CA-Markov模型的空间尺度敏感性研究

以广州市花都区为研究区,研究利用CA-Markov模型进行土地利用变化模拟的空间尺度敏感性特征,结论如下:①元胞尺寸的选择会明显影响模拟结果,元胞尺寸越大,模拟结果精度越低。模型中存在元胞尺寸的阈值,当元胞尺寸超出该阈值时,模拟结果的精度急剧下降,因此对于元胞尺寸的选择必须要慎重。②邻域类型的选择也会对模拟结果产生影响。采用3×3冯诺依曼邻域的模拟结果会比3×3摩尔邻域和5×5摩尔邻域生成更多的斑块数量和更高的斑块密度,但是模拟结果的Kappa系数值相差不大。

Spatial Scale Sensitivity of CA-Markov Model

Cellular Automata (CA) model is an efficient model to simulate land use/cover change (LUCC) process. However, the majority of geographic cellular automata researches now focus more on transition rules than on spatial scale selection. As a result, the spatial scale decisions of geographic cellular automata are often made arbitrarily. When using CA model in geographic context, nontrivial questions have to be answered about the choice of spatial scale and neighborhood configuration. This article takes Huadu district,Guangzhou City as the study area, to investigate the effect of changing spatial scale and neighborhood configuration on the result prediction accuracy of the CA-Markov model and the morphology of land use change simulation result. The result shows that (1)the spatial scale has great impact on the simulation results of CA-Markov model. Coarse cell size will reduce the result prediction accuracy of the model. There is a threshold of cell size in the model. When the cell size exceeds this threshold, the result prediction accuracy dropped rapidly. (2) Neighborhood configuration also has impact on the simulation results of CA-Markov model. The patch number and patch density of simulation result using 3?3 Von Neumann neighborhood are higher than simulation result using 3?3 and 5?5 Moore neighborhood, but there is little difference of Kappa coefficients of the simulation results.