在传统元胞自动机(CA)模型中,静态的模型参数和模型误差不能释放是影响城市扩张模拟效果的两个重要原因。文中引入集合卡尔曼滤波方法到CA模型中,提出了基于联合状态矩阵的地理元胞自动机。该模型在模拟过程中可以通过同化遥感观测数据,动态地调整模型参数和纠正模拟结果,使模型参数能够反映转换规则的时空变化,同时也能较好地释放积累的模型误差。将模型应用于东莞市的城市扩张模拟中,实验结果表明,模型能够准确地调整模型参数使之符合城市发展模式,同时也能有效地控制模型误差,其模拟的空间格局与真实情况吻合。

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.