基于多源遥感影像的青海云杉和祁连圆柏分类  

Classification of Picea crassifolia and Sabina przewalskii based on Multi-source Remote Sensing Images

作　　者：李萌 年雁云[1] 边瑞 白艳萍 马金辉[1] Li Meng;Nian Yanyun;Bian Rui;Bai Yanping;Ma Jinhui(College of Earth and Environmental Sciences,Lanzhou University,Lanzhou 730000,China)

机构地区：[1]兰州大学资源环境学院,甘肃兰州730000

出　　处：《遥感技术与应用》2020年第4期855-863,共9页Remote Sensing Technology and Application

基　　金：中国科学院A类战略性先导科技专项(XDA20100102)资助。

摘　　要：青海云杉和祁连圆柏是祁连山自然保护区的优势种,提取两种类型树木的空间分布对保护区森林资源的管理和监测方面有重要意义。使用Sentinel-2A(S2)、Sentinel-1A(S1)、Landsat-8(L8)3种遥感影像及来自SRTM DEM的地形数据,基于随机森林分类方法,设置8种组合方案共22个特征变量,以祁连山东段的甘肃连城自然保护区为例,对青海云杉和祁连圆柏进行分类试验。结果表明:融合Sentinel-1A(S1)数据的VV和VH两种后向散射信息的精度最高,达到92.85%,比使用单一影像Landsat-8提高了11.64%。实验表明:结合多源遥感影像的不同波段信息是提高森林类型分类精度的有效手段,有助于复杂山区森林资源调查、植被信息提取等需求。Picea crassifolia and Sabina przewalskii are the dominant species in Liancheng Nature Reserve.Extracting the spatial distribution of two types of trees is of great significance for the management and monitoring of forest resources in the reserve.Based on the method of random forest,22 feature variables in eight combinations from Sentinel-2A(S2),Sentinel-1A(S1),Landsat-8(L8)three remote sensing images and digital elevation model of SRTM DEM to classify Picea crassifolia and Sabina przewalskii in Liancheng Nature Reserve of Gansu Province.The results demonstrated that the accuracy of integrating VV and VH backscattering information of sentinels-1A(S1)was the highest,reaching 92.85%,which is 11.64%higher than that of single image Landsat-8.Experiments showed that combining different bands of multi-source remote sensing images is an effective means to improve the classification accuracy of forest types,which is beneficial to forest resource survey and vegetation information extraction in complex mountainous areas.

关 键 词：Sentinel-2A 特征变量 随机森林 信息提取 连城自然保护区 

分 类 号：S757[农业科学—森林经理学] TP79[农业科学—林学]