Simulating Wind Disturbances over Rubber Trees with Phenotypic Trait Analysis Using Terrestrial Laser Scanning

Abstract

Hurricanes often devastate trees throughout coastal China; accordingly, developing a method to quantitatively evaluate the changes in tree phenotypic characteristics under continuous strong winds is of great significance for guiding forest cultivation practices and mitigating wind hazards. For this research, we built a lifting steel truss carrying a large forced draft fan near a rubber plantation on Hainan Island, and we aligned three selected small rubber trees in a row in front of the fan (with separation distances from the forced draft fan outlet of approximately 1.3, 3.3, and 5.3 m) to explore the susceptibility of rubber trees to the mechanical loading of hurricane-level winds. By adjusting the power of the forced draft fan, four wind speeds were emitted: 0 m/s, 10.5 m/s, 13.5 m/s, and 17.5 m/s. Meanwhile, point clouds of the three rubber trees under different continuous wind speeds were acquired using two terrestrial laser scanners. Computer algorithms were applied to derive the key parameters of the three rubber trees, namely, the zenith and azimuth angles of each leaf, effective leaf area index (LAI), windward area of each tree, volume of the tree canopy, and trunk tilt angle, from these point clouds under all four wind speeds. The results show that by increasing the wind speed from 0 m/s to 17.5 m/s, the leaf zenith angles of the three rubber trees were unimodally distributed with the peak concentrated at 0°, while the leaf azimuth angles were bimodally distributed with the peaks concentrated at 0° and 360°. The effective LAI values of the three trees increased from 2.97, 4.77, and 3.63 (no wind) to 3.84, 5.9, and 4.29 (wind speed of 17.5 m/s), respectively, due to a decrease in the vertical crown projection area caused by the compression of the tree canopy. We also found that the effective LAI, windward area, and canopy volume of the third rubber tree (the tree farthest from the forced draft fan) varied less than those of the other two trees, reflecting the attenuation of the wind speed by the crowns of the two trees closer to the fan. The experimental results also indicate that the joint use of light detection and ranging (LiDAR) data with computer graphics algorithms to analyse the dynamic changes in tree phenotypic characteristics during the passage of a hurricane is promising, enabling the development of a novel strategy for mitigating wind hazards. The proposed method with the designed device capable of producing an adjustable wind speed also has the potential to study the impacts of wind damage under various forest conditions by further modifying the tree spacing and tree species.

Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail

“…Sun et al [ 23 ] extracted individual tree crowns and canopy width from aerial laser scanning data. Zhang et al [ 24 ] analyzed the dynamic phenotypic changes of trees under wind disturbance using LiDAR.…”

Point cloud segmentation for an individual tree combining improved point transformer and hierarchical clustering

“…At present, the common methods for obtaining point cloud data mainly include terrestrial laser scanning 18 (TLS), airborne laser scanning 19 , 20 (ALS), and mobile laser scanning 21 , 22 (MLS). Compared with ALS and MLS, the TLS is more suitable for complex and dense forests due to its higher resolution and lower cost.…”

SegForest: A Segmentation Model for Remote Sensing Images

“…Therefore, effective forest resource management and monitoring is critical to ensure sustainable development. Remote sensing techniques, such as satellite and unmanned aerial vehicle (UAV) image segmentation are widely employed for forest area estimation and land survey [7][8][9]. Remote sensing image segmentation refers to the process of analyzing and processing remote sensing image data to partition different regions or objects within the image into distinct sub-regions or sub-objects based on their independent characteristics.…”