Estimating Forest Aboveground Biomass in Tropical Zones by Integrating LiDAR and Sentinel-2B Data

This study developed an integrated approach for estimating tropical forest aboveground biomass (AGB) by combining UAV–LiDAR structural metrics and Sentinel-2B spectral data, optimized through successive projections algorithm (SPA) feature selection and random forest (RF) regression. Field surveys across three tropical forest sites in Hainan Province (49 plots) provided ground-truth AGB measurements, while UAV–LiDAR (1 m resolution) and Sentinel-2B (10 m) data were processed to extract 98 and 69 features, respectively. The results showed that LiDAR-derived elevation metrics (e.g., percentiles and kurtosis) correlated strongly with the AGB measurements (r = 0.652–0.751), outperforming Sentinel-2B vegetation indices (max r = 0.520). SPA–RF models with selected features significantly improved accuracy compared to full-feature RF, achieving R2 = 0.670 (LiDAR), 0.522 (Sentinel-2B), and 0.749 (coupled data), with the fusion model reducing errors by 46–54% in high-biomass areas. Despite Sentinel-2B’s spectral saturation limitations, its integration with LiDAR enhanced spatial heterogeneity representation, particularly in complex canopies. The 200-iteration randomized validation ensured a robust performance, with mean absolute relative errors of ≤0.071 for fused data. This study demonstrates that strategic multi-sensor fusion, coupled with SPA-optimized feature selection, significantly improves tropical AGB estimation accuracy, offering a scalable framework for carbon stock assessments in support of Reducing Emissions from Deforestation and Forest Degradation (REDD+) and climate mitigation initiatives.