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Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2007 Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Nets, and Cellular Automata Fei Wang Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Social and Behavioral Sciences Commons Recommended Citation Wang, Fei, "Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Nets, and Cellular Automata" (2007). LSU Doctoral Dissertations. 2433. https://digitalcommons.lsu.edu/gradschool_dissertations/2433 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. LAND-COVER AND LAND-USE STUDY USING GENETIC ALGORITHMS, PETRI NETS, AND CELLULAR AUTOMATA A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Geography and Anthropology by Fei Wang B.S., Wuhan Teacher’s College, 1984 M.S., Beijing Normal University, 1995 M.S., Southern University, 2002 December, 2007 ACKNOWLEDGEMENTS During my years in Baton Rouge, Louisiana, I have been blessed with guidance, help, and encouragement from more people than I could possibly cite in this acknowledgement. I deeply believe that I am indebted to those who have guided, helped, and encouraged me. Primary thanks must go to my major advisor Professor Nina Lam for her guidance during last four and half years. Her suggestions have greatly kindled my interest in artificial intelligence-based spatiotemporal analysis and modeling. I am thankful to my committee members – Professor Kam-Biu Liu, Mr. DeWitt Braud, Professor Robert V. Rohli, and Professor Max Z. Conrad – for their invaluable support. They not only introduce knowledge but also teach the way of thinking. Special thanks to Professor Qingxin Lin at Wetland Biogeochemistry Institute at Louisiana State University and Professor Exyie C. Ryder at the Department of Science and Mathematics Education at Southern University for all the help they have provided. Special thanks also to Professor Tony Lewis, Professor Craig E. Colten, Professor John Pine, Professor Andrew Curtis, and Mr. Farrell Jones at Department of Geography and Anthropology at Louisiana State University for their help. Without their help, I would not be able to finish this study. Thanks to all faculty and staff at the Department of Geography and Anthropology at Louisiana State University, all faculty and staff at the Department of Science and Mathematic Education at Southern University, and all faculty and staff at the Department of Social Sciences at Louisiana Tech University. Last but not least, I would like to thank my parents and parents-in-law, my wife and daughter, my brother, my sister and her family, and my brother-in-law and his family for their love, understanding, encouragement, and support. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS .…………………………………………………………..………… ii LIST OF TABLES .……………………………………………………………….………..……vii LIST OF FIGURES .…………………………………………………………….…..………..…. ix ABSTRACT ……………………………………………..………………….…..……………… xii CHAPTER 1 INTRODUCTION ………………………………………………..…....………… 1 1.1 Background ……………………………………………….………...….………………… 1 1.1.1 Remote Sensing and GIS ……….…………………………….....…………….…… 1 1.1.2 Land-Cover and Land-Use Study ……….….……………………..……………..… 2 1.1.3 Traditional Land-Cover and Land-Use Study Approaches …….…..……………… 3 1.1.4 Non-Traditional Land-Cover and Land-Use Study Approaches …...……………… 4 1.2 Problems Statement ……………………………………………………..…………..…… 7 1.3 Research Objectives ……………………………………………………...…………….…9 1.4 Research Hypotheses ………………………………………………..…………..……… 10 1.5 Expected Significance…………………………………………………………….……...11 1.6 Rationale of Dissertation Research ……………………………………….…………..…13 1.7 Chapter Organization ……..………………………………………………..……….…... 14 CHAPTER 2 LITERATURE REVIEW …………………………………………..……………16 2.1 Land-Cover and Land-Use Classification …………………………….…..…..............…16 2.1.1 Concept of Land-Cover and Land-Use Classification ……………..………...…… 16 2.1.2 Approaches to Land-Cover and Land-Use Classification …………..…….….……16 2.2 Land-Cover and Land-Use Change Detection …………………………………...…...…18 2.2.1 Concept of Land-Cover and Land-Use Change Detection ……………..………… 18 2.2.2 Approaches to Land-Cover and Land-Use Change Detection ………….…………19 2.2.3 Process-Oriented Land-Cover and Land-Use Change Detection ………..……..… 24 2.3 Land-Cover and Land-Use Predictive Modeling ……………………………..…………25 2.3.1 Concept of Land-Cover and Land-Use Predictive Modeling …………..…....…… 25 2.3.2 Approaches to Land-Cover and Land-Use Predictive Modeling ………..…..…… 26 2.4 Genetic Algorithms …………………………………………………………..……….…30 2.4.1 Concept of Genetic Algorithms …………….