State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
Sci Total Environ. 2020 Jun 20;722:137738. doi: 10.1016/j.scitotenv.2020.137738. Epub 2020 Mar 12.
Urbanization processes have accelerated over recent decades, prompting efforts to model land use change (LUC) patterns for decision support and urban planning. Cellular automata (CA) are extensively employed given their simplicity, flexibility, and intuitiveness when simulating dynamic LUC. Previous research, however, has ignored the spatial heterogeneity among sub-regions, instead applying the same transition rules across entire regions; moreover, most existing methods extract neighborhood effects with only one data time slice, which is inconsistent with the nature of neighborhood interactions as a long-term process exhibiting obvious spatiotemporal dependency. Accordingly, we propose a hybrid cellular automata model coupling area partitioning and spatiotemporal neighborhood features learning, named PST-CA. We use a machine-learning-based partitioning strategy, self-organizing map (SOM), to divide entire regions into several homogeneous sub-regions, and further apply a spatiotemporal three-dimensional convolutional neural network (3D CNN) to extract the spatiotemporal neighborhood features. An artificial neural network (ANN) is then built to create a conversion probability map for each sub-region using both spatiotemporal neighborhood features and factors that drive the LUC. Finally, the dynamic simulation results of entire study area are generated by fusing these probability maps, constraints and stochastic factors. Land use data collected from 2000 to 2015 in Shanghai were selected to verify our proposed method. Four traditional models were implemented for comparison, including logistic regression (LR)-CA, support vector machine (SVM)-CA, random forest (RF)-CA and conventional ANN-CA. Results illustrate that the proposed PST-CA outperformed four traditional models, with overall accuracy increased by 4.66%~6.41%. Moreover, three distinctly different "coverage rate-growth rate" composite patterns of built-up areas are shown in the SOM partitioning results, which verifies SOM's ability to address spatial heterogeneity; while the optimal time steps in 3D CNN generally maintained a positive correlation with the growth rate of built-up areas, which implies longer temporal dependency should be captured for rapidly developing areas.
城市化进程在最近几十年加速推进，促使人们努力建立土地利用变化 (LUC) 模型，以提供决策支持和城市规划。由于细胞自动机 (CA) 具有简单、灵活和直观的特点，因此在模拟动态 LUC 时被广泛应用。然而，之前的研究忽略了子区域之间的空间异质性，而是在整个区域应用相同的转换规则；此外，大多数现有的方法仅使用一个数据时间片提取邻域效应，这与邻域相互作用作为一个长期过程的本质不一致，邻域相互作用具有明显的时空依赖性。因此，我们提出了一种混合细胞自动机模型，该模型结合了区域划分和时空邻域特征学习，称为 PST-CA。我们使用基于机器学习的分区策略——自组织映射 (SOM) 将整个区域划分为几个同质的子区域，然后进一步应用时空三维卷积神经网络 (3D CNN) 提取时空邻域特征。然后，使用人工神经网络 (ANN) 为每个子区域构建一个转换概率图，该概率图使用时空邻域特征和驱动 LUC 的因素。最后，通过融合这些概率图、约束和随机因素来生成整个研究区域的动态模拟结果。我们选择了 2000 年至 2015 年上海采集的土地利用数据来验证我们的方法。实现了四个传统模型进行比较，包括逻辑回归 (LR)-CA、支持向量机 (SVM)-CA、随机森林 (RF)-CA 和传统的 ANN-CA。结果表明，所提出的 PST-CA 优于四个传统模型，总体精度提高了 4.66%~6.41%。此外，在 SOM 分区结果中显示了三种截然不同的“覆盖增长率”综合模式，这验证了 SOM 解决空间异质性的能力；而 3D CNN 的最佳时间步长通常与建成区的增长率保持正相关，这意味着对于快速发展的地区，应该捕获更长的时间依赖性。
