Individual tree branch and leaf metrics extraction in dense plantation scenario through the fusion of drone and terrestrial LiDAR

Abstract

In forest ecosystems, branch and leaf structures play crucial roles in hydrological and vegetative physiology. However, accurately characterizing branch and leaf structures in dense forest scenarios is challenging, limiting our understanding of how branch and leaf structures affect processes such as interception loss, stemflow, and throughfall. Both terrestrial and drone LiDAR technologies have demonstrated impressive performances in providing detailed insights into forest structures from different perspectives. By leveraging the fusion of point clouds, we classified the leaf and branch of three Japanese cypress trees. Leaf points occupied voxel space was calculated using voxelization, visible branches were fitted using line segments, and the angles and lengths of the invisible branches within the canopy were estimated using the tree-form coefficient. The quantitative analysis results showed that leaf points occupied voxel space at the single-tree and plot scales average were 0.89 ± 0.42 m³/m². Then, 82, 53, and 58 visible branches were fitted and 23, 14, and 12 invisible branches were estimated for the three trees, respectively. Destructive harvesting was conducted on a single tree to assess the accuracy of branch identification and parameter extraction at the individual branch level. The results yielded an $F1-score$ of 0.76 for branch identification and nRMSEs of 32.14 % for branch length and 13.68 % for branch angle, respectively. Our method solves the problem of extracting the branch and leaf structures of single trees in dense forest scenarios with heavy occlusion. The reconstructed tree model can be further applied to estimate tree attributes and canopy hydrology simulations accurately.