Various methods have been developed for the calibration of cellular automata (CA), which can produce a plausible cell-by-cell fit between modeling results and observed land-use data. However, traditional cell-based CA models still fail to characterize the aggregate landscape patterns of multiple land-use changes. To address this problem, we introduced a landscape-driven multiple CA model that can consider landscape patterns during the calibration procedure. Genetic algorithm was used to search for the optimal calibration parameters. We further investigated the performance of five important landscape metrics in calibration. Comparisons with two well-accepted cell-based CAs indicated that the modeling results of the proposed method are closer to the observed land-use data. Furthermore, we found that patch cohesion and edge density are appropriate landscape objectives for CA calibration in this study. More importantly, our method can effectively evaluate the performance of different landscape metrics, which could provide useful information for land-use planning.

已经开发了用于校准细胞自动机（CA）的各种方法，这些方法可以在建模结果和观察到的土地利用数据之间产生合理的逐个单元拟合。但是，传统的基于小区的CA模型仍然无法描述多种土地利用变化的总体景观格局。为了解决这个问题，我们引入了一个景观驱动的多CA模型，该模型可以在校准过程中考虑景观模式。使用遗传算法搜索最佳校准参数。我们进一步研究了五个重要景观指标在校准中的性能。与两个公认的基于单元的CA进行的比较表明，该方法的建模结果更接近于观测到的土地利用数据。此外，我们发现，在本研究中，斑块内聚力和边缘密度是用于CA校准的合适景观目标。更重要的是，我们的方法可以有效地评估不同景观指标的性能，这可以为土地使用规划提供有用的信息。