Remote sensing of impervious surfaces has matured using advances in geospatial technology so recent that its applications have received only sporadic coverage in remote sensing literature. Remote Sensing of Impervious Surfaces is the first to focus entirely on this developing field. It provides detailed coverage of mapping, data extraction, and modeling techniques specific to analyzing impervious surfaces, such as roads and buildings. Written by renowned experts in the field, this book reviews the major approaches that apply to this emerging field as well as current challenges, developments, and trends. The authors introduce remote sensing digital image processing techniques for estimating and mapping impervious surfaces in urban and rural areas. Presenting the latest modeling tools and algorithms for data extraction and analysis, the book explains how to differentiate roads, roofs, and other manmade structures from remotely sensed images for individual analysis.
The final chapters examine how to use impervious surface data for predicting the flow of storm- or floodwater and studying trends in population, land use, resource distribution, and other real-world applications in environmental, urban, and regional planning. Each chapter offers a consistent format including a concise review of basic concepts and methodologies, timely case studies, and guidance for solving problems and analyzing data using the techniques presented.
Country of Publication:
30 June 2020
Digital Remote Sensing Methods. Technology Advances in Impervious Surface Estimation and Mapping. Transport-Related Impervious Surfaces. Roof-Related Impervious Surfaces. Impervious Surface Data Applications.
Reviews for Remote Sensing of Impervious Surfaces
. . . well-organized and the chapters well-written . . . will serve as a comprehensive treatment for impervious surface remote sensing neophytes, as well as a valuable reference for veteran researchers and practitioners. - Daniel L. Civco, Department of Natural Resources Management and Engineering, University of Connecticut, in Photogrammetric Engineering & Remote Sensing, July 2008, Vol. 74, No. 7