Applied Remote Sensing for Urban Planning, Governance and Sustainability

Maik Netzband William L. Stefanov Charles Redman (Editors) Applied Remote Sensing for Urban Planning, Governance and Sus...

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Maik Netzband William L. Stefanov Charles Redman (Editors) Applied Remote Sensing for Urban Planning, Governance and Sustainability

Maik Netzband William L. Stefanov Charles Redman (Editors)

Applied Remote Sensing for Urban Planning, Governance and Sustainability

with 34 Figures

Dr. Maik Netzband Helmholtz-Centre for Environmental Research - UFZ Permoserstrasse 15 04315 Leipzig Germany Dr. William L. Stefanov NASA Johnson Space Center Houston, TX 77058 USA Professor Charles Redman Director, School of the Global Institute of Sustainability Arizona State University Tempe AZ 85287-3211 USA Cover image is a subset of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) orthorectified scene of the Phoenix, Arizona, USA metropolitan area acquired on May 2, 2007. Visible to near-infrared ASTER bands 1, 2, and 3N are mapped to blue, green, and red respectively. Image credit: NASA/GSFC/METI/ERSDAC/JAROS, and the U.S./Japan ASTER Science Team.

Library of Congress Control Number: 2007931201 ISBN

978-3-540-25546-8 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable to prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2007 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: deblik, Berlin Production: Almas Schimmel Typesetting: Camera-ready by the editors Printed on acid-free paper

30/3180/as 5 4 3 2 1 0

Preface As the global human population continues to expand, and cities become the locus of this expansion, the need to understand and monitor the function of cities from physical, social, and atmospheric perspectives becomes increasingly important. One of the most important tools, both for research and operational monitoring, is remotely sensed data. The 19th and 20th centuries saw the development of urban remote sensing as an applied science, progressing from airborne balloon photography in the United States Civil War to sophisticated multi-wavelength sensors onboard orbiting satellites. At the start of the 21st century, the amount of data available for urban remote sensing is staggering. Data can now be acquired at multiple times per day, and at spatial scales ranging from 1 kilometer to less than 1 meter per pixel. The computational power to extract meaningful quantitative results from remotely sensed data has also improved – tasks that once required the resources of a university or government laboratory can be done swiftly by a single analyst using a desktop computer and appropriate software. These developments in both data access and data processing ability present exciting and cost-effective opportunities for regional and local urban planners, developers, and managers. Over the past six years, scientists in the Urban Environmental Monitoring (UEM) Project - recently renamed the 100 Cities Project - based at Arizona State University (ASU) have been crafting a series of metrics to characterize the spatial and socio-ecological structure of cities, together with methods to validate inferred patterns. A wide range of disciplines has been involved, including the geological sciences, engineering, social science, geography, ecology, and anthropology. Much of this work has necessarily focused upon Phoenix, Arizona as that is the base of the UEM/100 Cities project. To further test our methods, we have formed an expanding network of partner cities in developed and developing countries. These partner cities offer readily available scientific resources and personnel (both academic and non-academic) eager to apply new remote sensingbased approaches to pressing environmental problems. This book is the first major cooperative effort of the 100 Cities Project resulting in a joint publication. It is intended as a reader for examples of applied remote sensing for urban environmental characterization, monitoring, and government decision-making, rather than a technical methodology volume – several of which have been published in recent years, and are referenced in the chapters. Our goal is to illustrate the most common and

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urgent problems facing both developed and developing cities, and present examples of how geo-information (remotely sensed data and GIS) can help solve practical and operational planning problems. We greatly appreciate the patience and cooperation of the chapter authors during the review and revision process. The chapter reviewers provided thoughtful critiques and suggestions to the authors. Their efforts have helped improve the quality and usefulness of this volume: Sharolyn Anderson, Mike Applegarth, Dan Blumberg, Jürgen Breuste, William Clark, Subhrajit Guhathakurta, Francisco Lara, Ray Quay, Julie A. Robinson, Richard Sliuzas, William D. Solecki, Frederick R Steiner, Paul C. Sutton, Christiane Weber, Douglas R. Webster, and Xiaojun Yang. Funding for the workshop, and partial support to the editors for completion of this book, was provided by NASA Earth Science Enterprise Research Program grant NNG04G057G to Philip R. Christensen, ASU; and NSF Long Term Ecological Research Program site grant DEB-9714833 to Nancy B. Grimm and Charles L. Redman, ASU. Philip R. Christensen conceived and promoted the original 100 Cities/Urban Environmental Monitoring Project as an ASTER Science Team Member, and we acknowledge his continued support of urban remote sensing research at ASU. We also acknowledge Michael Ramsey (University of Pittsburgh), for his contributions as lead scientist while a postdoctoral researcher (and later, visiting assistant professor) at ASU during the first few years of the project. We would like to thank the staff of the ASU Global Institute of Sustainability for contributions directly leading to production of this book, in particular Kathryn Kyle for technical editing of the book chapters; and Lauren Kuby for logistical support of the workshop and editing of the Introduction. The following personnel of the Mars Space Flight Facility, School of Earth and Space Exploration, at ASU provided valuable administrative, programming, and data wrangling support: Chris Eisinger, Tara Fisher, Jayme Harris, Chris Kurtz, Ed Maple, and Dale Noss. Stefanov also thanks the Image Science & Analysis Laboratory at NASA Johnson Space Center for providing computer resources used in the completion of this book. Mention of specific software packages, programs, or computer platforms does not indicate endorsement by the editors or chapter authors. Maik Netzband William L. Stefanov Charles L. Redman

Contents

Preface .................................................................................................... v List of Contributors .............................................................................xiii Glossary .............................................................................................xxiii Chapter 1 - Remote Sensing as a Tool for Urban Planning and Sustainability.............................................................................................. 1 1.1 Overview...................................................................................... 1 1.2 Social problems............................................................................ 4 1.3 Urban structure ............................................................................ 5 1.4 Climatic and atmospheric applications for urban remote sensing ......................................................................................... 6 1.5 Urban geohazards and environmental monitoring ....................... 8 1.6 Urban form and periphery............................................................ 9 1.7 Open space preservation ............................................................ 10 1.8 Evaluation of urban natural environments ................................. 11 1.9 Urban satellite sensors and mission legacy................................ 11 1.10 Urban monitoring initiatives .................................................. 13 1.11 Urban environmental monitoring project at Arizona State University...................................................................... 14

1.12 Outlook .................................................................................. 16 1.12.1 Case study Phoenix, USA .................................................. 16 1.12.2 Case study Rio de Janeiro, Brazil ...................................... 17 1.12.3 Case study Buenos Aires, Argentina ................................. 17 1.12.4 Case study Berlin, Germany .............................................. 17 1.12.5 Case study New Delhi, India ............................................. 17 1.12.6 Case study Chiang Mai, Thailand...................................... 17 1.12.7 Case study Chengdu and Guangzhou, China ..................... 18 1.12.8 Interurban comparison ....................................................... 18 1.13 References.............................................................................. 19 Chapter 2 - Automatic Land-Cover Classification Derived from HighResolution IKONOS Satellite Imagery in the Urban Atlantic Forest of Rio de Janeiro, Brazil, by Means of an Object-Oriented Approach... 25 2.1 Introduction................................................................................ 25 2.2 Methodology.............................................................................. 28 2.2.1 Study area .......................................................................... 28 2.2.2 Data.................................................................................... 28 2.2.3 Analysis ............................................................................. 29 2.3 Results and discussion ............................................................... 31 2.4 Conclusion ................................................................................. 34 2.5 References.................................................................................. 35 Chapter 3 - Advances in Urban Remote Sensing: Examples From Berlin (Germany)..................................................................................... 37 3.1 Introduction................................................................................ 37 3.2 New remote sensing technologies.............................................. 38 3.3 New remote sensing methods .................................................... 40 3.4 Examples.................................................................................... 42 3.4.1 Sensitivity analysis of Enhanced Thematic Mapper and ASTER data for urban studies ........................................... 42 3.4.2 Characterizing derelict urban railway sites with QuickBird data..................................................................................... 45 3.5 Outlook ...................................................................................... 47 3.6 Acknowledgments ..................................................................... 49 3.7 References.................................................................................. 49 Chapter 4 - Spatial Analysis of Urban Vegetation Scale and Abundance................................................................................................ 53 4.1 Introduction................................................................................ 53 4.2 Six urban landscapes.................................................................. 55 4.3 Spectral mixture analysis and image segmentation ................... 56

4.4 4.5 4.6 4.7 4.8

Vegetation fraction and patch size distributions ........................ 60 Comparison................................................................................ 64 Discussion.................................................................................. 71 Acknowledgements.................................................................... 75 References.................................................................................. 75

Chapter 5 - Urban Environmental Monitoring in Buenos Aires – Determining Green Areas ....................................................................... 77 5.1 Introduction................................................................................ 77 5.2 Background................................................................................ 79 5.3 Related work .............................................................................. 79 5.4 Materials and methods ............................................................... 81 5.4.1 Study area .......................................................................... 81 5.4.2 Data.................................................................................... 81 5.4.3 Preparatory work................................................................ 84 5.4.4 Remote sensing analyses.................................................... 86 5.5 Results........................................................................................ 88 5.6 Applications ............................................................................... 90 5.7 Conclusions................................................................................ 91 5.8 Acknowledgements.................................................................... 92 5.9 References.................................................................................. 92 Chapter 6 - Challenges in Characterizing and Mitigating Urban Heat Islands – A Role for Integrated Approaches Including Remote Sensing .................................................................................................... 117 6.1 Introduction.............................................................................. 117 6.2 Temporal and spatial scales in climatology ............................. 119 6.2.1 Regional to local scale ..................................................... 119 6.3 Factors controlling urban climates........................................... 120 6.4 Methods of evaluation ............................................................. 122 6.5 Remote sensing ........................................................................ 123 6.6 Urban heat island mitigation.................................................... 127 6.7 Conclusions.............................................................................. 128 6.8 References................................................................................ 129 Chapter 7 – Phoenix, Arizona, USA: Applications of Remote Sensing in a Rapidly Urbanizing Desert Region ............................................... 137 7.1 Introduction.............................................................................. 137 7.2 Regional setting and historic land use ..................................... 139 7.3 CAP LTER urban ecology research......................................... 140 7.4 Urban climate modeling........................................................... 141 7.5 Land cover characterization and change detection .................. 144

7.5.1 7.5.2 7.6 7.7 7.8

Expert system classification of the Phoenix area............. 148 Monitoring LULCC using object-oriented classification .................................................................... 151 High resolution commercial data use in Marana, AZ .............. 155 Conclusions.............................................................................. 159 References................................................................................ 160

Chapter 8 - Application of Remote Sensing and GIS Technique for Urban Environmental Management and Sustainable Development of Delhi, India ............................................................................................. 165 8.1 Introduction.............................................................................. 165 8.2 Urban environmental issues in Delhi....................................... 168 8.3 Application of remote sensing and GIS in urban studies......... 171 8.3.1 Aerial photographs and satellite data in urban studies..... 173 8.3.2 Urban spatial growth and sprawl ..................................... 174 8.3.3 Land-use and land-cover mapping................................... 177 8.3.4 Urban change detection and mapping.............................. 180 8.3.5 Base maps for urban areas ............................................... 181 8.3.6 Urban hydrology .............................................................. 182 8.3.7 Solid and hazardous waste ............................................... 183 8.3.8 Effective traffic management........................................... 184 8.3.9 Greenhouse gases and urban heat island mapping........... 185 8.3.10 Urban infrastructure recreational and utility mapping..... 186 8.4 Sustainable development and planning of Delhi ..................... 187 8.5 Conclusions.............................................................................. 190 8.5.1 Recommendations............................................................ 191 8.6 References................................................................................ 193 Chapter 9 - Berlin (Germany) Urban and Environmental Information System: Application of Remote Sensing for Planning and Governance - Potentials and Problems...................................................................... 199 9.1 Introduction.............................................................................. 199 9.2 Berlin urban and environmental information systems ............. 200 9.2.1 Definition and aims.......................................................... 201 9.2.2 The Berlin digital environmental atlas............................. 205 9.2.3 FIS-broker........................................................................ 206 9.2.4 Geo-data and geographic information systems................ 207 9.2.5 GIS and the internet ......................................................... 207 9.3 Application of remote-sensing data ......................................... 208 9.3.1 UEIS mapping of land use ............................................... 208 9.3.2 Area types ........................................................................ 209

9.3.3

Test of updating land-use mapping with remote-sensing data................................................................................... 209 9.3.4 Surface temperatures derived from satellite data ............. 212 9.3.5 Mapping of imperviousness (soil surface sealing)........... 213 9.3.6 Urban-biotope mapping ................................................... 215 9.4 Conclusions.............................................................................. 217 9.5 References................................................................................ 218 Chapter 10 - Views of Chiang Mai: The Contributions of RemoteSensing to Urban Governance and Sustainability .............................. 221 10.1 Introduction.......................................................................... 221 10.2 Views ................................................................................... 223 10.2.1 Access .............................................................................. 224 10.2.2 Interpretations .................................................................. 225 10.2.3 Resolution ........................................................................ 227 10.2.4 Social spaces .................................................................... 227 10.3 Histories ............................................................................... 230 10.3.1 Origins ............................................................................. 230 10.3.2 Urbanization..................................................................... 231 10.3.3 Ecosystem services .......................................................... 233 10.4 Models ................................................................................. 233 10.4.1 SLEUTH .......................................................................... 234 10.4.2 ELSE................................................................................ 236 10.5 Visions ................................................................................. 237 10.5.1 Space for time .................................................................. 238 10.5.2 Scenarios.......................................................................... 238 10.6 Actions ................................................................................. 240 10.6.1 Choices............................................................................. 241 10.6.2 Responsibilities................................................................ 242 10.7 Conclusions.......................................................................... 245 10.8 Acknowledgements.............................................................. 245 10.9 Notes .................................................................................... 245 10.10 References............................................................................ 246 Chapter 11 - 20 Years After Reforms: Challenges to Planning and Development in China’s City-Regions and Opportunities for Remote Sensing .................................................................................................... 249 11.1 Introduction.......................................................................... 249 11.2 Study areas........................................................................... 250 11.3 Remote sensing and GIS to monitor urban growth patterns................................................................................. 254 11.3.1 Pearl River Delta Case Studies ........................................ 254

11.3.2 Chengdu extended urban region ...................................... 257 11.4 Comparative urban development on the coast and in the west ...................................................................................... 258 11.5 Monitoring urban growth in China ...................................... 264 11.6 Challenges to planning and development and the role of remote sensing and geospatial data...................................... 265 11.7 References............................................................................ 267 Index ................................................................................................... 271

List of Contributors Brazel, Anthony, PhD, Professor School of Geographical Sciences, Arizona State University, Tempe, AZ 85287 USA; [email protected]. Anthony Brazel was born in Cumberland, Maryland in 1941. After receiving a BA in mathematics and MA in Geography from Rutgers University in New Jersey USA, he obtained a PhD in Geography at the University of Michigan. His early career involved several high latitude Arctic and Alpine expeditions studying glaciers and tundra environments. Upon taking a job in geography at Arizona State University in 1974, he began research on arid land and urban environments, in addition to snow and ice processes. He served as state climatologist of Arizona (governor-appointed position) from 1979-1999. His recent research relates to urban ecology and urban climatology. He is a Fellow of the American Association for the Advancement of Science, Arizona-Nevada Academy of Science, and the Explorer’s Club. Currently, he is in the School of Geographical Sciences and affiliated with the EPA National Center for Excellence at Arizona State University – whose goal is to study materials and heat island mitigation.

Fragkias, Michail, PhD, Executive Officer International Project Office, Arizona State University, Tempe, AZ 85287 USA; [email protected]. Michail Fragkias is the Executive Officer of the International Human Dimensions Programme's (IHDP) core project on Urbanization and Global Environmental Change (UGEC), based at Arizona State University in Tempe, Arizona, U.S.A. His interests focus on urban land use change modeling and its policy relevance, the evolution of urban landscape patterns and the interaction of urban spatial structure with the environment. He has employed spatial statistical analysis, simulations and geographical information systems (GIS) to study the significance of social, economic and political drivers of urban land use change in China and the USA. A native of Greece, he completed his undergraduate studies in Economics at the National University of Athens in Greece and his MA and PhD in Economics, with a focus on urban and environmental issues, at Clark University in Massachusetts, USA in 2004. From 2003 to 2006 he was a postdoctoral scholar with the Center for Environmental Science and Policy at the Freeman Spogli Institute for International Studies at Stanford University.

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Goedecke, Manfred Urban and Environmental Information System, Senate Department of Urban Development, Fehrbelliner Platz 1, 10707 Berlin, Germany. Manfred Goedecke was born in Berlin, Germany in 1956. He studied and graduated in landscape planning at the Technical University of Berlin, Germany. During his time at university he had special focus on environmental problems and land-use planning in developing countries. He worked several years as consultant. Since 1983 he is responsible for the Environmental Atlas of Berlin at the Department of Urban Development of the Senate Berlin. He concentrates his special attention at the processing and implementation of several instruments for collecting and activation of environmental data for planning purposes, and for the information of the public. His main focus is on soil conservation and urban water balance modeling.

Hostert, Patrick, PhD, Professor Department of Geomatics, Institute of Geography, Humboldt University, Unter den Linden 6, 10099 Berlin, Germany; [email protected] berlin.de. Patrick Hostert was born in Trier, Germany in 1967. He studied and graduated in Physical Geography at the University of Trier, Germany in 1994 and followed up his studies with his MSc-studies in GIS at the University of Edinburgh, UK (1995) before post-graduating (PhD) at the University of Trier (2001), where he worked as Assistant Lecturer in the Department of Remote Sensing after his post-graduation until 2002. With his change to Humboldt University in Berlin, first as Assistant Professor (until March 2006), then as Full Professor and Head of the Department of Geomatics, Patrick Hostert intensified his scientific focus on Remote Sensing and GIS. His actual focus is put on long-term monitoring of landscape change with satellite data, as well as hyperspectral and geometric high resolution data analysis. Important thematic issues for him are land change analysis in European transformation countries, as well as land degradation and desertification monitoring and assessment. Further research centres on remote sensing and geoinformation analysis for semi-arid and urban environments.

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Huaisai, Darika, Research Scientist Unit for Social and Environmental Research, Faculty of Social Sciences, Chiang Mai University, Chiang Mai, 50202 Thailand; [email protected]. Darika Huaisai was a researcher at the Unit for Social and Environmental Research at Chiang Mai University when the work for this book was done. She has an MSc in Geography from Chiang Mai University. Her research interests include application of remote-sensing and GIS to model land-use dynamics. She has also worked on floods and scenario analysis.

Krellenberg, Kerstin, Dipl.-Umweltw. Department of Geography, Humboldt University, Unter den Linden 6, 10099 Berlin, Germany; [email protected] Kerstin Krellenberg was born in Bad Oldesloe, Germany in 1977. After having studied and graduated in environmental sciences at the University of Vechta, Germany she collaborated on the binational research project “Perspectives of urban ecology for the metropolis Buenos Aires“ at the Humboldt University in Berlin, realising several stays in the Argentinean metropolis. Her special research interest lies in monitoring and evaluating environmental problems, urban ecology, land-use and planning using, among others, methods of remote sensing and geo-information. She is going to post-graduate (PhD) in April 2007 at the Humboldt University inBerlin and is currently looking for a new working challenge.

Lakes, Tobia, PhD, Postdoctoral Research Scientist Department of Geomatics, Institute of Geography, Humboldt University, Unter den Linden 6, 10099 Berlin, Germany; [email protected]. Tobia Lakes was born in Oberhausen, Germany in 1976. After having studied and graduated in Geography in Bonn she post-graduated (PhD) at the Technische Universität Berlin in a graduate study program on Urban Ecology (funded by the German research foundation). In her PhD she focused on the operational application of high-resolution remote-sensing data for urban planning. In particular, it has been her interest to analyse the integration of different types of geodata based on an information management approach. Her research interests lie in developing and applying geoinformatic methods for urban areas, spatial modeling and ecological and socio-economic data integration. She is now working at the Department of Geography at the Humboldt-Universität in Berlin in research and teaching. Her current research is on modeling urban development in postsocialistic countries by using remote-sensing and additional data.

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Lebel, Louis, PhD, Director Unit for Social and Environmental Research, Faculty of Social Sciences, Chiang Mai University, Chiang Mai, 50202 Thailand;[email protected]. Louis Lebel is the founding Director of the Unit for Social and Environmental Research at Chiang Mai University (see www.sea-user.org). Immediately after graduating with a PhD in Zoology from the University of Western Australia he travelled to Thailand on a three-month university exchange program and never returned. He has been living and working in Thailand for most of the past 16 years. During this time he has carried out theoretical and action-oriented research in epidemiology and public health, global environmental change, knowledge systems, urbanization and resilience, water governance and politics. Geographically most of his work is focused on Thailand and neighbouring countries in Southeast Asia.

Mack, Chris, MS, Senior GIS Analyst Department of Geographic Information Systems, Town of Marana, AZ 85653 USA; [email protected]. Chris Mack graduated from Washington State University in 1980 with a MS in Soil Genesis, Morphology and Classification. After several years working as a field soil scientist his interests became focused on remote sensing and GIS while employed as a research specialist at the Arizona Remote Sensing Center in Tucson, Arizona. In the 1990s, his career took him on two extended international assignments as a remote sensing specialist in Cairo, Egypt and a GIS expert for the United Nations in Dhaka, Bangladesh. In 2000, he relocated back to the United States and started his current position as a senior analyst in the GIS department with the Town of Marana, Arizona where his interests are the practical application of remote sensing and GIS in local government.

Moeller, Matthias S, PhD, Research Scientist Global Institute of Sustainability, Arizona State University, Tempe, AZ 85287 USA; and GIScience Research Unit, Austrian Academy of Sciences, Schillerstrasse 30, A-5020 Salzburg, Austria; [email protected]. Matthias S. Moeller graduated from the University of Osnabrueck, Lower Saxony, Germany in 1995 in Geography, Applied Geoinformatics and Remote Sensing. He has worked at the University of Vechta as an assistant researcher in the Research Center for Geoinformatics (FZG). Moeller received his PhD in natural science in 2002 for his thesis “Urban Environmental Monitoring with Digital Airborne Scanner Data”. From 2002 through 2003 he was ordered to build up the Center of Excellence for

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Geoinformatics in Lower Saxony (GiN). He went to Arizona State University, Global Institute of Sustainability in 2003 as a postdoctoral research associate, responsible for the coordination of tasks related to geoinformatics in the NSF funded project Agricultural Landscapes in Transition (AgTrans). Since 2006 Moeller is the Chair and Professor for Cartography and Geoinformatics at the University of Bonn, North Rhine Westphalia, Germany. He is interested in the development of new analysis techniques for extremely high resolution remote sensing data and the integration of the data in a GIS environment. The development of practical applications like 3D visualization, animated movies and web-based GIS solutions for these data are other topics of his research. His geography-related interests include human impacts on the environment and the interactions between human activities and ecology, especially in an urban environment. He is also involved in the field of distance learning. Moeller has a strong international teaching background in Applied Physical Geography, Geoinformatics and Remote Sensing at the University level.

Netzband, Maik, PhD, Scientific Consultant F & U Consult, UFZ-Helmholtz Centre for Environmental Research, Permoserstrasse 15, D-04318 Leipzig, Germany; [email protected]. Maik Netzband was born in Walsrode, Germany in 1965. After having studied and graduated in applied physical geography at the University of Trier/Germany he post-graduated (PhD) at the Technical University of Dresden. While having done further research in urban ecology and urban planning at the Institute for Ecological and Regional Research in Dresden, and later on, at the University of Leipzig and at Arizona State University he took the advantage to intensify his methodological knowledge of remote sensing techniques when approaching questions of urban ecology and urban planning. In particular, problems associated with urban land-use, climate, soil imperviousness, and land consumption, green areas, and open spaces caught his attention. His special research interest lies in monitoring and evaluating these complex issues with methods of remote sensing and geo-information. Currently he is working with the UFZ-Helmholtz Centre for Environmental Research Leipzig on the research initiative “Risk Habitat megacity” as a scientific consultant.

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Rahman, Atiqur, PhD, Assistant Professor Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia University, Jamia Nagar, New Delhi-110025, India; [email protected]. Atiqur Rahman was born in Ballia, India in 1971. After finishing schooling from Ballia, he studied at the prestigious Aligarh Muslim University (AMU), Aligarh and obtained the degree of BSc (Hons) and MSc. With keen interest in research and development, he pursued higher studies on urban environmental problems and management and obtained the degree of MPhil and PhD. His area of interest is application of geo-spatial tools (RS/GIS & GPS) for urban environmental planning and management, urban hydrology, land use/land cover change, and environmental impact assessment (EIA). He has worked at the UFZ-Centre for Environmental Research, Germany as a Postdoctoral Fellow. He was a member of the IndoGerman (DST-DAAD) joint research project and is also a collaborating scientist in the Arizona State University UEM/100 Cities project. He was awarded the prestigious Young Scientist Project (2001) by the Department of Science and Technology (DST), Govt. of India. Currently he is working as an Assistant Professor in the Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia University, New Delhi, India.

Redman, Charles L, PhD, Professor and Director Global Institute of Sustainability, Arizona State University, Tempe, AZ 85287 USA; [email protected]. Charles Redman received his BA from Harvard University, and his MA and PhD in Anthropology from the University of Chicago. He taught at New York University and at SUNY-Binghamton before coming to Arizona State University in 1983. Since then, he served nine years as Chair of the Department of Anthropology, seven years as Director of the Center for Environmental Studies and, in 2004, was chosen to be the Julie Ann Wrigley Director of the newly formed Global Institute of Sustainability. Redman's interests include human impacts on the environment, sustainable landscapes, rapidly urbanizing regions, urban ecology, environmental education, and public outreach. He is the author or co-author of 10 books including Explanation in Archaeology, The Rise of Civilization, People of the Tonto Rim, Human Impact on Ancient Environments and, most recently, The Archaeology of Global Change. Redman is currently working on building upon the extensive research portfolio of the Global Institute of Sustainability through the new School of Sustainability which is educating a new generation of leaders through collaborative learning,

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transdisciplinary approaches, and problem-oriented training to address the environmental, economic, and social challenges of the 21st century.

Rego, Luiz Felipe Guanaes, PhD, Professor Geography Department, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Gávea - Rio de Janeiro 22453, Brazil; [email protected]. Luiz Felipe Guanaes Rego was born in Syracuse, NY USA in 1962. After having studied and graduated in geography at the Catholic University of Rio de Janeiro, Brazil he post-graduated (PhD) at the Albert Ludwigs University of Freiburg, Germany. His basic interest of research involves geography knowledge to improve the automatic classifications of remote sensing data and define relations between the results of these classifications and the transformation of the landscape; understand this process and develop tools to analyze and support actions to reduce the negative effect of this process. He is Professor of the Geography Department and Director of the Multidisciplinary Institute of Environment, both of Catholic University of Rio de Janeiro PUC-RIO.

Sangawongse, Somporn, PhD, Lecturer Department of Geography, Faculty of Social Sciences, Chiang Mai University, Chiang Mai 50200, Thailand Somporn Sangawongse is a lecturer at the Department of Geography, Chiang Mai University. She has carried out extensive remote-sensing work on land-use changes in northern Thailand. Her research interests include applications of remote-sensing to support modelling of urbanization processes.

Schneider, Annemarie, PhD, Assistant Professor Department of Geography, University of California, Santa Barbara, CA 93106 USA; [email protected]. Annemarie Schneider is an Assistant Professor in the Department of Geography and Institute for Computational Earth System Science at the University of California, Santa Barbara. After completing her BS at the University of Wisconsin, Madison, she earned her MA and PhD in Geography and Environmental Science at Boston University. Her research interests include land cover change, urban geography and the urban environment, and the human dimensions of global environmental change. Her current projects focus on transforming the study of urban areas from local investigation to one of comparative analysis in support of global change research. She leads the 40 Cities Project, an effort to compare/contrast the rates, pat-

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terns, and socioeconomic drivers of land use change in a global crosssection of metropolitan areas. Her work also includes mapping urban land surface properties globally using the fusion of remote sensing data types, a task to help better model the impacts of urbanization on the regional and global environment.

Schneider, Thomas Urban and Environmental Information System, Senate Department of Urban Development, Fehrbelliner Platz 1, 10707 Berlin, Germany; [email protected]. Thomas Schneider was born in Coburg, Germany in 1953. He studied and graduated in landscape planning at the Technical University of Berlin, Germany. Since 1982, as employee of the Senate Department of Urban Development Berlin, he was involved with the formulation and implementation of the landscape program for Berlin, especially the problems of the urban natural environment. Further on his work focused on the collection and presentation of urban and environmental data for the Berlin Environmental Atlas, an extensive description of all natural and human-effected parts of the urban ecosystem.

Seto, Karen C, PhD, Assistant Professor Department of Geological & Environmental Sciences, Stanford University, Stanford, CA 94305 USA; [email protected]. Karen C. Seto is Assistant Professor in the Department of Geological and Environmental Sciences, and Center Fellow with the Freeman Spogli Institute for International Studies and the Woods Institute for the Environment at Stanford University. Her research focuses on optical remote sensing, understanding the causes and impacts of land-use change—especially urban growth—and evaluating the social and ecological impacts of land dynamics. She currently has active projects in China, Vietnam, India, and the U.S. She is the Co-Chair of the International Human Dimensions Programme’s (IHDP) core project on Urbanization and Global Environmental Change (UGEC), and is on the Scientific Steering Committee of the World Conservation Union’s (IUCN) Commission on Ecosystem Management.

Small, Christopher, PhD, Research Scientist Lamont Doherty Earth Observatory, Columbia University, Palisades, NY 10964 USA; [email protected]. Christopher Small is a geophysicist at the Lamont-Doherty Earth Observatory of Columbia University. Prior to receiving a PhD from the Scripps Institution of Oceanography in 1993, his formative experiences ranged

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from shipboard studies of the circulation of the Chesapeake Bay with the University of Maryland to satellite mapping for frontier petroleum exploration with the Exxon Production Research Company. Current research interests focus on measuring changes of Earth's surface and understanding the causes and consequences of these changes. Details available online at http://www.LDEO.columbia.edu/~small.

Thaitakoo, Danai, PhD, Lecturer Department of Landscape Architecture, Faculty of Architecture, Chulalongkorn University, Bangkok 10330, Thailand; [email protected]. Danai Thaitakoo is a lecturer in the Department of Landscape Architecture, Faculty of Architecture, Chulalongkorn University, Bangkok, Thailand. He received a bachelor's degree in landscape architecture from Chulalongkorn University, a Masters in landscape architecture from Harvard University and a PhD in environmental planning from the University of California at Berkeley. His research interest is in the field of landscape ecology, with an emphasis on the application of landscape spatial structure analysis and modeling to landscape planning and design. He is currently working on the research initiative "Urban-Rural Sustainability and Landscape Changes".

Stefanov, William L, PhD, Senior Geoscientist Image Science & Analysis Laboratory, Code KX, NASA Johnson Space Center, Houston, TX 77058 USA; [email protected]. William L. Stefanov was born in Webster, Massachusetts, USA in 1965. His undergraduate training in geology was completed at the University of Massachusetts in Lowell, MA. He completed his MS (physical volcanology, igneous petrology), and his PhD (geomorphology, thermal infrared remote sensing, laboratory spectroscopy) at Arizona State University (ASU). He led remote sensing research efforts for the Central ArizonaPhoenix Long Term Ecological Research site, and Urban Environmental Monitoring Project, while a postdoctoral researcher at ASU. His position at Johnson Space Center includes astronaut training and mission operations for acquisition of hand-held digital photography of the Earth from the International Space Station, and curation of the historical astronaut photography database. His research interests include the application of remotely sensed data to investigation of surface mineralogy, geomorphology, and geohazards in urban/peri-urban areas on Earth, with applications to future outposts on the Moon and Mars; biophysical aspects of urban heat islands and development of mitigation strategies; ecological disturbance mecha-

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nisms and patterns; and the role of humans as geological agents on the landscape.

Ueffing, Christoph, PhD, Scientific Consultant Ueffing Umwelt Consult, Im Ried 7A, D-79249 Merzhausen, Germany. Christoph Ueffing was born in Germany, 1962. After having studied and graduated in Forest Engineering he post-graduated (PhD) at the Albert Ludwigs University in Freiburg. His basic research interests involve geographic information systems, automatic classification of remote sensing data and developing geo-solutions to improve public administration using resources of the web. He is director of the Ueffing Umwelt Consult.

Vianna, Sérgio Besserman, Professor Economy Deparment, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Gávea - Rio de Janeiro 22453, Brazil. Sérgio Besserman Vianna was born in Rio de Janeiro, Brazil in 1957. He graduated and post-graduated in Economy at the Catholic University of Rio de Janeiro, Brazil. His main research interest is in sustainable development as well as climate change from the economic point of view. He was president of the Brazilian Institute of Geography and Statistics, and currently is professor at the Economy Department and Director of the Planning Institute of the City of Rio de Janeiro.

Glossary The following definitions of remote sensing terms, sensor acronyms, and other technical terms are provided as a supplement to text in the chapters. The definitions are provided from the perspective of remote sensing and omit other discipline-specific information. Interested readers are encouraged to consult the following works for information about the fundamental science, technology, and techniques of remote sensing: Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: Principles and practices. Lewis Publishers, New York, NY, ISBN 0-87371-986-7 Jensen JR (1996) Introductory digital image processing: A remote sensing perspective (2nd ed). Prentice-Hall, Upper Saddle River, NJ, ISBN 013-205840-5 Jensen JR (2000) Remote sensing of the environment: An earth resource perspective. Prentice-Hall, Upper Saddle River, NJ, ISBN 0-13489733-1 Sabins FF (1997) Remote sensing: Principles and interpretation (3rd ed). W.H. Freeman and Company, New York, NY, ISBN 0-7167-2442-1 Rashed T, Juergens C (2007) Remote sensing of urban and suburban areas, remote sensing and digital image processing vol 10. Springer, New York, NY, ISBN 1-4020-4371-6 ALI – Advanced Land Imager. Multispectral sensor onboard the NASA Earth Observing -1 (EO-1) technology demonstration satellite, acquiring data in the visible through shortwave infrared wavelengths. Data are publicly available. Website: http://eo1.gsfc.nasa.gov/Technology/ALIhome1.htm.

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ALK – German digital cadastral information system, also known as the Automated Real Estate Map. ARES – Airborne Reflective Emissive Spectrometer. German hyperspectral airborne sensor, acquiring data in the visible through midinfrared wavelengths. Data are publicly available. Website: http://www.ares.caf.dlr.de/intro_en.html. ASTER – Advanced Spaceborne Thermal Emission and Reflection Radiometer. Joint USA/Japan multispectral sensor onboard the NASA Terra satellite, collects data in the visible through midinfrared wavelengths. Data collection is by request, rather than continuous, therefore not all areas of Earth are imaged systematically. Data are publicly available. Website: http://asterweb.jpl.nasa.gov. ATLAS – Advanced Thermal and Land Applications Sensor. Multispectral sensor flown aboard NASA research aircraft, collects data in the visible through midinfrared wavelengths. Data not publicly available. Website: http://www.ghcc.msfc.nasa.gov/precisionag/atlasremote.html. AVHRR – Advanced Very High Resolution Radiometer. There have been several generations of these sensors onboard NOAA weather satellites in geostationary orbit around Earth. As there are multiple sensors, ground locations can be imaged several times per day depending upon the particular satellite. Data are acquired at coarse spectral and spatial resolution in the visible through midinfrared wavelengths. Data are publicly available. Website: http://eros.usgs.gov/products/satellite/avhrr.html. AVIRIS – Airborne Visible/Infrared Imaging Spectrometer. NASA hyperspectral airborne sensor that acquires data in the visible to shortwave infrared wavelengths at various ground resolutions. Data are publicly available. Website: http://aviris.jpl.nasa.gov/. Biotope – A distinct ecological region characterized by species particularly adapted to it, such as the Arctic. Breed Coefficient – A factor used in urban growth models to determine the probability of land-use change (from nonurban to urban; for example, the probability of change from agricultural use to a commercial development) in an isolated pixel, causing adjacent pixels to also become urbanized. Brute-force Calibration – A method used in modeling of urban systems, whereby elements of the model are assigned values purely on the basis of measurement data. One example would be to directly assign a value of “low density residential” to a model grid based upon field or remotely sensed data, rather than calculation of a value through statistical means.

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CIR – Color Infrared. This typically refers to photographic film sensitive to near-infrared wavelengths (0.7-1.0 micrometers). The peak reflectance of photosynthetically active vegetation is in this wavelength range, and color infrared film is typically used in airborne photography surveys of vegetation extent and health. DEM – Digital Elevation Model. A representation of the landscape using measured elevation data at known geographic coordinates. The resulting X, Y, Z grid (corresponding to latitude, longitude, and elevation) can be used to generate a three-dimensional representation of the landscape. Georeferenced, remotely sensed data can then be overlain on the DEM to produce accurate visualizations of spatial relationships in the data, as well as calculation of geomorphic and hydrologic parameters related to slope and aspect. Differential Global Positioning Systems (DGPS) – The Global Positioning System fixes a receiver’s location on the ground by using the difference between the transmission and receive time from a network of satellites orbiting Earth (requiring triangulation of at least three satellite signals for determination of latitude, longitude, and altitude). DGPS allows for greater accuracy by correcting the satellite signals with positional information from ground-based towers. Not all GPS units are capable of using DGPS. Diffusion Coefficient – A factor used in urban growth models to constrain the number of times a pixel will be randomly selected for urban landuse change. DigitalGlobe – The commercial provider of Quickbird high resolution remotely sensed imagery. Website: http://www.digitalglobe.com/. Dipterocarp – Tree belonging to the family Dipterocarpaceae and typically found in tropical rainforest climates. DSM – Digital Surface Model. A digital, three-dimensional representation of the landscape that includes all surface features, such as buildings and trees. These models are typically produced using LiDAR data or photogrammetric analysis of stereo visible imagery. ENVISAT – A European Space Agency (ESA) satellite equipped with a variety of sensors for environmental monitoring of Earth. Data are acquired for Earth’s surface and atmosphere in the visible through midinfrared wavelengths, together with active radar instruments, at a variety of spatial resolutions. Data are publicly available. Website: http://envisat.esa.int/.

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EOS – Earth Observing System. A constellation of earth-observing satellites launched and maintained primarily by NASA. The program is intended to collect information about the Earth’s atmosphere, hydrosphere, and geosphere using a variety of sensors designed for specific measurement tasks. Website: http://eospso.gsfc.nasa.gov/. ETM+ - Enhanced Thematic Mapper Plus. This sensor onboard the Landsat 7 satellite continues the long history of Earth observation by the Landsat program, and acquires data in the visible through midinfrared wavelengths. The sensor suffered a failure of its scan line corrector in 2003, significantly reducing the usefulness of the data. Data are publicly available. Website: http://edc.usgs.gov/products/satellite/landsat7.html. FDI – Fractal Dimension Index. A mathematical operation applied to remotely sensed data to indicate the shape complexity of ecological patches or classified land-cover and land-use types. For example, the FDI might be used to indicate the complexity of shapes of residential areas within a metropolitan area. GIFOV – Ground Instrument Field of View. A measure of the spatial area on the ground captured by a sensor in a single scene or frame. It is different from the Instrument Field of View as it also considers the altitude of the sensor. GIS – Geographic Information System. A now somewhat generic term for a geospatial database, in which descriptive data are identified by geographic (latitude, longitude) position. This allows for spatial, temporal, and statistical analysis of virtually any sort of spatially associated digital data (Census, power use, crop type, etc.). GISTDA – Geo-Informatics and Space Technology Development Agency of Thailand. A public agency whose mission is to provide geospatial data, and engage in research related to geospatial data collection and analysis, for the benefit of Thailand. Website: http://www.gistda.or.th/Gistda/HtmlGistda/Html/index2.htm. GCP – Ground Control Point. Geographic coordinates for land surface features, usually measured in the field using a DGPS system. These points are used to accurately georeference remotely sensed data acquired by airborne and satellite sensors. HRSC – High Resolution Stereo Camera. German high resolution stereo camera originally developed for use on the Russian Mars ’96 orbiter. Following failure of this mission, an HRSC was flown on the successful Mars Express mission. The camera collects multispectral data in the visible through near infrared wavelengths. Data are publicly available. Website: http://solarsystem.dlr.de/Missions/express/indexeng.shtml.

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HRSC-AX – High Resolution Stereo Camera Airborne Extended. An airborne version of the HRSC flown to Mars, with similar wavelength range and submeter ground resolution. Data are publicly available. Website: http://www.dlr.de/pf/en/desktopdefault.aspx/tabid-331/. (in German). HyMap – Commercial airborne hyperspectral sensors that can be flown in a variety of aircraft. Sensors can be configured to acquire data in the visible through midinfrared wavelengths. Data available via contract survey. Website: http://www.hymap.com/main.htm. Hyperion – A hyperspectral sensor on board the NASA EO-1 technology demonstration satellite. The sensor collects data in the visible through shortwave infrared wavelengths. Data collection is by request, rather than continuous, therefore not all areas of Earth are imaged systematically. Data are publicly available. Website: http://eo1.usgs.gov/hyperion.php. IFOV – Instrument Field of View. A measure of the area that a given sensor “sees.” The IFOV depends mainly on the type of lens used to focus incoming light onto the sensor detector array or film. IKONOS – A commercial high resolution multispectral satellite-based sensor. Data are collected in the visible and near infrared wavelengths. Data collection is by request, rather than continuous, therefore not all areas of Earth are imaged systematically. Data are publicly available. Website: http://www.geoeye.com/products/imagery/ikonos/default.htm. InSAR – Interferometric Synthetic Aperture Radar. An active remote sensing system that measures radar returns (reflections) from the land surface to the sensor, usually mounted on a satellite or airborne platform. The amount of energy returned to the sensor provides information on material composition and orientation. Repeat acquisition of the same location can indicate changes in the ground surface (subsidence for example) through phase changes in the returned radar signal. Website: http://quake.wr.usgs.gov/research/deformation/modeling/InSAR/whati sInSAR.html. IRS – Indian Remote Sensing system. A series of satellites launched by India beginning in 1988 that have carried a variety of multispectral sensors with different spatial resolutions and wavelength ranges in the visible through shortwave infrared. Data are publicly available. Website: http://www.geoeye.com/products/imagery/irs/irs_1c_1d/default.htm.

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IRS-1C – One of the currently operational satellites in the IRS. The IRS1C provides data with spatial resolutions ranging from 5 to 180 meters in the visible wavelengths. Data are publicly available. Website: http://www.geoeye.com/products/imagery/irs/irs_1c_1d/default.htm. Kappa Statistics – A measurement of accuracy for classifications derived from remotely sensed data. The statistic includes information on omission and commission errors not reflected in a simple measure of overall classification accuracy. Landsat - A general term applied to a series of satellites flown by the United States from 1972 to the present. Three multispectral sensors have been carried on the Landsat satellites: the visible to near infrared MSS, visible to midinfrared TM, and the current ETM+. Ground resolutions have increased from 80 to 15 meters during the program. The Landsat program has acquired the most extensive and temporally continuous remotely sensed dataset of Earth’s land surfaces. Data are publicly available. Website: http://landsat.usgs.gov/. Leica ADS40 Airborne Digital Sensor – Commercial airborne digital multispectral sensor that acquires submeter resolution data in the visible and near infrared wavelengths, and has stereo imaging capability for generation of digital surface models. Data are not publicly available. Website: http://gis.leica-geosystems.com/LGISub1x2x0.aspx. LIDAR – Light Detection and Ranging. An active sensor system that uses an aircraft-mounted laser to scan the Earth’s surface during flight. Travel time and amount of backscatter is measured for each laser pulse, and used together with precise GPS measurements to create a digital surface model along the flight line. These models typically return elevations accurate to the submeter level. Website: http://www.ghcc.msfc.nasa.gov/sparcle/sparcle_tutorial.html. LISS – Linear Imaging Self-Scanning System. A series of multispectral sensors flown on the Indian Remote Sensing satellites that obtain information in the visible to shortwave infrared wavelengths at various ground resolutions. MODIS – The Moderate Resolution Imaging Spectroradiometers are key instruments aboard the NASA Terra and Aqua satellites. Terra MODIS and Aqua MODIS together view the entire Earth's surface every 1 to 2 days, and acquire multispectral data in the visible to midinfrared wavelengths at ground resolutions of 250 to 1000 meters. Data are publicly available. Website: http://modis.gsfc.nasa.gov/.

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MSS – Multispectral Scanner. A sensor flown on Landsats 1-5 (1972present) that acquires multispectral data in the visible green, visible red, and near infrared wavelength regions at 80-meter ground resolution. The MSS onboard Landsat 3 also acquired midinfrared data in a single band. The currently orbiting Landsat 7 satellite does not include an MSS. Data are publicly available. Website: http://edc.usgs.gov/products/satellite/mss.html. MTI – Multispectral Thermal Imager. A United States Department of Energy sensor that acquires multispectral data in the visible through midinfrared bands. Some data from this sensor are publicly available. Website: http://www.arm.gov/xds/static/mti.stm. MST – Mean Surface Temperature. The temperature of a surface obtained from measurement by an airborne or satellite-based instrument in the midinfrared wavelengths. Temperatures obtained from remotely sensed data are spatially-weighted averages of the surface (or “skin”) temperatures of the materials present in a single pixel. Depending upon the pixel scale, the MST may include contributions from built materials, vegetation, water, soil/bedrock, etc. in urban or suburban settings. NDVI – Normalized Difference Vegetation Index. An index calculated from the ratio of pixel reflectance values measured in the visible red and near infrared channels of a given sensor. It is related to the fraction of photosynthetically active radiation available to plants, and calculation of the index provides relative plant abundance data or “greenness” maps. NOAA – National Oceanic and Atmospheric Administration of the USA. Responsible for climate modeling, weather forecasting, and oceanographic studies. Website: http://www.noaa.gov/. NRCT – National Research Council of Thailand. Organization responsible for support of research activities in Thailand. Website: http://www.nrct.net/eng/. OpenGIS – An open software-programming interface specification for geographic information systems (GIS) advanced by the Open Geospatial Consortium. This is a non-profit, international, voluntaryconsensus organization focused on development of standards for geospatial and location-based services. Website: http://www.opengeospatial.org/.

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ORBIMAGE – A commercial remote sensing company that currently operates the OrbView-3 and OrbView-2 ocean and land imaging satellites. Both satellites offer real-time data download capabilities for multispectral visible to near infrared data at resolutions of 1 km (OrbView2) to 1 meter (OrbView-3). The company was recently merged with SpaceImaging to form GeoEye. Data are publicly available. Website: http://www.geoeye.com/. PAN – Panchromatic. Refers to film or sensor that records information across a broad wavelength range – the visible wavelengths (0.4-0.7 micrometers) for example – rather than narrow wavelength bands. Commonly used in remote sensing to provide a high resolution band for sharpening coarser multispectral data. QUICKBIRD – A commercial high resolution multispectral satellite sensor. Data are collected in the visible and near infrared wavelengths to submeter resolution. Data collection is by request, rather than continuous, therefore not all areas of Earth are imaged systematically. Data are publicly available. Website: http://www.digitalglobe.com/product/basic_imagery.shtml. RADARSAT-1 – An active radar sensor launched by Canada in 1995 intended to monitor environmental change and natural resources. The satellite acquires synthetic-aperture radar data at a variety of spatial resolutions over the entire globe at one- to six-day repeat frequencies. Data are publicly available. Website: http://www.space.gc.ca/asc/eng/satellites/radarsat1/default.asp. RMS – Root Mean Square error. A mathematical measurement of the goodness of fit (or degree of similarity) between two sets of data. A typical remote sensing application is measurement of how well two images are coregistered to each other using tie points. If the RMS is high, the coregistration is poor, suggesting that the tie point locations are inaccurate; if RMS is low, tie points are accurately located on the two images and coregistration is good. RPC – Rational Polynomial Coefficients. An orthorectification technique used with data from various sensors (such as IKONOS, ASTER, and Quickbird) that does not require ground control points. Required inputs include the base image, an appropriate RPC model for the sensor, and elevation information. SAR – Synthetic Aperture Radar. An airborne or satellite-based coherent radar system that uses magnitude and phase of received signals over successive pulses from elements of a synthetic aperture to create an image. See also InSAR, above.

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SAVI – Soil Adjusted Vegetation Index. A vegetation index designed to minimize the effect of soil reflectance in calculation of vegetation abundance from remotely sensed imagery. It is based on the NDVI (see above) and adds a correction factor to account for soil reflectance at various levels of vegetation cover. Scene – Area on the ground that is captured by a satellite image or photograph, determined by the sensor or camera GIFOV (see above). The usual basic subset of a sensor dataset for purchasing purposes. SMA – Spectral Mixture Analysis. An image analysis technique for multispectral and hyperspectral data that determines relative abundance of a given set of pure “endmembers” on a per-pixel basis. Endmember spectra are assumed to represent pure materials or classes, such as concrete, Bermuda grass, or granite. Endmembers can be obtained from the image data, a spectral library, or ground measurements, and are combined mathematically in various percentage combinations to achieve minimum RMS (see above) error with the image pixel spectrum. The resulting endmember percentages reflect the composition of the image pixel. Space-For-Time Substitution/Chronosequence – A conceptual analysis approach that uses spatial variation in landscape elements to approximate a long temporal sequence of landscape change that cannot be viewed directly. An urban example of this concept would be the common land-use change sequence of a parcel of undeveloped land changing to agricultural use, then changing to residential or commercial use (this sequence of predictable change over time can also be called a chronosequence). This sequence cannot typically be observed in the dense urban core, but by examining other nearby areas (such as younger cities nearby, and plots of land slated for development), the developmental sequence can be inferred. Spatial Resolution – A measure of the spacing, in line-pairs per unit distance, of the most closely spaced lines that can be distinguished on an image or photograph. This is a primary factor in deciding whether or not a particular dataset or sensor will be adequate to address a given mapping need. For example, if the goal is to identify and map individual trees in a park, remotely sensed data acquired at 30 meters/pixel resolution would not be adequate. A more appropriate choice would be meter or submeter/pixel resolution data.

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SPOT – Systeme Probatoire d'Observation de la Terre. A series of multispectral satellite-based sensors launched by France beginning in 1986. Data are acquired at a variety of spatial resolutions in the visible through near infrared wavelengths, and digital elevation models can be obtained using the data. Data are publicly available. Website: http://www.spot.com/html/SICORP/_401_.php. Swidden – An area cleared for temporary cultivation by slash-and-burn of preexisting vegetation. This agricultural practice is now common in the tropics, but evidence suggests it was also practiced elsewhere (prehistoric Europe, for example). TIMS – Thermal Infrared Multispectral Scanner. An airborne NASA instrument that acquired multispectral information in the midinfrared wavelengths at various spatial resolutions, primarily for geological and environmental investigations. This sensor is no longer operational, and has been replaced by the MODIS/ASTER Simulator (MASTER; http://masterweb.jpl.nasa.gov/). Data are publicly available. Website: http://www.nasa.gov/centers/dryden/research/AirSci/ER-2/tims.html. TM – Thematic Mapper. A series of sensors flown onboard the Landsat series of satellites (Landsats 4 and 5). The sensor acquires multispectral information in the visible through shortwave infrared wavelengths and includes one midinfrared-wavelength band. Spatial resolution ranges from 30-120 meters/pixel. Data are publicly available. Website: http://eros.usgs.gov/products/satellite/tm.html. VHR – Very High Resolution. A term applied to remotely sensed data with a spatial resolution of 5 meters/pixel or less. VNIR – Visible to Near Infrared. The wavelength region from 0.4 to 1.0 micrometers that includes the “true color” (red, green, blue) portion of the electromagnetic spectrum to which human eyes are sensitive. The near infrared region (0.7-1.0 micrometers) is particularly useful for vegetation and soil studies.

Chapter 1 - Remote Sensing as a Tool for Urban Planning and Sustainability

Maik Netzband1, William L. Stefanov2, Charles L. Redman3 1

F&U Consult, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany

2

Image Science & Analysis Laboratory, NASA Johnson Space Center, Houston, TX, USA

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Global Institute of Sustainability, Arizona State University, Tempe, AZ, USA

1.1 Overview In coming decades, the rapid increase of large cities in the developing world and the transformation of urban landscapes in the developed world will be among the greatest challenges to human welfare and a viable global environment. Although cities occupy only 5% of the Earth’s terrestrial surface, they are home to almost half the global population, who consume 75% of the world’s natural resources and generate an equivalent proportion of pollution and waste. The United Nations estimates that virtually all net population growth over the next 30 years will occur in cities, doubling their population. This anticipated population growth will transform urban landscapes, create undreamed-of challenges and opportunities for political and social institutions, and require an unprecedented investment in infrastructure.

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The largest cities of the world are the engines of growth for the developing world’s economy and, in all parts of the world, are centers of creativity, culture, finance, and power. These same cities, especially in the developing world, are also the loci of poverty, pollution, disease, political instability, and social inequality. Nowhere are the challenges to environmental, economic, and social sustainability more daunting nor the opportunities more promising than in these rapidly urbanizing regions. Yet urban landscapes are changing faster than the forces that impel the changes can be understood. Changes and the forces behind them seem to be operating in opposing directions and at different spatiotemporal scales. For example, the demographic character of cities is rapidly changing, not just through growth, but through the changing identity of inhabitants. Rural-to-urban migration continues to depopulate rural areas and increase the dependency of urban migrants. On the other hand, in countries such as China, incentives and coercion are populating formerly rural areas. In North America and Western Europe, cross-border migration, largely in response to asymmetries in world economies and the demand for a low-wage labor force, is transforming the ethnic composition of cities, engendering daunting social and political challenges. In the later half of the 20th century, urban growth in western industrialized countries was generally concentrated on the edge of urbanized regions. In recent decades, however, the strongest per capita growth shifted to the more rural areas of the urban fringe (Soja 1995). Open spaces are increasingly included between cities, villages, and traffic axes. An urbanizing landscape, accompanying technical infrastructure, and uncontrolled urban spread are the results. The surface consumption progresses, i.e., predominantly agricultural surfaces are transformed into settlement and traffic surfaces, resulting in decreased settlement density, increased traffic, and costly infrastructure development. Increased building of single family houses and of shopping facilities in suburban spaces drives settlement surface growth. The settlement density and thus the utilization of infrastructure continue to decrease. This development of conurbations inevitably changes the structure and extent of natural spaces. On one hand, strong soil sealing (conversion of pervious to impervious surfaces) reduces natural spaces at urban margins. On the other hand, new green spaces result from breaking the “traps” of former industrial sites, transforming them into trade and housing locations. In addition, naturalizing spaces for recovery purposes can result from transforming land into park and forest surfaces in suburban areas. Observation and evaluation instruments are critically necessary in order to structure and plan for a sustainable future in these transforming cities.

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Although urbanization is the most visible anthropogenic force on earth, affecting its surface, atmosphere, and seas, its biodiversity and its people, we lack reliable baseline data to assess the ecosystem health and biodiversity of many urban areas. Progress obtaining this data is moving more slowly than our ability to alter the environment. Characterizing and monitoring land-cover and land-use change is of limited use in understanding the development pathways of cities and their resilience to outside stressors (Longley 2002). Geological, ecological, climatic, social, and political data are also necessary to describe the developmental history of an urban center and understand its ecological functioning (Grimm et al. 2000). Data available from the NASA Earth Observing System (EOS) satellite-based instruments presents an opportunity to collect information relevant to urban (areas of high population concentration with high building density and infrastructure) and periurban (adjacent agricultural and undisturbed regions with low population concentration) environments at various spatial, temporal, and spectral scales. EOS sensors offer two advantages essential for characterizing and monitoring urban and periurban regions: 1) a large volume of surficial multispectral data can be obtained at relatively low cost, and 2) data for the same region can be repeatedly acquired over short periods of time (days to weeks). Despite the promise of new and fast-developing remote sensing technologies, a gap exists between the research-focused results offered by the urban remote sensing community and the application of these data and products by the governments of urban regions. There is no end of interesting scientific questions to ask about cities, but sometimes these questions do not match the operational problems and concerns of a given city. On April 14-16, 2004, an international workshop focusing upon seven urban case studies (Phoenix, Mexico City, Lima, Berlin, Cairo, New Delhi, and Chiang Mai) took place in Tempe at Arizona State University. The international participants were engaged and familiar with the most urgent environmental tasks for the sustainable development of their urban regions, the planning challenges faced by the local authorities, and the application of geo-information data and techniques in urban areas to meet these challenges. These researchers gathered to determine the critical questions about urban remote sensing and how best to use data and scientific skills to answer these questions. The Tempe group agreed that, although we need to describe and monitor the processes of urbanization, we also need to better predict local and regional environmental effects and feedbacks associated with possible urban trajectories. To achieve this goal, researchers need to:

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Objective 1: track urban area growth and change: speed, density, direction, structures, impervious surfaces, land consumed Objective 2: assess the spatial arrangement of green/open space within cities and at the periphery: amount, distribution, connectivity Objective 3: monitor changes in periurban regions: farmland conversions, wetland infringement, biodiversity threats Objective 4: track land-cover and land-use changes that influence urban climatology and atmospheric deposition: impervious surfaces, vegetation cover, dust Objective 5: monitor urban growth as it intersects with areas of potential environmental hazards: earthquake, subsidence, mudslides, floods Objective 6: map environmental parameters (microclimate, heat island, access to open space, percent of impervious surface, percent of green space), assess the geographic differences within the region, and identify correlations with social, economic, and ethnic divisions. Though workshop participants aimed to compare the most common and urgent problems of the case-study cities and determine what geoinformation can offer to solve the practical and operational planning problems, they suggested that in-depth studies of their representative urban regions were needed to evaluate the realistic potential of remote sensing in urban areas. Key problems were identified that urban remote sensing can address.

1.2 Social problems Human settlements are a product of social evolution over long periods of time. Today, we face problems that require collective-action such as increasing ethnic and religious diversity, growing populations, and global social inequalities, and these challenges are concentrated in urban landscapes. The western urban-development model promotes consumption and growth in a time when resource availability lags behind projected population growth. Emerging technologies may offset some resource consumption, but not the increasing demands in the developing world. The developed world may be forced to reduce its resource use, which may lead to political and social upheaval. To study the global urban system, one requires a global, urban GIS dataset, based upon historic land-use and constantly updated with new data. It would be interesting, also, to examine global rates of suburbanization and how they differ among regions.

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What are the main, present-day challenges (threats) to urban populations? For one, growth and suburbanization make cities more vulnerable to crises (e.g., pipeline breaks, disease outbreaks). Urban growth consumes surrounding land resources that are generally nonrenewable in terms of surface water, groundwater, and food production. In arid climates the threat of dwindling water resources is especially critical. Dense cities may be more vulnerable to terrorist attack due to the interdependence of services, and concentrations of population, educational centers, and industry. Rindfuss and Stern (1996, http://books.nap.edu/books/0309064082/html/index.html) discuss the gap between social-science and remote sensing research and the potential benefits of bridging that gap. Remote sensing scientists emphasize remote sensing’s social utility as expensive, government-financed data and techniques become more valuable to society. Some social scientists view remote sensing as a tool for gathering information about social phenomena and the environmental consequences of various social, economic, and demographic processes. Social science itself can contribute to the accuracy of remote sensing research by validating and interpreting the data, as well as supporting the confidential use of public available information.

1.3 Urban structure A typology of urban structure that includes factors such as building density, spread of impervious surfaces, commute times, and other infrastructure issues needs to be developed. Such a typology could also consider open space areas such as parks, dedicated park areas in the hinterland, and green corridors, in order to determine dedicated recreational space per person. Private parks and open spaces are becoming correlative with power and wealth. Remote sensing and GIS can provide precise geo-referenced information on accessibility, size, shape, ownership, context, and distribution of open and green areas. Different technical approaches to the urban environment require a common spatial working basis that can integrate essentially heterogeneous investigation features by using an adequate surface classification. The "urban-structure type” concept was developed and used as a practical method for organizing urban spatial order and to provide a uniform methodological framework for different tasks within an interdisciplinary network of projects (Breuste 2002). Urban environmental planning decisions require characterization and rational analysis of urban landscapes according to ecologically relevant fea-

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tures. Integration of data from different sources (remotely sensed, fieldbased, and map-based) with differing spatiotemporal resolutions and thematic content is now operational in GIS environments and can deliver integrated data packages to planners. Biotope urban mapping using color infrared aerial photographs has been commonly used in the past to acquire structural information about urban areas on the basis of visual land-use classification. Now high-resolution remote sensing data (IKONOS, QuickBird), combined with advanced object-oriented analysis methods, offers a wealth of information for cities across the globe. Maps of growth and a classified urban structure derived from remotely sensed data can assist planners to visualize the trajectories of their cities, their underlying systems, functions, and structures. In order to share this information with policymakers in a usable format, we need to improve our capabilities in futures modeling and in combining remotely sensed, DEM, and GIS data to produce 3D visualizations and flythroughs. Arizona State University’s new Decision Theater (http://dt.asu.edu) is an example of an innovative facility that offers opportunities to visualize remote sensing research.

