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Automatic tree crown segmentation using dense forest point clouds from Personal Laser Scanning (PLS)
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-29 , DOI: 10.1016/j.jag.2022.103025
Andreas Tockner, Christoph Gollob, Ralf Kraßnitzer, Tim Ritter, Arne Nothdurft

Among digital-based technologies to monitor forest ecosystems, personal laser scanning (PLS) has high potential to characterize even complex deciduous and rainforests. PLS data include a complete and detailed 3D representation of forest stands, but tree individuals need to be segmented accurately before retrieving tree characteristics. As manual on-screen segmentation is time-consuming and labor intensive, we suggest an automatic voxel-based region growing crown segmentation algorithm. Diameter at breast height (dbh), tree height, crown base height (cbh), crown projection area (cpa) and crown volume were automatically extracted from single tree point clouds. The methodology was validated on previously published PLS raw data in terms of segmentation accuracy and measurement precision. Manual segmentation, field measurements, and geometrical crown models were used as reference data. The overall segmentation accuracy of the crowns was 87.02% and tree height was accurately measured with a bias of −0.05 m and a root mean square deviation (RMSD) of 1.21 m (6.33%). Existing geometric crown models proved to be a realistic approximation of the true crown architecture and matched the measured tree crown volume with a bias of −4.62 m3 and a RMSD of 63.02 m3 (31.72%). Tree height and cpa were not affected by segmentation accuracy, but a major challenge remained in estimating cbh. The proposed methodology provides an efficient and low-cost solution for a fully automatic and digital forest inventory.



中文翻译:

使用来自个人激光扫描 (PLS) 的茂密森林点云自动进行树冠分割

在监测森林生态系统的基于数字的技术中,个人激光扫描 (PLS) 具有很高的潜力来表征甚至复杂的落叶林和热带雨林。PLS 数据包括完整而详细的森林林分 3D 表示,但在检索树木特征之前,需要准确分割树木个体。由于手动屏幕分割既费时又费力,我们建议使用基于体素的自动区域生长冠分割算法。从单棵树点云中自动提取胸径(dbh)、树高、树冠基高(cbh)、树冠投影面积(cpa)和树冠体积。该方法在先前发布的 PLS 原始数据的分割精度和测量精度方面得到了验证。手动分割,现场测量,和几何牙冠模型被用作参考数据。牙冠整体分割准确率为87.02% 准确测量树高,偏差为 -0.05 m,均方根偏差 (RMSD) 为 1.21 m (6.33%)。现有的几何树冠模型被证明是真实树冠结构的真实近似,并且与测量的树冠体积相匹配,偏差为 -4.62 m 3和 RMSD 为 63.02 m 3 (31.72%)。树高和 cpa 不受分割精度的影响,但在估计 cbh 方面仍然存在重大挑战。所提出的方法为全自动和数字森林清查提供了一种高效且低成本的解决方案。

更新日期:2022-09-29
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