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.

Plantation forests, cultivated through artificial seeding and planting methods, are of great significance to human society. However, most experimental sites for these forests are located in remote areas. Therefore, in-depth studies on remote forest management and off-site experiments can better meet the experimental and management needs of researchers. Based on an experimental plantation forest of Triploid Populus Tomentosa, this paper proposes a digital twin architecture for a virtual poplar plantation forest system. The framework includes the modeling of virtual plantation and data analysis. Regarding this system architecture, this paper theoretically analyzes the three main entities of the physical world, digital world, and researchers contained in it, as well as their interaction mechanisms. For virtual plantation modeling, a tree modeling method based on LiDAR point cloud data was adopted. The transitional particle flow method was proposed to combine with AdTree method for tree construction, followed by integration with other models and optimization. For plantation data analysis, a database based on forest monitoring data was established. Tree growth equations were derived by fitting the tree diameter at breast height data, which were then used to predict and simulate trends in diameter-related data that are difficult to measure. The experimental result shows that a preliminary digital twin-oriented poplar plantation system can be constructed based on the proposed framework. The system consists of 2160 trees and simulations of 10 types of monitored or predicted data, which provides a new practical basis for the application of digital twin technology in the forestry field. The optimized tree model consumes over 67% less memory, while the R² of the tree growth equation with more than 100 data items could reach more than 87%, which greatly improves the performance and accuracy of the system. Thus, utilizing forestry information networking and digitization to support plantation forest experimentation and management contributes to advancing the digital transformation of forestry and the realization of a smart management model for forests.

The leaf area index (LAI) is an essential indicator for assessing vegetation growth and understanding the dynamics of forest ecosystems and is defined as the ratio of the total leaf surface area in the plant canopy to the corresponding surface area below it. LAI has applications for obtaining information on plant health, carbon cycling, and forest ecosystems. Due to their price and portability, mobile devices are becoming an alternative to measuring LAI. In this research, a new method for estimating LAI using a smart device with a fisheye lens (SFL) is proposed. The traditional Otsu method was enhanced to improve the accuracy and efficiency of foreground segmentation. The experimental samples were located in Gansu Ziwuling National Forest Park in Qingyang. In the accuracy parameter improvement experiment, the variance of the average LAI value obtained by using both zenith angle segmentation and azimuth angle segmentation methods was reduced by 50%. The results show that the segmentation of the front and back scenes of the new Otsu method is more accurate, and the obtained LAI values are more reliable. In the efficiency parameter improvement experiment, the time spent is reduced by 17.85% when the enhanced Otsu method is used to ensure that the data anomaly rate does not exceed 10%, which improves the integration of the algorithm into mobile devices and the efficiency of obtaining LAI. This study provides a fast and effective method for the near-ground measurement of forest vegetation productivity and provides help for the calculation of forest carbon sequestration efficiency, oxygen release rate, and forest water and soil conservation ability.

