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E-raamat: Remote Sensing Time Series Image Processing

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Edited by (Indiana State University, Terre Haute, USA)
  • Formaat: 263 pages
  • Sari: Imaging Science
  • Ilmumisaeg: 17-Apr-2018
  • Kirjastus: CRC Press
  • ISBN-13: 9781351680561
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Remote Sensing Time Series Image ProcessingSuurem pilt
  • Formaat: 263 pages
  • Sari: Imaging Science
  • Ilmumisaeg: 17-Apr-2018
  • Kirjastus: CRC Press
  • ISBN-13: 9781351680561
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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 worlds 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.
Preface vii
Acknowledgments xv
Editor xvii
Contributors xix
Part I: Time Series Image/Data Generation
1 Cloud and Cloud Shadow Detection for Landsat Images: The Fundamental Basis for Analyzing Landsat Time Series
3(22)
Zhe Zhu
Shi Qiu
Binbin He
Chengbin Deng
Brief Summary
4(1)
1.1 Introduction
4(1)
1.2 Landsat Data and Reference Masks
5(3)
1.2.1 Landsat Data
5(2)
1.2.2 Manual Masks of Landsat Cloud and Cloud Shadow
7(1)
1.3 Cloud and Cloud Shadow Detection Based on a Single-Date Landsat Image
8(6)
1.3.1 Physical-Rules-Based Cloud and Cloud Shadow Detection
8(1)
1.3.1.1 Physical-Rules-Based Cloud Detection Algorithms
8(1)
1.3.1.2 Physical-Rules-Based Cloud Shadow Detection Algorithms
12(2)
1.3.2 Machine-Learning-Based Cloud and Cloud Shadow Detection
14(1)
1.4 Cloud and Cloud Shadow Detection Based on Multitemporal Landsat Images
14(3)
1.4.1 Cloud Detection Based on Multitemporal Landsat Images
15(1)
1.4.2 Cloud Shadow Detection Based on Multitemporal Landsat Images
16(1)
1.5 Discussions
17(2)
1.5.1 Comparison of Different Algorithms
17(1)
1.5.2 Challenges
17(1)
1.5.3 Future Development
18(1)
1.5.3.1 Spatial Information
18(1)
1.5.3.2 Temporal Frequency
18(1)
1.5.3.3 Haze/Thin Cloud Removal
18(1)
1.6 Conclusion
19(1)
References
19(6)
2 An Automatic System for Reconstructing High-Quality Seasonal Landsat Time Series
25(18)
Xiaolin Zhu
Eileen H. Helmer
Jin Chen
Desheng Liu
2.1 Introduction
25(3)
2.2 Methods
28(5)
2.2.1 Classify Uncontaminated Pixels in Each Image
29(1)
2.2.2 Select Ancillary Data for Each Contaminated Pixel from the Time Series
29(1)
2.2.3 Interpolate Contaminated Pixels by NSPI
30(3)
2.3 Experiments
33(2)
2.4 Results
35(3)
2.4.1 Reconstruction of Real Landsat Time Series
35(1)
2.4.2 Reconstruction of Simulated Landsat Time Series
36(2)
2.5 Conclusion and Discussions
38(2)
Acknowledgments
40(1)
References
41(2)
3 Spatiotemporal Data Fusion to Generate Synthetic High Spatial and Temporal Resolution Satellite Images
43(26)
Jin Chen
Yuhan Rao
Xiaolin Zhu
3.1 Introduction
43(3)
3.2 NDVI Linear Mixing Growth Model (NDVI-LMGM)
46(8)
3.2.1 Description of NDVI-LMGM
46(3)
3.2.2 Test Experiment
49(1)
3.2.2.1 Assessment over Spatial and Temporal Contrasting Regions
51(1)
3.2.2.2 Assessment for Long Term Prediction
53(1)
3.3 Flexible Spatiotemporal Data Fusion Method (FSDAF)
