## 6 Discussion  
### Study Site 1  
The application of remotely sensed data was foreseen simultaneously with a field data validation campaign that could not be performed because of the COVID-19 pandemic. Nevertheless, the available data are still meaningful enough to address and uphold the discussion here proposed. We validated thorough comparison the recovery of the slope cover via Sentinel-2 and applying PRISMA remotely sensed data to evaluate their discrimination power to investigate burned areas. Although Sentinel-2 is widely adopted in research on post-fire related assessment [45], to our knowledge, this is the first study which adopted PRISMA data for acquiring additional hyperspectral information comparing it to Sentinel-2 computed indices. As reported in the results section, Sentinel-2 RBR has provided meaningful information regarding the fire severity that affected the area (Figure 6a,b), while most fire severity indices provide information only after the event RBR proved to be a useful instrument for the monitoring of vegetation recovery after the fire (Figure 6c-f). The applied classification allowed us to investigate the vegetation growth within the fire areas, making it possible to observe a phenomenon not previously considered, such as the immediate grass and shrubs resprouting following the fire (Figure 6b). The validation of the observations conducted with RBR has been performed through the comparison of biophysical indices as LAI fAPAR and fCover. All the computed parameters highlighted the temporal extent with which fire affected the area. Despite resprouting, as pointed out by the index graphs, two years after the event vegetation was still suffering lower leaf area index (Figure 7a-c; Figure 8a,b) and lower chlorophyll content (Figure 9a-c; Figure 10a,b). The indices agreed with the RBR temporal trend, and additionally allowed us to observe the phenological stages of vegetation: growth, diffusion, and greater chlorophyll content in spring until midsummer with a quiescence stage in autumn and winter. However, the establishment of stable and arboreal dominant vegetation ground cover was observed only after spring 2018 and still presented values 0.20 lower than unburned vegetation (Figure 11a-c; Figure 12a,b). The variations in soil properties, vegetation health, and recovery rates induced by fire determined an alteration of the abovementioned features, both in the land cover patterns and in their spectral signatures, as suggested by literature [85]. To assess the ability of PRISMA to detect the effects of fire on the area of interest, we examined the correlation between Sentinel-2 fire severity classes with PRISMA hyperspectral signatures, with  
Figure 18: (a) NDVI flame height evidence; (b) optical flame height. pixel-by-pixel comparison. This process led to an empirical classification of hyperspectral signatures that in turn led to a converging classification for burn severity as that computed via Sentinel-2. To our knowledge, this methodology is innovative since it provides an indication on ranges of hyperspectral signatures converging with Sentinel-2 data. The evidence collected with PRISMA proved that despite the elapsed time from the fire event until the acquisition, the alteration of the soil and vegetation persisted, highlighting a different recovery time depending on the severity the area was affected by. Additionally, PRISMA proved to be a valuable instrument for the determination of the residual fire severity in the investigated area basing the observations on the capability of the instrumentation and tool to correctly individuate different severity affected areas. Although this might be true, the application of such procedure must be aided with the adoption of a fire perimeter mask (Figure 15). However, because of the elapsed time from the event to the acquisition, hyperspectral images should be used with caution in order to evaluate burned areas after wildfires.