基于微粒群-马尔科夫复合模型的生态空间预测模拟——以长株潭城市群为例

生态空间具有重要的生态功能,对生态空间进行科学预测模拟可为保护国土空间生态安全提供决策依据。利用Arc GIS及MATLAB软件,在生态空间风险评价的基础上构建了微粒群-马尔科夫复合模型,并以长株潭城市群为研究区,基于2013年土地利用现状数据,对2020年的生态空间进行了预测模拟,最后在此基础上提出了生态空间重构的基本思路。结果表明:1)微粒群-马尔科夫复合模型(PSO-Markov)构建的基本步骤为:第一步:粒子的选择与设计,以2000 m×2000 m的正方形单元作为基本粒子。第二步:粒子的初始化设定,根据生态空间风险由低到高的原则进行选择。第三步:适应度函数的建立,用生态空间的风险值来确定生态空间的空间格局。第四步:空间位置的更新,根据自身的历史最优值及粒子群的全局最优值进行速度和位置更新。2)微粒群-马尔科夫复合模型(PSO-Markov)是一种土地利用格局预测的新途径,生态空间的数量规模可以通过改进后的马尔科夫模型进行预测,生态空间的格局可以通过微粒群模型进行预测。3)微粒群-马尔科夫复合模型具有4个特点:第一、数量预测较为合理。第二、搜索范围大、较好地考虑到局部对全局的影响。第三、受问题维数变化影响小,在求解多目标问题时具有明显优势。第四、收敛时间短、运算速度快、易于实现。4)2020年,长株潭城市群的生态空间总体数量减少,其中林地和未利用地面积变化最明显,空间变化主要集中分布在西南部地区。生态空间总面积减小的主要原因是建设用地的扩张。因此,要控制城市群的人口密度,优化城市群生产—生活—生态的数量结构及空间布局,尤其要合理规划与利用城市建设用地,充分发挥水体与未利用地的生态价值,重点保护好生态源地、廊道及关键结点,构建结构合理、功能齐全的生态网络系统,提高系统的生态服务价值功能,要在规划的指导下合理调整城市群的城乡局部空间结构,保护生态环境,提高生境质量和景观多样性。这是今后一段时期面临的主要任务。

Predictive simulation of ecological space based on a particle swarm optimization-Markov composite model: A case study for Chang-Zhu-Tan urban agglomerations

The distribution of ecological space has important functions, and its scientific prediction can provide a basis for making decisions regarding protecting the ecological security of a national landscape. A particle swarm optimization-Markov (PSO-Markov) composite model based on a risk assessment of ecological space was conducted using ArcGIS and MATLAB. The ecological space of the Chang-Zhu-Tan urban agglomeration in 2020 was predicted based on land-use data for 2013, and the resulting particle swarm was used to reconstruct the ecological space. The PSO-Markov composite model was established according to the following steps:First, a particle swam was selected and designed, and a 2000 m×2000 m square unit was selected as the basic particle. Second, the particle was initialized from low to high risk based on the principle of ecological space. Third, a fitness function was established, and the value of ecological space risk was used to determine the spatial pattern of ecological space. Finally, the spatial position and speed of a particle swarm were updated according to the history of optimal value and the global optimal value of the particle swarm. The PSO-Markov composite model is a relatively new method for land-use pattern prediction. The quantitative scale of ecological space is predictable with an improved Markov model, and the pattern of ecological space is predictable with a PSO model. Thus, the PSO-Markov composite model has the following features compared with other models:First, this model can yield more reasonable quantitative predictions. Second, it utilizes a large search area and thoroughly considers local and global influence. Third, it is less influenced by the problem of dimension change, and it has a significant advantage regarding the solution of multi-objective problems. Finally, with short convergence time and high arithmetic speed, it is easy to realize. The ecological space of the Chang-Zhu-Tan urban agglomeration was predicted to decrease by 2020 with woodland and unused land changing the most dramatically and spatial variation primarily concentrating in the southwest region. The decrease in total ecological space area was shown to be primarily due to the expansion of land development. Thus, we must control the population density of urban agglomerations and optimize the structure and spatial distribution of agriculture/industry, human settlements, and the ecosystem in the urban agglomeration. In particular, we must reasonably plan and utilize urban developed land, as well as make full use of the ecological value of water bodies and undeveloped land, with emphasis on the protection of ecological resources, ecological corridors, and key ecological areas so as to establish a rationally structured, functioning ecological net system and to improve the ecological services of the ecosystem. Urban and rural spatial structure must be regulated rationally under the guidance of city planning. Moreover, measures should be carried out to protect and improve the quality of the environment and to enhance landscape diversity. These are our priorities for the future.