1.4 Climatic and atmospheric applications for urban remote sensing A problem common to cities around the world is the formation and intensification of urban heat islands (UHI) (Voogt and Oke 2003). Phoenix, Arizona represents a classic example of the UHI affect, but heat islands are present in almost every city; they increase energy and water use, biodiversity change, and human discomfort (Brazel et al. 2000). The cost of dealing with rising urban temperatures may also intensify social and environmental injustices in cities (Harlan et al. 2006; Jenerette et al. 2007; Stefanov et al. 2004). The aggregate effects of UHI on regional and global climate are poorly understood. Day and night thermal infrared data acquired by ASTER, MODIS, and Landsat can be used to model the UHI effect and quantify the contributions of different materials to the thermal budget. Urban and periurban regions in both developed and developing countries often have poor air quality due to industrial processes, automobile use, residential wood and coal burning, agricultural activities, and disruption of soil surfaces due to construction or informal settlement (Krzyzanowsk and Schwela 1999; Williams 1999). Although many cities in developed regions use networks of in-place, ground-level sensors to

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measure air quality on a real-time basis, this capability does not exist in much of the developing world. The availability of surface and atmospheric remotely sensed data at a variety of scales (from <1 m/pixel to 40 km/pixel) presents an opportunity to improve climatic models and monitor urban air quality (Zehnder 2002; Stefanov et al. 2003; Grossman-Clarke et al. 2005). Important parameters that can be measured with remotely sensed data include high-resolution land cover (IKONOS, Quickbird), biogeophysical variables such as albedo, vegetation cover, and aerosols (ASTER, MODIS, Landsat), and constituents of air pollution such as ozone (TOMS). Incorporating these data sources would improve the effectiveness of climate models for developing cities. Urban climate is a function of urban structure and activity (e.g., microclimates), and in each specific case one is dealing with different physical and cultural conditions. How do we compare cities when each will have different priorities? In general, one must maintain the scientific point of view and also be practical, and structure basic work to provide a unit of analysis attractive to each city, or at least to subsets of cities. But at first one should take advantage of what has already been studied and then ask, “What else can we add by using remote sensing?” How can we integrate urban remote sensing data specific to particular urban areas with theoretical and conceptual frameworks for sustainable development and landscape resilience? As an example, global scenarios need to be linked with research scaled to a local level, with local impacts sorted out from global impacts, and vice versa. This can be achieved by using historical weather records when available, but the use of these records as input for global models is questionable, due to potential site-specific bias in the results (i.e., location of weather stations at airports). We perceive a clear need for calibration of comparative studies and integration of data from urban centers into global climatology models. Satellite-based remote sensing cannot currently provide atmospheric information at a small (i.e., <1 km) scale, but it is useful in describing the climate patterns of urban areas by recording surface temperatures, soil moisture, land cover, and vegetation density. Nevertheless, there is a need for other data sources for climate modeling. Remote sensing products that can provide 3D topographical information on cities are now available for many urban centers from commercial vendors. Remote sensing can provide cities with periodically updated land-use and land-cover data useful for revising and refining meteorological models for local climate prediction, and air-pollution models such as the NCAR MM5 code (GrossmanClarke et al. 2005). Remotely sensed data are useful for improving land-use and land-cover information to models, rather than for actual monitoring of air quality. At-

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mospheric sounders can also record data useful for analysis of atmospheric composition and opacity, but the spatial scale of these data is typically too coarse for monitoring pollution. In the case of large dust storms and pollution plumes, sensors are useful in tracking the movement and extent of these atmospheric phenomena. Urban-structure characterization derived from remote sensing can demonstrate how the urban structure correlates with air-quality data acquired through ground measurements. Another interesting and promising topic is the contribution of cities and urbanized areas to regional, national, and international pollution patterns. How far, and where does the atmospheric plume travel? How does it mix with the atmosphere of neighboring cities? These are components of the problem of modeling air flow and pollution production, and are closely connected to open-space preservation, urban form, and urban topography.

1.5 Urban geohazards and environmental monitoring The surrounding and underlying geology of an urban center determines the types of structures that can be built and the susceptibility of the city to various geological hazards (Valentine 2003). Some of these hazards are obvious: expanding into areas in close proximity to active volcanoes, or building on sediments that can fluidize during earthquakes. Mexico City, for example, is exposed to both of these geological hazards. Less obvious hazards include subsidence beneath cities due to groundwater withdrawal, slope failures related to building on unstable hillsides, and mobilization of contaminant-laden dust from agricultural fields, industry, and construction. Remotely sensed data can help to assess these potential hazards and monitor settlement incursions. The subtle decreases in elevation due to subsidence can be measured by radar remote sensing (InSAR) and LIDAR. Mapping of surficial deposits and landforms resulting from prior earthquakes and volcanic activity can be significantly augmented by using multispectral, superspectral, and hyperspectral sensors (Landsat, ALI, ASTER, Hyperion). Increased temperatures that can herald volcanic eruptions can be measured using AVHRR, ASTER, and MODIS (Ramsey and Flynn 2004). Spectral analysis can also be used to map areas of potential soil contamination, e.g., noting a certain clay species that can absorb heavy metals (Ben-dor et al. 1999). Urban geohazards and environmental geology are growing subfields within the discipline of geological sciences. An important synergy of these fields combines social data with geohazard knowledge for predictive purposes, resulting in greater knowledge of where geological disasters might

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occur (e.g., areas of earthquake rupture). This can help guide future expansion, hazard response planning, and the development of appropriate engineering guidelines for buildings and infrastructure. What are the implications for an urban center when a natural catastrophe occurs? Can slowertimescale “catastrophes” such as groundwater pollution or structural failure due to subsidence be averted by cities when they have adequate information? Why does urban growth move towards high-risk areas? Remote sensing combined with GIS (e.g., DEMs) could help cities to develop plans to mitigate geohazard risks and assess implications for urban growth.

1.6 Urban form and periphery The given penetration of settlement and open-space structures strongly shapes the development of urban landscapes (Kuehn 2003). Urban planning authorities are challenged to develop new planning strategies instead of negatively judging the existing urban landscape. Remotely sensed data can be used to easily detect and evaluate mixed-neighborhood structures of settlements and open spaces in suburban areas. The current reality of widespread urbanization suggests that we need to understand the urbanized landscape as a new, independent type of cultural landscape. Remote sensing might be helpful on the regional scale in evaluating the role that landscape plays in connecting different settlements within urban regions and in separating the core city from the surrounding countryside. Seventy percent of people in US urban regions live in suburbs (Leichenko 2001), the intermediate zones between city centers and the rural hinterland. This trend not only characterizes the US, but is also recognized as a worldwide phenomenon. What are the global implications of living in suburban structures? Who are these suburbanites, and how are they different from dwellers in the urban core? The main question for planners today seems to be how urban areas should grow. Can we influence growth to be sustainable? Is urban sustainability a global concern, or is it only of regional importance? A worldwide remote sensing network could develop sustainability parameters for the urban regions of the world, and develop tools to address these questions. Remote sensing provides timely, spatially accurate, and spatially continuous data at reasonable cost. Often, it is the only available data source in inaccessible, provisional, and insecure areas (e.g., informal settlements, shanty towns, rescue camps) that can monitor and evaluate infrastructure needs and growth, as well as short-term changes such as wars and natural disasters. Further research and applications are needed to calibrate and en-

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sure the accuracy of remotely sensed data and tools. Remote sensing is also a necessary tool for examining how urban forms modify the landscape. For example, remote sensing can help us to detect and evaluate the distribution of impervious or sealed surfaces, a key parameter of urban ecology (surface and groundwater availability and runoff, vegetation dynamics) and planning (stormwater runoff, flooding hazards, heat islands).

1.7 Open space preservation The preservation of open spaces, especially green areas, is critical to sustainable development in urban regions (Ward Thomson, 2002). A sharp contrast exists, particularly in former heavy-industrial regions, between large, industrial, abandoned “brownfield” sites with high remediation costs and insufficient traffic links to the core area, and a development boom in the suburbs with more favorable features. In the city centers, landscape consumption has led to the sealing of surfaces, contamination of soil and water, and increased air pollution. Thus, a fundamental goal for the sustainable development of urban regions is to improve, protect, and develop urban and suburban green spaces, understand their ecological functionality and economic load-carrying capacity, and increase the quality of life for urban residents (Breuste 2004). A regional open-space protection agenda covers both suburban space and the open spaces in a city. Urban and periurban features that are measurable by remote sensing and GIS techniques include: x x x x

semi-natural and protected areas within, and at the fringe of, urban areas, unused open spaces (fallow land), park systems and private green spaces, open spaces in the suburban landscape (protected areas, arable and forest land).

Many cities are developing or have developed open space models, concepts or strategies to prioritize areas for preservation (Cook 2002). Remotely sensed data can provide information such as vegetation indices and land cover, and enhance monitoring capabilities. Small urban gardens and urban agricultural areas are particularly important for subsistence, as well as for recreational purposes, in both rich and poor countries. Such areas also feed back into the urban ecological system (i.e., improving the green

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structure and urban climate). Urban garden areas also enhance the resilience of urban areas by decreasing vulnerability to food crises.

1.8 Evaluation of urban natural environments Scientists often evaluate urban landscapes based upon functions that refer to landscape features such as soil, groundwater, and biotope types. Such functions are characterized as indicators of performance for interconnected landscape factors. If one overlays these functions spatially in a GIS, it will be clear that in some regions individual landscape components not only overlay, but enhance their mutual function. They can therefore act as monitors and warning systems for ecological functions in urban environments. For example, soil and groundwater conditions are frequently monitored as indicators of habitat health for various species; remedial action is initiated when quality levels become low. Areas that are highly suitable for natural restoration and preservation are of special interest for biotope preservation and protection of species. An effective, integrative strategy for landscape evaluation is necessary to clarify which management options provide the most ecological, biodiversity, and health benefits while minimizing negative outcomes (pollution, reduced biodiversity). Spatial information that shows the connections among different aspects of the landscape can help guide development along ecologically sound and sustainable paths. The principal question remains: How do we value different aspects of cities? Intangible elements such as comfort and psychological well-being need to be introduced into the cost-benefit analysis that typically governs political and planning decisions. Using remotely sensed and GIS data to better define the physical context of urban centers may provide useful information for crafting better socioeconomic models.

1.9 Urban satellite sensors and mission legacy Urban and periurban analysis using automated, satellite-based sensors has a long history; however, much of this work has focused on delineation of urban vs. nonurban land cover at coarse to moderate spatial resolutions (Donnay et al. 2001; Longley 2002; Mesev 2003). The ready availability of both commercial and governmental satellite data has led to several programs for comparative studies of urban centers. The United States Defense Meteorological Satellite Program Operational Linescan Systems (DMSP

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OLS) nighttime imagery of global light distribution has been used to estimate urban and suburban population densities and urban extents throughout the world (Sutton 2003; Elvidge et al. 2003). Astronaut photography has also been used to track urban growth in several US cities (Robinson et al. 2000). Extensive use has been made of the Landsat series of sensors to characterize urban extent and materials (Forster 1980; Jackson et al. 1980; Jensen 1981; Haack 1983 Haack et al. 1987; Stefanov et al. 2001b, 2003), and to conduct basic comparisons of urban centers (Ridd 1995; Ridd and Liu 1998). These sensors provide coarse to moderately high spatial resolution (up to 15 m/pixel), fairly low spectral resolution (up to seven bands in the visible through shortwave infrared and one to two thermal infrared bands), and good temporal resolution (typically fourteen- to sixteen-day repeat cycle). The Landsat series of sensors has also developed a rich historical database extending from 1972 to the present. A new generation of satellite-based sensors with greatly improved spatial resolution (15 m/pixel to less than 1 m/pixel) has been developed by the commercial sector and includes the Système Probatoire d’ Observation de la Terre, or SPOT (Martin et al. 1988), IKONOS (Dial et al. 2003), and Quickbird (Sawaya et al. 2003). Highly detailed land-cover, land–use, and ecological characterizations of urban and suburban regions are being produced from these sensor data (Weber 1994; Greenhill et al. 2003; Sawaya et al. 2003; Small 2003; Weber and Puissant 2003). However, data from these commercial systems are costly compared to government-operated sensors, limited in both spatial and temporal coverage, and spectral coverage is limited to the visible and near infrared wavelengths (Jensen 2000). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board the Terra satellite, and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board both the Terra and Aqua satellites are well-suited for urban analysis. The ASTER instrument acquires surface data in the visible to near-infrared (three bands at 15 meters/pixel), shortwave infrared (six bands at 30 meters/pixel), and thermalinfrared (five bands at 90 meters/pixel) wavelength regions of the electromagnetic spectrum (Abrams 2000). Each ASTER scene captures a 60 km x 60 km area. The ASTER acquires data useful for characterization and investigation of urban and periurban land cover, and biogeophysical parameters such as biomass, albedo, spatial metrics, and surface temperature and emissivity (Ramsey et al. 1999; Stefanov et al. 2001a; Zhu and Blumberg 2002; Ramsey 2003; Netzband and Stefanov 2003; Stefanov and Netzband 2005, 2007). The MODIS sensors have a similar wavelength range to ASTER, but with 36 bands in the visible through mid-infrared wavelengths, and a swath width of 2300 km. However, the spatial resolution of MODIS data is

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significantly lower and ranges from 250 m/pixel (two visible bands), through 500 m/pixel (five visible to shortwave infrared bands), to 1000 m/pixel (29 visible, near-infrared, shortwave-infrared, and mid-infrared bands; Parkinson and Greenstone 2000). Both the Terra and Aqua satellites are equipped with MODIS sensors providing repeat coverage over any given area of the Earth every one to two days. This makes MODIS data attractive for high temporal resolution monitoring of regional land-surface processes associated with urban centers (Schaaf et al. 2002; Schneider et al. 2003). The MODIS science team also provides validated data products useful for characterization and monitoring of regional-scale biogeophysical and climatic variables in urban and periurban areas.

1.10

Urban monitoring initiatives

The ready availability of both commercial and governmental satellite data has led to several comparative studies of urban centers under national and multinational auspices. Several such programs have focused on European cities (Eurostat 1995; Churchill and Hubbard 1994; Weber 2001). The MOLAND project was initiated in 1998 (under the name “Murbandy”, or “Monitoring Urban Dynamics”) with the objective to monitor the development of urban areas and identify trends at the European scale (Lavalle et al 2001). The work includes the computation of indicators for, and the assessment of, the impacts of anthropogenic stress factors (with a focus on expanding settlements, transport, and tourism) in and around urban areas and along development corridors. To date, the MOLAND methodology has been applied to an extensive network of cities and regions, for an approximate total coverage in Europe of 50,000 km2. The Urban Dynamics Research project (UDR) (http://landcover.usgs.gov/urban/intro.asp) of the US Geological Survey (USGS) analyzes land-use change in urban environments in order to provide a historical perspective on land-use change and an assessment of the trends in spatial patterns, rates, correlation, and impacts of that change. Databases developed by the UDR program contain interpretations of urban extent, transportation routes, water features, and other important land uses. Selected regional studies are currently in progress across the nation. Data source availability for each region, in conjunction with historical significance, determines the time periods that are mapped. Features are interpreted from diverse data sources, including historical topographic maps, satellite images, census statistics, and aerial photographs.

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The US Environmental Protection Agency (www.epa.gov/urban/index.html) is using satellite imagery and map data for selected metropolitan areas to determine trends in spatial patterns, rates, and impacts of urban growth from 1970-2000. A major goal is to determine and track relationships between land-use and land-cover changes and environmental quality parameters. Both existing and new land-use and land-cover maps were collected and generated for this project. Existing thematic map products included the National Land Cover Database (NLCD) and various locally developed maps from county and academic organizations. The physical expressions and patterns of urban growth on landscapes can be detected, mapped, and analyzed using remote sensing and geographical-information-system (GIS) technologies and software. Patterns of urbanization and sprawl can be described with a variety of metrics generated by statistical software and other comparable programs. For example, earth scientists with the NAUTILUS (Northeast Applications of Useable Technology In Land-Use Planning for Urban Sprawl) program are using these technologies to characterize urbanizing landscapes over time and to calculate spatial indices that measure dimensions such as contagion, the patchiness of landscapes, fractal dimension, and patch-shape complexity (Hurd et al. 2001).

1.11 Urban environmental monitoring project at Arizona State University The Urban Environmental Monitoring (UEM) project - now known as the 100 Cities Project - was originally conceived to collect daytime and nighttime ASTER data over 100 urban centers twice per year to capture local winter and summer seasons. (http://100cities.asu.edu/index.html). Launch of the ASTER on board the Terra satellite was only achieved in 1999, and usable data became available in 2000. Baseline characterization of each urban center was to be accomplished by land-cover classification using a modification of the expert system of Stefanov et al. (2001b). This global baseline dataset would then enable comparison of land-cover changes during the duration of the Terra mission (nominally, six years) (Ramsey et al. 1999; Stefanov et al. 2001a; Ramsey 2003). The primary application of remotely sensed data in this study is to extrapolate detailed local measurements to a regional context. Specifically, multispectral image classification and texture analysis are used to identify land-cover types, including densities of vegetation, soils, manmade materi-

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als, and water. This information is combined within a classification matrix, using an expert-system framework, to obtain final pixel classifications. Because modifications of the urban environment are often coupled with alterations of spatial structure, the investigation of texture and shape parameters, or neighborhood relations, apart from the spectral investigation applied so far, represents another area of inquiry. Land-cover classification using ASTER data has been a central focus of the UEM project. An expert-classification system was constructed and used to identify land-cover types, such as different densities of vegetation, soils, manmade materials, and water for fifty-five urban centers with good ASTER data coverage. A methodological approach using and analyzing landscape metrics was applied to Phoenix, Arizona. A 10-by-10 km grid was used to calculate a variety of landscape metrics on a regular and comparable basis. This landscape metric analysis was placed in a global context using a fragmentation metric for comparison between fifty-five urban centers with varying geographic and climatic settings in North America, South America, Europe, central and eastern Asia, and Australia. Temporal variations in land cover and landscape fragmentation were assessed for a smaller subset of centers. The results of the fifty-five-city analysis suggested that the highest levels of fragmentation were associated with urban centers in Europe, Africa, Asia, and India (Netzband and Stefanov 2003). Recently, the UEM project was revised in response to a NASAmandated restructuring of the original EOS program grant, resulting in broader science goals for the project than those presented in Ramsey (2003). The current UEM project now integrates a wider range of remotely sensed data (ASTER, MODIS, Landsat, and astronaut photography) to characterize and model urban-development trajectories and resilience to environmental stressors for eight intensively studied cities: Chang Mai, Thailand; Berlin, Germany; Canberra, Australia; Delhi, India; Lima, Peru; Manila, Philippines; Mexico City, Mexico; and Phoenix, USA. A more portable land-cover classification scheme was implemented, and the gridded spatial metric analysis was refined for Phoenix in order to investigate the potential uses of both ASTER and MODIS for temporal monitoring of urban spatial patterns and biophysical response (Netzband and Stefanov 2004; Stefanov and Netzband 2005, 2007). This approach is now being modified for other UEM cities such as New Delhi, India.

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1.12

Outlook

Despite the promise of new sensors and technologies, we still perceive a gap between the academic, research-focused results offered by the urban remote sensing community and the application of these academic data and products by governmental bodies of urban cities and regions. A study entitled, “Transforming Remote Sensing Data into Information and Applications” (National Research Council 2000; http://books.nap.edu/catalog/10257.html), examined technology transfer in the remote sensing arena and identified problems that must be overcome in order to develop useful civilian applications: xInformation is needed about the realistic potentials and the limitations of remote sensing data; transformation of data into information is a critical step. xProducers and technical processors of remote sensing data must be able to understand the needs, cultural context, and organizational environments of end users. Education and training can help ensure that end users better understand the potential utility of the technology. xData purchase is not the only cost of a successful application; longterm financial investment in staff, training, hardware, software or, alternatively, purchase of services from a value-adding provider, is recommended. Through the UEM project and collaborations with partners from other urban regions, we hope to determine the important planning questions facing our study cities, and use our data and scientific skills to help answer these questions. With the experimental case studies presented in the following chapters, we hope to stimulate further discussion and bridge the gap between applied geo-information scientists and urban planners. 1.12.1 Case study Phoenix, USA Stefanov, Netzband, Möller, Redman and Marks (Ch. 7) describe the production of comparable land-use and land-cover datasets for urban and periurban analysis of the central Arizona-Phoenix area. Remotely sensed (spectral, vegetation indices, spatial texture) and other geospatially explicit ancillary data (water rights, land use) are used in an expert system to derive final land-cover classifications. Ecological variables in the study areas are identified, and landscape fragmentation and diversity are assessed using landscape metrics.

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1.12.2 Case study Rio de Janeiro, Brazil Rego, Ueffing, and Viana (Ch. 2) describe the development of an objectoriented classification system used to examine a growing informal settlement encroaching upon the protected, fragile ecosystem of the Atlantic Rain Forest adjacent to Rio de Janeiro. 1.12.3 Case study Buenos Aires, Argentina Krellenberg (Ch. 5) describes a strategy to obtain information about the ecological structures of Buenos Aires along an urban to rural gradient, with emphasis on eight green areas, using satellite imagery and fieldwork. 1.12.4 Case study Berlin, Germany Hostert and Schneider highlight urban and suburban change-detection studies in the formerly divided (and now very dynamic) capital region of Germany. The land-cover and land-use changes detected through remote sensing (Ch. 3) are examined for their utility in the Digital Environmental Atlas of the Senate Department of Urban Development Berlin, as well as in the department’s planning and environmental activities (Ch. 9). 1.12.5 Case study New Delhi, India Rahman (Ch. 8) demonstrates the potentials and limitations of mapping and monitoring changes in the urban core and peripheral areas of this megacity. Rahman describes the Urban Extension Plan to accommodate Delhi's changing needs and investigates the potential for mapping and monitoring informal settlements, which is a critical and often uncontrolled issue in many fast-growing cities in developing countries. 1.12.6 Case study Chiang Mai, Thailand Lebel, Thaitakoo, Sangawongse, and Huaisai (Ch. 10) perform a spatial structural analysis of Chiang Mai, the second-largest urban region in Thailand. They examine the city’s development over time and the implementation of geo-information methods in modeling landscape planning and design. Their contribution focuses upon urbanization and quality of the biophysical environment of urban tropical areas.

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1.12.7 Case study Chengdu and Guangzhou, China Seto, Fragkias, and Schneider (Ch. 11) have worked with urban planners and local governments to understand the impact of changing economic systems, policies, and institutions upon land use in rapidly developing Chengdu, in central Sichuan Province. China has recently targeted this region for industrialization and modernization in its “Go West” programs. The authors compare urban-growth patterns with major changes in economic systems in transition cities, and then compare the results to other urban centers in China. 1.12.8 Interurban comparison Small (Ch. 4) examines some characteristics of vegetation distributions and spatial structure for six contrasting urban settings using high spatial resolution Quickbird multispectral imagery. Stefanov and Brazel (Ch. 6) provide an overview how remotely sensed data can be used for characterization and monitoring of urban heat islands, and assess the effectiveness of mitigation strategies (increased vegetation, low albedo surfaces, etc.).

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References

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Netzband M, Stefanov WL (2004) Urban land cover and spatial variation observation using ASTER and MODIS satellite image data. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 35 (No.B7):1348 - 1353 Ramsey MS (2003) Mapping the city landscape from space: The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) Urban Environmental Monitoring Program. In: Heiken G, Fakundiny R, Sutter J (eds) Earth science in the city: A reader. American Geophysical Union, Washington, DC, pp 337-361 Ramsey MS, Stefanov WL, Christensen PR (1999) Monitoring world-wide urban land cover changes using ASTER: preliminary results from the Phoenix, AZ LTER site. Proceedings of the 13th international conference, applied geological remote sensing, Vol. 2. ERIM International, Vancouver, BC, Canada, pp. 237-244 Ramsey MS, Flynn LP (2004) Strategies, insights, and the recent advances in volcanic monitoring and mapping with data from the Earth Observing System. Journal of Volcanology and Geothermermal Research 135 (1-2):1-11 Ridd MK (1995) Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. International Journal of Remote Sensing 16:2165-2185 Ridd MK, Liu J (1998) A comparison of four algorithms for change detection in an urban environment. Remote Sensing of Environment 63:95-100 Rindfuss RR, Stern PC (1998) Linking remote sensing and social science: The need and the challenges. In: Liverman D, Moran EF, Rindfuss RR, Stern PC (eds) People and pixels. National Academy Press, Washington, DC, pp 1-27 Robinson JA, McRay B, Lulla KP (2000) Twenty-eight years of urban growth in North America quantified by analysis of photographs from Apollo, Skylab, and Shuttle-Mir. In: Lulla KP, Dessinov LV (eds) Dynamic earth environments: remote sensing observations from Shuttle-Mir missions). John Wiley & Sons, New York, NY, pp 25-41 Sawaya KE, Olmanson LG, Heinert NJ, Brezonik PL, Bauer ME (2003) Extending satellite remote sensing to local scales: Land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment 88:144156 Schaaf CB, Gao F, Strahler AH, Lucht W, Li XW, Tsang T, Strugnell NC, Zhang XY, Jin YF, Muller JP, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, d'Entremont RP, Hu BX, Liang SL, Privette JL, Roy D (2002) First operational BRDF, albedo and nadir reflectance products from MODIS. Remote Sensing of Environment 83 (1-2):135-148 Schneider A, McIver DK, Friedl MA, Woodcock CE (2003) Mapping urban areas by fusing coarse resolution remotely sensed data. Photogrammetric Engineering and Remote Sensing 69:1377-1386 Small C (2003) High spatial resolution spectral mixture analysis of urban reflectance. Remote Sensing of Environment 88:170-186

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Soja EW (1995) Postmodern urbanisation: The six restructurings of Los Angeles. In: Watson S, Gibson K (eds) Postmodern cities and spaces. Blackwell, Oxford, CA, pp 125–137 Stefanov WL (2002) Remote sensing of urban ecology at the Central ArizonaPhoenix Long Term Ecological Research site. Arid Lands Newsletter 51, http://ag.arizona.edu/OALS/ALN/aln51/stefanov.html, accessed 6 March 2007 Stefanov WL, Netzband M (2005) Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center. Remote Sensing of Environment 99 (1-2):31-43 Stefanov WL, Netzband M (2007) Characterization and monitoring of urban/periurban ecological function and landscape structure using satellite data. In: T. Rashed T, Jürgens C (eds) Remote sensing of urban and suburban areas.. Springer, New York, NY (in press) Stefanov WL, Christensen PR, Ramsey MS (2001a) Remote sensing of urban ecology at regional and global scales: Results from the Central ArizonaPhoenix LTER site and ASTER Urban Environmental Monitoring Program. Regensburger Geographische Schriften 35:313-321 Stefanov WL, Ramsey MS, Christensen PR (2001b) Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of Environment 77:173-185 Stefanov WL, Ramsey MS, Christensen PR (2003) Identification of fugitive dust generation, transport, and deposition areas using remote sensing. Environmental and Engineering Geoscience 9:151-165 Stefanov WL, Prashad L, Eisinger C, Brazel A, Harlan S (2004) Investigations of human modification of landscape and climate in the Phoenix Arizona metropolitan area using MASTER data. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 35 (B7):13391347 Sutton PC (2003) Estimation of human population parameters using night-time satellite imagery. In: Mesev V (ed) Remotely sensed cities. Taylor & Francis, London, pp 301-333 USGS (2003) MODIS reprojection tool user’s manual, release 3.1a. US Geological Survey, Sioux Falls, SD Valentine GA (2003) Towards integrated natural hazard reduction in urban areas. In: Heiken G, Fakundiny R, Sutter J (eds) Earth science in the city: A reader. American Geophysical Union, Washington, DC, pp 63-73 Voogt JA, Oke, TR (2003) Thermal remote sensing of urban climates. Remote Sensing of Environment 86:370-384 Ward Thomson C (2002) Urban open space in the 21st century. Landscape and Urban Planning 60:59–72 Weber C (1994) Per-zone classification of urban land cover for urban population estimation. In: Foody GM, Curran PJ (eds) Environmental remote sensing from regional to global scales. John Wiley & Sons, Chichester, pp 142-148

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Weber C, Puissant A (2003) Urbanization pressure and modeling of urban growth: Example of the Tunis metropolitan area. Remote Sensing of Environment 86:341-352 Williams ML (1999) Patterns of air pollution in developed countries. In: Holgate ST, Samet JM, Koren HS, Maynard RL (eds) Air pollution and health. Academic Press, San Diego, CA, pp 83-104 Zehnder JA (2002) Simple modifications to improve fifth-generation Pennsylvania State University-National Center for Atmospheric Research mesoscale model performance for the Phoenix, Arizona metropolitan area. Journal of Applied Meteorology 41:971-979 Zhu G, Blumberg DG (2002) Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel. Remote Sensing of Environment 80: 233-240

Chapter 2 - Automatic Land-Cover Classification Derived from High-Resolution IKONOS Satellite Imagery in the Urban Atlantic Forest of Rio de Janeiro, Brazil, by Means of an Object-Oriented Approach

Luiz Felipe Guanaes Rego1, Christoph Ueffing2, Sérgio Besserman Vianna3 1

Geography Department, Pontifical Catholic University of Rio de Janeiro, Brazil

2

Ueffing Umwelt Consult, Merzhausen, Germany

3

Economy Department, Pontifical Catholic University of Rio de Janeiro, Brazil

2.1 Introduction The Atlantic Forest is the most devastated Brazilian biome. This forest initially occupied the coastal region of the country from Ceará to Rio Grande do Sul and was approximately 1,000,000 km2 in size. Now the forest is limited to areas distributed across several states, and occupies approximately 91,000 km2. This level of devastation can be explained by the economic value of forest species, as well as by intense human occupation:

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approximately 70% of the Brazilian population lives in the region, creating all kinds of anthropic pressures (Thomas et al. 1998, Sips 1999). The Atlantic Forest, designated by UNESCO as a Biosphere Reserve, is one of the richest forests in the world in terms of biological diversity, with high levels of endemism: 30 % of the country’s arboreal species, 150 bird species, and 50 species of small animals. In addition to stabilizing steep slopes, the forest also contributes to the quality of human life, especially because of its esthetic value, and produces a healthy and balanced landscape (Mori et al. 1981). The City of Rio de Janeiro (Plate 2.1) is situated at 22o54’23” south latitude and 43o10’21” west longitude, in the Municipal District of Rio de Janeiro. It is the capital of its state and part of the southeast region of Brazil. Geographically, the City of Rio de Janeiro consists of great coastal and inland plains that are separated by two big mountain ranges occupied by limited areas of secondary-growth Atlantic Rain Forest of different ages, and even smaller areas of primary-growth Atlantic Rain Forest (Oliveira 1998). This geographic setting generates a particular distribution around which the city has developed, surrounded by the massif and in many areas also bordered by the sea on the east. The result is an extremely rich landscape, highlighted by verdant vegetation on the hills and urban expansion on the lowlands. An overview of Rio de Janeiro City can be seen in Plate 2.4. After São Paulo, Rio de Janeiro is the country’s second-biggest city, with a population of 5,848,914 (Statistics Yearbook 2000). The city presents contradictions that are spatially expressed as two cities: one with a well-developed infrastructure and services, and the other with no infrastructure, but with sanitation, electric power, and service problems. These two cities experience a negative, shocking transformation process that leads to social transgressions and environmental degradation. Their duality may be seen spatially, for instance, in the contrast between the fashionable boroughs situated in the south of the city and the favelas, or squatter settlements (illegal housing that is going through the process of being legalized, usually on hillsides, where people squat). Some of the latter areas are unsuitable for habitation because of the constant risk of landslides. Favelas are not slums, because they are not dilapidated areas of the original city. This spatial contradiction creates a link between service providers and service users, each depending upon the other and thus justifying itself (Corrêa 1997). These factors illustrate the complexity of the spatial occupation of the city and the attendant need for land-cover and land-use maps to facilitate the study of spatial arrangements that these pressures create. Understanding how space is being used now and monitoring land-use and land-cover

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change over time will facilitate the definition of standards for change and of priorities for preservation. Regular monitoring will also make it possible to evaluate the impacts of preservation initiatives, and to review and revise them. In spite of efforts to conserve the two mountain ranges, official data from City Hall has shows continuing urban pressure on the forest area, which changed from 18.3% of the municipality’s total area in 1884 to 13.35% in 1999; 6,242 hectares of the forest underwent coverage alteration (SMAC 2000). This fact makes the continuity of the monitoring process imperative and demonstrates the need for more detailed studies that allow spatial evaluation of the areas at risk for alternation of forest coverage, as well as the definition of the factors that favor this risk (Müller-Beilschmidt 2001). The city’s land-cover information is currently produced by visual interpreting techniques, primarily because of the complexity of the municipality’s coverage standards, which are obtained from orbital SPOT and LANDSAT imagery (SMAC 2000) and require highly qualified, costly labor. The acquisition of the image and the production of maps may last several months, making the process slow and, consequently, very expensive (Lillesand and Kiefer 1999). With spatial resolution of 30 m by 30 m, the land-cover maps produced by these images generate products compatible with the 1:50,000 scale that permits an overview of the problem. However, at that scale they make accurate definition of the boundaries of change difficult, which in turn makes more difficult the definition of the locally operational parameters that permit objective action towards the solution of problems (Mas and Ramirez 1996). Therefore, timeliness and accuracy are essential factors in the production of land-cover maps in Rio de Janeiro because of the speed and fragmentation of the transformation process of the municipality’s area (SMAC 2000). In short, it is necessary to find a classification process that enables the production of land-cover maps of the city within weeks. It is also necessary that these maps have a level of detail that makes it possible to identify the fragmented process of urban expansion into the forests. The proposed hypothesis, then, involves the verification of the capacity to reproduce automatically, and thus quickly, the city’s official coverage classes, using high-resolution IKONOS data to produce classifications that are compatible with the scale of 1:10,000 (Herold et al. 2002; Hofman 2001; Meinel et al. 2001).

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2.2 Methodology

2.2.1 Study area This work focused on part of the Pedra Branca State Park in the City of Rio de Janeiro, Brazil (Plate 2.1). The park was chosen because of the severe environmental problems it faces due to its location within the City. It was also chosen because of boundary limits such as irregular urban expansion, agricultural areas within the park’s limits, and problems caused by annual fires. Pedra Branca State Park (Figure 2.1) is situated in the Pedra Branca Massif, above the 100 m contour line and between the coordinates 22o53’ and 23o south latitude, and 43o 23’ and 43o 32’ west longitude. The average annual temperature is high, above 22o C. In the summer, temperatures ranges from 30o to 320 C, peaking at about 40o C. Winter is pleasant, with average temperatures above 18o C. Rainfall ranges from 1,500 to 2,500 mm, with summer the rainiest time of the year and winter the driest. The microclimatic variation is a result of the city’s proximity to the sea, the trend of urbanization towards the hillsides, and the abusive process of deforestation. 2.2.2 Data The data used was a cloud-free IKONOS scene from February 16, 2001, with 11-bit radiometric resolution, orthorectified with ER Mapper and Orthowarp software. The orthorectification of the 1 m-resolution panchromatic band has an overall accuracy of less than two pixels, based on an existing digital elevation model. The accuracy of the orthorectification of the 4 m-resolution multispectral bands of the IKONOS scene is about 0.5 pixels and fits very well with the rectified panchromatic band. The reference dataset for both rectifications was the mosaic of orthorectified aerial photos of Rio de Janeiro, map reference South American Datum 1969, projection UTM, zone 23 south. The IKONOS scene of the test area inside of Rio de Janeiro covers a section of the middle of the city, 11.5 km by 13.5 km in size, characterised by urban areas adjacent to forested, mountainous areas. The altitude of the area varies from sea level to 1,000 meters above sea level. After the rectification of the satellite scene, the multispectral bands of the image were topographically normalised with the C-factor

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method of the Silvics software package to reduce the effects caused by the high-relief energy inside the test area.

Fig. 2.1. Pedra Branca State Park

2.2.3 Analysis According to their hierarchy, the coverage classes used by the city administration in the study area may be grouped into generic classes that are then subdivided into subclasses. Most land-cover classification systems adopt this process to establish classifications at various levels of detail (Meinel and Hennersdorf 2002). The process to evaluate classification techniques used in this study used this hierarchical concept of classes, dividing the problem into three different levels of detail called phases. Within these phases, the results of the automatic classification techniques were tested from general to specific, beginning with three generic classes and ending with all the land-cover subclasses used by the city administration.

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The first phase evaluated a set of three generic classes in a relatively small subset (493 hectares), using a number of classification techniques. Phase Two evaluated a set of six classes in a subset four times larger than the first phase, and with fewer classification techniques. Phase Three evaluated a set of eight classes in a subset that represented half of the IKONOS scene, and applied one classification technique. Each phase was distinguished by a set of factors: the classes that were used, the area of the subset, and the number of options for classification. These factors varied from phase to phase: the classes and the subsets’ areas increased and the number of options for classification decreased, as seen in Figure 2.2. Generic categories in the first phase show that with the increase in the number of classes, subclasses were created that deepened the description of the landscape contained in the IKONOS image. The final product obtained in the third phase resulted in eight classes, which were then merged into the five classes used by the city administration. The increase in the number of classes from phase to phase coincided with the increase in area of the subsets evaluated. As a result, there was an attempt to confirm the stability of the classification results, while at the same time increasing the number of objects in the image that represented the classes intended to be extracted in each phase. We also attempted to make the study more realistic by looking for practical solutions rather than simple theoretical results that could be found in a fixed subset, which would clearly not embody the whole variance spectrum that is represented in a complete IKONOS image.

Fig. 2.2. Characteristics of the Phases in the Research Project

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2.3 Results and discussion Phase One tested three generic classes: Field, Vegetation, and Building, as well as a large number of automatic classification techniques for orbital images. These techniques involved pixel-based classifications (Experiment A), and object-based classification techniques (Experiment B; Herold et al. 2002; Hofman 2001; Smith and Hofmann 2001). In more than 100 classifications, the object-based system resulted in higher classification accuracy than the pixel-based system. This may be seen in Figure 2.3. Phase One also tested the membership classifier in the eCognition software which, despite the large number of available classifiers, proved inferior to the Nearest Neighbour classifier. Results suggest that in generic classes that involve objects with great variations within only one class, the best results were obtained by the Nearest Neighbour. This may be seen in Figure 2.4.

overall accuracy

1 0.8 0.6 0.4

Exp A Exp B

0.2 0

Fig. 2.3. The 40 best results from experiments A (pixel based) and the 40 best results from experiments B (object-based)

Phase Two involved six classes that were extracted in two steps. The first step involved the three classes from Phase One: Vegetation, Field, and Building. The second step involved six classes: Grouped Vegetation, Urban Vegetation, Urban Field, Grouped Field, Single Building, and Building Complexes identified by considering neighbourhood relations between classified objects. These classes were developed with the consistency prin-

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ciple used in the land-cover classifications. The second group is composed of subclasses of the generic classes extracted from the first group, Phase One, as shown in Figure 2.5.

overall accuracy

0.9 0.895

0.893

0.89 0.883

0.885 0.88 0.875 0.87 exp 1b2

exp 1b3

Fig. 2.4. The best classification results from Experiment 1b2 (Nearest Neighbour) and Experiment 1b3 (Membership)

Fig. 2.5. Classes from Phases One and Two

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Phase Two confirms the potential to describe contextual classes in the object environment. Contextual relations, however, proved to be very broad and flexible. The logic used by the analyst for the creation of these classes and the definition of their contextual relations are very specific and different definitions may lead to different results. Phase Three classes were produced using the classes developed in Phase Two. The Urban Vegetation, Urban Field, and Building Complexes classes were added to the Urban Area class, which was later subdivided into the Urban Not Consolidated and Urban classes. The Vegetation class was subdivided into Vegetation and Altered Vegetation, and the Grouped Field class was renamed Field as shown in Figure 2.6.

Fig. 2.6. Classes from Phase Three

Phase Three evaluated classifications produced inside the multihierarchic environment in eCognition software, with levels composed of objects of different sizes that enable classification of large objects according to the relative area of a class classified with small objects. Because of this two classes, Altered Vegetation and Urban Not Consolidated, were developed from proportion relationships between the sub-objects classified as Field and Urban Field. The relationship between objects and sub-objects proved to be powerful, but, once again, too dependent upon the analyst’s positions, which effect not only the definition of samples and evaluation using statistical parameters, but also the development of a spatial logic that enables the classifiers to be developed in sequential and interrelated phases. Therefore, the object-based classification process requires the creation of logical hierarchies that establish classes within the context of relationships between classes and subclasses, and among objects of different classes. This system resembles the one used for visual classification in which interpreters select big

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Luiz Felipe Guanaes Rego, Christoph Ueffing, Sérgio Besserman Vianna

Overall Accuracy

groups and, using varied parameters, subdivide these groups into subgroups, from the generic to the specific. The integrated results of the three phases of this project lead us to assert that the use of IKONOS images and the object-based classification process enable the reproduction of land-cover classes used by the Rio de Janeiro city government. This classification can be developed automatically and produces results compatible with a scale of 1:10,000, with higher accuracy (87%) than the current classification developed by City Hall (61%), as shown in Figure 2.7.

1

0 .8 7

0 .8

0 .6 1

0 .6 0 .4 0 .2 0 A utom atic

V isu a l

Fig. 2.7. Automatic classification (IKONOS) compared to the visual classification developed by City Hall (LANDSAT- SPOT)

2.4 Conclusion This work confirms our hypothesis that the city’s official coverage classes can be reproduced automatically at a scale of 1:10,000, using highresolution IKONOS images. The authors recommend that City Hall adopt the practice of using high-resolution IKONOS images to produce landcover maps, because such maps can be developed more quickly, with higher accuracy and more detailed class definitions, than maps produced from LANDSAT images. The acceleration of the whole process, from the purchase of a satellite scene to the visualisation of the classified information via the developed interfaces, should be noted. The old system of visual interpretation of the satellite scene took about one and one-half years to produce a useable map. With the new system, all information will be available in a platformindependent environment after only three to six months. This would ensure that information on the dynamics of transformation in the municipality’s

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space is constantly updated, and consequently, management and planning actions of the city can be quickly established. Despite the specificity of the study area and the classes used, this work enables us to generically substantiate the potential of the objectbased classification system, as well as of IKONOS images, for creating land-cover classifications. This method is especially effective in urban applications because of the complexity of the classes in this environment, and for mapping on the large scales required for actions at management and operational levels.

2.5 References Corrêa RL (1997) Trajetórias geográficas – Meio ambiente e sociedade. Bertrand, Brasil Herold M, Scepan J, Müller A, Günther S (2002) Object-oriented mapping and analysis of urban land use/cover using IKONOS data. In: Benes T (ed) Geoinformation for European-wide integration. Millpress, Rotterdam, pp 533-538 Hofmann P (2001) Detecting informal settlements from IKONOS image data using methods of object oriented analysis – An example from Cape Town (South Africa). Regensburger Geographische Schriften 35:107-118 Mas JF, Ramirez I (1996) Comparison of land use classifications obtained by visual interpretation and digital processing. ITC Journal 3-4:278-283 Meinel G, Hennersdorf J (2002) Classification systems of land cover and land use and their challenges for picture processing of remote sensing data – Status of international discussion and programs. Proceedings of the 3rd International Symposium, Remote Sensing of Urban Areas, June 11-13, Istanbul, Turkey, http://www.tudresden.de/ioer/PDF/PublikPDF/meinel_hennersdorf_istanbul2 002.pdf, accessed 6-March-2007 Meinel G, Neubert M, Reder J (2001) The potential use of very high resolution satellite data for urban areas–First experience with IKONOS data , their classification and application in urban planning and environmental monitoring. Regensburger Geographische Schriften 35:196-205 Mori SA, Boom BM, Prance GT (1981) Distribution patterns and conservation of easter brazilian coastal forest tree species. Brittonia 33 (2):233-245 Müller-Beilschmidt N (2001) Analysis of deforestation in the urban area of Rio de Janeiro with GIS. MS thesis, Albert Ludwigs University of Freiburg Lillesand TM, Kiefer RW (1999) Remote sensing and image interpretation (4th Ed). Wiley, New York, NY Oliveira RR (1998) Processos naturais e antrópicos na evolução da paisagem florestal em regiões tropicais. Revista da Pós Graduação Em Geografia, Rio de Janeiro 2 (2):120-135 Smith GM, Hoffmann A (2001) An object based approach to urban feature mapping. Regensburger Geographische Schriften 35:100-106

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Sips P (1999) The Atlantic forest of South Bahia, Brazil: A hotspot within a hotspot. European Tropical Research Network Newsletter 29, http://www.etfrn.org/etfrn/newsletter/frames/nl29.html, accessed 6-March2007. Statistics Yearbook (2000) Statistics Yearbook. City of Rio de Janeiro, Rio de Janeiro, Brazil SMAC (2000) Mapeamento e caracterização de usos das terras e cobertura vegetal no município do Rio de Janeiro entre os anos de 1984 e 1999, Technical report, Secretaria Municipal de Meio Ambiente, Rio de Janeiro Thomas WW, de Carvalho AM, Amorim AM, Garrison J, Arbeláez (1998) Plant endemism in two forests in southern Bahia, Brazil. Biodiversity and Conservation 7 (3):311-322

Chapter 3 - Advances in Urban Remote Sensing: Examples From Berlin (Germany)

Patrick Hostert Deparment of Geomatics, Institute of Geography, Humboldt University, Berlin, Germany

3.1 Introduction Urban remote sensing has long been a niche aspect of modern remote sensing. Aerial photo interpretation, based on national aerial photo surveys, is an established method in urban planning and in the context of urban ecological applications. Thermal remote sensing also has a long tradition in the urban context. Many other remote sensing approaches have become established in major fields of research, but not in urban remote sensing. There are several reasons for this limitation. Most urban features exhibit a great heterogeneity and hence vary substantially with regard to their object-wise spectral variance. Object size and heterogeneity are often related. Objects in complex urban environments (buildings, streets, cars) are, in general, relatively small when compared to objects commonly found in rural scenes, like agricultural fields, forest plots, or open water (Small 2003). Pixel mixing varies with pixel size, but is much higher in urban scenes. Additionally, the combination of natural and anthropogenic materials complicates the data analysis. Cities are not solely defined by the built environment. Typical urban objects are interspersed with large areas of natural materials (vegetation, soils, water). Pixels often contain intimate mixtures of natural and anthropogenic materials. Off-nadir viewing angles of many sensors result in extreme differences in object illumination. The strength of this effect is a spectrally varying function depending on sun-object-sensor geometry. Spectral heterogeneity

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is hence met by illumination-dependant spectral variability of otherwise identical spectral features. There is also the problem of shadows and shading in the built environment. Urban remote sensing presents numerous challenges when it comes to automated image-analysis approaches. It is therefore not surprising that urban remote sensing has failed to make substantial advances beyond the established methods in visual aerialphotography interpretation. However, a wealth of new analysis options and applications has been developed with the advent of new sensor generations and new methods during recent years. Important steps forward are possible thanks to enhanced geometric and spectral resolutions, object-based analysis, and hyperspectral image-processing techniques. The government of Germany’s capital, Berlin, has long used airborne, as well as satellite, remote-sensing data to solve environmental problems and generate advanced mapping products. The Environmental Atlas of Berlin has pioneered the structured approach towards environmental information collection, analysis, and presentation (Chapter 9, this volume). Past applications of remote sensing have not always resulted in satisfactory results. This is a direct consequence of the above-mentioned limitations concerning data quality and characteristics; additionally, the consequent lack of adequate methods did not allow for optimized results in this context. Future remote-sensing-based approaches for urban environmental applications in Berlin have to focus on a demand driven approach: A problem definition has to be followed by a proper characterization of underlying processes and resulting indicators. It will then be necessary to evaluate how far such indicators may be monitored and assessed with remote-sensing data and methods. Quality criteria have to be applied to ensure that limitations are well defined. These criteria should be judged against the available data, methods, and, last but not least, costs involved. A short overview of new technologies and methods will illustrate the future potential in urban remote sensing.

3.2 New remote sensing technologies A wealth of new sensor technologies has opened up new research possibilities during the last decade, and especially during the last seven years. Focusing on passive systems in the visible to shortwave-infrared wavelength domain, geometric and spectral high-resolution systems were developed that complement or supersede older technologies for many applications. Some of these systems are operational today and available to a wide community of remote-sensing scientists and users (Kramer 2002).

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Advances in the field of geometrically very-high-resolution (VHR) sensors are most obvious. Related developments are of particular interest in urban remote sensing, as the heterogeneity criterion is largely dependant upon the spatial resolution of a sensor. We may distinguish, mainly from the scale dependent point of view, between airborne and spaceborne sensor systems. Digital airborne sensors, such as the High Resolution Stereo Camera (HRSC) or the Leica ADS40 Airborne Digital Sensor, achieve spatial resolutions of a few centimeters or decimeters when flown at standard altitudes. This development will sooner or later lead to a replacement of standard aerial photography by digital products of similar or better quality. At the same time, falling costs and the direct link to the digitalprocessing chain, including near-realtime GIS integration, will dramatically increase the benefits of such data. Spaceborne VHR data from modern sensors have been available since 1999, when IKONOS opened up the field of multispectral satellite data with a resolution of 4 m accompanied by 1 m panchromatic data. In 2001, panchromatic sub-meter resolution became available from space with QuickBird data (0.61 m panchromatic, 2.44 m multispectral), a sensor with similar characteristics to IKONOS otherwise. Geometric capabilities close to aerial photographs, synoptically recorded, and extended datasets from geometrically stable platforms, are available from space since the advent of IKONOS and QuickBird data. Spectral high-resolution sensors, often referred to as hyperspectral sensors, are confined to airborne platforms at present. Spaceborne systems, such as Hyperion or MODIS, lack the spatial resolution needed for urban applications. Today, sensors like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) or HyMap are the most advanced hyperspectral systems available for urban applications. The ground sampling distance varies with operating altitude and can be less than 4 m in the case of HyMap data. AVIRIS and HyMap offer unique spectral information about urban surfaces with their 224 and 125 spectral bands, respectively. Several spectrally and geometrically less-advanced spaceborne sensors offer worthwhile capabilities for diverse applications. For example, the latest generation of the established Système Probatoire d’Observation de la Terre (SPOT) sensor family provides panchromatic data with a resolution of up to 2.5 m, along with 5 m multispectral data including visible, nearinfrared, and shortwave-infrared wavelengths. SPOT covers 60 x 60 km2 per scene, i.e., about 36 and 14 times the area covered by one scene of IKONOS and QuickBird, respectively. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) offers 15 bands, including 6 in the shortwave-infrared (SWIR) and 5 in the thermal-infrared wavelength regions. These spectral characteristics offer new opportunities,

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considering the fact that the urban environment consists largely of anthropogenic materials, which are mainly characterized by spectral absorption bands in the SWIR wavelengths.

3.3 New remote sensing methods It should be stressed that advancing sensor technologies do not, per se, lead to more advanced analysis results. Sensor improvements need always to be understood in the context of matching methodological progress, i.e., innovative data sets need to be adequately explored through adapted processing schemes. In the overwhelming number of cases, there is no simple “push-the-button” strategy for analyzing remote-sensing data effectively in the urban context. This is particularly true of information extraction techniques. A certain degree of automation is achievable when pre-processing remote sensing data (data calibration, radiometric and topographic correction, geometric rectification). The need for certain pre-processing steps and the attainable accuracy of results largely varies with the type of sensor, its internal layout, and the sensor platform (airborne or spaceborne). Without delving into too many details, two examples will illustrate how individual pre-processing issues influence the overall effort of pre-processing. VHR satellite data from QuickBird can be ordered along with relevant sensor orbit information to perform an orthorectification. It is a straightforward process to geometrically correct a QuickBird scene, having obtained the rational polynomial coefficient (RPC) files. A digital elevation model (DEM) and a few accurate ground control points (GCPs), e.g., from largescale maps or acquired with differential global position systems (DGPS), are additionally needed (Toutin et al. 2002). A radiometric correction is usually not needed, as the four-band data set will typically serve as input for qualitative analyses. In contrast, pre-processing of airborne hyperspectral data as obtained by the HyMap sensor always requires a dedicated geometric and radiometric pre-processing in order for its inherent properties to be adequately understood (Schläpfer and Richter 2002, Richter and Schläpfer 2002). With regard to geometry, this means synchronizing DGPS and inertial-navigationsystem (INS) data with the image, a DEM, and GCPs, and iteratively calculating new image positions until a satisfactory result is achieved. With regard to radiometry, an accurate sensor calibration by the provider must be taken for granted to achieve useful results. The scene-dependant correction of atmospheric and illumination effects requires a parametric atmos-

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pheric correction including topography (i.e., a DEM), relying on a pixelwise calculation of atmospheric water vapor. Particularly in urban environments, the essential correction of bidirectional effects in such data sets is important. These processing techniques have made major advances during the last decade, but are still a barrier to a broader use of hyperspectral imagery. Likewise, the advances achieved in methods of information extraction have been considerable during the last decade. The introduction of objectoriented approaches is probably the most prominent example, especially in terms of analysis of heterogeneous urban environments. Object-oriented image analysis focuses on a segmentation of data into rather homogeneous areas, for example, by means of the spectral and spatial characteristics of such areas (Baatz and Schaepe 2000). This procedure may be iteratively performed at different scales to achieve hierarchical levels of objects. Analyzing objects instead of pixels has substantial advantages, particularly in the case of geometric VHR data of urban environments; salt-and-pepper effects in analysis results are suppressed by appropriate object structures. Moreover, new options appear when analyzing hierarchical image objects. For example, a classification achieved at an initial level may be used to enhance final results by introducing scale dependencies in knowledgebased decision tree classifiers. Rules such as, “If at least 75% of the sublevel objects are trees, the super-object has to belong to the class park or forest” may be easily introduced. It is obvious that heterogeneous urban environments might particularly profit from such approaches. Data sets with contiguous and narrow bands are needed to differentiate the subtle spectral differences in urban environments, as spatial heterogeneity is met by an extreme diversity of natural and anthropogenic materials. However, “classical” approaches of supervised or unsupervised image classification are not necessarily suited to resolve such differences. Spectral unmixing or other hyperspectral image analysis tools hence have a great potential for urban analyses based on spectral high-resolution data (Hostert 2007). Reference data referring to the relevant surface components often cannot be derived from the imagery itself, as certain materials are simply not available as pure materials in such data sets. Alternatively, field-based spectrometric measurements have to be compared to radiometrically corrected hyperspectral data. Those may be obtained from available spectral libraries; however, reference spectra representing urban features are far from complete in available spectral libraries and hence, intensive field-data collection is needed to optimize such strategies in the future.

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3.4 Examples Two examples have been chosen to illustrate how new sensor technologies and appropriate analysis schemes can be utilized to enhance future remotesensing-based products for urban environmental applications in Berlin. First, a comparison between Landsat ETM+ data and data from ASTER explains similarities of past and potential future satellite-sensor generations for medium-scale environmental monitoring. Secondly, a study based on QuickBird data of Berlin exemplifies how geometric VHR satellite imagery can be analyzed based on object-oriented approaches. 3.4.1 Sensitivity analysis of Enhanced Thematic Mapper and ASTER data for urban studies With the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra-AM1, a new Earth Observation System (EOS) sensor concept was implemented – almost at the same time as the start of Landsat-7 ETM+ (Table 3.1). ASTER was developed for long-term global observations of the land surface, biosphere, solid Earth, atmosphere, and oceans. One of the specific topics followed by those who use ASTER is the application of ASTER data for monitoring urban change (Stefanov et al. 2001; Ramsey 2003). Table 3.1. Characteristics of the ASTER and ETM+ sensors spatial Sensor resolution [m] ASTER 15 (VNIR) ASTER 30 (SWIR) ASTER 90 (TIR) ETM+ 15 (Pan) ETM+ (VNIR- 30 SWIR) ETM+ 60 (TIR)

spectral resolution [no. of bands]

radiometric resolution [bit]

temporal resolution [days]

3* 8 6 5

12 16

1 6

8

1

* Band 3 (nIR) is designed with stereo capabilities as a nadir and a backward looking telescope

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The wide use of ASTER and ETM+ data was the motivation to compare the performance of both sensors for urban applications (Hostert and Petkov 2004). The presented study was based on two adjacent Level 1B ASTER scenes from 9th May 2002, and a corresponding Level 1R Landsat ETM+ dataset from 20th August 2002. The ETM+ data cover northeast Germany and include the complete administrative area of Berlin. The ASTER scenes tile the northern and southern areas of the city. Both datasets were restricted to the reflective region of the wavelength spectrum, leaving six bands for ETM+ and nine for ASTER. A parametric geometric and radiometric pre-processing scheme was applied to ensure comparable datasets. The two adjacent ASTER datasets, with an overlap in the center, of Berlin were mosaiced along a cutline with feathering. Fifteen-meter and thirtymeter datasets were produced to allow for sensitivity analysis. The first included SWIR bands resampled to visible/near infrared (VNIR) spatial resolution; the second, VNIR bands resampled to SWIR spatial resolution. An unsupervised clustering of principal-component transformed-image data was performed to successively separate spectrally similar surfaces. Classes exhibiting mixed pixels were clustered again to facilitate optimum separation. As the separation of water and shade is one of the most prominent problems in urban remote-sensing data analysis, their separation with ASTER and ETM+ should be a good indicator of how the two sensors differ. The ASTER dataset performed remarkably better than ETM+ in the separation of water and non-water when a focus was put on low errors of commission. Interestingly, this did not change substantially when reducing the ASTER subset to the five bands corresponding to ETM+. With zero reflectance values in the near-infrared and SWIR wavelength regions, water surfaces do not benefit from the additional SWIR bands of ASTER. However, classification accuracy clearly drops when pixels are aggregated to 30 m spatial resolution. A separation between water, its surroundings, and shade becomes more difficult with rising numbers of mixed pixels through pixel aggregation; this is particularly important considering the small linear structures of urban waterways in Berlin. Small structures are hence the reason why ETM+ data do not achieve the discriminating power for water and shade that ASTER does. Even the additional band in the blue wavelength region does not counterbalance the better geometric accuracy of ASTER. However, when ETM+ data are used without considering the wavelength region around 0.48 µm, about 11 % more shade and mixed pixels are classified as water than in the six-band dataset, while the error of omission rises by nearly 5 %. The enhanced spatial resolution in the VNIR region of ASTER is certainly an advantage compared to the ETM+ and TM sensor systems. How-

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ever, this is not always true, since two effects might partially counterbalance each other. On one hand, a relatively coarse spatial resolution will cause large amounts of mixed pixels between water and other surface components, resulting in general difficulties with object separability. On the other hand, improving the spatial resolution leads to purer shade pixels, which are difficult to separate from pure water, especially in eight-bit data. Nevertheless, analysis has shown that ASTER is usually superior to ETM+ in this respect. A second thematic aspect is the separability of high reflectance, impervious, urban surfaces and urban soils. Here, geometric considerations were less important: only features large enough to be identified in 15 m ASTER and 30 m ETM+ data were selected. Different types of urban soils were identified in the core area of Berlin. Open soil surfaces are abundant where land is being developed (pits and construction sites) or is derelict. Water, as identified from a previous classification, and vegetation, characterized by the NDVI, were initially separated from the rest of the images. Some of the resulting clusters contained spectrally similar soil and impervious surfaces. Classes containing both were considered impervious surfaces. Commission errors are therefore low for soils, with some soil features not identified, and high for impervious surfaces. Apparently, an effective stratification (in this case, excluding vegetation and water), combined with a problem-specific analysis (in this case, principal-component analysis of restricted feature space datasets and iterative clustering of relevant classes, considering pure reference pixels only), reveals the rather good discriminating power of both sensors. Differences are still perceptible. The number of pixels incorrectly classified as soil instead of impervious surface is almost exactly doubled in the case of ETM+. The discriminating power of ASTER data is significantly higher for soils and high reflectance, impervious targets. Since differences in spatial resolution have been eliminated by the dimension of the spatial reference targets, only the additional SWIR bands of ASTER can be regarded as the source of ASTER’s greater accuracy. This study shows that a better spatial resolution is particularly useful in separating water and shade in an urban environment, while their separation does not gain additional accuracy from additional SWIR bands. ASTER’s SWIR bands do, however, enhance identification of high reflectance impervious surfaces and urban soils. Nevertheless, separation capabilities would be greatly enhanced if a 15 m resolution were available for the SWIR bands (Plate 3.1). It can be concluded that ASTER is a welcome alternative to sensors like TM, ETM+, SPOT, and IRS for urban analysis. As has been shown, ASTER also offers additional information in some aspects. Moreover, the

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use of ASTER data can strengthen time-series analysis when combined with data from different Landsat or SPOT sensors. 3.4.2 Characterizing derelict urban railway sites with QuickBird data Various authors have shown that object-oriented image analysis can enhance information extraction from spaceborne VHR data in the urban context (e.g., Bauer and Steinocher 2001; Meinel et al. 2001; De Kok et al. 2003; Herold et al. 2002; Banzhaf and Netzband 2004). While advantages of object-oriented analyses are generally evident in the heterogeneous urban context, it is not feasible to generate universally applicable rules on how to apply object-based rule sets in urban image analysis. It will be necessary to develop concepts in response to specific questions related to equally specific environments. Brownfields, or urban derelict land, exhibit particular surface components and structures, that are not prevalent in other urban environments and hence need to be analyzed with specific processing schemes. Most importantly, derelict sites exhibit diverse surface types, encompassing different successional stages of plant communities, open soils, unused buildings, and sealed areas. Additionally, the patchiness of such sites often exceeds other environments’ heterogeneity because of their unplanned development over time. A QuickBird image was chosen for data analysis because VHR satellite data combine the advantage of large spatial coverage with simple image geometry when compared to aerial photographs. Here, a segmentation-based approach was adopted to analyze QuickBird data in an object-oriented way. The successional state of vegetation and the level of imperviousness were investigated through analysis of used and unused railroad tracks (Damm et al. 2005). A test site situated between the center and the southeastern periphery of Berlin was selected. It is a combination of used (and serviced), temporarily used, and unused railroad sections, along with used and unused buildings. Functional structures of diverse materials along railroads complete this setting. Vegetation succession is prominent in different parts of the area and includes all stages between early-succession species and tree coverage. Because security measures make the area inaccessible, remote sensing offers an opportunity to gain insight into the ecological processes on-site by analyzing the development of the site. A panchromatic and multispectral QuickBird full-scene bundle from March 28, 2002 was acquired and orthorectified with sub-pixel accuracy based on RPC information, a DEM, and additional GCPs from high-

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resolution aerial photographs. Panchromatic and multispectral data were merged by substituting the first principal component of the multispectral data with information from the panchromatic band (Plate 3.2). A hierarchical image-segmentation process enforcing common boundaries between different scale levels was performed. The red and near– infrared, pan-sharpened bands and the panchromatic data served as segmentation input. Three final segmentation levels were chosen for the object-based classification: the first represented small to medium objects mainly depending on spectral properties; the second represented equally large objects mainly depending on geometric properties; and the third aggregated objects into super-objects to allow for a coarse separation between railroad tracks and other areas. The third level was employed as context information for finer classification levels. A knowledge-based, hierarchical classification was subsequently performed based on the derived objects (Plate 3.3). The classification scheme included different vegetation classes (herbaceous vegetation, shrub and tree vegetation, mixed vegetation on unused railroad tracks), open soil, impervious surfaces (concrete sealing, non-concrete impervious areas), railroad tracks (including gravel foundation), and buildings (buildings with metal roofing, other buildings). The resulting overall accuracy achieved 85.04 %, with kappa statistics of 81.08 % accuracy. Most of the errors could be attributed to confusion about different vegetation types, and hence to poor phenological differentiation due to the early acquisition date of the imagery in late March. Confusion existed, for the same reason, about railroad tracks and vegetation on railroad tracks. The different vegetation classes could be interpreted as different successional states of plant communities on urban railway sites. Active railroad tracks are usually kept free of vegetation by mechanical clearing or herbicide treatment. It is thus easy to separate used and unused track systems if the spatial resolution of a sensor allows for distinguishing pixels with or without vegetation devoid of spectral mixing. The intermediate space between tracks shows a similar behavior, but lower herbicide quantities, increasing distance from tracks, and different substrates result in different dominant plant species. Generally, after an initial phase of dominant ruderal species, shrubs and eventually trees will replace early-succession plant communities. Early-succession vegetation on and between tracks is abundant, indicating significant derelict infrastructure. This seems to be a recent development, as almost no woody vegetation is found here. In contrast, the northern and southern sectors of the test site feature substantial stands of shrubs and trees, indicating that structures have been unused for ten or more years.

