## 4 Discussion  
The described methodology for determining the possibility of moving the fire brigade vehicles in forest vegetation can be used if tree position data or forest structure (generated from photogrammetric data or from LiDAR data) are available. In both cases, the same algorithm can be used to find the optimum forest path. In case we have more precise tree coordinates (from terrestrial or remote sensing sensors), the calculated route of the vehicle will be more reliable. Although the methods of directly determining the exact tree position by remote sensing data are constantly developing, the forest structure is often determined using DSM (CHM) methods. This is due to the financial cost of the high density of LiDAR data, as well as the personnel demand for data acquisition using photogrammetric methods. The quality of the photogrammetric evaluation depends on the scale of the images and the evaluator's experience. The main problem is to target the marker at the tree's top point, which is above the tree trunk. The accuracy of tree position evaluation is higher for coniferous trees than for leafy vegetation. The disadvantage of the photogrammetric method is the lower performance of manual evaluation compared to the possibility of automated evaluation of LiDAR data. LiDAR methods are faster than photogrammetric methods, and they allow a more efficient assessment of the forest structure and determination of the possibilities of vegetation passability without a manual evaluation. LiDAR methods can also be better combined with other remote sensing data sources (infrared, multispectral, radar, etc.). For example, an infrared spectrum can be used to map environmental and fire temperature characteristics, and at night, multispectral imagery can be used to classify species, and radar data is appropriate for mapping a burning forest covered with smoke or clouds. However, these methods have a disadvantage when  
Figure 10: Navigation on the shortest vehicle route—regular triangular forest structure—scale 1:1500.  
scanning vegetation with a low density of DSM elevation points (smaller than 1 m \(\times\) 1 m) and in the case of DTM data absence. On the other hand, the repeated photogrammetric evaluation of the representative forest stands and the data from DSM could bring about a new approach for forest growth analysis and could be a sufficient method for DSM updating in different growth conditions of forest stands. The results of this experiment showed that this method is fully applicable for the DSM generated from LiDAR data. The method can be appropriately implemented as a relatively inexpensive updating tool for GIS technology between two laser scanning campaigns of a territory. This method can also be refined using the growth curves of individual types of trees. Forest growth characteristics are very important due to the age of LiDAR DSM data. The results of photogrammetric measurements from aerial images taken at consecutive time intervals and statistical calculations show that the growth curves of the trees are initially steeper, but vegetation growth later slows. It is also necessary to investigate the relationships between the natural environment factors and specific canopy growth. The above-mentioned DSM updating method could be used for many applications, e.g., in forestry, military, etc.--see, e.g., [29; 30; 31; 32; 33]. It should be noted that the tree height correction values decrease with the increasing density of the LiDAR data. At the DSM density 1 m \(\times\) 1 m, the average correction is approx. +6 m. At the DSM points density of 1 dm \(\times\) 1 dm, it is possible to estimate the average height corrections of spruce trees in decimeters, depending on the age of the vegetation. Height corrections of the DSM can significantly affect the computationally generated forest structure and, hence, the vehicle motion models. The resulting model of forest crossing by a vehicle will depend, to a large extent, on the quality of the forest structure data.