Accurate individual tree crown (ITC) segmentation from scanned point clouds is a fundamental task in forest biomass monitoring and forest ecology management. Light detection and ranging (LiDAR) as a mainstream tool for forest survey is advancing the pattern of forest data acquisition. In this study, we performed a novel deep learning framework directly processing the forest point clouds belonging to the four forest types (i.e., the nursery base, the monastery garden, the mixed forest, and the defoliated forest) to realize the ITC segmentation. The specific steps of our approach were as follows: first, a voxelization strategy was conducted to subdivide the collected point clouds with various tree species from various forest types into many voxels. These voxels containing point clouds were taken as training samples for the PointNet deep learning framework to identify the tree crowns at the voxel scale. Second, based on the initial segmentation results, we used the height-related gradient information to accurately depict the boundaries of each tree crown. Meanwhile, the retrieved tree crown breadths of individual trees were compared with field measurements to verify the effectiveness of our approach. Among the four forest types, our results revealed the best performance for the nursery base (tree crown detection rate r = 0.90; crown breadth estimation R² > 0.94 and root mean squared error (RMSE) < 0.2m). A sound performance was also achieved for the monastery garden and mixed forest, which had complex forest structures, complicated intersections of branches and different building types, with r = 0.85, R² > 0.88 and RMSE < 0.6 m for the monastery garden and r = 0.80, R² > 0.85 and RMSE < 0.8 m for the mixed forest. For the fourth forest plot type with the distribution of crown defoliation across the woodland, we achieved the performance with r = 0.82, R² > 0.79 and RMSE < 0.7 m. Our method presents a robust framework inspired by the deep learning technology and computer graphics theory that solves the ITC segmentation problem and retrieves forest parameters under various forest conditions.

Rubber trees in southern China are often impacted by natural disturbances, and accurate rubber tree crown segmentation and property retrieval are of great significance for forest cultivation treatments and silvicultural risk management. Here, three plots of different rubber tree clones, PR107, CATAS 7-20-59 and CATAS 8-7-9, that were recently impacted by hurricanes and chilling injury were taken as the study targets. Through data collection using ground-based mobile light detection and ranging (LiDAR) technology, a weighted Rayleigh entropy method based on the scanned branch data obtained from the region growing algorithm was proposed to calculate the trunk inclination angle and crown centre of each tree. A watershed algorithm based on the extracted crown centres was then adopted for tree crown segmentation, and a variety of tree properties were successfully extracted to evaluate the susceptibility of different rubber tree clones facing natural disturbances. The results show that the angles between the first-order branches and trunk ranged from 35.1–67.7° for rubber tree clone PR107, which is larger than the angles for clone CATAS 7-20-59, which ranged from 20.2–43.2°. Clone PR107 had the maximum number of scanned leaf points, lowest tree height and a crown volume that was larger than that of CATAS 7-20-59, which generates more frontal leaf area to oppose wind flow and reduces the gaps among tree crowns, inducing strong wind loading on the tree body. These factors result in more severe hurricane damage, resulting in trunk inclination angles that are larger for PR107 than CATAS 7-20-59. In addition, the rubber tree clone CATAS 8-7-9 had the minimal number of scanned leaf points and the smallest tree crown volume, reflecting its vulnerability to both hurricanes and chilling injury. The results are verified by field measurements. The work quantitatively assesses the susceptibility of different rubber tree clones under the impacts of natural disturbances using ground-based mobile LiDAR.

Light detection and ranging (LiDAR) technology has become a mainstream tool for forest surveys, significantly contributing to the improved accuracy of forest inventories. However, the accuracy of the scanned data and tree properties derived using LiDAR technology may differ depending on the occlusion effect, scanning configurations, various scanning patterns, and vegetative characteristics of forest plots. Hence, this paper presents a computer simulation program to build a digital forest plot composed of many tree models constructed based on in situ measurement information and two mobile scanning patterns, i.e., airborne laser scanning (ALS) and ground-based mobile laser scanning (MLS). Through the adjustment of scanning parameters and the velocity of vehicle loading LiDAR sensors, the points scanned using two scanning patterns were compared with the original sampling points, derived from the constructed digital forest plots. The results show that only 2% of sampling points were collected by LiDAR sensors with the fastest vehicle speed (10 m/s) and coarsest scanning angular resolution (horizontal angular resolution 0.16° and vertical angular resolution 1.33°), and approximately 50% of sampling points were collected by LiDAR sensors with slow vehicle velocity (1.25 m/s) and a finer scanning angular resolution (horizontal angular resolution 0.08° and vertical angular resolution 0.33°). Meanwhile, the potential extended application of the proposed computer simulation program as a light model of forest plots was discussed to underpin the creation of the forest digital twin. Three main conclusions are drawn: (1) the collected points from airborne laser scanning (ALS) are higher than those collected from ground-based mobile laser scanning (MLS); (2) reducing the vehicle velocity is more efficient at improving the high density of the point cloud data than by increasing the scanning angular resolution; (3) the lateral extension of crown area increasing the light beams’ receptor area and the clumped leaf dispersion augmenting the light penetration with vertical elongation are the two paramount factors influencing the light transmittance of tree crowns.