## 5 Discussion  
In this study, we combined elevation, slope, aspect, TWI, land cover, settlement location, and road networks to create a fire risk model. Subsequently, we applied the model  
Figure 16: (**a**) The extent of the fire on 9 July 2021 relative to the fire risk map of 2021. (**b**) The updated fire risk distribution inside the affected area, after the fire on 9 July 2021. Figure 15: (**a**) The extent of the fire on 9 July 2021 relative to the fire risk map of September 2019. (**b**) The fire risk distribution inside the affected area, before the fire on 9 July 2021.  
to the National Park Forest of Dadia-Lefkimi-Soufli. Topography, vegetation, and human activity are the major categories represented in our model that affect wildfire generation and spread. Similar factors have been used by other studies, in combination with AHP to assign weights, in order to calculate fire risk in various areas [14; 24; 25; 26; 27; 28; 30]. In our analysis, land cover was proved to be the factor with the highest weight. This does not mean that topography and human activities are of less importance. In fact, human activity is the main cause of wildfire ignition. Approximately 93% of fires in Northern Europe are caused by humans, either intentionally or unintentionally [13]. Land cover, besides having the highest weight in the resulting model, also changes more frequently compared to human activities (road network, settlements) and topography. For this reason, we derived land cover classification from Sentinel 2 imagery since they are georeferenced, frequently updated, freely available, and have suitable spatial resolution. Classification was carried out with the use of SVM algorithm, since it has already been used in similar applications with promising results [31; 35]. With the combination of AHP (to determine the fire risk) and the SVM algorithm (to classify the land cover), we managed to identify the baseline fire risk of the National Park Forest of Dadia-Lefkimi-Soufli for September 2019 and September 2021. According to those fire risk maps, most of the areas in the National Park Forest of Dadia-Lefkimi-Soufli are classified as high risk. More specifically, the map of 2021 reveals that 5% out of the total area of the National Park was classified as extremely high risk, and 48% was high risk. Consequently, fire risk distribution in the National Park suggests that local authorities should be at high alert, especially during heatwaves and near the areas classified as extreme high risk. We also examined in detail the impact of the land cover change on fire risk in the areas affected by the two major fire incidents (October 2020 and July 2021). It is concluded that the average risk of those areas dropped significantly, while the rest of the fire risk map remained relatively unchanged between September 2019 and September 2021. It is evident that land cover changes caused by past fires have a significant drop on the fire risk of the affected areas. The main cause is the loss of the highly flammable pine tree forest near the settlement Lefkimi. Considering the effect of land cover changes on fire risk mapping, it is important that fire risk management plans incorporate those changes and reallocate resources accordingly on a local scale. In this way the SVM algorithm, along with other classification algorithms [33; 34; 35; 51], can offer a powerful tool for updating fire risk maps year by year. Finally, it is notable to point out that both October 2020 and July 2021 fires started near areas classified as 'extreme high' fire risk. This is yet another validity indicator of the proposed fire risk model. Additionally, the above-mentioned fire incidents spread mostly at areas classified as 'high risk' (Figures 13 and 15). We acknowledge that daily risk maps at the national [52] and European levels [53], which are mostly derived from weather data, are freely available and easily accessible to all stakeholders, including the citizens.