基于木质素和纤维素特征信息的荒漠稀疏植被遥感信息提取

项目名称： 基于木质素和纤维素特征信息的荒漠稀疏植被遥感信息提取

项目编号： No.41501477

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 曹晓明

作者单位： 中国林业科学研究院

项目金额： 20万元

中文摘要： 干旱区荒漠植被分布稀疏，叶片退化，叶面积指数较小，植株枝条所占比例大，采用传统方法利用对叶绿素敏感波段进行植被覆盖度信息提取遇到很大困难。同时，在荒漠稀疏植被信息提取方面，多源、多尺度遥感数据的综合应用还远远不够，高分辨率遥感数据对于解决大尺度上植被信息提取的潜力还没有得到有效挖掘。本项目拟以位于干旱荒漠区的乌兰布和沙漠边缘沙地为研究区，基于高光谱遥感数据，充分利用荒漠灌木植被枝条中的木质素和纤维素在短波红外波段的光谱响应特征，运用多元回归分析及模型构建等方法，提取能有效表征荒漠稀疏植被信息的植被指数，构建荒漠稀疏植被覆盖度高光谱遥感反演模型；综合运用多源、多尺度遥感数据，采用尺度推演方法，实现中、大尺度空间数据对干旱区荒漠稀疏植被信息的准确表达，为干旱区生态环境监测及荒漠化监测与评价提供技术支撑。

中文关键词： 荒漠稀疏植被；高光谱遥感；植被覆盖度；尺度；反演

英文摘要： It is difficult to extract the information of the desert sparse vegetation using traditional chlorophyll sensitive bands information, due to its sparse distribution, leaves reduction, small leaf area index, large proportion of plant shoots. And it is lack of the comprehensive application of multi-source, multi-scale Remote Sensing data in the extraction of sparse vegetation information. The potential of the high-resolution Remote Sensing data in large-scale information extraction has not been effectively tapped. The program chose the sand area in the edge of Ulan Buh desert in Dengkou county as the study area. Based on the high-resolution Remote Sensing data, and the spectral response of lignin and cellulose (lig-cell) in SWIR bands, using multiple regression analysis, the sensitive spectral characteristics of lig-cell was explored, and the hyperspectral inversion model for the sparse vegetation cover was established; then, based on the Medium Resolution Remote Sensing data, unmixing methods, the fractional vegetation cover of desert sparse vegetation was extracted. The program may explore the valid parameter selection to represent information of desert sparse vegetation, and the comprehensive use of multi-source Remote Sensing data. The study would provide some technical support of the macro ecological motoring and evaluation, the assessment of desertification.

英文关键词： desert sparse vegetation;hyperspectral remote sensing;fractional vegetation cover;scale;inversion