文献类型：期刊文献

中文题名：基于ICESat GLAS的云南省森林地上生物量反演

英文题名：Aboveground forest biomass estimation using ICESat GLAS in Yunnan,China

作者：黄克标[1,2] 庞勇[1] 舒清态[3] 付甜[1]

第一作者：黄克标

机构：[1]中国林业科学研究院资源信息研究所;[2]亚太森林恢复与可持续管理组织;[3]西南林业大学林学院

年份：2013

卷号：17

期号：1

起止页码：165-179

中文期刊名：遥感学报

外文期刊名：Journal of Remote Sensing

收录：CSTPCD;;Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；

基金：国家高技术研究发展计划(863计划)(编号:2012AA12A306;2007AA12Z173);国家林业局行业公益项目(编号:200804001);云南省自然科学基金(编号:2008ZC094M)~~

语种：中文

中文关键词：地上生物量估测;激光雷达;ICESat;GLAS;光学遥感数据

外文关键词：aboveground biomass estimation, LiDAR, ICESat GLAS, optical data

分类号：TP79

摘要：结合机载、星载激光雷达对GLAS(地球科学激光测高系统)光斑范围内的森林地上生物量进行估测,并利用MODIS植被产品以及MERIS土地覆盖产品进行了云南省森林地上生物量的连续制图。机载LiDAR扫描的260个训练样本用于构建星载GLAS的森林地上生物量估测模型,模型的决定系数(R2)为0.52,均方根误差(RMSE)为31Mg/ha。研究结果显示,云南省总森林地上生物量为12.72亿t,平均森林地上生物量为94Mg/ha。估测的森林地上生物量空间分布情况与实际情况相符,森林地上生物量总量与基于森林资源清查数据的估测结果相符,表明了利用机载LiDAR与星载ICESatGLAS结合进行大区域森林地上生物量估测的可靠性。
Accurate estimates of forest aboveground biomass （AGB） could reduce uncertainties in the characterization of terre- strial carbon fluxes. Light Detection and Ranging （LiDAR） provides an accurate measure of canopy height and vertical structure and information for the estimation of aboveground biomass of vegetation. Spaceborne large footprint LiDAR （ICESat GLAS） acquires over 250 million observations over forest regions globally and has been used successfully for forest height and biomass estimation in various sites. In this paper, airborne LiDAR and ICESat GLAS data were used to estimate aboveground biomass of forest at footprint level in Ytmnan, China. Vegetation products from EOS MODIS and ENVISAT MERIS were used to expand these discrete estimations from GLAS data to a wall-to-wall map. The R2 between ICESat GLAS waveform parameters and airborne LiDAR estimated forest AGB is 0.52 after training with 260 footprints. Results showed that the total forest AGB in Yunnan Province was 1272 million ton and the average was 94 Mg/ha. The amount and distribution of predicted aboveground biomass were in good agreement with the reference data. The results showed that the predict model using GLAS data could be used to estimate regional forest aboveground biomass successfully.