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

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

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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.

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

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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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Maik Netzband, William L. Stefanov, Charles L. Redman

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 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 (0.5) 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 (