Utilising mobile laser scanning point clouds to assess harvesting quality in thinning stands

Forestry is entering a new era where precision and innovation converge through advanced mobile laser scanning (MLS) technologies. Traditional methods of assessing harvesting quality, often manual, time-consuming, and prone to human error, are being replaced by objective, data-driven approaches. In this study, we conducted high-resolution point cloud scanning across four forest stands (11 ha) in Central Finland using the handheld GeoSLAM ZEB Horizon LiDAR system. We aimed to evaluate the capacity of MLS to measure harvesting attributes related to stand density, tree dimensions, and strip road characteristics, to assess the impact of the Ponsse Plc Thinning Density Assistant (TDA), and to detect defective tree stems. Within a 5-hectare subset, 11 potentially anomalous trees were identified. A spatially precise tree map was created using QGIS and a separate map application, enabling comparison between manual field measurements and digital measurements. The findings indicate a strong concordance between automated and traditional assessments. With few exceptions, the results were consistent with established Best Practices for Sustainable Forest Management. Preliminary tests of a novel algorithm for curved stem detection further suggest the potential of MLS for automated defect recognition. A strip road width model was also developed to estimate the average strip road width within the forest stand. These findings underscore MLS as a powerful tool for enhancing accuracy, efficiency, and objectivity in modern forest management. ● Defect identification is accurate, but tree detection deteriorates at low densities. ● Missed detections are caused by crown occlusion or suboptimal parameter settings. ● Lower point density reduces detected trees but not defect classification accuracy. ● Point clouds and derived tree maps reliably assess thinning quality. ● Thinning quality meets Finnish best-practice forest management guidelines.