54(8)
3.3.1 Description of FSDAF
54(4)
3.3.2 Test Experiment
58(1)
3.3.2.1 Assessment of Simulated Results
60(1)
3.3.2.2 Assessment of Fusing Real Satellite Images
60(2)
3.4 Conclusions and Discussion
62(2)
Acknowledgments
64(1)
References
64(5)
Part II: Feature Development and Information Extraction
4 Phenological Inference from Times Series Remote Sensing Data
69(20)
Iryna Dronova
Lu Liang
4.1 Introduction
69(1)
4.2 Single-Season Phenological Analyses
70(4)
4.2.1 Spectral Indicators of Phenology
70(1)
4.2.2 Basic Seasonal Phenological Trajectory
71(1)
4.2.3 Phenological Variation Represented by Seasonal Trajectories
72(2)
4.3 Common Applications of Single-Year Phenology
74(3)
4.3.1 Agricultural Mapping and Monitoring of Crops
74(1)
4.3.2 Forest Mapping and Ecosystem Analyses
74(2)
4.3.3 Hydro-Phenological Analyses of Complex Flooded Landscapes
76(1)
4.4 Multi-Year Phenological Inference
77(5)
4.4.1 Local-Scale: Phenocam Observations
77(1)
4.4.2 Regional Analyses of Greenness Trends
78(1)
4.4.2.1 Basic Trend Analyses
78(1)
4.4.2.2 Trajectory-Based Landscape Change Analyses
79(1)
4.4.2.3 Continuous Change Detection and Classification of Land Cover
81(1)
4.4.3 Broad-Scale Phenological Analyses with Multi-Year Seasonal Data
81(1)
4.4.3.1 Multi-Year Inference with Phenological Curves
81(1)
4.4.3.2 Percent above Threshold Approaches
82(1)
4.5 Applications and the Importance of Ancillary Factors
82(2)
References
84(5)
5 Time Series Analysis of Moderate Resolution Land Surface Temperatures
89(32)
Benjamin Bechtel
Panagiotis Sismanidis
5.1 Introduction
89(3)
5.2 Data and Methods
92(4)
5.2.1 MYD11A1 and MOD11A1 Land Surface Temperatures
92(2)
5.2.2 MODIS Land Cover and Urban Areas
94(1)
5.2.3 Annual Cycle Parameters
94(2)
5.3 Results and Discussion
96(11)
5.3.1 ACP for Central Europe
96(2)
5.3.2 Comparison between Collection-5 and Collection-6
98(4)
5.3.3 Latitudinal Gradients in ACP
102(5)
5.4 Applications
107(8)
5.4.1 Climatological SUHI Analysis
107(5)
5.4.2 Using the ACPs as Disaggregation Kernels for Downscaling LST Image Data
112(3)
5.5 Conclusions
115(1)
References
115(6)
6 Impervious Surface Estimation by Integrated Use of Landsat and MODIS Time Series in Wuhan, China
121(16)
Zhang Lei
Qihao Weng
6.1 Introduction
121(2)
6.2 Study Area
123(1)
6.3 Methodology
123(4)
6.3.1 Data Preprocessing
124(1)
6.3.2 Reconstruction of Time Series BCI
125(1)
6.3.3 Similarity of Temporal Features
126(1)
6.3.4 Classification Based on Semi-Supervised SVM
126(1)
6.4 Results and Discussion
127(4)
6.4.1 Annual Dynamics of Impervious Surfaces
127(1)
6.4.2 Classification Accuracy
128(3)
6.5 Conclusions
131(1)
Acknowledgments
131(1)
References
131(6)
Part III: Time Series Image Applications
7 Mapping Land Cover Trajectories Using Monthly MODIS Time Series from 2001 to 2010
137(20)
Shanshan Cai
Desheng Liu
7.1 Introduction
137(2)
7.2 Study Area and Data
139(2)
7.2.1 Study Area
139(1)
7.2.2 Image Data
140(1)
7.2.3 Reference Data
140(1)
7.3 Methods
141(4)
7.3.1 Algorithm Overview
141(1)
7.3.2 Detecting Change Dates
