Forest Aboveground Biomass Estimation using ICESat / GLAS and Imagery Remote Sensing Data in the Greater Mekong Subregion : 1 st result from Yunnan Province , China

This study aims to develop a forest aboveground biomass (AGB) mapping method in the Greater Mekong Subregion (GMS). Vertical structure of forest parameters of two forest farms in Yunnan province, China were derive using airborne LiDAR system (ALS). Regression models were built between field data of forest AGB and percentiles of canopy height, canopy density which derived from ALS point cloud data. The high accuracy ALS estimated forest aboveground biomass (AGB) were used as training data for building forest AGB estimation model with ICESat GLAS waveform indices. Then the forest ABG was estimated at ICESat GLAS footprint level in the whole province. The regression tree and MAXENT methods were investigated to extend the AGB estimation from GLAS footprint to continuous mapping using imagery remote sensing data of ENVISAT MERIS and EOS MODIS data. The preliminary results showed that: 1) The integrated method based on field measurements, airborne and spaceborne LiDAR data can be used to estimate forest aboveground biomass effectively. 2) The estimation agreed well with inventory based results, and the average difference was about 10%. 3) Both regression tree and MAXENT methods predicted AGB spatial distribution well. 4) These methods will be investigated further and used to the entire Greater Mekong Subregion with more reference training data. INTRODUCTION Forests play an irreplaceable role in maintaining regional ecological environment, global carbon balance and mitigating global climate change. Forest aboveground biomass (AGB) is an important indicator of forest carbon stocks. Estimating forest aboveground biomass accurately could significantly reduce the uncertainties in terrestrial ecosystem carbon cycle. The Greater Mekong Subregion (GMS) is rich in forest resources, the change of forest resources affect the regional even global climate change. It is important to estimate forest AGB with high accuracy methods in this region. Remote sensing is an efficient way to estimate forest parameters in large area, especially at regional scale where field data is scarce. LIDAR (Light Detection And Ranging) provides accurate information on the vertical structure of forests. Combining airborne LiDAR and spaceborne LiDAR for regional forest biomass retrieval could provide a more reliable and accurate quantitative information in regional forest biomass estimate. Foody et al (2003) es ma t ed tropi cal for est bi oma ss from Lands at TM dat a bet we en si tes in Br azi l, Ma l aysi a and Thailand and results showed that for each test site, the vegeta on i ndi ces of Lands at TM dat a mo s t strongl y related to the biomass of the training data. Muukkonen et al (2007) used forest inventory data and MODIS data to es ma t e for est bi oma ss and comp ar ed the resul ts wi th Na onal Forest Invent ory dat a f ound t he r ela ve RMSE was 9.9%. These researchers showed that op cal remo t e sens i ng can be us ed to bui ld emp i ri cal rel a ons hi ps between the forest biomass and spectral reflectance, especi al ly at regi onal scal e wh er e field dat a i s scarce ( Lim et al., 2003). Light Detec on and Rangi ng (Li DAR) i s one of the mo s t pr omi si ng technol ogi es for ret ri eval of var i ous for est biophysical proper es ( Lef sky et al ., 1999; 2005) . Al though ai rbor ne Li DAR can es mat e t r ee hei ght wi t h sub-meter ver cal accur acy and spa al resol u on but i ts u li ty is limited in lar ge area for it s high cos t ( Boudr eau et al. 2008). The first spacebor ne l ar ge foot pr i nt Li DAR sens or (ICESat /GL AS) acqui red over 250 mi llion Li DAR observa ons over for est regi ons gl obal ly and has been us ed successful ly for for est hei ght and bi oma ss es ma on in various sites (Lefsky et al., 2007; Boudreau et al. 2008; Duncanson et al., 2010; Pang et al., 2011). Lefsky et al (2005) used ICESat/GLAS and SRTM data to es ma t e for est hei ght and abovegr ound bi oma ss and demo ns trat ed that GLAS data were able to predict forest heights successfully over a wide range of canopy height and aboveground biomass. (Nelson et al., 2009) used op cal dat a f rom t he MO DI S and wa vef or m dat a f rom ICESat/GLAS to es ma t e mbe r vol ume i n Cent r al Siber i a. The encour agi ng r esul t showed t hat GLAS and MOD I S data can be used to develop accurate regional es ma t es of mbe r vol ume. In this paper, airborne LiDAR and ICESat/GLAS data were used to es ma t e f or est abovegr ound bi oma ss at footprint level in Yunnan Province of China and a con nue for est bi oma ss ma p wa s gener at ed by comb i ned op cal dat a and Li DAR es mat ed biomas s sampl es.