Uncovering Fragmentation Patterns: Optimal Vegetation Indices for Monitoring the Tuchola Forest Ecosystem

Forest fragmentation disrupts habitat continuity, reshapes ecosystem processes, and threatens biodiversity. Effective conservation efforts in fragmented landscapes rely on precise monitoring of these changes. This study leverages remote sensing through vegetation indices to evaluate forest health and detect fragmentation-induced alterations over time. Focusing on the Tuchola Forest in Poland, an area increasingly affected by windstorms, we analyzed Sentinel-2 imagery from 2016 to 2024 using 19 vegetation indices. Machine learning classifiers—Extra Trees, Random Forest, and LightGBM—were employed to assess which indices best capture fragmentation stress. The Extra Trees classifier outperformed the others in accuracy and generalization, identifying NDWI and GNDVI as the most effective indicators. These indices were particularly responsive to shifts in vegetation water content and canopy density linked to fragmentation. Our findings underscore the utility of targeted vegetation indices for precise ecological monitoring and inform conservation strategies in fragmented forests.