摘要
阴影是影响山地针叶林遥感识别精度的关键因素。选取天山一块面积约为10000 km2的区域为案例,基于太阳高度角和方位角差异较大的两期Sentinel-2影像,从遥感数据阴影分布的时相特性、分类特征以及分类器选择三方面进行综合分析,提出了一种适用于天山山地针叶林的遥感综合分类方案。该综合分类方案首先开展阴影识别以及阴影再分类以排除阴影对针叶林识别的影响;然后筛选出了海拔、归一化差值植被指数(NDVI)、红光到近红外波段斜率、蓝光波段、红光波段、短波红外波段和坡度作为区分天山山地针叶林的重要特征;最后比较支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)和BP神经网络(Back Propagation Neural Network,BPNN)3种分类器的分类效果。结果表明:采用地形校正方法来消除山体阴影的效果不但不明显,反而还会造成过矫正现象,从而影响后续的针叶林识别,但利用太阳高度角和方位角差异较大的两期影像开展阴影识别以及阴影再分类来排除阴影对针叶林识别的影响,可使针叶林的总体精度提高1.3%~3.7%;SVM、RF和BPNN 3种分类器都能取得较好的山地针叶林识别精度,但SVM分类器的分类精度最高,其总体分类精度和Kappa系数分别是93.33%和0.87。该遥感综合分类方案经参数调整之后有望应用于北方干旱半干旱区的其他山地针叶林区域。
Shadows are the key factors affecting the identification accuracy of mountains coniferous forests using multi-spectral remote sensing data.Taking Tianshan as the study area,a comprehensive classification scheme was proposed,which comprehensive considered three aspects:the time-phase features of shadow distribution,classification features and classifiers.Firstly,to eliminate the influences of shadows on coniferous forest identifi⁃cation,shadow recognition and shadow reclassification were carried out.Then the altitude,Normalized Differ⁃ence Vegetation Index(NDVI),spectral slope of red to near-infrared band,blue reflectance band,red reflec⁃tance band,short-wave infrared band and slope were selected as the important features for identifying the Tian⁃shan mountain coniferous forest.Finally,three often used classifiers(Support Vector Machine(SVM),Ran⁃dom Forest(RF)and Back Propagation Neural Network(BPNN))were compared.The results show that the terrain correction method can not effectively eliminate the mountain shadows,and it may cause over-correc⁃tion,which affects the subsequent identification of coniferous.However,using two-phase images with large dif⁃ferences in solar elevation and azimuth to eliminate the influence of shadows on coniferous forest identification can improve the overall accuracy of coniferous forest by 1.3%to 3.7%;The SVM,RF and BPNN classifier can all achieve better classification accuracy,but the SVM classifier got the highest classification accuracy and Kappa coefficient with a value 93.33%and 0.87,respectively.The proposed remote sensing comprehensive classification scheme is expected to be applied to other mountain coniferous forest areas in the north arid and semi-arid regions after adjusting the parameters.
作者
蒋嘉锐
朱文泉
乔琨
江源
Jiang Jiarui;Zhu Wenquan;Qiao Kun;Jiang Yuan(State Key Laboratory of Earth Surface Processes and Resource Ecology,Beijing Normal University,Beijing 100875,China;Beijing Engineering Research Center for Global Land Remote Sensing Products,Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China)
出处
《遥感技术与应用》
CSCD
北大核心
2021年第4期847-856,共10页
Remote Sensing Technology and Application
基金
国家自然科学基金重点项目“中国北方山地针叶林生长的时空分异及其对水热条件的响应”(41630750)。
关键词
天山
山地针叶林
阴影
特征选择
多光谱遥感
Tianshan
Mountains coniferous forest
Shadow
Feature selection
Multispectral remote sensing
