Modeling Urban Growth in the Atlanta, Georgia Metropolitan Area

MODELING URBAN GROWTH IN THE ATLANTA, GEORGIA METROPOLITAN AREA USING REMOTE SENSING AND GIS by ZHIYONG HU This study examines the dynamics of human-induced land use/cover changes, especially urban growth, in Atlanta, Georgia metropolitan area. Land use/cover changes of Atlanta between 1987 and 1997 were observed from LANDSAT TM satellite images and linked with the biophysical and socio-economic data using image processing, spatial analysis and modeling integrated in the framework of a geographic information system(GIS). Normalized Vegetation Difference Index (NDVI) differencing and temporal logic were employed to improve the accuracy of land use/cover change detection. A Markov chain model was used to conduct a ‘what-if’ analysis to predict the quantity distributions and spatial patterns of the future land use/cover based on the 1987-1997 land use/cover transition probabilities. In addition, a logistic regression model was used to identify the factors governing the process of urban growth and to predict the most probable sites of the growth. This study found that from 1987 to 1997 the most intense land use/cover change in Atlanta was deforestation for urban development. During the ten years, high-density urban area increased 12.94% from 876.78km2 to 990.27 km2; low-density urban area increased 42.57% from 2468.62 km2 to 3519.56 km2; and forest decreased 10.62% from 9217.04 km2 to 8238.28 km2. The Markov chain simulation revealed that urban use will continue to grow at the expense of forest. The proportion of urban area was 20.93% in 1987, and it will be 35.45% in 2020. Forest will decrease from 57.67% in 1987 to 43.64% in 2020. Urban growth will occur in the rural and urban-fringe areas, taking on a fragmented pattern. It was found from the logistic regression model that urban growth tends to occur around existing urban areas and close to major roads (road influenced growth), while some new urban clusters located at a distance from the existing urban areas can also form(diffusive growth). Whereas the logistic regression model can incorporate human dimensions, the Markov chain model is more temporally dynamic. Recommendations were made for further exploration of a more realistic land use/cover change model that can deal with space, time, and people simultaneously. INDEX WORDS: Land use, Land cover, Urban growth, GIS, Remote sensing, Temporal logic, NDVI, Markov chain, Logistic regression MODELING URBAN GROWTH IN THE ATLANTA, GEORGIA METROPOLITAN AREA USING REMOTE SENSING AND GIS by ZHIYONG HU B.S., Northeast University, China, 1992 M.S., Northeast University, China, 1995 A Dissertation Submitted to the Graduate Faculty of the University of Georgia in Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY ATHENS, GEORGIA 2004 © 2004 Zhiyong Hu All Rights Reserved MODELING URBAN GROWTH IN THE ATLANTA, GEORGIA METROPOLITAN AREA USING REMOTE SENSING AND GIS by ZHIYONG HU Major Professor: Chor-Pang Lo Committee: Thomas W. Hodler Xiaobai Yao Lynn E. Usery Hamid R. Arabnia Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia August 2004 DEDICATION To Ronghui, my loving wife, thank you for allowing me to spend more time on this venture than with you. You will never know how much I appreciated the many times you assumed my responsibilities at home and with family. To my parents and mother-in-law, thank you for your encouragement and for always letting me know how proud you were of my accomplishments. And, to my lovely daughter, Lucy, and son, James. Your smile has brought me happiness. iv ACKNOWLEDGEMENTS I would like to express my sincere appreciation to my major advisor, Chor-Pang Lo. Without Dr. Lo’s guidance, support, and encouragement, there were times that this dissertation may have never materialized. He can be credited for much of my personal development, and showed much patience in the pursuit of my research goals. His faith in my ability to complete this endeavor was what sustained me. He opened the doors for me to visualize myself as a researcher and educator with great potential for an academic career. I would also like to thank my advisory committee members, Thomas Hodler, Lynn Usery, Hamid Arabnia, and Xiaobai Yao. Their academic advice and guidance are greatly appreciated. Thanks are extended to Emily Duggar and Audrey Hawkins, who have provided helps in many ways. I also thank Bo Xu for her help. v TABLE OF CONTENTS Page LIST OF TABLES……………………………………………………………………………. ix LIST OF FIGURES ……………………………………………………………………………xi CHAPTER 1 INTRODUCTION …………………………………………………………………............1 Research background …………………………………………………………………..1 Study area ……………………………………………………………………...............8 Research objectives …………………………………………………………………..17 Dissertation structure …………………………………………………………. …….19 