Background. With the advancement of “digital forestry” and “intelligent forestry”, point cloud data have emerged as a powerful tool for accurately capturing three-dimensional forest scenes. It enables the creation and presentation of digital forest systems, facilitates the monitoring of dynamic changes such as forest growth and logging processes, and facilitates the evaluation of forest resource fluctuations. However, forestry point cloud data are characterized by its large volume and the need for time-consuming and labor-intensive manual processing. Deep learning, with its exceptional learning capabilities, holds tremendous potential for processing forestry environment point cloud data. This potential is attributed to the availability of accurately annotated forestry point cloud data and the development of deep learning models specifically designed for forestry applications. Nonetheless, in practical scenarios, conventional direct annotation methods prove to be inefficient and time-consuming due to the complex terrain, dense foliage occlusion, and uneven sparsity of forestry point clouds. Furthermore, directly applying deep learning frameworks to forestry point clouds results in subpar accuracy and performance due to the large size, occlusion, sparsity, and unstructured nature of these scenes. Therefore, the proposal of accurately annotated forestry point cloud datasets and the establishment of semantic segmentation methods tailored for forestry environments hold paramount importance. Methods. A point cloud data annotation method based on single-tree positioning to enhance annotation efficiency was proposed and challenges such as occlusions and sparse distribution in forestry environments were addressed. This method facilitated the construction of a forestry point cloud semantic segmentation dataset, consisting of 1259 scenes and 214.4 billion points, encompassing four distinct categories. The pointDMM framework was introduced, a semantic segmentation framework specifically designed for forestry point clouds. The proposed method first integrates tree features using the DMM module and constructs key segmentation graphs utilizing energy segmentation functions. Subsequently, the cutpursuit algorithm is employed to solve the graph and achieve the pre-segmentation of semantics. The locally extracted forestry point cloud features from the pre-segmentation are comprehensively inputted into the network. Feature fusion is performed using the MLP method of multi-layer features, and ultimately, the point cloud is segmented using the lightweight PointNet. Result. Remarkable segmentation results are demonstrated on the DMM dataset, achieving an accuracy rate of 93% on a large-scale forest environment point cloud dataset known as DMM-3. Compared to other algorithms, the proposed method improves the accuracy of standing tree recognition by 21%. This method exhibits significant advantages in extracting feature information from artificially planted forest point clouds obtained from TLS. It establishes a solid foundation for the automation, intelligence, and informatization of forestry, thereby possessing substantial scientific significance.

For extracting tree structural data from LiDAR point clouds, individual tree segmentation is of great significance. Most individual tree segmentation algorithms miss segmentation and misrecognition, requiring manual post-processing. This study utilized a hierarchical approach known as segmentation based on hierarchical strategy (SHS) to improve individual tree segmentation. The tree point cloud was divided into the trunk layer and the canopy layer to carry out trunk detection and canopy segmentation, respectively. The effectiveness of SHS was evaluated on three mixed broadleaf forest plots. The segmentation efficacy of SHS was evaluated on three mixed broadleaf forest plots and compared with the point cloud segmentation algorithm (PCS) and the comparative shortest-path algorithm (CSP). In the three plots, SHS correctly identified all the trunk portion, had a recall (r) of 1, 0.98, and 1, a precision (p) of 1, and an overall segmentation rate (F) of 1, 0.99, and 1. CSP and PCS are less accurate than SHS. In terms of overall plots, SHS had 10%–15% higher F-scores than PCS and CSP. SHS extracted crown diameters with R²s of 0.91, 0.93, and 0.89 and RMSEs of 0.24 m, 0.23 m, and 0.30 m, outperforming CSP and PCS. Afterwards, we evaluate the three algorithms’ findings, examine the SHS algorithm’s parameters and constraints, and discuss the future directions of this research. This work offers an enhanced SHS that improves upon earlier research, addressing missed segmentation and misrecognition issues. It improves segmentation accuracy, individual tree segmentation, and provides both theoretical and data support for the LiDAR application in forest detection.

Three-dimensional models of trees can help simulate forest resource management, field surveys, and urban landscape design. With the advancement of Computer Vision (CV) and laser remote sensing technology, forestry researchers can use images and point cloud data to perform digital modeling. However, modeling leafless tree models that conform to tree growth rules and have effective branching remains a major challenge. This article proposes a method based on 3D Gaussian Splatting (3D GS) to address this issue. Firstly, we compared the reconstruction of the same tree and confirmed the advantages of the 3D GS method in tree 3D reconstruction. Secondly, seven landscape trees were reconstructed using the 3D GS-based method, to verify the effectiveness of the method. Finally, the 3D reconstructed point cloud was used to generate the QSM and extract tree feature parameters to verify the accuracy of the reconstructed model. Our results indicate that this method can effectively reconstruct the structure of real trees, and especially completely reconstruct 3rd-order branches. Meanwhile, the error of the Diameter at Breast Height (DBH) of the model is below 1.59 cm, with a relative error of 3.8–14.6%. This proves that 3D GS effectively solved the problems of inconsistency between tree models and real growth rules, as well as poor branch structure in tree reconstruction models, providing new insights and research directions for the 3D reconstruction and visualization of landscape trees in the leafless stage.