Automatic Mapping of 10 m Tropical Evergreen Forest Cover in Central African Republic with Sentinel-2 Dynamic World Dataset

This dataset provides annual 10-m resolution maps of tropical evergreen forest cover in the Central African Republic (CAR) for the period 2017–2023. The Central African Republic hosts some of the most biodiversity-rich and conservation-critical tropical evergreen forests on Earth, making fine-scale, up-to-date mapping essential for ecological research, biodiversity conservation, and land management. The maps were generated using a novel automated method that leverages the Sentinel-2 Dynamic World dataset. Stable evergreen forest samples were first identified using the Copernicus Global Land Cover Layers (CGLC) and Global Forest Change (GFC) products. Initial evergreen forest cover maps were then produced by applying year-specific thresholds to the annual median forest cover probability layers derived from Dynamic World. Final refined maps were obtained by integrating a Non-Evergreen Forest Index (NEFI) with the thresholded initial maps. Independent accuracy assessment shows that the maps achieve an overall accuracy >94.10%, Cohen’s Kappa >87.63%, and F1-score >94.05% across all years, substantially outperforming existing products such as the CGLC evergreen forest layers and simple median probability maps from Dynamic World. These annual 10-m maps offer detailed spatial representation of evergreen forest extent and consistent temporal tracking of changes from 2017 to 2023 in the Central African Republic, providing a valuable resource for studying tropical forest dynamics, monitoring deforestation and degradation, and supporting conservation planning in this ecologically sensitive region.