基于交叉熵的北京市土地利用结构变化模拟及情景分析研究

Land-use Change Simulation and Scenario Analysis of Beijing with the Cross-entropy Optimization Method

土地利用政策是影响土地利用变化模拟和情景分析的重要因素.基于土地利用变化的历史规律,传统Markov方法能够实现土地利用结构变化的模拟和分析,但忽略了土地利用政策对土地利用变化的控制性影响.为更好地运用Markov方法,实现土地利用结构变化的模拟和分析,并为区域土地利用控制政策的制定提供科学参考,本文在传统Markov方法的基础上,基于土地利用系统的稳定性及交叉熵优化理论,建立了一种可用于多情景土地利用结构变化模拟和分析的研究方法(CE-Markov).在此基础上,对北京市2015~2030年不同土地利用控制情景下的土地利用结构变化进行模拟和分析.结果表明:(1)CEMarkov方法基于交叉熵优化的土地利用情景分析方法可实现土地利用总量控制政策下土地利用结构变化的优化模拟,相比于传统Markov方法,该方法具有更高的准确性;(2)北京市多情景土地利用结构变化模拟和分析的结果表明:虽然耕地总量动态平衡的控制政策对耕地非农化的过程具有显著的抑制作用,但是建设用地仍有明显的扩张趋势,同时林地面积将进一步减少,不利于北京市区域土地资源的可持续利用;(3)建设用地减量规划的控制政策不仅显著抑制耕地的非农化过程,同时也会显著促进建设用地的减量过程,是北京市区域土地资源可持续利用的较理想情景.研究为进一步研究土地利用变化提供了理论假设,同时可为区域土地可持续利用政策的制定提供科学支撑.

Land-use change is determined by both the inertial force from historical land-use changes and external control of land-use policies.To solve the challenge of coupling the control of land use policies in land-use change simulation with the regular Markov method,we proposed an improved Markov method(CE-Markov)based on the cross-entropy optimization theory to simulate land-use changes with multi-scenarios.With the application of CE-Markov in Beijing under three policy scenarios,results show that(1)CE-Markov can simulate land-use changes with higher accuracy than the regular Markov method due to the coupling of the control of landuse policies to the historical knowledge of land-use changes;(2)the dynamic equilibrium control policy of agricultural land area would go against the sustainable land use in Beijing since more forestland would be cultivated to equilibrate the loss of agricultural land due to the expansion of built-up land;(3)the scale-control policy of built-up land would promote the sustainable use of land resources in Beijing since it can both slow down the nonagriculturalization process of agricultural land and facilitate the reclamation process of built-up land.This research can contribute to the further understanding of land-use changes and provide evidence to inform policy making for sustainable land use.