Remote Sensing Time Series Image Processing
Today, remote sensing technology is an essential tool for understanding the Earth and managing human-Earth interactions. There is a rapidly growing need for remote sensing and Earth observation technology that enables monitoring of world’s natural resources and environments, managing exposure to natural and man-made risks and more frequently occurring disasters, and helping the sustainability and productivity of natural and human ecosystems. The improvement in temporal resolution/revisit allows for the large accumulation of images for a specific location, creating a possibility for time series image analysis and eventual real-time assessments of scene dynamics. As an authoritative text, Remote Sensing Time Series Image Processing brings together active and recognized authors in the field of time series image analysis and presents to the readers the current state of knowledge and its future directions.
Divided into three parts, the first addresses methods and techniques for generating time series image datasets. In particular, it provides guidance on the selection of cloud and cloud shadow detection algorithms for various applications. Part II examines feature development and information extraction methods for time series imagery. It presents some key remote sensing-based metrics, and their major applications in ecosystems and climate change studies. Part III illustrates various applications of time series image processing in land cover change, disturbance attribution, vegetation dynamics, and urbanization.
This book is intended for researchers, practitioners, and students in both remote sensing and imaging science. It can be used as a textbook by undergraduate and graduate students majoring in remote sensing, imaging science, civil and electrical engineering, geography, geosciences, planning, environmental science, land use, energy, and GIS, and as a reference book by practitioners and professionals in the government, commercial, and industrial sectors.
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