2 LITERATURE REVIEW …………………………………………………………. …….20 Introduction …………………………………………………………………………..20 Basic concepts and theories on urban land use/cover system and its modeling ……..20 Land use/cover change detection methods …………………………..........................29 Land use change models …………………………………………………………….34 Summary ……………………………………………………………………………56 3 MAPPING THE 1987-1997 LAND USE/COVER CHANGE IN ATLANTA FROM LANDSAT TM IMAGES: A NEW APPROACH TO ACCURACY IMPROVEMENT……………………………………………………………………58 Introduction ………………………………………………………………………….58 Satellite data …………………………………………………………………………60 vi Co-registration of multi-date images …………………………………………..........63 Computer-assisted land use/cover classification ……………………………………67 Principal components analysis and change vector analysis …………………………69 Errors in computer-assisted land use/cover classification from Landsat TM images ……………………………………………………………………….....77 Correction of classification errors caused by cloud shadows………………….. …..84 Error propagation in post-classification comparison…………………………... …..87 Using NDVI differencing and temporal logic to improve land use/cover classification accuracy …………………………………………...........90 Post-classification comparison for land use/cover change detection ……………...107 Discussions and conclusions ………………………………………………………112 4 MARKOV CHAIN ANALYSIS OF LAND USE AND LAND COVER CHANGE…………………………………………………………………………..117 Introduction ………………………………………………………………………..117 Markov chain model ………………………………………………………………118 Formulation of a Markov chain model of land use/cover change ………………...120 Testing for the Markov property ………………………………………………….124 Prediction of land use/cover proportions …………………………………………125 Prediction of future urban spatial patterns ………………………………………..129 Discussions and conclusions…………………………………………………... …130 5 LOGISTIC REGRESSION MODELING OF URBANIZATION PROBABILITY……………………………………………………………….......134 Introduction ……………………………………………………………………….134 vii Spatial data handling and integration ……………………………………………..134 Statement of model ……………………………………………………….............144 Multi-scale modeling and fractal analysis ………………………………………..153 Testing logistic regression residual for spatial autocorrelation …………………..163 Correcting for spatial autocorrelation ……………………………………………168 Refining the model at the resolution of 225 meters …………………………...…170 Testing for the significance of the model ………………………………………. 173 Assessment of goodness of fit …………………………………………………...179 Model interpretation …………………………………………………………......184 Prediction and validation ……………………………………………………... ...188 Prediction of spatial patterns of urban distribution.......………………………….194 Conclusions ……………………………………………………………………...196 6 SUMMARY AND CONCLUSIONS ……………………………………………… 199 REFERENCES ………………………………………………………………………….205 viii LIST OF TABLES Page Table 2.1: Summary of human drivers reflected in land use/cover change model variables……37 Table 3.1: Characteristics of the satellite images. ……………………………………………... 61 Table 3.2: PCA eigenvalues and percentage of variance explained by each component……… 71 Table 3.3: Factor loadings of each principal component with each band……………………... 72 Table 3.4: Accuracy assessment of the 1997 land use/cover map produced from Landsat TM image…………………………………………………………….79 Table 3.5: Parameters for the calculation of at-satellite spectral reflectance………………….. 94 Table 3.6: Interpretation of NDVI difference classes………………………………………… 102 Table 3.7: Using NDVI differencing and temporal logic to enhance land use/cover classifications………………………………………………………………………105 Table 3.8: Quantitative information of land use/cover change………………………………..109 Table 4.1: and use/cover transition probability matrix, 1987-1997…………………………...124 Table 4.2: Land use/cover proportions (%) from 1987 to 2020……………………………….127 Table 4.3: Simulated transition probability matrix, 1997-2020……………………………….129 Table 5.1: Original data for logistic regression modeling……………………………………..136 Table 5.2: List of variables in the logistic regression model…………………………………..146 Table 5.3: The coding of the design variables for land use/cover, coded at six levels………..146 Table 5.4: Effect of multi-resolution modeling………………………………………………..156 Table 5.5: Number of steps for calculation of fractal dimensions of predicted probability ix surfaces using triangular prism surface area method……………………………….161 x LIST OF FIGURES Page Figure 1.1: Urban growth indicated by night-time light brightness increase from 1992 for the Conterminous USA……………………………………………………….. 2 Figure 1.2: Percentage change in population and urbanized land in the Unite States, 1982-1997, by census region………………………………………………………...3 Figure 1.3: Urban and rural populations of more developed regions and less developed regions…………………………………………………………………..4 Figure

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