UUnniivveerrssiittyy ooff SSoouutthh CCaarroolliinnaa SScchhoollaarr CCoommmmoonnss Theses and Dissertations 12-15-2014 RReemmoottee SSeennssiinngg SSaatteelllliittee IImmaaggee AAccqquuiissiittiioonn PPllaannnniinngg:: FFrraammeewwoorrkk,, MMeetthhooddss aanndd AApppplliiccaattiioonn Shufan Liu University of South Carolina - Columbia Follow this and additional works at: https://scholarcommons.sc.edu/etd Part of the Earth Sciences Commons RReeccoommmmeennddeedd CCiittaattiioonn Liu, S.(2014). Remote Sensing Satellite Image Acquisition Planning: Framework, Methods and Application. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/2938 This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. REMOTE SENSING SATELLITE IMAGE ACQUISITION PLANNING: FRAMEWORK, METHODS AND APPLICATION By Shufan Liu Bachelor of Information Engineering Wuhan University 2004 Master of Cartography and Geographic Information System Wuhan University 2008 Submitted in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy in Geography College of Arts and Sciences University of South Carolina 2014 Accepted by: Michael E. Hodgson, Major Professor Diansheng Guo, Committee Member Sarah E. Battersby, Committee Member Csilla Farkas, Committee Member Lacy Ford, Vice Provost and Dean of Graduate Studies © Copyright by Shufan Liu, 2014 All Rights Reserved. ii Acknowledgements During my study in Department of Geography, University of South Carolina, many people have helped me in different ways. I would like to express my sincere gratitude to my advisor, Dr. Michael E. Hodgson for his guidance, patience, encouragement, and endless support throughout my degree program. I am grateful to my committee members, Dr. John R. Jensen, Dr. Diansheng Guo, Dr. Sarah E. Battersby, and Dr. Csilla Farkas for their assistance and committee work. I wish to especially thank my wife and best friend, Shiqing Zhao for her endless love and encouragement. Lots of thanks give to my parents, my sister, my brother in law, and my uncle for their continuous support and encouragement. My thanks also give to my parents in law for their support and understanding. My relatives and friends in China and the U.S. have been an integral part of my study here. Additionally, I would like to acknowledge all of the faculty, staff, and my fellow graduate students of the USC Geography department. iii Abstract This dissertation explores the theories and methods of satellite remote sensing image acquisition planning within a spatial temporal context. For many time sensitive applications, such as disaster emergency response, timely acquisition of critical information is the key to intelligent and effective decision making. Remote sensing plays an important role in information collection for these time sensitive applications. Imagery collected from hundreds of remote sensing satellite sensors offer accurate, frequent and almost instantaneous data covering the Earth in a relatively short time. However, determining which satellite sensors can provide an appropriated kind of imageries during a restricted collection window for the analysis is problematic. Satellite image acquisition planning is developed to solve the problem. In this research, we explore the design and implementation of s spatial decision support system (SDSS) for satellite image acquisition planning. A SDSS framework is proposed, and several novel models and algorithms are developed to derive optimized satellite image acquisition solutions. Chapter 2 describes the components of the framework; Chapter 3 and Chapter 4 present several models including composite satellite image collection opportunities modeling, collection opportunities evaluation model, and a spatial optimization model. Based on the framework, models, and algorithm, Chapter 5 presents an application of satellite image acquisition planning for tidally influenced salt marshes for vegetation mapping. Collectively, this research provides a foundation for research and development towards the satellite image acquisition planning. iv Table of Contents Acknowledgements ............................................................................................................ iii Abstract .............................................................................................................................. iv List of Tables ................................................................................................................... viii List of Figures .................................................................................................................... ix CHAPTER 1 Introduction................................................................................................... 1 1.1 Introduction ............................................................................................................... 1 1.2 Dissertation Structure ................................................................................................ 3 CHAPTER 2 The SDSS Framework .................................................................................. 5 2.1 Overview ................................................................................................................... 5 2.2 SDSS Framework ...................................................................................................... 9 2.3 Databases ................................................................................................................. 10 2.4 GIS-based models ................................................................................................... 12 CHAPTER 3 Optimizing Large Area Coverage from Multiple Satellite Sensors ............ 18 Abstract ......................................................................................................................... 18 3.1 Introduction ............................................................................................................. 19 3.2 Background ............................................................................................................. 