Aboveground forest biomass estimation using ICESat GLAS in Yunnan, China
基于ICESat GLAS的云南省森林地上生物量反演

Accurate estimates of forest aboveground biomass (AGB) could reduce uncertainties in the characterization of terrestrial 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 Yunnan, 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.