Remote Sensing of Natural Resources  book cover
1st Edition

Remote Sensing of Natural Resources

ISBN 9781466556928
Published July 12, 2013 by CRC Press
580 Pages 45 Color & 148 B/W Illustrations

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Book Description

Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for evaluation of natural resources, including sampling design, land use and land cover classification, natural landscape and ecosystem assessment, forestry, agriculture, biomass and carbon-cycle modeling, wetland classification and dynamics monitoring, and soils and minerals mapping.

The book combines review articles with case studies that demonstrate recent advances and developments of methods, techniques, and applications of remote sensing, with each chapter on a specific area of natural resources. Through a comprehensive examination of the wide range of applications of remote sensing technologies to natural resources, the book provides insight into advanced remote sensing systems, technologies, and algorithms for researchers, scientists, engineers, and decision makers.

Table of Contents

Remote Sensing Systems
Introduction to Remote Sensing Systems, Data, and Applications, Qihao Weng

Sampling Design and Product Quality Assessment
Remote Sensing Applications for Sampling Design of Natural Resources, Guangxing Wang and George Z. Gertner
Accuracy Assessment for Classification and Modeling, Suming Jin
Accuracy Assessment for Soft Classification Maps, Daniel Gómez, Gregory S. Biging, and Javier Montero
Spatial Uncertainty Analysis When Mapping Natural Resources Using Remotely Sensed Data, Guangxing Wang and George Z. Gertner

Land Use and Land Cover Classification
Land Use/Land Cover Classification in the Brazilian Amazon with Different Sensor Data and Classification Algorithms, Guiying Li, Dengsheng Lu, Emilio Moran, Mateus Batistella, Luciano V. Dutra, Corina C. Freitas, and Sidnei J. S. Sant’Anna
Vegetation Change Detection in the Brazilian Amazon with Multitemporal Landsat Images, Dengsheng Lu, Guiying Li, Emilio Moran, and Scott Hetrick
Extraction of Impervious Surfaces from Hyperspectral Imagery: Linear versus Nonlinear Methods, Xuefei Hu and Qihao Weng
Road Extraction: A Review of LiDAR-Focused Studies, Lindi J. Quackenbush, Jungho Im, and Yue Zuo

Natural Landscape, Ecosystems, and Forestry
Application of Remote Sensing in Ecosystem and Landscape Modeling, Chonggang Xu and Min Chen
Plant Invasion and Imaging Spectroscopy, Kate S. He and Duccio Rocchini
Assessing Military Training–Induced Landscape Fragmentation and Dynamics of Fort Riley Installation Using Spatial Metrics and Remotely Sensed Data, Steve Singer, Guangxing Wang, Heidi R. Howard, and Alan B. Anderson
Automated Individual Tree-Crown Delineation and Treetop Detection with Very-High-Resolution Aerial Imagery, Le Wang and Chunyuan Diao
Tree Species Classification, Ruiliang Pu
Estimation of Forest Stock and Yield Using LiDAR Data, Markus Holopainen, Mikko Vastaranta, Xinlian Liang, Juha Hyyppä, Anttoni Jaakkola, and Ville Kankare
National Forest Resource Inventory and Monitoring System, Erkki Tomppo, Matti Katila, and Kai Mäkisara

Remote Sensing Applications on Crop Monitoring and Prediction, Bingfang Wu and Jihua Meng
Remote Sensing Applications to Precision Farming, Haibo Yao and Yanbo Huang
Mapping and Uncertainty Analysis of Crop Residue Cover Using Sequential Gaussian Cosimulation with QuickBird Images, Cha-Chi Fan, Guangxing Wang, George Z. Gertner, Haibo Yao, Dana G. Sullivan, and Mark Masters

Biomass and Carbon Cycle Modeling
Remote Sensing of Leaf Area Index of Vegetation Covers, Jing M. Chen
LiDAR Remote Sensing of Vegetation Biomass, Qi Chen
Carbon Cycle Modeling for Terrestrial Ecosystems, Tinglong Zhang and Changhui Peng
Remote Sensing Applications to Modeling Biomass and Carbon of Oceanic Ecosystems, Samantha Lavender and Wahid Moufaddal

Wetland, Soils, and Minerals
Wetland Classification, Maycira Costa, Thiago S. F. Silva, and Teresa L. Evans
Remote Sensing Applications to Monitoring Wetland Dynamics: A Case Study on Qinghai Lake Ramsar Site, China, Hairui Duo, Linlu Shi, and Guangchun Lei
Hyperspectral Sensing on Acid Sulfate Soils via Mapping Iron-Bearing and Aluminum-Bearing Minerals on the Swan Coastal Plain, Western Australia, Xianzhong Shi and Mehrooz Aspandiar

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Guangxing Wang, PhD, is an associate professor of remote sensing and geographic information systems (GIS) in the Department of Geography and Environment Resources at Southern Illinois University at Carbondale (SIUC), Illinois.

Qihao Weng, PhD is the director of the Center for Urban and Environmental Change and a Professor of geography at Indiana State University.

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Author - Qihao  Weng

Qihao Weng

Professor; Director, Indiana State University

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"… a useful read for all identified audiences. The mixed levels of the content matter make it more appealing for readers to invest in specific sections of the book that match with their interest."
—Photogrammetric Engineering & Remote Sensing, J u l y 2015

"… a comprehensive view on and real world examples of remote sensing technologies in natural resources assessment and monitoring. … state-of-the-art knowledge in this multidisciplinary field. Readers can expect to finish the book armed with the required knowledge to understand the immense literature available and apply their knowledge to the understanding of sampling design, the analysis of multi-source imagery, and the application of the techniques to specific problems relevant to natural resources."
—Yuhong He, University of Toronto Mississauga, Ontario, Canada

"The list of topics covered is so complete that I would recommend the book to anyone teaching a graduate course on vegetation analysis through digital image analysis. … I recommend this book then for anyone doing advanced digital image analysis and environmental GIS courses who want to cover topics related to applied remote sensing work involving vegetation analysis."
—Charles Roberts, Florida Atlantic University, Boca Raton, USA, in Economic Botany