Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data

Abstract

Rubber trees along the southeast coast of China always suffer severe damage from hurricanes. Quantitative assessments of the capacity for wind resistance of various rubber tree clones are currently lacking. We focus on a vulnerability assessment of rubber trees of different clones under wind disturbance impacts by employing multidisciplinary approaches incorporating scanned points, aerodynamics, machine learning and computer graphics. Point cloud data from two typical rubber trees belonging to different clones (PR107 and CATAS 7-20-59) were collected using terrestrial laser scanning, and a connection chain of tree skeletons was constructed using a clustering algorithm of machine learning. The concept of foliage clumps based on the trunk and first-order branches was first proposed to optimize rubber tree plot 3D modelling for simulating the wind field and assessing the wind-related parameters. The results from the obtained phenotypic traits show that the variable leaf area index and included angle between the branches and trunk result in variations in the topological structure and gap fraction of tree crowns, respectively, which are the major influencing factors relevant to the rubber tree’s capacity to resist hurricane strikes. The aerodynamics analysis showed that the maximum dynamic pressure, wind velocity and turbulent intensity of the wind-related parameters in rubber tree plots of clone PR107 (300 Pa, 30 m/s and 15%) are larger than that in rubber tree plots of clone CATAS-7-20-59 (120 Pa, 18 m/s and 5%), which results in a higher probability of local strong cyclone occurrence and a higher vulnerability to hurricane damage.

A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory

“…Consequently, machine learning algorithms, e.g., Gaussian classifiers or support vector machines (SVMs), are employed to discriminate the category of each point based on the derived features. Furthermore, modelling of branches with a sequence of fitted cylinders with an adaptive radius scheme ( Huang et al., 2020 ) was conducted for wood volume estimates. The results for wood–leaf separation and branch segment reconstruction are shown in Figure 1 for five experimental trees.…”

“…After the suitable representation of every individual leaf in the tree crown using hexagonal prisms with automatic adaptive parameter assignment, the equivalent volume of every leaf V leaf t in the tree crown was calculated. Additionally, the wood volume V branch u was derived based on a set of cylinders in the branch direction with the fitted radii ( Huang et al., 2020 ) mentioned in subsection 2.2. The canopy volume V canopy was obtained using the 3D alpha shape algorithm ( Gardiner et al., 2018 ) based on the scanned points of vegetative elements above the heights of the lowest branches.…”

A Forest Fire Susceptibility Modeling Approach Based on Light Gradient Boosting Machine Algorithm

“…Altitude could result in different environmental conditions in terms of vegetation growth, humidity, and temperature [32,33]. Consequently, the probability of the occurrence of a fire may vary with altitude.…”

Modeling the Geometry of Tree Trunks Using LiDAR Data

“…Based on these data, a graph structure is generated by topologically arranging the central points of the layered segments of trunks and branches [67][68][69]. Huang et al [70] built a connection chain of tree skeletons with the use of a machine learning clustering algorithm. The proposed concept relied on foliage clumps composed of trunks and first-order branches.…”