文献类型：期刊文献

英文题名：Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

作者：Yu, Tao[1,2] Liu, Pengju[1,2] Zhang, Qiang[3] Ren, Yi[4] Yao, Jingning[5]

第一作者：Yu, Tao;余涛

通信作者：Liu, PJ[1];Liu, PJ[2]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Natl Nat Sci Fdn China, Ctr Sci Commun & Achievement Transformat, Beijing 100085, Peoples R China;[4]Natl Forestry & Grassland Adm, Survey & Planning Inst, Beijing 100029, Peoples R China;[5]Natl Forestry & Grassland Adm, Three North Shelterbelt Program Construct Bur, Yinchuan 750001, Ningxia, Peoples R China

年份：2021

卷号：13

期号：6

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20211310155377);Scopus(收录号：2-s2.0-85103287106);WOS:【SCI-EXPANDED(收录号:WOS:000651950900001)】；

基金：This research was funded by "The Three North Shelterbelt System Engineering Management and Service Platform" from the Three North Shelterbelt Program Construction Bureau of National Forestry and Grassland Administration of China.

语种：英文

外文关键词：forest degradation; indicators; MODIS; multi-scale; validation

摘要：Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.