Abstract Tree architecture, defined as the three-dimensional arrangement of tree above-ground elements, directly influences the biological and physical processes of vegetation such as photosynthesis and evapotranspiration. Accurate description of tree architecture is of central importance to understand the above biophysical processes. Terrestrial laser scanning (TLS) has been proved to be a promising tool to quantitatively describe tree architecture parameters. However, previous studies using TLS usually focused on architectural parameter measurements at individual tree, crown scale and leaf scales. Very few studies have achieved a comprehensive quantitative description of branch architecture (including angle, diameter, length and volume). In this study, we improved the Laplacian-Based Contraction skeletonization algorithm using the Dijkstra algorithm, developed a new path discrimination method to identify and encode branch orders, and retrieved branch architecture parameters based on branch order and topology information. To assess the influence of branching complexity and branching pattern on the estimation accuracy, we scanned 15 different sized magnolia trees without a leading stem and simulated 10 different sized trees with a leading stem. Results showed the overall branch order identification and parameters retrieval accuracy of trees with a leading stem was obviously higher than trees without a leading stem. The identification accuracy of branch order decreased with the increase in the number of branch and tree branching complexity. The estimated branch architecture parameters agreed well with ground truth measurements (R2 up to 0.99), except for the second- and third-order branch volume. Compared with branch angle and diameter, branch length showed the best correlations with manually measured values (0.14 vs 0.002, 8.48 in RMSE; 0.99 vs 0.99, 0.78 in R2). The second-and third-order branch volume estimations were highly underestimated compared with the ground truth values (R2 = 0.53, RMSE = 0.0239 and R2 = 0.70, RMSE = 0.0257 respectively). This study demonstrated that TLS was an effective way to retrieve branch architecture parameters and provided a useful tool for comprehensive studies of biophysical processes and metabolic theories in ecology.

中文翻译：

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使用拉普拉斯算法从地面激光扫描数据中检索树枝结构属性

摘要 树木建筑，定义为树木地上元素的三维排列，直接影响植被的光合作用和蒸发蒸腾等生物和物理过程。准确描述树结构对于理解上述生物物理过程至关重要。地面激光扫描 (TLS) 已被证明是定量描述树木结构参数的一种很有前途的工具。然而，以前使用 TLS 的研究通常侧重于单个树木、树冠尺度和叶尺度的建筑参数测量。很少有研究实现了对分支结构（包括角度、直径、长度和体积）的全面定量描述。在这项研究中，我们使用 Dijkstra 算法改进了基于 Laplacian 的收缩骨架化算法，开发了一种新的路径鉴别方法来识别和编码分支顺序，并根据分支顺序和拓扑信息检索分支架构参数。为了评估分枝复杂性和分枝模式对估计精度的影响，我们扫描了 15 棵不同大小的没有主干的木兰树，并模拟了 10 棵不同大小的有主干的树。结果表明，有主干树的整体枝序识别和参数检索准确率明显高于无主干树。分支顺序的识别精度随着分支数量和树分支复杂度的增加而降低。除了二阶和三阶分支体积外，估计的分支架构参数与地面实况测量结果非常吻合（R2 高达 0.99）。与分支角度和直径相比，分支长度与手动测量值的相关性最好（0.14 对 0.002，RMSE 为 8.48；R2 为 0.99 对 0.99，0.78）。与真实值相比，二阶和三阶分支体积估计值被严重低估（分别为 R2 = 0.53，RMSE = 0.0239 和 R2 = 0.70，RMSE = 0.0257）。这项研究表明，TLS 是检索分支架构参数的有效方法，并为生态学中生物物理过程和代谢理论的综合研究提供了有用的工具。与分支角度和直径相比，分支长度与手动测量值的相关性最好（0.14 对 0.002，RMSE 为 8.48；R2 为 0.99 对 0.99，0.78）。与真实值相比，二阶和三阶分支体积估计值被严重低估（分别为 R2 = 0.53，RMSE = 0.0239 和 R2 = 0.70，RMSE = 0.0257）。这项研究表明，TLS 是检索分支架构参数的有效方法，并为生态学中生物物理过程和代谢理论的综合研究提供了有用的工具。与分支角度和直径相比，分支长度与手动测量值的相关性最好（0.14 对 0.002，RMSE 为 8.48；R2 为 0.99 对 0.99，0.78）。与真实值相比，二阶和三阶分支体积估计值被严重低估（分别为 R2 = 0.53，RMSE = 0.0239 和 R2 = 0.70，RMSE = 0.0257）。这项研究表明，TLS 是检索分支架构参数的有效方法，并为生态学中生物物理过程和代谢理论的综合研究提供了有用的工具。0239 和 R2 = 0.70，RMSE = 0.0257）。这项研究表明，TLS 是检索分支架构参数的有效方法，并为生态学中生物物理过程和代谢理论的综合研究提供了有用的工具。0239 和 R2 = 0.70，RMSE = 0.0257）。这项研究表明，TLS 是检索分支架构参数的有效方法，并为生态学中生物物理过程和代谢理论的综合研究提供了有用的工具。