耦合混合像元分解和混合元胞模拟的土地覆盖变化推演  

Coupling Mixed Pixel Decomposition and Mixed-cell Simulation for Land Cover Change Deduction

作　　者：曹玮 肖瑶 梁迅 关庆锋[1,3] CAO Wei;XIAO Yao;LIANG Xun;GUAN Qingfeng(School of Geography and Information Engineering,China University of Geosciences,Wuhan 430078,China;Beijing Global Safety Technology Company,Beijing 100094,China;National Engineering Research Center for Geographic Information System,China University of Geosciences,Wuhan 430078,China)

机构地区：[1]中国地质大学(武汉)地理与信息工程学院,武汉430078 [2]北京辰安科技股份有限公司,北京100094 [3]中国地质大学(武汉)国家地理信息系统工程技术研究中心,武汉430078

出　　处：《地球信息科学学报》2024年第7期1611-1628,共18页Journal of Geo-information Science

基　　金：可持续发展大数据国际研究中心(CBAS2022GSP05);国家自然科学基金项目(42271437、42171466);湖北省自然资源厅科研计划项目(ZRZY2022KJ12)。

摘　　要：元胞自动机(CA)是土地利用/土地覆盖变化模拟的重要工具。以往基于纯质元胞的CA模型忽略了元胞内部的混合土地覆盖结构,难以模拟现实中混合土地系统在快速城市化进程的连续演化。而混合元胞CA模型(MCCA)虽能较好地处理这一问题,却因难以获取精细尺度的混合结构数据而不易于推广。为了解决这个问题,本研究提出了耦合混合像元分解方法和MCCA模型的模拟分析框架,利用混合像元分解算法从Landsat影像中直接获取MCCA模型所需的亚元胞尺度混合结构数据;并利用SHAP方法挖掘亚元胞尺度土地覆盖变化的驱动力。本文以武汉市为研究区开展了实验,研究结果表明:①耦合模型在混合土地结构的空间格局表征以及未来变化模拟方面具有较高的准确性(解混精度高于0.8,mcFoM指数达到0.38);②本文的耦合模型能够在有效模拟精细尺度的土地覆盖动态变化及其比例关系的同时,发现区域土地覆盖变化的相关规律。例如:未来的土地覆盖结构变化主要集中在建成区附近,土地混合度将经历先增后减的过程,靠近公司企业、市政府和高人口、高GDP是不透水面扩张的重要驱动因素,在离高铁站相对较远的地方,不透水面比近处增长更加明显;③本文模型模拟的未来土地覆盖变化趋势与武汉市的未来规划布局相符,多情景间的对比体现了MCCA模型在精确捕捉像元间土地覆盖比例细微差异方面的能力。本研究通过耦合遥感领域的混合像元分解方法和GIS领域的混合元胞CA模型,解决了混合土地覆盖模拟缺乏精细尺度数据源的问题,实现了在亚像元尺度下的未来混合土地覆盖结构变化模拟,可为城市管理和规划决策提供新的方法与工具。Cellular Automata(CA)provides an important tool for land use/land cover change simulation.However,previous CA models based on pure cells ignore the mixed land cover structure within cells,making it difficult to simulate the continuous evolution of mixed land systems during rapid urbanization.The Mixed-Cell Cellular Automata(MCCA)can address this issue,but its widespread application is hindered by the difficulty in obtaining fine-scale mixed structure data.To solve these problems,this study proposes a simulation analysis framework that couples the mixed pixel decomposition method with the MCCA model.This framework uses the mixed pixel decomposition algorithm to directly obtain the sub-pixel scale mixed structure data required by the MCCA model from Landsat images.The SHAP method is utilized to explore the driving forces of sub-pixel scale land cover change.To verify the proposed framework,we conducts an experiment in Wuhan city.Results show that:(1)The decomposition accuracy of the land cover data is above 0.8,and the mcFoM index of the simulation results is 0.38,indicating that this coupled model has high accuracy in characterizing the spatial pattern of mixed land structures and simulating future changes;(2)The proposed coupling model can effectively simulate the fine-scale dynamic changes of land cover proportions and discover relevant patterns of regional land use changes.For example,future land cover structure changes will mainly concentrate in built-up areas,and land mixture will experience a process of increasing first and then decreasing.Socio-economic factors such as proximity to companies,the municipal government,and high population and GDP are important driving factors for the expansion of impervious surfaces,and impervious surfaces in urban centers relatively far from high-speed railway stations grow more rapidly;(3)The future land cover change trends simulated by the proposed model are consistent with the future planning layout of Wuhan.The comparison between multiple scenarios demonstrates the MCCA m

关 键 词：土地利用变化 土地覆盖 地理模拟 混合元胞 元胞自动机 混合像元分解 

分 类 号：P237[天文地球—摄影测量与遥感]