The use of very high-resolution satellite data and the InVEST model to analyse carbon stock in the Budongo Forest Reserve, Uganda

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 eight spectral bands. PlanetScope images were used for the LULC classification for the Budongo Forest Reserve (BFR) area in Uganda. Using the detailed LULC map, the InVEST model was employed to estimate carbon stocks. Aboveground biomass estimation was achieved by combining GEDI LiDAR data with vegetation indices derived from PlanetScope imagery. The study produced a 3-metre-resolution LULC map. The classification performance was validated for accuracy based on ground-truth data, yielding a kappa coefficient of 0.80. Aboveground biomass mapping achieved 3 m resolution with an R² value of 0.84. The total carbon stock estimate for the BFR area, derived from these approaches, was 11 120 727 Mg C, with an average density of 136 Mg C ha−1. These findings underscore the importance of high-spatial-resolution satellite data in enhancing our understanding of carbon stock estimation and can be utilised to inform comprehensive strategies for the effective management of terrestrial carbon stocks.