Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed assess potential multispectral LiDAR data for estimating these a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) non-destructive 156) field measurements were collected from borisii-regis trees develop allometric equations each crown component enrich reference with non-destructively sampled trees. A set machine learning regression algorithms, including random (RF), support vector (SVR) Gaussian process (GP), tested individual-tree-level DB, NB BB using LiDAR-derived height intensity metrics different spectral channels green, NIR merged) predictors. results demonstrated that RF algorithm achieved best overall predictive performance DB (RMSE% = 17.45% R2 0.89), 17.31% 0.93) 24.09% 0.85) green channel. showed particularly when utilizing channel, accurately estimated conifer trees, specifically fir. Overall, can provide accurate estimates coniferous forests, further exploration this method’s applicability diverse structures biomes is warranted.