A Deep Learning-Based Method for Extracting Standing Wood Feature Parameters from Terrestrial Laser Scanning Point Clouds of Artificially Planted Forest

Abstract

The use of 3D point cloud-based technology for quantifying standing wood and stand parameters can play a key role in forestry ecological benefit assessment and standing tree cultivation and utilization. With the advance of 3D information acquisition techniques, such as light detection and ranging (LiDAR) scanning, the stand information of trees in large areas and complex terrain can be obtained more efficiently. However, due to the diversity of the forest floor, the morphological diversity of the trees, and the fact that forestry is often planted as large-scale plantations, efficiently segmenting the point cloud of artificially planted forests and extracting standing wood feature parameters remains a considerable challenge. An effective method based on energy segmentation and PointCNN is proposed in this work to address this issue. The network is enhanced for learning point cloud features by geometric feature balance model (GFBM), enabling the efficient segmentation of tree point clouds from forestry point cloud data collected by terrestrial laser scanning (TLS) in outdoor environments. The 3D Forest software is then used to obtain single wood point cloud after semantic segmentation, and the extracted single wood point cloud is finally employed to extract standing wood feature parameters using TreeQSM. The point cloud semantic segmentation method is the most important part of our research. According to our findings, this method can segment datasets of two different artificially planted woodland point clouds with an overall accuracy of 0.95 and a tree segmentation accuracy of 0.93. When compared with the manual measurements, the root-mean-square error (RMSE) for tree height in the two datasets are 0.30272 and 0.21015 m, and the RMSEs for the diameter at breast height are 0.01436 and 0.01222 m, respectively. Our method is a robust framework based on deep learning that is applicable to forestry for extracting the feature parameters of artificially planted trees. It solves the problem of segmenting tree point clouds in artificially planted trees and provides a reliable data processing method for tree information extraction, trunk shape analysis, etc.

A Multiple Criteria Decision-Making Method Generated by the Space Colonization Algorithm for Automated Pruning Strategies of Trees

“…below. Specifically, we utilized the following network architecture: (a) Input layer: 4 input features (x, y, z coordinates and categories); (b) MLP (Multi-Layer Perceptron) layers: 3 MLP layers, each with 64 neurons; (c) Feature propagation layers: 2 feature propagation layers; (d) Segmentation layer: 2 output neurons for segmenting the point cloud into different categories (0, 1) [22].…”

Automatic Vectorization of Power Lines from Airborne Lidar Point Clouds

“…The methods achieves an overall accuracy of 93.7% across four classes (i.e., ground, towers, transmission lines, and ground wires). It is also worth mentioning the work by (Zhu et al, 2024), that leverages on RandLA-Net as backbone network and proposes two final branches: one performs point cloud segmentation into conductors, tower, and others, while the second branch extracts distinctive embedding feature for distinguishing the points belonging to different cables. These features are finally used to clusterize the points via the Mean-Shift algorithm.…”

“…In particular, it is worth mentioning that lidar point clouds can be effectively used to detect encroaching objects on phase conductors, including vegetation growing in close prox-To fully exploit point clouds for monitoring power lines in an automated manner, it is necessary to perform appropriate data processing, with point cloud segmentation and modeling being the main phases. In the literature, attention is mainly focused on the point cloud segmentation task, with recent works proposing machine learning approaches (Toschi et al, 2019, Zhu et al, 2024 to identify points belonging to cables and pylons. A further required step is the grouping of individual objects, with subsequent mathematical modelling and vectorization of the cable geometry.…”

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion

“…Similarly, Brack et al (Brack et al, 2020) utilized TLS to acquire point cloud data in forests, measuring parameters such as tree diameter at breast height (DBH) and tree height. Shen et al (Shen et al, 2022), employing a robust deep learning framework, utilized TLS to obtain forest point cloud data and measured the DBH and tree height of wood in the forest. While TLS allows for large-scale and accurate measurement of some phenotypic parameters of trees, dense tree canopies or other obstructions around fruit tree trunks may hinder laser penetration, preventing the complete measurement of the trunk shape.…”