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2016 Adhikari Pradeep Dissert UNIVERSITY OF OKLAHOMA GRADUATE COLLEGE MAPPING AND ANALYZING URBAN GROWTH: A STUDY TO IDENTIFY DRIVERS OF URBAN GROWTH IN WEST AFRICA A DISSERTATION SUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY By PRADEEP ADHIKARI Norman, Oklahoma 2016 MAPPING AND ANALYZING URBAN GROWTH: A STUDY TO IDENTIFY DRIVERS OF URBAN GROWTH IN WEST AFRICA A DISSERTATION APPROVED FOR THE DEPARTMENT OF GEOGRAPHY AND ENVIRONMENTAL SUSTAINABILITY BY ______________________________ Dr. Kirsten M. de Beurs, Chair ______________________________ Dr. David Sabatini ______________________________ Dr. Renee McPherson ______________________________ Dr. Aondover Tarhule ______________________________ Dr. Yang Hong © Copyright by PRADEEP ADHIKARI 2016 All Rights Reserved. Acknowledgements First of all, I would like to express my sincere gratitude to my advisor Dr. Kirsten M. de Beurs for the continuous support during my Ph.D. study and research, for her patience, and for her motivation. Her guidance steered me on the right path in this journey. Thank you Dr. de Beurs! I would like to extend my sincere thanks to the rest of my committee members: Dr. David Sabatini, Dr. Renee McPherson, Dr. Aondover Tarhule, and Dr. Yang Hong for their insightful questions and encouragement along the way. Such questions and probing helped me to widen my research from various perspectives. I also thank my fellow students from the Landscape Land Use Change Institute for the stimulating discussions and comments on my research, presentations and write-ups. Additionally, I would also like to extend my gratitude to the faculty, staff and students of the department for their support and help, directly or indirectly during my graduate study. My parents, Jeevan and Saraswati, who are living on the other side of the globe, are the constant source of encouragement throughout for which I am always grateful. Last but not least, I thank my wife Kabita and children Pranshu and Pranjal for their continuous love and support. They played various roles including counsellors wherever needed, and kept me motivated during my study and research. This journey would not have been completed without their support and sacrifice. iv Table of Contents Acknowledgements iv List of Tables viii List of Figures xii Abstract xv CHAPTER 1: INTRODUCTION 1 Research questions and hypothesis 7 Study area 9 Kumasi, Ghana 11 Daloa, Cote d’Ivoire 14 Abuja, Nigeria 15 Kindia, Guinea 16 Ouagadougou, Burkina Faso 17 Kano, Nigeria 18 Methodology 19 Organization of dissertation 20 Literature cited 21 CHAPTER 2: AN EVALUATION OF MULTIPLE LAND COVER DATASETS TO ESTIMATE CROPLAND AREA IN WEST AFRICA 26 Introduction 27 Study area 31 Data 35 Methods 39 Pixel level validation 39 v Results and discussion 41 Eco-region level 43 Country level 44 Pixel level 50 Cropland data agreement 51 Conclusions 53 Literature cited 56 CHAPTER 3: MAPPING AND ANALYSIS OF URBAN EXPANSION IN WEST AFRICAN CITIES 62 Introduction 63 Materials and Methods 66 Study area 66 Data 68 Methods 70 Results 80 Urban land use maps 80 Urban growth-Transition matrices growth 85 Rate of growth in urban land use 90 Discussion 94 Conclusions 97 Literature cited 99 CHAPTER 4: IDENTIFYING AND ANALYZING FACTORS OF URBAN GROWTH IN WEST AFRICAN CITIES 108 Introduction 108 Materials and methods 110 Study area 110 vi Logistic regression 113 Data 114 Results 125 Accuracy of predicted urban land use maps 136 Discussion 141 Conclusions 143 Literature cited 144 CHAPTER 5: CONCLUSIONS 149 Literature cited 152 vii List of Tables CHAPTER 1 Table 1.1. Salient features of selected cities. 10 CHAPTER 2 Table 2.1. Selected eight eco-regions in West Africa 34 Table 2.2. The global LCLU datasets used in the study. 38 Table 2.3. Time stamps on randomly selected 1 Km × 1 Km Google EarthTM cropland points. 40 Table 2.4. Estimated cropland area (MHa) and coefficient of variation (CV) at the eco-region level. 46 Table 2.5. Estimated cropland area (MHa) and coefficient of variation (CV) at the country level. 47 Table 2.6. Sum of arable land and permanent crop (AL&PC) over the years. 48 Table 2.7. Accuracies (%) of the datasets to estimate cropland in West Africa. 50 CHAPTER 3 Table 3.1. Features of selected cities for the study of urban land use growth. 68 Table 3.2. Landsat path and rows, years of acquisition, number of scenes and sensors used in the acquisition. 70 Table 3.3. Land cover land use class (LULC) and number of pixels from Google Earth ™ images retained for training and validation. 76 Table 3.4. NDVI threshold used for refining ground truth pixels collected using Google Earth™. 