Urban expansion simulation by coupling remote sensing observations and cellular automata

Abstract

Traditional Cellular Automata (CA) requires parameter adjustments and results modification to improve performance especially in a long simulation period. This paper introduces the ensemble Kalman filter (EnKF) into the CA model and proposes a new geographical cellular automata model based on joint state matrix. The model will adjust model parameters and correct simulated results dynamically in the process of simulation by assimilating remote sensing observations. The change of model parameters can properly reflect temporal and spatial variations in the transition rules. Besides, the model can effectively release accumulated model errors. It was applied to the urban expansion simulation of Dongguan, Guangdong province, China. Experiments indicate that this model can modify the parameter value which can properly reveal the urban development pattern. It also can produce more reasonable results than logistics CA model and EnKF CA model in simulating this complex region.