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.

Species diversity and the distribution of forests are closely related to climate, and climate classifications have been used to characterize vegetation distribution for over a century at the global scale. In contrast, climate type and dominant forest species may not be accurately classified at the forestry stand scale due to limited observational data and the influence of terrain. The collaboration of Asian university forests traverses 37.4° of latitude, from Hokkaido in Japan to Sabah in Malaysia. This study used both long-term observations and Worldclim 1-km resolution gridded datasets to classify well-managed Asian university forests according to the Trewartha climate classification method. Outputs from circulation models of the Coupled Model

In order to overview the impact of climate change on runoff from forested catchments over Asian countries, we collected water balance data from fifteen long-term catchment monitoring stations (total monitoring period 1975–2018, not continuous), spanning from Sabah, Malaysia (our southernmost site), to Hokkaido, Japan (our northernmost site). We then employed an elasticity analysis to the dataset to examine how the annual runoff from each catchment responded to inter-annual fluctuations in annual rainfall and annual mean air temperature. As a result, we found that (1) the annual runoff was sensitive to annual rainfall for all the catchments examined. In addition, (2) the annual runoff from seven of the fifteen catchments was sensitive to inter-annual changes in the mean air temperature, which was likely due to changes in forest evapotranspiration. Three catchments, however, exhibited an increased runoff in a hot year. Finally, (3) the annual rainfall from the previous year (carry-over soil moisture) was important in explaining the variation in annual runoff in two tropical montane forest catchments. This study may serve as one of the pilot studies toward a comprehensive understanding of the climate elasticity of runoff in countries over Asia, because the examined catchments are unevenly and sparsely distributed over the area.

It is generally accepted that vegetation provides important ecosystem services especially in term of rainfall partitioning. This study aims to evaluate the influence of canopy structure namely crown area (CA), diameter at breast height (DBH), tree height (TH) and crown spread (CS) and stand density on the partitioning of rainfall. Twelve throughfall plots of 20 x 20 m with 64 gauges randomly placed within each plot were established. For stemflow measurements, all trees within a 100 m2 plot within the study area were collared. Interception loss was computed as the difference between precipitation and throughfall plus stemflow. Throughfall ranged from 73.47 – 82.32 % of the gross rainfall. Stemflow was found to be roughly around 2.01% of the gross rainfall. Highest interception was 24.52 % attributed to the plot having the highest above ground biomass (AGB) density. The relation between canopy interception and forest structure were analyzed by regression method. Multiple regression analysis on the potential influence of stand structure to the throughfall percentage shows that all the forest structures variables measured in this study are negatively correlated to the amount of throughfall generated. This study suggests that forests with higher value of DBH, CA, CS and TH had higher interception rate.

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