We present an approach for automatically detecting the positions of tree trunks, for determining their corresponding diameter at breast height (DBH), and for assessing the shape of tree trunks from 3D point clouds derived from unmanned aerial vehicle borne laser scanning (ULS). The experiments are carried out with point clouds from both a RIEGL miniVUX-1DL and from a RIEGL VUX-1UAV. The results reveal that the autonomous stem detection recognizes 91.0% and 77.6% of the stems, respectively, and that the DBH can be modeled with biases of 2.86 cm and 0.95 cm for 80.6% and 61.2% of the trees, when compared to field measurements. We further demonstrate that, compared to terrestrial laser scanning (TLS) data, the stem diameters along the tree can be estimated with biases below 3.4 cm and 1.4 cm for the two systems, respectively, up to a tree height of 12 m for stems with a DBH above 20 cm. Our experiments further reveal the accuracy of diameter estimations to be mainly dominated by the tree’s diameter with better accuracies for larger stems, while the completeness, with which a stem is covered by points, has little influence, as long as half of the stem circumference is captured. The absolute point count on the stem does not impact the estimation accuracy of all stem parameters, but is critical to the completeness with which a scene can be reconstructed. Conversely, we demonstrate the precision of the laser scanner to be a key factor for the accuracy of the stem diameter estimations, as in our experiments, we found the accuracies of the estimations from the VUX-1UAV to be higher than the ones from the miniVUX-1DL. The findings of our study assist to evaluate the potential of ULS for forest monitoring and management and allow for conclusions regarding the required point cloud qualities and, thus, the mission planning of ULS acquisitions, in order to deliver data products, which fulfill the requirements for an operational application in forest inventories.

我们提出了一种方法，用于自动检测树干的位置，确定其在胸高处的相应直径（DBH），以及从无人飞行器激光扫描（ULS）得出的3D点云中评估树干的形状。使用RIEGL miniVUX-1DL和RIEGL VUX-1UAV的点云进行了实验。结果表明，自动茎检测可分别识别91.0％和77.6％的茎，与野外测量相比，DBH可以对2.86 cm和0.95 cm的偏差对80.6％和61.2％的树木进行建模。 。我们进一步证明，与陆地激光扫描（TLS）数据相比，这两个系统的沿树的茎径可以分别以3.4 cm和1.4 cm以下的偏差估算，对于DBH超过20 cm的茎，最大树高为12 m。我们的实验进一步揭示了直径估计的准确性主要受树木直径的支配，对于较大的茎，其精度更高，而茎被点覆盖的完整性的影响很小，只要茎周的一半为被抓 词干上的绝对点数不会影响所有词干参数的估计准确性，但对于可重构场景的完整性至关重要。相反，我们证明激光扫描仪的精度是杆直径估计精度的关键因素，因为在我们的实验中，我们发现VUX-1UAV的估计精度高于miniVUX的估计精度-1DL。