Foreword
Introduction
Urban environment belongs to the most dynamic systems on Earth. Although city
areas are relatively small in size, they contain nearly half of the world’s
total population. Several decades of population explosion and accelerating
urban growth have had profound environmental and socioeconomic impacts felt
in both developing and developed countries alike (de Sherbinin et al.,
2002; Longley, 2002). Urbanization has often been viewed as a sign of the
vitality of a regional economy, but it has rarely been well planned, thus
provoking concerns over the degradation of our environmental and ecological
health. Understanding the growth and change brought on by urbanization is
critical to those who study urban dynamics and those who must manage resources
and provide services in these rapidly changing environments (Yang, 2002).
Management of the urban environment involves procedures of monitoring and modeling which require a reliable information base and robust analytical technologies. Conventional surveying and mapping methods cannot deliver the necessary information in a timely and cost-effective mode. Remote sensing and GIS, given their cost-effectiveness and technological soundness, are increasingly being used to develop useful sources of information and to support decision making in connection with a wide array of urban applications (e.g., Cowen and Jensen, 1998; Mesev, 1998; Lo and Yang, 2002; Yang and Lo, 2003). But the urban environment, given its heterogeneous landscape with highly dynamic changes, is challenging the applicability and robustness of these methods and technologies. Fortunately, recent innovations in data, technologies, and theories in the wider arena of remote sensing and GIS have provided scientists with invaluable insights into the study of urban environment.
Within the above context, a special issue of Photogrammetric Engineering & Remote Sensing on urban analysis is timely. From nearly 40 submissions in response to an open call for contributions to this special issue, I selected 11 papers. This special issue focuses on the applications of remote sensing and GIS technologies for the characterization, analysis, and modeling of the urban environment. The selected papers are grouped into five main categories, each of them representing a particular aspect of technological advancement in relation to urban analysis.
The first group of papers refers to the use of high-resolution satellite image data or alternative sources of image data (such as SAR and lidar) for urban landscape characterization. There are three papers in this category. The first paper is authored by Grey and Luckman, and represents a comprehensive analysis of the utility of ERS interferometric coherence data with a range of baselines and time-delays for urban extent mapping. The results are validated and classification kappa coefficients greater than 90 percent are achieved. The second article, by Thomas et al., discusses the effectiveness of several different classification methods for extracting land-use/land-cover information from high-resolution imagery of urban environments. The third paper, authored by Hodgson et al., presents a comparative analysis of two classifiers for mapping urban parcel imperviousness from color aerial photography fused with lidar-derived cover height.
The second group of articles represents the development of improved image processing techniques for deriving accurate and consistent information on urban attributes from remotely sensed data. Seto and Liu compare the ARCMAP neural network with the maximum-likelihood classifier for detecting urban change with different class resolutions. They also explore the effect of different levels of class aggregation on the change-detection performance. Herod et al. investigate an object-oriented method using spatial metrics and texture measures to extract detailed urban land-use information from high-resolution satellite imagery. Yang et al. develop an approach to detect urban land-cover changes by quantifying temporal changes of sub-pixel percent impervious surfaces using Landsat TM/ETM+ and high-resolution imagery. Rashed et al. evaluate and test the application of multiple endmember spectral analysis to map the physical composition of urban morphology using Landsat TM data.
The third group of papers addresses the development of analytical techniques and methodologies for deriving indicators of social and economic conditions that prevail within the urban landscape. Hasse and Lathrop develop a number of spatial metrics at the housing-unit level to analyze spatial patterns of urban growth for a better characterization of urban sprawl. They use these metrics to generate housing unit sprawl indicator measurements as a “report card” to quantify urban sprawl. Qiu et al. estimate population growth between two different dates in an urban environment using two different approaches: a conventional model based on remote sensing land-use change detection, and a newly devised approach using GIS-derived road development measurements.
The fourth group in this special issue addresses the development of new technologies for urban landscape simulation and predictive modeling. Yeh and Li present an approach that integrates neural networks and cellular automata to simulate urban development alternatives for planning purposes. Planning objectives are embedded in the model by modifying training data sets.
The last paper included in this special issue is authored by Lo and Quattrochi. They describe a new way of applying remote sensing to study the relationship between land-use/land-cover change, urban heat island phenomenon, and health implications, with Atlanta as a case.
In addition to the above peer-reviewed articles, Dr. Kamlesh Lulla from NASA’s Johnson Space Center contributes a feature article describing the use of new innovative imagery from the International Space Station for urban remote sensing.
Collectively, these papers report the current advancement of remote sensing and GIS technologies for the characterization, analysis, and modeling of the urban environment. I hope the remote sensing and GIS communities continue their efforts towards a better understanding and management of the highly dynamic urban environment.
