## 1 Introduction
Additionally, [PERSON] et al. [13] described the possibilities of the horizontal and vertical forest structure mapping from radar using data obtained by synthetic aperture radar tomography. The use of radar methods is, however, limited in terms of the accuracy of the determination of the characteristics of individual trees. Furthermore, Landsat or Sentinel 2 global satellite data can be used to monitor forests before and during a fire. Sentinel 2 with a multispectral instrument (MSI) with 13 spectral channels in the visible/near-infrared (VNIR) and shortwave infrared spectral range (SWIR) and three bands for vegetation mapping can provide the crisis management with actual data in the shortest possible time, especially during a forest fire. The data accuracy (about 20 m) does not allow a more accurate mapping of the internal forest structure--see, e.g., [PERSON] et al. [14]. Technologies for monitoring and modeling the spread of a fire are divided into stages: prediction, during the fire, and post-fire [15]. [PERSON] and [PERSON] [16] described the technologies of detection and the spread of the forest fires by using satellite-borne remote sensing techniques. However, the technologies of fire monitoring and distribution are limited by the availability of up-to-date data from satellites, planes, UAVs, or terrestrial observations. We also need to know the prediction of the spread of a fire to deploy rescue vehicles. [PERSON] et al. [17] described possible solutions using a physical model for the forest fire spread rate. This model successfully evaluated wind and slope effects of a fire on forest vegetation. The above-mentioned factors and technologies are very important for the teams (fire brigades, military units, health services, and police) that are deployed to rescue people and reduce the damage during forest fires. Remote sensing support is very important for rescue units when they are moving across vegetation before and during a fire and, also, for the decision to deploy aircraft. We can use LiDAR and aerial image data to create a navigation analysis for rescue (fire brigade or military) vehicles--see also [18; 19; 20]. These data can be supplemented by active fire scenes using infrared sensors or aerial or UAV images. Among the most effective data sources for Cross-Country Movement (CCM) navigation and optimal pathfinding across a forest are LiDAR data and the products of its analysis. A prerequisite for the success of this analysis is an up-to-date picture of vegetation data obtained by laser scanning. This precondition is especially crucial for forest stands, where data become quickly outdated due to vegetation growth. The primary focus of this article was the LiDAR data update for the forest stand structure, a simulation of the creation of a forest structure with the subsequent creation of a model for navigating the movement of a rescue vehicle between trees as obstacles in the terrain. The reason for designing the method of detecting the current forest structure was that LiDAR data in the Czech Republic is gradually becoming obsolete as a result of tree growth. The following procedure was chosen: (1) Selection of the most common type of forest stands in the territory of the Czech Republic with the predominant spruce tree (Picea abies). Obtaining LiDAR data characterizing DSM with a density of 1 \(\times\) 1 m. (2) Obtaining inventory data on the growth of spruce trees from MENDEL University, Brno. (3) Detection of DSM accuracy by geodetic and photogrammetric method. (4) Corrections of tree heights due to DSM density and tree growth. (5) Creation of forest structure by random distribution. (6) Selecting a simulated area where a fire could occur (older, drier forest). (7) Calculating the simulated shortest route for a particular vehicle (outside the area of the fire). The research results presented in this paper represent a new methodology of updating a digital surface model (DSM) or canopy height model (CHM) using the equations of tree growth and vegetation growth curves.