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Maik Netzband
William L. Stefanov
Charles Redman
(Editors)
Applied Remote Sensing for Urban Planning,
Governance and Sustainability
Maik Netzband William L. Stefanov Charles Redman (Editors)
Applied Remote Sensing for Urban Planning, Governance and Sustainability
with 34 Figures
Dr. Maik Netzband Helmholtz-Centre for Environmental Research - UFZ Permoserstrasse 15 04315 Leipzig Germany
Dr. William L. Stefanov NASA Johnson Space Center Houston, TX 77058 USA
Professor Charles Redman Director, School of the Global Institute of Sustainability Arizona State University Tempe AZ 85287-3211 USA
Cover image is a subset of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) orthorectified scene of the Phoenix, Arizona, USA metropolitan area acquired on May 2, 2007. Visible to near-infrared ASTER bands 1, 2, and 3N are mapped to blue, green, and red respectively. Image credit: NASA/GSFC/METI/ERSDAC/JAROS, and the U.S./Japan ASTER Science Team.
Library of Congress Control Number: 2007931201
ISBN 978-3-540-25546-8 Springer Berlin Heidelberg New York
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable to prosecution under the German Copyright Law.
Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2007
The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Cover design: deblik, Berlin Production: Almas Schimmel Typesetting: Camera-ready by the editors
Printed on acid-free paper 30/3180/as 5 4 3 2 1 0
Preface
As the global human population continues to expand, and cities become the locus of this expansion, the need to understand and monitor the func-tion 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 cen-turies 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 re-mote 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 excit-ing and cost-effective opportunities for regional and local urban planners, developers, and managers.
Over the past six years, scientists in the Urban Environmental Monitor-ing (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 sci-ence, geography, ecology, and anthropology. Much of this work has neces-sarily 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 sensing-based 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, monitor-ing, 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 au-thors during the review and revision process. The chapter reviewers pro-vided 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. Robin-son, Richard Sliuzas, William D. Solecki, Frederick R Steiner, Paul C. Sut-ton, Christiane Weber, Douglas R. Webster, and Xiaojun Yang.
Funding for the workshop, and partial support to the editors for comple-tion of this book, was provided by NASA Earth Science Enterprise Re-search 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 ac-knowledge 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 Sustain-ability for contributions directly leading to production of this book, in par-ticular Kathryn Kyle for technical editing of the book chapters; and Lauren Kuby for logistical support of the workshop and editing of the Introduc-tion. 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 ....................................................................................................vList of Contributors .............................................................................xiiiGlossary .............................................................................................xxiii
Chapter 1 - Remote Sensing as a Tool for Urban Planning and Sustainability..............................................................................................1
1.1 Overview......................................................................................11.2 Social problems............................................................................41.3 Urban structure ............................................................................51.4 Climatic and atmospheric applications for urban remote sensing .........................................................................................61.5 Urban geohazards and environmental monitoring .......................81.6 Urban form and periphery............................................................91.7 Open space preservation ............................................................ 101.8 Evaluation of urban natural environments ................................. 111.9 Urban satellite sensors and mission legacy................................ 111.10 Urban monitoring initiatives .................................................. 131.11 Urban environmental monitoring project at Arizona State University...................................................................... 14
1.12 Outlook .................................................................................. 161.12.1 Case study Phoenix, USA.................................................. 161.12.2 Case study Rio de Janeiro, Brazil ...................................... 171.12.3 Case study Buenos Aires, Argentina ................................. 171.12.4 Case study Berlin, Germany .............................................. 171.12.5 Case study New Delhi, India ............................................. 171.12.6 Case study Chiang Mai, Thailand...................................... 171.12.7 Case study Chengdu and Guangzhou, China..................... 181.12.8 Interurban comparison ....................................................... 18
1.13 References.............................................................................. 19
Chapter 2 - Automatic Land-Cover Classification Derived from High-Resolution IKONOS Satellite Imagery in the Urban Atlantic Forest of Rio de Janeiro, Brazil, by Means of an Object-Oriented Approach... 25
2.1 Introduction................................................................................ 252.2 Methodology.............................................................................. 28
2.2.1 Study area .......................................................................... 282.2.2 Data.................................................................................... 282.2.3 Analysis ............................................................................. 29
2.3 Results and discussion ............................................................... 312.4 Conclusion ................................................................................. 342.5 References.................................................................................. 35
Chapter 3 - Advances in Urban Remote Sensing: Examples From Berlin (Germany)..................................................................................... 37
3.1 Introduction................................................................................ 373.2 New remote sensing technologies.............................................. 383.3 New remote sensing methods .................................................... 403.4 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 ...................................................................................... 473.6 Acknowledgments ..................................................................... 493.7 References.................................................................................. 49
Chapter 4 - Spatial Analysis of Urban Vegetation Scale and Abundance................................................................................................ 53
4.1 Introduction................................................................................ 534.2 Six urban landscapes.................................................................. 554.3 Spectral mixture analysis and image segmentation ................... 56
4.4 Vegetation fraction and patch size distributions ........................ 604.5 Comparison................................................................................ 644.6 Discussion.................................................................................. 714.7 Acknowledgements.................................................................... 754.8 References.................................................................................. 75
Chapter 5 - Urban Environmental Monitoring in Buenos Aires – Determining Green Areas ....................................................................... 77
5.1 Introduction................................................................................ 775.2 Background................................................................................ 795.3 Related work .............................................................................. 795.4 Materials and methods ............................................................... 81
