## 6 Discussion
### Study Site 2  
UAV burned area analysis can hardly take advantage of multitemporal acquisitions for the computation of severity indices as its application is site-specific and \"on demand\", usually adopted after the event [86]. Nevertheless, this complexity is overcome with the increased detail and completeness of information deriving from the high-resolution sensors [87]. Consequently, indices computed from these images allowed for a more precise distinction of the thresholds necessary for the separation of different severity classes in the study area. This achievement, combined with ANNs (artificial neural network), proved to be an effective solution for burned area mapping and active fire individuation, also when tested with field validation [88]. Oftentimes, the adopted UAVs are military-grade vehicles equipped with multispectral sensors, specifically modified for the task [89]. The range of sensors these platforms can fit depends on the carrying capacity and the effective benefit they can provide to the analysis. Indeed, multispectral sensors can be coupled with LiDAR, thermal, optical, and hyperspectral sensors [90; 91; 92; 93]. Burned area mapping is operated to determine the extent of the burned area and to investigate fire severity affecting the soil-vegetation interface. Different fire severity classes correspond to heterogeneous modifications of soil and vegetation. The effects of fire severity on the soil and vegetal fractions cause the removal of vegetative cover, soil water repellency, and increase soil credibility as a consequence of organic matter and root breakdown. All of these factors combined strongly influence soil hydrological budget components as runoff, infiltration, and evapotranspiration [94; 95]. These parameters influencing the response of burned areas over time result in a modification of hydrogeological phenomena processes. These phenomena are landslides, debris flows, and general slope instability events [96]. Therefore, UAV multi-sensor acquisitions are strongly beneficial in the assessment of the parameters influencing slope instability processes. The adopted UAV mapping procedure proved to be a powerful tool in the burned area mapping due to its quickness of deployment, data collection, and accuracy. The three-dimensional representation of the orthophoto via a dense point cloud allowed us to investigate the effects of fire on different vertical sections of the vegetative cover, highlighting the influence of hill aspect, slope, and vegetation structure, Figures 16 and 17[97]. The possibility to visualize the NDVI index value on the point cloud allowed the selection and removal of points not representing the investigated area. As an example, to map the area using as discriminant the NDVI value of the ground cover, we could filter out the points that were over a certain z quota to obtain an index only representing the understory. Nevertheless, this result was obtained with image acquisition geometry performed with the sensor tilted towards the vertical elements of the woods, acquiring an under-the-canopy perspective. Generally, as the vegetation cover is dynamic and fast changing and experienced with PRISMA, timely acquisitions strongly influence the performance of the procedure. Indeed, the immediate application of the method after the fire would have resulted in less vegetation resprouting (_Robinia_, fern), reducing the index blurring and favouring a neater severity analysis and index distinction. The burned area perimeter delimitation computed via NDVI and ground-based mapping resulted in products differing for shape, detail, boundary position, and areal extent. The main differences in the boundary position were recorded in portions of the area with harsh morphology. In these areas, fire fighters' ground-based mapping included within the burned perimeter unaffected portions, as they adopted a compromise between burn marks and area accessibility. This difference can be only acknowledged by the comparison with UAV-based NDVI.