Speaker
Description
Precision forestry seeks to improve the sustainability and efficiency of forest management by applying new technologies to characterise individual trees. Accurate tree-level characterisation, typically through remote sensing, facilitates optimising timber yield and minimising waste. To that end, optimal bucking methods can be applied given accurate enough estimates of tree features. Among these features, stem shape and branching patterns are critical for stem bucking, as they determine the wood quality and the dimension and amount of products obtained. Yet, deriving this information from 3D point clouds remains challenging due to noise, non-circular cross-sections, and the difficulty of separating stems from branches.
In this study, we developed a novel stem shape characterisation and bucking method for tree point clouds capable of accounting for noisy data and irregular or curved trunks. Furthermore, we integrated tree branching information to estimate branch insertion points, find co-dominant or forked trunks, and gauge crown size. To do so, we leveraged graph theory to segment tree branches and isolate stem points. We then fit robust splines recursively across and along the stem, capturing curvature and irregular growth patterns through a recursive procedure. The resulting stem geometry and branching metrics served as inputs to the optimal bucking algorithm. We validated the proposed method on simulated point clouds, as well as LiDAR data acquired using a Mobile Laser Scanner (MLS) in Outokumpu, Finland. Our method successfully integrates branching patterns into bucking solutions, advancing tree-level decision-making. Moreover, it provides valuable insights into tree morphology for forest resources and biodiversity inventorying.
| Keywords | LiDAR; bucking; segmentation; reconstruction |
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