URBAN GROWTH MODELING OF PHNOM PENH, CAMBODIA USING SATELLITE IMAGERIES AND A LOGISTIC REGRESSION MODEL

Phnom Penh City is facing rapid population growth with Cambodia having the second highest urban expansion rate in Asia, and it has encountered poor urban planning that results in urban sprawl and the loss of natural areas and agricultural land. To solve this problem, spatial and temporal dynamic driving factors for land use and land cover change should be well understood for enhancing urban planning. The specific objectives of the study are (1) to assess the land cover status and its change; (2) to employ a logistic regression (LR) model to discover the driving factors for urban growth; and (3) to predict the future urban growth pattern of Phnom Penh in 2030. Four main components of the research methodology are here conducted comprising (1) data collection; (2) data preparation; (3) model simulation and validation; and (4) urban growth prediction. Results showed that the urban and built-up areas have continuously increased from 2002 to 2015 resulting in a major decline of arable land, vegetation and water bodies, while miscellaneous land was shown as fluctuating. Meanwhile, the pattern of urban growth expanded towards the southern, northern, and western areas of Phnom Penh during 20022009 with all types of growth including infill growth, expansion growth, linear branch, and isolated growth. However, during 2009-2015, the urban growth pattern occurred in all directions with expansion growth, clustered branch, and isolated growth. The driving factors for urban growth from the LR model for the 2002-2015, 2002-2009, and 2009-2015 periods varied according to the urban and built-up area pattern and time. Two common driving factors under the top 5 dominant factors, namely distance to the existing urban cluster and distance to an industrial area, showed a negative correlation with urban growth in the 3 periods. In addition, the final urban growth pattern from the LR model of the 3 periods showed a good result for overall accuracy at about 91%, 96%, and 94% a successful fit of urban allocation at about 58%, 54%, and 44%, and a relative operating characteristic at about 0.90, 0.95, and 0.90, respectively. Finally, the urban growth pattern prediction for Phnom Penh in 2030 from the optimum LR model of the 2002-2015 period with the estimated urban growth area using the Markov chain model revealed that urban growth tends to take place in the north, south, and east around the existing urban clusters, and some are expected to occur along the major roads and ring roads. In conclusion, the integration of the LR model, remote sensing, and GIS can be efficiently used as a tool to understand the driving factors and to predict the future urban growth pattern of Phnom Penh. It contributes significant information as a guideline for planners and decision makers.