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The separation of leaf and wood points remains challenging due to the diversity of tree species and structures. We propose an automatic leaf-wood separation method from tree point clouds, leveraging trees' shape information, growing patterns, and topological information for higher separation accuracy. First, the tree point clouds are roughly classified into initial planar, linear, and scattered points based on the dimensional structure of each point. Then, a slicing strategy combined with the DBSCAN (density-based spatial clustering of application with noise) clustering algorithm is used to exploit the potential wood clusters in each layer. A novel dynamic region growing algorithm based on an energy function is proposed for extracting tree trunk and large branches from initial planar points, incorporating the geometric features and growing patterns of trees. Finally, a novel inner-to-outer region growing method is proposed to extract the tiny branches based on voxel density and the principal direction. Twelve tree samples covering twelve species and locating tropical, temperate, and boreal areas are used to evaluate the method's performance. The mean separation accuracy and kappa coefficient of the proposed method are 0.942 and 0.805, respectively. Experimental results indicate that the proposed method exhibits a strong ability to distinguish the wood and leaf points for various trees with different species and structures. [ABSTRACT FROM AUTHOR]

While forest biodiversity faces a concerning decline, modern technology presents promising avenues for mitigation. However, a critical gap persists in reconciling ecological knowledge with the technical expertise required to use state-of-the-art technologies in 3D data classification. Currently, one main issue is the scarcity of 3D datasets for biodiversity, particularly within the context of machine learning applications. Unlike the straightforward classification of human-made structures, forest environments are uniquely intricate and nuanced due to its inherently complex nature. This study addresses this challenge by introducing a fully automated pipeline for tree stem 3D point cloud segmentation, focussing on a biodiversity indicator: tree-related microhabitats (TreMs). Furthermore, our research advances the field by demonstrating that machine learning models trained with labels generated by our proposed virtual reality (VR) method, Labelling Flora, yield predictions statistically similar to the traditional desktop-based labelling methods. This implies that existing 3D datasets could be augmented using the more rapid approach of VR labelling. Additionally, the findings of this paper demonstrate the potential integration of VR and immersive technology into the 3D labelling workflow, facilitating a quicker and more intuitive labelling process. This could empower users, who are non-familiar with 3D modelling, to contribute their expertise to the segmentation process.

In response to the slow processing speed and low reconstruction accuracy encountered during the 3D reconstruction of trees, a method for 3D reconstruction of single-tree skeletons using laser point cloud data is proposed. Firstly, a combination filtering method is determined based on the point cloud data type to remove outliers and ground points. Secondly, a hybrid registration algorithm based on ISS (Intrinsic Shape Descriptor) and CPD (Coherent Point Drift algorithm), called IS-CPD ( Intrinsic Shape-Coherent Point Drift), is employed to obtain complete point cloud data for individual trees. Finally, a method combining Laplace contraction of point sets and topological refinement is used to obtain the skeleton, and branch models are constructed using cylinders to achieve 3D skeleton reconstruction. Experimental results show that compared to traditional CPD algorithm, the proposed registration scheme improves accuracy and execution speed by 50% and 95. 8% respectively, with a final reconstruction error of no more than 2. 48%. The research demonstrates the effective reconstruction of the 3D skeleton of individual trees, with results close to the original trees, providing a reference for building digital twin environments of forest trees and forestry resource management. [ABSTRACT FROM AUTHOR]

The ISTTWN algorithm overcame the defect of separating the production process of skeleton points and skeleton lines in tree branch point cloud skeleton extraction and improved the accuracy of the extracted initial skeletons, but the skeletons need further optimization. In the existing skeleton optimization, it is difficult to see the stump adjustment, and most of the bifurcation optimization and skeleton smoothness adopt fitting. Based on the characteristics of the initial skeletons extracted by the ISTTWN algorithm, this research optimizes the skeleton from four aspects. An algorithm for the stump adjustment for reconstructing the stump based on the layer and hierarchical relationship and an algorithm for the bifurcation optimization based on the local branch point cloud and cosine correlation are proposed, and an existing pruning method and a skeleton smoothing method are used. The results show that the skeleton optimization method proposed or used in this research has a high computational efficiency in general and can ultimately retain the necessary skeleton lines. In a visual analysis, the optimized skeleton is obviously much more natural and more in line with the actual topology of trees. In the quantitative analysis, the completeness, accuracy and effectiveness reached 97.82%, 95.72% and 89.47%, respectively. In this study, in addition to the existing tree parameters extracted by the skeleton or generalized cylinder model, the generated skeleton is used to extract the branch attributes. The R 2 of the deflection angle of the branch tip, distance from branch tip and branch length are about 0.897, 0.986 and 0.988, respectively, which illustrates that their models are very good. This research can further expand the application of the skeleton. [ABSTRACT FROM AUTHOR]

The ISTTWN algorithm overcame the defect of separating the production process of skeleton points and skeleton lines in tree branch point cloud skeleton extraction and improved the accuracy of the extracted initial skeletons, but the skeletons need further optimization. In the existing skeleton optimization, it is difficult to see the stump adjustment, and most of the bifurcation optimization and skeleton smoothness adopt fitting. Based on the characteristics of the initial skeletons extracted by the ISTTWN algorithm, this research optimizes the skeleton from four aspects. An algorithm for the stump adjustment for reconstructing the stump based on the layer and hierarchical relationship and an algorithm for the bifurcation optimization based on the local branch point cloud and cosine correlation are proposed, and an existing pruning method and a skeleton smoothing method are used. The results show that the skeleton optimization method proposed or used in this research has a high computational efficiency in general and can ultimately retain the necessary skeleton lines. In a visual analysis, the optimized skeleton is obviously much more natural and more in line with the actual topology of trees. In the quantitative analysis, the completeness, accuracy and effectiveness reached 97.82%, 95.72% and 89.47%, respectively. In this study, in addition to the existing tree parameters extracted by the skeleton or generalized cylinder model, the generated skeleton is used to extract the branch attributes. The R2 of the deflection angle of the branch tip, distance from branch tip and branch length are about 0.897, 0.986 and 0.988, respectively, which illustrates that their models are very good. This research can further expand the application of the skeleton.

The classification of leaf and wood points is an essential preprocessing step for extracting inventory measurements and canopy characterization of trees from the terrestrial laser scanning (TLS) data. The geometry-based approach is one of the widely used classification method. In the geometry-based method, it is common practice to extract salient features at one single scale before the features are used for classification. It remains unclear how different scale(s) used affect the classification accuracy and efficiency. To assess the scale effect on the classification accuracy and efficiency, we extracted the single-scale and multi-scale salient features from the point clouds of two oak trees of different sizes and conducted the classification on leaf and wood. Our experimental results show that the balanced accuracy of the multi-scale method is higher than the average balanced accuracy of the single-scale method by about 10% for both trees. The average speed-up ratio of single scale classifiers over multi-scale classifier for each tree is higher than 30. [ABSTRACT FROM AUTHOR]