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It is also possible to assess the degree of imperviousness in more detail by generalizing the analysis results into three classes: impervious surfaces, vegetation, and tracks. It is hypothesized, for simplicity’s sake, that vegetation on tracks has similar permeability values as the railroad tracks themselves. As no values for imperviousness of railroad gravel beds were found in the literature, imperviousness was estimated to be 40 %. With 9.4 % of completely impervious surfaces, 64.1 % of tracks, and 26.5 % of vegetation, the degree of imperviousness averaged 35.1 %. Merging panchromatic and multispectral data into a 0.7 m-resolution, multispectral dataset appears to be the appropriate way to avoid most mixed pixels when analyzing railroad track corridors. A spatial resolution between 0.4 m and 0.5 m is probably the ideal pixel size for this kind of application. The analysis of QuickBird data revealed a distinctly reduced area of sealed surfaces compared to other available estimates. It is obvious that the variability of imperviousness is much higher than previously assumed, and different sites will probably exhibit diverse surface structures related to imperviousness. The early acquisition date and the correspondingly low photosynthetic activity may have led to a general underestimation of the vegetation classes and hence to an overestimation of impervious surfaces. These particular site conditions in spring may have influenced the accuracy of analysis results. The analysis of imperviousness allows conclusions to be drawn concerning the ecological function of railroad corridors. It is, for example, evident that such areas contribute to groundwater replenishment to a much higher degree than previously assumed, and thus reduce surface runoff and sewage overflow. While this effect would usually be rated as positive, the use of herbicides on railway tracks imposes the threat of groundwater contamination. Spaceborne VHR data may not be adequate to solve problems requiring a spatial resolution of a few decimeters or even centimeters. Problems due to in-track and cross-track pointing capabilities, the related overlap of 3-D structures, and shadows complicate image analysis in densely built urban areas. However, in regard to the described task, it is obvious that the adapted analysis of such data can substantially contribute to solving a variety of questions related to urban ecosystem studies.

3.5 Outlook Many of the issues described in the sensor, methods, and examples sections are not yet standard in image processing for urban applications. Nev-

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ertheless, an increasing number of applications and related publications illustrates that new opportunities are being embraced by the remote-sensing and geomatics communities. Future developments will include, among many others, satellite data with even higher ground-sampling distance. The Worldview sensors will offer at least 50-cm panchromatic, and 2-m multispectral, ground-sampling distances. Additional spectral bands will be available in the visible and near-infrared wavelength regions, while SWIR capabilities are not in sight for geometric VHR data from satellites (DigitalGlobe 2007). Nevertheless, problems of shading and shadows will always be inherently linked to remote-sensing data analysis in the urban context, and even increase with very-high-resolution data, i.e., an increased number of pure-shade pixels. Hyperspectral remote sensing has not yet been sufficiently explored in the urban context. Results presented by Heiden et al. (2001) and Herold et al. (2004) indicate that spectral high-resolution data are as promising as geometric high-resolution data for urban environmental analyses. Such data may even be used to analyze residual information in image shadows, which is, to a certain degree, possible in 16-bit data with more than ten bands (Hostert et al. 2005). However, sensor technology does not yet allow for operational hyperspectral systems with an adequate geometric resolution and signal-to-noise-ratio on satellite platforms; hence, there is a lack of spatially extended and widely available datasets. The integration of thermal IR bands in hyperspectral sensor concepts has not yet led to successful applications in the urban context. Advanced future sensor designs, such as the Airborne Reflective Emissive Spectrometer (ARES), will therefore be of particular interest for urban applications (Richter et al. 2005). The link between spectral, high–resolution, reflective data and thermal-infrared information is vital to the investigation of, for example, urban climate issues in relation to structural information. Also, hyperspectral data including a range of thermal-infrared bands should increase our ability to relate emissivity values from pre-defined databases to surface temperatures correctly. Linking such capabilities through a single sensor design will enable great advances in this respect. A more sophisticated integration of geoinformation with digital image processing (e.g., through seamless raster-vector integration) can be anticipated during the next few years. While producers of geoinformation systems, as well as those of image processing software, have achieved major improvements during the last decade, data exchange and data integration are still impediments for many applications. A strict adherence to OpenGIS standards will improve the situation. In conclusion, urban applications will still challenge remote-sensingbased strategies in the future. However, advances in technology, methods,

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and related research will initiate a wealth of opportunities for the scientific community, as well as for urban planners and ecologists.

3.6 Acknowledgments The author would like to thank Thomas Schneider for providing data from the Urban and Environmental Information System (UEIS) of the Berlin Department of Urban Development. ASTER data from the Urban Environmental Monitoring/100 Cities project at Arizona State University (ASU) in Tempe (PI: Philip Christensen, School of Earth and Space Exploration) has been used. I am also grateful to Will Stefanov (now at NASA Johnson Space Center) and Maik Netzband (now at UFZ-Leipzig) for supporting data access and initiating the collaboration with ASU.

3.7 References Baatz M, Schaepe A (2000) Multiresolution segmentation: an optimization approach for high quality multiscale image segmentation. In: Strobl J, Blaschke T (eds) Angewandte geographische informationsverarbeitung, vol. XII. Wichmann, Heidelberg, pp 12-23 Banzhaf E, Netzband M (2004) Detecting urban brownfields by means of high resolution satellite imagery. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 35 (B7):460-466 Bauer T, Steinnocher K (2001) Per parcel land use classification in urban areas applying a rule-based technique. GeoBIT/GIS 2001-6:24-27 Damm A, Hostert P, Schiefer S (2005) Investigating urban railway corridors with geometric high resolution satellite data. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 36 (8/W27), http://www.isprs.org/commission8/workshop_urban/damm.pdf, accessed 6-March-2007 De Kok R, Wever T, Fockelmann R (2003) Analysis of urban structure and development applying procedures for automatic mapping of large area data. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 34(7):41-45 DigitalGlobe (2007) WorldView 1, http://www.digitalglobe.com/about/worldview1.html, accessed 6-March-07 Heiden U, Roessner S, Segl K (2001) Potential of hyperspectral HyMap data for material oriented identification of urban surfaces. Regensburger Geographische Schriften 35:69-77

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Herold M, Roberts D, Gardner M, Dennison P (2004) Spectrometry for urban area remote sensing - Development and analysis of a spectral library from 350 to 2400 nm. Remote Sensing of Environment 91 (3-4):304-319 Herold M, Scepan J, Müller A, Günther S (2002) Object-oriented mapping and analysis of urban land use/cover using IKONOS data. In: Benes T (ed.) Geoinformation for European-wide integration. Millpress, Rotterdam, pp 533538 Hostert P (2007) Processing techniques for hyperspectral data. In: Rashed T, Juergens, C (eds) Remote sensing for urban and suburban areas. Springer, New York, NY (in press) Hostert P, Damm A, Diermayer D, Schiefer S (2005) Characterizing heterogeneous environments: Hyperspectral versus geometric very high resolution data for urban studies. Proceedings of the 4th EARSeL workshop on imaging spectroscopy, April 27-29, Warsaw, Poland, http://www.enge.ucl.ac.be/EARSEL/workshops/IS_Warsaw_2005/papers/Ter restial_Ecosystems/15_Hostert_127_133.pdf, accessed 6-March-2007 Hostert P, Petkov A (2004) A sensitivity study for urban change analysis – comparing Landsat-ETM+ and Terra-ASTER data. Proceedings of the SPIE conference on remote sensing for environmental monitoring, GIS applications, and geology, Sept. 8-12 Barcelona, Spain, pp 285-295 Kramer HJ (2002) Observation of the earth and its environment: Survey of missions and sensors. Springer, Berlin Meinel G, Neubert M, Reder J (2001) The potential use of very high resolution satellite data for urban areas–First experience with IKONOS data , their classification and application in urban planning and environmental monitoring. Regensburger Geographische Schriften 35:196-205 Ramsey MS (2003) Mapping the city landscape from space: The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) Urban Environmental Monitoring Program. In: Heiken G, Fakundiny R, Sutter J (eds) Earth science in the city: A reader. American Geophysical Union, Washington, DC, pp 337-361 Richter R, Schläpfer D (2002) Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction. International Journal of Remote Sensing 23(13): 2631-2649 Richter R, Müller AM, Habermeyer M, Dech S, Segl K, Kaufmann H (2005) Spectral and radiometric requirements for the airborne thermal imaging spectrometer ARES. International Journal of Remote Sensing 26 (15):3149-3162 Schläpfer D, Richter R (2002) Geo-atmospheric processing of airborne imaging spectrometry data. Part 1: Parametric orthorectification. International Journal of Remote Sensing 23(13): 2609-2630 Small C (2003) High spatial resolution spectral mixture analysis of urban reflectance. Remote Sensing of Environment 88:170-186 Stefanov WL, Christensen PR, Ramsey MS (2001) Remote sensing of urban ecology at regional and global scales: Results from the Central Arizona-Phoenix LTER site and ASTER Urban Environmental Monitoring program. Regensburger Geographische Schriften 35:313-321

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Toutin T, Chénier R, Carbonneau Y (2002) 3D models for high resolution images: examples with QuickBird, IKONOS and EROS. Proceedings of the ISPRS commission IV symposium on geospatial theory, processing, and applications, July 8-12, Ottawa, Canada, pp 547-551

Chapter 4 - Spatial Analysis of Urban Vegetation Scale and Abundance

Christopher Small Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA

4.1 Introduction The urban environment is strongly influenced by the physical properties of the urban mosaic across a wide range of spatial and temporal scales. The presence or absence of spatial scaling behavior in these physical properties has implications for the parameterization of physical process models that incorporate land surface mass and energy fluxes. The performance of these models is dependent on the accuracy and resolution of the land surface inputs that drive them. The spatial scaling behavior of land surface properties at fine (1-10 m) scales determines the aggregate properties at moderate (< 100 m) and meso (> 100 m) scales. Multiscale land surface properties like patch size and density could potentially be mapped at meter scales using the current generation of high resolution sensors. The results could then be used to scale up moderate resolution land cover maps to regional scales. In spite of its importance to physical and ecological processes, relatively little quantitative analysis has been done on the spatial scaling properties of urban land cover. Vegetation abundance and distribution are fundamental determinants of urban environmental conditions. Vegetation exerts a strong influence on mass and energy fluxes through the urban environment by modulating evapotranspiration and absorption of solar radiation. The abundance, distribution and connectedness of vegetation patches also influences ecological processes related to species diversity and spread of pests and diseases.

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Accurate mapping and monitoring of vegetation distribution and condition is central to the maintenance of parks and urban ecosystems, but it can also facilitate analyses of urban areas that can inform policy decisions and future development of urban environments. Mapping and monitoring urban vegetation in individual cities is important in its own right, but comparative analysis can provide an important complement to city-specific analyses. By comparing intraurban and interurban vegetation distribution it is possible to quantify consistencies and variances that determine which properties may be assumed and which properties must be measured. However, relatively few comparative analyses of urban vegetation distribution have been conducted to date. This chapter examines some characteristics of vegetation distributions for six contrasting urban settings. Spectral mixture analyses are used to estimate areal fractions of vegetation, substrate and nonreflective surfaces from high spatial resolution Quickbird multispectral imagery. Image segmentation is then used to quantify the size distribution of contiguous patches of vegetation and the effect of detection thresholds on the patch size distributions. Analyzed together, vegetation fraction and patch size distributions provide contrasting examples of vegetation characteristics for a variety of different urban land uses in each city. The vegetation fraction distributions quantify differences in canopy structure and foliage density among vegetation types, while the patch size distributions highlight intraurban contrasts and interurban consistencies in the density and connectedness of urban vegetation. The objective of the comparative analysis is to illustrate some basic characteristics of high spatial resolution vegetation fractions, and to highlight similarities and differences in the abundance and spatial distributions of vegetation in different urban environments. The results should not be interpreted as definitive but rather illustrative of fundamental properties of urban vegetation and methods for quantifying them. In the following sections, the study areas and data are described in a comparison of the six urban landscapes. The methodologies employed are summarized in brief descriptions of Spectral Mixture Analysis and image segmentation. The physical characteristics of urban vegetation are discussed in the context of the vegetation fraction and patch size distributions. The results of the analyses are presented in a comparison of the different urban areas. The chapter concludes with a discussion of the implications and caveats of the analysis.

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4.2 Six urban landscapes Quickbird multispectral imagery provides a detailed representation of the diversity of land cover types and vegetation distributions found in each city. Because the 2.8 m resolution oversamples the 10 to 20 meter characteristic scale of urban mosaic elements seen worldwide (Small 2003), the vegetation patch size distribution reflects the spatial scaling properties of the vegetation. The six urban areas were chosen primarily on the basis of data availability, but they provide numerous examples of interurban and intraurban variability and consistency in land use and land cover type. These urban areas span a wide range of historical, political, cultural, socioeconomic and environmental conditions but they obviously can’t represent all cities. The study areas and full resolution subscenes of the images are shown in Plates 4.1 and 4.2, respectively. The area of each image used is limited by the configuration of the built area, coastlines and cloud cover. As a result, the images span a range of sizes and the results for a given city are not necessarily representative of the entire city. However, the objective of the analysis is to highlight consistencies in urban environments that we expect to be different so equivalent areas are not necessary. For other types of analyses they would be. In this case, the point is to illustrate consistencies and differences. The consistencies and differences may suggest avenues for further research. Cairo, Egypt is well known as one of the cradles of urban civilization. It is situated within a steep physical and ecological gradient between the Nile delta and the Mukatim desert. The image area spans central Cairo on the east and Giza on the western side of the Nile River. The built environment ranges from spacious modern suburban housing developments in the west, through the historical city center and outward across a variety of distinct residential and municipal areas of varying building densities. An excellent discussion of Cairo’s land cover and urban reflectance properties is provided by Rashed et al. (2001). Cuzco, Peru is by far the oldest of the South American cities. As the center of the Inca empire for more than 400 years, it remains the spiritual capitol of the Andes and the longest continuously inhabited city on the continent. Located at the head of a broad valley at ~3300 meters elevation, the developed area extends up the valley walls across a range of elevations. The built environment is a mixture of Spanish and Incan architecture at the city center and extends to informal settlements on the outer hillslopes. Istanbul, Turkey also enjoys a long history as the urban center of numerous civilizations. Spanning the Bosporus Strait between Europe and Asia, Istanbul is a mosaic of built environments resulting from a succes-

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sion of cultural influences. The image area extends from the Sea of Marmara up the European side of the Bosporus encompassing the traditional city center, the Golden Horn and numerous parks and residential areas. An excellent discussion of Istanbul’s growth and urban reflectance properties is provided by Maktav and Erbek (2005). Los Angeles, California (or L.A) represents a 20th century suburban agglomeration. In comparison to the other cities in this study, L.A. is younger and represents the suburban end of the urban spectrum. The image area extends from the coastal scrub foothills of the Santa Monica Mountains in the northwest, through Beverly Hills and southeastward to Hollywood. The built environment includes parklands, industrial areas, spacious private estates and residential neighborhoods typical of the Los Angeles conurbation. New York City (NYC) represents the stereotypical urban environment of the industrial age. The vertical agglomeration is characterized by very high population densities in residential areas adjacent to very low densities in industrial and business districts, and one of the largest urban park systems in the world. The image area encompasses central Manhattan, and is dominated by Central Park; the flanking residential neighborhoods of Harlem, the Upper East and Upper West Sides; and the commercial center of Midtown Manhattan extending southward of the park. From suburban New Jersey in the NW corner of the image to the industrial boundary of Brooklyn and Queens in the SE corner, the image spans multiple industrial and business districts, numerous parks and public green spaces, municipal housing projects, and suburban developments with single family homes. A more detailed discussion of New York’s vegetation distribution is given by Small and Lu (2006). Rio de Janeiro, Brazil is a tropical metropolis characterized by multiple colonial influences and rapid population growth in recent years. The image contains the traditional Centro, and extends south and westward across a diversity of neighborhoods of varying building density. The topographic gradients often coincide with strong socioeconomic gradients spanning formal development at lower elevations and informal settlements on the hillslopes.

4.3 Spectral mixture analysis and image segmentation Comparative analyses require a robust, consistent metric to quantify vegetation distributions. In this study, Spectral Mixture Analysis is used to quantify the abundance and density of vegetation, and image segmentation

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is used to quantify the size distribution of contiguous patches of vegetation. Spectral Mixture Analysis (SMA) is a methodology in which an observed radiance is modeled as a linear mixture of spectrally pure endmember radiances. Linear mixture models are based on the observation that, in many situations, radiances from surfaces with different "endmember" radiances mix linearly in proportion to area within the IFOV (Johnson et al. 1983; Singer 1981; Singer and McCord 1979). This observation has made possible the development of a systematic methodology for spectral mixture analysis (Adams et al. 1993; Adams et al. 1986; Gillespie et al. 1990; Sabol et al. 1992; Smith et al. 1990) in which land surface reflectance variations are represented by a set of endmember fraction images describing spatial variations in the areal abundance of each endmember. The nature of the spectral mixing that occurs within the sensor’s IFOV can be inferred from the image’s mixing space. The mixing space is the N-dimensional cloud of image pixels corresponding to all of the image spectra represented by the mixture model. The mixing space can be represented graphically as scatterplots of the various band combinations corresponding to different 2D projections of the N- dimensional cloud. The dimensionality of the problem can be reduced by focusing on the dimensions of the mixing space that contain the majority of the image variance. A principal component transformation is used to reorient (rotate) the mixing space and provide quantitative estimates of the variance accounted for by each principal component (PC). Principal component analyses of Landsat and Ikonos imagery indicate that > 90% of image variance can generally be represented with the three primary PCs of the mixing space (Small 2001). This makes it possible to represent the topology of the mixing space with three orthogonal projections of the three primary PCs (Fig. 4.1). The triangular topology of the mixing space indicates that the mixed reflectances in the interior can be represented by a three endmember mixing model where the apexes of the 3D mixing space correspond to the spectral endmembers, while the mixed pixel reflectances lie within a convex hull circumscribing the apexes (Boardman 1993). The reflectance vectors residing at the three apexes of the NYC mixing space are shown in Figure 4.1. These endmember spectra correspond to High Albedo Substrate, Vegetation and Dark surfaces. The triangular topology of the NYC mixing space is very similar to that seen in Ikonos imagery of other urban areas worldwide (Small 2003), as well as the global ETM+ mixing space (Small 2004). The straight edges approaching the Dark surface endmember indicate that binary mixing between the Dark and Vegetation endmembers is strongly linear. The mixing continuum between the Dark and High Albedo Substrate endmembers also appears to be linear in the side view, but the third dimension of the mixing space reveals convexity suggesting a

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small degree of nonlinear mixing along this “gray axis”. The linear binary mixing continua between the Dark endmember and the other two indicates the importance of illumination and shadowing at the scale of the 2.8 m Quickbird pixels. Compared to Landsat ETM+ mixing spaces, the Quickbird (and Ikonos) mixing spaces are more dominated by binary mixing with the Dark endmember whereas the ETM+ mixing space indicates greater prevalence of ternary mixing among all three endmembers. In this analysis, the validated endmembers selected for the NYC mixing space are used in a least squares inversion of a three endmember linear mixture model as described in detail by Small (2003). The result of a spectral mixture analysis is a set of endmember fraction images and a RMS error image. The endmember fraction images show spatial variations in areal abundance of each endmember within the image. The RMS error image shows the corresponding misfit between the observed radiance and the mixed radiance obtained from a linear mixture of the endmember spectra by the estimated endmember fractions. For all of the models used in this study, 99% of RMS misfits were well under 0.2% of the amplitude of the dark surface endmember. These low RMS misfits indicate that the mixture model is able to replicate the observed radiance spectra very closely relative to the amplitude of the signal. However, it does not guarantee that the fraction estimates are accurate measures of the true fractional abundance of the endmember on the ground. Quickbird vegetation fraction estimates integrated to 30 m spatial scales agree well with Landsat ETM+ vegetation fraction estimates (Small and Lu 2006). This comparison can be used as a vicarious validation of Landsat ETM+ estimate accuracy in detection of meter scale vegetation. Analogous validation of Quickbird imagery would require centimeter scale mapping of vegetation abundance. Until such high resolution validations have been conducted, we must assume that the strongly linear mixing continua, and the low RMS misfit, are sufficient evidence that the fraction estimates are accurate. This is reasonable because we can identify the individual components of the urban vegetation mosaic (trees, bushes, grass) at the spatial scale of the Quickbird GIFOV. For analysis of urban vegetation the vegetation fraction is obviously the primary fraction of interest, but the fractions of the other two endmembers are also important for distinguishing the relative influence of soil reflectance and canopy shadow at meter scales for different types of vegetation.

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Fig. 4.1. Spectral mixing space and endmembers for the New York City Quickbird image. The side view shows the two primary dimensions of the mixing space, accounting for > 99% of spectral variance with the familiar triangular topology spanned by High Albedo, Vegetation, and Dark surface endmembers. The top and end views show the third dimension of the mixing space associated with 0.5% of the variance. The topology reveals that the mixing space is primarily planar along the mixing continuum extending to the Vegetation endmember, and that most of the non-linearity is associated with the gray axis spanning the other two endmembers. Sand ballfields are spectrally distinct.

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4.4 Vegetation fraction and patch size distributions The vegetation endmember fraction estimate, Fv, is based on directly illuminated healthy grass. The implicit assumption is that dense healthy grass is equivalent to 100% illuminated foliage. In reality, even manicured healthy grass has some degree of internal microshadow, but compared to other vegetation types at meter scales this small amount of internal shadow is negligible. A caveat of this approach is that the maximum vegetation fraction is not calibrated to a physical metric, so all vegetation estimates are relative to the densest grass on the bowling greens in NYC’s Central Park at the time the image was acquired. This method could be calibrated by using global endmembers (Small 2004), calibrated against agreed-upon reference targets. However, until such targets are established, it is necessary to use image-derived endmember spectra or local field spectra. The New York mixing space and endmember spectra used in this analysis are shown in Figure 4.1. The distribution of 2.8 m vegetation fractions quantifies foliage density as the relative abundance of illuminated foliage and shadow per unit area. It is important to keep in mind that the vegetation fractions correspond to areal fraction of illuminated foliage – not vegetative biomass or leaf area index. This is illustrated with the vegetation fraction image of Central Park in New York (Fig. 4.2) At meter scales, vegetated areas are imaged as a combination of illuminated foliage, shadow and illuminated soil and non-photosynthetic vegetation (stems, branches, etc). As a result, a typical deciduous tree crown will be imaged as pixels with illuminated foliage fractions between 0.2 and 0.7. Unvegetated surfaces have vegetation fractions well under 0.2. Poorly manicured or unhealthy grass shows considerable fractions of substrate endmember (soil and thatch), and typically has illuminated foliage fractions between 0.4 and 0.9. At meter scales the importance of image texture is even greater than at moderate (10-100m) spatial resolutions as the ranges of illuminated foliage fraction overlap more significantly. In spite of this complication, high resolution fractions are considerably more informative than their moderate resolution equivalents because the diversity of possible distinct mixtures is considerably reduced by the finer spatial scale. In other words, many more spatial combinations of soil, shade and illuminated foliage are possible within a 900 m2, than within a 7.8 m2, GIFOV. In terms of mapping vegetation abundance and distribution in urban areas, the bottom line is that Quickbird pixels are small enough to resolve the individual components that comprise the vast majority of the urban vegetation mosaic. This assertion is supported by the distribution of vegetation patch sizes.

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Patch segmentation refers to the spatial distribution of contiguous patches (segments) of pixels meeting specific criteria. In this study we are concerned with vegetation distribution so we quantify vegetation patches as contiguous sets of adjacent pixels with vegetation fractions greater than a particular fraction threshold. The threshold can be based on the bias or “noise floor” in the vegetation fraction distribution.

Fig. 4.2. Vegetation fraction distributions for closed canopy forest and grass in Central Park in Manhattan. Distributions of 2.8 m Quickbird vegetation fractions indicate the fraction of illuminated vegetation in typical forest canopy and varying density grass cover. 100% vegetation (Fv = 1.0) is calibrated to a well-maintained section of turf on the Bowling Greens. In comparison, typical grass cover is equivalent to 60% to 85% illuminated foliage, while closed canopy forest has median fractions in the 40% to 60% range.

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Fig. 4.3. Effect of vegetation fraction thresholds on segment distributions. Increasing the Fv threshold initially causes fragmentation of large segments connected by very low fractions, followed by areal shrinkage of a relatively stable number of segments, followed by attenuation of shrunken segments (upper left). All cities except L.A. fragment for thresholds up to ~10%, then segments shrink to thresholds of ~20% before small patches begin to be rapidly attenuated at higher thresholds. Vegetated area distributions (upper right) indicate that Cairo, Istanbul, New York and Rio de Janeiro have vegetation fraction noise floors in the 10-15% range, while Cuzco and L.A. have less pronounced noise floors and more continuous gradation between vegetated and non-vegetated pixels at low vegetation fraction.

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The fraction estimates are based on a least-squares minimization of a misfit function so non-vegetated pixels can often have a small, but nonzero, vegetation fraction corresponding to the degree of spectral ambiguity of the endmembers and nonlinearity of the mixing space. The most obvious example of spectral ambiguity is between vegetation and soil because both have positive spectral slopes between 0.6 and 0.9 ȝm wavelengths. Because urban vegetation generally occurs in discrete patches (rather than diffusely), the bias in the estimates is generally quite apparent on the histogram as an abrupt drop in the frequency of pixels with increasing fraction (Fig. 4.3). In other words, most of the urban surface is not vegetated and corresponds to a large peak in the distribution at low vegetation fractions. Segmenting the fraction image with a threshold slightly higher than the upper roll-off in the peak of the frequency distribution results in a vegetation fraction map that is easily field validated. Increasing the threshold above the roll-off results in less change in the actual spatial distribution of vegetated patches because the fraction frequency distributions generally have long tails that diminish much more slowly at fractions greater than ~0.1 (Fig. 4.3). A small difference in threshold results in little change in the spatial vegetation distribution so the resulting segment distribution is not strongly dependent on the actual threshold used – provided the threshold is chosen on the tail rather than the peak of the distribution. Thresholds can also be chosen on the basis of their influence on the resulting segment distributions. Increasing the Fv threshold from 0 to 1 has three sequential effects on the distribution. Thresholds below the noise floor result in smaller numbers of very large segments with little relationship to the actual vegetation distribution. Increasing the Fv threshold above the noise floor causes these large segments to fragment into larger numbers of smaller segments corresponding to the actual discrete patches of interconnected vegetation. As the threshold is increased further, these patches shrink as the partially vegetated pixels at the periphery of the patches (and in shadow between tree crowns) fall below the threshold. Further increases of the threshold eventually begin to attenuate smaller patches entirely as they shrink and disappear. The effect of this process on the distribution of vegetated patches can be quantified by plotting the number of segments versus the total vegetated area for increasing Fv thresholds. The segmentation threshold curves in Figure 4.3 show the Fv thresholds at which fragmentation, shrinkage and attenuation transitions occur for each city’s vegetation distribution. In each case, the fragmentation occurs at thresholds between 0.03 and 0.10 – consistent with the noise floors indicated on the Fv distributions. Further increase in the thresholds results in relatively little decrease in the number of segments as the vegetated area

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diminishes from patch shrinkage. Thresholds greater than 0.15 or 0.20 rapidly attenuate large numbers of small segments. Thresholds chosen at the transitions between fragmentation, shrinkage and attenuation therefore provide bounds on the true distribution of vegetation patches in the urban environment. Figure 4.3 illustrates these transitions for each city. In figures 4.4-4.9 thresholds are chosen at the fragmentation-shrinkage transition (noise floor) for each city. For the comparison in Figure 4.11 a common threshold of 0.20 is used (except for L.A. which uses 0.30). Patch sizes are given in units of circular equivalent diameter (2sqrt(area/pi)). Once images have been segmented into contiguous patches, we compare the patch size distributions and the vegetation fraction distributions both in terms of frequency and spatial distribution. The next section discusses the relationship between these distributions in the context of each city individually, and the following section will compare fraction distributions among cities to highlight similarities and distinctions.

4.5 Comparison Cairo is characterized by strong gradients and a nearly binary distribution of vegetation at neighborhood scales (Fig. 4.4). To the west, Imbabah, el Gezira, el Akwal and Ghabab are very densely vegetated with larger, closely spaced patches of vegetation on the west bank of the Nile and on the islands. There are some densely vegetated parts of Cairo (e.g. Garden City, Tahrir Square, Republic Palace) but much of the older city is characterized by small roof areas, narrow streets and passages and an apparent absence of vegetation at scales detectable by Quickbird. The non-uniform distribution of vegetation is evident at neighborhood scales. In this image, higher vegetation fractions are associated with larger patches while the less vegetated areas are characterized by small isolated patches. The larger patches are associated with parks and agriculture as well as interconnected networks of street trees. Cuzco is characterized by a relatively barren city center with the frequency of larger patches of higher vegetation fraction increasing radially outward from center (Fig. 4.5). The narrow streets and interconnected architectural style preclude street trees in most areas near the city center. Both natural and maintained vegetation patches increase in size and abundance toward the periphery. In contrast to Cairo, higher vegetation fractions are associated with smaller patches in Cuzco. In most areas this corresponds to urban agriculture, as the most common trees are open canopy

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eucalyptus associated with relatively low fractions of illuminated foliage with higher shadow and background substrate fractions than broadleaf trees. This could explain the absence of a prominent shoulder as seen on the fraction distributions of the other cities. Most of the higher vegetation fractions in this scene are associated with patches of maintained grass on the Plaza de Armas, Coricancha (Incan Gold Reserve) and Plaza San Francisco as well as smaller patches of weeds and scrub vegetation.

Fig. 4.4. Vegetation fraction and patch size distributions for Cairo. Visible-red band (upper left) shows full resolution subscene with corresponding grayshaded vegetation fraction (upper right), and patch size (lower left) maps. Distributions (lower right) are derived from full scenes shown in Plate 4.2. Gray scale applies to both vegetation fractions (x 100) and patch diameters (meters circular equivalent diameter).

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Fig. 4.5. Vegetation fraction and patch size distributions for Cuzco. Visible-red band (upper left) shows full resolution sub-scene with corresponding grayshaded vegetation fraction (upper right), and patch size (lower left) maps. Distributions (lower right) are derived from full scenes shown in Plate 4.2. Gray scale applies to both vegetation fractions (x 100) and patch diameters (meters circular equivalent diameter).

Istanbul is characterized by a more even distribution of vegetation than the other cities considered here (Fig. 4.6). Aside from several large contiguous patches of high fractions associated with parks and specific neighborhoods, (e.g. Tokapi Sarayi, Sultanahmet, Edirnekapi), there is a wide range of medium to small patches strongly intermixed throughout city. Street trees are abundant on larger thoroughfares, but most of the vegetation is associated with interior courtyard trees throughout most of the area imaged. There are very few barren neighborhoods in this image.

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Fig. 4.6. Vegetation fraction and patch size distributions for Istanbul. Visible-red band (upper left) shows full resolution sub-scene with corresponding grayshaded vegetation fraction (upper right), and patch size (lower left) maps. Distributions (lower right) are derived from full scenes shown in Plate 4.2. Gray scale applies to both vegetation fractions (x 100) and patch diameters (meters circular equivalent diameter).

Los Angeles is characterized by a strong contrast between: 1) open canopy scrub vegetation on the steeper slopes of the Santa Monica mountains (NW corner), 2) dense, interconnected vegetation in Beverly Hills and the foothill neighborhoods and 3) smaller, less interconnected patches in Hollywood to the east of the image (Fig. 4.7). Throughout the image, there is an abundance of thoroughly mixed patches of high vegetation fraction associated with lawns, and intermediate fractions associated with large trees. Street tree networks are well established in the foothill develop-

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ments, and even within the grid there is abundant vegetation across a range of scales. It is noteworthy that residential vegetation is denser than the indigenous scrub on the hillslopes. This is a consequence of Los Angeles’ mild climate and the profligate use of imported water. Compared to the other cities in the study, Los Angeles has a lower areal percentage of unvegetated (<0.1) area and a higher areal percentage of more densely vegetated area. Like Cuzco, L.A. lacks a shoulder in the fraction distribution because of the abundance of sparse, open canopy vegetation.

Fig. 4.7. Vegetation fraction and patch size distributions for Los Angeles. Visiblered band (upper left) shows full resolution sub-scene with corresponding grayshaded vegetation fraction (upper right), and patch size (lower left) maps. Distributions (lower right) are derived from full scenes shown in Plate 4.2. Gray scale applies to both vegetation fractions (x 100) and patch diameters (meters circular equivalent diameter).

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Fig. 4.8. Vegetation fraction and patch size distributions for New York. Visiblered band (upper left) shows full resolution sub-scene with corresponding grayshaded vegetation fraction (upper right), and patch size (lower left) maps. Distributions (lower right) are derived from full scenes shown in Plate 4.2. Gray scale applies to both vegetation fractions (x 100) and patch diameters (meters circular equivalent diameter).

New York is characterized by a strong contrast between large, densely vegetated parks and smaller residential patches of street trees (Fig. 4.8). The canyons in Midtown are nearly barren. The residential Upper East and Upper West Sides (flanking Central Park) contain both shadowed canyons between apartment buildings ranging from 10 to 50 floors, and densely vegetated cross-streets in which the street and courtyard trees approach, or exceed, the height of the 3 to 5 story residential brownstones. Harlem has an abundance of street trees along its wide, well illuminated streets. Suburban New Jersey and Queens have a greater number of single

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family homes with landscaped front and back yards while Brooklyn and the Bronx contain all of the above land cover types.

Fig. 4.9. Vegetation fraction and patch size distributions for Rio de Janeiro. Visible-red band (upper left) shows full resolution sub-scene with corresponding grayshaded vegetation fraction (upper right), and patch size (lower left) maps. Distributions (lower right) are derived from full scenes shown in Plate 4.2. Gray scale applies to both vegetation fractions (x 100) and patch diameters (meters circular equivalent diameter).

It is interesting to note that the vegetation fraction distribution in Manhattan is the socioeconomic inverse of what is often assumed. Neighborhoods with higher median incomes such as the Upper East Side often have much less street vegetation than lower income neighborhoods like Harlem. Public housing projects stand out as islands of green because they have

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numerous large trees and extensive landscaping between the residential towers. The shape of the cumulative distribution of segment area is strongly impacted by the presence of Central Park in the image. This continuous 4 km2 patch of dense vegetation in the center of Manhattan pushes the cumulative distribution downward so that smaller patches account for a lower percentage of the total area. In the comparison in Figure 4.10 the cumulative distribution minus the park area is also more similar to other cities. Rio de Janeiro is a mosaic of large, densely vegetated parks and hillslopes, interspersed patches of vegetation and barren areas (canyons & dense residential, possibly informal; Fig. 4.9). The lush tropical vegetation contrasts strongly with much of the vegetation in some of the semiarid cities described above. Even temperate cities rarely achieve the density of small patches of indigenous vegetation that thrive in tropical climates. Compared to the other cities, Rio de Janeiro contains a slightly greater fraction of its vegetation at smaller scales (<50 m).

4.6 Discussion Analysis of urban vegetation at high spatial resolutions differs considerably from moderate resolution analyses. This is primarily because, for most urban land cover mosaics, the diversity of mixing within the sensor IFOV increases with the area of the IFOV beyond the 10 to 20 m characteristic scale of the urban fabric (Small 2003). This could explain why the mixing spaces associated with high resolution imagery consistently show much greater degrees of distinct binary mixing lines between the dark endmember and the substrate and vegetation endmembers (Small 2003, 2005). In most cases, the dark fraction represents the shadowing that exists on almost all textured surfaces. In the case of urban vegetation, high spatial resolution (2.8m) makes it possible to resolve structure at the scale of individual tree crowns. Hence we can discern the effect of both canopy foliage density and closure, as well as intercrown shadowing in areas containing larger crowns. Unlike Landsat and other moderate resolution sensors, Quickbird images entire pixels with lower vegetation fractions (and higher shadow fractions) within densely vegetated areas. While this makes it more difficult to use high resolution imagery for traditional, per pixel, land cover classification (since shadow pixels can occur in almost all land cover types), it does provide valuable textural information at finer scales than moderate resolution sensors can resolve. This also makes it more difficult to use high spatial resolution imagery for per-pixel change detection since

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subpixel differences in image alignment can result in significant changes in individual fraction distributions - even when no actual change in the target has occurred. A coanalysis of Quickbird and Landsat imagery indicates that subpixel uncertainty in coregistration can account for much of the scatter observed when coregistered images are compared on a pixel-bypixel basis (Small and Lu 2006). The distributions of vegetation fraction show some consistency in the different cities (Fig. 4.10). Four of the six cities have a “shoulder” of intermediate fractions associated with gradations of dense foliage and shadow. These correspond to quasi-uniform frequency distributions of vegetation fraction within dense canopies. Similar shoulders are found in the distributions of moderate resolution vegetation fractions obtained from Landsat imagery in larger numbers of cities (Small 2005). The two cities lacking this shoulder (Cuzco & L.A.) both have open canopy scrub vegetation in large patches.

Fig. 4.10. Vegetation fraction distributions. Cairo, Istanbul, New York, and Rio de Janeiro have similar distributions with pronounced shoulders extending to fractions of 0.5. Los Angeles has a greater percentage of vegetated area at all fractions greater than 0.1, and lacks the shoulder between 0.2 and 0.5. The distribution of vegetation fractions in Cuzco is displaced toward lower fractions, with the lowest percentage of higher (>0.4) fractions.

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In vegetated areas this exposes greater fractions of the underlying soil and shadow at the expense of illuminated foliage. However, the overall percentage of non-vegetated area in Los Angeles is significantly lower than in the other cities, so the lack of a shoulder of dense vegetation appears to be offset somewhat by the greater abundance of open canopy vegetation throughout the city. Similarly, four of the six vegetation fraction distributions have similar peaks at low fractions. The frequency diminishes rapidly between 0.1 and 0.20 suggesting that these cities have similar biases, and that the spectral ambiguity produces a “noise floor” around vegetation fractions of 0.10. The similarity suggests that the detection threshold of ~0.20 observed in NYC may be applicable to other cities as well. The fact that Cuzco and L.A. do not show the same peak and shoulder structure suggests that semi-arid environments, with greater abundance of indigenous open canopy scrub vegetation, may have fundamentally different characteristics from the denser vegetation found in other temperate and tropical environments. In spite of diversity of patch configurations, all the urban areas in this study have similar distributions of small (<100m) patches (Fig. 4.11). In each case, 40 to 50% of the illuminated foliage occurs in the form of these smaller patches. This suggests that smaller isolated patches of “diffuse” vegetation distribution may be responsible for almost half of the energetic balance provided by vegetation in these urban areas. While the evapotranspiration associated with parks and other large patches is conducive to convection at moderate (>100 m) spatial scales, the more distributed fraction of the vegetation cover can reduce the contrast between cooler transpiring vegetated areas and hotter, radiative, unvegetated areas at finer spatial scales. This, in turn, reduces strong gradients in latent and sensible heat flux as well as evapotranspiration that maintains convection at these scales. Although maximum patch size increases with scene size, every urban area in this study has densely vegetated (Fv>0.5) areas within 1 km of the nominal Central Business District (CBD). These larger patches of vegetation are both maintained and indigenous. These results suggest that the abundance of small patch vegetation may be more significant to microclimate than previously believed. The prevalence of isolated small patches of vegetation also has ecological implications as the spread of diseases and pests is believed to be influenced by the spatial distribution and degree of connectedness of the urban forest.Some of the results of this comparative analysis are strongly dependent on the specific landcovers contained in the images. It is important to remember that none of these images cover the entire city so we are really comparing general characteristics of the urban mosaics. Specifically, the distribution of larger patches is strongly de-

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pendent on scene location and size. For this reason, the analysis focuses on the overall fraction distributions and the distributions of small patches. The consistency in these distributions suggests systematic characteristics for vegetation distributions in different urban settings, but it does not establish them as robustly as a more comprehensive analysis of a larger number of cities. Nonetheless, the analysis provides a methodology to quantify vegetation abundance and distribution that could be used on a larger, more representative sample.

Fig. 4.11. Vegetation patch size distributions. Despite large differences in the distribution of larger vegetation patches (e.g. parks, forests, hillslopes), all six cities have similar distributions of smaller vegetation patches. In each city, patches smaller than 50m diameter account for 30% to 45% of vegetated area except in Istanbul where smaller patches account for a somewhat larger percentage. E.C. Diameter is patch diameter (meters circular equivalent diameter).

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4.7 Acknowledgements The work described here was supported by the Doherty Foundation, the US EPA STAR program, the USDA Forest Service and the NASA Socioeconomic Data and Applications Center (SEDAC). Support was provided, in part, by the U.S. Environmental Protection Agency's National Center for Environmental Research (NCER) STAR Program, under Grant R-828733. Disclaimer: Although the research described in this presentation has been funded in part by the U.S. Environmental Protection Agency, it has not been subjected to the Agency's required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. The Quickbird data used in this study were provided by Digital Globe through the NASA Commercial Remote Sensing Scientific Data Purchase program. Includes materials © Digital Globe TM.

4.8 References Adams JB, Smith MO, Gillespie AR (1993) Imaging spectroscopy: Interpretation based on spectral mixture analysis. In: Pieters CM, Englert P (eds) Remote geochemical analysis: Elemental and mineralogical composition, Cambridge University Press, New York, NY, pp 145-166 Adams JB, Smith MO, Johnson PE (1986) Spectral mixture modeling; A new analysis of rock and soil types at the Viking Lander 1 site. Journal of Geophysical Research 91:8098-8122 Boardman JW (1993) Automating spectral unmixing of AVIRIS data using convex geometry concepts. In: Green RO (ed) Fourth Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) airborne geoscience workshop, NASA Jet Propulsion Laboratory, Pasadena, CA, pp 11-14 Gillespie AR, Smith MO, Adams JB, Willis SC, Fischer AF, Sabol DE (1990) Interpretation of residual images: spectral mixture analysis of AVIRIS images, Owens Valley, California. Proceedings of the 2nd Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, NASA Jet Propulsion Laboratory, Pasadena, CA, pp 243-270 Johnson PE, Smith MO, Taylor-George S, Adams JB (1983) A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures. Journal of Geophysical Research 88:3557-3561 Maktav D, and Erbek FS (2005) Analysis of urban growth using multi-temporal satellite data in Istanbul, Turkey. International Journal of Remote Sensing 26 (4):797-810

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Rashed T, Weeks JR, Gadalla MS, Hill AG (2001) Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: A case study of the greater Cairo region, Egypt. Geocarto International 16 (4):5-15 Sabol DE, Adams JB, Smith MO (1992) Quantitative sub-pixel spectral detection of targets in multispectral images. Journal of Geophysical Research 97:26592672 Singer RB (1981) Near-infrared spectral reflectance of mineral mixtures: systematic combinations of pyroxenes, olivine and iron oxides. Journal of Geophysical Research 86:7967-7982 Singer RB, McCord TB (1979) Mars: Large scale mixing of bright and dark surface materials and implications for analysis of spectral reflectance. In: 10th lunar and planetary science conference. American Geophysical Union, Washington, DC, pp 1835-1848 Small C (2001) Multiresolution analysis of urban reflectance. In: Bucciarelli T, and Hellwich O (eds) IEEE/ISPRS conference on remote sensing of urban areas, IEEE, Rome, Italy, paper 23 Small C (2003) High spatial resolution spectral mixture analysis of urban reflectance. Remote Sensing of Environment 88 (1-2):170-186 Small C (2004) The Landsat ETM+ spectral mixing space. Remote Sensing of Environment 93:1 –17 Small C (2005) Global analysis of urban reflectance. International Journal of Remote Sensing 26 (4):661-681 Small C, Lu J (2006) Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis. Remote Sensing of Environment 100:441-456 Smith MO, Ustin SL, Adams JB, Gillespie AR (1990) Vegetation in deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment 31:1-26

Chapter 5 - Urban Environmental Monitoring in Buenos Aires – Determining Green Areas

Kerstin Krellenberg Department of Geography, Humboldt University, Berlin, Germany

5.1 Introduction For urban planning and management, it is essential that detailed and up-todate information about the urban ecosystem, with its high level of pattern heterogeneity, is available. In urban areas the steadily increasing land requirements for housing, new areas zoned for economic activities, and new provision of infrastructure, all lead to a reduction of green and open spaces. Owing to their high dynamism, such changes call for the use of methods which allow not only the updating of the planning basis, but also the recording of the consequences (Spitzer 1998). Remote sensing data can provide essential information that is both inexpensive and up-to-date. Formerly, this was only possible with high-cost airborne data. Today, high-resolution satellite data such as IKONOS, with more spectral information and 1-4 m spatial resolution, as well as alternative classification methods based on object-oriented analyses, plays an important role in urban studies. Urbanization creates physical, ecological and social patches which are linked to each other through complex interactions. Because of the environmental conditions specific to urban areas, special ecological systems and combinations of communities occur. The development of urban biotopes (delimited characteristic habitats of a specific plant and animal community), is strongly influenced by human use (building structure, city zones, network of vegetation and sealed surfaces). Sukopp (1998) de-

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scribed how urban development fragments, changes, and weakens urban biotopes and species. In Germany, ecological data for the protection of nature in urban areas have been collected since 1978, as biotope-type inventories (homogenous or similar biotopes are called biotope-types). Today, biotope-type maps exist for all of the large, and nearly all of the middle-sized German cities (Schulte and Sukopp 2000). Such maps are very helpful for integrating ecological and environmental factors into the planning process. For Buenos Aires, no biotope-type maps were available for the present study. While the importance of green areas for social activities is well known, open spaces are rarely considered to be sources of biodiversity. Flora and fauna can be used as biological indicators of pressure on the environment. A high proportion of green area in the city is a prerequisite for high biological diversity. Fragmentation, connectivity, and isolation of urban green areas play an important role in their ecological function (Andrén 1997). The effect of green areas on urban climate and air quality is influenced by their size, shape, and composition (Sukopp and Wittig 1998). Knowledge of the ecological importance of green areas, and of the relationship between urban patterns and biodiversity, can be used to assess the potential of an ecological approach to urban planning. This is very important because environmental problems often arise more quickly and are more severe in urban areas than in non-urban areas. This chapter describes and discusses remote-sensing approaches to the analysis of urban green areas in Buenos Aires. Because satellite imagery provides a synoptic view of the urban mosaic, remote-sensing data can be an important complement to limited in situ measurements in urban areas (Small and Miller 1999). Since pixel-based analysis in complexly structured urban areas is limited because the semantic information necessary to interpret an image is usually not represented in single pixels, both pixeland object-based approaches were applied. Object-based analysis facilitates work with meaningful image objects and their mutual relationships. IKONOS multi-spectral data at 4 m spatial resolution (data at 1 m resolution was not available) and ASTER visible-to-near-infrared data at 15 m spatial resolution were used. Urbanization processes and environmental and economic problems of Argentina’s capital are described in order to demonstrate the need for ecological and remote-sensing analyses.

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5.2 Background Latin America is undergoing fast urbanization and currently is the most highly urbanized continent, with an urbanization rate of more than 75% (Schmitz 2001). The growth of megacities in the developing countries of Latin America, especially in recent years, has created serious environmental problems (Müller 2002). The main problems are increasing land requirements, insufficient water supply, wastewater load, refuse collection, and changes in urban climate and air-quality (Wehrhahn 1993). In terms of population, Buenos Aires is the third largest megacity in Latin America (Mexico City and São Paulo are first and second), and the eighth largest in the world. In 2000, 34% of the population of Argentina lived in the megacity region of Buenos Aires (2000 =12.6 million, 2003 =13 million, 2015 =14.5 million anticipated). From 1975 to 2000, the capital of Argentina had an average annual population increase of 1.28% (United Nations 2004). The federal district of Buenos Aires alone consists of about three million inhabitants (INDEC 2001). Since 1998, Argentina has been in a state of deep economic crisis. About 40% of the population are living near or below the poverty line (Zeitverlag Gerd Bucerius GmbH & CO. KG 2005). Social conditions worsened significantly during the decade 1991-2001 (Morello et al. 2003), and by the end of 2001, the economic crisis had become a national crisis. It is therefore understandable that environmental conservation continues to have less importance in Argentine politics than do economic and social problems. The development of environmental protection is difficult in both the public and private sectors. In recent years the public sector has launched environmental initiatives on a national scale. The first signs of amelioration are visible, although environmental problems still do not have priority on the political agenda. There is very little public awareness of conservation issues, and corruption in government is rampant (Krellenberg et al. 2005).

5.3 Related work Remote-sensing approaches to urban analysis have been implemented for several urban areas. Zhu et al. (2003) used ASTER data and an objectoriented approach to build a hierarchical multi-resolution structure for investigations of urban vegetation. Meinel et al. (2001) described the benefits of IKONOS satellite image data for urban applications. By using an image fusion of multispectral and panchromatic channels (1 m spatial reso-

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lution), urban biotope and land-use type mapping can be updated. For a part of the German city of Dresden, Neubert and Meinel (2002) tested a fusioned 1 m data set and, using eCognition software, distinguished among vegetated areas, grassland, trees, fruit plantations, agricultural land, and fallow fields. For other urban object-oriented approaches (e.g., Kleinschmit and Kim 2004, Banzhaf 2004, Bauer and Steinnocher 2001, de Kok et al. 2003), IKONOS data is also available at 1 m spatial resolution. Remote-sensing studies realized in Argentina have usually focused on analysis of natural resources and transformation processes. Using Landsat and/or SAR data, agricultural areas, wetland ecosystems (e.g., Karszenbaum et al. 2000, Kandus et al. 2001), and forests (e.g., Montenegro et al. 2002) were investigated. These areas of study have been described as the dominant branches of Argentine ecology by Rabinovich and Boffi Lissin (1992). The application of Geographic Information Systems (GIS) for analyzing the urban growth of Buenos Aires was presented in a case study by Matteucci et al. (1998). The first studies of urban green areas in Buenos Aires were conducted by analysing Landsat 5 TM data related to biomass, humidity and temperature at a maximal spatial resolution of 15 m (de Pietri et al. 2001). Between 1973 and 2004, only ten contributions (out of a total of 298) to the Journal of the Argentinian Ecological Society (ASAE/Ecología Austral) considered urban ecology, of which only one focused on urban green areas. The first inventories of flora and fauna in the green areas of Buenos Aires were made for the Costanera Sur nature reserve (e.g., Faggi and Cagnoni 1990, Burgueño et al. 1997), which is located 1 km from the center of Buenos Aires. Until now, urbanization processes of Buenos Aires have received little attention. Morello et al. (2003) described some ecological changes in periurban and rural areas caused by urban expansion of the metropolitan area of Buenos Aires between 1869 and 1991. They pointed out the lack of planning and control over the continuing urban expansion process. In general, little research and planning involving urban ecology and remote sensing of urban areas are going on for Buenos Aires. For the purposes of this study, there was very little basic work to build upon. Most of the data collection, including fieldwork and data processing, had to be done by the present author.

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5.4 Materials and methods

5.4.1 Study area The center of the federal district of Buenos Aires is located at 34º38’ south latitude and 58º28’ west longitude. The climate of the region is subtropically humid, characterised by long warm summers and mild winters, an average air temperature of 11 °C in July and 25.5 °C in January, and a mean annual precipitation of 1147 mm (climate station 34°35’S, 58°29’W) (Sträßer 1999). The study area was located in the southern portion of the Buenos Aires federal district. The location of the study area inside the metropolitan area, and the investigated green areas within, are shown in Figure 5.1.The chosen gradient extends from the city center to the suburban region, has a total area of about 51 km², and a high level of pattern heterogeneity in land use and human activities. The study area contains green areas of very different shape and composition. The area borders on the heavily contaminated rivers Río de la Plata and Riachuelo/Río Matanza. Other limits are the General Paz and the 25 de Mayo/Dellepiane highways. The study area is one of the poorest parts of the city and has many serious environmental problems. In the city center, parks are surrounded by densely built-up areas with few green or open spaces. Towards the west these conditions change and open areas prevail. Few patches of natural vegetation have been protected from urban sprawl. Inside the study area four urban parks were examined: Parque Chacabuco with 24.41 hectares, Parque España with 5.78 hectares, Parque Lezama with 7.36 hectares, and Parque Patricios with 15.41 hectares. Suburban green areas considered in the present study were the Parque Indoamericano with 55.2 hectares and the recreational area Parque Ribera Sur with 38.43 hectares. 5.4.2 Data Digital geographical data on Buenos Aires are scarce and only available in differing coordinate systems. Furthermore, the information is not necessarily up-to-date, validated, or on an appropriate scale. Updated aerial photographs and accurate thematic data are available from some private companies, but only at very high cost. In addition, in Argentina scientific institutes often do not make their data publicly available. Many interesting

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and important data may exist; however, if the data are not published and exchanged, they are not very useful. A central data archive is clearly needed.

Fig. 5.1. Location of the study area and the sampled green areas

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Basic data, such as topographical maps, are stored at the Military Geographic Institute (IGM: Instituto Geográfico Militar), although not all topographical maps exist in digital form. Analogue paper versions are obsolete. Satellite data are intensively analyzed but rarely published. A more intensive exchange of data and knowledge between scientific institutes and the IGM could lead to important findings. As one example of redundant data, the digital database on a block scale (cadastral data) for Buenos Aires was considered. Several different vector layers exist: at the governmental Department of Geographic Information Systems of Buenos Aires city (DGSIG: Dirección General de Sistemas de Información Geográfica del Gobierno de la ciudad de Buenos Aires), the Military Geographic Institute, and the Center of Metropolitan Information (CIM: Centro de Información Metropolitana). These layers are all in different coordinate systems and are not all updated or accurate. This can be proofed by a visual comparison of the IKONOS image and the DGSIG’s vector database below. The street layer of the latter was incomplete and in some cases lines are completely lacking (Fig. 5.2).

Fig. 5.2. DGSIG vector data and IKONOS-scene (2 January 2001)

This deficiency points out the advantages of using high-resolution satellite data to update existing databases. This application of satellite data was also described by Piñero (2003), who visually tested the use of IKONOS data for application to urban land registry questions for Buenos Aires, in order to avoid high-cost airborne data and intensive fieldwork. But, until now, this possibility of updating has not been put into practice. Aerial photographs from 1997 serve as the basis for the digital cadastral data of Buenos Aires. Airborne data from 2002 also exist but are not used

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directly to update the cadastral database. The photographs are visually analyzed at the land registry, and the cadastral changes are recorded by ground-checks made by fieldwork. The existing land-use information was collected in 1998. Information about socioeconomic conditions is based on the 1991 census; data from the last census in 2001 are still not integrated. Specific data of the various city government departments are collected at the DGSIG, integrated into the existing GIS, and made available via intranet to all of the departments which are concerned. Institutes and universities can apply for specific data for research purposes. Full datasets, such as large areas covered by airborne data, may occasionally be made available for a fee. The CIM created a territorial information system for the metropolitan area of Buenos Aires. A GIS was developed based on block units digitized from airborne data and later updates. The GIS consists of different analysis scales and units, topological and thematic, and was established to collect information about urban problems. Indicators were developed for thematic information. Much data, such as that for radiation and precipitation, are only available as points. Streets are stored as lines and contain further thematic information. Land-use activities are classified using the principal categories of residential, commercial, industrial, and public, and are divided into additional subclasses. For the present study, digital data on the block scale was obtained from the DGSIG for the whole federal district of the capital of Buenos Aires without any thematic information. Vascular plant richness and composition was sampled and listed during the vegetation period 2002-2003 in the six green areas along the urban to suburban gradient in Buenos Aires, in order to investigate total richness, richness of spontaneously growing plants, and tree abundance (Faggi et al. 2003). The gradient contains different types of urban green space and is subject to diverse management practices and objectives. Information on land-use and land-cover heterogeneity, existing biodiversity, and differences and similarities between the green areas was collected and evaluated. 5.4.3 Preparatory work An IKONOS scene was available (multispectral with 4 m spatial resolution, from 2 January 2001), covering the urban part of the study area. An ASTER scene (from 7 October 2002) was provided by Arizona State University. Subsequently, mapping of the study area was completed with IKONOS data (multispectral with 4 m spatial resolution, 2 January 2001 and 30 December 2000). Therefore, mosaicking had to be performed, including balancing the data range between the different images. Gains and

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offsets were calculated. The geometric correction of the IKONOS scene was done on the basis of the DGSIG vector layer on the block scale, because the low accuracy of the collected GPS-points precluded their use for image corrections. On the basis of the corrected IKONOS scene, an imageto-image registration of the subset of the ASTER scene was realised. Since a biotope-type map for Buenos Aires did not exist, intensive fieldwork, including the mapping of biotope-types and GPS-control points, was done to survey the study area. Problems occurred in taking GPSpoints for later geometric corrections, because all power cables were over ground, causing disturbances in the GPS point collection. Another problem was narrow streets surrounded by high buildings, which made the reception of satellite information difficult. Furthermore, not all of the areas were easily accessible. Some districts, like the slums, were dangerous to cross. Other districts, such as industrial areas, had to be visited with care. Sometimes it was prudent not to show the technical equipment (camera, GPS) because of the risk of being robbed. A biotope-type classification after Bede et al. (2000) and made for Brazil was chosen to map the area of the gradient, because the biotope-type units were considered to be similar to those of Buenos Aires. Based upon Bede et al.’s (2000) classification, fieldwork, and visual interpretation of the satellite data, the biotope types shown in Table 5.1 were assigned for the urban part of the study area using ArcView software (Plate 5.1). Classes were determined after area dominance inside one parcel: if one type was dominant with more than 50% of the whole area of the parcel (normally 100 m x 100 m in built-up areas), a clear biotope was assigned. If two types occurred, each with about 50% of the parcel area, mixed classes were assigned. A clear assignment was not always possible because, as is characteristic of urban areas, different mixed areas were predominant. A subset of images of the study area was compiled. For detailed analyses of vegetation, subsets of the selected green areas, including their specific radius, were used. Therefore, a 500 m radius was calculated for the Parque Lezama, taking into account expected ecological interactions between the park and its surroundings due to its size. Following this ratio of park size and surrounding distance of influence, the radii for the other green areas were calculated. One green area, the Parque Chacabuco with a 900 m radius, was chosen to represent the results of the pixel- and objectbased remote-sensing analyses done with IKONOS data.

