This book explores the current state of knowledge on remote sensing time series image processing and addresses all major aspects and components of time series image analysis with ample examples and applications.
This book explores the current state of knowledge on remote sensing time series image processing and addresses all major aspects and components of time series image analysis with ample examples and applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Qihao Weng is the Director of the Center for Urban and Environmental Change and a Professor of geography at Indiana State University, USA. He was a visiting NASA Senior Fellow (2008-09). Dr. Weng is a Guest/Adjunct Professor of several major universities including Peking University and a Guest Research Scientist at Beijing Meteorological Bureau, China. He received his Ph.D. degree in geography from the University of Georgia in 1999. At the same year, he joined the University of Alabama as an assistant professor. Since 2001, he has been a member of the faculty in the Department of Earth and Environmental Systems at Indiana State University, where he has taught courses on remote sensing, digital image processing, GIS, environmental modeling, and urban studies, and has mentored 15 doctoral and 13 master students. Dr. Weng's research focuses on remote sensing and GIS analysis of urban ecological and environmental systems, land-use and land-cover change, environmental modeling, urbanization impacts, and human-environment interactions. He is the author of over 200 peer-reviewed journal articles and other publications and 10 books. According to Web of Science, his publications have been cited by others 4,280 times with H-index of 35. His Google citation yields over 10,000 times. Dr. Weng has worked extensively with optical and thermal remote sensing data, and more recently with LiDAR data, primarily for urban heat island study, land-cover and impervious surface mapping, urban growth detection, image analysis algorithms, and the integration with socioeconomic characteristics, with financial support from US funding agencies that include NSF, NASA, USGS, USAID, NOAA, National Geographic Society, and Indiana Dept of Natural Resources. Dr. Weng was the recipient of the Robert E. Altenhofen Memorial Scholarship Award by the American Society for Photogrammetry and Remote Sensing (1999), the Best Student-Authored Paper Award by the International Geographic Information Foundation (1998), and the 2010 Erdas Award for Best Scientific Paper in Remote Sensing by ASPRS (1st place). At Indiana State University, he received the Theodore Dreiser Distinguished Research Award in 2006 (the university's highest research honor) and was selected as a Lilly Foundation Faculty Fellow in 2005 (one of the six recipients). In May 2008, he received a prestigious NASA senior fellowship.
Inhaltsangabe
Part I: Time Series Image/Data Generation 1. Cloud and Cloud Shadow Detection for Landsat Images: The Fundamental Basis for Analyzing Landsat Time Series 2. An Automatic System for Reconstructing High-Quality Seasonal Landsat Time Series 3. Spatiotemporal Data Fusion to Generate Synthetic High Spatial and Temporal Resolution Satellite Images Part II: Feature Development and Information Extraction 4. Phenological Inference from Times Series Remote Sensing Data 5. Time Series Analysis of Moderate Resolution Land Surface Temperatures 6. Impervious Surface Estimation by Integrated Use of Landsat and MODIS Time Series in Wuhan, China Part III: Time Series Image Applications 7. Mapping Land Cover Trajectories Using Monthly MODIS Time Series from 2001 to 2010 8. Creating a Robust Reference Dataset for Large Area Time Series Disturbance Classification 9. A General Workflow for Mapping Forest Disturbance History Using Pixel Based Time Series Analysis 10. Monitoring Annual Vegetated Land Loss to Urbanization with Landsat Archive: A Case Study in Shanghai, China
Part I: Time Series Image/Data Generation 1. Cloud and Cloud Shadow Detection for Landsat Images: The Fundamental Basis for Analyzing Landsat Time Series 2. An Automatic System for Reconstructing High-Quality Seasonal Landsat Time Series 3. Spatiotemporal Data Fusion to Generate Synthetic High Spatial and Temporal Resolution Satellite Images Part II: Feature Development and Information Extraction 4. Phenological Inference from Times Series Remote Sensing Data 5. Time Series Analysis of Moderate Resolution Land Surface Temperatures 6. Impervious Surface Estimation by Integrated Use of Landsat and MODIS Time Series in Wuhan, China Part III: Time Series Image Applications 7. Mapping Land Cover Trajectories Using Monthly MODIS Time Series from 2001 to 2010 8. Creating a Robust Reference Dataset for Large Area Time Series Disturbance Classification 9. A General Workflow for Mapping Forest Disturbance History Using Pixel Based Time Series Analysis 10. Monitoring Annual Vegetated Land Loss to Urbanization with Landsat Archive: A Case Study in Shanghai, China
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