文献类型：期刊文献

中文题名：浑善达克沙地榆树疏林的高分辨率遥感识别方法

英文题名：Research on high resolution remote sensing recognition method of elm sparse forest in Otindag sandy land

作者：薛传平[1] 高志海[1] 孙斌[1] 李长龙[1] 王燕[1] 张媛媛[1]

第一作者：薛传平

机构：[1]中国林业科学研究院资源信息研究所

年份：2018

卷号：30

期号：4

起止页码：74-81

中文期刊名：国土资源遥感

外文期刊名：Remote Sensing for Land & Resources

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD2017_2018】；

基金：国家重大专项"高分辨率对地观测系统"(编号:21-Y30B05-9001-13/15)资助

语种：中文

中文关键词：浑善达克沙地;榆树;稀疏;GF-2;GEOBIA;SEaTH

外文关键词：Otindag sandy land ;elm;sparse;GF-2;GEOBIA;SEaTH

分类号：TP79

摘要：榆树疏林是浑善达克沙地生态系统的重要组成部分,对于防风固沙具有重要意义。为了快速准确获取榆树空间分布信息,提出一种基于遥感技术的自动沙地榆树识别方法,选择内蒙古自治区锡林郭勒盟正蓝旗为研究区,基于国产GF-2高空间分辨率数据,结合该区榆树多分布在沙地上并且分布稀疏的特性,首先利用归一化植被指数(normalized difference vegetation index,NDVI)阈值快速粗提取榆树的分布;然后采用面向地理对象影像分析技术(geographic object based image analysis,GEOBIA)进一步准确提取榆树的分布。为弥补GEOBIA方法在特征选择和规则集构建上的不确定性,本研究采用SEa TH算法优选特征及自动计算特征阈值。结果表明,本研究提出的方法识别榆树的总体精度达到88. 17%,Kappa系数为0. 76,其中,榆树的制图精度达99. 14%。因此,采用GF-2数据与本研究提出的方法识别榆树疏林区是可行而有效的,该方法可为进一步开展整个浑善达克沙地榆树疏林调查提供技术支撑。
The elm sparse forest is an important component in the Otindag sandy land ecosystem,which is of great significance for windbreak and sand fixation.In order to obtain the spatial distribution information of elm trees quickly and accurately,this paper proposes a method of automatic sand elm identification based on remote sensing technology.With the data of domestic high spatial resolution satellite GF-2,the research was implemented on Zhenglan Banner,Xilin Gol League,Inner Mongolia.Combined with the characteristics of elm sparse distribution in the sand,normalize!difference vegetation index(NDVI)threshold was firstly used to quickly extract the coarse distribution of elm.Then,a method based on geographic object based image analysis(GEOBIA)was used to extract the distribution of elm accurately.To compensate for the uncertainty of GEOBIA method in feature selection and rule set construction,this study used SEaTH algorithm to optimize features and automatically calculate the feature threshold.The results show that the proposed methods reached the overall accuracy of88.17%and Kappa coefficient of0.76in identifying the sparse elm.Among them,elm mapping accuracy could reach99.14%.Therefore,it is effective to identify elms by using GF-2and the method proposed in this study.This method can provide technical support for the further research and production practices of elm sparse forest in the Otindag sandy land.