The soil carbon pool holds an enormous amount of carbon, making it the largest reservoir in the terrestrial ecosystem. However, there is growing concern that unsustainable logging methods damage the soil ecosystem, thus triggering the release of soil carbon into the atmosphere hence contributing to ongoing climate change. This study uses a replicated (n = 4) logging experiment to examine the impact of supervised logging with climber cutting (SLCC) and conventional logging (CL) on basic soil characteristics, litter input to soils, soil carbon pools, and soil respiration in a mixed dipterocarp forest 26 years after logging. This study found that there was no significant difference observed in the soil physicochemical properties and total carbon pools between the logging treatments and the virgin forest. Soil carbon pools dominated the total carbon pools, and the highest mean value was recorded in SLCC (87.95 ± 13.67 Mg C ha⁻¹). Conventional logging had a lower mean value (71.17 ± 12.09 Mg C ha⁻¹) than virgin forest (83.20 ± 11.97 Mg C ha⁻¹). SLCC also shows a higher value of soil respiration rate (161.75 ± 21.67 mg C m⁻² h⁻¹) than CL (140.54 ± 12.54 mg C m⁻² h⁻¹). These findings highlight the importance of accurate quantification of the effect of different logging methods on the forest’s carbon pools.

Monitoring anthropogenic disturbances on aboveground biomass (AGB) of tropical montane forests is crucial, but challenging, due to a lack of historical AGB information. We examined the use of spaceborne (Shuttle Radar Topographic Mission Digital Elevation Model (SRTM) digital surface model (DSM)) and airborne (Light Detection and Ranging (LiDAR)) digital elevation data to estimate tropical montane forest AGB changes in northern Borneo between 2000 and 2012. LiDAR canopy height model (CHM) mean values were used to calibrate SRTM CHM in different pixel resolutions (1, 5, 10, and 30 m). Regression analyses between field AGB of 2012 and LiDAR CHM means at different resolutions identified the LiDAR CHM mean at 1 m resolution as the best model (modeling efficiency = 0.798; relative root mean square error = 25.81%). Using the multitemporal AGB maps, the overall mean AGB decrease was estimated at 390.50 Mg/ha, but AGB removal up to 673.30 Mg/ha was estimated in the managed forests due to timber extraction. Over the 12 years, the AGB accumulated at a rate of 10.44 Mg/ha/yr, which was attributed to natural regeneration. The annual rate in the village area was 8.31 Mg/ha/yr, which was almost 20% lower than in the managed forests (10.21 Mg/ha/yr). This study identified forestry land use, especially commercial logging, as the main driver for the AGB changes in the montane forest. As SRTM DSM data are freely available, this approach can be used to estimate baseline historical AGB information for monitoring forest AGB changes in other tropical regions.

Identifying the land cover in plantations is crucial to assist the management of an area. Today, land cover classification can be achieved using free satellite data. The objective of this study is to perform a supervised classification using LANDSAT 8 OLI to differentiate the land cover in Brumas Camp which consists of non-vegetation, oil palm, forest, and forest plantations. The overall accuracy and Kappa’s coefficients were 71.64% and 0.62, respectively. We found out that the accuracy of classification for non-vegetation is relatively higher compared to vegetation land cover types. The non-vegetation land cover has distinct spectral reflectance which is useful to differentiate between non-vegetation and vegetation land covers.