Kalimantan poses one of the highest carbon emissions worldwide since its landscape is strongly endangered by deforestation and degradation and, thus, carbon release. The goal of this study is to conduct large-scale monitoring of above-ground biomass (AGB) from space and create more accurate biomass maps of Kalimantan than currently available. AGB was estimated for 2007, 2009, and 2016 in order to give an overview of ongoing forest loss and to estimate changes between the three time steps in a more precise manner. Extensive field inventory and LiDAR data were used as reference AGB. A multivariate linear regression model (MLR) based on backscatter values, ratios, and Haralick textures derived from Sentinel-1 (C-band), ALOS PALSAR (Advanced Land Observing Satellite’s Phased Array-type L-band Synthetic Aperture Radar), and ALOS-2 PALSAR-2 polarizations was used to estimate AGB across the country. The selection of the most suitable model parameters was accomplished considering VIF (variable inflation factor), p-value, R², and RMSE (root mean square error). The final AGB maps were validated by calculating bias, RMSE, R², and NSE (Nash-Sutcliffe efficiency). The results show a correlation (R²) between the reference biomass and the estimated biomass varying from 0.69 in 2016 to 0.77 in 2007, and a model performance (NSE) in a range of 0.70 in 2016 to 0.76 in 2007. Modelling three different years with a consistent method allows a more accurate estimation of the change than using available biomass maps based on different models. All final biomass products have a resolution of 100 m, which is much finer than other existing maps of this region (>500 m). These high-resolution maps enable identification of even small-scaled biomass variability and changes and can be used for more precise carbon modelling, as well as forest monitoring or risk managing systems under REDD+ (Reducing Emissions from Deforestation, forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks) and other programs, protecting forests and analyzing carbon release.

Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects.

Peatlands in Indonesia are one of the primary global storages for terrestrial organic carbon. Poor land management, drainage, and recurrent fires lead to the release of huge amounts of carbon dioxide. Accurate information about the extent of the peatlands and its 3D surface topography is crucial for assessing and quantifying this globally relevant carbon store. To identify the most carbon-rich peatlands—dome-shaped ombrogenous peat—by collecting GPS-based terrain data is almost impossible, as these peatlands are often located in remote areas, frequently flooded, and usually covered by dense tropical forest vegetation. The detection by airborne LiDAR or spaceborne remote sensing in Indonesia is costly and laborious. This study investigated the potential of the ICESat-2/ATLAS LiDAR satellite data to identify and map carbon-rich peatlands. The spaceborne ICESat-2 LiDAR data were compared and correlated with highly accurate field validated digital terrain models (DTM) generated from airborne LiDAR as well as the commercial global WorldDEM DTM dataset. Compared to the airborne DTM, the ICESat-2 LiDAR data produced an R² of 0.89 and an RMSE of 0.83 m. For the comparison with the WorldDEM DTM, the resulting R² lay at 0.94 and the RMSE at 0.86 m. We model the peat dome surface from individual peat hydrological units by performing ordinary kriging on ICESat-2 DTM-footprint data. These ICESat-2 based peatland models, compared to a WorldDEM DTM and airborne DTM, produced an R² of 0.78, 0.84, and 0.94 in Kalimantan and an R² of 0.69, 0.72, and 0.85 in Sumatra. The RMSE ranged from 0.68 m to 2.68 m. These results demonstrate the potential of ICESat-2 in assessing peat surface topography. Since ICESat-2 will collect more data worldwide in the years to come, it can be used to survey and map carbon-rich tropical peatlands globally and free of charge.