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Table 5.1. Biotope-type classification Green areas

Park, open space in succession, sports area, central reservation, open space extensively used Residential areas Detached, one-family house; detached, one-family house single-storey buildings with (visible) garden; detached, one-family house on smaller parcels with narrow streets; mixture of detached, one-family houses and multi-storey buildings; mixture of detached, one-family houses and trade area; mixture of detached, one-family houses and industrial area Residential areas Multi-storey buildings, multi-storey building complexes, multi-storey buildings mixture of multi-storey buildings and trade area Slums Wild slums; structured slums (formerly detached, onefamily house on smaller parcel with narrow streetsÆ increase in storeys); dilapidated, detached, one-family houses Industry/trade areas Trade area, industrial area, mixture of industrial and trade area Other areas Public space, hospital, cemetery, railway station, hypermarket, church, prison, railway or highway constructions

5.4.4 Remote sensing analyses Based upon fieldwork data and information from the created, digital biotope-type map (Plate 5.1), supervised classifications with the maximum likelihood classifier were realised using the available data from ASTER and IKONOS (described in section 5.4.3). Classifications were implemented using pixel- and object-based approaches. Pixel-based analyses were realised using RSI Envi 4.0 software, while the object-oriented approach was carried out by applying eCognition Professional 4.0 software. A simplified scheme of the methodological procedure is shown in Figure 5.3. Pixel-based analyses

Spectral signatures of training set pixels of selected surface types were implemented in the pixel-based, supervised hierarchical (a stepwise classification process) classification after Hildebrandt (1996). In this case “hierarchical” means that, first of all, those classes were prepared that could be best separated from other classes by their spectral features. These areas were excluded from further classification steps in the hierarchy. The following classes were detected during the hierarchical supervised classification process: grass, deciduous trees, coniferous trees, open space in succes-

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sion, sand, aluminium, concrete, asphalt, and shadow. In total, 872 training points were gathered using ground-truth information. Water/shadow areas were masked out, and the Normalised Difference Vegetation Index (NDVI) was calculated for the remaining areas.

Fig. 5.3. Simplified scheme of the methodological procedure

Object-based analyses

For the object-based analysis, a multi-scale, object-specific segmentation of the image subsets in homogeneous objects was completed. Based on the segmentation, a class hierarchy was worked out. This hierarchy contains the same classes as the pixel-based analyses. Objects are described by their specific spectral information, shape, and texture. The topological relationship of adjacent image objects was worked out explicitly. For that purpose, the relationships between objects (the relationship to neighbour objects,

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the relationship to sub- or super-objects, membership functions, and so on) were set. Several layers were produced to describe the class hierarchy. Based upon the objects and the class descriptions, a fuzzy classification was implemented. The main advantages of an object-oriented approach were observed. Shape characteristics such as size and density of an object, and neighbourhood characteristics for topological relationship analyses, were calculated. More characteristics were determined than are normally observed by pixel-based calculations. This information was particularly useful in order to resolve the increasing number of features of heterogeneous structures in urban areas (Baatz et. al 2000). Combination of object-and pixel-based analyses

For integration into the object-oriented approach, the NDVI, calculated using the spectral information of single pixels, was stacked as a synthetic channel to the four IKONOS multispectral channels. Because the NDVI represents plants’ photosynthetic efficiency, it is strongly correlated with the density and vitality of vegetation cover. Therefore, it was used as an additional feature to improve the object-based classification. For the combined approach, spectral signatures from the pixel-based analysis were added.

5.5 Results An NDVI map based on ASTER data for the whole study area is shown in Plate 5.2. It provides an overview of the vegetation conditions, showing an increase in vegetation density from the city center towards the suburban area. All other results of pixel- and object-based analyses are presented only for the urban park, Chacabuco. The results of the pixel- and the object-based approaches for the Chacabuco urban park are visualised as maps in Plate 5.3. To evaluate the methodology, the accuracy of the classifications was compared using ground-truth information. The calculation of the overall classification accuracy shows a slightly higher accuracy (97%) for the object-based results than for the pixel-based results (95%). During analysis of the pixel-based classification results, problems were encountered in separating sand and open spaces. All other classes were separated with high accuracy. The classes grass and concrete, which are of great importance for the thematic background, could be better classified by the object-based method. Classification accuracy assessment is given by a classification error matrix. The relationship between known reference data (ground truth) and the corre-

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sponding results is compared for both the pixel-based and object-based approaches (Table 5.2). The same training sets were used. The results of the combined methods show the highest overall accuracy (98%). Therefore, for this study, the combination of pixel- and object-based methods presents the best classification results. In evaluating the results, it should be taken into consideration that the presented calculations were only realised for a small sample site of 2.5 km². Not all of the classes (especially sand and coniferous trees) were well represented. This resulted in overlapping training sites which were used for spectral information of the classification and for reference data of the error matrix, and therefore produced highly accurate results. Because of the space-limited differentiation of the urban area, the principle of homogeneity was not always guaranteed. Table 5.2. Absolute error matrix for the pixel- (first number in every cell) and object-based (second number in every cell) classification (IKONOS) of the Parque Chacabuco (rows: classification; columns: ground truth) Classes Grass (G)

G DT CT OS 61/ 0/0 0/0 0/1 69 Deciduous trees (DT) 0/4 234/ 0/0 0/0 234 Coniferous trees (CT) 1/0 5/5 35/ 0/0 35 Open space in succes- 11/0 0/0 4/0 76/ sion (OS) 76 Sand (S) 0/0 0/0 0/0 1/0 Aluminium (AL) Concrete (C) Asphalt (A) Shadow (SH) Total

S AL C A SH Total 0/0 0/0 0/0 0/0 0/0 61/70 0/0 0/0 0/0 0/0 0/0 234/238 0/0 0/0 0/0 0/0 4/0 45/40 5/0 0/1 9/3 0/0 0/0 105/80

33/ 0/0 0/0 33 0/0 0/0 0/0 0/0 0/0 67/ 0/0 66 0/0 0/0 0/0 0/0 0/5 0/0 42/ 47 0/0 0/0 0/0 0/0 0/0 0/0 1/2

0/0 0/0 34/33 0/0 0/0 67/66 0/0 0/0 42/52

257/ 0/0 258/259 257 0/0 0/0 1/5 0/0 0/0 0/0 0/0 0/0 25/ 26/34 29 73/ 239/ 40/ 77/ 38/ 67/ 52/ 257/ 29/ 872/872 73 239 40 77 38 67 52 257 29

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Table 5.3. Classification results of the three methodological approaches (surfaces in pixel and percent) Number of Number of Number pixels/pixel- pixels/object- of pixels based based Class combined Grass 1898 2892 4043 Deciduous trees 4995 11108 12399 Coniferous trees 20516 15406 13233 Open space in 34200 16658 7544 succession Sand 1068 486 1230 Aluminium 21746 4149 6293 Concrete 39115 42928 47326 Asphalt 33282 59198 64747 Shadow 2108 6103 2105 Unclassified out- 49464 49464 49464 lying area

Percent pixelbased 0.91 2.40 9.84 16.41

Percent objectbased 1.39 5.33 7.39 7.99

Percent combined 1.94 5.95 6.35 3.62

0.51 10.44 18.77 15.97 1.01 23.74

0.23 1.99 20.60 28.41 2.93 23.74

0.59 3.02 22.71 31.07 1.01 23.74

Note: Total number of pixels of the image subset = 208,392

Comparing the classified surfaces of the three approaches, great differences can be observed (Table 5.3). The surface area classified as aluminium was five times greater using the object-based approach than it was using the pixel-oriented analyses. A visual comparison of the pixel- and object-based classification results shows that the edges of the aluminium roofs were heavily generalised with the pixel-based method. On the other hand, the segmentation of homogenous objects allowed a better separation of the edges. In all cases, problems occurred in classifying coniferous trees; even applying the combined approach the resulting surface area is too high. Overlaps with the shadow class were observed.

5.6 Applications As discussed above, the object-based approach permits analysis of the ecological functions of existing green areas. Correlations with fieldwork data and flora matrices can be undertaken. Based upon the object-based classifications, further information may be extracted, following the key ecological principles applicable to ecological research and land-use decisions in urban landscapes worked out by Zipperer et al. (2000). The ecological compensation can be observed by determining pattern size, biotope-types, plant diversity, and habitat fragmentation. Fragmenta-

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tion can be measured quantitatively as the ratio of green space to total surface area, calculated from satellite imagery and expressed as a percentage. A high proportion of green areas in the city is a prerequisite for high diversity, and a high shape index is an indicator of poor ecological quality (Venn 2001). The isolation of green patches is typical of urban sites. Therefore, connectivity needs to be considered together with patch size, the number of patches, and patch quality, since patch quality must be high in order for connectivity to have any effect on species richness in individual patches. Optimal characteristics for urban green areas are large patch sizes and continuous sites. The distance between the borders of neighbouring patches can be measured. Knowing the amount, spatial arrangement, and ecological function of urban green areas can help the Argentine government to take an ecological approach to urban planning. Advice can be given on how to improve existing green areas and where to create new ones. This information could be used, for example, to provide a sufficiently large area of habitat for various plant and animal species. Networks of green patches can be crucial to the survival of populations (Bastin and Thomas 1998). One option for conservation would be the maintenance and establishment of connectivity between patches by green corridors of sufficient width and quality, as well as improving the quality and increasing the quantity of patches. The possibilities for introducing deed restriction and conservation action need to be discussed.

5.7 Conclusions The analyses of remote-sensing data give an overview of the urban habitat network of Buenos Aires on different scales. For Buenos Aires, the use of remote-sensing data for urban planning and research is a new practice. By conducting extensive fieldwork and data research, abundant information on urban ecology in general, and on urban green areas in particular, was obtained. The biotope-type classification and the resulting map provide an important database for further investigations. For future investigations of Buenos Aires, it would be both necessary and interesting to model the changes in the urban ecosystem over time using remote-sensing data. Changes in vegetation composition, as well as in shape and connectivity of urban green areas, will lead to changes in the whole urban ecosystem. The combination of pixel- and object-based remote-sensing analyses allows a detailed separation of different classes inside of the urban green areas. This approach is therefore appropriate for detecting green areas within

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urban areas, to classify biotope-types of the surroundings, and to determine individual biotopes inside the green areas. Taking into account the availability, accuracy, and topicality of existing geodata in Buenos Aires, as well as the ecological and economical situation of the megacity, the presented methods reflect a conformist approach. As for future applications in Buenos Aires, the economic situation won’t allow the use of IKONOS scenes with 1 m spatial resolution; the present study conducted with 4 m IKONOS-data represents a realistic handling. Until now, the evaluation of the pixel- and object-based methods used for detailed investigation of urban green areas was only based on the classification accuracy assessment of the training sites and a comparison of the surface areas classified with each method. First conclusions for the overall accuracy of the classification results were made. For further evaluation and validation of the methods, it will be important to examine whether or not the methods are applicable to green areas of a different structure. An automation of the presented methods could be of interest to the Argentine government. Shadows will be considered individually in secondary studies because of their relatively high surface proportion in urban areas. By testing further object-based parameters, the object-oriented approach can be improved so that it becomes a satisfactory thematic basis for more detailed research on green areas, and therefore on urban ecology and urban planning.

5.8 Acknowledgements The author would like to thank all Argentinian institutes and companies mentioned in this paper for answering questions and providing data. The study on the urban ecology of Buenos Aires started in 2002 with the binational project, “Perspectives on Urban Ecology - the Metropolis Buenos Aires,” funded by DAAD/ PROALAR. Since April 2004 the work has been funded by a Ph.D. scholarship (NaFöG) from the state of Berlin.

5.9 References Andrén H (1997) Habitat fragmentation and changes in biodiversity Ecological Bulletin 46:171-181 Baatz M, Lessing R, Rott T, Schäpe A (2000) Objektorientierte, fraktalhierarchische auswertung von fernerkundungsdaten Rundgespräch der Kommission für Ökologie 17:27-35

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Banzhaf E (2004) Detektion von potenziellen industriebrachen mittels fernerkundungs- und GIS daten. Das beispiel Baltimore City, Maryland, USA Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation eV 13:271-278 Bastin L, Thomas CD (1998) The distribution of plant species in urban vegetation fragments. Landscape Ecology 14:493-507 Bauer T, Steinnocher K (2001) Per parcel land use classification in urban areas applying a rule-based technique. GeoBIT/GIS 6:24-27 Bede LC, Weber M, Resende S, Piper W, Schulte W (2000) Manual para mapeamento de biótopos no Brasil, base para un planejemento ambiental eficiente. Belo Horizonte, Brazil Burgueño G, Codignotto J, Faggi A, Grass E, Roberts C (1997) Análisis y propuesta de restauración ecológica de la Reserva Costanera Sur. Actas 1er Congreso Ambiental No Gubernamental Area Metropolitana Buenos Aires, pp 191-193 de Kok R, Wever T, Fockelmann R (2003) Analysis of urban structure and development applying procedures for automatic mapping of large area data. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 34 (7/W9):41-45 de Pietri DE, Karszenbaum HY, Zamorano J (2001) Convergencia de los impactos negativos de la ciudad de Buenos Aires en la vegetación urbana: Aplicación de datos satelitales. ASAUEE – Buenos Aires – Primeras Jornadas de Economía Ecológica, p 23 Faggi AM, Cagnoni M (1990) Flora del Parque Natural Costanera Sur. Parodiana 6 (1):49-66 Faggi A, Castro R, Krellenberg K, Milesi J (2003) Indicadores de flora y fauna en un gradiente urbano-periurbano. Boletin de la Sociedad Argentina Botanica 38:224-225 Hildebrandt G (1996) Fernerkundung und luftbildvermessung für forstwirtschaft, vegetationskartierungen und landschaftsökologie. Wichmann, Heidelberg, Germany INDEC (2001) Instituto Nacional de Estadísticas y Censos Censo 2001, http://www.indec.mecon.gov.ar/webcenso/index.asp, accessed 6-March-2007 Kandus P, Karszenbaum H, Pultz T, Parmuchi G, Bava J (2001) Influence of flood condition and vegetation status in the radar backscatter of wetland ecosystems: Analysis of multitemporal /multiangle radarsat SAR data Canadian Journal of Remote Sensing 27 (6):651-662 Karszenbaum H, Kandus P, Martinez JM, Le Toan T, Tiffenberg J, Parmuchi G (2000) ERS-2, RADARSAT SAR backscattering characteristics of the Parana River Delta Wetland, Argentina. Proceedings of the ERS-Envisat symposium: Looking down to Earth in the new millennium, Oct. 16-20, Gothenburg, Sweden, on CDROM

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Kleinschmit B, Kim HO (2004) Anwendung sehr hochauflösender Satellitenbilddaten zur urbanen Biotop- und Nutzungstypenkartierung – dargestellt am Beispiel der südkoreanischen Megastadt Seoul. Publikationen der deutschen gesellschaft für photogrammetrie, fernerkundung und geoinformation eV 13:263-270 Krellenberg K, Faggi AM, Endlicher W (2005) Umweltpolitik in Argentinien – dargestellt am beispiel des flusses Riachuelo/ Río Matanza in Buenos Aires im vergleich zur Emscher im Ruhrgebiet. Geo-Öko 26:19-34 Matteucci SD, Buzai GD, Baxendale CA (1998) Sistemas ambientales complejos: herramientas de análisis espacial. Universidad de Buenos Aires, Buenos Aires, Argentina Meinel G, Neubert M, Reder J (2001) The potential use of very high resolution satellite data for urban areas–First experience with IKONOS data , their classification and application in urban planning and environmental monitoring. Regensburger Geographische Schriften 35:196-205 Montenegro C, Minotti P, Karszenbaum H, Strada M, Parmuchi G (2002) Integration of the different phases of the argentine first national native forest inventory in a GIS environment. Proceedings of the 29th international symposium on remote sensing of environment (IRSE), April 8-12, Buenos Aires, Argentina, on CDROM Morello J, Matteucci SD, Rodríguez A (2003) Sustainable development and urban growth in the Argentine Pampas region. Annals of the American Academy of Political and Social Science 590:116-130 Müller U (2002) Räumliche konzentration und dekonzentration von bevölkerung und wirtschaftsstandorten im großraum von Buenos Aires. Petermanns Geographische Mitteilungen 146:8-15 Neubert M, Meinel G (2002) Segmentbasierte auswertung von IKONOS-daten – Anwendung der bildanalyse-software eCognition auf unterschiedliche testgebiete In: Blaschke T (ed) Fernerkundung und GIS: Neue sensoren – innovative methoden. Wichmann Verlag, Karlsruhe, Germany Piñero CA (2003) La alternativa satelitaria para la captura de datos catastrales urbano. Primera congreso de la ciencia cartográfica y VIII semana nacional de cartografía, June 25-27, Buenos Aires, Argentina Rabinovich JE, Boffi Lissin LD (1992) La Ecología en la República Argentina. Ecología Austral 2:109-122 Schmitz S (2001) Nachhaltige stadtentwicklung – Herausforderungen, leitbilder, strategien und umsetzungsprobleme. Petermann Geographische Mitteilungen 145:6-15 Schulte W, Sukopp, H (2000) Stadt- und dorfbiotopkartierungen. Naturschutz und Landschaftsplanung 5:140-147 Small C, Miller RB (1999) Digital cities II: Monitoring the urban environment from space. Proceedings of the international symposium on digital Earth, Beijing, China, pp 671-677

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Spitzer F (1998) Bestimmung der bebauungsdichte aus satellitenbilddaten für das stadtgebiet von Regensburg In: Breuer T, Jürgens C (1998) Luft- und satellitenbildatlas Regensburg und das östliche Bayern. Pfeil Verlag, Munich, Germany Sträßer M (1999) Klimadiagramm-atlas der Erde, teil 2: Asien, Lateinamerika, Afrika, Australien und Ozeanien, Polarländer: Monats- und jahresmittelwerte von temperatur und niederschlag für den zeitraum 1961-1990. Duisburger Geographische Arbeiten 20 Sukopp H (1998) Urban ecology - Scientific and practical aspects. In: Breuste J, Feldmann H, Uhlmann O (eds) Urban ecology. Springer, Berlin, Germany, pp 5-17 Sukopp H, Wittig R (1998) Stadtökologie. Fischer Verlag, Stuttgart, Germany United Nations (2004) World urbanization prospects the 2003 revision: Data Tables and Highlights. United Nations, New York, NY, http://www.un.org/esa/population/publications/wup2003/2003Highlights.pdf, accessed 6-March-2007 Venn S (2001) Development of urban green spaces to improve the quality of life in cities and urban regions: Ecological criteria. URGE project deliverable 7, http://www.urge-project.ufz.de/PDF/D7_Ecological_Report.pdf, accessed 6March-2007 Wehrhahn R (1993) Ökologische probleme in lateinamerikanischen großstädten. Petermanns Geographische Mitteilungen 137:79-94 Zeitverlag Gerd Bucerius GmbH & CO. KG (2005) Die ZEIT: Das Lexikon in 20 Bänden. Zeitverlag Gerd Bucerius GmbH & CO. KG, Hamburg, Germany Zipperer WC, Wu J, Pouyat RV, Pickett STA (2000) The application of ecological principles to urban and ubanizing landscapes. Ecological Applications 10 (3):685-688 Zhu G, Bian F, Zhang M (2003) A flexible method for urban vegetation cover measurement based on remote sensing images. Proceedings of the ISPRS joint workshop high resolution mapping from space 2003, Oct. 6-8, Hannover, Germany, http://www.ipi.unihannover.de/html/publikationen/2003/workshop/zhu.pdf, accessed 6-March2007

Plate 2.1. The City of Rio de Janeiro (Landsat image).

Plate 3.1. Tegel Airport (left) and the city center of Berlin (right) in ASTER RGB data; bands 3-2-1 (top) and 3-4-2 (bottom). The 3-4-2 band combination reveals the influence of the lower spatial resolution in the SWIR bands.

Plate 3.2. False-color composite (RGB - 432) of multispectral QuickBird data for a test site in southeast Berlin. The diagonal structure from NW to SE marks the partially derelict marshalling yard.

Plate 3.3. Classification over false color composite. Final result of the hierarchical, object-oriented classification.

Plate 4.1. Quickbird imagery used in this analysis. Reduced-resolution, falsecolor composites (RGB=341) with 2% linear stretch. The Cuzco image is 2x2 km. Includes materials © Digital Globe TM.

Plate 4.2. Quickbird imagery used in this analysis. Full resolution false color composites (RGB=341) with 2% linear stretch. Each subscene is 1680x1680 m2. Includes materials © Digital Globe TM.

Plate 5.1. Biotope-type map for the urban part (federal district) of the study area (own presentation, based on DGSIG data, visual interpretation of IKONOS data, and fieldwork).

Plate 5.2. NDVI calculation for the whole study area using ASTER data.

1)

2)

Plate 5.3. Classification maps (IKONOS) for the urban park, Chacabuco 1) pixelbased approach and 2) object-based approach.

Plate 6.1. Nighttime surface temperature map for Phoenix, AZ obtained from ASTER data acquired at 10:34:46 local time on Oct 03, 2003. Temperature scale is in Celsius. PM – Phoenix Mountains, SM – South Mountain, SE – Sierra Estrella, SH – Sky Harbor Airport.

Plate 7.1. Land use change in the Phoenix metropolitan area from 1912 (top), through 1955 (middle), to 1995 (bottom). Figure after Knowles-Yanez et al. (1999).

Plate 7.2. Comparison of pixel resolution for a portion of the Phoenix metropolitan area using 80 m/pixel Landsat MSS data acquired in 1980 (top) with 15 m/pixel ASTER data acquired in 2000 (bottom). See text for description of features visible in the images. North is to top of images.

natural land

farmland

farmland to urban

urban

water

natural to urban

Plate 7.3. LULC change in the Phoenix metropolitan area using classified data. Upper image shows change 1973-1979; lower image shows change 1973-2003. Image orientation and location same as in Fig. 7.4.

Plate 8.1. Unplanned and illegal constructions on the outskirts of Delhi.

Plate 8.2. Location of large urban projects in Delhi.

Plate 8.3. Urban sprawl and expansion onto productive lands.

Plate 8.4. Land-use and land-cover class in Central Delhi.

Plate 8.5. Land-use and land-cover class in Trans Yamuna Delhi.

Plate 8.6. Urban land-use change from agricultural to built up.

Plate 8.7. Traffic congestion during peak-use hour.

Plate 8.8. Recreational facilities like sports complexes and golf courses. Source: Delhi Development Authority.

Source: D.D.A

Plate 8.9. Master Plan for Delhi Perspective 2001.

Plate 9.1. Map presentation, Berlin Digital Environmental Atlas.

Plate 9.2. Presentation of maps, plans, and data via the FIS-Broker.

Plate 9.3. Surface temperature at night, City of Berlin, with the cold-air generating areas of the Berlin-Tempelhof airport.

Plate 9.4. Surface temperature, Berlin Digital Environmental Atlas.

Imperviousness Environmental Atlas 1993-Satellite data 2000 100

90

80

Satellite data %

70

60

50

40

30

20 y = -0.0034x2 + 1.3034x + 4.5333

10

0 0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Environmental Atlas %

Plate 9.5. Imperviousness degrees of reference surfaces, from the Environmental Atlas (combined procedure from satellite-image interpretation, CIR air photo evaluation, and evaluation of topographical maps; as of 1993), and satellite-image interpretation (as of 2000) n = 10751.

Imperviousness Environmental Atlas 2001 / Waterworks 2000 100 y = 0.7236x + 4.7782

90

80

Berlin Waterworks %

70

60

50

40

30

20

10

0 0

10

20

30

40

50

60

70

80

90

100

Environmental Atlas %

Plate 9.6. Imperviousness degrees of reference surfaces according to the Environmental Atlas (mixed method from satellite-image interpretation, CIR air photography evaluation and evaluation of topographical maps; as of 2001), and lot specific survey by the Berlin Waterworks (as of 2000) = n 15482.

Plate 9.7. HRSC-AX: Multispectral information with a high geometric accuracy used for visual interpretation. Left: multispectral view with ALK-data; right: new habitat type mapping for updating (old data in hatchings).

Plate 10.1. ASTER satellite image of the upper Mae Nam Ping basin in 2001. Most of the urbanized area around Chiang Mai municipality lies just below the middle of the image.

Plate 10.2. ASTER VNIR image of central built-up area of Chiang Mai on 20 Feb 2001 (Level 1B Band 1, 2, 3N) at a 15x15m resolution.

Plate 10.3. ASTER Level 1B with spatial enhancement (top), and spatial and spectral enhancement (bottom).

Plate 11.1. Landscape metrics for Chengdu extended urban region: Landscape shape index.

Plate 11.2. Landscape metrics for Chengdu extended urban region: Mean patch size.

Chapter 6 - Challenges in Characterizing and Mitigating Urban Heat Islands – A Role for Integrated Approaches Including Remote Sensing

William L. Stefanov1, Anthony J. Brazel2 1

Image Science & Analysis Laboratory, NASA Johnson Space Center, Houston, TX, USA

2

School of Geographical Sciences, Arizona State University, Tempe, AZ, USA

6.1 Introduction Over the last several decades, scientists have investigated urban influences on atmospheric conditions (Oke 1987). Much of this work has been conducted using historical climate records of urban and rural sites; doing spatial sampling in and around a given urban area with mobile transects and at instrumented tower sites; and using satellite and airborne remote-sensing technology. In the 21st century, more and more people are moving to cities. Soon, cities will contain the majority of the Earth’s population. It is thus critical to maintain urban environments in sustainable ways that ensure acceptable levels of health, welfare, and safety of citizens. Scientists from many disciplines have converged upon several urban themes at the interdisciplinary juncture of climate, meteorology, information technology, space technology, architecture, planning, and engineering of the built environment, in order to understand the interactions between a city and its overlying atmosphere. One major facet of this convergence could broadly

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be labeled “urban climatology.” This area of research has advanced knowledge at the nexus of climate and urbanization over the last several decades, but there are many challenges yet to be met, with the ultimate goal of applying scientific understanding to maintain sustainable urban environments and quality of life (e.g., Arnfield 2003, review of the field of urban climate). This chapter reviews temporal and spatial scales in climatology, the basic factors controlling urban climates, methods used to investigate urban climate, how remote sensing is contributing to this area of study, and strategies to mitigate unwanted consequences of the inadvertent modification by cities of their overlying atmospheres. Oke (1997) suggests that urban atmospheres demonstrate the strongest evidence we have of the potential for human activities to change climate. Rapid expansion of cities has produced concurrent alterations in the urban climatic environment (Landsberg 1981). In general, there are many apparent anthropogenic impacts on our atmospheric environment (Changnon 1983). These range from microscale (e.g., replacing trees with a parking lot) to macroscale (e.g., carbon dioxide effects on global climate caused by fossil fuel combustion and emissions). An extensive literature addresses the specific problem of air pollution on regional and global scales as it relates to general processes of urbanization. This discussion, however, does not focus on air pollution effects per se. Urban climate (e.g., the heat island) can be understood, to a large degree, through the study of modifications which develop primarily because of the effects of land-cover types and land-use changes and their feedbacks into the energy, moisture, and local air motion systems. Air quality certainly plays a role. However, when studying urban climates it is essential to identify the variety of land-cover conditions, surface attributes of small areas, and the three-dimensionality of the components of the urban mosaic. This is where remote sensing attains great significance in the field of urban climatology. The following discussion is limited to local , micro-, and mesoscales. It is also restricted to processes taking place in what are called the urban canopy layer (UCL — beneath roof level) and the urban boundary layer (UBL — extending from roof level to the height at which urban influences are absent; Oke 1998). There are many scholarly reviews of urban climate and accompanying bibliographies that address the overall problem of how cities alter their climatic environment (e.g., Beryland and Kondratyev 1972; Bonan 2002; Brazel 1987; Brazel et al. 2000; Chandler 1976; Landsberg 1981; Lee 1984; Oke 1974, 1979, 1980). Today, urban climatology has achieved recognition as a sub-discipline in climatology and within allied disciplines

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such as planning, ecology, environmental science, and meteorology (e.g., as evidenced in de Dear et al. 2000).

6.2 Temporal and spatial scales in climatology Climate is considered as an ensemble of weather processes and varies in its characteristics depending upon the time scale chosen and the spatial area considered (Barry 1970). If one were to study wind gusts as extreme events that might affect buildings, observational methods or calculations would have to address processes that occur in seconds or minutes, and resolve effects over areas less than a square kilometer (e.g., a microburst from a cloud). On the other hand, forecasting mid-latitude cyclones requires a coarser time and space domain to assure awareness of impending storms across a region (e.g., hundreds-of-kilometers spatial resolution). Climatology consists of concepts about how frequently variable processes occur, and their magnitudes and dimensions. Are these frequencies and magnitudes static over time, close to some overall mean state, or are there significant cycles, step jumps, or subtle long-term trends that are evident in the climate system? The expression “climate system” is typically used to indicate a series of complex and dynamic physical processes that interact to characterize a climate, including “feedbacks” (positive and negative) in the system. In the field of urban climatology, climatologists face issues that require an interdisciplinary approach. They are constantly asked pragmatic questions by local, state, and regional governments and their citizens. What difference does climate make to our citizens, cities, towns, companies, agencies, governments, and what is it about the climate system that must be understood so that we can provide effective strategies to combat the negative feedbacks on climate and take advantage of the positive ones? Design and mitigation concepts that are related to weather and climate must get at these questions. Applied problems, design-related or otherwise, more often than not begin with an appreciation of the spatial scale of any issue and the temporal scale required for consideration. 6.2.1 Regional to local scale If we consider, for example, the whole contiguous desert Southwestern United States as a uniform region, and dismiss the notion of variability and factors of climate that operate at the local scale, we would ignore variability that threatens the sustainability of human settlements in the region. Much of this within-region variability is due to several factors: (a) eleva-

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tion, slope, and aspect of terrain, i.e., basic geography of relief from place to place; (b) watershed variation, orientation of river channels and wind drainage paths; (c) surface type—soil, vegetation, and heating and cooling rates over these surfaces, their impermeability to water; and, (d) climate within and over the built or human-affected lands (e.g., urban, desert, agricultural). Each of these factors requires careful thought and analysis regarding its significance for living in the desert, and is equally applicable to other geographic regions and landscapes. Through mesoscale modeling, remote sensing of land cover, remote sensing of surface parameters such as albedo and surface temperature, and local field monitoring it is possible to resolve potential environmental problems in the regional to local cascade of processes. Embedded in the diverse desert climates of the Southwest are human settlements with their own distinct local climatic regimes. Cities in desert areas are growing rapidly, are within climate regimes dominated by localscale processes (stable air, fewer storms, terrain influences), and experience heat island effects for a large proportion of the time during a seasonal cycle, more so than storm-dominated, moist climates typical of humid continental and marine regions of the mid-latitudes. Many questions are posed by various agencies and clients worrying about excess heat and how to ameliorate its effects. Is it daytime or nighttime that dominates the socalled “heat island effect,” and what can be expected as large cities get even larger? Are there natural negative feedback mechanisms counteracting runaway heat island growth beyond some threshold of heat island development? For example, if we heat the city, perhaps increased wind from the heating will feed back naturally to ventilate the urban area. If we plan on no growth, maximum growth, or moderate growth, what kind of local climate effects must we cope with—heat, increased rain, local wind circulation, increased microbursts—that could be beneficial or detrimental? These are complex questions that must be addressed by many researchers sharing their perspectives and ideas, and focusing first on the scales of analysis needed to address issues.

6.3 Factors controlling urban climates Factors that contribute to urban heat excesses include: (a) increased surface area absorbing the sun’s energy due to multi-story buildings; (b) the rates of heat absorption and storage by different materials during the day; (c) relative impermeability of surfaces, amount of vegetation, and number of lakes; (d) the geometry of building arrangements and their canyon-like

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heat-trapping effect; (e) emitted heat from buildings and roofs; (f) transportation emissions and air-quality effects on heating and cooling within the city; and, (g) the land-use geography of the city. All of these aspects require that knowledge be shared by specialists from a variety of disciplines. Many aspects of urbanization change the physical environment and lead to alterations in energy exchanges, thermal conditions, moisture fluxes (evaporation, precipitation, and runoff), and wind-circulation systems. These include air pollution, anthropogenic heat, surface waterproofing, thermal properties of the surface materials, and surface geometry (Oke 1981). Other factors that must be considered relate to the setting of the city, such as topography, proximity to water bodies, city size, population density, and land-use distributions. Oke (1997) provides a summary of the typical alterations of climatic elements in cities as compared to rural areas (Table 6.1). The magnitude-frequency concept is important but less studied in urban climatology. How large are climatic alterations in a city and how often are they that large? The latter part of the question requires analysis of the links between microscale and mesoscale alteration magnitudes and more macroscale factors, such as the role of synoptic climatology on urban climate variations (e.g., Unwin 1980). Typically, cloudy or windy days reduce heat island magnitudes for a city. Urbanization causes changes in the energy, moisture, and circulation systems, but few studies address the relationship between pre- and posturban climate conditions (Lowry 1977). One excellent example is Landsberg’s experiment demonstrating how a city, as it developed from a rural environment, affected the local and regional climate (Landsberg 1981). Most urban climate studies rely on geographic comparisons between the city and its surroundings to estimate the urban effect on local climate. Considerable attention has been given to the study of historical weather records in cities to evaluate temperature trends that are urban in origin rather than attributable to global change (e.g., global warming). A large number of stations with long records are needed for trend analyses, and many of the world’s weather stations are in or near urban-affected locales. It is difficult to detect global change using data from such sites (e.g., Hansen et al. 1999 and 2001). Furthermore, placing weather stations in urban areas to study how cities alter climate is a challenging endeavor in and of itself (Oke 2000).

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Table 6.1. Urban climate effects for a mid-latitude city with about 1 million inhabitants (values for summer unless otherwise noted) Variable Change Turbulence intensity Greater Wind speed Decreased Increased Wind direction Altered UV radiation Much less Solar radiation Less Infrared input Greater Visibility Reduced Evaporation Less Convective heat flux Greater Heat storage Greater Air temperature Warmer

Magnitude/Comments 10-50% 5-30% at 10 m in strong flow In weak flow with heat island 1-10 degrees 25-90% 1-25% 5-40%

About 50% About 50% About 200% 1-3 degrees C per 100 years; 1-3 degrees C annual mean up to 12 degrees C hourly mean Humidity Drier Summer daytime More moist Summer night, all day winter Cloud More haze In and downwind of city More cloud Especially in lee of city Fog More/less Depends on aerosol and surroundings Precipitation ? ? Snow Less Some turns to rain Thunderstorms More Tornadoes Less Total More? To the lee of rather than in the city Source: adapted from Oke (1997), page 275

6.4 Methods of evaluation Many methods are used to determine how a city affects climate. Early methodologies included sampling the differences between urban and rural environments; upwind and downwind portions of the urban area; urban and regional ratios of various climatic variables; time trends of differences and ratios; time segment differences such as weekday versus weekend; and point sampling in mobile surveys throughout the urban environment (Lowry 1977). This point sampling approach led to the discovery of the famous heat-island phenomenon. Methodological inadequacies of many of the early studies have been pointed out in recent decades and accurate time-series data remain a problem of analysis. Lowry (1977) reviews myriad studies on filtering out urban effects to reveal global trends. These inadequacies led to increased

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studies of processes involved, particularly fluxes of energy, moisture, and momentum of mass in urban environments. Process studies help to provide a better characterization of how urbanization alters the surface-atmospheric system (Oke 1979). In fact, much attention has been given to internal variability of climate conditions within the urban environment and to the importance of the UCL (Arnfield 1982; Grimmond 1992; Grimmond and Oke 1999; Johnson and Watson 1984; Oke 1981).

6.5 Remote sensing Extensive use has been made of data from sensors such as the Landsat Thematic Mapper, Enhanced Thematic Mapper Plus (TM/ETM+), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to characterize urban extent and materials (Donnay et al. 2001; Gong and Howarth 1990; Grossman-Clarke et al. 2005; Haack 1983; Haack et al. 1987; Kato and Yamaguchi 2005; Longley 2002; Mesev 2003; Stefanov and Netzband 2005, 2007; Stefanov et al. 2001, 2003a; Vogelmann et al. 1998; Zhu and Blumberg 2002). These sensors provide excellent temporal resolution (typically a 16-day repeat cycle from 1982 to present) over the majority of the globe at spatial resolutions ranging from 15 m/pixel to 30 m/pixel in the visible through shortwave-infrared bands. Measurement of reflected energy in these bands is useful for characterization of important surface parameters of interest in urban climate modeling, such as land-cover type, albedo, and vegetation density (Grossman-Clarke et al. 2005; Hawkins et al. 2004; Huete et al. 1988; Humes et al. 2004). The Landsat TM and ETM+ sensors, Advanced Very High Resolution Radiometers (AVHRRs), ASTER, and the Moderate Resolution Imaging Spectroradiometer (MODIS) also measure emitted energy in the midinfrared (or thermal) wavelengths (Abrams 2000; Jensen 2000; Kerr et al. 2004; Stefanov and Netzband 2007). Spatial resolutions of these measurements range from 60 m/pixel (ETM+) to 1100 m/pixel (AVHRR). These data are necessary to derive emissivity and surface temperature variables required by urban climate models (Humes et al. 2004; Lougeay et al. 1994 and 1996). Landsat ETM+ mid-infrared data have been used to investigate the relationships between settlement patterns, vegetation density, and urban climate in Phoenix, AZ (Harlan et al. 2006; Jenerette et al. 2007). Satellite-based sensors with moderately high to very high spatial resolution (15 m/pixel to less than 1 m/pixel) have been developed primarily by the commercial sector and include the Système Probatoire d’ Observation de la Terre or SPOT (Martin et al. 1988), IKONOS (Dial et al. 2003), and

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Quickbird (Sawaya et al. 2003). These very high resolution systems enable highly detailed land-cover and land-use, albedo, and ecological characterization of urban and suburban regions of potential use to urban climate research (Greenhill et al. 2003; Jensen 2000; Sawaya et al. 2003; Small 2003; Weber 1994; Weber and Puissant 2003). Data from these commercial systems are typically expensive, and limited in both spatial and temporal coverage compared to the extensive Landsat and AVHRR data record. The spectral coverage of the commercial systems is also limited to the visible and near-infrared wavelengths, typically incorporating a very high spatial resolution, wide bandpass panchromatic band to sharpen lower spatial resolution bands with narrower bandpasses (i.e. SPOT, IKONOS, Quickbird; see Table 6.2.). An additional data source for moderately high to potentially very high resolution visible to near-infrared digital data is astronaut photography. Photographs are acquired by astronauts from both the Space Shuttle and International Space Station and represent a rich potential database for urban and periurban climate studies (Gebelein and Eppler 2006; Robinson et al. 2000, 2002; Stefanov et al. 2003b; http://eol.jsc.nasa.gov). The increasing availability of Synthetic Aperture Radar (SAR) data from satellites has also spurred research in the areas of urban feature mapping and land-cover classification (Dell’Acqua et al. 2003). Table 6.2 presents the technical specifications of selected past and current sensor systems including those with thermal infrared measurement capability. Data from US Government sensor systems may be downloaded through the EROS Data Center Data Gateway (http://edcimswww.cr.usgs.gov/pub/imswelcome/). This website also contains information on the pricing structure for various datasets. Multispectral (several bands), superspectral (tens of bands), and hyperspectral (hundreds of bands) remote sensing in the visible through midinfrared wavelengths at moderately high to high spatial resolution is currently only available using airborne sensor systems. The interested reader is directed to Jensen (2000) for a general review of airborne sensor systems. There have been relatively few land-use and land-cover studies of urban and periurban systems performed using airborne multispectral to hyperspectral sensors acquiring data in the visible through shortwave infrared wavelengths (Herold et al. 2003; Meinel et al. 1996; Roessner et al. 2001; Wharton 1987). Studies of urban and periurban areas with mid-infrared (or thermal infrared) airborne multispectral sensors such as the Thermal Infrared Multispectral Scanner (TIMS), Airborne Terrestrial Applications Sensor (ATLAS), and MODIS/ASTER Simulator (MASTER) have been primarily driven by urban heat island research and have focused on cities in the developed world (Nichol 1994, 2004; Voogt and Oke 2003). These studies have used the multispectral thermal data available from these sen-

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sors to produce accurate models of urban climatic effects such as heat islands (Quattrochi et al. 2000); correlate specific heat inputs with landcover types and vegetation (Quattrochi and Ridd 1994); explore the climatic interactions between social and biophysical parameters in urban environments (Harlan et al. 2006; Stefanov et al. 2004); and monitor environmental effects of regional climate change and increasing urbanization (Hook et al. 2001). Table 6.2. Selected satellite sensor specifications Wavelength Range [Bands] VSWIR (2), TIR (3)

Repeat Cycle [Temporal Coverage] 9 days at nadir; sensors on several platforms allow for multiple daily acquisitions (1984-) 16 days (1982-)

Sensor AVHRR

Pixel [m] 1100

TM

30 - 120

VSWIR (6), TIR (1)

ETM+

15 - 60

16 days (1999-)

ASTER

15 - 90

Pan VNIR (1), VSWIR (6), TIR (1) VSWIR (10), TIR (5)

MODIS

250 - 1000

VSWIR (20), TIR (16)

SPOT 1-5

2.5 - 20

16 days; sensors on both Terra and Aqua platforms allow for 1-2 days (1999-) 26 days (1986-)

IKONOS

1-4

Pan VNIR (1), VNIR (3), or VSWIR (4) Pan VNIR (1), VNIR 3 days off-nadir (4) (1999-)

Quickbird

0.6 - 2.8

Pan VNIR (1), VNIR 1-3.5 days off-nadir (4) (2001-)

RADARSAT

10 - 100

5.7 cm (C-band)

24 days (1995-)

Hyperion

30

VSWIR (220)

16 days (2000-)

Astronaut Photography

Variable (4 or Pan visible, VNIR greater)

16 days (1999-)

Variable (1961-)

Note: HRV (High Resolution Visible) and HRVIR (High Resolution VisibleInfrared).

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Table 6.3 presents the technical specifications of selected airborne sensor systems. Data acquired by airborne sensor systems is typically limited to specific research sites and generally not as freely accessible as satellitebased sensor data. Some sensor data archives are available, however. For example, MASTER data are available to search and request from the MASTER website (http://masterweb.jpl.nasa.gov). Recent research linking measures of urban climate to remote sensing highlights several ongoing issues in urban climate research utilizing remote sensing platforms (e.g., reviewed in Voogt and Oke 2003). Fluxes of energy from urban canyons, roof-top levels, and various land sectors of the city must be calculated or observed in three-dimensional space, taking into account differing scale characteristics of the city. Research by Voogt and Oke (1998), Voogt and Grimmond (2000), and reviews by Arnfield (2003) and Voogt and Oke (2003) point out several interesting questions for investigation: What is the nature of the urban surface as seen by a remote sensor? How do sensor-detected radiant temperatures relate to the true temperature of the urban-air interface? What is the nature of effective thermal anisotropy? What is the relation between satellite-derived surface urban heat islands and those measured in the air? How appropriate is thermal remote-sensing data as input to models of urban climate? Thus, several issues must be resolved in order to effectively use remote sensing in modeling the urban climate of cities. Table 6.3. Selected airborne sensor specifications Sensor MASTER

Pixel[m] 5 - 50

Wavelength Range [Bands] VNIR (11), SWIR (14), TIR (25)

Temporal Coverage 1998-

AVIRIS

4 - 20

VSWIR (224)

1994-

HyMap

3 - 10

VSWIR (126), and/or TIR (32)

1996-

AirSAR

3 – 10

5.6 cm (C), 23.5 cm (L), 68 cm (P)

1988-

LIDAR

< 1 cm

NIR to SWIR

1987-

Aerial Photography

~1

Pan visible, color visible, VNIR

mid-1800s to present

Note: AVIRIS (Airborne Visible/Infrared Imaging Spectrometer); AirSAR (Airborne Synthetic Aperture Radar); and LIDAR (Light Detection and Ranging).

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6.6 Urban heat island mitigation Although some increase in temperature is probably unavoidable during urbanization, current urban planning and design practices tend to exacerbate local warming by strictly applying zoning, land-use regulations, and design standards and practices that may not be appropriate for the specific region. For example, almost all subdivisions in the Phoenix, Arizona, USA metropolitan area are designed to accommodate long hook-and-ladder trucks, with widths up to ~10 meters (32 feet) for streets and ~12 meters (40 feet) for collector roads, even though most housing in the Phoenix region is single story, with very little housing over two stories that would require hookand-ladder trucks. Furthermore, because most housing was built after widespread automobile ownership became the norm, nearly all residents have off-street parking in garages or carports, so on-street parking that reduces functional street width is atypical. Commercial land use regulations often require excessive parking lot sizes because the ratio of building square footage to the number of parking spaces is based on the busiest shopping days of the year. The same regulations have scant requirements or incentives for shading. The overabundance of black asphalt roads and parking lots is a key cause of urban warming because these surfaces have high heat-absorption rates during the day and also high heat-storage capacity to retain heat at night. The link between urban form and climate change is being made in several cities. For example, high-resolution thermal imagery, collected by the National Aeronautical and Space Administration (NASA) for Atlanta, Georgia demonstrates the relationship between single-family residential design and the emission of radiant heat energy (Stone and Rogers 2001). The NASA research showed that lower-density housing patterns contribute more radiant heat than higher-density development within the Atlanta region. As a result, the researchers suggested that, "Compact moderate-tohigh-density new construction and area-based tree ordinances . . . [can mitigate] the effects of urban development on regional climate change." (Stone and Rogers 2001). Ongoing research conducted by the Arizona State University Consortium for the Study of Rapidly Urbanizing Regions is using ASTER thermal infrared data to explore urban heat island mitigation strategies (http://www.asusmart.com/). The primary use of this data is to characterize daytime and nighttime surface temperatures and emissivity from urban (built and impervious materials), agricultural, and natural surfaces in the Phoenix metropolitan area. Once the relative contributions of different landscape elements to the urban diurnal heat cycle is known, these data can

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be used to test the effectiveness of mitigation strategies (increased vegetation, use of high-albedo surfaces, etc.) using experimental ground sites. Plate 6.1 is a surface temperature map derived from ASTER data of the Phoenix region. This image clearly illustrates the large range of surface temperatures between vegetated and nonvegetated land-cover types. Note the striking similarity in high surface temperatures between built surfaces such as Sky Harbor Airport (SH) and surrounding mountain slopes (PM, SM, SE). Urban climatologists, planners, and others have found that the scientific issues of urban warming revolve around several factors that must be considered in the arena of mitigation. Some standard paradigms of mitigation of urban warming in desert environments revolve around judicious use of low-water-use vegetation for daytime shading, use of reflective materials, considerations of density of structures for shade but optimum spacing to reduce nighttime retention of heat, taking advantage of terrain-induced windiness for ventilation (in Phoenix there is a pronounced east-west, upand-down-valley wind regime), and use of evaporative cooling of moisture available from lakes, lawns, etc. to reduce the enormous daytime heat load. Many of these strategies are equally applicable to other urban centers. Urban planning and design policies can be redesigned to mitigate urban warming. Construction of narrower roads, more green spaces interspersed between built-up areas, more use of high-albedo materials for roofs and streets, and greater use of shading are among practices that could mitigate warming or reduce its impact (Arendt 1996; Calthorpe and Van der Ryn 1986; Pijawka and Shetter 1995; Steiner 2000; Thayer 1994; Thompson and Sorvig 2000). Such practices were once common in desert cities worldwide, but have fallen out of favor with contemporary architectural design and urban planning.

6.7 Conclusions The climate of cities will continue to be a phenomenon of significance as urban populations grow in the coming decades. The incorporation of remotely sensed data into the study of urban climate is essential in order to obtain the synoptic view required to understand the interactions between natural processes and human modification of those processes. Satellitebased, high–resolution, thermal infrared sensor data (30 m/pixel or less spatial resolution) would greatly enhance our ability to monitor and model urban heat islands by resolving the surface temperatures of discrete surface materials on a repeatable basis. While the availability of very high to high

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resolution visible-to-shortwave infrared data for urban and periurban areas seems assured for the foreseeable future, the continuing availability of corresponding thermal infrared data is in question. Current plans for the Landsat Data Continuity Mission (to replace Landsats 5 and 7) do not include a thermal infrared band. While proposals for new missions are being developed (ex. Vidal et al. 2004), there are currently no planned missions with thermal infrared capabilities especially designed for urban remote sensing. Thermal infrared data from sensors such as ASTER, MODIS, AVHRR, and ENVISAT should continue to be available for the near future; however, none of these datasets are high resolution. The Multispectral Thermal Imager (MTI), a United States Department of Energy satellite-based sensor launched in 2000, acquires thermal infrared data in three bands at 20 m/pixel (Clodius 2000). Data from this sensor are not freely available to the public; however, the sensor design itself presents a potentially useful model for future Earth-orbiting missions. The increasing power of desktop computer systems and the commercial availability of image analysis software for the desktop encourage the use of remotely sensed data by urban planners and governments. Collaborations between research institutions, agencies, and city governments will also facilitate the transfer of knowledge and expertise necessary to effectively use remotely sensed data in the operational city planning environment. The application of research findings of urban climatology in building designs and urban environmental planning is beginning to emerge but is not yet widespread (Bonan 2002). Due to the complexity of the urban landscape and the variability of dimensions, land use, morphology, and other characteristics, much research still remains to be done on just how a city affects its surface and atmospheric climatic environment and its overall urban ecology. Equally, if not more, important are the interactions of the urban climate system with other elements of the ecosystem (Bonan 2002; Douglas 1981). The discovery of these interrelationships will eventually aid in planning solutions to problems of pollution, health, comfort, water supply, and general quality of life for urban dwellers (Harlan et al. 2006).

6.8 References Abrams M (2000) The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): Data products for the high spatial resolution imager on NASA’s Terra platform. International Journal of Remote Sensing 21:847-859 Arendt R (1996) Conservation design for subdivisions: A practical guide to creating open space networks. Island Press, Washington, DC

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Arnfield AJ (1982) An approach to the estimation of the surface radiative properties and radiation budget of cities. Physical Geography 3:97-122 Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence exchanges of energy and water, and the urban heat island. International Journal of Climatology 23:1-26 Barry RG (1970) A framework for climatological research with particular reference to scale concepts. Transactions and Papers of the Institute of British Geographers 49:61-70 Beryland ME, Kondratyev KY (1972) Cities and the global climate. Atmospheric Environment Service, Downsview, Ontario, Canada Bonan GB (2002) Ecological climatology concepts and applications. Cambridge University Press, Cambridge, UK Brazel AJ (1987) Urban climatology. In: Oliver J, Fairbridge RW (eds) Encyclopedia of Earth sciences, vol XI: The encyclopedia of climatology. Van Nostrand Reinhold Co., New York, NY, pp 889-901 Brazel AJ, Selover N, Vose R, Heisler G (2000) The tale of two climates: Baltimore and Phoenix LTER sites. Climate Research 15:123-135 Calthorpe P, Van der Ryn S (1986) Sustainable design: A new synthesis for cities, suburbs, and towns. Sierra Club Books, San Francisco, CA Chandler TJ (1976) Urban climatology and its relevance to urban design: Technical note 149. World Meteorological Organization, Geneva, Switzerland Changnon SA, Jr (1983) Purposeful and accidental weather modification: Our current understanding. Physical Geography 4:126-139 Clodius WB (2000) The MTI data reference guide for Level 1 imagery. Publication LA-UR-00-5948. Los Alamos National Laboratory, Los Alamos, NM De Dear RJ, Kalma JD, Oke TR, Auliciems A (2000) Biometeorology and urban climatology at the turn of the millennium: Selected papers from the conference ICB-ICUC'99, WCASP-50, WMO/TD-No. 1026 Dell’Acqua F, Gamba P, Lisini G (2003) Improvements to urban area characterization using multitemporal and multiangle SAR images. IEEE Transactions on Geoscience and Remote Sensing 41:1996-2004 Dial G, Bowen H, Gerlach F, Grodecki J, Oleszczuk R (2003) IKONOS satellite, imagery, and products. Remote Sensing of Environment 88:23-36 Donnay J-P, Barnsley MJ, Longley PA (2001) Remote sensing and urban analysis. In: Longley PA, Donnay J-P, Barnsley MJ (eds) Remote sensing and urban analysis. Taylor and Francis, London, UK, pp 3-18 Douglas I (1981) The city as an ecosystem. Progress in Physical Geography 5:315-367 Gebelein J, Eppler D (2006) How Earth remote sensing from the International Space Station complements current satellite-based sensors. International Journal of Remote Sensing 27(13): 2,613-2,629 Gong P, Howarth PJ (1990) The use of structural information for improving land – cover classification accuracies at the rural – urban fringe. Photogrammetric Engineering and Remote Sensing 56:67-73

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Greenhill DR, Ripke LT, Hitchman AP, Jones GA, Wilkinson GG (2003) Characterization of suburban areas for land use planning using landscape ecological indicators derived from Ikonos-2 multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing 41:2015-2021 Grimmond CSB (1992) The suburban energy balance: methodological considerations and results for a mid-latitude west coast city under winter and spring conditions. International Journal of Climatology 12:481-497 Grimmond CSB, Oke TR (1999) Evapotranspiration rates in urban areas. In: Impacts of urban growth on surface water and groundwater quality. IAHS Publication 259:235-243 Grossman-Clarke S, Zehnder JA, Stefanov WL, Liu Y, Zoldak MA (2005) Urban modifications in a mesoscale meteorological model and the effects on near surface variables in an arid metropolitan region. Journal of Applied Meteorology 44:1281-1297 Haack B (1983) An analysis of Thematic Mapper Simulator data for urban environments. Remote Sensing of Environment 13:265-275 Haack B, Bryant N, Adams S (1987) An assessment of Landsat MSS and TM data for urban and near-urban land-cover digital classification. Remote Sensing of Environment 21:201-213 Hansen J, Ruedy R, Glascoe J, Sato M (1999) GISS analysis of surface temperature change. Journal of Geophysical Research 104:30,997-31,022 Hansen, J, Ruedy R, Sato M, Imhoff M, Lawrence W, Easterling D, Peterson T, Karl T (2001) A closer look at United States and global surface temperature change. Journal of Geophysical Research 106:23,947-23,963 Harlan SL, Brazel AJ, Prashad L, Stefanov WL, Larsen L (2006) Neighborhood microclimates and vulnerability to heat stress. Social Science & Medicine 63:2,847-2,863 Hawkins TW, Brazel AJ, Stefanov WL, Bigler W, Safell EM (2004) The role of rural variability in urban heat island determination for Phoenix, Arizona. Journal of Applied Meteorology 43:476-486 Herold M, Gardner ME, Roberts DA (2003) Spectral resolution requirements for mapping urban areas. IEEE Transactions on Geoscience and Remote Sensing 41:1907-1919 Hook SJ, Myers JJ, Thome KJ, Fitzgerald M, Kahle AB (2001) The MODIS/ASTER airborne simulator (MASTER) – a new instrument for earth science studies. Remote Sensing of Environment 76:93-102 Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25:295-309 Humes K, Hardy R, Kustas WP, Prueger J, Starks P (2004) High spatial resolution mapping of surface energy balance components with remotely sensed data. In: Quattrochi DA, Luvall JC (eds) Thermal remote sensing in land surface processes. CRC Press, Boca Raton, FL, pp 110-132 Jenerette GD, Harlan SL, Brazel A, Jones N, Larsen L, Stefanov WL (2007) Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landscape Ecology 22:353-365

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Jensen JR (2000) Remote sensing of the environment: An Earth resource perspective. Prentice Hall, Upper Saddle River, NJ Johnson GT, Watson ID (1984) The determination of view-factors in urban canyons. Journal of Climate Applications in Meteorology 23:329-335 Kato S, Yamaguchi Y (2005) Analysis of urban heat-island effect using ASTER and ETM+ Data: Separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sensing of Environment 99 (12):44-54 Kerr YH, Lagouarde JP, Nerry F, Ottle C (2004) Land surface temperature retrieval techniques and applications: Case of the AVHRR. In: Quattrochi DA, Luvall JC (eds) Thermal remote sensing in land surface processes. CRC Press, Boca Raton, FL, pp 33-109 Landsberg HE (1981) The Urban climate. Academic Press. New York, NY Lee DO (1984) Urban climates. Progress in Physical Geography 8:1-31 Longley PA (2002) Geographic information systems: will developments in urban remote sensing and GIS lead to ‘better’ urban geography? Progress in Human Geography 26:213-239 Lougeay R, Stoll M, Brazel A[J] (1994) Surface emissivity calibration of Landsat thermal data: creating an urban surface temperature map. Geographical Bulletin 32:74-82 Lougeay R, Brazel A[J], Hubble M (1996) Monitoring intraurban temperature patterns and associated land cover in Phoenix, Arizona using Landsat thermal data. Geocarto International:79-90 Martin LRG, Howarth PJ, Holder G (1988) Multispectral classification of land use at the rural-urban fringe using SPOT data. Canadian Journal of Remote Sensing 14:72-79 Meinel G, Netzband M, Amann V, Stätter R, Kritikos G (1996) Analysing an ATM-Scanner flight over the city of Dresden to identify urban sealing. International Archives of Photogrammetry and Remote Sensing 31:486-492 Mesev V (2003) Remotely sensed cities: An introduction. In: Mesev V (ed) Remotely sensed cities. Taylor & Francis, London, UK, pp 1-19 Nichol JE (1994) A GIS-based approach to microclimate monitoring in Singapore’s high-rise housing estates. Photogrammetric Engineering & Remote Sensing 60:1225-1232 Nichol J[E] (2003) GIS and remote sensing in urban heat islands in the Third World. In: Mesev V (ed) Remotely sensed cities. Taylor & Francis, London, UK, pp 243-264 Oke TR (1974) Review of urban climatology 1968-1973: Technical note 134. World Meteorological Organization, Geneva, Switzerland Oke TR (1979) Review of urban climatology 1973-1976: Technical note 169. World Meteorological Organization, Geneva, Switzerland Oke TR (1980) Climatic impacts of urbanization. In: Bach W, Pankrath J, Williams J (eds) Interactions of energy and climate. Reidel, Boston, MA, pp 339356 Oke TR (1981) Canyon geometry and the nocturnal urban heat island: comparison of scale model and field observations. Journal of Climatology 1:237-254

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Oke TR (1987) Boundary layer climates. Methuen, London, UK Oke TR (1997) Urban climates and global environmental change. In: Thompson RD, Perry AH (eds) Applied climatology: Principles and practice. Rutledge Publishers, New York, NY, pp 273-288 Oke TR (1998) Observing weather and climate. Proceedings of the technical conference on meteorological and environmental instruments and methods of observation: Instruments and observing methods report 70, WMO/TD-No. 877:1-8 Oke TR (2000) Observing urban weather and climate using 'standard' stations. In De Dear RJ, Kalma, JD, Oke TR, and Auliciems A (eds) Biometeorology and urban climatology at the turn of the millennium: Selected papers from the conference ICB-ICUC'99, WCASP-50, WMO/TD-No. 1026, pp. 443-448 Pijawka KD, Shetter K (1995) The environment comes home: Arizona Public Service environmental showcase home. Herberger Center for Design Excellence, Arizona State University, Tempe, AZ Quattrochi DA, Luvall JC, Rickman DL, Estes Jr MG, Laymon CA, Howell BF (2000) A decision support information system for urban landscape management using thermal infrared data. Photogrammetric Engineering and Remote Sensing 66:1195-1207 Quattrochi DA, Ridd MK (1994) Measurement and analysis of thermal energy responses from discrete urban surfaces using remote sensing data. International Journal of Remote Sensing 15:1991-2022 Roessner S, Segl K, Heiden U, Kaufmann H (2001) Automated differentiation of urban surfaces based on airborne hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 39:1525-1532 Robinson JA, Amsbury DL, Liddle DA, Evans CA (2002) Astronaut-acquired orbital photographs as digital data for remote sensing: Spatial resolution. International Journal of Remote Sensing 23:4403-4438 Robinson JA, McRay B, Lulla KP (2000) Twenty-eight years of urban growth in North America quantified by analysis of photographs from Apollo, Skylab and Shuttle-Mir. In: Lulla KP, Dessinov LV (eds) Dynamic Earth environments: Remote sensing observations from Shuttle-Mir missions. John Wiley & Sons, New York, NY, pp 25-42 Sawaya KE, Olmanson LG, Heinert NJ, Brezonik PL, Bauer ME (2003) Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment 88:144156 Small C (2003) High spatial resolution spectral mixture analysis of urban reflectance. Remote Sensing of Environment 88:170-186 Stefanov WL, Netzband M (2005) Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center. Remote Sensing of Environment 99 (1-2):31-43 Stefanov WL, Netzband M (2007) Characterization and monitoring of urban/periurban ecological function and landscape structure using satellite data. In: Rashed T and Jürgens C (eds.) Remote sensing of urban and suburban areas. Springer, New York, NY (in press)

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Stefanov WL, Ramsey MS, Christensen PR (2001) Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of Environment 77:173-185 Stefanov WL, Ramsey MS, Christensen PR (2003a) Identification of fugitive dust generation, deposition, and transport areas using remote sensing. Environmental and Engineering Geoscience 9:151-165 Stefanov WL, Robinson JA, Spraggins SA (2003b) Vegetation measurements from digital astronaut photography. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 34 (7/W9):185-189 Stefanov WL, Prashad L, Eisinger C, Brazel A[J], Harlan S (2004) Investigations of human modification of landscape and climate in the Phoenix Arizona metropolitan area using MASTER data. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 35:1339-1347 Steiner F (2000) The living landscape: An ecological approach to landscape planning. McGraw-Hill, New York, NY Stone B Jr, Rogers MO (2001) Urban form and thermal efficiency - how the design of cities influences the urban heat island effect. Journal of American Planning Association 64:186-198 Thayer RL Jr (1994) Gray world, green heart: Technology, nature, and the sustainable landscape. John Wiley and Sons, New York, NY Thompson JW, Sorvig K (2000) Sustainable landscape construction: A guide to green building outdoors. Island Press, Washington, DC Unwin DJ (1980) The synoptic climatology of Birmingham's urban heat island 1965-1974. Weather 35:43-50 Vidal A, Duthil P, Ottlé C, Caselles V, Yagüe A, Murtagh J (2004) MUST – A medium scale surface temperature mission dedicated to environment and agriculture. In: Quattrochi DA, Luvall JC (eds) Thermal remote sensing in land surface processes. CRC Press, Boca Raton, FL, pp 405-428 Vogelmann JE, Sohl T, Howard SM (1998) Regional characterization of land cover using multiple sources of data. Photogrammetric Engineering and Remote Sensing 64:45-57 Voogt JA, Grimmond CSB (2000) Modeling surface sensible heat flux using surface radiative temperatures in a simple urban area. Journal of Applied Meteorology 39:1769-1699 Voogt JA, Oke TR (1998) Effects of urban surface geometry on remotely-sensed surface temperature. International Journal of Remote Sensing 19:895-920 Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sensing of Environment 86:370-384 Weber C (1994) Per-zone classification of urban land cover for urban population estimation. In: Foody GM, Curran PJ (eds) Environmental remote sensing from regional to global scales. John Wiley & Sons, Chichester, UK, pp 142148 Weber C, Puissant A (2003) Urbanization pressure and modeling of urban growth: example of the Tunis metropolitan area. Remote Sensing of Environment 86:341-352

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Wharton S (1987) A spectral-knowledge-based approach for urban land-cover discrimination. IEEE Transactions on Geoscience and Remote Sensing 25:272282 Zhu G, Blumberg DG (2002) Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel. Remote Sensing of Environment 80:233-240

Chapter 7 – Phoenix, Arizona, USA: Applications of Remote Sensing in a Rapidly Urbanizing Desert Region

William L. Stefanov1, Maik Netzband2, Matthias S. Möller3, Charles L. Redman3, Chris Mack4 1

Image Science & Analysis Laboratory, NASA Johnson Space Center, Houston, TX, USA

2

F & U Consult, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany

3

Global Institute of Sustainability, Arizona State University, Tempe, AZ, USA

4

GIS Department, Town of Marana, Marana, AZ, USA

7.1 Introduction The Phoenix metropolitan agglomeration (Fig. 7.1) is one of the fastestgrowing conurbations in the United States, and is the focus of the Central Arizona-Phoenix Long-Term Ecological Research Project (CAP LTER) (Grimm et al. 2000, Grimm and Redman 2004). This project has been the locus of significant remote sensing investigation and characterization of

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the Phoenix urban and peri-urban areas (Stefanov 2002), combined with ground truthing and allied studies (Hope et al. 2003). The information derived from these and other studies is increasingly being used by local governments and regional planners (GP2100 2003). As an example, the City of Scottsdale, Arizona has used high-resolution, airborne, multispectral data to assess impervious and pervious land-cover percentages for surface water runoff studies. This use of remotely sensed information, rather than traditional ground-based surveys, produced estimated cost savings of eight to fifteen million dollars for the city (W. Erickson 1999). Construction of an advanced visualization and modeling environment (a “decision theater”) that integrates remotely sensed and other geospatial data for the Phoenix metropolitan region was completed in 2005 at Arizona State University (J. Fink 2005). Use of such an advanced system enables near realtime modeling of the impact of planning and development decisions. Remotely sensed data acquired at a variety of spatial, spectral, and temporal resolutions provides the basic biophysical information necessary to initialize models of urban resilience and sustainability. Section 7.5 below presents specific examples for the Phoenix metropolitan area using data acquired by government sponsored sensors, while Section 7.6 presents an example application of high resolution, commercial sensor data for the Town of Marana, Arizona.

