Potential tree cover under current and future climate scenarios.

Forests play a key role in the global commitments to reach carbon neutrality in the coming decades. Global maps of potential tree cover at high spatial resolution for current and future climate scenarios are needed to assess the risk of future forest carbon loss and carbon storage potential through afforestation/reforestation projects. Here, we present data integrating satellite-based tree cover observations into a machine learning framework to estimate tree cover carrying capacity (percentage of tree coverage), which reflects the maximum potential tree cover, accounting for natural disturbances. Our model improves upon previous estimates by reducing prediction errors, better aligning with tree cover observations in intact areas, and lowering spatial variance in areas without topographical variation. However, uncertainties remain, particularly in regions where human activity has significantly altered landscapes. The tree cover carrying capacity provides an estimate of potential tree cover based on climatic and soil conditions. This serves as an initial step in identifying afforestation/reforestation opportunities but should be further assessed for land-use competition, ecological feasibility, and other limitations.