作者：Liu, DF (Liu, Dianfeng); Tang, WW (Tang, Wenwu); Liu, YL (Liu, Yaolin); Zhao, X (Zhao, Xiang); He, JH (He, Jianhua)
来源出版物：APPLIED GEOGRAPHY 卷：86 SI 页码: 165-182 DOI：10.1016/j.apgeog.2017.05.012 出版年：SEP 2017
摘要：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.
作者关键词： Rural land use allocation; Spatiotemporal patterns; Policy interventions; Particle swarm optimization
扩展关键词： PARTICLE SWARM OPTIMIZATION; SPATIAL OPTIMIZATION; RESOURCE-ALLOCATION; GENETIC ALGORITHM; CELLULAR-AUTOMATA; PROGRAMMING-MODEL; PARETO FRONT; LARGE AREAS; SYSTEM; PERSPECTIVE
通讯作者地址：Liu, DF (reprint author), Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
[Liu, Dianfeng; Liu, Yaolin; Zhao, Xiang; He, Jianhua] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
[Liu, Dianfeng; Tang, Wenwu] Univ North Carolina Charlotte, Dept Geog & Earth Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA.
[Liu, Dianfeng; Tang, Wenwu] Univ North Carolina Charlotte, Ctr Appl Geog Informat Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA.
[Liu, Yaolin; He, Jianhua] Wuhan Univ, Collaborat Innovat Ctr Geospatial Informat Techno, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
版权所有 © 武汉大学资源与环境科学学院
电话：027-68778381，68778284，68778296 传真：027-68778893 技术支持：尚网互联