Terrestrial laser scanning (TLS) accurately captures tree structural information and provides prerequisites for tree-scale estimations of forest biophysical attributes. Quantifying tree-scale attributes from TLS point clouds requires segmentation, yet the occlusion effects severely affect the accuracy of automated individual tree segmentation. In this study, we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects. Firstly, region growing and point compensation algorithms are used to determine the location of tree roots. Secondly, the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor (KNN). Thirdly, neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption. Finally, a graph describing connectivity between a point and its neighbors is constructed, and it is utilized to complete individual tree segmentation based on the shortest path algorithm. The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo, Finland, and it reached the highest mean accuracy of 77.5%, higher than previous studies on tree detection. We also extracted and validated the tree structure attributes using manual segmentation reference values. The RMSE, RMSE%, bias, and bias% of tree height, crown base height, crown projection area, crown surface area, and crown volume were used to evaluate the segmentation accuracy, respectively. Overall, the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.

中文翻译：

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通过椭球体方向搜索和点补偿在被遮挡的复杂林分中分割单个树木


地面激光扫描 （TLS） 可准确捕获树木结构信息，并为森林生物物理属性的树木规模估计提供先决条件。量化 TLS 点云中的树尺度属性需要分割，但遮挡效应会严重影响自动单个树分割的准确性。在这项研究中，我们提出了一种使用椭球方向搜索和点补偿算法的新方法来减轻遮挡效应。首先，使用区域增长和点补偿算法来确定树根的位置;其次，在椭球体邻域内提取邻点，以减轻与 k 最近邻 （KNN） 相比的遮挡效应。再次，通过基于斐波那契原理的定向搜索算法在多个空间方向上对相邻点进行均匀子采样，以减少内存消耗;最后，构建了一个描述点与其邻居之间连通性的图，并利用该图完成基于最短路径算法的单个树分割。所提出的方法在芬兰 Evo 的公共 TLS 数据集上进行了评估，该数据集包含六个具有三个复杂度类别的森林图，它达到了 77.5% 的最高平均准确率，高于以前的树木检测研究。我们还使用手动分割参考值提取并验证了树结构属性。分别使用树高、树冠基高、树冠投影面积、树冠表面积和树冠体积的 RMSE、RMSE%、bias% 和 bias% 来评价分割精度。 总体而言，所提出的方法避免了当前方法的许多固有局限性，并且可以准确绘制遮挡复杂林分中的树冠结构。