The advancement of Light Detection and Ranging (LiDAR) technology has revolutionized forest inventory practices by providing precise and detailed data on forest structure. This study aims to compare the accuracy, efficiency, and cost-effectiveness of two LiDAR platforms, Unmanned Aerial Vehicles (UAV) LiDAR and Handheld Mobile Laser Scanners (MLS), in capturing key forest inventory parameters such as stem mapping, diameter at breast height (DBH), and tree height. This study was conducted in a pine forest located in Chalkidiki, Greece. LiDAR data were collected using UAV LiDAR (DJI Matrice 350 RTK with Zenmuse L2) and a Handheld Mobile Laser Scanner (Leica BLK2GO) over the same forest plots. Additionally, manual measurements were conducted to serve as ground truth data. The raw LiDAR data were processed to extract forest inventory parameters, including point-cloud classification, segmentation, and parameter extraction. The platforms were compared based on data accuracy, time efficiency, ease of use, and cost, with statistical analyses performed to evaluate the differences in measurements obtained from each platform against ground truth data. Preliminary results indicate that both LiDAR platforms can effectively capture forest inventory parameters, but with varying degrees of accuracy and efficiency. UAV LiDAR demonstrated high efficiency and coverage, particularly in capturing canopy structure. The handheld mobile laser scanner offered flexibility and detailed ground-level measurements, providing more information below the canopy, but was strongly affected by complex terrain. These technologies offer significant potential for creating comprehensive 3D forest structure models, which are essential for future forest inventories in Greece and can aid in sustainable forest management.