The use of unmanned aerial vehicles (UAVs) as a useful tool in forestry studies is increasing rapidly. The level of accuracy in UAV-supported measurement data has been increasing in the field of forestry. Recently, biophysical and morphological tree information has been calculated using the three-dimensional (3D) point cloud data. The accuracy of studies on different types of trees (i.e. coniferous and deciduous) may vary depending on the selected instruments and methods. Within the scope of the present study, height (H) and crown projection area (CPA) of 105 stone pines (Pinus pinea, L.) measured using both conventional methods and UAV-based Structure-from-Motion (SfM) derived 3D dense point cloud were evaluated. Tree volumes in the study area were calculated using the allometric formulas generated based on the linear diameter model produced with the field measurements. For field measurements and UAV based data, the tree volumes were calculated using the single- and double-entry over-bark stem volume equations, and their numerical comparisons were conducted. To search for the differences, RMSE (Root mean square error), RMSE% (Root mean square percentage error), MAE (Mean absolute error) and MAE% (Mean absolute percentage error) values were taken into consideration. According to the results of the paired t-test, it was revealed that there were no significant differences between the field- and SfM- measurements based methods. Considering the average values instead of individual (single) trees in the information obtained using the 3D point cloud in such stands gave more accurate results. It was found that forest parameters at plot levels in stands could be quickly revealed by UAV photogrammetry. In addition, these data can be evaluated as a metric measurement technique for sustainable and precise operational planning in forest lands.

在林业研究中，无人驾驶飞行器（UAV）作为有用工具的使用正在迅速增加。在林业领域，无人机支持的测量数据的准确性水平一直在提高。最近，已经使用三维（3D）点云数据计算了生物物理和形态树的信息。对不同类型树木（即针叶树和落叶树）的研究准确性可能会因所选择的仪器和方法而异。在本研究范围内，105个石松（松树，L.）使用传统方法和基于无人机的动感结构（SfM）得出的3D密集点云进行了评估。研究区域中的树木体积是使用根据野外测量产生的线径模型生成的异速方程计算得出的。对于野外测量和基于无人机的数据，使用单次和两次进入的超树皮茎体积方程式计算树木体积，并进行数值比较。为了寻找差异，考虑了RMSE（均方根误差），RMSE％（均方根误差），MAE（均值绝对误差）和MAE％（均值绝对误差）值。根据配对t的结果-t测试表明，基于现场测量和SfM测量的方法之间没有显着差异。在这样的看台上使用3D点云获得的信息中考虑平均值而不是单个（单个）树，可以得到更准确的结果。人们发现，无人机摄影测量法可以迅速揭示林分地段的森林参数。此外，这些数据可以作为衡量森林土地可持续和精确运营计划的度量标准技术进行评估。