Wang G., Weng Q. (Eds.) Remote Sensing of Natural Resources

CRC Press. Taylor & Francis Group. 2014. - 565 p.This book focuses on remote sensing systems, algorithms, and their applications for evaluation of natural resources, especially in the areas of sampling design, land use and land cover (LULC) characterization and classification, natural landscape and ecosystem assessment, forestry and agriculture mapping, biomass and carbon cycle modeling, wetland classification and dynamics monitoring, and soils and minerals mapping. It 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. This book aims at providing undergraduate and graduate students with a focused text or a supplementary text for those whom the principles of remote sensing, digital image processing, or remote sensing applications is a course to be taken. It may also be useful for researchers, scientists, engineers, and decision makers in the area of geospatial technology and/or its applications to natural resources as a reference text.Acknowledgments.
Editors.
Contributors.
Introduction to Remote Sensing of Natural Resources.
Remote Sensing Systems.
Introduction to Remote Sensing Systems, Data, and Applications.
Sampling Design and Product Quality Assessment.
Remote Sensing Applications for Sampling Design of Natural Resources.
Accuracy Assessment for Classification and Modeling.
Accuracy Assessment for Soft Classification Maps.
Spatial Uncertainty Analysis When Mapping Natural Resources Using Remotely Sensed Data.
Land Use and Land Cover Classification.
Land Use/Land Cover Classification in the Brazilian Amazon with Different Sensor Data and Classification Algorithms.
Vegetation Change Detection in the Brazilian Amazon with Multitemporal Landsat Images.
Extraction of Impervious Surfaces from Hyperspectral Imagery: Linear versus Nonlinear Methods.
Road Extraction: A Review of LiDAR-Focused Studies.
Natural Landscape, Ecosystems, and Forestry.
Application of Remote Sensing in Ecosystem and Landscape Modeling.
Plant Invasion and Imaging.
Assessing Military Training–Induced Landscape Fragmentation and Dynamics of Fort Riley Installation Using Spatial Metrics and Remotely Sensed Data.
Automated Individual Tree-Crown Delineation and Treetop Detection with Very-High-Resolution Aerial Imagery.
Tree Species Classification.
Estimation of Forest Stock and Yield Using LiDAR Data.
National Forest Resource Inventory and Monitoring System.
Agriculture.
Remote Sensing Applications on Crop Monitoring and Prediction.
Remote Sensing Applications to Precision Farming.
Mapping and Uncertainty Analysis of Crop Residue Cover Using Sequential Gaussian Cosimulation with QuickBird Images.
Biomass and Carbon Cycle Modeling.
LiDAR Remote Sensing of Vegetation Biomass.
Carbon Cycle Modeling for Terrestrial Ecosystems.
Remote Sensing Applications to Modeling Biomass and Carbon of Oceanic Ecosystem.
Wetland, Soils, and Minerals.
Wetland Classification.
Remote Sensing Applications to Monitoring Wetland Dynamics: A Case Study on Qinghai Lake Ramsar Site, China.
Hyperspectral Sensing on Acid Sulfate Soils via Mapping Iron-Bearing and Aluminum-Bearing Minerals on the Swan Coastal Plain, Western Australia.