Optimal rural land use allocation in central China: Linking the effect of spatiotemporal patterns and policy interventions

- Optimal land use allocation is adjusted to dynamic rural land use transition process and policy interventions of China.
- Land use patterns and policy impacts were simulated and incorporated into an improved particle swarm optimization.
- Effects of land use patterns and policies on allocation solutions were examined with a scenario analysis in central China.

Rural land use development is experiencing a transition stage of socioeconomic and land use development in China. Historic land use transition process and policy interventions have key influence on the applicability of land use allocation solutions in future land use management. Strategic land use allocation is therefore required to possess a good adjustment capability to the transition process. Although heuristic optimization methods have been promising to solve land use allocation problems, most of them ignored the spatially explicit effect of historic land use transition and policies. To help resolve this issue, this study aims to optimize future land use pattern in the context of rural land use development. We took Yunmeng County, one of the typical major grain producing and rapidly urbanizing areas in central China, as a case study and solved the sustainable land use allocation problem by using an improved heuristic optimization model. The model was constructed based on the integration of a spatial discrete particle swarm optimization and cellular automata-Markov simulation approach. The spatiotemporal land use patterns and policy interventions were represented by the CA-Markov as in spatially explicit transition rules, and then incorporated into the discrete PSO for optimal land use solutions. We examined the influence of the joint effect of spatiotemporal land use patterns and policy interventions on the land use allocation outcome. Our results demonstrate the robustness and potential of the proposed model, and, more importantly, indicate the significance of incorporating the spatiotemporal land use patterns and policy interventions into rural land use allocation.