Fig. 7.1. Location map for Phoenix, AZ metropolitan region. Map on right indicates location of Arizona within the continental United States of America.

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7.2 Regional setting and historic land use The metropolitan area of Phoenix, Arizona is located in the Sonoran Desert. Desert areas contain ecosystems in which the main limiting factor is availability of water. With population growth, the desert surrounding the Phoenix area is experiencing increasing environmental pressure. Historically, native desert in the area was converted primarily into arable farmland with development of irrigation systems. Many of these farmlands have been, or are now undergoing, conversion to commercial, industrial, or residential uses; there is also continuing conversion of native desert to the same land uses. The metropolitan area is situated on an alluvial plain formed by the Salt River, and alluvial fans derived from the surrounding mountain ranges (Arrowsmith and Stefanov 2003). It has an average elevation of 305 m and a climate with an average of less than 20 centimeters of annual precipitation. The region is quite hot, with mean monthly temperatures ranging from 12º C in January to 34º C in July. This area contains 300,000 hectares of highly productive farmland, and 3.2 million people are concentrated in an expanding metropolitan area. The region was first occupied by a successful irrigation- and agricultural-based society now referred to as the Hohokam. At the civilization’s peak (ca. 1000-1200 AD), over 150 kilometers of canal-irrigated fields supported a population in the thousands. The Hohokam developed an advanced social organization and trade relations with distant peoples. For a variety of environmental and social reasons, Hohokam society dissolved by 1400 AD (Redman 1999). The region was largely unoccupied until the 1860s, when traces of the ancient Hohokam canal system indicated the agricultural potential of this region. Phoenix grew quickly as an agricultural center with the construction of federally supported dam and irrigation projects early in the 20th century. By 1940, the region supported a population of 186,000 and was on the verge of a transformation from farming center to regional capital of national and international importance. The growth of industry related to World War II, the introduction of air conditioning, the rise of automobile use, expanding tourism, and a growth-minded citizenry propelled Phoenix to become the largest population center of the American Southwest, converting it into an industrial, commercial, and administrative hub, and the fastest-growing metropolitan area in the United States (Kupel 2003; Gammage 1999).

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7.3 CAP LTER urban ecology research Recognition that virtually all ecosystems on Earth have experienced some degree of human alteration highlights the need to incorporate humans and their environmental effects into ecosystem models. This change in ecological thinking was demonstrated by the incorporation of two urban sites, Phoenix, Arizona and Baltimore, Maryland, into the National Science Foundation’s Long-Term Ecological Research Network in 1997 (Grimm et al. 2000, Grimm and Redman 2004). These two cities were chosen because they represent two ecological endmembers in human-dominated systems: Phoenix is a rapidly expanding urban center in an arid climate, while Baltimore is not rapidly expanding and is located in a temperate climate. The large areal extent of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) site (~7,900 km2, centered on the Phoenix metropolitan area) necessitates a significant remote sensing component to many research activities, as does the ruggedness of the terrain in some areas. Results of this project and allied studies are increasingly being used to justify long-range regional planning activities (Quay 2004). The first phase of CAP LTER research featured three activities associated with land-use and land-cover change (LULCC): 1) classifying, monitoring, and modeling LULCC; 2) examining urban form; and, 3) investigating the human drivers of those changes. A hypothesis-testing (or expert classification) system developed from Landsat Thematic Mapper data (Stefanov et al. 2001) provided the initial characterization of urban patch structure and its changes over the past decade. Analysis of land use since 1912 shows agricultural expansion in the first half of the 20th century, and urban expansion in the second half (Plate 7.1); this analysis contributed to the urban growth model (Berling-Wolff and Wu 2004, Jenerette and Wu 2001). Primary data for the historical land-use project included existing maps, historical records, and aerial photographs (Knowles-Yánez 1999, Wentz et al. 2006). The model suggests that by 2030, urban growth will consume all available agricultural and desert lands adjacent to the current built metropolitan area. Associated ecosystem simulation modeling efforts aim to simulate LULCC and study the effect of urbanization on ecological processes. This work also has resolved methodological issues in modeling spatially complex ecological systems (Reynolds and Wu 1999; Wu 1999, 2004; Wu and David 2002; Wu et al. 2000, 2002). CAP LTER research has also updated urban fringe morphology studies, using a much shorter time frame and the finer geographic scale (Gober 2000, Gober and Burns 2002) afforded by digital geographic data. New residential developments resemble a tidal wave, covering a surprisingly

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wide geographic area but remaining within a narrow, donut-shaped ring of territory surrounding the urban core (i.e., Gober and Burns 2002). Recent expansion in parts of Phoenix has occurred at a rate of one mile per year, compared to the one mile per decade typical of cities during the first half of the 20th century (Blemenfeld 1954). Land taken out of agriculture is quickly covered with housing, inspiring CAP -LTER ecologists to adapt a model of housing spread borrowed from population-diffusion models (Fagan et al. 2001). Urban environments increasingly influence biophysical processes and the quality of life of their inhabitants. The Phoenix Area Social Survey (PASS) of eight neighborhoods (302 respondents) captures the spatial variation in the human attributes that comprise the social fabric of Phoenix (Harlan et al. 2004). While most respondents believe in preserving pristine desert lands, paradoxically, half the respondents believe housing density is too high—particularly those on the urban fringe! More than 40% of the respondents are also concerned about the water supply, drinking water safety, accidental releases of industrial chemicals, air pollution, allergens, and soil and groundwater contamination. Half the respondents believe environmental conditions in Phoenix are worsening; only one in five thinks the environment is improving.

7.4 Urban climate modeling Modification of regional to local climate associated with urban centers is a well-known phenomenon, particularly as expressed by urban heat islands and oases (Brazel et al. 2000; Chapter 6, this volume; Voogt and Oke 2003). The Phoenix urban heat island is formed primarily by atmospheric inversions caused by the topographic effects of surrounding mountain ranges. A significant portion of urban heating also is caused by solar heating and re-emission from built materials such as asphalt, concrete, and buildings (Brazel et al. 2000). Mountain ranges both within and surrounding the urban area may also contribute significant radiated heat, particularly at night. The Phoenix metropolitan area has experienced a high degree of conversion of natural to built materials due to rapid expansion of the urban area over the past 60 years (Gammage 1999). This conversion of surface materials is thought to be a major contributor to the significant rise of mean annual air temperatures observed in the Phoenix region over the same 60 years (Fig. 7.2).

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Fig. 7.2. Mean annual air temperatures for Maricopa and Pinal Counties, Arizona. Figure after Brazel et al. 2000.

Moderately high spatial resolution satellite and airborne remotely sensed data (TM, ETM+, ASTER, MASTER; see Glossary for acronym definitions) are a major component of ongoing urban climate research in the Phoenix region. These data are used to characterize land cover, vegetation density, and surface temperature in order to improve the determination of urban-to-rural gradients for heat island calculation (Hawkins et al. 2004); to improve mesoscale climate modeling and weather forecasting for urban centers (Zehnder 2002, Grossmann-Clarke et al. 2005); and to explore the relationships between social and physical variables important to urban climate (Harlan et al. 2006, Jenerette et al. 2007, Stefanov et al. 2004). The results of social and biophysical interactions on urban climate were explored using the PASS information, remotely sensed data, and additional interviews with local residents (Harlan et al. 2006, Stefanov et al. 2004). Surface temperature data derived from the airborne MASTER, Landsat TM and ETM+, and ASTER sensors were used to map surface temperature in eight Phoenix neighborhoods (Fig. 7.3). The neighborhoods were selected to provide a socioeconomic gradient, and were oriented roughly north-south through the city center. The results of the study indicated a strong negative correlation between mean household income and surface temperature, suggesting that poorer households (the highest percentage of which are Hispanic in Phoenix) generally experience higher environmental temperatures. Weaker positive correlations were observed between increased percent Hispanic population per mile and higher surface temperatures. Some of this correlation can be explained by vegetation density in the neighborhoods (with higher income neighborhoods having greater vegetation cover), but statistical analysis suggests this is not the only factor. These results suggest that environmental inequities exist in the Phoe-

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nix metropolitan area, and provide quantitative data useful for city planners to address this problem.

0

0.7 km

Fig. 7.3. MASTER surface temperature image of a low-income neighborhood in central Phoenix (left). Data was acquired at approximately 12:00 local time on June 3, 2000. Light areas correspond to bare soil and built materials (black arrows). Image on right is a coregistered digital aerial orthophotograph of the neighborhood. Surface temperatures are in ºC. Figure after Stefanov et al. 2004. North is to top of images.

Other climate research of potential use in urban planning and governance includes modification of land-cover input to the mesoscale climate model MM5 (Grossman-Clarke et al. 2005). A 1998 land-cover classification derived from Landsat TM data (described below) was used to expand the single urban land-cover category used in the MM5 model to three subclasses (built-up urban, mesic residential, and xeric residential). Additional improvements included refinement of physical models used in the MM5 algorithm (emission from urban materials and trapping of heat). These modifications of the model produced significant improvement in the ability to simulate diurnal temperature cycles as measured by ground stations. Implementation of city-specific models using remotely sensed data delivered in near real-time could increase weather forecasting accuracy and improve readiness for extreme weather events.

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7.5 Land cover characterization and change detection Airborne and satellite-based imagery have been used together with GISbased approaches to perform LULCC characterization and monitoring of Phoenix as part of the CAP LTER, Urban Environmental Monitoring (UEM), and Agricultural Lands in Transition (AgTrans) projects (Chapter 1, this volume). This provides a reliable source of objective land surface information spanning decades (and for some urban regions perhaps also centuries). A common problem in image analysis of urban areas is the mixture of surface materials and features (i.e., natural desert, active and fallow farmland, and urban settlements or industrialized areas) at a variety of pixel and sub-pixel spatial scales (Woodcock and Strahler 1987). This difficulty is compounded by using time-series data that also have different spatial scales, wavelength coverage, and spectral responsivity. In subsections 7.5.1 and 7.5.2, below, we present different methods for the analysis of land-cover and land-use changes in the CAP LTER test site from 1979 through 2001, using satellite imagery as well as GIS-based mapping. Satellite imagery can often be analyzed easily in a qualitative fashion by a human interpreter, because different features typically exhibit clear differences in shape, color, neighborhood relations, etc. (Wentz et al. 2006). The major limiting factor in datasets with similar spectral coverage is the spatial resolution, or cell size, of each picture element (or pixel). Plate 7.2 shows two scenes acquired by different sensors over the metropolitan area of Phoenix, AZ. Table 7.1 presents the specifications for satellite sensor datasets used in ongoing multitemporal studies of Phoenix. Both images in Plate 7.2 show a false color band combination where the near-infrared portion of the reflected spectrum is displayed in the red band. This produces visual signatures such that urban surfaces are represented in dark red to grey and light blue tones, whereas healthy vegetation appears in intense red tones, and fallow farmland appears in greenish tones. Natural (or undisturbed) land consists of the light beige, grey, and turquoise tones. Once an observer has become familiar with the nature of these material signatures, a clear differentiation depending only upon color and shape information is possible, even for an untrained person. In this qualitative fashion, the growth of the Phoenix metropolitan area can be clearly recognized by an interpreter over the 35-year time period covered by the data. Quantitative time-series analysis and change detection for both research and operational usage requires image-analysis software and a standardized analysis approach.

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Table 7.1. Properties of the MSS, TM, ETM+, ASTER Sensors Sensor MSS

Resolution TM/ETM+

Resolution

VNIR bands 1: 0.50 - 0.60 µm 2: 0.60 - 0.70 µm 3: 0.70 - 0.80 µm 4: 0.80 - 1.10 µm 80 m/pixel

SWIR bands

TIR bands

1: 0.45 - 0.52 µm 2: 0.53 - 0.61 µm 3: 0.62 - 0.69 µm 4: 0.78 - 0.91 µm 30 m/pixel (15 m/pixel for additional ETM+ panchromatic band)

5: 1.570 - 1.780 µm 6: 2.080 - 2.350 µm

7:10.420 - 12.500 µm

30 m/pixel

120 m/pixel (60 m/pixel for ETM+)

ASTER

1: 0.52 - 0.60 µm 4: 1.600 - 1.700 µm 10: 8.125 - 8.475 µm 2: 0.63 - 0.69 µm 5: 2.145 - 2.185 µm 11: 8.475 - 8.825 µm 6: 2.185 - 2.225 µm 12: 8.925 - 9.275 µm 3: 0.76 - 0.86 µm (two bands, one nadir and one backward looking) 7: 2.235 - 2.285 µm 13: 10.25 - 10.95 µm 8: 2.295 - 2.365 µm 14: 10.95 - 11.65 µm 9: 2.360 - 2.430 µm Resolution 15 m/pixel 30 m/pixel 90 m/pixel VNIR = visible to near infrared; SWIR = shortwave infrared; TIR = thermal (or mid-infrared); µm = micrometers

One such approach is statistical differentiation depending upon the reflectance values of the image pixels (Jensen 1996). An unsupervised classification approach calculates clusters of the most identical spectral pattern from the pixels in an image. The interpreter need only specify the desired number of output classes. The supervised classification approach requires more input from the human interpreter. Specific areas (training samples) are selected that are most representative of the desired class in the image. A variety of algorithms (minimum distance, maximum likelihood, parallelepiped, etc.) may then be used to search the whole image for pixels which are very close to the average of the training samples with regard to their spectral properties. The success of this approach is well demonstrated for spaceborne high- and medium-resolution imagery (Donnay et al. 2001,

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Mesev 2003), but did not work well for the Phoenix metropolitan area due to significant spectral similarities between classes (Stefanov et al. 2001). Stefanov et al. (2001) used a more sophisticated expert systems approach to incorporate additional geospatial knowledge into the classification process for Landsat data on Phoenix obtained in 1998 (described in Section 7.5.1, below). Unfortunately, this additional geospatial (or a priori) knowledge is not available for earlier images. Other sophisticated approaches were necessary to provide useable results for long-term change detection analysis of the Phoenix metropolitan region. The application of one such approach (object-oriented classification) is described in Section 7.5.2, below. During the long and ongoing operational period of the Landsat program (from 1972 to present), a rich dataset of multispectral imagery has been acquired and archived at the United States Geological Survey EROS Data Center (http://edc.usgs.gov/products/satellite.html). The Landsat series sensors’ technical specifications have been altered and modified carefully over time in order to make sure that data can be compared from one sensor generation to another. Likewise, the ASTER sensor onboard the Terra satellite was designed to provide some overlap in band coverage with the Landsat ETM+ sensor (Abrams 2000). Both the Landsat sensors and ASTER acquire imagery that is stored as raw data and can be processed to higher-level products upon individual request. Some of the archived imagery has been processed (system corrected and georeferenced) and made freely available for use by the public via various image archives on the World Wide Web (http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp for Landsat data; http://edcdaac.usgs.gov/datapool/datapool.asp for ASTER data). Current satellite sensors typically used for multitemporal urban analysis, such as the TM, ETM+ and ASTER, acquire data in the visible, nearinfrared, shortwave-infrared, and mid-infrared (or thermal) portions of the electromagnetic spectrum (Abrams 2000; Jensen 2000). This wavelength coverage enables the differentiation of vegetation which has strong reflectance values in the near-infrared, compared to reflectance in the visible red, wavelength (see Plate 7.2). This typical reflectance curve is known as the red edge (Jensen 1996, Sabins 1997). Most minerals have distinct reflectance and emittance curves in the shortwave and mid-infrared wavelengths that can be used for differentiation with appropriate image-analysis software (Christensen et al. 2000, Kahle et al. 1998, Salisbury 1991). A variety of indices, ratios, and spectral signatures have been developed for the detection of features like vegetation density and mineralogical composition (Jensen 2000). Most, if not all, of these analytical tools can be applied to urban analysis since urban surface

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materials are comprised of various combinations of naturally occurring materials such as vegetation, soil, and rock (Donnay et al. 2001, Herold et al. 2003, Roessner et al. 2001). Radar and LIDAR (Light Detection and Ranging) data have also been used to map urban features, topography, and land cover (Dell’Acqua et al. 2003, Fujii and Arikawa 2002), but these approaches have not yet been rigorously applied in the Phoenix metropolitan area. Table 7.2 provides some examples of applications of potential interest to urban planners and environmental managers using data from sensors such as TM, ETM+ and ASTER.

Table 7.2. Example Applications of TM, ETM+, and ASTER Data Spectral Region [µm] Wavelength Region 0.45 - 0.52 visible blue

0.52 - 0.60

visible green

0.52 - 0.90

visible green through near infrared (panchromatic bands) visible red

0.63 - 0.69 0.76 - 0.90 1.55 - 1.75

2.08 - 2.43 8.12- 12.50

µm = micrometers

Application measurement of water clarity; differentiation of vegetation from soils; measurement of albedo (surface reflectance) estimation of vegetation health; measurement of albedo urban change studies; measurement of albedo

measurement of chlorophyll absorption; vegetation differentiation; measurement of albedo near infrared biomass surveys; delineation of water bodies; measurement of albedo shortwave infrared vegetation and soil moisture measurements; differentiation between snow and clouds; measurement of albedo shortwave infrared hydrothermal and mineralogical mapping; measurement of albedo mid-infrared (or ther- measurement of surface temperamal infrared) ture; soil moisture studies; plant heat stress measurement; mineralogical and lithologic mapping

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7.5.1 Expert system classification of the Phoenix area A land cover classification for the Phoenix, AZ metropolitan area was produced using mosaiced Landsat TM data acquired on May 24 and June 18, 1998, and ancillary geospatial data (Stefanov et al. 2001). The visible to shortwave-infrared bands of the TM data (bands 1-5 and 7) were georeferenced to the Universal Transverse Mercator (UTM) coordinate system using nearest neighbor resampling to an estimated 0.3-0.5 pixel location accuracy. Data for the study area was atmospherically corrected to surface reflectance with the MODTRAN3 radiative transfer code and a midlatitude summer atmospheric profile within a commercial software environment (GEOSYSTEMS GmbH 1997). Removal of the atmospheric component from remotely sensed data is necessary to obtain the true reflectance signature of surface materials for subsequent image processing and classification (Jensen 2000). The six TM bands were used as the initial base data for land-cover classification. A soil-adjusted vegetation index, or SAVI (Huete 1988), was calculated from the visible and near-infrared bands and used to map vegetation density or “greenness.” Use of this index also minimized shadow effects in the data. The 120 m/pixel, mid-infrared band 6 was not used in image classification. An initial maximum likelihood supervised classification (Jensen 1996) was performed on the TM data using 8 land-cover classes: Vegetation, Undisturbed, Water, Disturbed (mesic residential materials), Disturbed (xeric residential materials), Disturbed (commercial/industrial materials), Disturbed (asphalt + concrete), and Disturbed (compacted soil). Spatial variance texture was calculated using the visible wavelength bands and a 3 x 3-pixel moving window to discriminate between urban and nonurban regions. This operation highlights large changes in reflectance between adjacent pixels and correlates well with urban versus non-urban land-cover types (Gong and Howarth 1990; Irons and Petersen 1981; Stefanov and Netzband 2005, 2007; Stefanov et al. 2001, 2003; Stuckens et al. 2000). Both the vegetation index and spatial variance texture data were used as additional layers in subsequent processing. Qualitative assessment of the initial classification results indicated unacceptable misclassification of pixels, both within and between the various soil, vegetation, and built classes. A knowledge-based or expert classification system was then constructed to perform post-classification recoding of the initial classification result. Post-classification recoding was necessary because the surface reflectance data obtained from the TM did not adequately discriminate between some important classes. For example, river gravels and asphalt roadways were highly confused in classification approaches that used only surface reflectance information; this was due to the

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high proportion of local aggregate (river gravel) used in asphalt that led to a very similar spectral character in the TM reflectance bands. An expert classification system applies a sequence of decision rules to a set of georeferenced datasets using Boolean logic (Stuckens et al. 2000, Vogelmann et al. 1998). This approach allows for the introduction of a priori knowledge into the classification data space and can significantly reduce errors of omission. A typical decision rule might be, “If pixel A is classified as Disturbed (asphalt + concrete), and has a vegetation index value lower than 0.4, and has a spatial texture value lower than 0.3, and is located within a polygon defined as a Waterway, recode the pixel to Undisturbed.” Multiple decision pathways can be created in this fashion to recode misclassified pixels, and most major image processing software packages now have interfaces for constructing such models. The initial maximum likelihood land-cover classification, SAVI, spatial variance texture data, and several vector polygon datasets were used in the expert system framework to produce a final land-cover classification (Fig. 7.4). The vector datasets included land use, water rights, Native American reservations, and municipal boundaries in the metropolitan area.

Fig. 7.4. Expert system land-cover classification for Phoenix using 1998 TM data. Figure after Stefanov et al. (2001).

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Table 7.3. Accuracy Assessment Results Class Name

Ref. Class. No. Producer User Totals Totals Correct Accuracy Accuracy k

Cultivated Vegetation 99 Cultivated Grass 77 Fluvial and Lacustrine Sedi77 ments (canals) Compacted Soil (prior agricul- 81 tural use) Vegetation 80 Disturbed (commercial/ 54 industrial) Disturbed (asphalt and concrete) 67 Undisturbed 101 Compacted Soil 110 Disturbed (mesic residential) 70 Disturbed (xeric residential) 86 Water 79 Totals 981

99 78 88

93 76 72

93.94 98.70 93.51

93.94 97.44 81.82

0.933 0.972 0.803

84

71

87.65

84.52

0.831

84 71

61 35

76.25 64.81

72.62 49.30

0.702 0.463

71 95 87 72 74 78 981

61 86 83 59 62 77 836

91.04 85.15 75.45 84.29 72.09 97.47

85.92 90.53 95.40 81.94 83.78 98.72

0.849 0.894 0.948 0.806 0.822 0.986

Overall Accuracy = 85.22 Results from Stefanov et al. (2001); Ref. = Reference, Class. = Classified

0.836

Use of the expert system also allowed for the identification of four additional land-cover classes: Cultivated Vegetation, Cultivated Grass, Fluvial and Lacustrine Sediments (canals), and Compacted Soil (prior agricultural use). Accuracy assessment of the final classification was performed using a reference dataset constructed from 3 m/pixel, digital aerial orthophotos for the Phoenix metropolitan region collected in 1999, field verification data, and the original 1998 TM data (to minimize reference dataset error due to temporal change). A useful reference for accuracy assessment techniques is Congalton and Green (1999). A total of 981 assessment points were used, with each class represented by at least 70 points. Points were randomly selected using a 3 x 3 moving window and a class majority filter. Table 7.3 presents the accuracy assessment results of the 1998 land-cover classification. The expert system model was then applied to TM data of the Phoenix metropolitan region for 1985, 1990, and 1993 (Stefanov 2000), using appropriate ancillary datasets. These other land-cover classifications have been used primarily for visualizations that illustrate LULCC in the Phoenix metropolitan area, while the 1998 classification has been used extensively for CAP LTER, and allied, research (Stefanov 2002).

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7.5.2 Monitoring LULCC using object-oriented classification Change detection analysis for the Phoenix metropolitan area was performed as a part of the Agrarian Landscapes in Transition (AgTrans) project (http://sustainability.asu.edu/AGTRANS/). This analysis focuses on the major transition processes affecting agricultural and natural land, primarily conversion to urban land use, in the Phoenix metropolitan area. Satellite remotely sensed imagery is an important data source for this study. The imagery used covers more than 30 years, with data initially acquired in 1973 by the MSS onboard the Landsat 1 satellite. Later datasets were acquired by the Landsat TM launched in 1982, and the ETM+ launched in 1999. The most recent dataset used for this investigation was acquired by the ASTER sensor. All of these sensors are readily comparable in terms of spectral, spatial, and temporal properties (Table 7.1; Abrams 2000, Sabins 1997). The Landsat and ASTER repeat overpass rate is approximately 1416 days; ideally the same urban area can be imaged at this temporal frequency as long as weather conditions (e.g., cloud cover) are favorable. For long-term monitoring, acquisition of satellite imagery during the same season every year of observation is recommended. This minimizes variations due to seasonal changes in vegetation (both natural and agricultural) and soil moisture. This study used imagery acquired in seven different years during the spring season (a period of about six weeks; Table 7.4). The spring season was selected because vegetation abundance (and reflectance) is highest during this time of year in Phoenix (MacMahon 1988). Table 7.4. Remote Sensing Imagery Used in Change Detection Analysis Sensor

Path/Row

Date Acquired

MSS 39/37 May 5, 1973 MSS 39/37 April 1,1979 MSS 40/37 March 24,1979 TM 37/37 May 4,1985 TM 37/37 March 18,1991 TM 37/37 March 13,1995 ETM+ 37/37 April 19, 2000 N/A March 20, 2003 ASTER* *The ASTER scene does not cover the entire metropolitan area.

The relatively new object-oriented analysis approach considers not only the reflectance values, but also makes use of other feature parameters such as object size, shape, texture and neighborhood relations (Baatz and Scha-

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epe 2000; Benz et al. 2004; Moeller 2004, 2005). The eCognition software (http://www.definiens.com/products/eiis_definiensprofessional.php) is used to calculate image segments on several levels (Fig. 7.5). The individual segment sizes and shape parameters can be determined by the user. Typically, a number of iterative trials are necessary to determine the optimum segment size and shape for the best representation of the desired image objects, on each segmentation level. Each segmentation level is directly connected to all other levels in a parent-child relationship. Levels with a large number of small segments are used for the later classification of small objects; levels with a small number of large segments represent large image objects.

Segmentation Levels

Level IV

Level III

Level II

Level I

Fig. 7.5. Schematic diagram of multi-level image segments and their parent-child relationship.

Once an appropriate segmentation scheme has been established, the classification itself can be performed when the classes have been described. This is usually done by defining the spectral ranges of individual classes in all of the image bands. Also, the neighborhood relations of the objects can be defined based upon specific rules. For example, a heavily vegetated area with spectral features similar to common croplands but surrounded by urban features (e.g., built materials, pavement), should be classified as Urban Park. The inheritance from one segmentation level to the others can be used for the definition of classes as well. A high-level urban class with large segments can be differentiated on a lower level (smaller

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segments) into subclasses like Dense Urban Settlement, Sparse Urban Settlement, Commercial Areas, Parks, Recreational Areas, and so on. The definition of inheritance parameters allows linkage between specific high– level classes and subclasses. Segmentation of the Phoenix imagery was carried out on two levels for all data of all years. On a rough scale consisting of large segments, a manual classification was performed first. The classes Urban, Farmland, and Natural Land could be classified in about ten minutes for each image based upon approximately 150 segments of the rough classification. Smaller segments inherited with the manually classified rough segments were used for a more detailed classification into subclasses. The classification scheme established for both levels is presented in schematic form in Figure 7.6.

Level I, large segments, manually classification urban

Level II, small segments, rule-based classification farmland farmland high vegetation

natural land farmland

farmland medium vegetation fallow farmland dark fallow farmland water urban urban vegetation urban developed sparse urban developed dense urban transport/industrial/commercial water natural land water

Fig. 7.6. Schematic diagram of two-level classification scheme based upon image segments.

Initial results of this study, presented below, are based upon the work of Moeller (2004, 2005). The overall accuracy for all scenes analyzed is 83%; this result is similar to that achieved by Stefanov et al. (2001) for classification of the Phoenix area. A 500 m x 500 m mesh net was overlaid on the classified data using a GIS, in order to provide a standard framework for change detection analysis. The Level II classification was mapped to each

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high level LULC class for each of the grid cells. These classes are Urban Area, Natural Land, Farmland, and Water. Plate 7.3 presents change detection maps based upon the classification output. Changes are mapped on a 0.25 km2 grid scale. Time-series maps such as these are useful visualization tools for highlighting urban growth and LULC change. Table 7.5 presents LULC results on a grid cell basis. Table 7.5. LULC Changes of the Phoenix Metropolitan Area

Period

Farmland to Urban [cells] [km2]

Natural Land to Urban [cells] [km2]

Overall LULC changes [cells] [km2]

1973 - 425 106.25 433 108.25 858 1979 1979 - 1039 259.75 926 231.50 1965 1985 1985 - 529 132.25 1157 289.25 1686 1991 1991 - 793 198.25 1340 335.00 2133 1995 1995 - 867 216.75 811 202.75 1678 2000 2000 - 599 149.75 1292 323.00 1891 2003* overall 4252 1063 5959 1489.75 10211 * ASTER satellite scene does not cover the whole metropolitan area

214.5 491.25 421.5 533.25 419.5 472.75 2552.75

While the LULC results are useful, we also need to know when and where changes took place over time. To answer these questions the LULC change pattern was spatially analyzed within a GIS. The centroid of the 1973 urban extent has been used as a starting point. This point has been buffered with 50 concentric circles of 1 km width each. The circles were divided into eight sectors, each with an arc of 45 degrees. This produced a schema similar to a navigation wind rose consisting of 400 single segments. The LULC changes for the six time periods have been assigned to each segment; this enables change analysis with regard to location in the wind rose sector, as well as distance from the metropolitan centroid point. The results of this change analysis show several buildup stages that move away from the city center, similar to the urban fringe “waves” of Gober and Burns (2002). Outward growth has been limited to the north-northeast and southwest, where Native American reservations directly border the

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Phoenix metropolitan area. Growth in the south-southwest is restricted by the South Mountain Preserve, an urban park. The preceding discussion has focused primarily on the use of moderately high resolution, government-sponsored satellite data for urban studies of Phoenix. Characterization of LULC and vegetation patterns at the scale of individual dwellings is now possible with very high spatial resolution multispectral satellite data – 5 m/pixel or less – from commercial satellites such as Quickbird (Sawaya et al. 2003) and IKONOS (Dial et al. 2003) launched within the past eight years. Incorporation of these very high resolution datasets to ongoing urban ecological studies and urban management in the Phoenix metropolitan area is in its initial stages. The last section of this chapter (7.6, below) presents some example applications of very high resolution satellite data to urban management for the Town of Marana, AZ - many of the concerns for the town are similar to those in the Phoenix metropolitan area.

7.6 High resolution commercial data use in Marana, AZ The use of high resolution commercial satellite imagery by a small town in southern Arizona demonstrates that data from spaceborne sensors can greatly assist in satisfying the high data demands present in a dynamically developing community on the urban fringe. It is in these suburban areas that some of the highest demands are made on the timeliness, accuracy, and spatial resolution of image data. The Town of Marana is located 140 km southeast of Phoenix in Pima County, Arizona, just northwest of the city of Tucson (Fig. 7.7). During the 1990s Marana’s 518% growth rate was the highest in Arizona. Marana’s 2004 population of 23,520 is expected to grow to over 100,000 by 2030 (PAG 2004). Accelerated growth in Marana, with almost the same patterns and speed as in the Phoenix agglomeration, has placed increasing pressure on the town’s infrastructure and planning mechanisms. Town management has responded, in part, by acquiring high resolution satellite imagery to supplement other imagery and vector data for use in the town administration’s GIS applications. The acquisition of IKONOS (1 m/pixel resolution) and Quickbird (.70 m/pixel resolution) imagery on an annual basis has proven to be invaluable in helping Marana better manage its rapid growth. Table 7.6 presents the specifications for these sensors.

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Fig 7.7. Location map for Marana, Arizona and high resolution satellite imageacquisition area.

Table 7.6. Properties of the IKONOS and Quickbird Sensors Sensor IKONOS

Resolution Quickbird

Resolution

VNIR bands 1: 0.45 - 0.52 µm 2: 0.52 - 0.60 µm 3: 0.63 - 069 µm 4: 0.76 – 0.90 µm 4 m/pixel (1 m/pixel for additional panchromatic band)

SWIR bands n/a

1: 0.45 - 0.52 µm n/a 2: 0.52 - 0.60 µm 3: 0.63 - 0.69 µm 4: 0.76 - 0.90 µm 2.4 m/pixel (0.6 m/pixel for additional panchromatic band)

TIR bands n/a

n/a

VNIR = visible to near infrared; SWIR = shortwave infrared; TIR = thermal (or mid-infrared); µm = micrometers

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The town’s government also has access to higher spatial resolution (0.15 to 0.3 m/pixel) digital orthophotography from the Pima Association of Governments (PAG). PAG implemented regional (more than 3000 km2) orthophoto projects in 1998, 2000, and 2002. Digital orthophotography was acquired over all or part of the Town of Marana by these projects, as was digital elevation data. Because of the large scope of these projects, the normal turnaround time between data capture, processing, and delivery to Marana has historically been between nine and twelve months. One major advantage of high resolution satellite imagery over photo-based orthophotography is faster data processing and delivery. The turnaround time for processed, high resolution satellite imagery is getting better every year. In 2004, the Town contracted for IKONOS data capture during June; orthorectified and mosaiced imagery was delivered less than three weeks after the last data were acquired by the satellite sensor. While for some applications (e.g., visual location of fire hydrants, signs, water meter boxes) the higher spatial resolution (0.15 to 0.3 m/pixel), aerial photo-based orthophotography was necessary, for most uses the commercial high resolution satellite data was adequate. Most use of high resolution satellite imagery in Marana is with desktop applications in the town’s planning, engineering, public works, parks and recreation, environmental, and GIS departments. Most often the imagery is used in combination with vector data layers (e.g., parcels, zoning), and users normally request the most recent image data available. While the most recent image set is the most frequently utilized and changes annually, it is important not to underestimate the archival value of an annual image acquisition schedule. Historical imagery is often requested by users because it allows them to view an area of interest at specific periods in time. High resolution satellite imagery is also used in the Town of Marana for 3-D visualization products. The power of high resolution, satellite based, 3-D visualization products lies in their ability to effectively communicate complex geographic phenomena to the general public. As image processing, GIS, and 3-D visualization software become more powerful and pervasive, they will offer users a chance to see what information means at a glance, rather than requiring them to analyze an array of engineering plans and associated project documents. In these products, real-world terrains in three dimensions are created using high resolution satellite imagery, digital elevation models, and 3-D visualization software. These products (Figs. 7.8 and 7.9) have proven to be an effective tool in Marana because communication of landscape-based information is a key element in the local government planning process.

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Fig. 7.8. May 2002 IKONOS image with preliminary plat housing development data draped over a digital elevation model. (Includes material © Space Imaging LLC).

Fig. 7.9. Oblique view of the rapidly developing urban fringe in Marana, provided by draping June 2004 IKONOS imagery over digital terrain data (Includes material © Space Imaging LLC)

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7.7 Conclusions Data from moderate to very high spatial- and temporal-resolution satellite and airborne sensors are nowadays available from a variety of sources. Multispectral (tens of bands) to hyperspectral (hundreds of bands) data, together with laser and radar topography data (LIDAR, InSAR), can be acquired for many cities. The remote sensing community now has data in its hands to characterize and monitor urban surfaces and processes in three dimensions (x, y, z), from the scale of sidewalks to that of entire watersheds. Many of these datasets are particularly well-suited to operational use in semi-arid and arid cities, which generally have low vegetation cover density and frequently clear atmospheric conditions. The data used for studies of the Phoenix metropolitan area discussed in this chapter were acquired by governmental agencies such as the United States National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS). Such data are frequently either free of charge or are subsidized for end users like scientists and urban planners. Commercial providers now also offer very high resolution panchromatic to multispectral datasets (IKONOS, Quickbird) with high potential for urban planning uses, as is demonstrated by the Town of Marana (Section 7.6 above). However, the frequently high data licensing cost and limited spatial coverage of these datasets currently constrains their use for urban research, and urban governments in developing regions may have difficulty obtaining these data. Many government-sponsored satellite sensor programs are now required to make data available in a timely fashion and at low cost to the general public. Much of this data transfer has been made possible by Web-based servers and ordering interfaces such as the EROS Data Center’s Data Gateway (http://edcimswww.cr.usgs.gov/pub/imswelcome/), the USGS Global Visualization Viewer (GLOVIS; http://glovis.usgs.gov/) and the NASA Gateway to Astronaut Photography (http://eol.jsc.nasa.gov). The ever-increasing speed and computational power of desktop computers and local area networks are likewise increasing the capability for local and regional governments to do in-house image processing and analysis. The Phoenix region presents a useful model for successful collaboration among academic, governmental, and private sector entities, and use of advanced technologies and data (Quay 2004). Such a model may be exportable to urban centers in developing countries.

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7.8 References Abrams M (2000) The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): Data products for the high spatial resolution imager on NASA’s Terra platform. International Journal of Remote Sensing 21:847-859 Arrowsmith JR, Stefanov WL (2003) Geology overview for the CAP-LTER region. Central Arizona-Phoenix Long Term Ecological Research Project virtual tour, http://caplter.asu.edu/home/capltertour/geology.htm (accessed 6-March2007) Baatz M, Schaepe A (2000) Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. In: Strobl J, Blaschke T, Griesebner G (eds) Angewandte geographische informationsverarbeitung, XII. Wichmann, Karlsruhe, Germany, pp 12–23 Benz UC, Hoffmann P, Willhauk G, Lingenfelder I, Heynen M (2004) Multiresolution, object-oriented fuzzy analysis of remote sensing data for GISready information. ISPRS Journal of Photogrammetry and Remote Sensing 58:239-258 Berling-Wolff S, Wu J (2004) Urban growth models: A historical review. Ecological Research 19:119-129 Blemenfeld H (1954) The tidal wave of metropolitan expansion. Journal of the American Institute of Planners 10:3-14 Brazel AJ, Selover N, Vose R, Heisler G (2000) The tale of two climates: Baltimore and Phoenix LTER sites. Climate Research 15:123-135 Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: principles and practices. Lewis Publishers, New York, NY Christensen PR, Bandfield JL, Hamilton VE, Howard DA, Lane MD, Piatek JL, Ruff SW, Stefanov WL (2000) A thermal emission spectral library of rockforming minerals. Journal of Geophysical Research 105:9735-9739 Dell’Acqua F, Gamba P, Lisini G (2003) Improvements to urban area characterization using multitemporal and multiangle SAR images. IEEE Transactions on Geoscience and Remote Sensing 41:1996-2004 Donnay J-P, Barnsley MJ, Longley PA (2001) Remote sensing and urban analysis. In: Longley PA, Donnay J-P, Barnsley MJ (eds) Remote sensing and urban analysis. Taylor and Francis, London, UK, pp 3-18 Erickson W (1999) Division of Stormwater Management, Municipal Services Department, City of Scottsdale, Arizona (personal communication) Fagan WF, Meir E, Carroll S, Wu J (2001) The ecology of urban landscapes: Modeling housing starts as a density-dependent colonization process. Landscape Ecology 16(1): 33-39 Fink J (2005) Vice President for Research and Economic Affairs, Arizona State University, Tempe, Arizona (personal communication) Fujii K, Arikawa T (2002) Urban object reconstruction using airborne laser elevation image and aerial image. IEEE Transactions on Geoscience and Remote Sensing 40:2234-2240

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Gammage G Jr (1999) Phoenix in perspective: Reflection on developing the desert. Arizona State University, Tempe, AZ GEOSYSTEMS GmbH (1997) ATCOR2 for ERDAS Imagine user manual. GEOSYSTEMS GmbH, Germering, Germany Gober P (2000) In search of synthesis. Annals of the Association of American Geographers 90:1-11 Gober P, Burns EK (2002) The size and shape of Phoenix’s urban fringe. Journal of Planning Education and Research 21:379-390 Gong P, Howarth PJ (1990) The use of structural information for improving land – cover classification accuracies at the rural – urban fringe. Photogrammetric Engineering and Remote Sensing 56:67-73 GP2100 (2003) Greater Phoenix regional atlas: A preview of the region’s 50-year future. Arizona State University, Tempe, AZ Grimm NB, Grove JM, Redman CL, Pickett STA (2000) Integrated approaches to long-term studies of urban ecological systems. BioScience 70:571-584 Grimm NB, Redman CL (2004) Approaches to the study of urban ecosystems: The case of Central Arizona-Phoenix. Urban Ecosystems 7:199-213 Grossman-Clarke S, Zehnder JA, Stefanov WL, Liu Y, Zoldak MA (2005) Urban modifications in a mesoscale meteorological model and the effects on near surface variables in an arid metropolitan region. Journal of Applied Meteorology 44:1281-1297 Harlan SL, Brazel AJ, Prashad L, Stefanov WL, Larsen L (2006) Neighborhood microclimates and vulnerability to heat stress. Social Science & Medicine 63:2847-2863 Hawkins TW, Brazel AJ, Stefanov WL, Bigler W, Safell EM (2004) The role of rural variability in urban heat island determination for Phoenix, Arizona. Journal of Applied Meteorology 43:476-486 Herold M, Gardner ME, Roberts DA (2003) Spectral resolution requirements for mapping urban areas. IEEE Transactions on Geoscience and Remote Sensing 41:1907-1919 Hope D, Gries C, Zhu W, Fagan WF, Redman CL, Grimm NB, Nelson AL, Martin C, Kinzig A (2003) Socioeconomics drive urban plant diversity. Proceedings of the National Academy of Science 1000 (15):8788-8792 Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25:295-309 Irons JR, Petersen GW (1981) Texture transforms of remote sensing data. Remote Sensing of Environment 11:359-370 Jenerette GD, Harlan SL, Brazel A, Jones N, Larsen L, Stefanov WL (2007) Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landscape Ecology 22:353-365 Jenerette GD, Wu J (2001) Analysis and simulation of land use change in the central Arizona - Phoenix region. Landscape Ecology 16:611-626 Jensen JR (1996) Introductory image processing: A remote sensing perspective (2nd ed). Prentice Hall, Upper Saddle River, NJ Jensen JR (2000) Remote sensing of the environment: An Earth resource perspective. Prentice Hall, Upper Saddle River, NJ

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Kahle AB, Palluconi FD, Christensen PR (1993) Thermal emission spectroscopy: Application to the Earth and Mars. In: Pieters CM, Englert PAJ (eds) Remote geochemical analysis: Elemental and mineralogical composition. Cambridge University Press, Cambridge, MA, pp 99-120 Knowles-Yánez K, Moritz C, Bucchin M, Redman C, Fry J, McCartney P, Marruffo J (1999) Historic land use team: Generalized land use for CAP LTER study area. Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) First Annual Poster Symposium, Tempe, AZ, p 22 Kupel DE (2003) Fuel for growth: Water and Arizona’s urban environment. University of Arizona Press, Tucson, AZ MacMahon JA (1988) Warm deserts. In: Barbour MG, Billings WD (eds) North american terrestrial vegetation. Cambridge University Press, New York, NY, pp 231-264 Mesev V (2003) Remotely sensed cities: An introduction. In: Mesev V (ed) Remotely sensed cities. Taylor and Francis, New York, NY, pp 1-19 Moeller M[S] (2004) Monitoring long term transition processes of a metropolitan area with remote sensing. Proceeding of the IGARRS 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, pp 3398-3401 Moeller MS (2005) Remote sensing for the monitoring of urban growth patterns. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 36 (on CDROM) PAG (2004) Pima Association of Governments, 177 North Church Avenue, Suite #405, Tucson, AZ 85701, http://www.pagnet.org/default.htm, accessed 6March-2007 Quay R (2004) Bridging the gap between ecological research and land use policy: The North Sonoran Collaboration. Urban Ecosystems 7:283-294 Redman CL (1999) Human impacts on the ancient environment. University of Arizona Press, Tucson, AZ Reynolds J, Wu J (1999) Do landscape structural and functional units exist? In: Tenhunen JD, Kabat P (eds) Integrating hydrology, ecosystem dynamics, and biogeochemistry in complex landscapes. John Wiley, New York, NY, pp 273296 Roessner S, Segl K, Heiden U, Kaufmann H (2001) Automated differentiation of urban surfaces based on airborne hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 39:1525-1532 Sabins FF (1997) Remote sensing: principles and interpretation (3rd ed). W. H. Freeman, New York, NY Salisbury JW, Walter LS, Vergo N, D'Aria DM (1991) Infrared (2.1- 25 micrometers) spectra of minerals. Johns Hopkins University Press, Baltimore, MD Stefanov WL (2000) 1985, 1990, 1993, 1998 Land cover maps of the Phoenix, Arizona metropolitan area. Geological Remote Sensing Laboratory, Department of Geological Sciences, Arizona State University, Tempe. 4 Plates, scale 1:115,200

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Stefanov WL (2002) Remote sensing of urban ecology at the Central ArizonaPhoenix Long Term Ecological Research site. Arid Lands Newsletter 51, http://ag.arizona.edu/OALS/ALN/aln51/stefanov.html, accessed 6-March2007 Stefanov WL, Netzband M (2005) Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center. Remote Sensing of Environment 99 (1-2):31-43 Stefanov WL, Netzband M (2007) Characterization and monitoring of urban/periurban ecological function and landscape structure using satellite data. In: Rashed T, Jürgens C (eds) Remote Sensing of urban and suburban areas. Springer, New York, NY (in press) Stefanov WL, Ramsey MS, Christensen PR (2001) Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of Environment 77:173-185 Stefanov WL, Ramsey MS, Christensen PR (2003) Identification of fugitive dust generation, deposition, and transport areas using remote sensing. Environmental and Engineering Geoscience 9:151-165 Stefanov WL, Prashad L, Eisinger C, Brazel A[J], Harlan S (2004) Investigations of human modification of landscape and climate in the Phoenix Arizona metropolitan area using MASTER data. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 35:1339-1347 Stuckens J, Coppin PR, Bauer ME (2000) Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sensing of Environment 71:282-296 Vogelmann JE, Sohl T, Howard SM (1998) Regional characterization of land cover using multiple sources of data. Photogrammetric Engineering and Remote Sensing 64:45-57 Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sensing of Environment 86:370-384 Wentz EA, Stefanov WL, Gries C, Hope D (2006) Land use and land cover mapping from diverse data sources for an arid urban environments. Computers, Environment, and Urban Systems 30 (3):320-346 Woodcock CE, Strahler AH (1987) The factor of scale in remote sensing. Remote Sensing of Environment 21:311-332 Wu J (1999) Hierarchy and scaling: Extrapolating information along a scaling ladder. Canadian Journal of Remote Sensing 25(4): 367-380 Wu J (2004) Effects of changing scale on landscape pattern analysis: Scaling relations. Landscape Ecology 19:125-138 Wu J, David JL (2002) A spatially explicit hierarchical approach to modeling complex ecological systems: Theory and applications. Ecological Modeling 153:7-26 Wu J, Jelinski DE, Luck M, Tueller PT (2000) Multiscale analysis of landscape heterogeneity: Scale variance and pattern metrics. Geographic Information Sciences 6(1):6-19 Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landscape Ecology 17:761-782

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Zehnder JA (2002) Simple modifications to improve fifth-generation Pennsylvania State University-National Center for Atmospheric Research mesoscale model performance for the Phoenix, Arizona metropolitan area. Journal of Applied Meteorology 41:971-979

Chapter 8 - Application of Remote Sensing and GIS Technique for Urban Environmental Management and Sustainable Development of Delhi, India

Atiqur Rahman Department of Geography, Jamia Millia Islamia, New Delhi, India

8.1 Introduction India no longer lives in villages, and rapid urban development has increased the size of India’s urban population. During the last fifty years the population of India has grown two-and-a-half times, but urban India has grown nearly five times. In 2001, 306.9 million Indians (30.5%) were living in nearly 3700 towns and cities spread across the country, compared to 62.4 million (17.3%) who lived in urban areas in 1951. This is an increase of about 390% in the last five decades. The urban population is expected to increase to over 400 million and 533 million by 2011 and 2021, respectively. In 1991 there were 23 metropolitan cities in India; the number increased to 35 in 2001. Among the megacities of the world (those with a population greater than 10 million), Mumbai with 16.37 million, Delhi with 13.78 million, Kolkata with 13.22 million, and Chennai with 6.42 million people figure prominently (Raghavswamy et al. 1996). The high rate of urban population growth is a cause of concern among India’s urban and town planners. The term urbanization once conveyed an image of a city’s radial expansion into its rural surroundings. Urban areas of today are more aptly described as sprawling regions that become interconnected in a dendritic fashion (Carlson and Arthur 2000). The positive aspects of urbanization have often been overshadowed by deterioration in the physical environment and quality of life caused by the widening gaps between supply and demand for essential services and infrastructure. Substandard land and housing and exorbitant increases in land prices have left the urban

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poor with virtually no alternative to informal housing, leading to the mushrooming of slums. There has been tremendous slum growth in Delhi, from 12,749 in 1951 to over 500,000 in 2005. The current number of slum dwelling units is estimated to be about 0.6 million, and the population living in these slums and Yamuna clusters is about 3 million. Metropolitan areas like Delhi are the main engines of urban growth in the country. Rapid, haphazard urban growth and accompanying population pressures result in changes in urban land use and land cover; loss of productive agricultural land and open green spaces; loss of surface water bodies; depletion of ground water; deterioration of infrastructure facilities; micro climate change; air, water, and noise pollution; and an increase in public health hazards. To address these issues effectively, up-to-date and accurate data must be available at regular intervals of time. The process of urbanization is influenced by birth rate and immigration. Infrastructure development and large-scale migration of people from rural to urban areas results in the growth of villages into towns, towns into cities, and cities into metropolitan areas. Planning for ecologically sustainable development of urban areas requires understanding of growth dynamics. Unplanned urbanization leads to serious problems with infrastructure development and may have unforeseen consequences. Effective infrastructure planning and development require spatial and socio-economic data for different time periods. Spatial data for different time periods can be obtained through remote sensing and, along with population data, would help identify patterns and trends in urban growth. GIS would help to integrate spatial and statistical data, and generate various theme-based maps for planning purposes. Satellite remote sensing offers excellent possibilities for mapping, monitoring, measuring, and managing various features of the urban environment. The information that is generated helps governments, city administrators, and planners to formulate suitable plans and strategies for effective urban planning and management. Over the years, satellite remote sensing data have been used for assessing urban environmental conditions. Data is used for urban land-use and land-cover mapping, land-use dynamics, urban landscape design, and urban base map preparation. Remote sensing and GIS are repeatedly proving to be very valuable in urban studies, and particularly in the fields of urban management and planning (Shekhar 2004). Land-use and land-cover assessment is one of the most important tasks in planning for land-resource management. Land-use and land-cover inventories are assuming increasing importance in various resource sectors, like settlement surveys, environmental studies, and operational planning based on agro-climatic zones (Jayakumar and Arockiasamy 2003). With the successful launch of IRS-1C and -1D satellites, the availability of high spatial resolution data of 5.8 meter/pixel in a panchromatic single band (0.50-0.75

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µm), 23.5m LISS-III data in four bands corresponding to Landsat MSS with revisit capability of five days, and IKONOS 1 meter/pixel resolution, it is now possible to explore the potential of remote sensing data, either singularly or in combination, in different areas. High resolution satellite imagery including IKONOS 1 meter/pixel, Quick Bird 0.61 meter/pixel, and IRS-1D 5.8 meter/pixel pan and multispectral data were used to detect, identify, and delineate the slums in Dehradun; to see new slum development; and to map the condition of the slum environment with support from ground verification (Sur et al. 2004). Urban greenbelt (or open space) mapping, urban encroachment, growth of slums on vacant lands, urban housing, urban utilities and infrastructure, solid waste management, urban transportation and traffic planning, urban hydrology, urban cadastral and real estate, urban ecological hazards, and urban census data all can be mapped, monitored, and analyzed using remote sensing. The next section of this chapter provides a demographic profile of Delhi and describes environmental issues that affect urban planning and management of Delhi in particular and India in general. The third part of the chapter describes how aerial photographs, satellite-based remotely sensed data, and GIS technology can help solve urban environmental problems in various Indian cities, including Delhi, so that growth can take place in a sustainable, planned way. The fourth section of the chapter describes land– use and land-cover classification that has been done using ASTER 2003 data, using a 1.5 m by 1.5 m grid as an example to compare different landuse patterns in two areas of Delhi. The last section of the chapter deals with planning for sustainability in urban areas. The chapter will be useful to local and higher-level governmental authorities, and planners and decision makers who monitor urban issues such as land-use and land-cover change, urban sprawl, and illegal encroachment. Delhi has been taken as a case city because it is one of the “intensive study” cities of the 100 Cities project at Arizona State University (ASU). Delhi, the capital city of India, is one of the prime megacities of the world and is located at 28º 30' N latitude and 77º 00' E longitude. It lies at an altitude of between 213 and 305 meters above sea level, and covers an area of 1,483 km². It is situated on the bank of one of the most polluted rivers of the world, the River Yamuna, a tributary of the Ganges River. It is bordered on the east by the state of Uttar Pradesh, and on the north, west, and south by Haryana. The region has a tropical steppe climate. The general prevalence of continental air leads to relatively dry conditions with extremely hot summers. Mean monthly temperatures range from 14qC in January (min. 3qC) to 35qC in June (max. 47qC). The main seasonal climatic influence is the monsoon, which typically occurs from June to October. The mean annual rainfall is 70 cm. Maximum rainfall occurs in July

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with an average of 210 cm. Northwesterly winds usually prevail; however, in June and July southeasterly winds predominate.

8.2 Urban environmental issues in Delhi India's population explosion places great strain on the country's environment. Rapid population growth, along with increased urbanization and industrialization, places significant pressure on India's infrastructure and natural resources. With rapid urbanization, Delhi has progressively lost its green cover. It has merely 88 km2 (5.93%) of forest cover in the total geographical area of 1,483 km2. The natural ridge forests, which served as the “lungs” of Delhi, have dwindled considerably because of human activities. Less than a century ago, Delhi was an idyllic place, with the river Yamuna flowing fresh and pure, and the ridge forests undisturbed, green, and providing clean air. Today the story is totally different. An ever-increasing number of trucks, buses, cars, three-wheelers, auto rickshaws, motorcycles, and motor scooters—all spewing uncontrolled fumes—surge in sometimes haphazard patterns over city streets jammed with jaywalking pedestrians, cattle, and goats. Every major city of India faces the same proliferating problems of grossly inadequate housing, transportation, sewerage, and water supplies, and hence, unrest, making urban governance a difficult task. In 2001, the total population of Delhi was 13,782,976, with the northeast district the most densely populated at 29,397 person/km² (Table 8.1). Of 2,554,149 households in Delhi, 101,747 were without shelter. Twentyfive percent of the population lives more than three to a room. There were 150,339 houses with permanent roof material of mud, thatch, bamboo, grass, plastic, and polythene. The Master Plan of Delhi 2001 suggested that 1.61 million new dwelling units should be made available during 2001. Delhi is a sprawling metropolis and is among India's fastest growing cities. The problem of rapid urbanization in Delhi is exacerbated due to inmigration, from rural areas of nearby states, of people in search of jobs and better livelihood. Migrants account for a 50% increase in population every year. This large-scale immigration has led to unplanned urban development that is characterized by major infrastructure bottlenecks and environmental degradation (Plate 8.1). Water shortages, vehicular congestion, loss of open space, power outages, and increased pollution of various kinds are very common problems.

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Table 8.1. Demographic and housing conditions in Delhi, by district Districts

Population Population Area [km2] Density* Use Type

Northwest North

2,847,395 440 779,788 60

6,472 13,019

Northeast East New Delhi

1,763,712 60 1,448,770 64 171,806 35

29,397 22,638 4,909

Central

644,005

25,759

West

2,119,641 129

16,431

Southwest South

1,749,492 420 2,258,367 250

4,166 9,034

25

Total 13,782,976 1,483 9,296 Source: Census of India (2001); * person/km²

No. of units

Residence 2,316,996 Residence-with other 135,406 use Shop, Office 319,233 School, College 7,620 Hotel, Lodge, Guest 6,005 House, etc. Hospital, Dispensary, 7,661 etc. Factory, Workshop, 80,165 etc. Place of Worship 8,249 Other Non-residential 120,831 Use 3,002,166

India’s urban air quality is among the world’s worst overall, and Delhi is no exception. Vehicles are the major source of this pollution, with more than three million vehicles in Delhi, of which about two million are on the road during the daytime hours. With vehicle ownership rising along with population and income, India's efforts to improve urban air quality have focused on pollution by vehicles. The level of pollution of air, water, and land has increased also because of poor environmental management. Pollution has a direct impact on quality of life, affecting human efficiency and productivity, and thus overall socioeconomic development. The current trends of urban growth and development in Delhi, particularly as viewed in the context of India Vision 2020, point towards grim outcomes. Authentic statistical data on the above-mentioned urban issues is vital for all kinds of developmental decision-making. Similarly, urban resource accounting is necessary to better understand how policies are affecting the current development trends. Collection, collation, integration, and mapping of data on urban conditions is important to determine current conditions and to develop a concise set of urban indicators for monitoring the effects of development. Furthermore, public access to such information is essential, so that everyone who is interested can know what is happening to the urban environment and what factors contribute to its deterioration.

