Changes in land use/cover are among the most prominent impacts that humans have on the environment. Therefore, exploring land use/cover change is of great significance to urban planning and sustainable development. In this study, we preprocessed multiperiod land use and socioeconomic data, combined with spatial zoning, multilayer perception (MLP) artificial neural network, and Markov chain (MC), to construct a cellular automaton model of spatial zoning. Moreover, with the help of ArcGIS 10.2 and TerrSet 18.07 software, we explore the current status of land use and predict future changes. The results showed that drastic changes have occurred among different land use classes in Jinzhou District over the past 13 years owing to the impact of economic development and reclamation projects. Construction land, arable land, and waters have changed by +85.09, −24.42, and −23.62 km², respectively. By comparing the FoM and Kappa coefficients, we concluded that the prediction accuracy of partitioned MLP-MC is better than that of unpartitioned MLP-MC. Therefore, using the spatial zoning approach to identify the conversion rules among land use classes in different zones can more effectively predict future land use changes and provide a reference for urban planning and policy making.

中文翻译：

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时空土地利用/覆盖变化动态监测与模拟

土地使用/覆盖的变化是人类对环境的最重要影响。因此，探索土地利用/覆盖变化对城市规划和可持续发展具有重要意义。在这项研究中，我们预处理了多时期的土地利用和社会经济数据，并与空间分区，多层感知（MLP）人工神经网络和马尔可夫链（MC）结合，构建了空间分区的细胞自动机模型。此外，借助ArcGIS 10.2和TerrSet 18.07软件，我们可以探索土地使用的当前状态并预测未来的变化。结果表明，在过去的13年中，由于经济发展和开垦工程的影响，锦州区不同土地利用类别之间发生了急剧变化。建设用地，耕地和水域已改变+85。^2个。通过比较FoM和Kappa系数，我们得出结论，分区的MLP-MC的预测精度优于未分区的MLP-MC。因此，使用空间分区方法识别不同区域土地利用类别之间的转换规则，可以更有效地预测未来的土地利用变化，并为城市规划和政策制定提供参考。