Forest Cover in the Congo Basin: Consistency Evaluation of Seven Datasets

Tropical forests play an essential role in the carbon and water cycles of terrestrial ecosystems, but they are increasingly threatened by human activities and climate change. For places where ground observations are scarce, like in Equatorial Africa, remote sensing is a key source of information for monitoring the temporal and spatial dynamics of forests over large areas. Several Earth Observation-based global maps were developed in recent decades using different definitions of the land-use/land-cover (LULC) classes. While such products are widely used for monitoring land use and planning land management, the consistency of these LULC maps for the Congo Basin has never been analyzed and quantified at the ecosystem level. Here, we selected seven of the most-used global maps and analyzed their consistency over the Congo Basin. After reclassification into forest/non-forest masks and spatial resampling, we assessed the agreement and disagreement percentage across the different tropical ecoregions of Africa, from moist forest to miombo, including savanna. The datasets showed differences in forest area as a function of spatial resolution, with higher forest area levels at coarser resolutions (e.g., from 74.1% to 88.5% forest cover when upscaling the GLCLU from 30 m to 1 km over the Congo Basin). A higher agreement between the datasets was found for forest area over moist forest (between 88.18% and 99.38%) in comparison to savanna (32.82%–99.84%) and miombo (53.83%–99.7%). These discrepancies led to large differences in forest cover, varying from a net loss of 205,704 km2 to a net gain of 50,726 km2 over 2001–2019 depending on the dataset used. This study draws attention to the uncertainty associated with these products with regard to forests, particularly in regions of biological importance, such as the miombo and savanna regions, which remain poorly understood. Indeed, the two major uncertainties affecting the quality of LULC products are related to the different spatial resolutions and biological definition of “forest” adopted by each product.