## 5 Discussion
Considering the \(K\) factor's influence, which is a factor that does not take into consideration the post-fire state, it showed some interesting patterns in Varympompi, where moderate to high values corresponded to high erosion risk. However, due to the fact that soil properties hardly ever face significant alterations over the years, the \(K\) factor is considered to contribute effectively in soil erosion assessment offering reliable results. With regards to the \(P\) factor, it did not play an important role in the model's results, making it evident that it is not taken into consideration in many studies [58]. The NDVI-based \(C\) factor before and after the wildfires clearly showed a homogenous increase within the affected areas contributing to the soil loss increase. In the last factor considered, the \(R\) factor, there was a moderate correlation with the erosion risk which in some cases was related to increased soil loss. Some comparisons can be made between burn severity and land cover with the erosion risk. In Schinos, higher erosion risk was found in coniferous forests, transitional woodland-shrubs, and mainly in areas of higher burn severity values. Passing to Varympompi, higher erosion risk values were located in the burned forest areas. It is worth mentioning that in the discontinuous urban fabric, in the north of the area, the risk was also high, presenting a trend of higher soil loss to the higher burn severity areas. In Lavreotiki, there was not a clear correlation between burn severity and erosion risk while the significant concentrations in high and moderate soil erosion risk were found in sclerophyllous vegetation and natural grasslands. Lastly, the soil erosion risk was higher in forest areas within the Vilia burned area and areas of moderate-high values. Lately, RUSLE modeling has been applied to several studies in the Mediterranean region regarding post-fire assessment [2; 26; 47; 27]. [PERSON] et al. [2] implemented RUSLE modeling in Central Greece so as to detect spatial correlations regarding the erosion risk after a fire event. Results of this study demonstrated as well that there was a rise in soil loss values immediately after the fire incident. The outputs of this study were also formed mostly based on the \(LS\) factor, while there was also a correlation between factors \(R\) and \(C\) and the outputs which could be attributed to the total length of the study area. Another interesting study is the one of [PERSON] et al. [47], in which RUSLE modeling was applied in Crete, Greece in order to evaluate the factor's influence in soil erosion mapping. The outputs showed that the \(R\) and \(C\) factors seemed to affect mostly the erosion risk assessment. In addition to our study, the \(LS\) factor was characterized as a static factor regarding its effect on soil loss mapping. Similarities were also detected in a study published by [PERSON] et al. [26] regarding the erosion risk after the wildfires that broke out in Greece in 2021. Results also demonstrated a significant increase in soil loss after the fire incidents using a Boolean logic-based model. Comparable patterns to our study have been also demonstrated in research carried out by [PERSON] et al. [27], in which soil erosion risk was assessed after wildfire events in a southern Italy region. In this research, soil erosion risk is significantly increased after the fire, complying with the spatial patterns of our results. At this point, the empirical part of the study is evaluated. In the mapping of burned areas, satellite image availability plays an important role operationally. Sentinel-2 has a very good temporal resolution but all the optical sensors can be severely affected by cloud and smoke cover over an area of interest thus limiting the burned area mapping capabilities which is a factor that affected the image selection of this study. Although, despite the fact that the 10 m resolution of Sentinel-2 is sufficient, the availability of higher resolution datasets could enhance the mapping's detail.