Estimation of stem volume and tree structural attributes using backpack LiDAR: effects of leaf-on and leaf-off conditions

Mobile light detection and ranging (LiDAR) technologies offer a promising alternative to traditional forest inventory methods by enabling rapid acquisition of three-dimensional point cloud data. This study evaluates the performance of a backpack laser scanning system in terms of stem volume, diameter at breast height (dbh), tree height, and stem taper estimation in deciduous forests under contrasting phenological conditions (leaf-off and leaf-on). Two sample plots dominated by Betula pendula and Populus tremula in Tatra National Park (Slovakia) were scanned using a LiBackpack DGC-50 system, and LiDAR-derived estimates were compared with reference measurements from destructively sampled trees. The results showed that stem volume can be estimated directly from point clouds with acceptable accuracy for most forest inventory applications, without relying on allometric models. The highest accuracy was achieved under leaf-off conditions (RMSE = 0.03 m 3 ), whereas errors increased under leaf-on conditions, particularly in the Populus plot (up to 0.06 m 3 ). Despite this decline, no significant differences were detected between LiDAR-derived and reference stem volumes across seasons ( p > 0.05). dbh and tree height were also estimated more accurately under leaf-off conditions (RMSE = 0.97–1.87 cm and 0.91–0.93 m, respectively), although tree height was consistently underestimated (bias = −0.65 to −1.53 m) due to incomplete canopy representation. Overall, backpack laser scanning can be considered as a viable tool for estimating certain structural attributes in young- to mid-aged Betula and Populus stands; however, reliable stem taper reconstruction remains challenging in structurally complex trees because of occlusion and limited point density in upper stem sections. Another challenge is its limited feasibility for large-scale forest inventory applications, as operational forest inventories often require highly automated workflows.