A Comparative Analysis of UAV LiDAR and Mobile Laser Scanning for Tree Height and DBH Estimation in a Structurally Complex, Mixed-Species Natural Forest

Accurate measurement of tree height and diameter at breast height (DBH) is essential for forest inventory, biomass estimation, and habitat assessment but remains challenging in structurally complex, multi-layered forests. This study evaluates the accuracy and operational feasibility of Unmanned Aerial Vehicle (UAV) LiDAR and Mobile Laser Scanning (MLS) for estimating tree height and DBH in such stands with a diverse structure in the Romanian Carpathians. Field measurements from six plots encompassing mixed-species (Fagus sylvatica L., Abies alba Mill., Picea abies (L.) H.Karst.) and single-species (Picea abies) stands were compared against UAV- and MLS-derived metrics. MLS delivered near-inventory-grade DBH accuracy across all species (R2 up to 0.98) and reliable height estimates for intermediate and suppressed trees, while UAV LiDAR consistently underestimated tree height, especially in dense, multi-layered stands (R2 < 0.2 in mixed plots). Voxel-based occlusion analysis revealed that over 93% of area under canopy and interior crown volume was captured only by MLS, confirming its dominance below the canopy, whereas UAV LiDAR primarily delineated the outer canopy surface. Species traits influenced DBH accuracy locally, but structural complexity and canopy layering were the main drivers of height underestimation. We recommend hybrid UAV–MLS workflows combining UAV efficiency for canopy-scale mapping with MLS precision for stem and sub-canopy structure. Future research should explore multi-season acquisitions, improved SLAM robustness, and automated data fusion to enable scalable, multi-layer forest monitoring for carbon accounting, biodiversity assessment, and sustainable forest management decision making.