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当前位置: 首页 > 文章 > 基于Sentinel-2A影像的矿区土地利用信息提取方法 山东农业大学学报(自然科学版) 2020,51 (3) 441-446
Position: Home > Articles > Extraction Method of Mining Land Use Information Based on Sentinel-2A Image Journal of Shandong Agricultural University(Natural Science Edition) 2020,51 (3) 441-446

基于Sentinel-2A影像的矿区土地利用信息提取方法

作  者：
邵安冉;李新举;周晶晶
单  位：
山东农业大学资源与环境学院
关键词：
Sentinel-2A;矿区;土地利用信息;提取方法
摘  要：
为研究中高分辨率遥感影像大范围精确提取矿区土地利用信息的技术方法,选取山东省兖州市兴隆庄煤矿作为研究区,针对遥感影像的光谱特征,采用监督分类和归一化指数计算相结合的方式进行矿区土地利用信息提取试验。结果表明,选择融合后Sentinel-2A卫星影像的四个高分辨率波段可应用于土地利用的精确识别;根据地物光谱特征,最大似然监督分类法提取建设用地、裸地效果较好,可作为精确提取两种地类的方法;结合归一化植被指数运算和归一化水体指数运算提取植被、水体信息可明显提高总体分类精度,提取精度可达93.54%,进而可利用该分类结果精确高效地识别和测算矿区土地利用类型及其分布特征,为矿区土地整治、村庄搬迁等提供依据。
译  名：
Extraction Method of Mining Land Use Information Based on Sentinel-2A Image
作  者：
SHAO An-ran;LI Xin-ju;ZHOU Jing-jing;College of Resources and Environment/Shandong Agricultural University;National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources;
关键词：
Sentinel-2A;;mining area;;land use information;;extraction method
摘  要：
In order to study the technical method of accurate extraction of land use information in mining area in a wide range of high-resolution remote sensing images, Xinglongzhuang Coal Mine, Yanzhou City, Shandong Province, was selected as the study area. According to the spectral characteristics of remote sensing images, the experiment of mining area land use information extraction was carried out by combining supervised classification and normalized index calculation. The results show that four high resolution bands of the combined sentinel-2a satellite images can be used for accurate identification of land. According to the spectral characteristics of ground objects, the maximum likelihood supervised classification method is effective in extracting construction land and bare land, which can be used as an accurate method for extracting two types of land. Combined with normalized vegetation index operation and normalized water body index operation, the extraction of vegetation and water body information can obviously improve the overall classification accuracy, and the extraction accuracy can reach 93.54%. Then, the classification results can be used to accurately and efficiently identify and calculate the land use types and their distribution characteristics, and provide the basis for land renovation and village relocation in mining areas.

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