5.4.1 Study area .......................................................................... 815.4.2 Data.................................................................................... 815.4.3 Preparatory work................................................................ 845.4.4 Remote sensing analyses.................................................... 86
5.5 Results........................................................................................ 885.6 Applications ............................................................................... 905.7 Conclusions................................................................................ 915.8 Acknowledgements.................................................................... 925.9 References.................................................................................. 92
Chapter 6 - Challenges in Characterizing and Mitigating Urban Heat Islands – A Role for Integrated Approaches Including Remote Sensing .................................................................................................... 117
6.1 Introduction.............................................................................. 1176.2 Temporal and spatial scales in climatology ............................. 119
6.2.1 Regional to local scale ..................................................... 1196.3 Factors controlling urban climates........................................... 1206.4 Methods of evaluation ............................................................. 1226.5 Remote sensing ........................................................................ 1236.6 Urban heat island mitigation.................................................... 1276.7 Conclusions.............................................................................. 1286.8 References................................................................................ 129
Chapter 7 – Phoenix, Arizona, USA: Applications of Remote Sensing in a Rapidly Urbanizing Desert Region ............................................... 137
7.1 Introduction.............................................................................. 1377.2 Regional setting and historic land use ..................................... 1397.3 CAP LTER urban ecology research......................................... 1407.4 Urban climate modeling........................................................... 1417.5 Land cover characterization and change detection .................. 144
7.5.1 Expert system classification of the Phoenix area............. 1487.5.2 Monitoring LULCC using object-oriented classification .................................................................... 151
7.6 High resolution commercial data use in Marana, AZ .............. 1557.7 Conclusions.............................................................................. 1597.8 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.............................................................................. 1658.2 Urban environmental issues in Delhi ....................................... 1688.3 Application of remote sensing and GIS in urban studies......... 171
8.3.1 Aerial photographs and satellite data in urban studies..... 1738.3.2 Urban spatial growth and sprawl ..................................... 1748.3.3 Land-use and land-cover mapping................................... 1778.3.4 Urban change detection and mapping.............................. 1808.3.5 Base maps for urban areas ............................................... 1818.3.6 Urban hydrology .............................................................. 1828.3.7 Solid and hazardous waste ............................................... 1838.3.8 Effective traffic management........................................... 1848.3.9 Greenhouse gases and urban heat island mapping........... 1858.3.10 Urban infrastructure recreational and utility mapping..... 186
8.4 Sustainable development and planning of Delhi ..................... 1878.5 Conclusions.............................................................................. 190
8.5.1 Recommendations............................................................ 1918.6 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.............................................................................. 1999.2 Berlin urban and environmental information systems ............. 200
9.2.1 Definition and aims.......................................................... 2019.2.2 The Berlin digital environmental atlas............................. 2059.2.3 FIS-broker........................................................................ 2069.2.4 Geo-data and geographic information systems................ 2079.2.5 GIS and the internet ......................................................... 207
9.3 Application of remote-sensing data ......................................... 2089.3.1 UEIS mapping of land use ............................................... 2089.3.2 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 ............. 2129.3.5 Mapping of imperviousness (soil surface sealing)........... 2139.3.6 Urban-biotope mapping ................................................... 215
9.4 Conclusions.............................................................................. 2179.5 References................................................................................ 218
Chapter 10 - Views of Chiang Mai: The Contributions of Remote-Sensing to Urban Governance and Sustainability .............................. 221
10.1 Introduction.......................................................................... 22110.2 Views ................................................................................... 223
10.2.1 Access .............................................................................. 22410.2.2 Interpretations .................................................................. 22510.2.3 Resolution ........................................................................ 22710.2.4 Social spaces .................................................................... 227
10.3 Histories ............................................................................... 23010.3.1 Origins ............................................................................. 23010.3.2 Urbanization..................................................................... 23110.3.3 Ecosystem services .......................................................... 233
10.4 Models ................................................................................. 23310.4.1 SLEUTH .......................................................................... 23410.4.2 ELSE................................................................................ 236
10.5 Visions ................................................................................. 23710.5.1 Space for time .................................................................. 23810.5.2 Scenarios.......................................................................... 238
10.6 Actions ................................................................................. 24010.6.1 Choices............................................................................. 24110.6.2 Responsibilities ................................................................ 242
10.7 Conclusions.......................................................................... 24510.8 Acknowledgements.............................................................. 24510.9 Notes .................................................................................... 24510.10 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.......................................................................... 24911.2 Study areas........................................................................... 25011.3 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 ...................................... 25711.4 Comparative urban development on the coast and in the west ...................................................................................... 25811.5 Monitoring urban growth in China ...................................... 26411.6 Challenges to planning and development and the role of
remote sensing and geospatial data...................................... 26511.7 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 receiv-ing 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 Al-pine 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 Ad-vancement of Science, Arizona-Nevada Academy of Science, and the Ex-plorer’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 Di-mensions 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 pat-terns 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 na-tive of Greece, he completed his undergraduate studies in Economics at the National University of Athens in Greece and his MA and PhD in Econom-ics, 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 Free-man Spogli Institute for International Studies at Stanford University.