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Remotely sensed data can be effectively integrated with conventional maps in a GIS environment to extract data useful in planning for the sustainable development of Delhi. GIS and remote sensing technology have been used during the last decade in the planning and development of large urban projects like Rohini, Dwarka, DLF, Palam Vihar, and the Narela sub-city of the Delhi Development Authority (Uttarwar 2001). The Narela project in the north, Rohini project in the west, and Dwarka project in southwest Delhi (Plate 8.2) each have about one million inhabitants. Planning for, implementing, and managing such large projects requires continuous monitoring of plan proposals. Identification of land for acquisition, physical possession of land, and the development of roads and other infrastructure takes considerable time. During this incubation time, ground realities can change, which creates problems for implementation of the original planning proposals. Some of the major lacunae identified by planners are lack of base maps, absence of any technique for updating base maps, and lack of any monitoring mechanism to track project progress. Multitemporal remotely sensed data can provide quickly available and reliable information about land use and land cover, transformation of agricultural and other lands into developed lands, and other information essential for planning purposes. Three organizations in Delhi, the New Delhi Municipal Corporation (NDMC), Municipal Corporation of Delhi (MCD), and the Delhi Development Authority (DDA) are responsible for managing Delhi’s growth and development under the overall governance of the Delhi city government. Each organization blames another for the deteriorating conditions in the urban area (Tiwari 2003). India’s large cities have mayors, municipal commissioners, and counselors or ward representatives who are responsible for managing their respective areas. However, even these people are not doing what has to be done to protect the city environment. This is mainly due to the lack of up-to-date spatial data, skill to use the data properly, and most importantly, lack of commitment to protect the sustainability of the city. Only one part of Delhi, the NDMC enclave, has an adequate level of services. The rest of the huge area of Delhi suffers from a multitude of problems. An efficient urban information system is a vital prerequisite for planned and sustainable development, and remotely sensed data is an essential component of such a system.

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8.3 Application of remote sensing and GIS in urban studies It is necessary and fundamental for policy makers to integrate technology like remote sensing into urban planning and management. Traditional approaches and techniques designed for towns and cities may prove to be inadequate tools when dealing with metropolises. New approaches are required, and new methods must be incorporated into current practice. Until recently, maps and land-survey records from the 1960s and 70s were used for urban studies, but now the trend has shifted to using digital, multispectral images acquired by EOS and other sensors. The trend towards using remotely sensed data in urban studies began with first-generation satellite sensors such as Landsat MSS, and was given impetus by a number of second-generation satellites: Landsat TM, ETM+ and SPOT HRV. The recent advent of a third generation of very high spatial resolution (<5 meter/pixel) satellite sensors is stimulating the development of urban remote sensing still further (Fritz 1999; National Remote Sensing Centre 1996). The high resolution PAN and LISS III merged data can be used together effectively for urban applications. Data from IRS P-6 satellites with sensors onboard, especially LISS-IV Mono and Multispectral (MX) with 5.8 meter/pixel spatial resolution, is very useful for urban studies. A new chapter has begun in the Indian Space Mission, with its recent successful launch of the IRS P-5 (Cartosat-1) on May 5, 2005 from Sriharikota; its high resolution 2.5 meter/pixel PAN is earmarked for cartographic applications. It has two PAN cameras that will give data in mono and stereo modes. A tilt of +26º and –5º will provide stereo images needed for making DEMs/DTMs. The Cartosat mission is intended to fill the gap for geo-engineering and advanced mapping applications. Apart from cartographic applications, P-6 data will be used for cadastral mapping and updating, terrain visualization, generation of a national topographic database, utilities planning and other GIS applications needed for urban areas. The satellite will provide cadastral-level information up to a 1:5,000 scale, and will be useful for making 2-5 meter contour maps (NRSA 2005). Remote sensing technology can also be used for identifying large-scale encroachments, land conversion, and revenue boundaries of villages for Delhi. The revenue boundaries of villages in the west Delhi urban fringe area of Rohini have been correlated using satellite imagery (Uttarwar 2001). Large-scale detection of changes in terrain is possible with the help of satellite imagery of different time periods. All states in India except Delhi have facilities to access and analyze data from the country's IRS remote sensing satellites. There are five Regional Remote Sensing Centers

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(RRSC) in India working on various spatial problems for state government using remotely sensed data. The Delhi government has decided to pay Indrapastha University to set up a remote sensing service center. By 2007, Delhi will have its own remote sensing center, where satellite images will be analyzed and processed to extract information and prepare cadastral and general-purpose maps on a scale of 1: 4,000; larger, district-level land–use and land-cover maps to aid assessment of the degradation of Delhi’s ridge forest; and selection of suitable sites for the development of parks and recreational areas. Application of remote sensing technology can lead to innovation in the planning process in various ways: 1. Since information and maps are available in digital format, correlating various layers of information about a feature from satellite imagery, planning maps and revenue maps is feasible with the help of image processing software like ERDAS Imagine, ENVI and PCI Geomatica, ILWIS. Such superimposed maps in GIS software like Map Info, Geomedia, ArcView, AutoCAD Map and ArcGIS provide valuable information for planning, implementing, and managing development in urban areas. 2. Digital maps are scale-free, and different thematic maps and raster imagery can be stacked with high geolocation precision and accuracy. 3. Remote sensing techniques are extremely useful for changedetection analysis and selection of sites for specific facilities, such as hospitals, restaurants, solid waste disposal, and industry. Remote sensing technology in urban studies is also very useful for: 1. Monitoring large-scale urban encroachments in fringe areas, i.e., conversion of agricultural land into non-agricultural land. 2. Acquisition of vacant pockets of agricultural land for construction purposes. 3. Change detection (mapping can be done with time-series satellite images). 4. Landscape analysis and monitoring of urban sprawl. 5. Monitoring air and water pollution. 6. Identification of revenue boundaries of villages on satellite images. 7. Urban heat island characterization and monitoring. 8. Development of traffic management systems.

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Some of the urban issues in which remote sensing and GIS technology have an important role to play are discussed in the following sections. 8.3.1 Aerial photographs and satellite data in urban studies Aerial photography has long been employed as a tool in urban analysis (Jensen 1983, Garry 1992). The application of remote sensing techniques in Indian city planning has been largely confined to aerial photography. Aerial photographs can provide a visualization of three-dimensional urban features that is very important for urban planning. Various types of cameras and sensors—black and white, color, and color infrared—are used for aerial photography. Because of security concerns related to aerial photography, the use of photogrammetric techniques was confined to smaller cities. For Delhi and some other cities (e.g., Jaipur, Ujjan, Hyderabad, and Bangalore) aerial photographs are available on different scales (1:8,000, 1:12,000). Almost all of Delhi is photographed on a scale of 1:12,000, with the exception of high-security zones. Aerial photographs provide information that can significantly improve the effectiveness of city and town planning and management in India. They are also relatively low in cost, accurate, reliable, and can be obtained on the desired scale, but they are not useful for large metropolitan areas. Most aerial photographs of urban areas are classified documents. They can be acquired for educational purposes, and for some specific projects that require clearance from the Ministry of Defense (MoD), Ministry of Home (MoH), and Ministry of Civil Aviation. Acquisition is a long process and can take more than one year. Acquisition of aerial photographs of border regions, the coast,, and other sensitive areas is not permitted for security reasons. Aerial photography surveys (over 450,000 km²) have been done by the National Remote Sensing Agency (NRSA) in Hyderabad of all states in India, with scales ranging from 1:6,000 to 1:50,000, for different users such as the Town and Country Planning Organization (TCPO), Government of India, and state town-planning departments. The creation of a digital terrain database and preparation of large-scale topographic maps with 1m contours, using Survey of India (SoI) toposheets, is underway on a 1:50,000 scale for all of India. DEMs are being generated by analytical photogrammetric systems for the following projects: x x

Updating of large-scale topographical maps for the Delhi Development Authority (DDA) 20 km² in Okhla area in southeast Delhi for the National Informatics Center (NIC)

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600 km² in and around Hyderabad for the Hyderabad Metropolitan Water Supply and Sewerage Board (HMWSSB)

As discussed above, India has been very dependent on photogrammetry to provide information for urban planning purposes. But since the March 17, 1988 launch of its first satellite (IRS 1A) equipped with the LISS-I sensor acquiring 72.5 meter/pixel data, the application of remotely sensed data (from various sensors) in urban and regional planning processes has gained momentum. LISS-I gathered data in four spectral bands (0.45-0.86 µm) and was mainly used for broad land-use, land-cover, and urban sprawl mapping. The IRS-1C and 1D satellites launched in 2003, carrying LISSIII and LISS-IV sensors with spatial resolutions of 23.5 meter/pixel and 5.8 meter/pixel using Landsat MSS optical bands (0.52-0.86µm), have contributed to the effectiveness of urban planning and management. Early experiments with the first-generation Landsat satellites found the data very useful for mapping large urban parcels and urban extensions. The development of Landsat TM data with 30 meter/pixel spatial resolution has helped in mapping Level-II urban land-use classes. Some of the salient features of different satellite sensors and the extractable levels of urban information are summarized in Table 8.2. Cities and towns in India exhibit complex land use-patterns, with the size of urban parcels varying frequently within very short distances. The extraction of urban information from remotely sensed data therefore requires higher spatial resolution. 8.3.2 Urban spatial growth and sprawl Urban sprawl has been traditionally assessed from demographic information, and most communities continue to rely on aerial photography to update land-use and land-cover maps. Since regional land-cover changes caused by human activity tend to occur incrementally, communities often do not realize the extent of their development and therefore, possible changes in their environment. In this situation, satellite data has great potential for practical application to regional planning and urban ecology (Carlson and Arthur 2000). In Delhi, the environment is under stress because of rapid urbanization. Population growth, immigration of poor people, industrial growth, inefficient and inadequate traffic corridors, and poor environmental infrastructure are the main factors that have caused deterioration of the city’s resources. Delhi's urban area had grown from just 182 km2 in the 1970s to more than 1,483 km2 in 2001. This urban sprawl is mainly occurring at the expense of productive agricultural land (Plate 8.3).

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Table 8.2. Remote sensing platforms and sensor application in urban studies

Platform and Sensor System Landsat (MSS) IRS-1A & 1B (LISS-I) Landsat TM IRS-1A & 1B (LISS-II) IRS-1C & 1D (LISS- \III) SPOT HRV-I (MLA) IRS-1D (LISS-IV) ASTER VNIR (0.52 0.86ȝm) SWIR (1.60 2.43ȝm) TIR (8.125 11.65ȝm) SPOT HRV-II (MLA) IRS-1C & 1D (PAN) MOMS-II

Spatial resolution Year of op- Mapping [m/pixel] eration scale Extractable information 80 1972 1: 1,000,000 Broad land-use/land72 1988 & 1991 1: 250,000 cover and urban sprawl 30 36

1982 1: 50,000 1988 & 1991

23

1995 & 1997

20

1998

5.8

2003 1999

1:5000 1: 250,000 Land-use/ land-cover, 1: 50,000 urban sprawl, ecological monitoring data

10

1998

1: 25,000

5.8

1995 & 1997 1:10,000

4

1983

1: 8,000

IKONOS Quickbird

1 0.61

1999 2001

1: 4,000 1: 2,000

Cartosat-1

2.5

2005

1:4000

Cartosat-2

1.0

2007

1:1000 1:2000

15

Thematic data for broad structural plans and spatial strategies

30 90

Source: Modified after Rashid (1999)

Data for land-use/landcover for urban area Land-use/land-cover details Cadastral map, detailed information extraction for urban planning and infrastructure mapping Large scale cartographic work and DEM generation Cartographic applications at cadastral level, urban and rural infrastructure development and management

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There have been many debates on how to confine urban sprawl and con serve agricultural land resources (Bryant et al. 1982, Ewing 1997, Daniels 1997). Most farmland areas have coarse and loamy soils, with good-tomoderate moisture retention, that have been converted to built-up land, leaving less fertile land for agriculture. There is a sharp increase in land price due to the increase in demand for land for housing, and that has made agriculture less profitable, so agricultural lands are fast converting into built-up urban areas. Urban form and the structure of Indian cities have mostly evolved from the ancient urban core, with sprawling urban corridors penetrating the highly productive agricultural areas of the rural fringe. The twentieth century witnessed the rapid urbanization of the world’s population. The global proportion of urban population increased from a mere 13 per cent in 1900 to 29 per cent in 1950, and reached 49 per cent in 2005. Since the world is projected to continue to urbanize, 60 per cent of the global population is expected to live in cities by 2030. The rising numbers of urban dwellers give the best indication of the scale of these unprecedented trends: the urban population increased from 220 million in 1900 to 732 million in 1950, and is estimated to have reached 3.2 billion in 2005. Almost five billion people are expected to be urban dwellers in 2030 (World Urbanization Prospects 2006). It is essential to have updated information on urban growth patterns and their impacts upon the human environment. The current trend of urban spatial growth patterns is haphazard along the rural-urban fringe areas. This haphazard growth pattern in transitional rural-urban areas, along with the dense population of the urban core, is of great concern to urban administrators and planners. Unplanned land use change, such as conversion of agricultural land to residential or commercial uses, is proceeding without prior approval of the Delhi Development Authority (DDA) and planning departments. This presents an obvious need for periodic studies of growth patterns. In these situations satellite remote sensing, with its repetitive coverage and multispectral capabilities, is a powerful tool for mapping and monitoring the emerging changes in the urban core and peripheral areas of any urban entity. This same is true in the Indian context. Land-degradation assessment for 241 districts in India was carried out on a 1:50,000 scale, using Landsat TM data, IRS-LISS-II data, and using a 13-fold watershed classification (Rao et al. 1996). The loss of agricultural land because of rapid urbanization has been detected using remote sensing techniques in some cities of India (i.e., Hyderabad, Madras, and Nagpur; NRSA 1990, 1994). The situation is more severe in India than in some other countries due to the unplanned development of the cities in all directions. The spatial patterns of urban sprawl in all directions, over different time periods, can be systematically mapped, monitored, and accurately assessed using re-

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motely sensed data along with conventional ground data (Lata et al. 2001). Urban sprawl mapping on a 1:50,000 scale, with visual- and digitalinterpretation techniques, using the multi-date Landsat MSS, TM, and SPOT data (Pathan et al. 1993); and land-use change studies using Landsat MSS data from 1975 and 1982 (Rao et al. 1983) reveal that overall growth of built-up (both residential and industrial) areas has taken place in the fertile, peripheral rural areas. Urban sprawl can be monitored using satellite images of LISS-III or IV data, with the help of GIS software, using Shannon’s Entropy model (Rahman 2005a). This model compares built-up area to the total area at different time periods and calculates qualitative entropy value (En). It varies from 0 to logn; values closer to logn means sprawl has occurred (Yeh and Li 2001). 8.3.3 Land-use and land-cover mapping From the 1972 launch of the first “public” remote sensing satellite, Landsat I, land-use and land-cover studies were carried out on different scales for different users. Urban planners and administrators require detailed land-use and land-cover information on 1:25,000 or larger scales. So, as a part of the Remote Sensing Application Mission (RSAM), the Department of Space (DoS) has undertaken a major project to create land-use and landcover maps of all of India’s metropolitan cities. The National Remote Sensing Agency (NRSA 1995) provided a broad land-use and land-cover classification scheme for remotely sensed images for Delhi, on a scale of 1:12,500. There are ten land-use classes in Level One and 36 classes in Level Two (Table 8.3). This classification scheme is being used for all data from IRS, Landsat, and SPOT. The changes caused by human activities, particularly by encroachments, unplanned land-use change, and haphazard urban growth, can be mapped using remotely sensed data. Remote sensing can also be an important tool to assess both the negative and positive impacts on the environment caused by changes in urban land-use and land-cover. Based upon the data provided by satellites, suggestions can be made for alternative strategies or action plans that might solve current environmental problems. Satellite data can be used to acquire information about land-use, geomorphology, geological features, and hydrological features. Land-use maps derived from imagery can provide the baseline data upon which forecasts can be based, as well as estimates of the urban area and types of land-cover that would be affected by proposed development (Meaille and Wald 1990, Pathan et al. 1993).

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Table 8.3. Major land-use and land-cover classification scheme for Delhi 1.

2.

Residential i) Regular ii) Irregular iii) Squatter iv) Farm house Institutional i) Government offices ii) Educational Institutions iii) Other institutional areas

6.

Agriculture i) Cultivated land ii) Vegetable cultivation iii) Fallow land

7.

Forests i) Dense forest ii) Degraded forest iii) Plantation

3.

Commercial/Industrial 8. Waste land i) CBD/Shopping mall i) Degraded land ii) Other commercial areas ii) Abandoned land iii) Heavy industrial areas iii) Land with & without scrub 4. Historical/ Religious 9. Water bodies i) Fort i) River streams ii) Tombs ii) Canals iii) Ancient monuments iii) Lake/reservoirs iv) Mosques/Temples/Churches iv) Seasonal water logged areas v) Graveyard/Cemeteries v) Permanent water logged 5. Recreation/Parks 10. Others i) Regional parks i) Degraded forest ridge ii) Stadium ii) Vacant lands iii) Golf course/Race course Source: Modified after NRSA (1995). Note: Level II classification, details mappable at 1:25,000 scale, amenable to the interpretation of LANDSAT, IRS, and SPOT data supplemented by collateral and ground data

There has been large-scale transformation of land in Delhi since independence, but the magnitude of change was never as great as that which occurred during the last decade of the 20th century. This tremendous change resulted mainly from immigration, industrialization, and globalization. Many multinational companies (MNCs) have come to Indian metropolitan areas like Delhi, Bangalore, and Hyderabad and eaten up valuable, fertile, agricultural lands. Five major land-use and land-cover classes were made in ERDAS Imagine 8.6. Supervised classification was done using maximum-likelihood classification for ASTER data (cell size 15 m/pixel) of Delhi in September 2003. A flow chart of the methodology for land-use and land-cover mapping is shown in Figure 8.1. Two sites were selected, one in central Delhi and other in east Delhi (Trans Yamuna). The first site is located between 28q 31’-28q 36’N and 77q 10’-77q 15’E; the other between 28q 36’-28q 41’N and 77q 17’-77q 22’E (i.e., using a grid of 1.5 m by 1.5 m). These sites were selected with a view to comparing the land-use

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pattern between the old (central) and new (eastern) parts of the city. Accuracy assessment was done for classified data that shows 96.41% overall and 91.02% kappa accuracy for the central Delhi area, and 95.15% overall and 89.04% kappa accuracy for the Trans Yamuna area (eastern side of Delhi).

ASTER Satellite Images 2003

Preprocessing

Image Enhancement

Geometric Corrections

Supervised Classification

Maximum-Likelihood Method Post processing

Ground truthing Classification Accuracy Assessment

Land-use /land-cover map of Delhi

Fig. 8.1. Flow chart of methodology for the land-use and land-cover map

The classified land-use and land-cover map (Plate 8.4) shows that the central part of Delhi is sparsely built up, with less than 40% of built-up area. Open green space and forests/plantations/orchards occupy 39% and 17% of the area, respectively (Table 8.4). When compared with a classified map of the Trans Yamuna (Plate 8.5), it is evident that the built-up area of the Trans Yamuna is greater than in central Delhi, occupying 55% of the total area. In the Trans Yamuna, open green space and forests/plantations/orchards occupy 26% and 12% of the area, respectively. Density of built-up land is higher in east Delhi because of lower land values (current rates are Rs. 10,000 to 20,000/sq. mts.) than in central Delhi,

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where land is priced at over 2,50,000 Rs./sq. mts. ($1.00 = 44 Indian Rupees (Rs.)). Most of the construction in east Delhi is unplanned, i.e., not approved by the DDA, and many of these constructions are illegal. Planning regulations and checks are not effectively enforced, leading to the mushrooming of buildings on vegetated lands (Plate 8.5). The result is less open and green space, and hardly anyplace for children to play. Another feature of the Trans Yamuna and other fringe areas is that buildings are usually more than five stories, while in the central area they are mostly single story, with more than 60% of the plot area open and green, except at few commercial points. This leads to a significantly different environment (better) in central Delhi than in east Delhi. If we look at the classified image of the Trans Yamuna, we see that in the eastern part of the area there is less built-up land because city expansion is eastward; agricultural land will be filled up in the due course of time. Farmers are selling their vegetated and agricultural land to developers and builders at very high prices. They do so because agriculture has become a less profitable business in the current age of globalization; this is especially true when the demand for land is very high, as in the fringe areas of Delhi or in any other urban center in India where expansion is taking place. Table 8.4. Comparative land-use and land-cover of Delhi Land-use/land-cover

Built up lands Forests/plantation Open green/vacant lands Water bodies Others Total

Area in [km²] Central Delhi 28.74 12.65 27.91 0.07 3.12 72.86

Area in [% total] 39.45 17.36 38.31 0.61 4.28 100.00

Area in Area in [km²] [% total] Trans Yamuna 39.81 54.64 8.58 11.77 18.83 25.84 1.69 2.84 3.58 4.91 72.86 100.00

8.3.4 Urban change detection and mapping Urban areas are highly dynamic. Remote sensing can enable urban planners and decision makers to assess land-use conversions from agricultural to non-agricultural (i.e., residential, commercial, and industrial), loss of greenery (Plate 8.6) and water bodies, development along main transport routes and drainage lines, and changing quality of the urban environment. Detection of large-scale conversion of agricultural land into non-

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agricultural land has been useful for determining the extent of built-up areas on the day the government decides to acquire land (Uttarwar 2001). This technique has already been used in Delhi for the purpose of identifying the new, unauthorized colonies, slum clusters, and encroachments on government land that keep mushrooming all over Delhi. It is also useful in dealing with legal and compensation cases. Studies have been conducted in India on urban change detection using Survey of India toposheets at 1:50,000, Landsat MSS, TM, IRS LISS-I and II, and SPOT HRV data. With the use of IRS LISS-II False Color Composite (FCC), the transformation of agricultural land into residential and industrial land was carried out by the Kukatpally municipal area in Hyderabad City in 1990. Landuse/land-cover change detection mapping of Delhi has been done in ERDAS Imagine software and ArcGIS using Landsat TM and IRS LISS-III data from 1992 and 2004 respectively. Rapid conversion of agricultural land to non-agricultural uses has been recorded. The total agriculture land area was 65,214 hectares (45%) in 1992; this decreased to 54,152.6 hectares (37%) in 2004 (Singh and Singh 2007). Urban change-detection mapping can be done with digital image processing (DIP) software using satellite data from different time periods (Yuan et al. 1998). 8.3.5 Base maps for urban areas Base maps are important inputs to the planning and management of urban areas. Base maps at a scale of 1:4,000 that were prepared by ground surveys in 1969 and 1971 are available for some areas. But now base maps are being produced at scales ranging from 1:4,000 to 1:10,000, depending upon the specific urban applications for which they are prepared. Some urban development authorities have switched to using aerial photographs instead of conventional existing base maps (Rashid 1999). Cities like Bangalore, Delhi, Chennai, and Hyderabad have detailed base maps at a scale of 1:10,000 that were prepared from large-scale photographs (Raghavswamy 1994). Base maps can be made from orthophotographs for inaccessible areas that are difficult to survey, high-altitude towns like Leh Laddakh, Shimla, Nainital, and Massoorrie, and coastal cities like Panji, Puri, and Vishakhapatnam. In such situations, remote sensing has made information collection possible for base maps where field surveying has fallen short due to prohibitive factors such as cost, timing, and terrain. These base maps can provide the backbone for the development of information that was previously unavailable to the community, urban, regional, and natural resource planners and managers. IRS P-6 (multispectral) data with 2.5 m/pixel spatial resolution can meet the ever-growing demand for current,

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accurate base maps at a scale of 1: 5,000 for urban planning purposes and for developing new residential sites. 8.3.6 Urban hydrology A huge gap exists between demand and supply of drinking water in Delhi despite the availability of adequate water resources, and this gap is bound to rise further, even up to 6,000 million liters per day (MLD). Other concerns are the poor quality of the water supply, inefficient services, and inequities in water supply levels. Groundwater levels have been depleted by 2-6 m in the Alipur block (block is a smaller unit of district), 10 m in the Najafgarh block in west Delhi, and about 20 m in the Mehrauli block in southwest Delhi. A groundwater survey using remotely sensed satellite data has been done by NRSA, Hyderabad, as part of the National Technology Mission on Drinking Water (NTMDW). Mapping of potential groundwater resources has been carried out for the entire country. What is attractive about remote sensing is the possibility of monitoring water quality changes caused by urbanization, which may help urban development agencies like the Delhi Development Authority and other public health services to enforce regulatory control measures. With regard to wastewater, almost 46% of the population still does not have access to piped carriage systems. The problem is compounded further by malfunctioning sewer systems and blocked drains. The total volume of domestic wastewater generation is highest in Mumbai with 2,228 million l/day, followed by Kolkata with 1,383 ml/day, and Delhi with 1,270 ml/day. The sewerage also carries industrial effluents through the same drainage network as domestic waste. Unchecked domestic and industrial discharges of 211 tonnes/day into the Yamuna River have led to the significant depletion of dissolved oxygen levels in the Yamuna river stretch in Delhi (CPCB 2000). The use of remotely sensed data for surface water quality monitoring is well established and performed routinely in some water resource management areas. Parameters such as color of water, depth of water, turbidity, eutrophication, and water surface temperature can be recognized with remote sensing techniques. The extent of light penetration in different spectral bands provides information on water quality. Remotely sensed data are also being used to detect fluoride concentration in groundwater. The Water Index (WI) and Normalized Differential Water Index (NDWI) models can be used for change-detection mapping of urban surface water bodies using Landsat and IRS satellite data (Rahman 2005a). Satellite imagery can be used to locate domestic Sewage Treatment Plants (STP), and has been used to do so in 15 cities along the banks of the Yamuna River

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using drainage slope information from topographical maps together with the land use information obtained from the satellite data (Subuddhi et al. 2001). GIS techniques have been applied for groundwater-vulnerability mapping with the DRASTIC model in Aligarh City, using the weighted sum overlay method (Rahman 2005b). The DRASTIC model considers seven groundwater parameters: depth of water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity. Vulnerability ratings were assigned from one to ten and a weight multiplier between one and five was applied for each parameter; then the model was run in ILWIS 3.0 GIS software to produce ground water vulnerability maps. Indian cities face problems of insufficient water for domestic and industrial purposes, poor water quality, and inadequate urban stormwater runoff disposal. Models have been developed to assess runoff and water quality using remotely sensed data. Chakraborti (1989) discussed an approach for urban stormwater runoff modeling, water supply assessment, and water quality surveillance of Delhi, Najafgarh, Patna, and Hyderabad. The operational assessment of Hyderabad city has been dealt with using Landsat TM and IRS LISS-I and II data (Satyanarayana 1991). However, there is an urgent need to map potential water resource zones for all of Delhi, which is comprised of nine administrative districts; remotely sensed multispectral data can play a vital role for such a study. Remote sensing can be applied to drainage studies using proxies or surrogates (Campbell 1987). Satellite data have been successfully used to map surface drainage patterns. However, in applications such as mapping of tile lines, where high-resolution data are required, satellite data have been found to be mostly inappropriate (Barrett and Curtis 1992, Haralick et al. 1985, Merritt 1982). 8.3.7 Solid and hazardous waste Solid waste is a potential nightmare for India's large and growing urban population, due to inadequate policy and legislative instruments and to the deplorable organizational and financial capacities of local urban governments. The informal sector should be organized, and the private sector participate more widely in collection and recycling of solid waste (Joardar 2000). At present, municipal waste generation in Delhi is about 8,000 tonnes per day, with 0.61 kg generated per capita, per day; by 2021 it is anticipated to be 17,000-25,000 tonnes per day. About 40% of waste remains uncollected by the Municipal Corporation of Delhi. The current estimated quantity of infectious biomedical waste in Delhi is 20-25 tonnes per year, most of which finds its way (with or without treatment) into municipal

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waste dumps. Waste is disposed of in vacant, open plots in the absence of any proper disposal facility. The informal practice of waste collection by rag pickers and the recycling of various household, industrial, and commercial wastes reduce the burden. But hazardous waste poses a threat to the health of rag pickers, as well as to that of the general population which uses such recycled waste materials. In this context, the most acceptable strategy for solid waste management in Delhi is first to categorize waste streams as biodegradable, nondegradable, and recyclables. In Delhi, NGOs have started collecting waste door-to-door for a charge of $1 (US) per month in upper- to middle-class colonies, and they segregate it into organic and inorganic for disposal and recycling. Then the problem is where to dispose of it, and it is not easy to locate the disposal sites: a geospatial database generated from remotely sensed satellite data could be used to help solve this problem. Efforts should also be made to reclaim abandoned landfill sites. Attention should be focused on identification of suitable new sanitary landfill sites to isolate waste from human society and the ecosystem, and monitoring of the existing landfill sites for environmental impact assessment. Remotely sensed data can aid in identification and location of such landfill sites, and in monitoring the changes in land-use within and near hazardous waste and sanitary landfills (Radhakrishnan 1996). Land-use and land-cover analysis of part of Delhi has been carried out using IRS data to analyze the feasibility of locating a site for hazardous waste disposal in the NCR (Javed and Pandey 2004). IKONOS satellite data has been used to identify proper landfill sites and dustbin locations for garbage and solid waste management in Dehradun and Lucknow. Integrating data derived from IRS-IC, LISS III, and PAN merged imagery in a GIS environment has been useful to identify potential waste disposal sites for Ranchi Municipal Corporation (Shrivastava and Nathawat 2003). The overlay-index method can be used in GIS to identify the potential waste disposal sites using IRS, LISS IV, and PAN imagery with 5.8 m/pixel resolution, and ASTER visible to nearinfrared data with 15 m/pixel resolution. Path optimization can be carried out using a network analysis model in GIS for solid waste dumping. 8.3.8 Effective traffic management The transportation network is an important infrastructure element of the whole urban area. It allows connectivity and movement of people, traffic, and goods both within and between urban centers. Radar remotely sensed data can be used effectively for urban transportation network management. All roads with a width of 3 meters or more can be seen on high resolution

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(IKONOS) satellite data; such data facilitate the identification of roads that need to be widened to ease congestion (Plate 8.7). Using satellite images, road information can be updated and the approximate width of a road can be determined (Hazarike et al. 1998). Road width can be assessed using data from SPIN-2 with 2 m/pixel resolution, ADEOS Pan with 8 m/pixel resolution, SPOT Pan with 10 m/pixel resolution, ADEOS multispectral data with 16 m/pixel resolution, and Landsat TM with 30 m/pixel resolution. A 5 m wide road can be measured with a maximum error of 1 m using SPIN-2 data. A 35 m wide road varies from 34 m to 36 m in SPIN-2 data, giving a maximum error of 1 m. Width of the same road or road section varies from 32 m to 40 m and 30 m to 40 m in ADEOS Pan data and SPOT Pan data respectively, with a maximum error of 5 m in each case (Hazarike et al. 1999). New traffic signal sites can also be suggested based upon the analysis of these data. The effects of urban traffic on the environment in Jaipur, in terms of population affected by air and noise pollution, was studied using predictive and dispersion models in a GIS environment using 1998 data from IRS1C, LISS-III, FCC, and PAN. The study showed that a significant percentage of the population was effected by air (94.3%) and noise (34.8%) pollution (Maithani et al. 2002). About 52.7% of the total population residing in a 0-425 m buffer zone was affected by all air pollutants, and 41.6% of the total population living in 425-1500 m buffer zone was affected only by suspended particulate matter. Such data are vital for formulating strategies to mitigate traffic-related air and noise pollution hazards, such as mass transit, telecommuting, and enacting stricter automobile emission standards. 8.3.9 Greenhouse gases and urban heat island mapping Delhi, being the capital of India and a major commercial center, has a high density of human population and commercial activity, as has been discussed above. CO2 emissions from transport, residential, and power sectors in Delhi are 15.9 million tonnes annually. These emissions may rise to 32.6 million tonnes annually if the present rate of development continues without any major emission mitigation efforts (DUEIIP 2001). On the other hand, some steps have been taken, and others are being developed, that will limit emission levels to 22.5 million tonnes by 2021 without adversely affecting growth and development. Some of the steps that have been taken in this respect include adopting stringent Euro II/Euro III emission norms for automobiles, promoting clean fuels like Compressed Natural Gas (CNG), and phasing out vehicles more than 8 years old in the pub-

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lic transport sector. In the industrial and power sectors, all the capacity additions being proposed for the future are gas based. The growing number of vehicles (>3.5 million in 2005) in Delhi, and the increase in high-rise structures, lead to concrete jungles and reduction in trees and forest cover. With rapid industrialization and growth all around the city, there is general warming and change in weather conditions. Thermal infrared remotely sensed data with a bandwidth of 10.4-12.5 µm, available from Landsat (TM and ETM+), can be used to identify urban heat islands. Thermal infrared data acquired over urban areas during the day and at night can be used to monitor the heat island effect associated with urban areas, as well as atmospheric pollution (Eliasson 1992, Kim 1992, Cornelis et al. 1998; Chapter 6, this volume). To ascertain the influence of land-use and landcover categories and vegetation density on surface temperature in Delhi, Landsat 7 ETM+ and ASTER (band 10-14) datasets were used. These data were converted into spectral radiance; surface emissivity was also calculated by taking the proportion of vegetation cover per pixel in conjunction with NDVI, and surface temperature was estimated at the channel wavelength using Inverse Penk’s Law (Mallick 2006). 8.3.10 Urban infrastructure recreational and utility mapping Parks and playgrounds, schools and colleges, hospitals, public transportation, telephone lines, sewers and discharge pipes are some of the urban infrastructure and utility services. With the use of remotely sensed data, accurate information can be obtained for mapping such facilities (Plate 8.8). The IRS-1C and 1D data in panchromatic mode offer capabilities for mapping and analyzing urban transportation networks and urban greenery. Assessment of the Delhi population served by urban facilities and services has been done using 1:8,000 aerial photographs. SPOT and Landsat MSS data have been used to evaluate urban land use and transport systems in Delhi (Sokhi 1999). The well-known Konkan Railway on the western coast of India, and pipeline routing have also been studied using satellite data (NRSA 1993). Projects and Development India Ltd. (PDIL) Noida studied the optimum routing for the Chennai-Bangalore pipeline for Gas Authority of India Ltd. (GAIL), New Delhi. Two different methods of semi-automated alignment of pipelines using remote sensing and GIS tools have been developed and tested in Delhi. The cost in terms of budget and time for implementing such methods seems high at first glance, but the experiments carried out so far indicate the benefits of using such methods is high compared to the cost (Dubey 2003). Remotely sensed and GPS data have the potential to be highly use-

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ful in mapping urban infrastructure features such as recreational and utility facilities. The National Informatics Center (NIC) in Delhi has set up facilities for processing remotely sensed information with programs that utilize data from IRS and other satellites. Realizing the crucial role of utility mapping in the management and utility service systems, NIC established its Utility Mapping Group in 1989 with headquarters in New Delhi. The project was conceptualized with various departments including the Delhi Development Authority (DDA), Water Supply and Sewerage Undertaking, Delhi Fire Service, Delhi Traffic Police, Public Works Department, and Mahanagar Telephone Nigam Ltd. The group endeavors to map various utilities on large scale, common format, digital base maps. It also attempts to create a database of all the utilities integrated with the digital base maps for their proper management. All the base maps are stored in GINIS/MAPMAN software. Ancillary data related to various utilities such as water, sewer, and fire were collected from old records, plans, and maps.

8.4 Sustainable development and planning of Delhi Since the publication of the Brundtland Report in 1987, the concept of sustainable development has achieved worldwide recognition. Sustainable development is not simply a passing fashion, but rather a fundamental goal. If we are going to propose policies for achieving sustainability, we should have a clear understanding of the term. Sustainable development is development that meets the needs of present generations without compromising the ability of future generations to meet their own needs (World Commission on Environment and Development 1987). Sustainable development aims to improve environmental quality, especially in those areas that are degraded and highly polluted. The Habitat Agenda addresses the issue of social and economic participation, as well as organizational and political participation. For example, the Agenda's Global Plan of Action deals with "Adequate Shelter for All" and "Sustainable Human Settlements in an Urbanizing World." Equality, eradication of poverty, sustainable development, livability, family, civic engagement and government responsibility, partnerships, solidarity and international cooperation and coordination are goals and principles of the Habitat Agenda. The recent Master Plan, Delhi Perspective 2001 rightly described Delhi as the focus of the socio-economic and political life of India, and a symbol of ancient values and current aspirations. It is the capital of the world’s largest democracy, and assumes increasing eminence among the great cities of the world. To ensure planned development of the city, the Delhi De-

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velopment Act was enacted in 1957; the Master Plan for Delhi, completed in 1962, was the first exercise in comprehensive planning. The plan was modified in 2001 and renamed “Master Plan for Delhi Perspective 2001”, or MPD-2001 (Plate 8.9). It has been the framework for guiding development since then. The preparation of meaningful and effective development plans requires a variety of information, including social and physical data derived from up-to-date records and maps. The preparation of such databases and maps on required scales is, however, a gigantic task. The problem of keeping databases and maps up-to-date is an additional dimension because of the fact that in metropolitan cities like Delhi, urbanization is taking place at a rapid pace. DDA is currently engaged in extensive modification of the MPD-2001, and is preparing MPD 2021 to cater to the increasing population and changing requirements of the city. At present, hardly 30% of the urban centers in India have some sort of Master Plan, which in many cases is just a policy document and is only partially implemented. Keeping in mind the aspirations of India, an operational exercise was carried out in a major mission project called Integrated Mission for Sustainable Development (IMSD), where resource information is generated using remotely sensed and ancillary data sources (including ground verification). This experiment has yielded very encouraging results. The approach needs to be further refined, taking into account the need to identify indicators for sustainability. The project, which aims to improve the environmental conditions, monitor improvements through remote sensing, and assess the impact of implementation activities on the social fabric at the grassroots level, will demonstrate the utility and acceptability of using remote sensing techniques. For development planning, it is imperative to integrate the various data available from different sources - which may be on different scales - in different formats and projections. Data from different sources provide the scope necessary to explore and analyze the sustainable development of cities. The use of remotely sensed data in planning processes can facilitate the sustainable development of urban areas. Efforts to use remote sensing techniques to facilitate sustainable development in Delhi have been underway since 1990, but very little has been achieved. The Government of Delhi does not have the necessary personnel, so it has assigned the work to consulting companies that work for the Government on a project basis. Planning of expressway alignment in Delhi by using remote sensing techniques has been done (Javed et al. 1999). That work demonstrated that remote sensing is effective in planning alignments of big transportation projects like expressways, and that it could also be adopted in other metropolitan areas and large cities of India such as Mumbai, Chennai, Kolkata, Hyderabad, and Bangalore where traffic and transport

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management requires immediate attention. Delhi Metro Rail Transport Corporation (DMRTC) is working day and night to solve the urban traffic and transportation problem. Remote sensing techniques can play a role in acquiring data necessary for city development plans. But unfortunately, no single agency has all of the data related to land records and socio-economic conditions in a format in which is it accessible to all users. One has to move around from one office to another in order to acquire data. Existing survey and mapping practices have remained distant from modern spatial techniques that are used for studies related to social aspects of development. In India, whatever modernization in survey and mapping techniques has taken place has remained confined to the big national mapping and data collection organizations like Survey of India (SoI), Census of India (CoI), and the National Thematic Mapping Organization (NATMO). Thus, the benefits of such modernization have not percolated down to lower-level planning departments. In addition, some information is not readily and easily available because of organizational mandates, clearance requirements, and methods of data collection, map preparation, and storage. The result is that until now, most of the information generated at various levels could not be utilized properly and efficiently. During the last 10 years, the Indian Space Research Organization (ISRO) has been trying to generate a National Spatial Data Infrastructure (NSDI) that can be put in an open domain for the general public, but this goal has not yet been realized. The World Bank (Pieri et al. 1995) proposed a long-term urban development strategy establishing a National Capital Region (NCR) of Delhi, to cater to the growing population and to solve a number of related problems by adopting a comprehensive planning approach.. The proposed NCR covers an area of 30,242 km², including 1,483 km² of Delhi itself with the remainder in neighboring states: Haryana 13,413 km², Uttar Pradesh 10,853 km², and Rajasthan 4,493 km². The Delhi government is trying to reduce the heavy pressure of population growth on Delhi’s environment by encouraging emigration to the satellite townships of the NCR, such as Meerut and Noida in Uttar Pradesh; Faridabad, Gurgaon, Rohtak, and Sonipat in Haryana; and Bharatpur in Rajasthan. Once population pressures have been reduced by establishing an urban-rural link through satellite townships, then pressure on exiting resources will also go down. In fact, the higher-income people now prefer to migrate to new townships like Dwarka and Greater Noida in the NCR region. Properly planned development of the NCR can be accomplished with the help of remote sensing and GIS-based techniques such as accurate land–use/land-cover mapping, and identification of suitable sites for housing, marketplaces, recreational facilities, and transportation networks.

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8.5 Conclusions The problems and challenges faced by mankind are global, but they have to be dealt with at the local level. To tackle the problems of environmental degradation and meet the challenges of sustainable development, remote sensing and GIS are tools of vital importance. The critical issues and challenges of sustainable urban developmental and management of growing urban centers like Delhi and Mumbai have been the subject of extensive discussion and debate in recent years. The major problems associated with urban areas in India are lack of developed urban land-use plans; and loss of natural resources in fringe areas, for example, loss of water bodies, flora, fauna, and productive agricultural land. The high rate of population growth in Delhi has resulted in increased demand for basic services and infrastructure facilities. Satellite remote sensing, with repetitive and synoptic viewing capabilities, as well as multispectral capabilities, is a powerful tool for mapping and monitoring the ecological changes in the urban core and in the peripheral areas of any urban center. Sustainable urban design, including appropriate land-use planning, will help to reduce unplanned urban sprawl and the associated loss of natural surroundings and biodiversity. Sustainable construction methods will promote comfort, safety, accessibility, and a good quality of life. The preceding sections of this chapter demonstrate that remote sensing is an effective and powerful tool for the management and potential amelioration of urban and regional spatial problems in Delhi, indeed in all of India. Realizing the potential of this tool, the state and national governments of India are making use of remotely sensed data in nearly all plans for sustainable development. These plans include the Integrated Mission for Sustainable Development (IMSD); ISRO; Department of Space block-level (local) databank generation using remotely sensed data under the National Natural Resource Management (NNRMS) program of the Department of Science and Technology; Ministry of Science and Technology, and Biodiversity Characterization at the Landscape Level of NRSA. Programs are mainly implemented by NRSA and supported by the Department of Space, Government of India, and other sister organizations. There is evidence that major environmental problems occur because of uncontrolled and mismanaged urban expansion that leads to a dramatic rise in air, water, noise, and solid waste pollution. Management of huge volumes of garbage and solid waste, including medical waste, is very difficult and this tends to lead to unhealthy conditions in urban areas. Satellite data, together with GIS, can help identify potential waste disposal sites and monitor changes in land-use within and near hazardous waste and sanitary landfills. Land-

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degradation assessment can also be done using Landsat TM and IRS-LISSII data. The effects of traffic on the urban environment, in terms of population affected by air and noise pollution, can be assessed using inversedistance point-interpolation models in GIS. Thermal infrared data can be used to monitor urban heat islands and areas with high atmospheric pollution. SPOT and Landsat MSS data can be used to evaluate urban transportation systems. High resolution satellite imagery (IKONOS, Quickbird) can be used to generate detailed base maps, and help monitor urban expansion and illegal housing constructions over a period of time. Cartosat-1 data can be widely used for cartographic and large-scale base map preparation for urban areas. Cartosat-2, recently launched in January 2007, is an advanced remote sensing satellite capable of providing scene-specific spot imagery. It carries a panchromatic camera to provide imagery with a spatial resolution of better than one meter and a swath width of 9.6 km. The data from this satellite can be used for cartographic applications at the cadastral level; urban and rural infrastructure development and management; and applications in GIS and Land Information Systems (LIS). A database generated from remotely sensed imagery in DIP software, and thematic maps prepared using socio-economic data in a GIS environment, could be helpful for sustainable development planning and good governance of Delhi and other cities. The Master Plan of Delhi is a design for the physical, social, economic, and political framework for the city, which could greatly improve the quality of the urban environment. 8.5.1 Recommendations Recommendations for policy measures that could be taken up on a priority basis to improve Delhi’s environment and promote its sustainable development are as follows: x

x

Links should be established and coordination take place among the Government of India, Delhi Government, Delhi Development Authority, Municipal Corporation of Delhi, National Capital Regional Planning Board, New Delhi Municipal Corporation, and other departments to work together for the sustainable development of Delhi. Use urban information databases that have been generated using remote sensing and GIS techniques.

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x

x

x x

x

Improve the expertise of the administrative, technical, and managerial staff of the urban local government bodies through continuing training of existing, and recruitment of new, qualified personnel. Personnel from DDA, MCD, NDMC, and urban planning departments should be given thorough training in the use of remote sensing and GIS for application in urban environmental management plans. Establish a remote sensing and GIS capability in every planningrelated government department. Top priority should be given to issues of urban sustainability, for example: planned development, pollution, traffic congestion, and commuting time. Recognize that remote sensing and GIS provide essential information for formulation and execution of any urban project in a sustainable manner.

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8.6 References Barrett EC, Curtis LF (1992) Introduction to environmental remote sensing (3rd Ed). Chapman Hall, London Bryant CR, Russwarm LH, McLellan AG (1982) The city’s countryside: Land and its management in the rural-urban fringe. Longman Group Ltd., New York, NY Campbell JB (1987) Introduction to remote sensing. Guilford Press, New York, NY Carlson TN, Arthur ST (2000) The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and Planetary Change 25:49-65 Census of India (2001) District census handbook. New Delhi, India, http://www.censusindia.net/ accessed 6-March-2007 Chakraborti AK (1989) Role of water management in urban settlement in India: A remote sensing based assessment. National Remote Sensing Agency, Hyderabad Technical Report 40:1-12 Cornelis B, Binard M, Nadasdi I (1998) Potentials urbains et ilots de chaleur. Publications de l’Association Internationale de Climatologie 10:223-229 CPCB (2000) Water quality status of Yamuna River. Central Pollution Control Board, New Delhi Annual Report ADSORBS 32 (99) Daniels TL (1997) Where does cluster zoning fit in farmland protection? Journal of the American Planning Association 63 (1):129-137 Dubey RP (2003) A remote sensing and GIS based least cost routing of pipelines. GIS Development 7 (9), http:// www.gisdevelopment.net/application/utility/transport/utilitytr0025.htm, accessed 6-March-2007 DUEIIP (2001) Delhi Urban Environment and Infrastructure Improvement Project (DUEIIP), Ministry of Environment & Forests and Government of National Capital Territory of Delhi (Planning Department), New Delhi Eliasson I (1992) Infrared thermography and urban temperature patterns. International Journal of Remote Sensing 13:869-879 Ewing R (1997) Is Los Angeles-style sprawl desirable? Journal of the American Planning Association 63 (1):107-126 Fritz LW (1999) High-resolution commercial remote sensing satellites and spatial information systems. ISPRS Highlights 4 (2):19-30 Garry G (1992) Environment et aménagement: L’usage des photographies aériennes. Service Technique de l’Urbanisme, Paris-La Défense, Éditions du STU Haralick RM, Wang S, Shapiro LG, Campbell JB (1985) Extraction of drainage networks by using the consistent labeling technique. Remote Sensing of Environment 18:163-175

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Hazarike MK, Samarakoon L, Kiyoshi H (1998) Application of remote sensing and GIS for road (Asian Highway) network database creation and risk assessment. Proceedings of the 19th Asian Conference on Remote Sensing, Nov. 16-20, Manila, Philippines, http:// www.gisdevelopment.net/aars/acrs/1998/ts11/ts11003.asp, accessed 6-March2007 Hazarike MK, Kiyoshi H, Samarakoon L, Murai S (1999) Application of remote sensing for extraction of road information. Proceedings of the 20th Asian Conference on Remote Sensing, Nov. 22-25, Hong Kong, China, http://www.gisdevelopment.net/aars/acrs/1999/ps1/ps1101.asp, accessed 6March-2007 Javed A, Pandey S (2004) Land use/land cover analysis for waste disposal. GIS Development 8 (6), http:// www.gisdevelopment.net/magazine/years/2004/jun/landuse.asp, accessed 6March-2007 Javed A, Bansal S, Murtaza H (1999) Planning of expressway alignment by using remote sensing techniques-a case study of Delhi. Indian Journal of Transport Management 23 (12):725–735 Jayakumar S, Arockiasamy DI (2003) Land use/land cover mapping and change detection in part of Eastern Ghats of Tamil Nadu using remote sensing and GIS. Journal of the Indian Society of Remote Sensing 31 (4):251-260 Jensen JR (1983) Urban/suburban land use analysis. In: Colwell RN (ed) Manual of Remote Sensing (2nd Ed), American Society for Photogrammetry and Remote Sensing, Falls Church, VI, pp 1571-1666 Joardar SD (2000) Urban residential solid waste management in India: Issues related to institutional arrangements. Public Works Management & Policy 4 (4):319-330 Kim HH (1992) Urban heat island. International Journal of Remote Sensing 13 (12):2319-2336 Lata KM, Prasad VK, Badarinath KVS, Raghavaswamy V, Rao CHS (2001) Measuring urban sprawl: A case study of Hyderabad. GIS Development 5 (12), http://www.gisdevelopment.net/application/urban/sprawl/urbans0004.htm, accessed 6-March-2007 Maithani S, Sokhi BS, Subudhi AP, Herath KB (2002) Environmental effects of urban traffic - A case study of Jaipur City. GIS Development 6 (12), http://www.gisdevelopment.net/application/utility/transport/utilitytr0023.htm, accessed 6-March-2007 Mallick J (2006) Satellite-based analysis of the role of land use - land cover and vegetation density on surface temperature regime of Delhi, India. MS thesis, ITC Meaille R, Wald L (1990) Using geographical information system and satellite imagery within a numerical simulation of regional growth. International Journal of Geographical Information Systems 4:115-156

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Merritt ES (1982) The role of resource information companies: Bridges to application. In: Johannsen JJ, Sanders JL (eds) Remote sensing for resource management, Soil Conservation Society of America, Ankeny, IA, pp 479-489 NRSA (1990) Mapping and monitoring urban sprawl of Madras. Unpublished project report, National Remote Sensing Agency, Balanagar, Hyderabad, pp. 1-55 NRSA (1993) Studies on Konkan Railway alignment using remote sensing and ancillary data, Maharashtra Section (380km), Unpublished project report, Ministry of Railway, Government of India, New Delhi NRSA (1994) Mapping and monitoring urban sprawl of Hyderabad. Project report, National Remote Sensing Agency, Balanagar, Hyderabad, pp 1-85 NRSA (1995) Report on area statistics of land use/land cover generated using remote sensing techniques, India. Project report, National Remote Sensing Agency, Balanagar, Hyderabad, pp 19–23 NRSA (2005) National Remote Sensing Agency Quarterly Newsletter 2 (2), Balanagar, Hyderabad Pathan SK, Sastry SVC, Dhinwa PS, Rao M, Majumdar KL, Kumar DS, Patkar VN, Phatak VN (1993) Urban growth trend analysis using GIS techniques - a case study of the Bombay metropolitan region. International Journal of Remote Sensing 14 (17):3169-3179 Pieri C, Dumanski J, Hamblin A, Young A (1995) Land quality indicators. Discussion Paper 315, World Bank, Washington, DC Radhakrishnan K, Adiga S, Varadan G, Diwakar PG (1996) Enhanced geographic information system application using IRS-1C data - Potential for urban utility mapping and modeling. Current Science 70 (7):629-637 Raghavswamy V (1994) Remote Sensing for urban planning and management. In: Guatam NC, Raghavswamy V, Nagaraja R (eds) Space technology and geography, NRSA Publishers, Hyderabad, India, pp 295-323 Raghavswamy V, Pathan SK, Mohan PR, Bhanderi RJ, Priya P (1996) IRS-1C applications for urban planning and development. Current Science 70 (7):582588 Rahman A (2005a) Urban sprawl and its environmental impact assessment (EIA) of twin city Hyderabad-Secundrabad using remote sensing and GIS techniques. Project Report, Center for Space Science and Technology, India Institute of Remote Sensing, Dehradun Rahman A (2005b) Assessing ground water quality and vulnerability to pollution in shallow aquifers of Aligarh city using GIS technique. Project Report, Department of Science and Technology, Government of India, New Delhi Rao M, Thakker PS, Jaleja (1983) Urban land use change in Ahmedabad city using Landsat MSS data. Proceedings of the International Symposium on Socioeconomic aspects of Remote Sensing, Yokyakarta, Indonesia Rao DP, Gautam NC, Nagaraja R, Mohan PR (1996) IRS-1C applications in land use mapping and planning. Current Science 70 (7):575-581

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Rashid SM (1999) Remote sensing of urban environment. In: Sokhi BS, Rashid SM (eds), Remote sensing of urban environment, Manak Publications Ltd., Delhi, p 6 Satyanarayana R (1991) Remote sensing studies of the land and water resources of Hyderabad city and its environs. PhD thesis, Sri Venkateswara University Shekhar S (2004) Urban sprawl assessment entropy approach. GIS Development 8 (5), http://www.gisdevelopment.net/magazine/years/2004/may/urban.asp, accessed 6-March-2007 Shrivastava U, Nathawat MS (2003) Selection of potential waste disposal sites around Ranchi urban complex using remote sensing and GIS techniques. Proceedings of the Map India 2003 Conference, Jan. 28-31, New Delhi, India, http://www.gisdevelopment.net/application/utility/transport/mi03203.htm, accessed 6-March-2007 Singh A, Singh B (2007) Spatio-temporal analysis of land use/land cover transformation of Delhi-A remote sensing approach. Unpublished project report, Department of Geography, Jamia Millia Islamia, New Delhi Sokhi BS (1999) Remote sensing in urban land use structure-transportation system relationship: a case study of Delhi. In: Sokhi BS, Rashid SM (eds) Remote sensing of urban environment, Manak Publications Ltd., Delhi, pp 174-195 Subuddhi AP, Sokhi BS, Roy PS (2001) Remote sensing and GIS application in urban and regional studies. Indian Institute of Remote Sensing, Dehradun Sur U, Jain S, Sokhi BS (2004) Identification and mapping of slum environment using IKONOS satellite data: A case study of Dehradun, India. Proceedings of the Map India 2004 Conference, Jan. 28-30, New Delhi, India, http://www.gisdevelopment.net/application/environment/pp/pdf/mi04011.pdf, accessed 6-March-2007 Tiwari DP (2003) Remote sensing and G.I.S. for efficient urban planning. Proceedings of the Map India 2003 Conference, Jan. 28-31, New Delhi, India, http://www.gisdevelopment.net/application/urban/overview/ma03224.htm, accessed 6-March-2007 Uttarwar PS (2001) Applications of GIS and remote sensing in urban planning, implementation and monitoring of urban projects - Case study of Rohini and Dwarka project, New Delhi. Proceedings of the Map India 2001 Conference, Feb. 7-9, New Delhi, India, http://www.gisdevelopment.net/application/urban/overview/urbano0015.htm, accessed 6-March-2007 Yuan D, Elvidge CD, Lunetta RS (1998) Survey of multispectral methods for land cover change analysis. In: Lunetta RS, Elvidge CD (eds) Remote sensing change detection: Environmental methods and applications, Ann Arbor Press, Chelsea, MI, pp 21-39 Yeh AG-O, Xia L (2001) Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogrammetric Engineering & Remote Sensing 67 (1):83-90

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World Commission on Environment and Development (1987) Our Common Future. Oxford University Press, Oxford World Urbanization Prospects (2006) The 2005 Revision Executive Summary. United Nations New York, NY, http://www.un.org/esa/population/publications/WUP2005/2005wup.htm, accessed 6-March-2007

Chapter 9 - Berlin (Germany) Urban and Environmental Information System: Application of Remote Sensing for Planning and Governance - Potentials and Problems

Thomas Schneider1, Manfred Goedecke1, Tobia Lakes2

1

Berlin Department of Urban Development, Unit III F Urban and Environmental Information System, Berlin, Germany

2

Department of Geomatics, Institute of Geography, Humboldt University, Berlin, Germany

9.1 Introduction In accordance with the resolutions of the United Nations Conference on the Environment and Development in Rio de Janeiro in 1992, the state and federal capital of Berlin has made the principle of sustainability a guideline for its further development. This principle, in addition to socially just and economically sustainable development, contains a third component: The development of the environment in a way that ensures the long-term protection of our basis of life. To accomplish this objective, good planning and governance are essential. The crucial prerequisite for those however, is reliable information on the condition and development of the environment. Different information technologies for collecting and analyzing environmental information are in use to support planning and governance. Remote-sensing technologies can

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contribute by providing up-to-date and area-wide data on different aspects of the environment. In addition to the traditionally used aerial photography, new airborne scanner and satellite data with steadily increasing spatial resolution opens up new application fields in urban areas. Until recently, the actual application of these new remote-sensing data and methods to planning and governance has been hindered by complex technological, organizational, financial, legal, and social factors. Today, different initiatives, as well as developments in web-based technologies, provide the basis for a new generation of environmental information systems to assess, communicate, and exchange environmental data. Recent developments in public administration and planning have the potential to improve integration of remote-sensing data in urban and environmental information systems used for planning and governance. The main tasks of remote sensing are to obtain environmental information, and to support the expert knowledge of specialists in a coherent and transparent way.

9.2 Berlin urban and environmental information systems Urban and environmental information systems have been used in Germany since the 1980s; the Berlin Environmental Atlas was one of the first. These systems perform various support functions for urban planning and governance. Urban and environmental planning processes require continual monitoring. Urban planning (e.g., the development of master plans, urban development plans, and local plans) must balance competing demands for space and ensure compromise among interests. Detailed understanding of the current land-use situation is also needed for the tasks and issues of landscape planning (e.g., Program for the Protection of Land and Endangered Species, landscape plans). For example, evaluation of a community’s needs for recreational opportunities near to home requires information about the location of residential areas, inhabitants, and open spaces. The close proximity of certain polluting activities to sensitive areas, such as commercial activities near residential areas, or allotment gardens in the neighborhood of commercial areas, can indicate existing conflicts (noise pollution, air quality, heavy-metal pollution of the soil), and strategies for solutions can be developed. A basic understanding of all components of the city corpus is important for the development of tools for ecological planning, i.e., for soil concept maps, biotope typing, or the delimitation of climatic zones. Specific planning instruments, such as the legally required environmental impact assessment (EIA), require a reliable database for all components of the envi-

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ronment (soil, water, air, climate, energy, noise, biotopes). The aim of the EIA is to examine a specific construction project while still in the planning stage with respect to its probable environmental impact. Measures to compensate for the damage to nature and the landscape must be carried out and monitored. Water management and water-resource operations oriented toward sustainability require precise knowledge of surface runoff, percolation, and groundwater recharge. The Berlin area has limited water resources so balancing the water economy is particularly important. For water-economic issues and for the calculation of percolation, the influence of sealing and the sewage system must be considered as a crucial effect on the water supply of urban areas. Basic data for these tasks of planning and governance in Berlin must be provided by the Environmental Information System. 9.2.1 Definition and aims For Berlin, the different components of environmental data are summed up in an Urban and Environmental Information System (UEIS) (Fig. 9.1). The aim of this UEIS is to prepare, store, assess, and graphically present environmental data for planning and governance. A geographic information system (GIS), and an expert GIS such as the Berlin UEIS as well, comprises hardware, software, data, and intended users. The users include workers in different levels of administration, planning, and governance; and members of the private sector, civil society, non-governmental, and non-profit organizations. The role of government planners has changed in recent years. Planners are now seen as mediators in a cooperative management process. Egovernment initiatives have modernized government administration. These changes depend upon information processing and exchange on vertical levels, among groups from local to European Union levels, as well as on horizontal levels, among various experts and department sections. Legal requirements of new European Union directives, in particular, impose new challenges for managing environmental information; for example, the Water Framework directive, the Strategic Environmental Assessment for plans, programs and policies, the Flora-Fauna-Habitat directive, and the directive on open public access to environmental information. The municipal government has stored and distributed extensive amounts of heterogeneous environmental data since the 1980s (Fig. 9.2). The Berlin Digital Environmental Atlas represents one of the first systems to support planning and governance in Germany (Berlin Department of Urban Development 2004a).