Acknowledgments
I would like to thank the American Society for Photogrammetry and Remote Sensing
(ASPRS) and Dr. James Merchant, Editor of Photogrammetric Engineering & Remote
Sensing (PE&RS), for dedicating this issue to the theme of urban
analysis. Many thanks to all of those who contributed papers, those who revised
their papers one or more times, those who reviewed papers according to my
requests and timetables, and a large number of authors whose manuscripts
were not included here because of space limitations. This special issue would
not have been completed without the help and assistance from several staff
members at the American Society for Photogrammetry and Remote Sensing, in
particular, Kimberly Tilley, Jim Case, and Rae Kelley.
The group of reviewers who contributed their time, talents, and energies are listed here: Steve Ackerman, Thomas Allen, John D. Althausen, Peter Atkinson, Mike Barnsley, Stuart Barr, Mike Batty, Daniel Civco, Kelley A. Crews-Meyer, Curt H. Davis, Chikashi Deguchi, Chris Elvidge, Thomas Evans, G.M. Foody, Andrea I. Frank, Philip Giles, Peng Gong, Doug Goodin, Berry Haack, Luoheng Han, Richard Harris, Uta Heiden, Floyd M. Henderson, Michael Hodgson, Ming-Chih Hung, John Jensen, Minghe Ji, John W. Jones, Eric S. Kasischke, Liz Kramer, Richard G. Lathrop, Jr., Jay Lee, Sangbum Lee, Christopher Lee, Lin Li, Xia Li, Hongxing Liu, David Landgrebe, Xiaohang Liu, C. P. Lo, Jeffrey C. Luvall, Steven Manson, Stuart E. Marsh, Jeffrey Masek, Victor Mesev, Andrew Millward, Lasse Moller-Jensen, Jan-Peter Muller, Alan Murray, Soe Myint, Janet Nichol, Peter Noble, Tim Owen, Steve Prisley, Sandy Prisloe, Hong-Lie Qiu, Fang Qiu, Volker Radeloff, Douglas Ramsey, Jane Read, Charles Roberts, Brian Steele, Yukio Sadahiro, Jeong-Chang Seong, Karen C. Seto, Ryosuke Shiabasaki, Christopher Small, Jonathan Smith, William D. Solecki, Conghe Song, Clyde H. Spencer, Tozio Strozzi, Daniel Sui, Mohamed Sultan, Mingjun Sun, Paul Sutton, Karen Thundiyil, Peter Tischer, Paul M. Torrens, Lynn Usery, Stephen Walsh, Yong Wang, Yeqiao Wang, Jialing Wang, Tim Warner, David Williams, Changshan Wu, Jianguo Wu, Fulong Wu, Zongguo Xia, Yichun Xie, Bing Xu, Xianghe Yang, Limin Yang, and Yunhe Zhao.
References
Cowen, D.J., and J.R. Jensen, 1998. Extraction and modeling of urban attributes
using remote sensing technology, People and Pixels: Linking Remote
Sensing and Social Science (D. Liverman, E.F. Moran, R.R. Rindfuss,
and P.C. Stern, Editors), National Academy Press, Washington, D.C., pp. 164-188.
de Sherbinin, A., D. Balk, M. Jaiteh, F. Pozzi, C. Giri, and A. Wabbebo, 2002. A CIESIN thematic guide to social science applications of remote sensing, URL: http://sedac.ciesin.columbia.edu/tg/guide_main.jsp., Columbia University, New York, N.Y. (last date accessed: 20 June 2003).
Lo, C.P., and X. Yang, 2002. Drivers of land-use/land-cover changes and dynamic modeling for the Atlanta, George Metropolitan Area, Photogrammetric Engineering & Remote Sensing, 68(10):1073-1082.
Longley, P.A., 2002. Geographical information systems: Will developments in urban remote sensing and GIS lead to ‘better’ urban geography? Progress in Human Geography, 26(2):231-239.
Mesev, V., 1998. Remote sensing of urban systems: Hierarchical integration with GIS, Computer, Environment, and Urban Systems, 21(3/4):175-187.
Yang, X., 2002. Satellite monitoring of urban spatial growth in the Atlanta Metropolitan Area, Photogrammetric Engineering & Remote Sensing, 68(7):725-734.
Yang, X., and C.P. Lo, 2003. Modelling urban growth and landscape changes in the Atlanta Metropolitan Area, International Journal of Geographical Information Science, 17(5):463-488.
Guest Editor
Xiaojun Yang is an assistant professor of Geography
at the Department of Geography, Florida State University. His research
interests are remote sensing, geographic information science, urban
analysis, and environmental modeling. His specific research areas
are information extraction from remotely sensed imagery; scale dependence
and information scaling; spatial data integration and analysis; integration
of remote sensing and GIS with dynamic modeling; urban indicators
and spatial growth dynamics; environmental indicators and landscape
dynamics; and coastal studies.
Department of Geography, Florida State University
Tallahassee, FL 32306-2190, U. S. A.
850-644-1796, 850-644-5913 (fax),
yang12337@itc.nl
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