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基于地理分区与FLUS模型的城市扩张模拟与预警
引用本文:赵林峰,刘小平,刘鹏华,陈广照,何家律.基于地理分区与FLUS模型的城市扩张模拟与预警[J].地球信息科学,2020,22(3):517-530.
作者姓名:赵林峰  刘小平  刘鹏华  陈广照  何家律
作者单位:1. 中山大学地理科学与规划学院,广州 510275;2. 广东省城市化与地理环境空间模拟重点实验室,广州510275
基金项目:国家重点研发计划项目(2017YFA0604404);国家自然科学基金面上项目(41671398);生态环境部生物多样性 调查、观测和评估项目(2019—2023年)
摘    要:城市的快速扩张不断改变着土地资源的转化,带来了诸多生态环境问题。分析和模拟城市扩张的机制,并对城市未来土地利用变化的风险进行预警,利于合理调控城市的发展。本文提出了一种基于地理分区和未来用地模拟(Future Land Use Simulation,FLUS)模型的城市扩张模拟模型,用于模拟和预测复杂的土地利用变化。该模型利用多指标数据进行空间聚类,耦合地理分区结果进行城市扩张模拟。珠江三角洲2005-2015年的城市扩张模拟结果显示,分区下的模拟精度(FoM=0.2329,提高了9%)明显高于未分区,说明不同分区在土地利用转化上存在空间差异,该模型能更有效地模拟城市土地利用变化。另外,本文构建了一种城市扩张预警指标评价体系,用于评估城市扩张的警情。根据在2005-2015基础上预测的2025-2045年土地利用变化结果,对珠江三角洲城市扩张进行多尺度预警分析。综合预警结果显示该区域大部分城市至2045年城市扩张警情将达到中警和重警,其中东莞警情一直维持在重警。由此,未来需要加强对珠三角城市扩张的宏观调控,以此来缓解未来城市扩张的警情。

关 键 词:城市扩张  预警  地理分区  FLUS模型  元胞自动机  土地利用变化  马尔科夫链  珠江三角洲
收稿时间:2019-08-29

Urban Expansion Simulation and Early Warning based on Geospatial Partition and FLUS Model
ZHAO Linfeng,LIU Xiaoping,LIU Penghua,CHEN Guangzhao,HE Jialv.Urban Expansion Simulation and Early Warning based on Geospatial Partition and FLUS Model[J].Geo-information Science,2020,22(3):517-530.
Authors:ZHAO Linfeng  LIU Xiaoping  LIU Penghua  CHEN Guangzhao  HE Jialv
Institution:1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China;2. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China
Abstract:The rapid urbanization has constantly changed the transformation of land resources. With rapid growth of economic and population, urbanization has also led to many ecological and environmental problems. Simulating the mechanism of urban expansion and providing early warning of the risk of urban land use change in the future can help to regulate and control urban development. In addition, most urban expansion simulation studies select common drivers and uniform transformation rules for simulation and prediction. Insufficient consideration of spatial heterogeneity increases the simulation error.This paper proposed an urban expansion simulation model based on the geospatial partition and Future Land Use Simulation (FLUS) model for simulating and predicting complex land use change. The model used multiple indicator data for spatial clustering in township streets and grid units, and divided Pearl River Delta into 10 sub-regions. Urban expansion simulation was performed under unpartitioned and partitioned scenarios with geospatial partition results. The simulation results of the urban expansion in the Pearl River Delta from 2005 to 2015 show that the simulation accuracy under the partitioned (FoM=0.2329, increase 9%) scenario is significantly higher than that of the unpartitioned scenario. Land use conversion potentials in different districts display spatial differences. Combining geospatial partition with FLUS model can simulate urban land use change more effectively. The Markov chain model was used to predict the number of future land types. The model was further applied to simulate land use changes in the Pearl River Delta in 2025, 2035 and 2045. Based on the impact of urban expansion on urban form, ecology and intensity, this paper constructed an urban expansion early warning indicator evaluation system to assess the alert of urban expansion. Furthermore, this system can predict the level of urban expansion deterioration and provide a scientific reference for urban development planning and monitoring. Based on the simulated results of land use change between 2025 and 2045, an early warning analysis of urban expansion in Pearl River Delta was conducted at the partition and city level. The results show that the urban expansion of most cities in the region will reach above the middle and heavy level in 2045. Dongguan will always maintain heavy level in the future. Urban expansion in Pearl River Delta is not optimistic. The analysis suggests that strengthening macro-control on urban expansion in Pearl River Delta to alleviate alarm of future urban expansion.
Keywords:urban expansion  early warning  geospatial partition  FLUS model  cellular automata  land use change  Markov chain  Pearl River Delta  
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