Data on forests (e.g., height, diameter at breast height and volume) are increasingly being collected using remote sensing methods, leading to improved forest inventories. At present, a popular method for data collection is the use of unmanned aerial vehicles (UAVs) equipped with LiDAR sensors, which allow for a more detailed assessment of structural parameters in both space and time, facilitating the practical application of more complex forest management systems. Therefore, the main objective of this study was to measure the structural elements of stands (e.g., volume, basal area, tree count, height, diameter at breast height, crown width, and area) from LiDAR images and determine the accuracy of the obtained results. The research was conducted in a 5500 ha area containing some of the most valuable forests in Croatia, namely, lowland oak forests. The results of this study show that there are no statistically significant differences between the diameters and heights measured in the field and those from LiDAR images and, consequently, no difference in the calculated volume. This study also concluded that the use of unmanned aerial vehicles with various sensors can significantly reduce fieldwork while achieving the same accuracy in the results, thus leading to substantial savings in both time and money.

Data on forests (height, diameter at breast height, volume, etc.) are increasingly being collected using remote sensing methods, which improve forest inventories. The latest popular method of data collection is through unmanned aerial vehicles (UAVs) equipped with LiDAR sensors, which allow for a more detailed assessment of structural parameters in space and time, facilitating the practical application of more complex forest management systems. Therefore, the main objective of this study was to measure the structural elements of the stand (volume, basal area, tree count, height, diameter at breast height, crown width and area, etc.) from LiDAR images and determine the accuracy of the results obtained. The research was conducted in the area of the most valuable forests in Croatia – the lowland oak forests, covering an area of 5500 ha. The results of the study showed that there is no statistically significant difference between the diameters and heights measured in the field and those from LiDAR images, and consequently, no difference in the calculated volume. The study also concluded that the use of unmanned aerial vehicles with various sensors can significantly reduce fieldwork while achieving the same accuracy in the results, thus leading to substantial savings in both time and money.

Negativne posljedice propadanja šuma nastoje se umanjiti provođenjem gospodarskih mjera i praćenjem zdravstvenog stanja stabala, odnosno procjenom oštećenosti krošanja. Utvrđivanje zdravstvenog stanja šuma provodi se, osim terestričkim načinom, i met...

One of the priorities in sustainable forest management is monitoring the health status of trees and stands. From the aspect of remote sensing (RS), the best way of doing this is by interpreting color infrared (CIR) aerial photographs; however, this raises the issue of sample size. For this reason, to apply this method in practice, it is indispensable to determine an appropriate sample size to ensure sufficient reliability of the health status assessment of trees in CIR aerial photographs. This research was conducted in lowland forests of pedunculate oak in Croatia. To determine damage in the photographs of the main tree species, a systematic sample with varying dot grid densities—100 × 100 m, 200 × 200 m, 300 × 300 m, 500 × 500 m, 1000 × 1000 m—was used with combinations of different numbers of interpreted trees per sample. Damage indicators were also calculated based on tree distributions obtained by interpreting four trees, two trees and one tree in different sample sizes. The results of the testing showed that there were no statistically significant differences between different sample densities and numbers of interpreted trees in relation to mean damage assessment. Regardless of the fact that there were no statistically significant differences during damage assessment, it was found that by lowering sample densities, starting with 200 × 200 m, the number of trees and the number of sample points per particular sub-compartment significantly decreased, and so did the desired accuracy. Consequently, the participation (distribution) of particular species and damage degrees in the sample were lost, which significantly affected the overall tree health assessment. In contrast, grid densities of 100 × 100 m with one interpreted tree at the raster point proved to be the optimal sample size. This confirms the fact found in earlier research, that is, that the selected sample should have several spatially well-distributed points with a smaller number of trees in the point, and samples with larger numbers of trees in a smaller number of points should be avoided.