| 顾及空间尺度效应的城市土地利用变化精细化模拟 |
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| 引用本文： | 李岩,林安琪,吴浩,吴霞,岑鲁豫,刘荷,江志猛. 顾及空间尺度效应的城市土地利用变化精细化模拟[J]. 地理学报, 2022, 77(11): 2738-2756. DOI: 10.11821/dlxb202211004 |
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| 作者姓名： | 李岩 林安琪 吴浩 吴霞 岑鲁豫 刘荷 江志猛 |
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| 作者单位： | 1.华中师范大学城市与环境科学学院，武汉 4300792.地理过程分析与模拟湖北省重点实验室，武汉 430079 |
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| 基金项目： | 国家自然科学基金项目(42071358);国家自然科学基金项目(41671406) |
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| 摘 要： | 城市土地利用变化模拟是优化土地资源配置的科学依据，提高其精细化程度和可靠性有助于准确把握城市用地发展趋势，对城市土地资源精准调控具有重要意义。基于宏观遥感分类的土地利用变化模拟，难以在街区尺度上揭示城市用地社会功能变化及精细化模拟中空间尺度效应来源和作用机理。本文联合遥感影像和POI数据识别出城市土地利用精细化特征，运用响应面法率定土地利用精细化模拟的最优空间尺度组合，在此基础上，利用CA-Markov模型开展了未来土地利用变化的精细化模拟。以武汉市中心城区为应用案例，研究结果表明：基于POI 的城市土地利用精细化识别方法，可以深度解析城市建设用地的社会功能，极大改善了传统基于遥感的土地覆被宏观解译效果；研究区土地利用变化元胞自动机精细化模拟的最优空间尺度组合是30 m元胞、7×7邻域以及冯诺依曼邻域类型，采用最优空间尺度组合能够提高土地利用变化精细化模拟的可靠性。响应面试验设计结果可有效识别精细化模拟过程中空间尺度效应的主要来源，并区分其对模拟精度的影响程度与正负效应；预计到2025年，研究区建设用地范围将继续向周边扩张，各类型用地之间互为交织，土地利用空间格局将呈更加破碎化趋势。
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| 关 键 词： | 城市土地利用变化 精细化模拟 空间尺度效应 元胞自动机 武汉市 |
| 收稿时间： | 2021-12-20 |
| 修稿时间： | 2022-07-19 |
Refined simulation of urban land use change with emphasis on spatial scale effect |
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| LI Yan,LIN Anqi,WU Hao,WU Xia,CEN Luyu,LIU He,JIANG Zhimeng. Refined simulation of urban land use change with emphasis on spatial scale effect[J]. Acta Geographica Sinica, 2022, 77(11): 2738-2756. DOI: 10.11821/dlxb202211004 |
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| Authors: | LI Yan LIN Anqi WU Hao WU Xia CEN Luyu LIU He JIANG Zhimeng |
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| Affiliation: | 1. College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China2. Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China |
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| Abstract: | Simulation of urban land use change is the scientific basis for optimizing land resource allocation, and improving its refinement and reliability is helpful to accurately grasp the development trend of urban land use. This is immensely crucial for accurate regulation of urban land resources. The simulation of land use change based on remote sensing classification is macroscopic and simple. However, it is difficult to apply this approach to reveal the change in urban land use social functions, as well as the source and mechanism of spatial scale effect in the refined simulation at block scale. This study identified the refined urban land use characteristics by combining remote sensing images and POI data. Moreover, the optimal spatial scale combination was calibrated for refined land use simulation with the response surface method. Based on the optimal spatial scale combination, the refined simulation of future land use change was performed by using the CA-Markov model. Considering the Wuhan core urban area as an example, the results demonstrate that: (1) POI-based refined urban land use identification method can deeply analyze the social functions of urban construction land, which greatly improves the traditional remote sensing-based macro interpretation of land cover. (2) Optimal spatial scale combination of CA-Markov model for refined land use change simulation in the study area is at the cell size of 30 m and neighborhood size of 7 using the Von Neumann neighborhood type, at which the reliability of refined land use change simulation can be improved. The results of the response surface design can effectively distinguish not only the main sources of the spatial scale effect, but also the magnitude of their influence and the positive or negative effects on the simulation accuracy in the refined simulation process. (3) It is predicted that by 2025 the construction land scope of the study area will continue to expand to the periphery with various types of land interlaced, and the spatial pattern of land use will become more fragmented. |
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| Keywords: | urban land use change refined simulation spatial scale effect cellular automata Wuhan city |
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