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Fig. 9.1. Structure of the Berlin Urban and Environmental Information System (UEIS)

Networking of users User - and service -orientation Interoperability

Fig. 9.2. Development of the Berlin Urban and Environmental Information System (UEIS)

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Based upon the 1985 printed version of the Berlin Environmental Atlas, a digital and web-based system was introduced in 1995. A number of other specialized, web-based, expert systems, such as the “Open-Space Information System” (urban green areas), have been developed in Berlin during recent years. The most important challenges to these systems were to improve data quality with regard to spatial, temporal and contextual resolution, and to provide better access to the data. The limited ability of users to estimate the availability and applicability of the dataset was a further challenge. Decentralized data capture, heterogeneous systems of different departments without data overview and processing tools, and different data formats were significant constraints. In the late 1990s, the problems of the earlier systems were partly addressed by science and practice focusing on meta-information, which created procedures and techniques to systematically collect, document, and provide digital environmental data. In Berlin, the first version of the Fachübergreifendes Informationssystem (FIS-Broker) comprised such a meta-information system. Other environmental meta-information systems have been developed on all levels of the German administration and on various specific topics, for example, the Environmental Data Catalogue (UDK). These systems and their data needed to be linked by an overall system, the German Environmental Information Network (GEIN), which has been accessible through the Internet since 2000. Technical development of modular, web-based systems, for exchange of data and services made possible a new generation of environmental information systems. The FIS-Broker in Berlin (Berlin Department of Urban Development 2004b) is an example of such a system. It builds upon a complex structure of heterogeneous databases and systems resulting from the earlier development of environmental information systems. FIS-Broker has a growing networking of users, higher user- and service-orientation, and greater interoperability than earlier systems. It has become increasingly important to develop concepts and methods to integrate growing networks of data systems with the demands and requirements of data providers and data customers. New initiatives to develop a spatial-data infrastructure at different administrative levels are underway (Greve 2002), such as the European program, “Infrastructure for Spatial Information in Europe.” In Berlin, as in other German states, such development will strongly influence the use of environmental information and also the application of remote-sensing data. Organizational, technological, social, contextual, financial, political, and legal changes have influenced development of the new information system. A main focus has been standardization, because this is the prerequisite for exchange of data

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and services. With the technical development of environmental information systems, a shift in standardization can be observed (Table 9.1). Table 9.1. Development of GIS and Standards Development of GIS Geodata

Development of standards 1985

Proprietary systems and services, 1990 linking single PCs Web-based systems

1995

Data conversion Standardized open data exchange formats (dxf), quasi-standard data formats (shape, Yade) APIs (ArcSDE), OGC Simple Feature Specification for SQL

ISO 19115, OGC web services Web-based, open, modular sys- 2000 tems and services, metainformation Web-based GIS + CMS single in devel- in development solution opment Note: dxf,shape,YADE: formats of GIS-systems, API: Application Programming Interface, ArcSDE: “middleware" software package made by ESRI, SQL: Structured Query Language, CMS:Content Management System, ISO: International Standard Organization, OGC: Open Geospatial Consortium

In the data-centric approach, data conversion tools and adapters were the main issue. Then proprietary GIS, such as Yade in the Berlin administration, imposed a “quasi-standard” that conflicted with other “quasistandards,” such as ESRI in the private and scientific sectors. In recent years, the shift towards open GIS and exchange of geo-services has been supported by the standardization committees Open Geospatial Consortium (OGC) and the International Standard Organization (ISO). A possible future trend might be the development of standards for communication and exchange of environmental information. Egovernment initiatives may also contribute to such development. Initiatives such as the Berlin eGovernment have successfully addressed environmental issues in planning and government. One goal should be to integrate data and systems in the operational processes of planning and governance and not to stress the exclusiveness of environmental data. New technological developments are reflected in changing goals and methods of urban and environmental information systems. The following two examples show different generations of information systems in Berlin.

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9.2.2 The Berlin digital environmental atlas The Berlin Digital Environmental Atlas (BDEA) systematically presents and integrates basic urban and environmental data. The approximately eighty topics and more than 400 maps are organized under the subjects of soil, water, air, climate, land use, traffic, noise, and energy. Pollution and dangers, as well as existing positive qualities and development potentials, are included. (See Plate 9.1 for an example of BDEA map presentation.) At a scale of 1: 50,000, a Berlin-wide overview of different environmental issues is provided, and supported by careful data evaluation, interpretation, and documentation in text and figure. The existence of editions from different years allows changes in the environment to be followed over time. Most data are generated by sections of the Berlin Senate Department of Urban Development. Additional measurements and reports from other departments, universities, and colleges, and from the numerous scientific institutions located in Berlin, have been processed. All this data is being stored and updated on a central server within the administration of the Senate Department of Urban Development. Web-based access to the data is built upon interactive html pages, controlled by simple user windows which allow the selection of fundamental map parameters. Different areas can be selected, specific topics and maps can be presented, and the level of detail can be adjusted. However, large-scale analyses requiring more details are limited by data quality in terms of spatial, temporal, and contextual resolution. The small scale of 1:50,000 and the block-wise reference units frequently are too coarse for planning tasks, and the specific geometry does not allow overlaying other data sets. Due to growing budget deficits, the time- and cost-intensive updating and organizing of the centralized data has been contracted to the lowest level, resulting in some outdated data. Furthermore, the main data transfer is restricted to downloading pictures (gif format) and printing in pdf format, without being able to further process the data. Transfer of digital data is confined to traditional-post mail or downloading from the server. Another challenge is to overcome the problems linked to the proprietary Yade format, which cannot be converted to other prevailing GIS, such as ESRI, without data loss. To sum up, the focus of the Environmental Atlas is on information storage, with communication and transfer of environmental information only recently being considered. A serious challenge is to update such a large number of datasets. Remote-sensing technologies can provide solutions to the problem of data collection.

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9.2.3 FIS-broker FIS-Broker, a key part of the UEIS, has been developed to address the problem of heterogeneous and distributed data. Initiated in 1999, it has been accessible on the Berlin municipal intranet since 2000, and on the Internet since 2004 (Berlin Department of Urban Development 2004b). Exceeding the environmental information and meta-information system functions by offering a broker solution with a modular and serviceoriented design, FIS-Broker can be considered a new-generation system. Its aim is to make information and specialized systems accessible and available to the Berlin public administration and, in part, to the public. FIS-Broker enables access to data and services via its core tool, a metainformation system which describes, searches, and retrieves information. Data suppliers document, distribute, and update their own data, enabling the system to handle more, and better-quality, data than its predecessors. The user has direct access to the distributed datasets, services, and systems via a standard web browser, without the need for a specialized GIS (Plate 9.2). Several services, including data inquiry, requests, and viewing and overlaying of datasets, are provided. These services can be linked, staggered, and combined, and they can also be started from different expert systems or linked with content-management systems. The Berlin Environmental Atlas, for example, uses the advanced functions of the FIS-Broker for the search of local addresses or coordinates. GIS and database adapters allow geodata from different GIS systems, different coordinate systems and scales, different spatial references, and numerical and alphanumerical data from different data base systems with different spatial references, to be presented. The major benefit of the system is improved data access: the data is better-documented and can be searched more easily. Services that enable simple analysis functions, such as overlaying different datasets or calculating areas, are provided and frequently used. To date, use is limited by the number and type of integrated datasets, mainly due to financial and legal reasons. Although they focus on interoperability, neither the included standards nor the adapters allow data exchange without data loss. Use is also limited by the small number of available services; more sophisticated analysis functions are not possible. Besides, using the web-browser restricts further processing of the output, because the Internet-user receives a picture in gif format and no geodata that can be further processed. Another problem that needs to be solved is slow system performance. The FIS-Broker is an example of the new generation of environmental information systems and of a good approach for integrating heterogeneous and distributed data and systems. Although still under development, it al-

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ready improves data access and exchange. However, more sophisticated applications and analysis are restricted by the data and services included. 9.2.4 Geo-data and geographic information systems Environmental and planning data usually refer to concrete locations or areas. Different spatial references are used to link the geo-basis and specific thematic geo-data. In Berlin, there is a choice between the cartographic database Automatisierte Liegenschaftskarte (Automated Map of Properties, ALK), at a scale of 1:1,000, with block maps at 1:5,000 and 1:50,000; or the Amtliches Topografisches Kartographisches Informationssystem (Official Topographic Cartographic Information System, ATKIS), at a scale of 1:25,000. An additional graphic representation of statistical blocks is derived from the Regionales Bezugssystem Berlin (Berlin Regional Reference System, RBS) of the Statistisches Landesamt (State Statistical Agency). It has been supplemented with block segments with homogeneous use. RBS can also be used for locating environmental data at precise addresses. In addition to this topographic data, a large number of thematic databases exist, and are provided in various GIS formats. These maps include, for example, those of the Berlin Environmental Atlas, the Monument Map, the map of old contaminated sites, maps of the Central Construction Planning Office, and data for the implementation of the EU Water Framework Directive. Another important basis for decision-making in planning, investment and construction proposals, and evaluation of redevelopment priorities is the Soil Pollution Register, which assembles information on polluted or potentially polluted soils. Comprehensive expert data are drawn from sources in several departments, such as the CO2 register, which links data from the emissions register, energy providers, the State Statistical Agency, and the UEIS. In addition to these digital geo-databases, more information is collected in databases on, for example, law, environmental literature, and GSBL (Joint Database for Materials of the federal and state governments). 9.2.5 GIS and the internet The main task of an environmental information system is to facilitate the use of environmental information with geo-processing tools and services. The evaluation, cross-computation, and spatial differentiation of numerous indicators require the development of expert methods. Visualization of data and preparation for printing require additional services. A run-off

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formation model, for example, enables the precise calculation of important dimensions of the water economy, such as surface run-off and percolation, for very small areas throughout the entire urban area. Another possible application is habitat network planning, which is compulsory in Germany with the new Federal Conservation Law (BNatSchG). It requires the analysis of the prevailing criteria, which can be derived from biotope-mapping supported by remote-sensing data via GIS. With the growing importance of the Internet, web-based services open up new possibilities for information provision, communication, and interaction among users of environmental information in planning and governance. The importance of the Internet is increased by current legal requirements such as the Free Environmental Information Act of the European Union, and the Convention on Open Access to Environmental Information and Public Participation of the UN ECE (Århus Convention). Such legal regulation forces all member states of the European Union to provide free access to all environmental information stored in public institutions. Furthermore, one important aspect of sustainable development, the participation of the civil society, can be strongly supported by web-based systems.

9.3 Application of remote-sensing data The following section describes several UEIS remote-sensing data applications to clarify the problems which are associated with the use of remotesensing data. We focus on the usability of remote-sensing data under the specific conditions and demands of the existing Berlin UEIS mapping type. The detailed differentiation of urban structures requires a high level of methodological and technical accuracy with regard to data and resolution, which in some cases is not yet available. 9.3.1 UEIS mapping of land use Over the course of time, a multilayered structure of construction and open space has emerged in Berlin. The various urban structural types must be delimited and described. They are based on various area types, which are defined according to their typical use, time of origin and construction, and open-space structure. Knowledge of structural types is an essential basis for all urban development and landscape-planning standards, both at local and higher levels. This knowledge also permits information to be derived regarding the formation of biotopes and vegetation structures, climate relationships, condi-

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tion of the soil, degree of soil impermeability, and the new formation of groundwater. The data on actual land use are stored in a use database. This database contains, in addition to a statement about the actual land use of a block or a block segment (a total of 25,000 area units), additional information about the area size, area type, sealing degree (percentage of imperviousness of the soil surface), and distribution of differently permeable surface types. 9.3.2 Area types Area types of predominantly residential use and those with other uses are differentiated (Berlin Department of Urban Development 2002a, 2002b). The area types of predominantly residential use are further subdivided according to their typical construction and open-space structure, and their date of construction. In addition, each area type is described by its percentage of imperviousness, percentage of built-up surface area, and distribution of differently permeable surface covers. Altogether, sixty different area types have been assessed on the basis of the categories in the UEIS records. The categories correspond to the statistical blocks, which are further subdivided into block segments with homogeneous use. The assignment to structure types is shown in Figure 9.3. 9.3.3 Test of updating land-use mapping with remote-sensing data Following the dynamic development in Berlin since the early 1990s, the database for land-use had to be updated. Because of declining financial resources, new ways of updating had to be tested. A joint application project (Joint Research Centre 1997) was implemented with the European Statistical Agency (EUROSTAT) at the end of the 1990s, to analyze the potential of satellite data for such purposes. The objective of the Statistical Atlas of Urban Agglomerations in Europe (ATLAS) Berlin project was to assess the potential of high-and very high-resolution satellite data (IRS-1C and KVR 1000) for statistical and land-use mapping purposes.

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Fig. 9.3. Classification of structure type groups and area http://www.stadtentwicklung.berlin.de/umwelt/umweltatlas/eia607.htm

types

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Based on the four hierarchical levels of the CLUSTERS nomenclature (Classification for Land-Use Statistics: EUROSTAT Remote Sensing Program, from low-detail Level I to high-detail Level IV), processed and classified satellite images were compared to ancillary data (area types) from the Berlin UEIS. Assignment of classes comprising low-detail Level I (e.g., built-up areas or water) and major classes from Level II (e.g., urban areas or forests) of the CLUSTERS nomenclature are achieved by combining PAN and LISS images without external data for the whole site. The procedures are automatic, repeatable, and operational. The high-detail Levels III (e.g., residential or recreational areas) and IV (e.g., closed-courtyard housing, or housing areas built during the 1990s) of CLUSTERS could not attain sufficient classification accuracy without the integration of ancillary data or higher-resolution satellite imagery. Ancillary data of UEIS were much more detailed than the CLUSTERS nomenclature. Hence, a transformation of the UEIS nomenclature (area types) to CLUSTERS was necessary. Mixed pixels are one of the major problems in this context. Quite a few factors have hampered the application of the CLUSTERS nomenclature to the conditions of urban land use in Berlin:

Many buildings are used both for residential and commercial purposes. Abandoned railway areas are a problem. The boundaries between gardens of residential areas and the adjacent forests are difficult to identify, even on large-scale aerial photographs. Parks and leisure areas are usually covered by trees, grassland, bodies of water, bare soil, impervious areas, or small sports fields. Hence, the whole range of spectral signatures of urban classifications occurs within this category, with varying patterns. There are 17,500 hectares of forest within the city limits of Berlin, and in most cases it is extremely difficult to distinguish between forest land and parks or leisure areas. However, these categories have to be separated for management purposes; both categories can easily be confused with the category of “green” housing areas, which have been constructed under a remaining forest canopy in large areas.

Comparing the satellite based classifications with UEIS classes, the overall accuracy of certain classes seems to be quite poor. The classification accuracy is closely related to the various nomenclatures. Satellite images are useful to determine land cover, but do not necessarily provide information about land use. By adding very high-resolution imagery (Russian KVR 1000) better classification results were obtained than from IRS imagery alone. Im-

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provements were due to the geometric accuracy of the combined information, particularly for areas where vegetation is mixed with man-made areas. Further enhancements were possible in the case of areas with saturated response on IRS imagery, like industrial areas and continuous, dense residential areas (Joint Research Centre 1997). The next example shows a more successful application of remote-sensing data for the Berlin UEIS.

9.3.4 Surface temperatures derived from satellite data There are two different climatological research methods for surveying urban climate: mobile field surveys, and the application of climate models. Thermal maps are useful for (urban) climate analysis because they combine two advantages of both methods: they provide digitally-processable information, and they cover the total area at a particular time (also see Chapter 6, this volume). Thus, surface temperature provides very valuable baseline information that isn’t obtainable with other applications. Because of the impressive cartographic presentation, especially for larger areas like airport fields, thermal maps based upon satellite data can be used right from the design or planning stage (Plates 9.3, 9.4). The inclusion of additional climatological parameters, like air temperature and wind velocity, allows surface-temperature maps to be used to determine climate-function areas for direct planning purposes like the Berlin Landscape Program and the Environmental Impact Assessment Proceedings. Surface temperatures derived from Landsat 5 and Landsat 7 data were analyzed for a late-summer day in 1991 and a midsummer day in 2000. For both dates, a scene from night and a scene from day of the following morning were processed and compared. Surface temperatures and the differences between night and day were combined with land-use data (area types of UEIS) for further investigations of the relationship between temperature behavior and urban structures. The thermal bands of the satellite images were transformed into surface temperature values and these layers were intersected with land-use cover polygons. Multivariate statistical methods were applied. The Normalized Difference Vegetation Index (NDVI) was used to examine the relationship between thermal behavior and vegetation cover. Water and forest cover types showed low day-night temperature differences compared to residential, commercial, and agricultural cover types. The main problem with change detection based on mean surface temperature (MST) for urban blocks is the insensitivity of this parameter because of high variability within each urban structure type, and within each polygon. The variability for all variables is rather high for

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small polygons, and one can assume that the internal structure of large polygons is not as homogeneous as the GIS database indicates (Berlin Department of Urban Development 2001, Munier and Burger 2001). 9.3.5 Mapping of imperviousness (soil surface sealing) Data about imperviousness are needed not only for various environmental protection issues, but also for urban-space and open-space planning. In addition to their use in the relatively new discipline of soil protection, data about imperviousness are important as input for models of water balance and municipal climate. The first map of imperviousness appeared in the Berlin Environmental Atlas more than twenty years ago and contributed significantly to the discussion of imperviousness and use of open space. It was an evaluation of aerial photography in analog form, and predated the fall of the Berlin Wall; hence it covered West Berlin only. During the mid-1980s, interest in these data was already so great that an extrapolation of the data was carried out. At that time, digital technology was used, both for data collection and management. For the first time, satellite data were used to determine the extent of imperviousness. Satelliteimage interpretation was carried out using Landsat-TM scenes between 1985 and 1988, and these results were calibrated and classified on the basis of test areas with known degrees of imperviousness determined from mappings on the spot, or from CIR photos. At that time however, the intermediate result available as a grid value still had to be overlaid visually with the basic digital map, which forced us to laboriously count the number of pixels for each of the approximately 20,000 areas by imperviousness class, and calculate the degree of imperviousness for each square. The level of detail for the overall degree of imperviousness was insufficient to inform the various environmental protection tasks. It was necessary, also, to make statements about the type of imperviousness. First, a distinction had to be made between built-up areas (buildings) and nonbuilt-up, impervious areas (walkways, roads, parking lots). Statements were also required about the materials (concrete, asphalt, paving stones, artificial lawns) with which the non-built-up, impervious areas were covered. This was necessary in order to be able to determine the water permeability of impervious areas, and the charging of new groundwater, for example. The values ascertained from the satellite data were combined with topographical maps and CIR aerial photography for all reference surfaces, to assess their respective shares. These time- and labor-intensive methods were also used at the beginning of the 1990s for East Berlin (Berlin Department of Urban Development 1993).

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After the turn of the millennium, the information needed to be updated, as considerable changes had taken place after the reunification of Berlin. Tight financial resources allowed only for satellite-based approaches (Roesrath et al. 2001). After the evaluation of IRS 1C scanner data failed to yield the desired results, Landsat ETM+ data were evaluated in combination with SPOT data. The software ERDAS-Imagine 8.3/8.4 and a subpixel classifier were used. The data evaluated on a pixel basis were then aggregated for the approximately 23,000 areas of the base geometric structure. Since the new data were aggregated to the same geometric basis as the older data, juxtaposition of the 1993 and 2000 data was possible (Plate 9.5). The ascertainable result is that the major deviations have nothing to do with real changes in the city during that time interval, although a further increase in imperviousness has surely taken place. Yet an increase in the mean degree of imperviousness, from 42% to 51% – an increase by a quarter, does not seem plausible, nor does any reduction in imperviousness in many areas. This result can therefore only be explained by systematic errors, which may be found in both the old mapping and in the satelliteimage interpretation. The possible systematic errors of satellite-image interpretation, despite improved methods, are known: shading of large areas by high-rise buildings in the inner-city area; coverage of impervious areas by treetops, primarily in the outer areas; interpretation of mixed pixels; distinction between shade and water, sand, open soil and concrete-covered areas; and phenology of vegetation. For these reasons, great hopes were placed on another data set on imperviousness which had been developed in a completely different way. Between 1999 and 2001, the Berlin Waterworks assembled a dataset which certified the built-up area and the impervious, but not-built-up, area for each property. The data were necessary to enable fixing of fees for rainwater run-off into the sewage system. Detailed maps at a scale of 1:1000 (ALK), aerial photos, and interviews with approximately 150,000 property owners constituted the basis of this survey. Plate 9.6 shows the juxtaposition of these data with data from the Environmental Atlas. The Environmental Atlas data had in the meantime been updated for the urban development areas (approximately 15% of the city area) as part of an analysis of key sites, with the aid of a visual aerialphoto evaluation in 2001. The property-specific evaluation shows consistently lower values than the Environmental Atlas does. There are now three data sets which differ considerably from one another, in spite of the fact that all three have been assembled with adequate care, although with different methods.

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9.3.6 Urban-biotope mapping Urban biotope mapping provides a basic dataset for planning and governance by systematically collecting information on different types of urban habitats (Werner 1999). A Berlin-wide biotope-type map is available in the Environmental Atlas; however, it has been acquired by different, not necessarily comparable, methods and is partly out of date. Reliable and up-todate information on Berlin’s habitats is needed for a number of planning tasks., One example is the proposed establishment of a habitat-network system (Biotopverbundsystem) on 10% of each parcel of Federal land, mandated by the amendment of the German Federal Nature Conservation Act in 2002. Hence, the existing habitat-type mapping needs to be updated. The first method of urban biotope mapping was developed in Berlin in the late 1970s and it has always been supported by remote-sensing data since; more precisely, by visual interpretation of aerial photography. Recently, a new habitat-type list and mapping manual were released. New technical developments in remote sensing, such as aerial scanning by the HRSC-AX (High Resolution Scanner), open up new possibilities. In a recent study, HRSC-AX imagery acquired by the German Aerospace Center in Berlin-Adlershof was analyzed. In addition to the multispectral and panchromatic bands (spatial resolution of 20 cm), a Digital Surface Model (DSM, spatial resolution of 1 m) was available. Digitized CIR photos and vector data, such as the Automated Real Estate Map (ALK), have been used. Benefits and drawbacks of this new high-resolution data are shown in Table 9.2. Table 9.2. Benefits and drawbacks of HRSC-AX Benefits of HRSC-AX Problems of HRSC-AX x Digital data x No market price x Multispectral data x No multi-temporal analysis x High geometric accuracy x Problematic automatic x User-friendly visual interpretation classification x Ready-to-go-products (no digitizx Large data amount ing, geo-referencing, mosaicking) x Limited availability x Digital surface model is integrated

In contrast to color films of analog photography, which are restricted to three emulsion layers resulting in either red-green-blue or color-infrared images, aerial scanner data (HRSC-AX) offer additional spectral information with different channels (red, green, blue, near infrared). They can be fused and analyzed with different indices, permitting more accurate identi-

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fication of habitat types. In combination with the high geometrical accuracy of the scanner data, complex urban structures and shadow areas can be analyzed (Plate 9.7). HRSC-AX can collect new data and update existing datasets. The available DSM information helps to distinguish built objects and vegetation by their height (Fig. 9.4). It is very useful in shadow areas, but the information is limited by smooth edges of high objects and the geometrically lower resolution of 1m. Another advantage of HRSC-AX is the userfriendliness of the digital and pre-processed format, which eliminate the need for film developing, digitizing, aerotriangulation, mosaicking, and georeferencing. A

B

A

B

Fig. 9.4. The DSM of the HRSC-AX: useful for detecting complex urban structures (left: DSM and ALK, right: urban surface profile)

However, this new type of data presents some challenges. One significant problem that is caused by the camera characteristics is color shift; that is, the color mismatches with high (Fig. 9.4) and moving objects (Leser 2003). The amount of data produced is very high compared to digital aerial photos, and requires specific hardware for processing. Another limitation is that the usefulness of this kind of data for multi-temporal analysis is not yet known. To exploit the benefits and minimize the problems, a combined method of automatic and visual interpretation proved to be the best solution. Automatic, pixel-based approaches are limited by the very high resolution leading to increased variation of spectral values within one object. The spectral variety and complexity of urban materials lead to a large number of misclassifications (Kim et al. 2005). Additional information about the context, and the ability to generalize, can be provided by visual interpretation. The analysis also examined the possibilities of biotope-mapping supported by aerial scanner data to provide the basic data necessary for an ur-

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ban-habitat network approach. Information about criteria for habitat networks, e.g., area, buffer, fragmentation, and dynamics, was acquired via a GIS. Although the technical characteristics of the new aerial scanner data and automatic classification methods suggest applications, a fully automatic classification remains to be realized. Mapping urban biotopes on that level of detail will remain a task for visual interpretation, which can be supported by automatic classification. When applying such a combined method, use of aerial scanner data provides benefits compared to the wellproven method of visual interpretation of aerial photos.

9.4 Conclusions Initiated by technological developments, principally the Internet and client-server architecture, a new generation of environmental information systems is developing in the planning sector. This enables improved vertical and horizontal exchange of environmental information between different actors, leading to a possible multi-usage of data. The latter is of special importance because the budgets of public administrations will probably continue to decrease in the future. New methods of updating data (Kim et al. 2005), and multifunctional use of environmental data need to be explored. Providing access to environmental information can contribute to the growing demand for participation of the civil society in decisionmaking (BLAK UIS 2003). Apart from the technological possibilities of the Internet, one needs to be aware of possible negative consequences, such as increased segregation of users due to unequal access to information technology (Haklay 2003). Experiences with remote-sensing applications for the Berlin Urban and Environmental Information System, except for mapping of surface temperatures, have been unsatisfactory. A study for mapping urban land uses could not achieve the necessary precision. This was caused by the high differentiation of urban-use categories that could not be derived completely from the land-coverage signals received by the satellite. Mapping of soilsurface sealing also yielded unsatisfactory results. Problems of shadows, distinguishing various types of imperviousness, and mixed pixels could not be solved. Urban-biotope mapping supported by aerial-scanner data proved to be an alternative to visual interpretation of aerial photography; however, the hopes for fully automatic classification could not be fulfilled. In future, it will be important to combine various databases to gain acceptable results

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with relatively low financial expenditure. Remote-sensing data will necessarily be only one of these resources. With respect to the continuously increasing quantity and quality of available remote-sensing data, there is a need for further user-relevant research (Beule et al. 2004). Remote-sensing science should develop methods which are able to identify structures defined, for example, by a special pattern of houses and open spaces, or by a mixture of various vegetation types. What would seem particularly important from our experience is to have a sufficient number of non-homogeneous reference areas with mixed uses which have been mapped in the field. The financial resources of public administrations will probably decrease further in the future. Increased use of high-resolution, multi-spectral satellite pictures can only be expected when their cost decreases considerably.

9.5 References Berlin Department of Urban Development (1993) Berlin digital environmental atlas, edition 1993, map 01.02 surface sealing, 1:50000, Berlin, Germany, http://www.stadtentwicklung.berlin.de/umwelt/umweltatlas/eia406.htm, accessed 6-March-2007

Berlin Department of Urban Development (2001) Berlin digital environmental atlas, edition 2001, map 04.06 surface temperatures day and night, 1:50000, Berlin, Germany, http://www.stadtentwicklung.berlin.de/umwelt/umweltatlas/ei102.htm, accessed 6-March-2007 Berlin Department of Urban Development (2002a) Berlin digital environmental atlas, map 06.01/2, 1:50000, Berlin, Germany, http://www.stadtentwicklung.berlin.de/umwelt/umweltatlas/eda601_03.htm, accessed 6-March-2007 Berlin Department of Urban Development (2002b) Berlin digital environmental atlas, map 06.07, 1:50000, Berlin, Germany, http://www.stadtentwicklung.berlin.de/umwelt/umweltatlas/eda607_03.htm, accessed 6-March-2007 Berlin Department of Urban Development (2004a) Berlin digital environmental atlas, Berlin, Germany, http://www.stadtentwicklung.berlin.de/umwelt/umweltatlas/eiinhalt.htm, accessed 6-March-2007 Berlin Department of Urban Development (2004b) FIS-broker, http://www.stadtentwicklung.berlin.de/geoinformation/fis-broker/, accessed 6March-2007 Beule B, Backhaus R, Bittner M, Bock M, Borg E, Gege P, Holzer-Popp T, Kästner M, Keil M, Lemm C, Neumann A, Paliouras E, Rossner G, Roth A, Strunz G, Wissen M (2004) Anforderungsanalyse der nutzung von

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satellitenbasierten erdbeobachtungssystemen für die umweltpolitik (SATUM). Umweltbundesamt, Berlin, Germany BLAK UIS (2003) Bund/Länder arbeitskreis umweltinformationssysteme, http://www.portalu.de, accessed 6-March-2007 Greve K (2002) Vom GIS zur geodateninfrastruktur. Standort - Zeitschrift für angewandte Geographie 26 (3):121-125 Haklay ME (2003) Public access to environmental information: past, present and future. Computers, Environment and Urban Systems 27:163-180 Joint Research Centre (1997) Statistical ATLAS of Urban Agglomerations in Europe (ATLAS) – Berlin. Final report 13351-97-11 F1ED ISP D Kim HO, Lakes T, Kenneweg H, Kleinschmit B (2005) Different approaches for urban habitat type mapping – The case study of Berlin and Seoul. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 36 (on CDROM) Leser C (2003) Entwicklung operationell einsatzfähiger methoden zur biotoptypen-kartierung anhand hochauflösender HRSC-daten. PhD thesis, Technische Universität Berlin Munier K, Burger H (2001) Analysis of land use data and surface temperatures derived from satellite data for the area of Berlin. Regensburger Geographische Schriften 35:206-221 Roesrath C et al. (2001) Concept study for updating surface sealing data set by high-resolution satellite data. Final report for Berlin Department of Urban Development. TU Berlin Institut für Landschaftsentwicklung, Berlin, Germany Werner P (1999) Why biotope mapping in populated areas? DEINSEA 5:9-26

Chapter 11 - 20 Years After Reforms: Challenges to Planning and Development in China’s CityRegions and Opportunities for Remote Sensing

Karen C. Seto1,2, Michail Fragkias1, Annemarie Schneider3

1

Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA

2

Department of Geological and Environmental Sciences, Stanford University, Stanford, CA, USA

3

Department of Geography, University of California, Santa Barbara, CA, USA

11.1

Introduction

Since economic and agricultural reforms were initiated in the late 1970s, China’s cities have grown at a remarkable pace. Urban population increased from 172 million in 1978 to 517 million in 2003, increasing the urbanization level from 19 percent to 40 percent (2004 State Statistical Bureau data). The number of Chinese cities has increased from 132 in 1949 to 667 in 1999 (Anderson and Ge 2004). It is estimated that urban population

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will grow to almost 5 billion by 2030, an expected increase of 2 billion people from the estimated level for 2003 (United Nations 2004). However, aggregate growth measures give limited information regarding spatial patterns of urbanization or the underlying processes that shape urban areas. Many of the urban processes and urban growth phenomena we seek to understand and manage operate across extensive areas and over time. Most methods and tools for collection of data on urban growth, however, are developed for comparatively small areas and over short periods of time. As a result, there has been a lack of a time series of spatially explicit information available for regional- and city-level assessments, planning, and decision-making. The routine collection of satellite imagery for most of Earth’s surface area has revolutionized environmental and urban mapping, monitoring, and planning. In particular, historical satellite data provide invaluable information about the temporal and spatial dynamics of urban development. Used in conjunction with geographic information systems (GIS), remote sensing is an indispensable tool for modern day urban planning. In this chapter, we provide a quantitative assessment of the spatial and temporal dynamics of urban development in the four most developed cities in South China: Guangzhou, Shenzhen, Dongguan, and Zhongshan, as well as the Chengdu extended urban region. We calculate landscape metrics for a time series of imagery and compare the shape, size, and growth of urban areas across the five cities. One of the aims of the remote sensing analysis is to describe the spatial and temporal patterns of urban development, which in turn can be used to catalyze research on the underlying social, economic, and political processes that shape urban growth. We then address some of the urgent challenges in rapidly growing city-regions in China, and the utility of remote sensing and GIS to help solve planning problems.

11.2

Study areas

Our study regions are the Pearl River Delta, located in Guangdong Province (Figure 11.1), and the greater Chengdu metropolitan area, located in Sichuan Province (Figure 11.2). The Pearl River Delta is the economic hub of the province and generates more than 70% of the provincial Gross Domestic Product. It is home to 21 million people, nearly one-third of the province’s official population (2001 Statistical Bureau of Guangdong data). The region is fertile, has a long history of high agricultural productivity, and can support two to three crops per year. The semi-tropical monsoon climate, combined with rich alluvial soils, makes the Delta a national

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leader in the production of rice, lychees, bananas, pond fish, and sugar cane. Within the Pearl River Delta, we focus on four of the most developed cities in the region: Guangzhou, Dongguan, Shenzhen, and Zhongshan (Figure 11.3). Guangzhou (Canton), capital of the Guangdong Province, is the oldest of the four cities in the Delta. Located at the mouth of the Pearl River, Guangzhou has been the cultural, economic, and industrial focal point of southern China. It is also a transportation hub: it has an international airport, one of the most active regional seaports, and railroad connections to all regions of the country. In contrast, Shenzhen is a relatively young city. Just a small fishing village until it was declared a Special Economic Zone in 1979, Shenzhen is located on the Hong Kong-China border and has experienced the most dramatic economic growth and landscape changes of the cities in the study. Regionally, it receives the bulk of foreign direct investment, has been the focus of special economic policies, and has a large population of temporary workers, estimated to be between 5 and 10 million.

Fig. 11.1. Pearl River Delta study area.

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Fig. 11.2. (a) Map of China indicating provinces targeted in the “Go West” program, Special Economic Zones, and Open Cities, and (b) map of administrative boundaries of Chengdu municipality where counties are shown in white and districts are shown with hatched lines. Urban land use is shown in dark grey.

Located between Guangzhou and Shenzhen in the northeastern part of the Delta, Dongguan is a leader in export-oriented industries such as textiles, toys, and food processing. It has developed rapidly, in part because of its close proximity to Hong Kong. Zhongshan is the smallest of the four Pearl River Delta cities examined, and is located in the low-lying western mouth of the Delta, approximately 80 kilometers south of Guangzhou. It differs from the other three cities in that it has received relatively little foreign direct investment. Whereas Guangzhou, Dongguan, and Shenzhen have focused on manufacturing and export processing, Zhongshan has developed more slowly, with a significant focus on “green development.” Geographically, it is situated among hills and river ways, giving it a comparative ecological advantage in the region. These differences have resulted in a city that has developed more slowly and retains a more domestic Chinese character than the other three cities.

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Fig. 11.3. Case study cities in the Pearl River Delta region and buffer zones.

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Chengdu, the provincial capital of Sichuan, has also undergone rapid transformation in the last two decades. The leading city in southwest China in terms of services, aviation, and business headquarters, Chengdu is second only to Chongqing in population. Between 1978 and 2002, there was a significant urban shift in the population, with the population of Chengdu city increasing by 35%, from 1.7 to 2.3 million, while the population of the entire municipality grew by 25%, from 8 to 10 million. Over this same period, the GDP of Chengdu municipality grew seven-fold, with a restructuring of the economy away from the primary sector to the secondary and tertiary sectors. Chengdu is a leading center in western China for finance, higher education, electronics, research and development, and aviation.

11.3 Remote sensing and GIS to monitor urban growth patterns The spatial configuration of an urban landscape is as much a reflection of past, as it is an indicator of future, processes; it provides a snapshot of various economic, social, and political factors that influenced land-use decisions. The character of emergent cities and their infrastructure requirements will be significantly defined by urban form. Similarly, the social, economic, cultural, and political character of a city will be influenced by its spatial configuration. Once established, urban structures, transportation networks, and other infrastructure patterns are unlikely to change. Therefore, judicious urban planning and sustainable urban growth management are critical for human, economic, and environmental well-being during periods of high growth, such as are currently being experienced in China. Time-series satellite imagery can play an integral role in observing patterns of growth. Post-hoc land assessments can provide insight into the efficacy of the planning process, and help to understand how incentives and policies promote or inhibit sustainable urban growth. Information provided by remote sensing analysis can help inform the planning process to promote sound land management. 11.3.1 Pearl River Delta Case Studies For all five cities in our study, we used a time series of Landsat images to calculate estimates of urban extent. For the Pearl River Delta study cases, we used a two-step change-detection procedure to extract annual estimates of urban growth for ten images acquired between 1988 and 1999 (Table 11.1). First, we developed a two-date map for the years 1988 and 1996.

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The map has nine categories, three of which are urban: 1) established urban areas, 2) new urban areas converted from agricultural land, and 3) new urban areas converted from natural vegetation or water. Following the completion of the two-date map, the three urban classes were used to generate a mask which was applied to the seven intermediate images (19891995) and the 1999 image. The rationale behind using a mask to restrict the areas to be analyzed is twofold. First, we had conducted a field-based accuracy assessment for the 1988-1996 analysis and were confident of the high level of accuracy for that map. Secondly, using a mask to limit the areas of study reduced the potential for error for the intervening years. Details of the image processing methods are documented in Seto et al. (2002). Table 11.1. Images used for Pearl River Delta and Chengdu case studies Study Area Acquisition Date Pearl River Delta 10 December 1988 13 December 1989 30 October 1990 02 February 1991 20 January 1992 24 December 1993 08 November 1994 30 December 1995 03 March 1996 15 November 1999

Study Area Chengdu

Acquisition Date 21 August 1978 1 May 1988 24 April 1991 16 August 1992 5 May 1995 2 November 2000 23 December 2001 7 October 2002

The classified images were used to generate annual maps of urban extent, and spatial-pattern metrics were calculated and analyzed spatiotemporally across three buffer zones for each city for each year. Originally developed in the field of landscape ecology, where understanding habitat fragmentation, landscape heterogeneity, and the distribution of landscape disturbance is important for understanding ecological processes, spatialpattern metrics are quantitative characterizations of the landscape (Wickham and Norton 1994, Kareiva and Wennergren 1995, Ives et al. 1998). A variety of indices to characterize the landscape have been developed, some of which describe the proportion of the landscape with a particular land-cover class, the size, number, and perimeter of each land-cover patch, and the complexity of the shape of the patch (McGarigal et al. 2002). Although these indices of landscape patterns have been used widely in ecology for decades, only recently have they been applied specifically to the study of urban morphology (Geoghegan et al. 1997, Herold et al. 2002,

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Luck and Wu 2002, Herold et al. 2003, Cifaldi et al. 2004, Schneider et al. 2005, Seto and Fragkias 2005). We chose metrics that describe three aspects of the urban landscape: absolute size, relative size, and complexity of urban form. Absolute size is described by two metrics, total urban area (UA) and number of urban patches (NUMP). As urban growth occurs, total urban area continually increases due to the non-reversible nature of urbanization. The number of urban patches metric is a measure of discrete urban areas in the landscape, and is expected to increase during periods of rapid urban nuclei development, but may decrease if urban areas expand and merge into continuous urban fabric. Relative size is described by the mean urban patch size (MPS) and urban patch-size coefficient of variation (PSCOV). The mean urban patch size is a function of the number of urban patches and the size of each urban area, and can either increase or decrease over time. Decreasing values of mean urban patch size imply that new urban centers are growing faster than existing urban areas. That is, urban growth occurs more as a process of new and multiple urban-nuclei formation than of envelopment or annexation. The urban patch-size coefficient of variation is a normalized metric of the urban area and can either decrease or increase over time. Urban edge density (ED) measures the total edge of urban areas relative to the total landscape and should increase with new urban nuclei, but may decline as urban areas fuse together and boundaries dissolve. The areaweighted mean patch fractal dimension (AWMPFD) metric describes the degree to which the shape of an urban area is irregular or complex. The more irregular the shape of the urban area, the higher the value of the fractal dimension. Of the many shape and complexity measures available, we chose the area-weighted mean patch fractal dimension because it is normalized. The area-weighted mean patch fractal dimension is hypothesized to increase during the early periods of urban growth when new urban nuclei and expansion of existing urban space creates irregularly shaped landscape patterns. This metric is expected to decline as urban form becomes more regular. We calculated six landscape metrics for each of the ten years for three buffer zones drawn at 0-3 km, 3-10 km, and 10-20 km from the city centers of Guangzhou, Shenzhen, Zhongshan, and Dongguan. An ad hoc definition of a city and its urban-rural fringe boundaries can be a problem for the study of metropolitan urban areas. The choice of a variety of concentric rings and the width of these buffers is largely based on the experience of the authors. Our rationale for a concentric-ring partitioning of urban space and the selection of buffer size was based on the following: 1) a standard buffer size by which the cities in the study could be compared

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through time was needed, 2) the buffers needed to capture variation within the city center and areas surrounding the center, and 3) the buffers needed to capture changes at the urban-rural fringe and the forces that drive landscape changes at the edges of cities. 11.3.2 Chengdu extended urban region In the case of Chengdu, we used eight Landsat images distributed between 1978 and 2002 (Table 11.1). The image-processing algorithm involved stacking all images and identifying five periods of urban change in the series of images. Decision-tree analysis was used to classify the images (Schneider et al. 2005). The methodology used for the Chengdu analysis differs from that employed in the Pearl River Delta case studies in two respects. For the Pearl River Delta, pairs of images were analyzed to differentiate between two categories of urban change classes, agriculture to urban and vegetation to urban, for two dates. In the case of Chengdu, all the images were analyzed simultaneously to evaluate when urban growth occurred in a time series of images. In this case, the timing of urban growth was more important to the analysis than the type of urban conversion. Increasingly, policymakers, planners, and researchers aim to link patterns of land conversion with economic activity and policy levers. In these cases, it is particularly important to assess not only the location, but also the date, at which urban growth occurs. After classification of the images, two spatial-pattern metrics, mean patch size (MPS) and landscape shape index (LSI), were calculated for five binary maps of urban/non-urban land for the years 1988, 1991, 1995, 2000, and 2002. LSI is the sum of the perimeter of all patches relative to the amount of edge that would be present in the same landscape with simple shapes: the greater the amount of edge, the more fragmented the growth. Increasing LSI values over time reflect increased fragmentation in urban areas, while increasingly continuous urban fabric results in decreasing LSI values. While concentric rings were drawn around each of the Pearl River Delta case studies for the spatial pattern metric analysis, in the Chengdu analysis the metrics were calculated for seven major transportation corridors that extend outward from Chengdu’s central business district (Figure 11.4). The underlying principle behind the corridor analysis is to partition the urban fabric into zones identified as vectors or regions of rapid growth, and to quantify the pattern of urban growth, which is traditionally measured by summary statistics alone.

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Fig. 11.4. Growth corridors analyzed for Chengdu. Stable urban areas are shown in gray, urban change 1978 to 2002 is shown in dark gray, agriculture and vegetation in white. A sample of the economic drivers of land cover change is illustrated in each corridor. County-level satellite cities are indicated in capital letters.

11.4 Comparative urban development on the coast and in the west Remote sensing and GIS analysis reveal astonishing rates of urban growth. For the 11-year period between 1988 and 1999, Shenzhen’s urban land area grew by 132%, Guangzhou’s by 248%, Dongguan’s by 833%, and Zhongshan’s by 631%. Similar rates of growth were experienced in Chengdu. Between 1978 and 2002, urban land area in Chengdu increased

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by 373%, with growth rates ranging from 92.3% for the Chenghua district, to 1257% for the Longquanyi district. For all case studies, the landscapepattern metrics results show that regardless of the initial patterns of urban structure, disconnected urban areas eventually expanded, their physical boundaries dissolved, and they became seamless urban regions. Most disconnected urban areas converge toward a pattern of contiguous urban fabric. This spatiotemporal pattern is consistent for all cities across all buffer zones and growth corridors. Guangzhou is the most densely settled city for our study period, with all developable land within the 3 km buffer zone already urban in 1988 (Figure 11.5A - E). Shenzhen did not experience substantial change within this inner buffer zone, but was constrained by the landscape and topography. Dongguan and Zhongshan experienced high rates of urban growth within a 3-km radius after 1992. From 1988 to 1992, total urban area for the middle buffer zone of 3-10 km and the outer buffer zone of 10-20 km was similar for all cities in the Pearl River Delta. After 1992, the total urban area in these two buffer zones started to diverge. For example, in Guangzhou, the total urban area more than doubled between 1988 and 1999 in the middle buffer zone, and increased six-fold in the outer buffer zone. Zhongshan experienced a less dramatic, but still significant, divergence compared to the other three cities. The measure of edge density tends to increase, but shows a sudden drop between 1995 and 1996 for the inner and middle buffer zones for all cities; this combined with other metrics is evidence of urban- area fusion during that time period. Complexity of urban form as measured by the area- weighted mean patch fractal dimension also has a tendency to increase for all buffer zones and cities (except the inner buffer zones of Shenzhen and Guangzhou), while several periods of stabilization are observed in the mid-nineties.

Fig. 11.5A. Time series landscape metrics for Pearl River Delta cities, normalized by quarter; number of urban patches.

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Fig. 11.5B. Time series landscape metrics for Pearl River Delta cities, normalized by quarter; urban area.

Fig. 11.5C. Time series landscape metrics for Pearl River Delta cities, normalized by quarter; area-weighted mean patch fractal dimension.

Fig. 11.5D. Time series landscape metrics for Pearl River Delta cities, normalized by quarter; urban edge density.

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Fig. 11.5E. Time series landscape metrics for Pearl River Delta cities, normalized by quarter; urban patch size coefficient of variation.

For the cities in the Pearl River Delta, the number of urban patches partially reveals the temporal patterns of new urban nuclei development: across all buffer zones and for all cities, the number of urban patches first increases as urban land-use change occurs in areas non-adjacent to existing urban centers, and then eventually decreases until 1996 as cores that were disconnected become contiguous urban entities. After 1996, the number of urban patches remained constant or increased because the new urban areas still tended to remain disconnected from one another. Additional information regarding the dynamics of urban nuclei development is provided by the mean patch-size measure. For example, in the case of Shenzhen and its middle and outer buffer zones, between 1988 and 1991 the mean urban patch size declined, while the number of urban patches increased in the middle buffer zone. This shows that urban growth during this time and in this region was mainly due to new nuclei development. Between 1991 and 1996, the number of urban patches declined while the mean patch size increased, evidence that urban growth occurred primarily through expansion of extant urban areas rather than spontaneous and detached development. Between 1996 and 1999 we again observe a declining mean patch size and an increasing number of patches, denoting a second period of new urban nuclei development. Dongguan and Zhongshan experience the same cycle but at a different intensity for each stage. Guangzhou follows the same cycle for the first two periods but during the last period, between 1996 and 1999, the two metrics show stabilization, signifying a balanced mix of envelopment-type and new-nuclei urban development. These results highlight several key points about urban growth in the four coastal cities. First, urban growth is a result of cycles of two expansion processes: envelopment—the annexation of the surrounding landscape through the growth of extant urban areas, and multiple-nuclei develop-

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ment. Satellite data reveal that urban growth occurs mostly at the expense of cultivated land and far from the city center in rural farm communities. Secondly, the landscape metrics reflect land-use decision making that occurred at all levels of the Chinese administration and at different stages in the evolution of development policies. The original 1987 land administration law allowed industrial development zones to be sanctioned by the central government’s State Council, as well as by lower-level administrative units such as municipal and local governments. This gave rise to internal competition across multiple administrative levels to develop hightech and industrial specialty zones that, in turn, led to a polynucleated urban space. Thirdly, incorporating a temporal component into landscape metrics reveals that the urban form of cities can change relatively quickly over short periods of time. This is particularly true during the early stages of urbanization, when infrastructure is less likely to constrain patterns of growth or urban form. While there is a certain level of urban growth-path dependency, the results show that urban form can vary greatly during early stages of economic development. The pattern of urban growth in the four cities is neither linear nor nodal. Some areas developed out of older city cores, but others developed out of new industrial zones. In the latter case, the lack of services such as roads and telecommunications did not inhibit the development of new urban areas. The results suggest that given a rural or agricultural landscape with minimal infrastructure and zoning restrictions, urban growth can be manifested in multiple spatial configurations during the early stages of economic growth. Urban land-use change is constrained only after more advanced stages of economic development, when infrastructure is in place. Despite differences in levels of economic development and local policies, there are common patterns in the shape, size, and growth of urban land across urban zones and cities. In our study of the greater-Chengdu metropolitan area, the simultaneous drop of the mean patch size and increase in the landscape shape-index values within a short distance from the urban core reveals the intensity of fragmentation and discontinuity forces close to the city center (Plates 11.1 and 11.2). Further evidence using these two metrics suggests that fragmentation was the dominant pattern of urban development in the west of the city for the periods 1991-95 and 1995-2000, while areas of specific corridors and distances from the core start experiencing infill during the 20002002 period. For further distances from the core (e.g., 25-40 km), fragmentation of the landscape is not as strong but still exists. The results of this type of analysis show that a standard diffusion-coalescence-furtherdiffusion story may not be adequate, either due to the particularities of

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Chengdu or to the corridor partitioning of urban space, since an overlap of diffusion and coalescence is observed during the same time period. A major advantage of the analysis is the ability to capture more detailed intracity (geographical neighborhood) spatial variation through landscape metrics by using multidirectional transects. We performed a dynamic gradient analysis of landscape metrics for the city of Chengdu, along several transects following road networks that originate at the urban core. Neither of the two studies summarized in this chapter vary the chosen scale (a 30-m image resolution); recent studies, though, indicate that the landscape metrics used in this study show consistent patterns through changes in grain size, supporting the concept that there is no correct texture or scale at which to evaluate these landscape patterns (Wu et al. 2002). The results highlight several aspects of post-reform urban development trends in the city of Chengdu. First, the development of multiple nuclei outside the core city is identified in the pattern-metric analysis. Chinese cities have adopted more contemporary, westernized, multiple-nucleibased industrial and post-industrial forms; this is in part due to the compatibility of planning goals and market forces, pairing the planning for good urban form with economies of agglomeration (Schneider et al. 2005). Secondly, there is a trend towards “increased spatial and functional land-use specialization” (Schneider et al. 2005). This move away from mixed land uses and the agglomeration of similar land uses has occurred for several reasons: housing reforms such as the liberalization of the household registration system hukou and the weakening of the work unit system danwei, land market policy and law reforms, and the creation of development zones accommodating national and international investment flows. Thirdly, there is a trend towards periurbanization, which is a process of urban development driven by the agglomeration of investment in exurban and periurban areas outside of the rural-urban fringe (Webster 2002). In the case of Chengdu, periurbanization processes can be identified as far as 40 km from the core of the city proper. While often driven by preferential tax policies and viewed as a possible engine of regional growth, periurban investment has significant impacts, including the introduction of urban lifestyles to rural populations, environmental changes resulting from increased fragmentation of agricultural and natural lands, and, often, intergovernmental conflicts associated with the high fragmentation of urban governance in periurban areas.

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Monitoring urban growth in China

Currently, we lack quantitative studies of the patterns of urban development of Chinese—or more generally, non-U.S.—city-regions. Given the rapid urbanization that is occurring in China and most of the developing world, an understanding of emerging spatial patterns of urban form is a necessity, and further research should not be delayed. Such studies have large data requirements and require suitable methodological frameworks. We believe that time-series analysis of remotely sensed images and spatialpattern-metrics analysis constitute effective ways to bridge existing gaps. The two studies presented in this chapter provide robust guidelines for undertaking such a task. Our research evidence shows that these five Chinese cities follow similar physical-growth trends and that urban growth is largely fragmented in a sprawl-like pattern. In particular, the emerging spatial patterns reveal that the physical development of the extended Chengdu region is following the coastal city patterns in the southeast. The importance of different drivers of change, though, varies as one moves westward from the coast (since, for example, the west has experienced lower levels of foreign direct investment to date). Reforms toward open markets that lead to higher rates of economic growth in China have unquestionably affected the rates of urbanization. In all five cities, urban development rates and form have been affected by the growth of the private sector economy. Differences in the timing and level of reforms has, until recently, led to lower economic growth in the medium-sized cities of western China than in those on the coast. Nevertheless, western cities face rapid urban growth and the associated problems of urban management. In addition to the increased role of the private-sector economy in urban land development, in the four southeastern coastal cities of Shenzhen, Guangzhou, Dongguan, and Zhongshan, formal planning has had relatively little influence compared to that of large-scale investments by overseas interests. In the city of Chengdu, identified processes of urban expansion (spatial clustering, land-use specialization, and periurban land development) have also been weakly driven by formal planning, but strongly driven by large-scale investment by external public agencies uncoordinated with municipal, provincial, and national investment processes (Schneider et al. 2005). This is a materialization of the problematic unisectoral planning process rather than the more desirable crossjurisdictional (both horizontal and vertical) process. The problem of the limited effect of local planning is demonstrated by the fact that officials

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and urban planners are often unaware of the extent and patterns of land conversion (Schneider et al. 2003). The results have important implications for future city design and planning in China. Urban development in the Pearl River Delta has been the result of interactions among local, regional, national, and international forces, while for metropolitan Chengdu and the non-coastal areas, international forces have not played a big role. In terms of directing urban growth, formal planning has played a relatively minor role compared to that of private sector actors such as farmers, real estate developers, and foreign investors. Foreign investors have also been instrumental in shaping urban development in other Chinese cities, such as Shanghai (Wu 2003, Zhao et al. 2003) and Beijing (Leaf 1998, Gu and Shen 2003). This is different from urban planning in China during the pre-reform period, when officials easily controlled the amount and location of new urban land development. In the post-reform period, patterns of growth have been more extensive and fragmented than policy intended. Considering the large amount of urban growth and extensive nature of new urban areas, it is necessary to re-examine land management policies and the role of planning in post-reform Chinese urban environments. While agricultural and economic policy reforms have raised income levels and spurred economic growth, they have also accelerated the rate and extent of urban land-use change. A key question facing policymakers now is how to manage urban growth and its direct and indirect consequences, such as the loss of farmland, high-cost delivery of urban services and infrastructure, environmental degradation, and growing urban populations. The issue of farmland loss will be especially salient given the cumulative effect of urban land-use change throughout China.

11.6 Challenges to planning and development and the role of remote sensing and geospatial data Urbanization in China has opened up many opportunities for the Chinese and facilitated rapid economic growth, but new challenges have arisen. How can the urbanization process be more effective so that city-regions become both engines of growth and livable places that foster environmental and human well-being? How can Chinese cities house high densities of people and accommodate growing floating populations while simultaneously reducing their exposure to environmental risks and negative environmental impacts? Discussions with planners indicate that cities need development strategies that are specific to their geographic and economic

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situations. Empirical evidence suggests that geographic advantage is less important than connections with foreign investors in determining economic growth (Seto 2004). Thus, how can cities best utilize their competitive advantages? Local governments in China have a relatively high level of autonomy to develop strategies that work for their region. All the cities in our study have used incentive policies tied to zoned economic or hightech development regions to attract investors. What incentive structures are most effective for attracting strategic investments and influencing where they locate? Clearly, access to timely and relevant geographic information can help the urban planning and development process. Understanding historical land-use patterns and relating them to policy and investment can help inform the planning process and foster development of effective policies. The examples shown in this chapter highlight the utility of time-series satellite data and spatial analysis for understanding patterns and drivers of growth. Unfortunately, there are a number of barriers that constrain the use of spatially explicit land-use information that can be derived from remote sensing. Perhaps the most common obstacles are limited access to and availability of satellite imagery, the level of technical expertise required to utilize the data beyond visual interpretation, and the costs associated with developing and maintaining extensive geospatial databases. Satellite data are often unavailable to project managers engaged in planning and implementing development strategies. In order to meet the challenge of developing effective and comprehensive planning strategies, it is essential for information resources to be more readily available to people engaged in development and planning at all levels. Difficulties in accessing geospatial information limit our ability to plan and implement successful development strategies. All too often, land-use assessments are static, and master plans of urban development are more of an aesthetic exercise than a dynamic process tied to real market and public-investment processes. City master plans in China produce a “final product”—a picture of a mature and fully developed city. These master plans reflect little, if any, coordination between departments and bureaus or strategies to reach the “final product.” There is very little coordination between city offices that provide services (e.g., transportation and sanitation) and the city planning bureaus. Master plans are often poorly tied to urban management and operational investment functions. Currently, there is also very little effort to involve stakeholder communities and the wider civil society in China. However, as urban planning cultures and processes change, there is a move toward more stakeholder involvement. Remote sensing and web-based geographic data on land-use could be effective tools for increasing stakeholder involvement. Little is shared between and

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among cities on best practices and lessons learned. There is an urgent need to enable planners, stakeholders, and decision-makers across multiple administrative scales to more effectively interact, form networks, and collaborate. Another challenge facing rapidly growing cities in China is how to efficiently and effectively manage and direct growth that is both environmentally and socially sustainable. Remote sensing and GIS can contribute to and inform all of the aforementioned planning challenges. Specifically, remote sensing and GIS can contribute to: 1) land use planning and sustainable development; 2) land market development and land market monitoring; 3) infrastructure planning; 4) informing stakeholder communities; 5) facilitating collaborations among cities and within city departments through shared information; and 6) environmental and resource planning, especially with regard to conversion of agricultural land, and preservation of cultural sites and areas of historic value. As China’s cities and networks of cities continue to grow, the need for sustainable and sound urban planning and management will become more acute. The environmental, economic, political, and social impacts of urban growth are profound. The way Chinese cities develop in the next decade will affect not only their citizens and the local economy, but also the national—and potentially global—economy and environmental health. Urban growth-related microclimatic changes, urban-heat-island effects, loss of natural vegetation and agricultural land, pollution of water, air, and soils, and overuse of natural resources will have impacts well beyond China’s borders. As shown by the examples in this chapter, time-series satellite imagery and spatial analysis can significantly inform the planning process and contribute to understanding urban development. Our results indicate that the methodology is useful for monitoring the spatial configuration of cities over time, and comparing the changes in urban form across cities. By linking landscape-pattern analysis with temporal and spatial information from satellite imagery, a more complete understanding of urbanization can be developed than by using urban growth rates alone.

11.7

References

Anderson G, Ge Y (2004) Do economic reforms accelerate urban growth? The case of China. Urban Studies 41:2197-2210 Cifaldi RL, Allan JD, Duh JD, Brown DG (2004) Spatial patterns in land cover of exurbanizing watersheds in southeastern Michigan. Landscape and Urban Planning 66:107-123

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Geoghegan J, Wainger LA, Bockstael NE (1997) Spatial landscape indices in a hedonic framework: An ecological economics analysis using GIS. Ecological Economics 23:251-264 Gu CL, Shen JF (2003) Transformation of urban socio-spatial structure in socialist market economies: The case of Beijing. Habitat International 27:107-122 Herold M, Goldstein NC, Clarke KC (2003) The spatiotemporal form of urban growth: Measurement, analysis and modeling. Remote Sensing of Environment 86:286-302 Herold M, Scepan J, Clarke KC (2002) The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environment and Planning A 34:1443-1458 Ives AR, Turner MG, Pearson SM (1998) Local explanations of landscape patterns: Can analytical approaches approximate simulation models of spatial processes? Ecosystems 1:35-51 Kareiva P, Wennergren U (1995) Connecting landscape patterns to ecosystem and population processes. Nature 373:299-302 Leaf M (1998) Urban planning and urban reality under Chinese economic reforms. Journal of Planning Education and Research 18:145-153 Luck M, Wu J (2002) A gradient analysis of urban landscape pattern: A case study from the Phoenix metropolitan region, Arizona, USA. Landscape Ecology 17: 327-339 McGarigal K, Cushman SA, Neel MC, Ene E (2002) Fragstats: Spatial pattern analysis program for categorical maps. Computer software program, University of Massachusetts, Amherst, MA, http://www.umass.edu/landeco/research/fragstats/fragstats.html, accessed 6March-2007 Schneider A, Seto KC, Webster DR (2005) Urban growth in Chengdu, western China: Application of remote sensing to assess planning and policy outcomes. Environment and Planning B 32:323-45 Schneider A, Seto, KC, Webster D, Cai J, Luo B (2003) Spatial and temporal patterns of urban dynamics in Chengdu, 1975-2002: The urban dynamics of east Asia series. Asia Pacific Research Center, Stanford University, Stanford, CA Seto KC (2004) Urban growth in south China: Winners and losers of China’s policy reforms. Petermanns Geographische Mitteilungen 148(5):50-57 Seto KC, Fragkias M (2005) Quantifying spatiotemporal patterns of urban landuse change in four cities of China with time series landscape metrics. Landscape Ecology 20: 871-888 Seto KC, Woodcock CE, Song C, Huang X, Lu J, Kaufmann RK (2002) Monitoring land-use change in the Pearl River Delta using Landsat TM. International Journal of Remote Sensing 23(10):1985-2004 United Nations (2004) World urbanization prospects: The 2003 revision. UN Press, New York, NY Webster DR (2002) On the edge: Shaping the future of peri-urban east Asia: The urban dynamics of east Asia series. Asia Pacific Research Center, Stanford University, Stanford, CA

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Wickham JD, Norton DJ (1994) Mapping and analyzing landscape patterns. Landscape Ecology 9:7-23 Wu FL (2003) Globalization, place promotion and urban development in Shanghai. Journal of Urban Affairs 25:55-78 Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effect of changing scale on landscape metrics. Landscape Ecology 17:761-782 Zhao B, Nakagoshi N, Chen JK, Kong LY (2003) The impact of urban planning on land use and land cover in Pudong of Shanghai, China. Journal of Environmental Sciences-China 15:205-214

Chapter 10 - Views of Chiang Mai: The Contributions of Remote-Sensing to Urban Governance and Sustainability

Louis Lebel1, Danai Thaitakoo2, Somporn Sangawongse3, Darika Huaisai1 1

Unit for Social and Environmental Research, Faculty of Social Sciences, Chiang Mai University, Chiang Mai, Thailand

2

Department of Landscape Architecture, Faculty of Architecture, Chulalongkorn University, Bangkok, Thailand

3

Department of Geography, Faculty of Social Sciences, Chiang Mai University, Chiang Mai, Thailand

10.1

Introduction

From space, the rice fields of the Mae Nam Ping basin around Chiang Mai curve like a fetus around the square heart of the old city, nourished by a placenta of forested hills (Plate 10.1). If we zoom in closer to the heart, the square moat, road and ramparts of the old city (Plate 10.2), we can make out the arterial roads streaming out from ancient city gates to feed the growing body marked by the curves of new ring roads (Plate 10.2). The lowland rice farmer’s view is of a society nurtured by, but separate from, wild nature, with the river, Mae Nam, literally the “mother.” Aircraft and satellite-based remote-sensing technology came to the fore during the Cold War as a way for states to gather military intelligence from

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space. Most of the early applications in the Chiang Mai region of northern Thailand were concerned with such strategic matters but are rarely discussed. Civilian applications have followed, but often start from the similar logic of the state defining boundaries and monitoring its subjects. In northern Thailand, remote sensing has primarily been viewed as a tool for documenting and monitoring changes in forest and agricultural land-cover. Rarely has it been directed toward urban areas, not just because of problems with resolution, but rather because the problems of sustainable development were framed as rural and mountain ones. When urban areas have been the focus, the end use has not gotten much beyond making large prints of the sprawling Bangkok metropolis and using them as high-tech symbols of the modernization project. The image of Chiang Mai is more ambiguous, combining as it does the footprint of history with the constraints of mountain geography. Until recently, because of constraints of high costs, secrecy, and lack of skills, most products of remote sensing have remained in the hands of a narrow group of technocrats in bureaucracy and academia. Increasingly wider access, improved capacities to interpret remote-sensing products, and the availability of enhanced products combining remote-sensing and more conventional map data, are changing the playing field. Various groups apart from the state agencies can make their own interpretations of changes and recent conditions. To date, interest has remained focused on forests in mountain areas and agriculture. This is unfortunate because it means that the critical role urbanization of the main valley plays in determining overall regional sustainability has been overlooked. For the most part, researchers concerned with environmental and social issues have adopted a conventional view of Chiang Mai in which the expansion of human settlements in any form constitutes “sprawl,” implying that urbanization itself is a bad, if unstoppable, process and is not sustainable. Selfishness and nostalgia may lie behind these sentiments. In our view, urbanization in the Mae Nam Ping basin could make a significant positive contribution to regional sustainability, promoting efficient use of land, energy, and water resources, while also providing a good environment for building skills, strengthening education, and adding value to businesses. That urbanization often hasn’t contributed much to improving the welfare of disadvantaged groups, or created opportunities for additional conservation of complex ecosystems, is evidence of poor urban planning and management rather than of the undesirability of urbanization per se. For example, poor layout of roads and scattered development of housing estates leave pockets of high-quality agricultural and valuable urban land difficult to access or even to manage as public green spaces. A high and still growing dependence on private motor vehicles is encouraged by road

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and suburban layouts that disrupt irrigation systems and sever social connections with avenues of danger. Wide, fast, and elevated roads with little or no pedestrian access or protection pose great risk and inconvenience to the poor, who are without cars and who cannot afford to live close to places of work. Lack of low-cost housing near important places of work, especially in service and informal sectors, leave the poor to choose between long commutes and living in squatter settlements. Remote-sensing images and derived products are part of the multifaceted revolution in digital communication and information technology. Skilled governments and citizens could use them to improve governance and make it more transparent, accountable, and evidence-based. But the opposite is also possible. As the digital divide between government and less wealthy and educated citizens expands, technocratic approaches to urban planning and governance come more and more to the fore, substituting purportedly neutral data in the form of images, maps, multi-criteria analyses, and models, for deliberation and negotiation. In this chapter, we look at the ways in which remote-sensing technologies have been, and could be, used in the planning and management of Chiang Mai city and its surrounds in northern Thailand. We do this with illustrations from applications of our own work, shorter reviews of a few key studies by other groups, and by asking questions we hope will stimulate more innovative and socially responsible uses of remote sensing in urban development. We emphasize that remote-sensing products should not be understood as ends in themselves, but rather as tools that contribute and complement visualizing, mapping, analyzing historical changes, and thinking about the future.

