Developing a robust and cost-effective method for accurately estimating tropical forest's carbon pool over large area is a fundamental requirement for the implementation of Reducing Emissions from Deforestation and forest Degradation (REDD+). This study aims at examining the independent and combined use of airborne LiDAR and Landsat 8 Operational Land Imager (OLI) data to accurately estimate the above-ground biomass (AGB) of primary tropical rainforests in Sabah, Malaysia. Thirty field plots were established in three types of lowland rainforests: alluvial, sandstone hill and heath forests that represent a wide range of AGB density and stand structure. We derived the height percentile and laser penetration variables from the airborne LiDAR and calculated the vegetation indices, tasseled cap transformation values, and the texture measures from Landsat 8 OLI data. We found that there are moderate correlations between the AGB and laser penetration variables from airborne LiDAR data (r =-0.411 to-0.790). For Landsat 8 OLI data, the 6 vegetation indices and the 46 texture measures also significantly correlated with the AGB (r = 0.366 to 0.519). Stepwise multiple regression analysis was performed to establish the estimation models for independent and combined use of airborne LiDAR and Landsat 8 OLI data. The results showed that the model based on a combination of the two remote sensing data achieved the highest accuracy (R 2 adj = 0.81, RMSE = 17.36%) whereas the models using Landsat 8 OLI data airborne LiDAR data independently obtained the moderate accuracy (R 2 adj = 0.52, RMSE = 24.22% and R 2 adj = 0.63, RMSE = 25.25%, respectively). Our study indicated that texture measures from Landsat 8 OLI data provided useful information for AGB estimation and synergistic use of Landsat 8 OLI and airborne LiDAR data could improve the AGB estimation of primary tropical rainforest.

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This study was conducted in the alluvial forest and heath forest in the lowland tropical forest of Sepilok Forest Reserve, Sabah, Malaysia. The main objective was to assess how forest structure regulates rainfall partitioning in both forests. Field monitoring involved a series of forest inventory work to determine the forest stand characteristics. Mann Whitney U test was performed to compare physical characteristics between the two forests. Meanwhile rainfall partitioning was quantified by measuring the throughfall (Tf) for a period of 12 months in ten (15 x 15 m) Tf plots and a simple linear regression was conducted to obtain a regression model to estimate Tf. In terms of stand structure characteristics, data in the alluvial forest indicates wider variation. Percentage of Tf as of gross rainfall (Pg) is higher in the heath forest than in alluvial forest with the value of 89.5 % and 76.8 %, respectively. Representative trees were selected for stemflow (Sf) estimation at each forest type. The estimated Sf is 0.2 % in alluvial forest and 0.5 % in heath forest. In this study, tree diameter at breast height (Dbh) and height as well as aboveground biomass were identified to have some influence in Tf and Sf production. Keywords: rainfall partitioning; gross rainfall; throughfall; stemflow; Mann Whitney U; simple linear regression