Uncovering the Hidden Carbon Treasures of the Philippines’ Towering Mountains: A Synergistic Exploration Using Satellite Imagery and Machine Learning

Tropical montane forests (TMFs) are highly valuable for their above-ground biomass (AGB) and their potential to sequester carbon, but they remain understudied. Sentinel-1, -2, biophysical data and Machine Learning were used to estimate and map the AGB and above-ground carbon (AGC) stocks in Benguet, Philippines. Non-destructive field AGB measurements were collected from 184 plots, revealing that pine forests had 33.57% less AGB than mossy forests (380.33 Mgha⁻¹), whilst the grassland summit had 39.93 Mgha⁻¹. In contrast to the majority of literature, AGB did not decrease linearly with elevation. NDVI, LAI, fAPAR, fCover and elevation were the most effective predictors of field-derived AGB as determined by Random Forest (RF) feature selection in R. WEKA was used to evaluate and validate 26 Machine Learning algorithms. The results show that the Machine Learning K star (K*) (r = 0.213–0.832; RMSE = 106.682 Mgha⁻¹–224.713 Mgha⁻¹) and RF (r = 0.391–0.822; RMSE = 108.226 Mgha⁻¹–175.642 Mgha⁻¹) exhibited high modelling capabilities to estimate AGB across all predictor categories. Consequently, spatially explicit models were carried out in Whitebox Runner software to map the study site’s AGB, demonstrating RF with the highest predictive performance (r = 0.982; RMSE = 53.980 Mgha⁻¹). The study area’s carbon stock map ranged from 0 to 434.94 Mgha⁻¹, highlighting the significance of forests at higher elevations for forest conservation and carbon sequestration. Carbon-rich mountainous regions of the county can be encouraged for carbon sequestration through REDD + interventions. Longer wavelength radar imagery, species-specific allometric equations and soil fertility should be tested in future carbon studies. The produced carbon maps can help policy makers in decision-planning, and thus contribute to conserve the natural resources of the Benguet Mountains.