Remote Sensing And Image Interpretation, 6th Edition.pdf
Now in full color, the sixth edition of this leading text features new chapters on remote sensing platforms (including the latest satellite and unmanned aerial systems), agriculture (including agricultural analysis via satellite imagery), and forestry (including fuel type mapping and fire monitoring). The book has introduced tens of thousands of students to the fundamentals of collecting, analyzing, and interpreting remotely sensed images. It presents cutting-edge tools and practical applications to land and water use analysis, natural resource management, climate change adaptation, and more. Each concise chapter is designed as an independent unit that instructors can use in any sequence. Pedagogical features include over 400 figures, chapter-opening lists of topics, case studies, end-of-chapter review questions, and links to recommended online videos and tutorials. New to This Edition *Discussions of Landsat 8 and Sentinel-2; the growth of unmanned aerial systems; mobile data collection; current directions in climate change detection, fire monitoring, and disaster response; and other timely topics. *Additional cases, such as river erosion; the impact of Hurricane Sandy on Mantoloking, New Jersey; and Miami Beach as an exemplar of challenges in coastal communities. *Revised throughout with 60% new material, including hundreds of new full-color figures. *New chapters on remote sensing platforms, agriculture, and forestry.
Remote Sensing and Image Interpretation, 6th Edition.pdf
James B. Campbell, PhD, is Professor of Geography at Virginia Tech, where he teaches remote sensing, quantitative methods, and geomorphology. He has worked closely with students and faculty in forestry, geology, agronomy, and environmental sciences. The author of numerous technical articles and several books, Dr. Campbell has received the Outstanding Service Award and the Fellow Award from the American Society for Photogrammetry and Remote Sensing, as well as the Outstanding Service Medal from the Remote Sensing Specialty Group of the Association of American Geographers. In 2020, Dr. Campbell received the AmericaView Lifetime Achievement Award. He has served as a principal investigator for the VirginiaView consortium and as a member and chair of the AmericaView Board of Directors. Randolph H. Wynne, PhD, is Professor in the Department of Forest Resources and Environmental Conservation at Virginia Tech. He also serves as Director of the Interdisciplinary Graduate Education Program in Remote Sensing. He teaches courses focused on the environmental and natural resources applications of remote sensing at the senior and graduate levels. Dr. Wynne's research interests are in the applications of remote sensing to forestry, natural resource management, ecosystem ecology, and earth system science. He is a recipient of the Award in Forest Science from the Society of American Foresters. Dr. Wynne is Coeditor of the journal Science of Remote Sensing and Associate Editor of Remote Sensing of Environment. Valerie A. Thomas, PhD, is Professor in the Department of Forest Resources and Environmental Conservation at Virginia Tech. She also serves as Co-Director of the Center for Environmental Analytics and Remote Sensing within the College of Natural Resources and Environment. Dr. Thomas teaches remote sensing courses in forest lidar applications and hyperspectral applications for natural resources. She also teaches about the linkages between forests, society, and climate. Dr. Thomas's research related to remote sensing of forest cover, function, and change has been funded through federal and state agencies and by industry.
Over the last two decades, technological developments have reduced the cost and the infrastructure required to develop on-the-ground monitoring networks that can provide data on vegetation phenology in real time and at high spatial and temporal resolution9. The data from this near-surface remote sensing, whether derived from radiometric instruments10,11 or imaging sensors12,13,14, is critical for improving our understanding of the strengths and limitations satellite remote sensing of phenology, and for conducting cross-scale phenological data integration5.
Our analysis has shown a generally high level of agreement between phenological transition dates derived from near-surface PhenoCam imagery and from MODIS satellite remote sensing. Relative to other studies that compared transition dates derived from MODIS data with ground observations recorded by citizen scientists29,30, our analysis shows as good or better agreement between what is seen from space and what is happening on the ground. The inherent subjectivity of ground observers, and the ability of PhenoCam imagery to integrate across the canopy, may be key factors contributing to these patterns. 350c69d7ab