Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy structure of trees in natural forests and the presence of occlusion in TLS data pose significant challenges to achieving this goal. In this study, we present a novel algorithm, L-Tree, for the construction of 3D tree models and the estimation of architectural traits from TLS data. This algorithm is grounded in the L-Median algorithm and integrates a tree skeleton optimization procedure that considers the structural characteristics of tree branches. By comparing modeling results and manually derived branch traits for 24 trees of 24 species, we found that the L-Tree algorithm achieved precision, recall, and F-score values of 0.94 for branch identification, coefficient of determination, root-mean-squared error, and normalized root-mean-squared error of 0.998, 0.068 m, and 0.3 % for branch length estimation, and a respective value of 0.958, 0.257 cm and 0.9 % for branch radius estimation. Additionally, the branch identification accuracy and accuracy in branch architectural trait estimation remained satisfactory across branch orders. Compared to established 3D tree model construction algorithms (e.g., TreeQSM), our L-Tree algorithm demonstrated a superior capability in handling noisy environments and data gaps, making it a robust tool for TLS data-based tree architecture studies. Leaf-wood separation emerged as a crucial step influencing the performance of the L-Tree algorithm. We observed significant drop in branch identification accuracy when using an automatic leaf-wood separation algorithm as input, highlighting the urgent need to develop effective leaf-wood separation algorithms to generate high-quality wood point clouds for tree architecture studies.

中文翻译：

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L1-Tree：一种使用地面激光扫描数据构建 3D 树模型并估计分支结构特征的新算法


分支结构为理解植物性状变异和树木响应环境所采用的适应性策略提供了重要信息。高保真地面激光扫描 (TLS) 数据为构建三维 (3D) 树木模型和评估建筑特征提供了准确、高效且无损的方法。然而，天然林中树木复杂的冠层结构以及 TLS 数据中遮挡的存在给实现这一目标带来了重大挑战。在本研究中，我们提出了一种新颖的算法 L-Tree，用于构建 3D 树模型并根据 TLS 数据估计架构特征。该算法以 L-Median 算法为基础，并集成了考虑树枝结构特征的树骨架优化过程。通过比较 24 个物种的 24 棵树的建模结果和手动导出的分支特征，我们发现 L-Tree 算法在分支识别、确定系数、均方根误差方面实现了 0.94 的精度、召回率和 F 得分值，分支长度估计的归一化均方根误差为 0.998、0.068 m 和 0.3%，分支半径估计的相应值为 0.958、0.257 cm 和 0.9%。此外，分支识别的准确性和分支架构特征估计的准确性在分支订单中仍然令人满意。与已建立的 3D 树模型构建算法（例如 TreeQSM）相比，我们的 L-Tree 算法在处理噪声环境和数据间隙方面表现出卓越的能力，使其成为基于 TLS 数据的树结构研究的强大工具。叶木分离成为影响 L 树算法性能的关键步骤。 当使用自动叶木分离算法作为输入时，我们观察到树枝识别精度显着下降，这凸显了迫切需要开发有效的叶木分离算法来生成用于树木结构研究的高质量木材点云。