Simulating urban expansion using an improved SLEUTH model

Accelerated urbanization creates challenges of water shortages, air pollution, and reductions in green space. To address these issues, methods for assessing urban expansion with the goal of achieving reasonable urban growth should be explored. In this study, an improved slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) cellular automata model is developed and applied to the city of Tangshan, China, for urban expansion research. There are three modifications intended to improve SLEUTH: first, the utilization of ant colony optimization to calibrate SLEUTH to simplify the calibration procedures and improve their efficiency; second, the introduction of subregional calibration to replace calibration of the entire study area; and third, the incorporation of social and economic data to adjust the self-modification rule of SLEUTH. The first two modifications improve the calibration accuracy and efficiency compared with the original SLEUTH. The third modification fails to improve SLEUTH, and further experiments are needed. Using the improvements to the SLEUTH model, forecasts of urban growth are performed for every year up to 2020 for the city of Tangshan under two scenarios: an inertia trend scenario and a policy-adjusted scenario. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI:10.1117/1.JRS.6.061709]