杨昌兰,关雪峰,李静波,吴华意

• 1:武汉大学测绘遥感信息工程国家重点实验室

摘要(Abstract)：

鉴于当前较难获取最新高精度长时序遥感城市土地监测图，现有城市扩展空间驱动分析实践识别主要驱动因子时未检验因子集的全局解释性且较少关注空间溢出效应。以东莞为实验对象，集合Landsat影像和MODIS归一化植被指数（normalized differential vegetation index,NDVI）时序产品，构建深度学习分类器，获取高精度土地覆盖分类图，识别城市扩展，使用基于logistic回归的探索性回归识别解释东莞全局城市扩展的最优因子集，进而使用auto logistic回归测度空间溢出效应的影响并进行驱动分析。研究发现：(1)基于深度学习分类器，融合Landsat光谱、纹理和MODIS NDVI时序变化信息可获取高精度（Kappa>93%）土地覆盖分类图；(2)基于探索性回归可良好识别解释全局城市扩展的最优因子集，受试者工作特征曲线（receiver operating characteristic curve,ROC）>0.85；(3)2000—2020年，最优解释东莞全局城市扩展的主要空间驱动因子有城市规划方案、距建成区距离、空间加权城市密度、城市扩展的空间溢出效应，主要限制因子有高程、坡度、1 km2水体密度和土地可得性。

关键词(KeyWords)： 卷积神经网络(convolutional neural network,CNN);长短期记忆(long short-term memory,LSTM)网络;城市扩展;空间驱动因子;auto logistic;空间溢出效应

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（41971348）;; 面向大规模地理智能体模拟的多元交互关系建模与并行调度

作者(Author): 杨昌兰,关雪峰,李静波,吴华意

DOI: 10.14188/j.2095-6045.2022128

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