Remote sensing of land use and land cover : principles and applications
著者
書誌事項
Remote sensing of land use and land cover : principles and applications
(Taylor & Francis series in remote sensing applications / Qihao Weng, series editor)
CRC Press, 2012
- : Hardback
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Filling the need for a comprehensive book that covers both theory and application, Remote Sensing of Land Use and Land Cover: Principles and Applications provides a synopsis of how remote sensing can be used for land-cover characterization, mapping, and monitoring from the local to the global scale. With contributions by leading scientists from around the world, this well-structured volume offers an international perspective on the science, technologies, applications, and future needs of remote sensing of land cover and land use.
After an overview of the key concepts and history of land-use and land-cover mapping, the book discusses the relationship between land cover and land use and addresses the land-cover classification system. It then presents state-of-the-art methods and techniques in data acquisition, preprocessing, image interpretation, and accuracy assessment for land-use and land-cover characterization and mapping.
Case studies from around the world illustrate land-cover applications at global, continental, and national scales. These examples use multiple data sources and provide in-depth understanding of land cover and land-cover dynamics in multiple spatial, thematic, and temporal resolutions. Looking to the future, the book also identifies new frontiers in land-cover mapping and forecasting.
The availability and accessibility of accurate and timely land-cover data sets play an important role in many global change studies, highlighting the need for better land-use and land-cover change information at multiple scales. A synthesis of current knowledge in remote sensing of land-use and land-cover science, this book promotes more effective use of Earth observation data and technology to assess, monitor, and manage land resources.
目次
Overview: Brief Overview of Remote Sensing of Land Cover. History of Land-Cover Mapping. Basic Principles: Semantic Issues in Land-Cover Analysis: Representation, Analysis, and Visualization. Overview of Land-Cover Classifications and Their Interoperability. Revisiting Land-Cover Mapping Concepts. Evaluating Land-Cover Legends Using the UN Land-Cover Classification System. Long-Term Satellite Data Records for Land-Cover Monitoring. Preprocessing: Need for Sensor Calibration. Classification Trees and Mixed Pixel Training Data. Comparison between New Digital Image Classification Methods and Traditional Methods for Land-Cover Mapping. Land-Cover Change Detection. Supervised Classification Approaches for the Development of Land-Cover Time Series. Forest-Cover Change Detection Using Support Vector Machines. Global Land-Cover Map Validation Experiences: Toward the Characterization of Uncertainty. Role of Remote Sensing for Land-Use and Land-Cover Change Modeling. Application Examples: Operational Service Demonstration for Global Land-Cover Mapping: The GlobCover and GlobCorine Experiences for 2005 and 2009. Continental and Regional Approaches for Improving Land-Cover Maps of Africa. Land-Cover Mapping in Tropical Asia. Land Cover and Its Change in Europe: 1990-2006. North American Land-Change Monitoring System. The Application of Medium-Resolution MERIS Satellite Data for Continental Land-Cover Mapping over South America: Results and Caveats. Mapping Land-Cover and Land-Use Changes in China. An Approach to Assess Land-Cover Trends in the Conterminous United States (1973-2000). Is Africa Losing Its Natural Vegetation? Monitoring Trajectories of Land-Cover Change Using Landsat Imagery. Looking Ahead: The NASA Land-Cover and Land-Use Change Program: Research Agenda and Progress (2005-2011). Building Saliency, Legitimacy, and Credibility toward Operational Global and Regional Land-Cover Observations and Assessments in the Context of International Processes and Observing Essential Climate Variables. Index.
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