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Goedecke, Manfred Urban and Environmental Information System, Senate Department of Ur-ban 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 environ-mental 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] ber-lin.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 Uni-versity of Edinburgh, UK (1995) before post-graduating (PhD) at the Uni-versity of Trier (2001), where he worked as Assistant Lecturer in the De-partment 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 Geo-matics, 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 envi-ronments.
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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 go-ing to post-graduate (PhD) in April 2007 at the Humboldt University in-Berlin 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 fo-cused 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 manage-ment 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 Depart-ment of Geography at the Humboldt-Universität in Berlin in research and teaching. Her current research is on modeling urban development in post-socialistic 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 Environ-mental Research at Chiang Mai University (see www.sea-user.org). Imme-diately after graduating with a PhD in Zoology from the University of Western Australia he travelled to Thailand on a three-month university ex-change 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 resil-ience, 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 spe-cialist 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 Sci-ences, 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 re-ceived his PhD in natural science in 2002 for his thesis “Urban Environ-mental 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 Univer-sity, Global Institute of Sustainability in 2003 as a postdoctoral research associate, responsible for the coordination of tasks related to geoinformat-ics in the NSF funded project Agricultural Landscapes in Transition (Ag-Trans). Since 2006 Moeller is the Chair and Professor for Cartography and Geoinformatics at the University of Bonn, North Rhine Westphalia, Ger-many. 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 in-clude 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 interna-tional teaching background in Applied Physical Geography, Geoinformat-ics and Remote Sensing at the University level.
Netzband, Maik, PhD, Scientific Consultant F & U Consult, UFZ-Helmholtz Centre for Environmental Research, Per-moserstrasse 15, D-04318 Leipzig, Germany; [email protected] 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 re-mote 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 Habi-tat megacity” as a scientific consultant.
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Rahman, Atiqur, PhD, Assistant ProfessorDepartment of Geography, Faculty of Natural Sciences, Jamia Millia Isla-mia 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, ur-ban hydrology, land use/land cover change, and environmental impact as-sessment (EIA). He has worked at the UFZ-Centre for Environmental Re-search, Germany as a Postdoctoral Fellow. He was a member of the Indo-German (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 DirectorGlobal 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 Uni-versity of Freiburg, Germany. His basic interest of research involves geog-raphy knowledge to improve the automatic classifications of remote sens-ing data and define relations between the results of these classifications and the transformation of the landscape; understand this process and de-velop 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 Uni-versity, 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 in-clude 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 Geog-raphy and Institute for Computational Earth System Science at the Univer-sity of California, Santa Barbara. After completing her BS at the Univer-sity 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 investi-gation 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 cross-section 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 Ur-ban 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 implemen-tation 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 Environ-mental 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 Insti-tute 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 ur-ban growth—and evaluating the social and ecological impacts of land dy-namics. She currently has active projects in China, Vietnam, India, and the U.S. She is the Co-Chair of the International Human Dimensions Pro-gramme’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 Observa-tory 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 explora-tion with the Exxon Production Research Company. Current research in-terests 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, Chu-lalongkorn University, Bangkok 10330, Thailand; [email protected]. Danai Thaitakoo is a lecturer in the Department of Landscape Architec-ture, Faculty of Architecture, Chulalongkorn University, Bangkok, Thai-land. He received a bachelor's degree in landscape architecture from Chu-lalongkorn 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 Land-scape 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 volcanol-ogy, 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 Arizona-Phoenix 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 In-ternational Space Station, and curation of the historical astronaut photog-raphy 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 geo-graphic 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 devel-opment 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 in-formation 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 0-13-205840-5
Jensen JR (2000) Remote sensing of the environment: An earth resource perspective. Prentice-Hall, Upper Saddle River, NJ, ISBN 0-13-489733-1
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, acquir-ing 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 hyperspec-tral 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 Radi-ometer. Joint USA/Japan multispectral sensor onboard the NASA Terra satellite, collects data in the visible through midinfrared wave-lengths. 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. Multispec-tral 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 hyper-spectral 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 particu-larly 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 exam-ple, the probability of change from agricultural use to a commercial development) in an isolated pixel, causing adjacent pixels to also be-come 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 re-motely sensed data, rather than calculation of a value through statisti-cal 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 reflec-tance 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 result-ing X, Y, Z grid (corresponding to latitude, longitude, and elevation) can be used to generate a three-dimensional representation of the land-scape. 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 Position-ing System fixes a receiver’s location on the ground by using the dif-ference between the transmission and receive time from a network of satellites orbiting Earth (requiring triangulation of at least three satel-lite 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 land-use change.
