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| 基于GEE的东北三省城市建设用地扩张研究 | |
| 引用本文： | 王明常,刘鹏,陈学业,王凤艳,宋玉莲,刘瀚元.基于GEE的东北三省城市建设用地扩张研究[J].吉林大学学报(地球科学版),2022,52(1):292-304. |
| 作者姓名： | 王明常 刘鹏 陈学业 王凤艳 宋玉莲 刘瀚元 |
| 作者单位： | 1.吉林大学地球探测科学与技术学院，长春130026 2.自然资源部城市国土资源监测与仿真重点实验室，广东深圳518000 3.中国地质调查局西安矿产资源调查中心，西安711500 |
| 基金项目： | 国家自然科学基金;吉林省教育厅十三五科学研究规划项目;吉林省自然科学基金;自然资源部城市土地资源监测与仿真重点实验室开放基金 |
| 摘 要： | 根据社会经济和人口变化，基于云平台实现中高分辨率遥感影像城市建设用地的快速提取，可以高效准确地对长时间序列大范围城市建设用地扩张情况进行动态变化监测，为城市的管理和规划提供借鉴和参考。本文基于Google Earth Engine（GEE）云平台，利用NPP/VIIRS（suomi national polar orbiting partnership/ visible infrared imaging radiometer suite）年度平均夜间灯光数据和阈值分割法提取城市区域，获得3 142景覆盖东北三省城市的Landsat影像；在原始光谱波段的基础上构建指数、纹理和地形特征，利用SEaTH算法进行特征优化，根据JM距离的取值将特征数量从20个精简到12个；在城市区域中利用随机森林（random forest, RF）算法结合最优特征对Landsat月度合成影像进行再次分类，更加精确地提取建设用地。研究结果表明，东北三省城市建设用地提取平均总体精度和Kappa系数分别为96.19%、0.92，有较高的效率和可靠性；1989—2019年东北三省城市建设用地总面积扩张49.07%，省会城市中沈阳扩张速率较快，长春次之，哈尔滨最慢；人口因素和经济因素是推动城市建设用地扩张的主要因素。 |
| 关 键 词： | GEE 特征优化 随机森林算法 建设用地扩张 驱动机制 |
| 收稿时间： | 2021-03-30 |
Land Expansion of Urban Construction in the Three Provinces of Northeast China Based on Google Earth Engine |
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| Wang Mingchang,Liu Peng,Chen Xueye,Wang Fengyan,Song Yulian,Liu Hanyuan.Land Expansion of Urban Construction in the Three Provinces of Northeast China Based on Google Earth Engine[J].Journal of Jilin Unviersity:Earth Science Edition,2022,52(1):292-304. | |
| Authors: | Wang Mingchang Liu Peng Chen Xueye Wang Fengyan Song Yulian Liu Hanyuan |
| Institution: | 1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China 2. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, Guangdong, China 3. Xi’an Center of Mineral Resources Survey, China Geological Survey, Xi’an 711500, China |
| Abstract: | Combined with social economy and population changes, the rapid extraction of urban construction land from medium and high resolution remote sensing images based on cloud platform can be used to effectively and accurately monitor the dynamic changes of urban construction land expansion in a large range and a long time series, thereby providing reference for urban management and planning. Based on Google Earth Engine (GEE) cloud platform, the urban area was extracted by NPP/VIIRS (suomi national polar-orbiting partnership/visible infrared imaging radiometer suite) annual average night light data and threshold segmentation. We obtained 3 142 Landsat images covering the cities of the three provinces of Northeast China. On the basis of the original spectral band, we constructed exponential features, texture features, and terrain features, and used SEaTH algorithm for these features optimization. Based on the JM distance, the number of features was reduced from 20 to 12. Within the urban area, combined the optimal features with random forest (RF) algorithm, the Landsat monthly composite images were reclassified, and more accurate extraction of construction land was obtained. The experimental results show that the average overall accuracy and Kappa coefficient of urban construction land expansion in the three provinces are 96.19% and 0.92, the method is more efficient and reliable. The urban construction land of the three provinces had expanded by 49.07% from 1989 to 2019. Among the provincial capitals, the expansion rate of Shenyang is the fastest, followed by Changchun, and Harbin is the slowest. Population and economy are the main factors that promote the expansion of urban construction land. |
| Keywords: | Google Earth Engine feature optimization random forest algorithm construction land expansion driving mechanism |
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