An assessment of conventional and drone-based measurements for tree attributes in timber volume estimation: A case study on stone pine plantation

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. •CHM data obtained from low-cost UAV-SfM were found satisfactory to manage forest resources.•SfM-based studies, which requires low-cost is promising technique considering LiDAR use in forestry studies.•In small-scale studies, monitoring technique for biophysical and morphological tree information for sustaining ecosystems.•New user-friendly point cloud processing algorithms should be developed and tested for tree detection.