Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances
1
School of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
2
Co-Innovation Centre for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
3
Danzhou Investigation and Experiment Station of Tropical Crops, Ministry of Agriculture, Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Danzhou 571737, China
4
Advanced Analysis and Testing Centre, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2019, 11(8), 903; https://doi.org/10.3390/rs11080903
Received: 5 March 2019 / Revised: 10 April 2019 / Accepted: 11 April 2019 / Published: 13 April 2019
(This article belongs to the Special Issue Advances in Active Remote Sensing of Forests)
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.
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Keywords:
tree crown segmentation; ground-based mobile LiDAR; rubber tree properties retrieval; natural disturbance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Yun, T.; Jiang, K.; Hou, H.; An, F.; Chen, B.; Jiang, A.; Li, W.; Xue, L. Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances. Remote Sens. 2019, 11, 903. https://doi.org/10.3390/rs11080903
AMA Style
Yun T, Jiang K, Hou H, An F, Chen B, Jiang A, Li W, Xue L. Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances. Remote Sensing. 2019; 11(8):903. https://doi.org/10.3390/rs11080903
Chicago/Turabian StyleYun, Ting; Jiang, Kang; Hou, Hu; An, Feng; Chen, Bangqian; Jiang, Anna; Li, Weizheng; Xue, Lianfeng. 2019. "Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances" Remote Sens. 11, no. 8: 903. https://doi.org/10.3390/rs11080903
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