## 2 Materials and Methods
### Forest Fire Susceptibility Index
Coniferous forest, terrain slopes with value over 35 degrees, slopes oriented to the south, and also areas that are the least remote to residential and other buildings types, roads, and agricultural areas are considered the most susceptible to forest fire occurrence. Terrain slope and aspect were derived from a 25-meter cell size digital elevation model over Europe (EU-DEM) [34]. Slope, expressed in degrees, was then reclassified based on certain values with assignment of values from 1 to 5 to each class. Distance from residential buildings and other buildings types and roads was calculated according to data digitized from open satellite imagery. Vector data (buffer zones) were then rasterized to layer with the same cell size and projection of layers with data obtained from EU-DEM. Vegetation type index was calculated on the basis of the random forest (RF) classification of Sentinel-2 multispectral imagery. Remote sensing technology can be used to obtain forest information from areas with rough terrain or that are difficult to reach. Therefore, it is necessary to explore the potential of remote sensing data to obtain adequate information about forest types [35]. The random forest (RF) algorithm is an influential method of collaborative learning developed for classification, regression, and unsupervised learning [36]. The objective of RF is to identify the appropriate model for analyzing the relationship between independent variables and a dependent variable for weight determination for each factor [1]. Sentinel-2 images Level-1C from 11 July 2017, 19 October 2017, and 22 April 2018 [37] were used for the purpose of obtaining the vegetation types in Nature Park Golija. With semi-automatic classification plugin (SACP) in QGIS software, the atmospheric correction of Sentinel-2 granules was performed, together with converting to reflectance, image mosaicking, and clipping to the nature park area. The following Sentinel-2 bands were used: blue (492 nm central wavelength), green (560 nm), red (665 nm), NIR (833 nm), and SWIR1 (1614 nm), for each aforementioned date. All bands have a spatial resolution of 10 m, except SWIR1 of which the spatial resolution is originally 20 m, but was automatically resampled to 10 m with SACP. Along with them, the Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) were also used for each aforementioned date, in order to distinguish different types of vegetation more precisely, but also to distinguish artificial land cover and barren soil/rock. NDVI and BSI indexes are calculated as follows:  
\[\text{NDVI}=(\rho\text{NIR}-\rho\text{Red})\ /\ (\rho\text{NIR}+\rho\text{Red}) \tag{3}\]  
\[\text{BSI}=((\rho\text{SWIR1}+\rho\text{Red})-(\rho\text{NIR}+\rho\text{Blue} ))\ /\ ((\rho\text{SWIR}+\rho\text{Red})+(\rho\text{NIR}+\rho\text{Blue})) \tag{4}\]  
where \(\rho\text{Blue}\), \(\rho\text{Red}\), \(\rho\text{NIR}\), and \(\rho\text{SWIR1}\) are the reflectance from the blue band (B02), red band (B04), near-infrared band (B08), and shortwave-infrared band 1 (B11), respectively. The training/test dataset consisted of 3.2 km\({}^{2}\) digitized data classified in 8 classes: artificial areas, agricultural areas, grassland, shrub, broad-leaved forest, mixed forest, coniferous forest, and barren soil/rocks. Polygon data were transformed to point data, so they match 10 m pixels from the Sentinel-2 bands. Vegetation type data for Nature Park Golija were extracted based on RF in RStudio by the best combination of used Sentinel-2 bands, NDVI, and BSI, with an overall accuracy of 97.7% and a kappa coefficient of 0.97. Vegetation type index was obtained by the classification of random forest raster