Extraction Of Leaf Attributes And Reconstruction Of Single Tree Based On Terrestrial Laser Scanning Data

Leaf attribute estimation is crucial for understanding photosynthesis,respiration,transpiration,and carbon and nutrient cycling in vegetation and evaluating the biological parameters of plants or forests.Terrestrial laser scanning(TLS)has the capability to provide detailed characterisations of individual trees at both the branch and leaf scales and to extract accurate structural parameters of stems and crowns.In this paper,we developed a computer graphic-based 3D point cloud segmentation approach for accurately and efficiently detecting tree leaves and their morphological features(i.e.,leaf area and leaf angle distributions(leaf azimuthal angle and leaf inclination angle))from single leaves.To this end,we adopted a sphere neighbourhood model with an adaptive radius to extract the central area points of individual leaves with different morphological structures and complex spatial distributions;meanwhile,four auxiliary criteria were defined to ensure the accuracy of the extracted central area points of individual leaf surfaces.Then,the density-based spatial clustering of applications with noise(DBSCAN)algorithm was used to cluster the central area points of leaves and to obtain the centre point corresponding to each leaf surface.We also achieved segmentation of individual leaf blades using an advanced 3D watershed algorithm based on the extracted centre point of each leaf surface and two morphology-related parameters.Finally,the leaf attributes(leaf area and leaf angle distributions)were calculated and assessed by analysing the segmented single-leaf point cloud.To validate the final results,the actual leaf area,leaf inclination and azimuthal angle data of designated leaves on the experimental trees were manually measured during field activities.In addition,a sensitivity analysis investigated the effect of the parameters in our segmentation algorithm.The results indicate that the proposed method is effective and operational for providing accurate,detailed information on single leaves and vegetation structure from scanned data.This capability facilitates improvements in applications such as the estimation of leaf area,leaf angle distribution and biomass.The complexity of the geometric structure of the 3D tree makes the construction process of the real 3D tree model very complicated.Many studies have been focused on the reconstruction of the TLS-based 3D trunk structure,but the leaves are an important part of the tree due to the complexity of the structure and The problem of large data volume is often simplified to the operation of simulating the overall tree crown as a geometric model.Therefore,this paper has developed a simple method for reconstructing real three-dimensional trees(including trunks and leaves).This method is based on the combined information of the point cloud spatial distribution related to the accurately extracted feature parameters of the crown and the hierarchical branch structure parameters of the trunk,thus Realize accurate modeling of a single tree.We implemented 3D reconstruction operations on three real trees(including trunk and crown).The results show that the 3D reconstruction model has good consistency with the measured point cloud data.The reconstructed tree can meet the basic requirements of the computer simulation model,and Keep the gap score between the forest and the real tree highly similar.Although this method is limited by the point occlusion effect,the method has achieved satisfactory consistency both visually and in the quantitative evaluation of three-dimensional structures.