141(1)
7.3.3 Generating Adaptive Time Series
142(1)
7.3.4 Modified SVM Classification
143(1)
7.3.5 Integrated Training and Classification
143(1)
7.3.6 Trajectory Reconstruction
144(1)
7.3.7 Comparison of Adaptive Time Series with Full Length Time Series
144(1)
7.3.8 Accuracy Assessment
144(1)
7.4 Results
145(5)
7.4.1 Trajectory Mapping Results
145(1)
7.4.2 Accuracy Assessment of Adaptive Classification Results
146(2)
7.4.3 Accuracy Assessment of Trajectory Mapping Results
148(1)
7.4.4 Comparison of Adaptive Time Series with Full Length Time Series
149(1)
7.5 Discussion
150(2)
7.6 Conclusions
152(1)
References
153(4)
8 Creating a Robust Reference Dataset for Large Area Time Series Disturbance Classification
157(16)
Mariela Soto-Berelov
Andrew Haywood
Simon Jones
Samuel Hislop
Trung H. Nguyen
8.1 Introduction
157(1)
8.2 Study Area
158(1)
8.3 Methods
159(3)
8.3.1 Quality Control and Quality Assurance
161(1)
8.4 Case Study
162(6)
8.4.1 Quality Control and Quality Assurance
164(1)
8.4.2 Mapping Disturbance
165(3)
8.5 Discussion and Conclusion
168(1)
Acknowledgments
169(1)
References
170(3)
9 A General Workflow for Mapping Forest Disturbance History Using Pixel Based Time Series Analysis
173(32)
Feng Zhao
Chengquan Huang
9.1 Introduction
173(2)
9.2 Overview of the NAFD-NEX Processing Flow
175(4)
9.3 Image Selection and Preprocessing
175(1)
9.3.1 Image Selection
176(1)
9.3.2 Image Preprocessing
177(1)
9.3.3 Image Compositing
177(2)
9.4 VCT Disturbance Analysis
179(5)
9.4.1 Need for Annual Landsat Time Series
180(2)
9.4.2 Western US Sparse Forests Adjustment
182(2)
9.5 Post Processing
184(8)
9.5.1 Quality Assessment
185(4)
9.5.2 Adjustment of Minimum Mapping Unit (MMU) to Address Erroneous Forest Disturbance Rates in Low Forest Cover Counties
189(2)
9.5.3 Map Re-Projection
191(1)
9.5.4 Annual Disturbance Maps Mosaic
192(1)
9.6 NAFD-NEX Product Generation
192(7)
9.6.1 NAFD-NEX Product at Oak Ridge National Laboratory (ORNL)
192(3)
9.6.2 Validation
195(4)
9.7 Summary
199(1)
References
200(5)
10 Monitoring Annual Vegetated Land Loss to Urbanization with Landsat Archive: A Case Study in Shanghai, China
205(16)
Qingling Zhang
Bhartendu Pandey
10.1 Introduction
205(2)
10.2 Methodology
207(5)
10.2.1 Data and Preprocessing
207(1)
10.2.2 Constructing Time Series of Annual Cloud/Shadow Free Landsat NDVI Composites
208(1)
10.2.3 Properties of the NDVI Mosaics
209(1)
10.2.4 Simulating NDVI Trajectory Models
209(1)
10.2.5 Pinpointing Changes
210(1)
10.2.6 Post Processing
211(1)
10.2.7 Accuracy Assessment and Evaluation
211(1)
10.3 Results
212(2)
10.3.1 Results of Change Detection
212(1)
10.3.2 Change Detection Accuracy
212(1)
10.3.3 Comparison with Official Statistics Data
213(1)
10.4 Discussions
214(3)
10.5 Conclusions
217(1)
10.6 Acknowledgment
217(1)
References
217(4)
Index 221
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. Wengs 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 universitys 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.
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