Monitoring and Simulation of Dynamic Spatiotemporal Land Use/Cover Changes

Abstract

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.