78 Table 3.5. Accuracy assessments of classified images. 81 Table 3.6. Transition matrices-Kumasi, Ghana. 87 Table 3.7. Transition matrices-Daloa, Cote d’Ivoire. 87 Table 3.8. Transition matrix – Abuja, Nigeria. 88 Table 3.9. Transition matrices – Kindia, Guinea. 88 viii Table 3.10. Transition matrix – Ouagadougou, Burkina Faso. 89 Table 3.11. Transition matrix – Kano, Nigeria. 89 Table 3.12. Kumasi, Ghana - Growth of urban area. 91 Table 3.13. Daloa, Cote d’Ivoire - Growth of urban area. 91 Table 3.14. Abuja, Nigeria - Growth of urban area. 92 Table 3.15. Kindia, Guinea - Growth of urban area. 92 Table 3.16. Ouagadougou, Burkina Faso - Growth of urban area. 93 Table 3.17. Kano, Nigeria - Growth of urban area. 93 CHAPTER 4 Table 4.1. Features of selected cities. 113 Table 4.2. Cities and years for which the urban area are used in the study. 115 Table 4.3. A list of explanatory variables used in the logistic regression model. 116 Table 4.4. Population - Kumasi, Ghana. 122 Table 4.5. Population - Daloa, Cote d’Ivoire. 122 Table 4.6. Population - Abuja, Nigeria. 123 Table 4.7. Population - Kindia, Guinea. 123 Table 4.8. Population - Ouagadougou, Burkina Faso. 123 Table 4.9. Population - Kano, Nigeria. 124 Table 4.10a. Kumasi - Odds ratios and R2 to predict urban pixels for 1986. 128 Table 4.10b. Kumasi - Odds ratios and R2 to predict urban pixels for 2005. 128 Table 4.10c. Kumasi - Odds ratios and R2 to predict urban pixels for 2010. 129 Table 4.11a. Daloa - Odds ratios and R2 to predict urban pixels for 1985. 129 Table 4.11b. Daloa - Odds ratios and R2 to predict urban pixels for 2000. 130 Table 4.11c. Daloa - Odds ratios and R2 to predict urban pixels for 2008. 130 Table 4.12a. Abuja - Odds ratios and R2 to predict urban pixels for 1990. 131 Table 4.12b. Abuja - Odds ratios and R2 to predict urban pixels for 2001. 131 Table 4.12c. Abuja - Odds ratios and R2 to predict urban pixels for 2005. 131 Table 4.13a. Kindia - Odds ratios and R2 to predict urban pixels for 1995. 132 Table 4.13b. Kindia - Odds ratios and R2 to predict urban pixels for 2008. 132 ix Table 4.14a. Ouagadougou - Odds ratios and R2 to predict urban pixels for 1986. 133 Table 4.14b. Ouagadougou - Odds ratios and R2 to predict urban pixels for 2005. 133 Table 4.14c. Ouagadougou - Odds ratios and R2 to predict urban pixels for 2010. 134 Table 4.15a. Kano - Odds ratios and R2 to predict urban pixels for 1985. 134 Table 4.15b. Kano - Odds ratios and R2 to predict urban pixels for 2000. 135 Table 4.15c. Kano - Odds ratios and R2 to predict urban pixels for 2010. 135 Table 4.16. Kumasi-Accuracy of predicted of urban pixels. 140 Table 4.17. Daloa-Accuracy of predicted of urban pixels. 140 Table 4.18. Abuja-Accuracy of predicted of urban pixels. 140 Table 4.19. Kindia-Accuracy of predicted of urban pixels. 140 Table 4.20. Ouagadougou-Accuracy of predicted of urban pixels. 141 Table 4.21. Kano-Accuracy of predicted of urban pixels. 141 x List of Figures CHAPTER 1 Figure 1.1. Study area of West Africa and selected cities. 9 Figure 1.2. Selected cities and their eco-regions in West Africa. 10 Figure 1.3. A view of Central Market, Kumasi, Ghana. 13 Figure 1.4. A view of an outskirt area of Kumasi, Ghana. 13 CHAPTER 2 Figure 2.1. The study area, West Africa. 33 Figure 2.2. The selected eight eco-regions in West Africa. 33 Figure 2.3. Two data pixels (1 Km × 1 Km) collected using the GoogleEarthTM. 41 Figure 2.4. Cropland map from the global LCLU datasets. 42 Figure 2.5. Country wide estimated cropland area (EstCrp) estimated by the selected datasets (X-axis) vs sum of arable land and permanent crop area (AL&PC) from FAOSTAT (Y-axis). 49 Figure 2.6. Agreement/disagreement of datasets to identify croplands in the study area. 52 CHAPTER 3 Figure 3.1. Selected cities in West Africa. Also shown are the eco-regions in West Africa. 66 Figure 3.2. Landsat image (raw image without atmospheric correction) of 1973 (left panel) and 1986 (right panel) over Kumasi, Ghana. 69 Figure 3.3. Flow chart of mapping and analyzing urban growth in West Africa. 71 Figure 3.4. Two data points (30 m × 30 m) collected using the Google EarthTM. 74 Figure 3.5. Urban area of the city of Kumasi, Ghana. 82 xi Figure 3.6. Urban area of the city of Daloa, Cote d’Ivoire. 82 Figure 3.7. Urban area of the city of Abuja, Nigeria. 83 Figure 3.8. Urban area of the city of Kindia, Guinea. 83 Figure 3.9. Urban area of the city of Ouagadougou, Burkina Faso. 84 Figure 3.10. Urban area of the city of Kano, Nigeria. 84 CHAPTER 4 Figure 4.1. Location of six cities in West Africa.
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