文献类型：期刊文献

中文题名：基于遥感的湄公河次区域森林地上生物量分析

英文题名：Forest Aboveground Biomass Analysis Using Remote Sensing in the Greater Mekong Subregion

作者：庞勇[1] 黄克标[2] 李增元[1] 覃先林[1] 陈尔学[1]

第一作者：庞勇

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网络

年份：2011

卷号：33

期号：10

起止页码：1863-1869

中文期刊名：资源科学

外文期刊名：Resources Science

收录：CSTPCD;;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；CSSCI:【CSSCI2010_2011】；

基金：国家973项目(编号:2007CB714404);国家自然科学基金课题(编号:41071272);亚太森林恢复与可持续管理网络项目(编号:2011PA004)

语种：中文

中文关键词：大湄公河次区域;森林地上生物量;激光雷达;光学遥感

外文关键词：Greater Mekong Subregion （GMS）; Forest aboveground biomass; LiDAR; Opticalremote sensing

分类号：S812

摘要：森林对维护区域生态环境及全球碳平衡、缓解全球气候变化发挥着不可替代的作用,对森林地上生物量进行精确估测能够大大减小陆地生态系统碳储量的不确定性。本文结合机载激光雷达、星载激光雷达和成像光学遥感等数据进行大湄公河次区域的森林地上生物量估测,生成连续的森林地上生物量图。结果表明:①基于星机地协同观测数据可以有效地估测森林地上生物量,模型总体平均误差为34t/hm^2,相关系数为0.7;②估测结果与FAO FRA 2010报告以及其它报告公布的结果相比,一致性较好,平均差异为13.3%;③根据本文的遥感反演结果,大湄公河次区域森林生物量总量为62.72亿t,其中常绿阔叶林占71%,落叶阔叶林占10%,常绿针叶林占16%,混交林占3%;④从各国(地区)的生物量总量来看,缅甸森林地上生物量总量最大,占22%,其次是中国云南省、老挝、泰国、越南、中国广西壮族自治区和柬埔寨。
Forests play a key role in maintaining the regional environment and global carbon balance and mitigating global climate change. Forest aboveground biomass （AGB） is an important indicator of forest carbon stocks. Accurately estimating forest aboveground biomass can significantly reduce uncertainties in investigating the terrestrial ecosystem carbon cycle. The Greater Mekong Subregion （GMS） is rich in forest resources; changes in forest resources can affect regional and even global climate change. It is therefore important to estimate forest AGB in this region. Remote sensing is an efficient way to estimate forest parameters over large areas, especially at regional scales where field data are scarce. Light Detection And Ranging （LIDAR） provides accurate information on the vertical structure of forests. Combining airborne LIDAR with spaceborne LIDAR for regional forest biomass estimation could provide a more reliable and quantitative information regarding regional forest biomass. In this study, the vertical structure of forest parameters of two forest farms in Yunnan Province, China, was derived using airborne LIDAR system （ALS）. Regression models were built using field data of forest AGB and percentiles of canopy height and canopy density derived from ALS point cloud data. Forest AGB estimated from ALS with high accuracy were used as training data for building a forest AGB estimation model with ICESat GLAS waveform indices. Then the forest ABG was estimated at ICESat GLAS footprint levels in GMS. In terms of different types of ecological zones, a set of categorical regression models was built between ICESat GLAS estimates and MERIS spectral variables. Then, a forest aboveground biomass map with continuous biomass values was generated. Results show that： 1） integrating field measurements with airborne and spaceborne LiDAR data can be useful in effectively estimating forest aboveground biomass. Ten estimation equations were built using the regression decision tree method. The overall average error of the estimation models is 34 t/hm2, with a correlation coefficient of 0.7. 2） The estimation agrees well with the FAO FRA 2010 report and other published results, and the average difference is 13.3%. 3） The total forest aboveground biomass in GMS estimated from remote sensing data is 6.27 billion tons, consisting of 71% evergreen broadleaf forest, 10% deciduous broadleaf forest, 16% evergreen coniferous forest, and 3% mixed forest. 4） According to the total aboveground biomass map, Myanmar has the largest AGB in the region which account for 22% of the total regional biomass, followed by Yunnan Province in China, Laos, Thailand, Vietnam, Guangxi Zhuang Nationality Autonomous Region in China, and Cambodia.