Advancing Sustainable Forest Management in Darkhan-Uul province, Mongolia using the Spectral Forest Index (SFI)

Abstract. Forest ecosystems in semi-arid boreal regions, such as those in Darkhan-Uul province, Mongolia, serve as critical reservoirs of biodiversity and carbon while facing escalating anthropogenic and climatic pressures. Despite covering 22.4% (~733 km2) of the province, these birch- and larch-dominated forests exhibit declining resilience due to unsustainable land-use practices, illegal logging, and climate-induced disturbances, necessitating advanced monitoring frameworks for sustainable forest management (SFM). This study introduces the Spectral Forest Index (SFI), a novel composite metric derived from Sentinel-2 multispectral data within a Google Earth Engine (GEE) platform, to quantify spatiotemporal variations in forest health, productivity, and species composition. By integrating normalized difference and ratio-based indices (e.g., NDVI, RVI), the SFI synthesizes canopy structural attributes, photosynthetic activity, and biomass dynamics across monthly intervals (May–October 2020–2024), enable monitoring of forest cover, health, and species composition, with quality control measures ensuring data reliability. Results reveal pronounced spatial heterogeneity in forest degradation, with SFI depressions strongly correlated with overgrazing and anthropogenic land conversion, while regenerative trajectories align with targeted reforestation initiatives. The SFI’s sensitivity to ecological stressors (e.g., drought, pest infestations) underscores its utility as a scalable, policy-relevant tool for monitoring carbon sequestration potential and guiding adaptive management. This research advances remote sensing applications in SFM, offering a transferable framework for reconciling ecological preservation with socio-economic demands in vulnerable boreal ecosystems.