The Use of Very High-Resolution Satellite Data and the InVEST Model to Analyse Carbon Stock in the Budongo Forest Reserve, Uganda

Background: Forests, especially tropical forests, act as major carbon sinks and regulate the atmospheric carbon content. Conventional methods for quantifying carbon stocks are highly dependent on the accuracy of spatial mapping of land use and land cover (LULC). Recent developments in high-resolution remote sensing technology have increased the potential to produce accurate LULC classifications as a prerequisite for assessing such carbon stocks. This research examines the advantages of remote sensing techniques to support accurate LULC mapping via satellite imagery at 3 m spatial resolution with 8 spectral bands. Results: PlanetScope images were used for the LULC classification for the Budongo Forest Reserve area in Uganda. The classification performance was validated for accuracy based on ground truth data, yielding a kappa coefficient of 0.80. Using the detailed LULC map, the InVEST model was used to determine carbon stock estimates. Aboveground biomass estimation was achieved by combining GEDI LiDAR data with vegetation indices derived from PlanetScope imagery. The total carbon stock estimate from these approaches for the Budongo Forest Reserve area was 11,120,727 MgC, and the average density was 136 MgC/ha. Conclusions: These findings highlight the value of high spatial resolution satellite data for improving our understanding of carbon stock estimation and can be used to facilitate comprehensive strategies for the effective management of terrestrial carbon stocks.