## 1 Introduction  
Forest fires are very frequent crisis situations, especially in dry or arid landscapes [1]. The prediction of forest fire occurrence depends on knowledge of the factors that affect the fires and on the technologies that facilitate the monitoring and modeling the spread of the fire. [PERSON] et al. [2] analyzed the most common human and environmental factors driving forest fire ignition. The primary factors that directly cause forest fires are natural (lightning strikes, seismic and volcanic activity, etc.) or human (carelessness and activities such as as, slash-and-burn agriculture, fire-fallow cultivation, machinery sparks, discarded glass bottles or cigarette buts, military activity, etc.). The factors that determine fire spread are as follows: forest type and structure (distances between trees, \(DBH\), canopy height, tree crown density, etc. ); meteorological conditions (precipitations, temperature, wind speed, air humidity, cloudiness, soil moisture, etc. ); topographic (morphological shapes of terrain, orientation of relief slopes, etc. ); geological and pedological (underground structure, soil structure, and terrain surface color); and season and time of day, which determine the amount of available sunlight and temperature, etc. The technologies that facilitate the monitoring and modeling of a forest structure and the spread of forest fires include the sensor types for vegetation data collection and forest structure determination and technologies for monitoring and modeling the spread of fire. [PERSON], [PERSON], and [PERSON] [3] described the Laser Vegetation Imaging Sensor (LVIS), which operates at altitudes of up to 10 km aboveground and is capable of producing data for topographic mapping with dm accuracy and vertical height and structure measurements of vegetation. The LVIS instrument is also suitable for subcanopy ground elevation mapping. [PERSON] et al. [4] described many of the initial studies of the application of LiDAR for forestry focused on verifying through statistical analysis that LiDAR could be used to accurately measure forest attributes. The focus has been on canopy tree heights given the nature of this attribute as a predictor variable for other forest attributes, such canopy density. [PERSON] et al. [5] analyzed existing algorithms and methods of airborne laser scanning that are used for extraction of the canopy height and individual tree information. [PERSON] and [PERSON] [6] described an algorithm for detecting trees in a semiautomatic way. [PERSON] and [PERSON] [7] analyzed the effects of forest growth on laser-derived canopy metrics. [PERSON] et al. 2004 [8], [PERSON] et al. [9], and [PERSON] et al. [10] provided the overview of LiDAR applications in forestry. By comparing these methods based on laser scanning, it can be stated that, at present, an approximate forest structure for modeling the movement of rescue vehicles can be determined. At the metric density of the DSM (CHM), the error in tree positioning can reach values in decimeters, sometimes up to meters, depending on the structure and type of the canopy. At the decimeter density of the DSM points out, it is possible to calculate the tree position errors in centimeters to decimeters. When comparing the possibilities of using LiDAR and aerial optical images, both methods have advantages and disadvantages. Aerial images provide both spatial and image information, but they do not allow, unlike LiDAR, full automation to determine the forest structure. However, the use of LiDAR and aerial optical images may be problematic in the area of fire because of the clouds or smoke generated by the fire. In these situations, radar methods can be used to measure forest parameters. The mapping of forest units by radar is described, for example, by [PERSON] et al. in [11]. [PERSON] et al. [12] compared the LiDAR and radar methods for determining the heights of the forest in three areas: boreal, temperate, and tropical. The correlations achieved confirm the possibility of combining the use of both forest mapping methods.