20 v 3.3 Modeling the satellite image collection opportunities for a large area ................... 23 3.4 Modeling the best satellite image acquisition plan ................................................. 25 3.5 Solving the optimization problem ........................................................................... 27 3.6 Results ..................................................................................................................... 29 3.7 Conclusions ............................................................................................................. 31 CHAPTER 4 Satellite Image Acquisition Planning for Large Area Disaster Emergency Response ...................................................................................... 43 Abstract ......................................................................................................................... 43 4.1 Introduction ............................................................................................................. 44 4.2 Background ............................................................................................................. 45 4.3 Methodology ........................................................................................................... 48 4.4 Results ..................................................................................................................... 54 4.5 Conclusion and Discussion ..................................................................................... 55 CHAPTER 5 Remote Sensing Image Acquisition Planning for Salt-Marsh Vegetation . 66 Abstract ......................................................................................................................... 66 5.1 Introduction ............................................................................................................. 67 5.2 Tide Prediction Stations and Data Description ....................................................... 72 5.3 Methodology ........................................................................................................... 72 5.4 Results ..................................................................................................................... 76 5.5 Conclusion ............................................................................................................... 78 vi CHAPTER 6 Conclusions................................................................................................. 90 6.1 Summary of results.................................................................................................. 90 6.2 Future research ........................................................................................................ 92 References ......................................................................................................................... 94 Appendix A - Permission to Reprint ............................................................................... 108 vii List of Tables Table 3.1 Example high spatial resolution satellite sensors revisit frequency ................. 32 Table 3.2 Working assumptions of the application case ................................................... 33 Table 3.3 Example satellite image collection opportunity ................................................ 34 Table 4.1 Example EEIs and their minimum spatial/spectral resolution requirements .... 57 Table 5.1 Example satellite image collection opportunity on 07/08/2014 ....................... 80 Table 5.2 Tide prediction results of 07/08/2014 ............................................................... 81 Table 5.3 Working assumption variables .......................................................................... 82 viii List of Figures Figure 2.1 Sprague’s three-level framework for developing a SDSS ............................... 13 Figure 2.2 Amstrong and Densham’s architecture for a SDSS ........................................ 14 Figure 2.3 Proposed SDSS framework for remote sensing image acquisition planning .. 15 Figure 2.4 1:N relationship between satellite, sensor, and band in the database .............. 16 Figure 2.5 The primary tables used to support the satellite-sensor-band database ........... 17 Figure 3.1 Relatively large disaster impact area requires multiple satellite images to be fully covered ............................................................................................................ 35 Figure 3.2 CIPS (small squares) as potential satellite pointing angles representative points ......................................................................................................... 36 Figure 3.3 Basic flow of the optimization model ............................................................. 37 Figure 3.4 Algorithm structure for optimization model.................................................... 38 Figure 3.5 493 CIPS and 11 RCIPS representing the multitude of satellite pointing angles for the study area ..................................................................................... 39 Figure 3.6 (a) Spatial coverage of the example satellite image collection opportunity. (b) Spatial coverage detail of 143 daytime collection opportunities ................................ 40 Figure 3.7 Best three image collection plans derived from the optimization model ........ 41 Figure 3.8 Top three image collection plans derived from the optimization model, (a), (b) and (c) represents #1, #2 and #3 solution in Figure 7 respectively. (d) shows the spatial coverage of the first solution over 143 available image collection opportunities ............ 42 Figure 4.1 Satellite image requirements identification via Spatial/Spectral resolution specification or EEI selection .......................................................................... 58 Figure 4.2 Multiple polygons are interpolated with points to derive RCIPS for future collection opportunities prediction ......................................................................... 59 ix