Stem-Level Bucking Pattern Optimization in Chainsaw Bucking Based on Terrestrial Laser Scanning Data

Cross-cutting of a tree into set assortments (»bucking pattern«) presents large potential for optimizing the volume and value recovery; therefore, bucking pattern optimization has been studied extensively in past. However, it not seen widespread adoption chainsaw bucking, where time consuming costly manual measurement input parameters is required taper curve estimation. The present study investigated an alternative approach, curves are fit based on terrestrial laser scanning data (TLS), how deviations from observed (REF) affect result optimization. In addition, performance TLS was compared to traditional, segmental estimation approach (APP) experienced operator’s solution (CHA).A mature Norway Spruce stand surveyed by stationary scanning. TLS, were mixed-effects B-spline regression stem diameters extracted 3D point cloud data. A network analysis technique algorithm used during harvesting. Stem diameter profiles obtained measurement. former post-operation REF same as TLS. APP part For 35 trees, tree, trunk crown section level. patterns optimized with 30 CHA operation level.Taper estimated high accuracy precision (underestimated 0.2 cm average (SD=1.5 cm); RMSE=1.5 cm) outperformed APP. Volume recovery marginally higher (0.6%; 0.9%) than level, while substantial differences (–6.1%; –4.1%). Except cumulated nominal length, no significant between inferior throughout. significantly (2.1%; 2.4%), but mainly due small disadvantage algorithm.The proved provide highly accurate precise estimations curves. Therefore, can be considered further step towards increased accuracy, efficiency bucking.