## 5 Results
### Study Site 1
#### 5.1.3 Hyperspectral Burned Scar Detection Assessment  
The classified hyperspectral image (Figure 13) provided an adequate individuation of high severity areas, especially in the northwest portion of the fire perimeter. Unburned vegetation patches within the fire perimeter were correctly classified. However, in contrast with this, the area outside the fire perimeter was also classified as previously affected by high severity.  
Given the amount of time elapsed between the fire and the image acquisition, the actual classification of the whole scene was not precise enough to delimitate the fire perimeter nor severity. This meant that in order to achieve a correct classification, Prisma needed to compare the burnt area with a previously computed fire perimeter image. The abovementioned classification could work as a guideline for the determination of the fire effects on natural spectral signatures. Indeed, extreme classes as high burn severity and unburned areas were accurately detected. Nevertheless, the classification of intermediate classes was partially impaired by the vegetation regrouting phenomena. To determine whether the classification performed better within the fire-affected area, we used a mask of the fire perimeter to run the tool. This fire perimeter-bounded classification (Figure 14) performed better regarding the extent of homogeneous classified areas. Indeed, if in the whole scene classification (Figure 13) only extreme classes were correctly classified, with the application of the mask we saw an improvement in the capability of mapping intermediate classes. The results suggest that in order to increase the accuracy of the classification, the use of a mask could improve the accuracy of the classification. This could be due to the fire spectral signature modification combined with vegetation regrouting, occurring from the fire event until the image acquisition. This scenario is a complex spectral mix where charred soil and tree stumps signatures contribute with the newly settled vegetation ones, creating mixed signatures variable in reflectance and depending on the fire severity of  
Figure 12: fCover index timeseries computed for: (**a**) the burned area (medium severity); (**b**) unburned area.  
Overall, high-burn severity areas suffer marked modifications regarding soil denudation and charring resulting in a longer modification of their spectral behavior. In Figure 15, the areas interested by intermediate severity classes were more abundant and better resembled the Sentinel-2 RBR distribution. Generally, the classification workflow proved to find resemblance among the spectra of the different classes. This can be inferred by Figure 15 homogeneity and the statistical validation provided by the tool itself. The accuracy and probability of correct classification is reported, highlighting the accuracy for extreme classes with less sound classification for intermediate values. The lower performance in intermediate classes can be addressed to the variability in the acquired spectra as lower severity resulted in less alteration and vegetation destruction. However, fire severity, post-fire weather conditions, topography, and soil properties were all factors that influenced recovery patterns and therefore the areas' spectral signatures [84]. As shown above, PRISMA accuracy in assessing the burns scar was not sufficiently performing through the application of the classification workflow tool. Nevertheless, the tool proved how spectral modifications operated by fire persisted through time and affected vegetation spectral signatures growing in the same area.