…………………………….…...……30 2.4.2 Applications of Genetic Algorithms ………………………….…….….....………. 31 2.4.3 Genetic Parameters ………………………………….……………...….…….…… 31 2.5 Petri Nets ..……………………………………………………………………..….….… 35 2.5.1 Concept of Petri Nets …………………………………………………..……...….. 35 2.5.2 Applications of Petri Nets ………..……………………………..…….………….. 35 2.5.3 Limitation of Pure Petri Nets …………………………………………..…….…… 36 2.6 Cellular Automata ……………………………………………….…………..……..……38 2.6.1 Concept of Cellular Automata ……………………………………………..…...… 38 2.6.2 Applications of Cellular Automata ………………………………..……..…..…… 40 2.6.3 Transition Rules of Cellular Automata …………………………………...…….… 41 2.7 Summary ………………………………………………………………………………... 43 iii CHAPTER 3 RESEARCH METHODOLOGY………………………………………...…...… 45 3.1 Study Area and Data Sources ……………………………………………………………45 3.1.1 Natural and Social Environment …………...…………………………...………… 45 3.1.2 Data Sources ………………………………………………………..…………..… 50 3.2 Data Preprocessing………………………………………………………………….……54 3.2.1 Geometric Correction and Radiometric Correction ……………..…...…………… 55 3.2.2 Transformation and Indexing ………………………………………………………56 3.2.3 Conversion and Layer Stack ….………………………………………………….…57 3.3 Research Methods ………………………………………………………..…….…..…… 58 3.3.1 Classification …………………………………………………………………….…58 3.3.2 Change Detection …………………………………………………..……...………60 3.3.3 Predictive Modeling ……………………………………………………….………60 3.4 Algorithms …………………………………………………………..………………..… 62 3.4.1 Genetic Algorithms …………………………………………..……….……..…… 62 3.4.2 Petri Nets ...…………………………………………………..…………………… 65 3.4.3 Cellular Automata …………………………………………..……….………….… 77 3.5 Implementation ……………………………………………………..…….………..…… 81 3.6 Summary ………………………………………………………………..…………….… 82 CHAPTER 4 IMPACT OF GENETIC PARAMETERS ON LAND-COVER AND LAND-USE CLASSIFICATION ACCURACY…………………………..…… 84 4.1 Experiment #1 Design ………………………………………………………………..… 84 4.2 Results ………………………………………………………..………………………… 88 4.2.1 Number of Generations ……………………………………………………………90 4.2.2 Initial Population Size …………………………..…………………………………93 4.2.3 Crossover Rate ………………………………….………………………………… 96 4.2.4 Mutation Rate …………………..………………………………………………… 99 4.2.5 Generation Gap …………………………………………………...………………102 4.2.6 Land-Cover and Land-Use Classification ………………………………………..105 4.3 Discussion ………………………………………………………………………...…… 106 4.3.1 Impact of Premature Convergence and Local Optimization ……………..………109 4.3.2 Impact of Low Convergence Rate and Unstable Population ……….……….……110 4.3.3 Impact of Small Number of Generations and Small Size of Population …….…...111 4.3.4 Recommendation of Genetic Parameters Configuration …………………………112 4.4 Hypothesis #1 Review ……………………………………………………….…………112 4.5 Summary ………………………………………………………………….…………… 113 CHAPTER 5 IMPACT OF IMAGE PARAMETERS ON LAND-COVER AND LAND-USE CLASSIFICATION ACCURACY…………….………………… 114 5.1 Experiment #2 Design ……………………………………………….…………………114 5.2 Results ………………………………………………………………….………………117 5.2.1 Individual Data Characteristics …………………………………………………..121 5.2.2 Training / Testing Data Size …………………………………………..………… 121 5.2.3 Spatial Resolution …………………………………………………..…………… 127 5.2.4 Data Layer Combinations …………………………………………..…………… 128 5.2.5 Land-Cover and Land-Use Classification ……………………………..…………131 5.3 Discussion …………………………………………………………...………………… 134 5.3.1 Impact of Individual Data Layer …………..………………………………..……134 iv 5.3.2 Impact of Data Layer Combinations ……………………………………………..140 5.3.3 Impact of Training / Testing Data Size …………………………………......……141 5.3.4 Impact of Spatial Resolution ……………………………………………...……...142 5.3.5 Recommendation of Image Parameters Configuration ...………...……………....143 5.4 Hypothesis #2 Review ………………………………………………………..……….. 144 5.5 Summary ……………………………………………………………...……………….. 145 CHAPTER 6 COMPARISON OF TRADITIONAL AND GA-BASED LAND-COVER AND LAND-USE CLASSIFICATION……………………….………….….…147 6.1 Experiment #3 Design ……………………………………………………...……..……147 6.2 Results ……………………………………………………………………………...…..150 6.2.1 ISODATA-based Unsupervised Land-Cover and Land-Use Classification …......152 6.2.2 MLC-based Supervised Land-Cover and Land-Use Classification ………………152 6.2.3 Hybrid-Approach-based Land-Cover and Land-Use Classification …...……..….154 6.2.4 GA-based Land-Cover and Land-Use Classification ………..………………..….154 6.3 Discussion ……………………………………………………………………………....157 6.3.1 Advantages and Disadvantages ………………………………………...………...157 6.3.2 Ease of Operation ………………………………………………...………………160 6.3.3 Overall Classification Accuracy………………….………………………..…….. 160 6.3.4 Individual
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