Adaptive Shortest Path Tracking for Robust Leaf–Wood Separation in Individual Trees from TLS Point Clouds

Abstract

Leaf-wood separation is crucial for single-tree aboveground biomass estimation and 3D reconstruction. Although the non-destructive and efficient acquisition of fine-grained, high-density point cloud data can be performed using terrestrial laser scanning (TLS) technology, existing methods suffer from various drawbacks, including insufficient detection of fine branches, limited robustness to point cloud subsampling, and weak adaptability across different tree species and crown structures. A core issue lies in the over-reliance on prior values for key algorithm parameters. This study proposes an adaptive shortest path tracking for robust leaf–wood separation (ASPTS) in individual trees. First, a graph is constructed, and the shortest path backtracking is employed to extract skeleton points. Second, an improved k-nearest neighbor (KNN) algorithm is proposed to adaptively optimize the number of neighboring points based on the shortest path, thereby obtaining initial wood points. Third, the feature descriptor construction for characterizing trunk and branch structures is optimized using principal component analysis (PCA) by implementing an enhanced adaptive neighborhood radius selection strategy. Finally, final wood points are extracted using a region-growing approach guided by a stepwise feature thresholding scheme. Twenty-two individual trees, which represent different species, heights, and crown structures, are selected as test subjects. The results demonstrate the capability of ASPTS to make a good balance between type I and type II errors. ASPTS consistently exhibits strong fine-branch detection capability and robust performance under varying conditions, including different tree species, crown structures, and point cloud densities. ASPTS demonstrates superior performance compared to four state-of-the-art methods.

Plant phenotyping relevance

TLS点群から個体樹木の葉・木部を分離し、枝構造やバイオマス推定・3D再構成に用いる新規アルゴリズムを開発・比較検証しており、植物形態の取得・抽出が中心である。

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