DigitalGlobe – The commercial provider of Quickbird high resolution re-motely sensed imagery. Website: http://www.digitalglobe.com/.
Dipterocarp – Tree belonging to the family Dipterocarpaceae and typi-cally 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 ac-quired for Earth’s surface and atmosphere in the visible through midin-frared 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 satel-lites launched and maintained primarily by NASA. The program is in-tended to collect information about the Earth’s atmosphere, hydro-sphere, 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 Land-sat 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 pub-licly available. Website: http://edc.usgs.gov/products/satellite/landsat7.html.
FDI – Fractal Dimension Index. A mathematical operation applied to re-motely 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 dif-ferent from the Instrument Field of View as it also considers the alti-tude of the sensor.
GIS – Geographic Information System. A now somewhat generic term for a geospatial database, in which descriptive data are identified by geo-graphic (latitude, longitude) position. This allows for spatial, temporal, and statistical analysis of virtually any sort of spatially associated digi-tal 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 ac-quired 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 success-ful 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 air-borne 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 systemati-cally. Data are publicly available. Website: http://eo1.usgs.gov/hyperion.php.
IFOV – Instrument Field of View. A measure of the area that a given sen-sor “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 plat-form. The amount of energy returned to the sensor provides informa-tion 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. Web-site:http://quake.wr.usgs.gov/research/deformation/modeling/InSAR/whatisInSAR.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. Web-site: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 IRS-1C 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 omis-sion and commission errors not reflected in a simple measure of over-all 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 reso-lutions have increased from 80 to 15 meters during the program. The Landsat program has acquired the most extensive and temporally con-tinuous remotely sensed dataset of Earth’s land surfaces. Data are pub-licly available. Website: http://landsat.usgs.gov/.
Leica ADS40 Airborne Digital Sensor – Commercial airborne digital multispectral sensor that acquires submeter resolution data in the visi-ble and near infrared wavelengths, and has stereo imaging capability for generation of digital surface models. Data are not publicly avail-able. 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 re-turn 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 in-formation 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 wave-lengths 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 (1972-present) that acquires multispectral data in the visible green, visible red, and near infrared wavelength regions at 80-meter ground resolu-tion. 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 En-ergy sensor that acquires multispectral data in the visible through mid-infrared 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”) tempera-tures 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 calcula-tion 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 oceano-graphic 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 Geospa-tial Consortium. This is a non-profit, international, voluntary-consensus organization focused on development of standards for geo-spatial and location-based services. Website: http://www.opengeospatial.org/.
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ORBIMAGE – A commercial remote sensing company that currently op-erates the OrbView-3 and OrbView-2 ocean and land imaging satel-lites. Both satellites offer real-time data download capabilities for mul-tispectral visible to near infrared data at resolutions of 1 km (OrbView-2) 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 mi-crometers) for example – rather than narrow wavelength bands. Com-monly used in remote sensing to provide a high resolution band for sharpening coarser multispectral data.
QUICKBIRD – A commercial high resolution multispectral satellite sen-sor. Data are collected in the visible and near infrared wavelengths to submeter resolution. Data collection is by request, rather than continu-ous, 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 in-tended 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 photo-graph, 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 mul-tispectral 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 con-crete, 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 spec-trum. 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 approxi-mate a long temporal sequence of landscape change that cannot be viewed directly. An urban example of this concept would be the com-mon land-use change sequence of a parcel of undeveloped land chang-ing 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 dis-tance, 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 individ-ual 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 multis-pectral 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 (prehis-toric Europe, for example).
TIMS – Thermal Infrared Multispectral Scanner. An airborne NASA in-strument 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 multispec-tral 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.
