Abstract
Remote sensing , GIS, and land change models (LCMs) are critical for mapping urban land use/cover and simulating “what if” urban growth scenarios, particularly in developing countries experiencing rapid urbanization . The purpose of this chapter is to describe briefly the methodology used to produce land use/cover maps, and simulate land use/cover changes for selected metropolitan areas in Asia and Africa. Land use/cover maps were classified from Landsat imagery for 1990, 2000, 2010, and 2014 using the random forest (RF) classifier. Quantitative accuracy assessment was not conducted for the 1990 land use/cover maps due to lack of reference data. However, qualitative and quantitative accuracy assessment was performed for the 2000, 2010, and 2014 land use/cover maps based on Google Earth imagery. Overall land use/cover classification accuracy for all land use/cover maps ranged from 70 to 90%. Land use/cover changes were simulated based on the boosted regression trees-cellular automata (BRT-CA) and RF-CA LCMs. We evaluated the goodness-of-fit of transition potential maps, and validated the simulated land use/cover changes based on robust statistical measures. Generally, the BRT-CA and RF-CA LCMs for all metropolitan areas in Asia and Africa performed relatively well. In particular, the BRT-CA and RF-CA LCMs for metropolitan areas in Africa had the best performance. The modeling and simulation results presented in this chapter provide an initial exploration of BRT-CA and RF-CA LCMs in Asia and Africa. This chapter demonstrates the significance of robust calibration, validation , and simulation of spatial LCMs for all metropolitan areas in Asia and Africa.
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Kamusoko, C. (2017). Methodology. In: Murayama, Y., Kamusoko, C., Yamashita, A., Estoque, R. (eds) Urban Development in Asia and Africa. The Urban Book Series. Springer, Singapore. https://doi.org/10.1007/978-981-10-3241-7_2
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