Abstract
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.
Highlights
Rubber trees (Hevea brasiliensis) are a widely planted hardwood genus in tropical areas and are important suppliers of natural rubber and wood
If the trees that are planted in forests have heavily intersected tree crowns, seriously sloped trunks and unobvious tree crown features, these interference factors complicate the tree crown segmentation based on scanned data
Some researchers have adopted mobile ground-based light detection and ranging (LiDAR), such as the man-portable backpack scanning mode, to scan the data of tree trunks growing in forests [10] and combined the region growing method based on trunk data to perform individual tree segmentation
Summary
Rubber trees (Hevea brasiliensis) are a widely planted hardwood genus in tropical areas and are important suppliers of natural rubber and wood. The deficiency of local scanned data makes the distribution of scanned points discontinuous, i.e., difficulties in adopting a region growing method based on the scanned trunk points to determine the architecture of the whole tree branch These disturbances result in difficulties in detecting the tree crown centre and unclear representation of the tree crown shape, which complicates the delineation of rubber tree crowns from ground-based mobile LiDAR data. In face of the above issues, this paper proposed a new approach for delineating individual rubber tree crowns and effectively retrieving the tree parameters from mobile ground-based scanned data This method was used to quantitatively assess the severity of the impacts of wind disturbances on different rubber tree clones, including PR107, CATAS 7-20-59 and CATAS 8-7-9. Several valuable conclusions are presented based on the comparison of these retrieved tree attributes among the three rubber tree clones under natural disturbance regimes with various severities
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