Mapping hard-to-access and hazardous parts of forests by terrestrial surveying methods is a challenging task. Remote sensing techniques can provide an alternative solution to such cases. Unmanned aerial vehicles (UAVs) can provide on-demand data and higher flexibility in comparison to other remote sensing techniques. However, traditional georeferencing of imagery acquired by UAVs involves the use of ground control points (GCPs), thus negating the benefits of rapid and efficient mapping in remote areas. The aim of this study was to evaluate the accuracy of RTK/PPK (real-time kinematic, post-processed kinematic) solution used with a UAV to acquire camera positions through post-processed and corrected measurements by global navigation satellite systems (GNSS). To compare this solution with approaches involving GCPs, the accuracies of two GCP setup designs (4 GCPs and 9 GCPs) were evaluated. Additional factors, which can significantly influence accuracies were also introduced and evaluated: type of photogrammetric product (point cloud, orthoimages and DEM) vegetation leaf-off and leaf-on seasonal variation and flight patterns (evaluated individually and as a combination). The most accurate results for both horizontal (X and Y dimensions) and vertical (Z dimension) accuracies were acquired by the UAV RTK/PPK technology with RMSEs of 0.026 m, 0.035 m and 0.082 m, respectively. The PPK horizontal accuracy was significantly higher when compared to the 4GCP and 9GCP georeferencing approach (p < 0.05). The PPK vertical accuracy was significantly higher than 4 GCP approach accuracy, while PPK and 9 GCP approach vertical accuracies did not differ significantly (p = 0.96). Furthermore, the UAV RTK/PPK accuracy was not influenced by vegetation seasonal variation, whereas the GCP georeferencing approaches during the vegetation leaf-off season had lower accuracy. The use of the combined flight pattern resulted in higher horizontal accuracy; the influence on vertical accuracy was insignificant. Overall, the RTK/PPK technology in combination with UAVs is a feasible and appropriately accurate solution for various mapping tasks in forests.

Close-range photogrammetry (CRP) can be used to provide precise and detailed three-dimensional data of objects. For several years, CRP has been a subject of research in forestry. Several studies have focused on tree reconstruction at the forest stand, plot, and tree levels. In our study, we focused on the reconstruction of trees separately within the forest stand. We investigated the influence of camera lens, tree species, and height of diameter on the accuracy of the tree perimeter and diameter estimation. Furthermore, we investigated the variance of the perimeter and diameter reference measurements. We chose four tree species (Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Picea abies (L.) H. Karst. and Abies alba Mill.). The perimeters and diameters were measured at three height levels (0.8 m, 1.3 m, and 1.8 m) and two types of lenses were used. The data acquisition followed a circle around the tree at a 3 m radius. The highest accuracy of the perimeter estimation was achieved when a fisheye lens was used at a height of 1.3 m for Fagus sylvatica (root mean square error of 0.25 cm). Alternatively, the worst accuracy was achieved when a non-fisheye lens was used at 1.3 m for Quercus petraea (root mean square error of 1.27 cm). The tree species affected the estimation accuracy for both diameters and perimeters.