Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas offers a complete and thorough system for using optical and synthetic aperture radar (SAR) remote sensing data for improving impervious surface estimation (ISE). Highlighting tropical and subtropical areas where there is significant cloud occurrence and varying phenology, the book addresses the challenges impacting impervious surfaces in tropical and subtropical zones. It examines the potential for estimating urban impervious surfaces in a rainy and cloudy environment, considers the difficulties encountered when using optical remote sensing in this type of climate, and assesses existing methods employing remote sensing data for accurate ISE in tropical and subtropical regions.
Using the results of comparative studies conducted during the four seasons and in six different cities (Guangzhou, Shenzhen, Hong Kong, Mumbai, Sao Paulo, and Cape Town), the authors develop a framework for ISE using optical and SAR image data. They address the advantages and disadvantages of optical and SAR data, consider fusion strategies for combining optical and SAR data, and examine different feature extractions for optical and SAR data. They also detail the limitations of the research, suggest possible topics for future analysis, and cover previous findings on the synergistic use of optical and SAR data.
Concentrates on the effect a tropical and subtropical urban climate can have on impervious surface estimation (ISE)
Reviews literature on the significance of ISE and the phonological and climatic characteristics of tropical and subtropical regions
Describes datasets including satellite data, digital orthophoto data, in situ data, and more
Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas investigates the state of the art in creating new algorithms for digital imag
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Für Beruf und Forschung
Academic and Professional Practice & Development
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Höhe: 234 mm
Breite: 156 mm
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978-0-367-87062-1 (9780367870621)
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Hongsheng Zhang is currently a research assistant professor at the Institute of Space and Earth Information Science, The Chinese University of Hong Kong. He received a B.Eng in computer science and technology in 2007, and an M.Eng in computer applications technology in 2010 from South China Normal University, Guangzhou. In addition, he received a Ph.D in earth system and geoinformation science from The Chinese University of Hong Kong in 2013. Currently, his research interests are on remote sensing applications in tropical and subtropical areas, with a focus on urban environment and natural disasters monitoring, using multi-source remote sensing data fusion and image pattern recognition techniques.
Hui Lin is Chen Shupeng professor of geoinformation science and director of the Institute of Space and Earth Information Science of The Chinese University of Hong Kong. He is director of the Hong Kong Base of National Remote Sensing Center of China. He graduated from the Wuhan Technical University of Surveying and Mapping in 1980, and received his M.Sc from the Graduate School of Chinese Academy of Sciences in 1983, and his Ph.D. from the University at Buffalo in 1992. His research interests include microwave remote sensing image processing and analysis, virtual geographic environments (VGE), spatial database and data mining, spatially integrated humanities, and social science.
Yuanzhi Zhang is a professor of environmental remote sensing, remote sensing of lunar and planetary science at the Key Laboratory of Lunar and Deep-Space Exploration, Chinese Academy of Sciences. He is also a research fellow and adjunct professor at the Center for Housing Innovations, Chinese University of Hong Kong. He received his Ph.D. in Technology at Helsinki University of Technology (HUT) in Finland in 2005, a postgraduate diploma in remote sensing and geological survey at the International Institute for Geo-Informa
Autor*in
The Chinese Univ of Hong Kong, Hong Kong
The Chinese Univ of Hong Kong, Hong Kong
The Chinese University of Hong Kong
Indiana State University, Terre Haute, USA
Introduction. Impervious Surface Estimation Using Remote Sensing. Methodology of Combining Optical and SAR. Impact of Climate Zone on Impervious Surface Estimation and Mapping. Assessing the Urban Land Cover Complexity. Comparative Studies with Different Image Data and Fusion Methods. In-Depth Study: ISE Using Optical and SAR Data. Conclusions and Recommendations. Bibliography.