10.2

Views

From space, the sprawling city of Chiang Mai is also like a drain hole through which the resources of the mountains and fields are sucked into the bowels of the earth (Plate 10.1). If we zoom in closer to the drain, the square moat, road, and ramparts of the old city (Plate 10.2), we can make out the plumbing as it squeezes through the narrow city gates. An alternative view is of a society sucking nature dry. Remote-sensing – whether it is from the balcony of Doi Suthep temple, an airplane window, or the IKONOS satellite – is about vantage points. An image captured from far away, when viewed on the page or screen, puts things close together and we see proximities that are otherwise impossible to perceive. Such an image gives a sense of boundaries, territory and its

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control. Remote sensing also provides a sense of the similarities and differences in land cover between places, which lends itself to the conventions of mapping complexity into a few basic land-use types. Thus we have “urban” and “rural” land uses. 10.2.1 Access But not everyone gets a look, and when they do they don’t necessarily see the same things. Access to remote-sensing data has been constrained by costs, skills, and societal position. Most systematic, early remote-sensing applications in northern Thailand were based on aerial photos and lowresolution satellite data, such as Landsat MSS and Landsat TM. Microwave-length sensors like RADARSAT, NOAA, and the Japanese Earth Resource Satellite, and high-resolution data from sensors like SPOT, have rarely been used (Rakariyatham et al. 1998). In the early years, satellite imagery could formally be obtained only through the Thailand Remote Sensing Center, a division within the National Research Council of Thailand (NRCT). 1 During the 1990s, improved computer software, algorithms, and technological processing capacities resulted in an important shift away from manual digitizing and visual interpretation, and toward computer-assisted image processing. At the same time, there was some decentralization of services outside Bangkok, with the establishment of regional remotesensing centers. The Northern Remote Sensing and GIS Center was set up with financial support from NRCT in 1994 at Chiang Mai University (Rakariyatham et al. 1998). Integrated into a university as it is, the Center has played an important role in building government and non-government capacities in the use of remote-sensing data. One of its important early roles was to work closely with the Office of the Narcotics Control Board, a government agency, in using remote sensing to detect opium and other illegal crops cultivated in the uplands around Chiang Mai. In November 2000, the Geo-Informatics and Space Technology Development Agency (GISTDA) was created as a central public organization in Thailand with a mandated mission to provide remote-sensing and geospatial data to support national development. The agency also has a significant role in research and development. The new structure is more businesslike than other agencies and is independent of immediate government structures and control. Nevertheless, government agencies can obtain satellite data at no or very low cost, while non-governmental organizations must pay. Research outputs are supposed to be returned to the agency if data are obtained for free. International collaboration has often provided

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early access to new imagery for well-connected and technically advanced research groups in Thailand. Today these differences are smaller, and overall access to products via GISTDA is similar to most international sources, and can be cheaper and faster. Today, private companies like ESRI and Map Point Asia use remotesensing-based data as part of mapping and GIS services to help with business siting and planning. For the most part, however, use of remotesensing products is limited to a small group of trained people in government and academic institutions or large firms. This reflects both a lack of knowledge about availability of information like aerial photographs or satellite imagery, and lack of capacity to use it effectively, even when the existence is known. 2 10.2.2 Interpretations Most remote-sensing work has been done by academics and a few government agencies (Rakariyatham et al. 1998). Early applications using satellite-based imagery were concerned primarily with identifying major land-use and land-cover types and deriving image classification algorithms. For many researchers accustomed to segregated land-use patterns of present-day Europe and North America, land-use change studies around Chiang Mai were, at least at first, annoying, because they required more ground-truthing and validation. Here, you were supposed to be documenting deforestation and environmental degradation, and the crop fields in your images were turning into secondary forests; there, it looked as if there were orchards and even rice paddies next to the new housing estates, and a lot of green spaces were appearing near the city (Fig. 10.1), even though your tourist map hardly shows any public parks. What is urban? What is forest? Is bare land abandoned land that is being, or about to be, used? You can almost hear the poor expert mumbling to herself: “If we leave too much as other they will think we are no good at our job!” There are several related challenges to applying remote-sensing technologies to the study and monitoring of urbanization in the Mae Nam Ping basin. First, the older parts of Chiang Mai and Lamphun cities are mostly low-rise, with narrow streets and gardens with mature, mostly nondeciduous trees. Moreover, many walls and some older roofs are made of timber or covered with plant growth. As a consequence, the outlines of buildings in intermediate-resolution images like ASTER are not always very clear (Plate 10.2) and NDVI, a simplistic measure of greenness, is high even within the urban matrix (Figure 10.1). The overall mix of surfaces in lower-resolution products suggests that simplistic algorithms will

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significantly underestimate built-up areas and cause problems for change detection.

Fig. 10.1. Normalized difference vegetation index from ASTER bands 3N and 2. Index range is from 0.56 to – 0.40.

Secondly, shadow effects in mountain areas, and mixed reflectance from surface covers and anthropogenic activities, create additional classification challenges (Sangawongse 1996). The test of separability between landcover classes from Landsat Thematic Mapper images of Chiang Mai, imaged in February 1988, 1989, and 1991, revealed that bare soil, dry paddy field, and construction sites have close spectral similarity, so their reflectance is mixed. Fine-scale interpenetration of rural and urban land use in the Mae Nam Ping basin compounds these problems (Wara-Aswapatti 1991). Thirdly, the monsoonal climate of Chiang Mai is strongly seasonal, with pronounced dry (November-April) and wet (May-October) seasons. During the wet season, cloud cover is high. In the second half of the dry season fires, deliberately lit to burn off rice crop stubble in the lowlands and as part of land preparation in swidden systems in the uplands, frequently cover the city with dense smoke. Special techniques and more processing time are needed for removing such effects. All of these factors can reduce classification accuracy. One consequence of these challenges is that comparability and quality remain major issues for land-cover maps produced

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by different international groups, state agencies, and academic institutions. Very often products are distributed with little information about classification boundaries, decision rules, or validation procedures. Decisions about classes matter. Choices made can change the map. One of the responsibilities of remote-sensing researchers is to ensure that the assumptions and definitions behind their products are communicated along with the processed image in ways that are comprehensible. Another is to challenge poor and misleading representations and interpretations. Rather than seeing these classification challenges as annoying, they should be viewed as intriguing. 10.2.3 Resolution The availability of higher-resolution or multi-spectral bands creates opportunities for extracting additional information about urban structure and environments. In Plate 10.2 we zoomed in on the main built-up area around Chiang Mai municipality. Even at the modest intermediate resolution of 15m, key features of the spatial organization of the city stand out, including the older part of the city around the moat, the main roads emanating from the old city gates, and the newer ring roads. Closer inspection reveals substantial differentiation in building sizes, reflecting urban, old mixed commercial, and newer commercial centers, and, very significantly, pockets of abandoned land, orchards, and crop land. Higher resolutions, however, don’t eliminate problems with classification; they replace them with new ones as the temptation to read more into the image grows. 10.2.4 Social spaces Urban areas are complex mixtures of arranged and built, inert and organic land covers. Looking at the ASTER images in Plate 10.2 and Figure 10.1, and aided by knowledge of the city, one can see that it is composed of objects of different materials, different ages, and different sizes and shapes. In addition, it is composed of different spatial arrangements or grouping patterns, such as large buildings grouped together in compact or dispersed arrangements. The intensity and nature of human activities varies greatly across an urban landscape. For example, some patches of fragmented vegetation, like land awaiting development, may be left alone for extended periods and self-organize, while other areas, like manicured parks, are intensively managed by teams of gardeners (Figure 10.1). To an architect or urban designer, the satellite imagery of Chiang Mai provides a view filled with clues about how space is - and is not - meeting people’s needs.

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The structure of the built urban environment influences and reflects the way people work, live, and play (Rapoport 1977, Jensen 2000). The physical environment defines an operational environment or context for human activities. Within the operational environment are perceptual environments of individuals that give places and relations symbolic meanings. Furthermore, within the perceptual environment, there is a behavioral or action “space,” the space that people are directly aware of and respond to (Rapoport 1977). A better understanding of the “social spaces” of perception, meaning, and experience, and their congruity or incongruity with three-dimensional physical space, would greatly improve our capacity to make cities better places to live. Is it possible to see, in a reproducible fashion, more than physical landcover in remotely sensed images? Can we get a handle on social space? Such a view is not clearly visible in the VNIR image in Plate 10.2. With simple image enhancement, such as spatial and spectral enhancement, these patterns start to be more visible (Plate 10.3). Enhancing the image in Plate 10.2 to emphasize the boundaries between areas with similar spectral characteristics produces an image that emphasizes different kinds of boundaries (Plate 10.3 bottom). In this image, for example, red indicates boundaries between water bodies and other surfaces, while dark blue marks boundaries between short vegetation, like grasses, and bitumen surfaces, like the airport runway. In addition, the spatial arrangement and grouping pattern of various surface materials and buildings is more pronounced. A spectral enhancement (principle components) was applied to the spatial enhancement (texture) in Plate 10.3 top. Edges are more visible. As a result, shape, size, and grouping patterns such as clustering are more detectable in Plate 10.3 bottom. Several questions arise. Why do different parts of the city share distinct patterns? Were they built at the same time or for similar purposes? Or were there conditional factors that guided spatial organization, like pre-existing communal irrigation systems, what guided urban development along similar spatial pathways? The questions above cannot be answered by satellite remote sensing alone (Donnay et al. 2001). Satellite remote sensing is only suggestive of what might be going on in the city. Boonyarit Boonyawong (2001), for example, used field-survey data to complement remote sensing imagery, in order to analyze structural patterns and functions of Chiang Mai’s central business district. The boundaries of social space may not be distinct. People may become aware of boundaries through changes in activities. Socioeconomic and cultural activities create socioeconomic and cultural spaces and interactions that make issues of congruence and non-congruence between social and

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physical space important (Rapoport 1977). Mismatches between social and physical space, for example, may explain why people use or do not use a particular space, or why one marketplace is more successful than another. The cluster of social spaces and the interactions among them can be viewed as a dynamic actor network unfolding in and through the physical urban space. More information on the ground is necessary to analyze spatial patterns in the context of their underlying socio-economic processes. Diverse spatial patterns arise for qualitatively similar centers of activity. Figure 10.2 shows 1 x 1 km cells around five different, socially important, commercial locations in Chiang Mai. These clusters are commercial complexes in Figure 10.2 (1, 4); local marketplaces in Figure 10.2 (2, 5); and central marketplaces in Figure 10.2 (3). There are similarities and differences in patterns beyond the immediate market spaces among the images. All five locations are relatively densely built with rectangular organization. Commercial clusters involve larger blocks and lighter colored areas than local markets, whereas the central market has intermediate characteristics.

Fig. 10.2. Samples of different clusters that play a major role as market places in Chiang Mai.

In the brief illustrations above, a first-cut analysis was made possible by familiarity with the city where three of the authors live, and some site visits with the images in-hand. A more detailed investigation based on systematic interviews about use, residence times, arrival and departure data, and key network linkages could be collected to identify and locate social space and test the value of this conceptual approach. Geographic information systems could help to effectively combine enhanced remote-sensing imagery with ground-based information on cores, nodes, and spatial linkages. Together these analyses could improve understanding of the spatial characteristics of social space, linkages, networks, and their influence on behavior of urban dwellers.

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Histories

Individual images can say a lot, but it is the capacity to construct a series of images for comparative work that is critical to understanding urban histories. Pairs of images taken decades apart can show the ways in which a city has grown or declined in influence. Animated sequences of rapidly urbanizing regions often give the impression of an evolving amoeba in a Petri dish. Remote sensing can help provide up-to-date layers easily and cheaply for areas that have not been mapped by conventional means for a long time, and, in some cases, provide a way of validating or obtaining historical information on spatial organization of land uses that is otherwise unrecoverable. The problems of interpreting a single image are compounded when trying to combine data from multiple sources, but the tradeoff in terms of possible insights into historical dynamics is attractive. 10.3.1 Origins Chiang Mai is a city of historical legacies. Chiang Mai was founded in 1296 as the administrative capital of the Lanna Kingdom. It has a history that is distinct from, but often intertwined with, that of Burma and Siam (Winichakul 1994, Wyatt and Wichienkeeo 1998). For two centuries after 1558, the city was the centre of multiple battles, and changed hands several times. From 1687 to 1763, it was self-governing again, but in response to threats of another Burmese take-over, the city was completely abandoned for two decades (1774-1796). In 1796 it became a vassal state of the Thai kingdom, but continued to be directly ruled by its own line of royalty. It was formally annexed by the Bangkok government in 1896. The introduction of mapping technologies and concepts during the colonial period was crucial to the subsequent construction of the Siamese state. It changed the way people thought about the state by shifting the emphasis of politics from the control of labor to territory (Winichakul 1994). The introduction of satellite-based remote sensing did not, at first, change much, but there was growing realization that as a tool for monitoring land use, it could improve local government, state agencies, and community-based management programs. The important point is that technologies don’t just describe, they also shape the way societies imagine space and territory, and consequently, social control.

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10.3.2 Urbanization Remote sensing, in combination with other methods of collecting spatiallylinked information, has helped scholars and regional planners better understand changes in land use and spatial organization of the Chiang Mai region. They view satellite imagery as stills taken from an epic movie. The few earlier studies of urbanization in the Mae Nam Ping basin typically opted for easier-to-obtain, but more time-consuming-to-analyze, aerial photographs of limited parts of the municipalities of Chiang Mai and Lamphun. Siripunya (1987) compared aerial photographs from 1979 to landuse planning documents and his own land suitability assessment in the eastern part of the Chiang Mai basin. Veerapol Sauvaluk (1988) used aerial photographs, maps, and field work to analyze land-use patterns in Lamphun Municipality, the twin city of Chiang Mai. Yarnasarn (1985) aggregated data from multiple sources, including aerial photography and Landsat, to document some of the broader changes in urban form. Somporn Sangawongse extended these analyses by systematically combining aerial photography taken in 1954, 1972, 1977, and 1983 with Landsat TM images from 1988, 1991, and 1996 in order to explore the creation of a digital land-cover archive for the northern region of Thailand (Sangawongse 1996). The archive was initially intended primarily for forest monitoring, but other administrative functions, such as rural and town planning, were anticipated for the future. Sangawongse and Peterson (1997) suggest that such an archive would help establish credibility for central planners who would otherwise have to depend upon local folk law. The focus of the initial study by Sangawongse (1996) was on describing and verifying land-use changes in a 42 km2 area around the Mae Kwang Dam, which was built between 1976 and 1993 in Doi Saket District, a satellite town of Chiang Mai. The technical challenge of bringing different kinds of data into the same format was, at the time, formidable, but administrative challenges that hindered access to data, and its sharing and updating, were noted to be significant constraints that had to be overcome in order to establish an effective database service (Rakariyatham et al. 1998). This experience was a good foundation for the next generation of applied studies in the Mae Nam Ping basin (Fig. 10.3). These studies, taken together, have helped us better understand the way Chiang Mai has grown, and some of the problems that have been created for regional sustainability as a result.

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Fig. 10.3. Growth of Chiang Mai urban land-use, based upon digitized analog maps and classification of Landsat imagery.

After almost a century of modest growth centered primarily around trade, extraction of teak, and rice production, over the last two decades the main inter-montane valley of the Mae Nam Ping Basin around Chiang Mai (Fig. 10.1) has been spectacularly transformed (Lebel et al. 2004). The built environment, in terms of dwellings and road infrastructure, has grown faster than the population. The scattered location of new housing estates, however, has left pockets of abandoned agricultural land unused and driven up the cost of infrastructure and services. Poor road layouts and little or no collection of property taxes on vacant land allows speculators to leave land idle at low cost (Yarnasarn 1985, Pearson 1999). Industrial investments, tourism, manufacturing, and trade have grown in importance, and along with overall economic growth of the Thai state, have resulted in profound transformation of economic activities and livelihoods (Tran Hung and Yasuoka 2000). During this period urbanization has strongly interacted with industrialization, literally building upon the foundations of agricultural expansion and intensification in the 1960s and 1970s (Tran Hung 1998). Today, Chiang Mai is a mat of merging towns and villages, intimately connected to, and interpenetrating with, rural activities and the industrial and urban zones in neighboring Lamphun province.

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10.3.3 Ecosystem services Most studies of urban growth are purely descriptive and have not tried to link land-cover or land-use information with ecosystem services. Land-use changes are often interpreted uncritically as indicating a wide range of environmental changes when, in fact, they may be indicators of only a few. For example, bank-erosion and flood-control ecosystem services provided by trees and other vegetation may be largely maintained by a relatively narrow, vegetated riparian corridor and irrigated wet-season agricultural fields that effectively behave as wetlands. Other services, however, such as maintaining productive fisheries or a biologically diverse wetland flora and fauna, may not be maintained. There are several opportunities to use remote-sensing to enhance management of environmental changes that affect urban areas in the Chiang Mai region. There is recurring poor air-quality at the end of each dry season as a result of fires in the main valley and surrounding mountains. Hot spots are already being monitored at high temporal frequencies using AVHRR and NOAA across the mainland Southeast Asia region. This information could be analyzed with climatic and ambient air quality information to develop early-warning and perhaps preventative systems, like “no burning days,” when there is high risk of haze formation in urban valley corridors. Another application for monitoring and planning would be to manage irrigation water in the dry season, and to map flood history and risk at the end of the wet season. Finally, urban vegetation as measured by NDVI (Fig. 10.1), or related indices from ASTER images, could be correlated with surface radiant temperatures and used as a measure of microclimate control services provided by urban vegetation. This information could be helpful in prioritizing street-side, roof-top, and car-park treeplanting schemes.

10.4

Models

Analyzing time series of land-cover maps invariably involves models of one kind or another for exploring change. Quantitative spatial models are frequently used to analyze land-use changes detected with remote-sensing data, in the hope of understanding historical processes and applying such understanding to exploring future scenarios for change. In contrast to planners, to model-users satellite imagery is viewed somewhat like stills from the beginning of an animated film, inspiring stories of what happened in between and how it might end.

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Most modeling studies carried out in northern Thailand that have used remote-sensing information as inputs have focused on changes in land use, in particular, changes in forest cover in the more remote mountain areas. Relatively few researchers have focused on developments and changes in their own urban environments, and of these, almost none have moved beyond descriptive analyses of changes in land cover. In the following sections we describe two examples of ongoing urban modeling exercises, SLEUTH and ELSE. 10.4.1 SLEUTH SLEUTH is a modified cellular-automaton model that consists of an urban growth sub-model and a land-cover-change sub-model (Clarke 2002, Silva and Clarke 2002). Sangawongse adapted the SLEUTH model to the 2,500 km2 area over Chiang Mai city and its environs (Sangawongse et al. 2005). Remote-sensing and cartographic data were compiled and processed to obtain grey-scale GIF images of the same extent. Raster-based, spatial remote-sensing data is easily incorporated into cellular-automaton models as they both have a cellular structure. Satellite data input for the land-cover modeling consisted of Landsat-5 TM and Landsat-7 ETM+, which were acquired in 1989, 1992, and 2000, respectively. Data for other periods was obtained from an existing land cover database, with earlier maps based primarily on aerial photography. Due to the inconsistency of land-use data from various sources, much time and effort was spent converting data into the same format. The metadata and land-use classification scheme used for manipulating the Chiang Mai data were modified from the 1996 Department of Land Development of Thailand and the 2000 Thailand Environment Institute schemes. The final product was a spatio-temporal land-cover and land-use database for the Chiang Mai study area spanning 1952 to 2004. This database consists of five major land-cover types: (1) Agriculture, (2) Forest, (3) Urban and Built-Up Land (4) Water Body, and (5) Miscellaneous Land. These data were used as inputs for land-cover modeling. Agriculture includes paddy field, orchards, and swidden cultivation. Forest consists of hill evergreen, mixed deciduous, dipterocarp, and forest plantations. Urban and Built-Up Land consists of cities and towns, rural residential land, institutional land, airport, and recreational land. Miscellaneous Land refers to grassland, barren land, salt pans, and vacant land, which are not considered important land-cover types. Input data for the Chiang Mai area were prepared to calibrate the SLEUTH model at resolutions or cell sizes of 200, 100, and 50m.

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SLEUTH simulates four types of urban land-use changes: spontaneous growth, new spreading center growth, edge growth, and road-influenced growth (Clarke 2002). Spontaneous growth simulates urbanized pixels randomly and is controlled by a diffusion coefficient. Low-density development patterns can be captured regardless of proximity to urban areas or the transportation infrastructure. New spreading center growth determines whether any of the new, spontaneously urbanized cells will become new urban-spreading centers by the breed coefficient. Edge growth simulates growth that stems from existing urban centers. It is controlled by the spread coefficient, which influences the probability that a non-urban cell with at least three neighbors will become urbanized. Road-influenced growth simulates the influence of transportation networks on growth patterns by generating spreading centers adjacent to roads, and it is controlled by the breed coefficient. The growth rules are applied sequentially during each cycle. Brute-force calibration involved fitting the model to historical data on land use, transportation, and urban extent. Three phases of calibration using a set of spatial, goodness–of-fitness statistics, and corresponding to progressively higher spatial resolutions, were carried out (Sangawongse et al. 2005). After the final calibration, the model suggests the best parameter-value combinations that can then be used in forward-looking applications. The SLEUTH model separates urban growth into several components. The way these change over time provides insight into urban dynamics. Thus, from Figure 10.4, we can see the growing importance of edgeinfluenced growth, while most other processes remain remarkably constant over time. The edge growth reflects the conversion of agricultural lands to housing estates, starting from ribbon development along the major roads. According to Porananond (1993), the expansion of Chiang Mai city occurred in an irregular pattern and dispersed in all directions, resulting in very locally mixed types of land use and their associated environmental problems, including traffic bottlenecks and long-distance commutes, and difficulties for waste collection and other basic services (Lebel et al. 2004). As with all models, the quality of the SLEUTH model depends upon the quality of the input data and decisions about resolutions. For example, when the resolution of the road data (1952 and 1977) was changed from 50 m to 100 m and 200 m, some small roads were missed. In the future, higher-resolution satellite imagery could be used to better calibrate or test the model.

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Fig. 10.4. Number of urban pixels generated by different growth processes in the SLEUTH model of Chiang Mai in three time periods.

10.4.2 ELSE The Ecosystem Landscape Scenario Explorer (ELSE) model was built primarily to help quantify and make spatially explicit a set of multi-scale qualitative scenarios that were being developed (Lebel et al. 2004, Lebel 2006). There are three variants of the model, each with the same basic structure but working at radically different scales. The first covers the entire Mekong region and is based on a 10-km grid cell. The second is a study of the Mae Chaem upper tributary watershed of the Mae Nam Ping basin and is based on a 1-km grid. The ELSE-Urban model is restricted to the districts around Chiang Mai and Lamphun towns and is also based on a 1-km grid. Individual grid cells have mixtures of land use, for example 30% orchard, 20% rice, and 50% urban residential. It is thus a low–spatialresolution model.

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Separate datasets were prepared and analyzed for the two target regions by gridding acquired data. All candidate-predictor variables were transformed into multiple-level categorical variables prior to analysis, in order to facilitate exploring non-linear associations. For this initial analysis of the Chiang Mai urban region, we settled on a small set of seven land-cover types: Forest, Field Crop, Water, Urban, Orchard, Paddy Field and Bare Land. The core of the model is based on fitting historical-regression equations of categorical predictor variables to changes in land-use cover of cells. This yields a set of differential equations describing how each landuse cover has changed. Additional process-oriented realism is brought into the model, when used for quantification of scenarios, by introducing rule-based algorithms for how some of the background map layers change over time; in particular, roads, population densities, and the boundaries of excluded areas like national parks. These are similar to cellular-automata rules and are based on statistical analysis of, for example, where roads are more likely to be built in relation to existing roads, but instead of presence or absence, changes in road-length density per cell area are calculated. In comparison to the SLEUTH model, the ELSE-Urban model is computationally simple, allowing greater emphasis on sensitivity analysis to explore assumptions. It provides a basis for capturing the spatial and quantitative implications of more qualitative uncertainties captured by scenario storylines. On the other hand, the results are spatially more aggregated and the explorations into the future are likely to be not as well constrained. A well-calibrated model allows for use of the descriptive tool to help generate alternative landscapes under different scenarios for Chiang Mai’s urban development.

10.5

Visions

It may seem unusual to write about remote-sensing the future, but we believe that technologies have changed the way the people think about the future, in particular about scales of change. Moreover, remote-sensingderived data are often an important part of the historical analyses from which models for exploring alternative futures are constructed. It is as if after viewing a movie for awhile, we were allowed to pause and choose among a set of optional events and characters and then continue to watch a story unfold. Two approaches seem plausible. The first is time and space-for-time substitutions, in which we look for changes in places that modernized early

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to understand what may next happen in more resistant or remote locations. The second is to explicitly take understanding from history, using enhanced modeling of series of remotely sensed land-cover data, and explore different sets of assumptions with these models to get ideas about magnitudes, rates, and spatial organization of change. 10.5.1 Space for time Although rarely acknowledged as an analytical approach, using logical space-for-time substitution to interpret possible changes is quite common. Urban places perceived to be more developed or modern are taken as reference for the future of other more ordinary places (Robinson 2006). The experiences of satellite towns of Bangkok are a first–cut model for Chiang Mai. Changes in Chiang Mai are helpful for thinking about other provincial capitals in northern Thailand. Chiang Mai municipality is of growing interest to the emerging towns forming around district capitals. But there are risks in such an approach. At broader regional levels, the social context for development today may be substantially different from what it was a decade or two ago. Processes of urban change may not unfold as they did in the past nor should they necessarily follow the ideal symbolized by the capital. For example, in northern Thailand, the rapid transition to lower fertility rates led to below replacement-level birth rates being reached in the mid-80s. As a result, the population is now aging rapidly. Birth rates are even lower in urban areas, meaning that further growth of Chiang Mai will primarily be through rural-to-urban labour migration or rural-to-urban transformation of livelihoods in situ. The implication for rural land use is that without the availability of cheap labor from Burma/Myanmar, pressure for expansion of agriculture would fall or even reverse. Scenarios are an effective way for dealing with these kinds of uncertainties. 10.5.2 Scenarios Chiang Mai is in a critical stage of its development. Two decades of rapid economic growth and infrastructure developments have made people wealthier, healthier, and much more mobile. At least two major uncertainties need to be addressed. The first is the fundamental degree to which development in the urbanizing region is linked to and driven by external relations, in terms of trade, investment, and material flows in particular, and water and labor resources. The second is the degree to which agricultural

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production, as crops or trees, will persist as a land use and livelihood activity in the main and larger valley. For these reasons we constructed a set of four scenarios based upon different assumptions about these key uncertainties in regional development (Fig. 10.5). Our purpose was not to create a set of straw-man options from which the reader was forced to pick a winner, but rather to provide a more realistic situation in which each of the scenarios could be viewed as positive or negative by specific actors, and which each contain some opportunities and challenges for regional sustainability. Two parallel sets of scenarios were developed at the northern-Thailand scale to capture processes affecting the Mae Nam Ping Basin as a whole, including upland areas, and for the main inter-montane basin containing the Chiang Mai-Lamphun urbanized area. The scenarios were constructed to capture the large uncertainties about how livelihoods and regions could engage with wider markets and social structures. At the northern-Thailand scale of analysis, these four scenarios are labeled according to the kind of market integration they imply, while at the finer scale, the labels are evocative of particular lifestyles.

Fig. 10.5. The urban scenarios are embedded in larger regional socio-economic scenarios for how the landscapes may evolve (adapted from Lebel et al. 2004).

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We used soft models, or causal-link diagrams, and sequence diagrams of events, to help improve the internal consistency of the qualitative storylines and to link the stories to specific assumptions about changes in sets of parameters in the ELSE model. Scenario runs of the model were implemented by altering the functional form of relationships between the outcome land-cover-change variable and a small set of predictors in a way consistent with the soft model and storyline for that scenario. Scenarios are potentially important boundary objects for exploring urban development and growth in Chiang Mai. In meetings with selected business, state official, and academic stakeholders, a purely qualitative scenario exercise showed some promise in getting everybody to think about alternatives, even when there was not much agreement about which were or were not desirable. Quantitative and spatially explicit models based upon historical time series can help people understand likely limits on rates and patterns of urban change. In general public exposure to models and scenarios exercises in Chiang Mai context, beyond single “vision” speeches is infrequent and not widespread. The arena with greatest engagement with the public has probably been with respect to spatial planning of road infrastructure where simple maps or artist’s impressions have played an important communication role about transport and layout “options”. The assumptions behind traffic modeling exercises that lay behind these plans are generally invisible in the public arena. Moreover, the dominant mode of engagement by state authorities is still that of marketing decisions already made so as to gain public acceptance for them rather than seeking public comment and deliberation over alternatives. Visualization tools, like animations of past remotesensing imagery and “fly-overs’ of current states superimposed on digital elevation maps have a strong “gee-whiz” factor, but have not really proven themselves useful in influencing public debate above more conventional photos and maps. Other ways of thinking about space in ways people can more easily relate to are needed.

10.6

Actions

Remote sensing is not quite the neutral technology it is claimed to be. Historically, it has been wielded as a tool of control, but it can also be a tool of resistance and challenge. One problem is that those who might benefit by resisting are rarely on the right side of the digital divide. Another is that important information is often not acted upon because it is not communicated well and its significance is not understood.

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10.6.1 Choices The science and technology community is fond of assuming that their work, the images and maps they produce, are objectively neutral and independent of political interests. We are skeptical. Important biases, conscious or otherwise, and with political consequences, are introduced, for example, through choice of scales, filters, classes, coloring schemes, and visualization techniques. Distributions and differences between mature, secondary regrowth forest, pine plantations, and orchards can be emphasized or minimized through aggregation of classes and choice of resolution where different covers are finely interspersed. Image processing can be used to highlight boundaries or similarities among different adjacent land covers giving different visual cues about continuity of changes across the landscape. Although there have not been many lowland urban applications around Chiang Mai yet, we anticipate similar challenges to occur in lowland studies as those that have arisen in studies focused on upland areas. Researchers, urban planners, and real estate developers alike are excited about the promise of high-resolution imagery in urban environments. Newer satellite sensors such as IKONOS and Quickbird are helpful, but detailed classification of land uses, as is often required for urban planning and investment decisions, is still very difficult. Alternatives, like the mounting of high-resolution sensors on unmanned aerial vehicles that stay in position for months, are becoming technically feasible. The acquisition and use of high-resolution imagery of urban areas in which individual households and business properties can be identified raises several legal and justice issues. Should such information be made available only to state agencies? Should it and could it be linked to other spatial datasets? Could this lead to unfair discrimination? Experts working with remote-sensing products in urban environments have a growing burden of responsibility. We suggest that each time a remote-sensing product is created for use in a policy arena, we should ask: Whose interests do they make invisible? Which structures and variables do they elevate? What would a map from a different perspective look like? The power of maps and images places a burden of responsibility for transparency and honesty on researchers. Information technology could be used to reduce and overcome some of these problems by placing decisions about aggregation of classes, colors for emphasis, and which variables to even display, back in the hands of the userviewer.

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10.6.2 Responsibilities To date, remote-sensing-based products and interpretation services have primarily served state and elite interests. Could they be used to empower the urban poor? If high-resolution imagery in simple and more processed forms were made available to all, would it contribute to more open, transparent, and fairer negotiations over urban development? Would civil society be able to use it to monitor urban development and the impacts of policies? Or will these tools reinforce existing unfair structures of power and the continuance of unsustainable patterns of regional development? The conventional model of how remote-sensing-based information and its expert interpreters contribute to urban planning, management, and policy is very linear and technocratic (Fig. 10.6A). The pathway from imagery to maps, through classification schemes and then land-use plans and policies, is seen as part of technical-decision support, and thus somehow beyond politics. This is sleight-of-hand. In this paper we have begun making the case for a different model of how remote sensing can be used and for the roles and responsibilities of remote-sensing experts (Fig. 10.6B). In the innovative future, remote-sensing imagery and derived products are easily accessible to many actors in the system, and even the way they are prepared - for example, classes and uncertainties in defining them - is not left unquestioned. The rational basis for decision support is more transparent. The spatial positions adopted by decision makers, civil society, and lobby groups must be more explicitly articulated and open to scrutiny. Performance can be monitored independently, and thus key actors held accountable. In Chiang Mai this is critical. Starting in 1969 the Department of Town and Country Planning launched a 25-year plan that was never fully implemented. Since then there have been dozens of plans with similar fates (Lebel et al. 2004). In the innovative future, remote-sensing researchers are no longer the handmaidens of planning agencies, but see their role in development as arena creators or contributors to deliberative processes. They no longer provide e-governance or geospatial solutions to a single set of interests, but explore options for many. They no longer hide behind a mask of technical wizardry, but actively seek and create opportunities for meaningful participation in interpretation, mapping, and organization of spatial information (e.g. McCall 2003). To date, there have been very few cases of non-state agencies using remote-sensing imagery to challenge plans or monitor impacts of urban development. In early 2005 we were shown a presentation and a video CD, created by a Chiang Mai-based NGO with GIS and remote-sensing capacities, that projected IKONOS images onto a digital elevation map and then

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created an animated fly-over with added features to explain to nearby villagers the impacts of a proposed strip mine and its tailings Although not strictly an urban example, it is an interesting glimpse into possible future actions.

Fig. 10.6. Two perspectives on the contribution of remote-sensing to urban planning and management. (A) conventional past; (B) innovative future. Dotted lines focus on primary remote-sensing products, while solid lines are more processed and integrated products within communications. Boxes represent individual and collective actors.

Most urban applications have been by for-profit organizations. Environmental Systems Research Institute (ESRI) of Thailand has used satellite data in urban planning and zoning. The company Map Point Asia developed, along with Global Positioning Systems, tools for road navigation. Both applications have been within the Bangkok metropolis. Even there, cooperation has been limited. A key challenge for Chiang Mai is to get be-

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yond the history of inter-agency competition, and a culture of borrowing without acknowledgement that has undermined trust and led groups to be secretive and protective with respect to datasets in which they have invested. Transparent, shared standards and norms of cooperation and acknowledgement need to be negotiated. Remote sensing is part of the digital transformation of society and governance. Conceptually, it could make interactions between government and citizens easier and faster. By providing independent views of the information going into decision-making, it could help improve transparency and accountability. With decentralization it could lead to multiple agencies, both state and non-state, contributing to urban planning and development as they have done, for example, in Bangalore (Heitzman 2003). But egovernance could be used as a way to move key decisions outside the realm of deliberation and negotiation and into black boxes of decision support. This is an important issue in Thailand and Chiang Mai because the capacity of government agencies to access and use information technology grows rapidly, while that of citizens, apart from a few academics, is not keeping pace. Governance mechanisms are therefore important to ensure that unequal access to public information does not become another tool that reinforces entrenched interests, but instead becomes a tool for liberation and social development. Imagery, models, integrated social-spatial analyses and scenarios could contribute to social justice, but are most likely to do so when information-communication technology projects are done by grass-root intermediaries with strong incentives to work with marginalized groups (Cecchini and Scott 2003). More innovative approaches to research are needed. Better ways need to be found for analyzing, projecting, and visualizing data in ways that are meaningful to a wide range of stakeholders, rather than just representing the interests of a small group of technocratic elites. Remote-sensingderived products, especially when combined with other spatial or networkbased information, could become useful tools in creating and supporting forums in which to negotiate alternative futures for the Chiang Mai region. Higher-resolution imagery in urban settings could become more useful to non-governmental organizations in their environmental and social advocacy work. Up-to-date information and regular coverage could help them get involved in an informed way in planning and decision-making at local levels, but tellingly, we were told by an official: “The NGOs can get involved if they participate with government activities, but only as long as they get on well with government policies.”

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245

Conclusions

The capacity of a society to respond creatively and adaptively to the underlying processes that shape, and are shaped by, urban forms and functions, is enhanced by the interaction of multiple, diverse views. In this paper we described several ways in which remote sensing can inform different views. Satellite-based imagery, especially if organized and enhanced with accessible web-based tools, could, for example, be used by the public to hold local and regional authorities and firms accountable for paths taken and opportunities missed. Of course, it must be a two-way deal. Their own behavior and performance as responsible stakeholders in managing floodplain land uses or maintaining tree cover, for example, would also be open to scrutiny by neighbors and authorities. The contributions to democratizing the governance or regional sustainability of urban Chiang Mai made by experts wielding remote-sensing tools have, however, been very modest. For the most part, their gaze has been fixed on poor farmers in the mountains rather than on the comparatively wealthy old and new suburbs of an expanding Chiang Mai. As a result, new environmental and social traps are being created at their doorstep, and opportunities for urbanization to contribute to regional sustainability are being overlooked. Remote-sensing technologies have been viewed and used primarily as tools for social control. If experts and their patrons were to assume greater social responsibility, they might instead become tools for social justice and urban sustainability.

10.8

Acknowledgements

The authors are grateful to Arizona State University for sponsoring participation in an urban remote-sensing workshop that led to this first collaboration among three research groups in Thailand. Thanks to Rajesh for comments on earlier drafts of this paper.

10.9

Notes

1. Interview with Pong-in Rakariyatham Head of the Northern Thailand Remote Sensing and GIS Centre, Chiang Mai University 2. Interview with Dr. Sukij Wisessinthu of ESRI (Thailand)

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10.10 References Boonyawong B (2001) An analysis of structural patterns and functions of Chiang Mai’s central business district. MS thesis, Chiang Mai University Cecchini S, Scott C (2003) Can information and communications technology applications contribute to poverty reduction? Lessons from rural India. Information Technology for Development 10:73-84 Clarke KC (2002) Land use change modeling using SLEUTH. Advanced training workshop on land use and land cover change study, National Central University/START, Taiwan, pp 525-573 Donnay JP, Barnsley MJ, Longley PA (2001) Remote sensing and urban analysis. In: Donnay JP, Barnsley MJ, Longley PA (eds) Remote sensing and urban analysis. Taylor & Francis, New York, NY, pp 3-18 Heitzman J (2003) Geographic information systems in India's 'Silicon Valley': The impact of technology on planning Bangalore. Contemporary South Asia 12:57-83 Jensen JR (2000) Remote sensing of the environment : An earth resource perspective. Prentice Hall, Upper Saddle River, NJ Lebel L (2006) Multi-level scenarios for exploring alternative futures for upper tributary watersheds in mainland Southeast Asia. Mountain Research and Development 26:263-273 Lebel L, Manuta J, Garden P, Huaisai D, Khrutmuang S, Totrakool D (2004) Urbanization in the Mae Nam Ping Basin: are transitions in the Chiang Mai Lamphun corridor contributing to regional sustainability? USER Working Paper WP-2004-02. Unit for Social and Environmental Research, Chiang Mai University, Chiang Mai McCall MK (2003) Seeking good governance in participatory-GIS: Review of processes and governance dimensions in applying GIS to participatory spatial planning. Habitat International 27:549-573 Pearson R (1999) A political economy analysis of the impact of agrarian change and urbanisation on communal irrigation systems in the Chiang Mai valley, northern Thailand. PhD thesis, Macquarie University Porananond A (1993) The expansion of Chiang Mai City: The changing of physical environment. Chiang Mai University, Chiang Mai, Thailand Rakariyatham P-i, Sangawongse S, Chamnivikaipong P (1998) Application of remote sensing data in Northern Thailand. Space Technology for the National Development, Regent Hotel, Bangkok, Thailand Rapoport A (1977) Human aspects of urban form: Towards a man-environment approach to urban form and design. Pergamon Press, New York, NY Robinson J (2006) Ordinary cities: between modernity and development. Routledge, New York, NY Sangawongse S (1996) Landscape change detection in the Chiang Mai area: An appraisal from remote sensing, GIS and cartographic data developed for monitoring applications. PhD thesis, Monash University

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Sangawongse S, Peterson JA (1997) Integration of remote sensing data to land-use /land-cover change detection in the Chiang Mai area northern Thailand: A case study from the Doi Saket district. In: McDonald AD, McAleer M (eds) MODSIM'97 international congress on modelling and simulation. The Modelling and Simulation Society of Australia, Hobart, Australia, p 99-103 Sangawongse S, Sun CH, Tsai BW (2005) Urban growth and land cover change in Chiang Mai and Taipei: Results from the Sleuth model. MODSIM 2005 international congress on modelling and simulation. The Modelling and Simulation Society of Australia and New Zealand, Hobart, Australia, p 2622-2628 Sauvaluk V (1988) The analysis of land use pattern of Lamphun Municipality. Chiang Mai University, Chiang Mai, Thailand Silva E, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems 26 (6):525-552 Siripunya U (1987) A comparative study of land use on the eastern part of the Chiang Mai basin with the land use plan using physical characteristics as the criteria. MS thesis, Chiang Mai University Tran Hung (1998) Integrating GIS with spatial data analysis to study the development impacts of urbanization and industrialization: A case study of Chiang Mai-Lamphun area, Thailand. PhD thesis, Asian Institute of Technology Tran Hung, Yasuoka Y (2000) Integration and application of socio-economic and environmental data within GIS for development study in Thailand. Proceedings of the 21st asian conference on remote sensing, Taipei, Taiwan, http://www.aars-acrs.org/acrs/proceeding/ACRS2000/Papers/GIS00-4.htm, accessed 6-March-2007 Wara-Aswapatti P (1991) Image processing technique for urban and rural land use monitoring in northern Thailand. Journal of Thai Geosciences 1:59-63 Winichakul T (1994) Siam mapped: A history of the geo-body of a nation. Silkworm Books, Chiang Mai, Thailand Wyatt DK, Wichienkeeo A (1998) The Chiang Mai chronicle (2nd ed). Silkworm Books, Chiang Mai, Thailand Yarnasarn S (1985) Land-use patterns and land-use changes in Chiang Mai, northern Thailand. PhD thesis, Columbia University

Index

A accuracy, 46, 58, 88, 92, 148, 153, 179, 211, 216, 226, 255 accuracy assessment, 150 ADEOS, 185 aerial photography, 173, 174, 213, 215, 231 aerial photograph, 83 aerial photo interpretation, 37 agglomeration, 56 AgTrans, 144, 151 air quality, 233 airborne sensor system, 124, 126 albedo, 120, 123, 128 ALI, 8 Aligarh City, 183 Alipur, 182 Andes, 55 anthropogenic impact, 118 ArcGIS, 172, 181 ArcView, 85, 172 ARES, 48 Argentina, 78, 79, 80, 81 Arizona State University, 138 asphalt, 127, 141, 148, 213 ASTER, 8, 12, 14, 39, 42, 43, 44, 78, 79, 84, 123, 127, 129, 142,

146, 151, 178, 184, 186, 225, 227, 233 astronaut photography, 12, 124 Atlanta, 127 Atlantic Forest, 25, 26 ATLAS, 124 atmospheric component, 148 atmospheric correction, 41 AutoCAD Map, 172 Automated Map of Properties, 207 automation, 40 AVHRR, 8, 123, 129, 233 AVIRIS, 39 B Baltimore, 140 band, 41, 227 Bangalore, 173, 178, 181, 188, 244 Bangkok, 222, 230 base map, 181 Berlin, 3, 15, 17, 38, 42, 45, 199, 201, 203, 207, 208, 211, 213 area type, 209, 211, 212 Digital Environmental Atlas, 201, 205 Environmental Atlas, 38, 200, 214 eGovernment, 204

272 Regional Reference System, 207 Senate Department of Urban Development, 205 Urban and Environmental Information System, 201, 217 Waterworks, 214 Bharatpur, 189 biological diversity, 78 Biosphere Reserve, 26 biotope, 77, 78, 85, 90, 92, 201, 208, 215, 216, 217 biotope-type map, 78, 86 Brazil, 26, 85 breed coefficient, 235 brownfield, 10, 45 Buenos Aires, 17, 78, 79, 80, 81, 83, 84, 91 building, 46 C Cairo, 3, 55, 64 Canberra, 15 canopy structure, 54 carbon dioxide, 118 Cartosat, 171, 191 Census of India, 189 Central Arizona-Phoenix LongTerm Ecological Research, 140 Central Arizona-Phoenix LongTerm Ecological Research Project (CAP LTER), 137, 144, 150 C-factor method, 29 Chacabuco, 88 Chang Mai, 15 change detection analysis, 71, 144, 151, 153, 154, 181, 182, 212, 226, 254 Chengdu, 18, 250, 254, 257, 258, 262, 264, 265 Chenghua, 259 Chennai, 165, 181, 188 Chiang Mai, 3, 17, 221, 223, 224, 225, 226, 227, 229, 230, 232,

233, 234, 235, 237, 238, 239, 242, 243, 245 China, 2, 249, 264, 265 Chongqing, 254 class, 30, 148 contextual, 33 coverage, 29 classification, 46, 86, 88, 217, 225, 227, 234, 241 expert system, 15, 140, 146, 148 fuzzy, 88 hierarchical, 46, 86 maximum likelihood, 86, 145, 178 membership, 31 minimum distance, 145 object-based, 31, 33, 35, 46, 90 parallelepiped, 145 pixel-based, 31, 78 supervised, 86, 145, 148, 178 unsupervised, 43, 145 climate, 119 cluster, 43, 44 CLUSTERS, 211 CO2, 185, 207 Cold War, 221 concrete, 141, 213 Consortium for the Study of Rapidly Urbanizing Regions, 127 contaminant, 8 conurbation, 2, 56 Convention on Open Access to Environmental Information and Public Participation, 208 Costanera Sur, 80 Cuzco, 55, 64, 72 D danwei, 263 decision pathway, 149 decision rule, 149 decision theater, 6, 138 Defense Meteorological Satellite Program, 11 Dehradun, 167, 184

273 Delhi, 15, 165, 166, 167, 168, 169, 171, 173, 174, 178, 179, 181, 182, 183, 186, 189 Master Plan, 168, 187, 191 Delhi Development Authority (DDA), 170, 173, 176, 180, 182, 187, 188 desert, 120 differential global position system, 40 diffusion coefficient, 235 digital elevation model, 40, 157 digital orthophotography, 157 DLF, 170 Doi Saket District, 231 Dongguan, 252, 256, 258, 259, 261, 264 DRASTIC, 183 Dresden, 80 Dwarka, 170, 189 E Earth Observing System, 3 Earth Resource Satellite, 224 earthquake, 8 eCognition, 31, 33, 80, 86, 152 Ecosystem Landscape Scenario Explorer, 236 ecosystem service, 233 ELSE, 236, 237, 240 emission, 121, 146 emissivity, 48, 123, 127, 186 endmember, 57, 71 fraction image, 58 ENVI, 86, 172 environmental impact assessment, 200 environmental inequity, 142 ENVISAT, 129 ER Mapper, 28 ERDAS Imagine, 172, 178, 181, 214 EROS Data Center, 146, 159 Data Gateway, 124 ESRI, 204, 205, 225, 243

Enhanced Thematic Mapper Plus (ETM+), 42, 43, 44, 57, 123, 142, 146, 151, 171, 186, 214, 234 eucalyptus, 65 Europe, 2 European Union, 201 evapotranspiration, 53, 73 F false color, 144 Faridabad, 189 favelas, 26 FIS-Broker, 203, 206 foliage density, 54, 60, 71 forecasting, 119 fossil fuel, 118 fragmentation, 91, 217, 257, 262, 263 Free Environmental Information Act, 208 G Ganges River, 167 Gateway to Astronaut Photography, 159 Geo-Informatics and Space Technology Development Agency, 224 geoinformation system, 48 geological hazard, 8 Geomedia, 172 geometric correction, 85 German Environmental Information Network, 203 Germany, 78, 200 GIS, 84, 170 global warming, 121 GLOVIS, 159 grass, 60, 65 gravel bed, 47 ground control point, 40 groundwater, 47, 182, 201, 209, 213 Guangdong Province, 250 Guangzhou, 18, 251, 256, 258, 259, 261, 264

274 Gurgaon, 189 H Habitat Agenda, 187 Haryana, 167, 189 hazardous waste, 184 heat absorption, 120 herbicide, 47 hierarchic environment, 33 High Resolution Stereo Camera (HRSC), 39, 215, 216 histogram, 63 Hohokam, 139 hukou, 263 human interpreter, 144 Hyderabad, 173, 174, 176, 178, 181, 183, 188 HyMap, 39, 40 Hyperion, 8, 39 hyperspectral, 48, 124 I IFOV, 57, 71 IKONOS, 12, 27, 28, 30, 34, 39, 57, 77, 78, 79, 83, 84, 92, 123, 155, 167, 184, 185, 191, 241, 242 ILWIS, 172, 183 image analysis, 144 image segmentation, 46, 54, 56, 87, 90 inheritance, 152 segment, 152 impervious surface, 44, 45, 46, 47 imperviousness, 213, 214, 217 India, 165, 168, 176 Indian Space Research Organization, 189 inertial navigation system, 40 infrastructure, 166, 186, 232, 240, 254, 262, 267 InSAR, 8, 159 Integrated Mission for Sustainable Development, 188 International Space Station, 124

International Standard Organization, 204 IRS, 44, 166, 171, 174, 176, 181, 182, 183, 184, 186, 191, 211, 214 Istanbul, 55, 66 J Jaipur, 173, 185 K Kolkata, 165, 182, 188 Konkan Railway, 186 Kukatpally, 181 KVR, 211 L Lamphun, 225, 231, 232, 236, 239 land cover, 26, 27, 120, 123, 124, 128, 142, 147, 166, 174, 177, 211, 225, 226, 228, 234, 241, 255 change, 140 class, 34 classification, 15, 29, 71, 143, 148 land use, 26, 124, 127, 140, 151, 166, 177, 205, 209, 212, 217, 225, 230, 233, 234, 237, 254, 261, 262, 263, 265, 267 change, 176 landfill, 184, 190 Landsat, 8, 12, 27, 34, 57, 58, 71, 80, 123, 140, 146, 151, 167, 174, 177, 182, 212, 231, 254, 257 Landsat Data Continuity Mission, 129 Landsat 7, 42 landscape metrics, 15, 256, 255, 262 area- weighted mean patch fractal dimension, 256, 259 landscape shape index, 262, 257 mean patch size, 261, 256, 257, 262 number of urban patches, 256 total urban area, 256

275 urban edge density, 256, 259 urban patch-size coefficient of variation, 256 Lanna Kingdom, 230 Latin America, 79 Leh Laddakh, 181 Leica ADS40 Airborne Digital Sensor, 39 LIDAR, 8, 147, 159 Lima, 3, 15 LISS, 167, 171, 174, 176, 181, 183, 184, 191, 211 Longquanyi, 259 Long-Term Ecological Research Network (LTER), 140 Los Angeles, 56, 67, 73 Lucknow, 184 M Madras, 176 Mae Kwang Dam, 231 Mae Nam Ping Basin, 221, 222, 225, 231, 232, 236, 239 magnitude-frequency concept, 121 Manila, 15 Map Info, 172 Map Point Asia, 225, 243 Marana, 138, 155, 157 Massoorrie, 181 MASTER, 124, 126, 142 mean surface temperature, 212 Meerut, 189 megacity, 79, 165 Mehrauli, 182 mesoscale modeling, 120 Mexico City, 3, 8, 15, 79 mid-infrared, 123, 124, 146, 148 migration, 166 mineral, 146 mitigation strategy, 128 mixing space, 57 MM5, 143 MODIS, 8, 12, 39, 123, 129 MODTRAN3, 148 MOLAND, 13

monsoon, 167 Multispectral Scanner (MSS), 151, 167, 171, 174, 177, 181, 186, 191, 224 MTI, 129 multispectral, 39, 124 Mumbai, 165, 182, 188 Municipal Corporation of Delhi, 170 N Nagpur, 176 Nainital, 181 Najafgarh, 182, 183 Narela, 170 NASA, 159 National Informatics Center, 187 National Remote Sensing Agency, 173, 177 National Research Council of Thailand, 224 National Technology Mission on Drinking Water, 182 National Thematic Mapping Organization, 189 NAUTILUS, 14 NDVI, 44, 87, 88, 186, 212, 225, 233 nearest neighbour resampling, 31 near-infrared, 144, 146, 184, 215 New Delhi, 3, 17 New Delhi Municipal Corporation, 170 New York City, 56, 69, 73 NOAA, 224, 233 Noida, 189 Normalized Differential Water Index, 182 North America, 2 Northern Remote Sensing and GIS Center, 224 NRSA, 190 O object, 33, 37, 41, 46, 87, 152 object illumination, 37

276 object-oriented image analysis, 41, 45, 78, 79, 87, 90, 91, 151 Official Topographic Cartographic Information System, 207 Okhla, 173 Open Geospatial Consortium, 204 open space, 10 OpenGIS, 48 Open-Space Information System, 203 orthorectification, 28 Orthowarp, 28

post-classification recoding, 148 pre-processing, 40 principal component, 43, 44, 46, 57, 228 Puri, 181

P

Radar, 147, 184 RADARSAT, 224 radiant heat, 127 railroad corridor, 47 railroad track, 45, 46 Rajasthan, 189 rational polynomial coefficient, 40 red edge, 146 reference dataset, 28, 150 reflectance, 146, 148 Regional Remote Sensing Center, 171 Remote Sensing Application Mission, 177 Riachuelo, 81 Rio de Janeiro, 17, 26, 56, 71 Río de la Plata, 81 Río Matanza, 81 river gravel, 148 RMS error image, 58 Rohini, 170, 171 Rohtak, 189

Palam Vihar, 170 Panji, 181 Parque Chacabuco, 81, 85 Parque España, 81 Parque Indoamericano, 81 Parque Lezama, 81, 85 Parque Patricios, 81 Parque Ribera Sur, 81 passive system, 38 patch, 61, 64, 66, 67, 73, 77, 91, 140, 255, 256 segmentation, 61 size, 53, 54, 55 Patna, 183 PCI Geomatica, 172 Pearl River Delta, 250, 254, 257, 259, 261, 265 Pedra Branca State Park, 28, 29 periurban, 3 periurbanization, 263 permeability, 47 Phoenix, 3, 15, 16, 127, 137, 139, 140, 141, 144, 148, 150, 155, 159 Phoenix Area Social Survey (PASS), 141, 142 Pima Association of Governments, 157 pixel, 37, 144, 145, 214 plant community, 46 pollution, 169, 185, 186, 190, 200, 205, 267

Q QuickBird, 12, 39, 40, 42, 45, 47, 54, 55, 58, 60, 71, 124, 155, 167, 191, 241 R

S Salt River, 139 São Paulo, 26, 79 SAR, 80, 124 SAVI, 148 scale, 27, 39, 53, 60, 118, 126, 144, 172, 181, 205, 263 scatterplot, 57 Scottsdale, 138

277 segmentation threshold, 63 sensor airborne, 39 hyperspectral, 39, 40 satellite, 144 spaceborne, 39, 47 Worldview, 48 settlement, 9 sewage, 47, 201, 214 shade, 43, 44, 48, 60, 128, 214 Shannon’s Entropy Model, 177 Shenzhen, 251, 256, 258, 259, 261, 264 Shimla, 181 shortwave-infrared, 146 Sichuan Province, 250 Silvics, 29 Sky Harbor Airport, 128 SLEUTH, 234, 237 slope failure, 8 slum, 166, 167, 181 social science, 5 soil, 44, 46, 58, 60, 63, 73, 120, 141, 147, 151, 176, 200, 205, 209, 211, 213, 217, 226 Soil Pollution Register, 207 solar radiation, 53 solid waste, 183 Sonipat, 189 Sonoran Desert, 139 South Mountain Preserve, 155 Space Shuttle, 124 space-for-time substitution, 238 spatial heterogeneity, 41 spatial model, 233 spatial pattern, 229 spatial resolution, 27, 39, 44, 46, 47, 71, 123, 124, 144 spatial variance texture, 148 spectral library, 41 spectral mixing, 57 spectral mixture analysis, 56, 58 spectral signature, 86 spectral unmixing, 41 SPIN, 185

SPOT, 12, 27, 39, 44, 123, 171, 177, 181, 185, 186, 191, 214, 224 spread coefficient, 235 Statistical Atlas of Urban Agglomerations in Europe, 209 subdivision, 127 sub-objects, 33 subsidence, 8 suburb, 9, 56 superspectral, 124 surface runoff, 47, 121 surface temperature, 48, 120, 123, 127, 142, 186, 212, 217, 233 Survey of India, 189 sustainability, 222 sustainable development, 187, 188, 190, 191 swidden, 226, 234 SWIR, 43, 44, 48 T texture, 151 Thailand, 222, 224, 231, 234, 238 thermal-infrared, 48, 124, 127, 128, 186 threshold, 73 TIMS, 124 Thematic Mapper (TM), 44, 123, 140, 142, 146, 148, 151, 171, 174, 176, 181, 183, 185, 186, 191, 213, 224, 226, 231, 234 topography, 41 training sample, 145 training set, 86 Trans Yamuna, 178, 179 U UEIS, 208, 209, 211, 212 Ujjan, 173 urban, 3 air quality, 6, 78, 169 atmosphere, 118 boundary layer, 118 canopy layer, 118 canyon, 126

278 climate, 7, 48, 78, 118, 121, 126, 129, 142, 212 climatology, 118 development, 242, 250, 263, 264, 266 ecology, 80 environment, 228 environmental planning, 5 fringe, 140, 154, 176, 256 geology, 8 growth, 2, 5, 176, 250, 254, 261, 267 heat island, 6, 120, 122, 125, 127, 128, 141, 186, 191, 267 history, 230 planning, 77, 91, 127, 128, 143, 171, 200, 222, 242, 266 remote sensing, 37, 38 scenario, 239, 240 sprawl, 222 structure, 5 transportation, 184 Urban Dynamics Research Project, 13 Urban Environmental Monitoring (UEM) Project, 14, 144 100 Cities Project, 14 urbanization, 165 US Environmental Protection Agency, 14 USGS, 159 Uttar Pradesh, 167, 189 V vector data, 157

vegetation, 45, 46, 53, 54, 58, 64, 67, 70, 71, 73, 79, 85, 88, 91, 120, 123, 128, 142, 146, 148, 151, 186, 208, 212, 214, 216, 218, 227, 228, 233, 255, 257, 267 vegetation index, 148 very high resolution, 39 Vishakhapatnam, 181 visualization (3D), 157 VNIR, 43 volcano, 8 W wastewater, 182 water, 43, 44, 120, 139, 141, 166, 182, 183, 201, 205, 208, 211, 213, 228, 233, 238, 255 Water Index, 182 weather forecasting, 142, 143 weather record, 121 wind gust, 119 World Bank, 189 World War II, 139 Y Yade, 204, 205 Yamuna, 166, 167 Yamuna River, 182 Z Zhongshan, 252, 256, 258, 259, 261, 264