Traditional forest inventory requires substantial manual effort and resources for measuring dendrometric parameters. This study compares field-based inventory with two automated remote-sensing approaches in Eucalyptus urograndis plantations: Canopy Height Model (CHM) analysis and direct LiDAR point cloud processing. Both methods employ robust preprocessing pipelines including statistical outlier filtering (median + 3 MAD), multi-scale peak detection, and watershed segmentation for individual tree delineation. CHM processing utilises morphological operations and dynamic Gaussian smoothing, whilst LiDAR analysis applies DBSCAN clustering on height-normalised point clouds. Evaluation across two commercial stands revealed that CHM slightly overestimated mean tree height (+10.5% and +1.6%), whereas LiDAR underestimated (–4.6% and –4.2%), both remaining within acceptable operational tolerances (±10%). Volume estimates achieved high precision, with maximum deviation of 6.7% from manual measurements. Tree density was consistently underestimated by 10% across both methods. Despite these systematic biases, both approaches demonstrated operational viability for commercial forest inventory, offering substantial improvements in efficiency and spatial coverage whilst maintaining accuracy comparable to traditional sampling methods.