A LiDAR-Based Method for Incorporating Foliar Biomass in Aboveground Carbon Estimates in Tropical Forest Enrichment Plantations

Accurately quantifying aboveground biomass (AGB) in tropical forests remains challenging, particularly in regenerating stands where tree crown architecture, size structure, and species composition strongly diverge from those used to calibrate classical allometric equations. Here, we evaluate whether leaf biomass typically ignored in LiDAR workflows can be predicted and improve AGB estimation derived from LiDAR data. We combined destructive measurements on 83 trees with high-resolution Mobile LiDAR Scanner (MLS) point clouds to quantify biomass. We also calibrate leaf mass models and assess the contribution of foliar biomass to total AGB. Stems accounted for most biomass (65%), while leaves contributed only 3% on average. Among models tested, the model 3, integrating DBH, projected crown area and wood density showed the best adjustment (R² = 54.4%; RMSE = 2.43 kg). This model outperformed both regional (-20.4%) and pantropical (-25.6%) allometric equations. This work highlights the importance of canopy features in forest carbon assessments and refines the use of LiDAR data as a robust alternative for estimates and monitoring AGB across tropical forests.