论文摘要：

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.