Estimating Aboveground Biomass and Surface Fuels in Semi-Arid Oak–Pine Forests Using Sentinel-2 Spectral Indices and Gamma GLMs

Estimation of forest biomass and surface fuels is essential for wildfire risk assessment, carbon accounting, and ecosystem management in semi-arid forests. This study evaluated Sentinel-2 spectral indices and Gamma generalized linear models (GLMs) for estimating aboveground live biomass, forest floor biomass, and downed woody debris in oak–pine forests of Northern Mexico. Spectral predictors were grouped according to chlorophyll activity, vegetation vigor, physiological condition, and soil/background correction effects. Measurements of live biomass, forest floor biomass, and downed woody debris were linked to Sentinel-2 spectral information. Aboveground live biomass was most strongly associated with the chlorophyll-related index CIre8A (p < 0.0001), whereas both forest floor biomass and downed woody debris showed stronger relationships with indices associated with red–NIR reflectance gradients, vegetation senescence, and soil/background correction effects, including the Normalized Difference Vegetation Index (NDVI), the Alpha-weighted Red–NIR Vegetation Index (PVIα), the Normalized Pigment Chlorophyll Index (NPCI), and the Atmospherically Resistant Vegetation Index (ARVI) (p < 0.05). Single-index Gamma GLMs showed significant predictive relationships (p < 0.05), supporting the use of individual Sentinel-2 indices as practical predictors of biomass and surface fuels. In contrast, multivariate models combining several spectral indices were affected by collinearity and parameter instability. Principal Component Analysis (PCA) integrated four representative indices into a single orthogonal predictor. The first principal component (Prin1) explained 90.30%–95.25% of total spectral variance, eliminated collinearity effects (VIF = 1), and produced highly significant Gamma GLMs across all biomass components (p ≤ 0.0009). PCA-based models also yielded lower AIC and BIC values, providing the most parsimonious framework when multiple spectral predictors were combined. These results indicate that individual spectral indices offer practical alternatives for biomass estimation, whereas PCA provides a robust approach for integrating complementary spectral information while maintaining model stability.