生态网络约束下的长沙市景观格局模拟

以保护生态网络为目标约束景观格局模拟,对于促进区域环境保护和生态文明建设具有重要意义。以长沙市为例,基于生态网络识别结果,兼顾生态重要性和邻域空间特征,提出了一种生态网络约束下的景观适宜性修正方法,并借助CA-Markov模型模拟在生态网络约束下的长沙市2035年景观格局。结果表明:(1)长沙市共提取生态源地89个、生态廊道213条、生态节点87个,其中生态源地主要为东西部的大型山体,廊道沿主要河流、山体分布;(2)2035年长沙市景观类型依旧以林地为主,耕地减少较多,林地稍有增加,建设用地涨幅较大;(3)通过对比有无生态网络约束下模拟的2035年景观格局发现,两者景观指数与生态网络内林地占比率有较大差异,表明基于生态网络约束进行景观格局模拟可以重点保护生态关键区域、优化景观格局。研究结果旨在为长沙市国土空间规划、景观格局优化提供参考。

Landscape Pattern Simulation Under the Constraint of Ecological Network in Changsha City

It is of great significance for promoting the regional environmental protection and ecological civilization construction to constrain landscape pattern simulation with the goal of protecting ecological network. Taking Changsha as a case, based on the results of ecological network optimization, we proposed a landscape suitability correction method with the direction of ecological network, and simulated the landscape pattern of Changsha City in 2035 by using the CA-Markov model integrating ecological network constraint. The results are as follows.(1)A total of 89 ecological sources, 213 ecological corridors and 87 ecological nodes were extracted in Changsha City. The ecological sources mainly included the large mountains in the east and west of Changsha City. The ecological corridors distributed along rivers or mountains.(2)In 2035, the landscape type of Changsha City will still be dominated by woodland and the area of cultivated land will decrease, forestland will increase slightly, the construction land will increase greatly.(3)By comparing the simulated landscape pattern in 2035 with or without the ecological network constraint, we found that the landscape index and the proportion of forest land in the ecological network were significantly different, which showed that the simulation of landscape pattern based on ecological network constraints could focus on protecting the ecological key areas and optimizing the landscape distribution. These research results are intended to provide reference for the spatial planning and landscape pattern optimization of Changsha City.