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系統識別號 U0026-0708201714445600
論文名稱(中文) 應用Dyna-CLUE模式預測石門子集水區土地利用改變
論文名稱(英文) Predicting Land Use Changes Using Dyna-CLUE Model in Shihmen Sub-Watershed, Taiwan
校院名稱 成功大學
系所名稱(中) 自然災害減災及管理國際碩士學位學程
系所名稱(英) International Master Program on Natural Hazards Mitigation and Management
學年度 105
學期 2
出版年 106
研究生(中文) 雷諮曼
研究生(英文) Laxman Maharjan
學號 NC6047040
學位類別 碩士
語文別 英文
論文頁數 127頁
口試委員 指導教授-游保杉
召集委員-陳憲宗
口試委員-林妤蓁
口試委員-楊道昌
口試委員-郭振民
中文關鍵字 none 
英文關鍵字 Shihmen sub-watershed  predicting land use change  Dyna-CLUE model 
學科別分類
中文摘要 none
英文摘要 Anthropogenic activities combined with the physiographical attributes in the watershed can largely affect the land use change pattern. Land use change in one hand indicates the human development whereas in other hand if overexploited cause harm to ecosystem causing global warming, climate change or even trigger natural disasters. The purposes of this study are: (1) to analyze the relationship between driving factors and their response to land use change and (2) to investigate the possible land use changes in Shihmen sub-watershed by using Dynamic Conversion of Land Use and its Effects (Dyna-CLUE) model. Firstly, the land use pattern in 2004 is recognized as the base map from Landsat satellite image by using a classification algorithm. Five different land use types are classified which are water, forest, built-up, grassland and bare land. Secondly, a logistic regression model is built to quantify the relationship between estimated driving factors and land use types. Simple linear extrapolation method is adopted to calculate the future demand of each land use types. Then, spatial allocation model, Dyna-CLUE is used to simulate the evolvement of land use pattern from 2004 to 2011 and the parameters of Dyna-CLUE (i.e., conversion elasticities for various land use types) are tuned to match with the observed land use map in 2011. Conversion elasticities of 1, 0.95, 0.8, 0.3, and 0.05 for water, forest, built-up, grassland and bare land, respectively, give the most reliable result. Relative operating characteristics (ROC) curve, kappa and accuracy indices are selected for statistical validation of the generated maps. Finally, the study used tuned Dyna-CLUE to project the land use pattern in 2020 for four different scenarios: (1) Linear trend of land use demand without restriction areas, (2) Linear trend of land use demand with restriction areas, (3) Input of minimum and maximum time steps of conversion sequence in the conversion matrix, and (4) Higher rate of land transformation. All the results show an increase in built-up and bare land area on the expenses of forest and grassland area in 2020.
論文目次 ABSTRACT I
ACKNOWLEDGEMENTS II
TABLE OF CONTENTS III
LIST OF TABLES V
LIST OF FIGURES VI
CHAPTER 1 INTRODUCTION 1
1.1. Background 1
1.2. Research Problem 3
1.3. Research Objectives 4
1.4. Organization of thesis 5
CHAPTER 2 LITERATURE REVIEW 6
2.1. Model 6
2.2. Types of Model 6
2.3. Modeling land use changes 8
2.4. Dyna-CLUE model and its development 9
2.5. Application of Dyna-CLUE model 9
2.6. Study Area 16
CHAPTER 3 METHODOLOGY 18
3.1. Dyna-CLUE Model Description 18
3.1.1. Model Structure 18
3.1.2. Iteration Procedure 19
3.1.3. Allocation Procedure 22
3.2. Statistical Analysis 24
3.2.1. Logistic Regression 24
3.2.2. Evaluation of the Logistic Regression Models 25
3.3. Model Simulation 27
3.3.1. Model User Interface 27
3.3.2. Model Parameters 28
3.3.3. Model Input Files 33
3.3.4. Display of Model Outputs 37
CHAPTER 4 APPLICATION AND RESULTS 40
4.1. Research Methodology 40
4.2. Various Scenarios Proposed 42
4.3. Model Settings 42
4.3.1. Delineation of Shihmen Reservoir Watershed 42
4.3.2. Grid Size and Grid Extent 45
4.3.3. Available Land Use Maps 46
4.3.4. Image Classification 46
4.3.5. Classification of Land Use Types 47
4.3.6. Maps of Individual Land-Use Classes 50
4.3.7. Accuracy Assessment of Image Classification 52
4.3.8. Quantitative method of evaluation 53
4.3.9. Classification of Land use map 2011 for validation 55
4.3.10. Demand file 57
4.3.11. Conversion matrix 60
4.3.12. Conversion Elasticity 61
4.3.13. Map of no restriction and restriction map 62
4.3.14. Driving factors for Logistic Regression 64
4.3.15. Logistic Regression and ROC curve 75
4.3.16. Evaluation of the Logistic Regression Models 78
4.4. Model Run and Calibration 82
4.5. Model Output 83
4.6. Model Validation 83
CHAPTER 5 DISCUSSION AND CONCLUSIONS 85
5.1. Results from Different Scenarios 85
5.1.1. Results from Scenario 1 85
5.1.2. Results from Scenario 2 89
5.1.3. Results from Scenario 3 94
5.1.4. Results from Scenario 4 96
5.2. Comparisons between Scenarios 100
5.3. Conclusions 106
5.4. Thesis Contribution 107
5.5. Recommendations 107
REFERENCES 109
APPENDICES 113
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