Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin

Abstract

The external environment in the transitional zone of the ecological barrier is fragile, and economic growth has resulted in a series of land degradation issues, significantly impacting regional economic development and the ecological environment. Therefore, monitoring, assessing, and predicting land use changes are crucial for ecological security and sustainable development. This study developed an integrated model comprising convolutional neural network, cellular automata, and Markov chain to forecast the land use status of western Jilin, located in the transitional zone of the ecological barrier, by the year 2030. Additionally, the study evaluated the role of land use policies in the context of land use changes in western Jilin. The findings demonstrate that the coupled modeling approach exhibits excellent predictive performance for land use prediction in western Jilin, yielding a Kappa coefficient of 93.26%. Policy drivers play a significant role in shaping land use patterns in western Jilin, as evidenced by the declining farmland accompanied by improved land utilization, the sustained high levels of forest aligning with sustainable development strategies, the ongoing restoration of waters and grassland, which are expected to show positive growth by 2030, and the steady growth in built-up areas. This study contributes to understanding the dynamics of land use in the transitional zone of the ecological barrier, thereby promoting sustainable development and ecological resilience in the region.

Keywords

• land use change
• land use policy
• land use prediction
• ecological barrier
• convolutional neural network (CNN)
• cellular automata (CA)