Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass

doi:10.1016/j.isprsjprs.2015.02.007

CORC > 北京大学 > 城市与环境学院

	Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass
	Li, Le ; Guo, Qinghua ; Tao, Shengli ; Kelly, Maggi ; Xu, Guangcai
刊名	isprs journal of photogrammetry and remote sensing
	2015
DOI	10.1016/j.isprsjprs.2015.02.007
英文摘要	Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: (1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, (2) linear regression, among four tested models, achieve the best performance (R2=0.74; RMSE=183.57Mg/ha); (3) for MODIS-derived vegetation indices at varied spatial resolution (250-1000m), accumulated NDVI, accumulated LAI, and accumulated FPAR could explain 53-74% variances of forest AGB, whereas accumulated NDVI derived from 1km MODIS products gives higher R2 (74%) and lower RMSE (13.4Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB. ? 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).; SCI(E); EI; 0; ARTICLE; qguo@ucmerced.edu; 198-208; 102
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/262896]
专题	城市与环境学院
推荐引用方式 GB/T 7714	Li, Le,Guo, Qinghua,Tao, Shengli,et al. Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass[J]. isprs journal of photogrammetry and remote sensing,2015.
APA	Li, Le,Guo, Qinghua,Tao, Shengli,Kelly, Maggi,&Xu, Guangcai.(2015).Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass.isprs journal of photogrammetry and remote sensing.
MLA	Li, Le,et al."Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass".isprs journal of photogrammetry and remote sensing (2015).