Linking structural forest heterogeneity and ecological processes using Sentinel-2 and FAD-based zoning

Structural heterogeneity strongly influences forest ecological function, yet zone-specific spectral diagnostics remain limited. This study integrated Sentinel-2 imagery (2016, 2020, 2024) with field-observed ecological attributes across Foreground Area Density (FAD)-based structural zones in the Tuchola Forest Biosphere Reserve, Poland. The aim was to evaluate whether open Sentinel-2 vegetation indices can capture ecological variation across structurally distinct forest zones using interpretable machine-learning models. Correlation and cluster analyses of 17 vegetation indices revealed substantial multicollinearity, supporting the selection of a reduced set of spectrally distinct indices for modelling. Extra Trees (ET) and LightGBM (LGBM) produced comparable predictive performance, although ET achieved equal or lower RMSE values in most zone × year combinations and was retained for interpretation. Test-set RMSE remained below one degradation class (0.70-0.96), 2.04 moisture units, 2.35 site-type categories, and 34.4 years for stand age. Permutation importance and partial dependence analyses revealed clear zone-specific spectral-ecological relationships. Rare zones exhibited stronger stress-related spectral responses and greater variability in moisture and stand-age-related patterns, whereas Core zones displayed more stable response surfaces across years. NDRE emerged as the most consistent predictor across ecological attributes, while MCARI, NDMI, and CVI provided complementary information depending on the response variable. By combining FAD-based structural stratification, cluster-driven multicollinearity reduction, and interpretable ensemble learning, this framework provides a reproducible approach for linking spectral traits to ecological gradients across fragmentation contexts and supports open-data monitoring of fragmented forest landscapes.