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Global terrestrial biodiversity hotspots (GBH) represent areas featuring exceptional concentrations of endemism and habitat loss in the world. Unfortunately, geospatial data of natural habitats of the GBHs are often outdated, imprecise, and coarse, and need updating for improved management and protection actions. Recent developments in satellite image availability, combined with enhanced machine learning algorithms and computing capacity, enable cost-efficient updating of geospatial information of these already severely fragmented habitats. This study aimed to develop a more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, and to update the knowledge on its spatial extent, level of fragmentation, and conservation status. We tested 1023 model possibilities stemming from a combination of Sentinel-1 (S1) and Sentinel-2 (S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series, and topographic information, using random forest modelling approach. We compared the best CCEF model with existing spatial forest products from the EAM through independent accuracy assessment. Finally, the CCEF model was used to estimate the fragmentation and conservation coverage of the EAM. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296,000 ha of Eastern Arc Forests (EAF) left. Furthermore, acknowledging small forest fragments (1–10 ha) implies that the EAFs are more fragmented than previously considered. Currently, the official protection of EAFs is disproportionally targeting well-studied mountain blocks, while less known areas and small fragments are underrepresented in the protected area network. Thus, the generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans to balance this situation. The results highlight that spatial texture of S2, seasonality, and topography are the most important variables describing the EAFs, while spatial texture of S1 increases the model performance slightly. All in all, our work demonstrates that recent developments in Earth observation allows significant enhancements in mapping, which should be utilized in areas with outstanding biodiversity values for better forest and conservation planning.

Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots