Quantifying architectural uniqueness of Scots pine trees using terrestrial laser scanning: toward individual tree fingerprinting

Abstract Tree architecture reflects a hierarchical growth pattern shaped by the interplay between genetics and the environment. Environmental variation leads to unique resource availability, resulting in each tree developing distinct structural features, akin to the uniqueness of a human fingerprint. In this study, we propose a nondestructive method for quantifying this architectural uniqueness using terrestrial laser scanning for tree identification. While tree identification is commonly based on their precise geospatial location, this information may not always be available. Instead, we hypothesized that a tree’s stem profile (diameters along the stem) and branching arrangement (locations of branch origins on the stem surface) could distinguish individuals within a population. The experimental setup included 65 Scots pine (Pinus sylvestris L.) trees in a managed boreal forest stand, scanned with terrestrial laser scanning in September 2021 (T1) and November 2022 (T2). We investigated whether individual trees could be identified based on architectural similarities between their point cloud reconstructions from T1 and T2. In total, 52 trees (80.0%) were identified based on their architectural characteristics. The results supported our hypothesis, showing that identifying ≥10 branch origins from independent reconstructions was sufficient to establish architectural uniqueness, resulting in 100% identification accuracy (n = 20 trees). These findings suggest that the complex three-dimensional tree architecture can be condensed into a two-dimensional pattern of points representing branch arrangement, which we term the “tree fingerprint.” These architectural characteristics, which can be reconstructed from the lower half of the tree, are well suited for acquisition via ground-based sensing techniques such as terrestrial or mobile laser scanning. If point cloud data capable of characterizing individual branches is acquired during forest operations, the proposed methodology can facilitate tree identification for applications such as wood tracking, even without geospatial coordinates.