Narrow-leaved ash (Fraxinus angustifolia Vahl), a highly valued European forest tree species, has been severely affected by a large-scale decline, which is most probably driven by a complex of multiple interacting factors including fungi, which contribute to and accelerate this process. Red deer (Cervus elaphus L.) can be considered as one of the contributing factors, as they inflict damage on the stems of young trees by stripping the bark. These wounds not only represent suitable entry points for fungi which can cause tissue necroses and decomposition, they can lead to changes in the wood structure as well. The aims of this research were to analyze chosen parameters of bark stripping wounds in narrow-leaved ash stands, identify fungi present in the tissue exposed by wounding, and inspect the effect of wounding on the wood structure. Bark stripping was observed on ash trees from 2 cm up to 18 cm of DBH and between 0.1 m and 1.9 m of stem height. The most susceptible trees were those with an average DBH of 5 ± 2.5 cm. On most of the ash trees (51%), one-third to two-thirds of the circumference was damaged. In wounded tissue, 174 fungal isolates were found, most of which belonged to known endophytic fungi from the genera Trichoderma, Fusarium, and Clonostachys. It was observed that earlywood cells in the wounding zone had narrower lumens compared to ones in adjacent healthy zone with regard to different trees and stem heights.

Planning in forestry should be based on accurate and reliable data. UAVs equipped with RGB cameras can enable fast and relatively cheap surveys, but their accuracy depends on many factors. Therefore, it is necessary to determine when UAVs can be used and when this type of survey gives data that does not reflect the true ground situation. This research analyzed the usability of a UAV, equipped with a RGB camera, for recording normal cross-sections and side ditch depths of the forest road in a lowland forest. The research was conducted in two time periods: during winter and spring, i.e., outside and during the vegetation season. DTMs of the area researched were created based on aerial photographs taken with the UAV, Z values of terrain points were read, and the depths of side ditches were calculated based on read Z values. The water depth in the side ditches and the vegetation height on the entire road body width were recorded to determine the influence of these two variables on the UAV survey error. Terrain points were recorded with the total station, which was the reference measurement method. An analysis of the obtained (read) DTM Z values revealed RMSE values of 10.09 cm for winter (outside vegetation) and 36.41 cm for spring (vegetation) UAV survey. The side ditch, calculated based on the DTM of the winter and spring periods of UAV recording, were statistically significantly different from the side ditch depths measured using the total station. Correcting the obtained data with water depth and vegetation height lowered the differences in Z values, as well as the ditch depths visible from ${RMSE}_Z$ (7.70 cm) for the winter UAV survey, with no statistically significant difference in side ditch depths. In the case of the correction of spring recording data, ${RMSE}_Z$ was smaller (23.41 cm) than before correction (36.41 cm), and the depth of the side ditches was still statistically significantly different. The authors conclude that water and ground vegetation can significantly affect UAV survey accuracy. In the winter period, side ditch depth measurement is possible in areas where water is not present. If water is present, manual measurement of water height and correction of obtained UAV data can improve data accuracy. On the other hand, spring or vegetation period UAV surveys are highly affected by ground vegetation height and the authors do not recommend surveys in that period.

Field data, as the basis for planning and designing forest roads, must have high spatial accuracy. Classical (using a theodolite and a level) and modern (based on total stations and GNSSs) surveying methods are used in current field data survey for forest road design. This study analyzed the spatial accuracy of classical and modern surveying methods, the accuracy of spatial data recorded using a UAV equipped with an RGB camera at different flight altitudes, and the accuracy of lidar data of the Republic of Croatia. This study was conducted on a forest area where salvage logging was carried out, which enabled the use of a GNSS receiver in RTK mode as a reference method. The highest RMSE values of the spatial coordinates were recorded for measurements obtained with the classical surveying method (0.89 m) and a total station (0.33 m). The flight altitude of the UAV did not significantly affect the spatial error of the collected data, which ranged between 0.07 and 0.09 m. The cross-terrain slope, as one of the factors that significantly affect the amount of earthworks, did not differ statistically significantly between the methods. The ALS error was strongly influenced by the cross-terrain slope. The authors conclude that the new survey methods (SfM and lidar data) provide high-accuracy data but also draw attention to challenges in their use, such as vegetation and biomass on the ground.

Field data of high accuracy and precision is the basis for creating the high-quality design of a forest road. In this study, three survey methods for collecting field data were tested: ALS UAV, LiDAR data of the Republic of Croatia, collected by airplane, and UAV SfM. A total of three detailed forest road projects were created based on the collected data. The designed forest roads had the same horizontal and vertical development, thus eliminating the human factor from the design process. Four important forest road parameters were tested: earthwork cut and fill volume, cross-terrain slope, and carriageway value. No significant statistical difference was found for any of the tested parameters between designs. The design based on ALS data had a total number of earthworks of 1026.03 m³, the amount was 1449.56 m³ for SfM design, and the number of earthworks for the State Geodetic Administration LiDAR data was 889.02 m³. The calculated amount of cut volume was significantly affected by the error of the carriageway value for the State Geodetic Administration LiDAR data-based design. The results indicate the possibility of using all used methods on terrain with a moderate slope, but there is a need for further testing on different terrain slope classes.