## 3 Results and Discussion
### Correlations of VARI with Vegetation Cover Using Remote Sensing Techniques  
Figure 5(a),b reports the maps of VARI distribution in the experimental areas at both catchment and hillslope scales. In particular, Figure 5(b) highlights that VARI is higher in the areas subjected to post-fire treatments and lower in burned and untreated land.  
Figure 5: Vegetation cover (mean \(\pm\) standard deviation of 62 plots) in plots under four land conditions (UB = unburned; BNA = burned and no action; CFD = contour-felled log debris; LEB = log erosion barriers) after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain). Mean values that do not share a lower case letter (top of graph) are significantly different from each other (HSD, \(p<0.05\)).  
A low and not significant R\({}^{2}\) (0.03, \(p\) > 0.05) was found regressing the LANDSAT8-derived VARI with the vegetation cover measured in field (Figure 6(a), catchment scale), while the regression of UAV-derived VARI versus vegetation cover was significant (R\({}^{2}\) = 0.84, \(p\) < 0.05) (Figure 6(b), hillslope scale). This difference is clearly due to the lower spatial resolution of satellite data compared to surveys by UAV.  
Figure 6: Spatial distribution of VARI surveyed by satellite (**a**) and UAV (**b**) images of 2016 among four land conditions after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain). Legend: Fajinas = log erosion barriers; condones = contour-felled log debris; sin tratamiento = burned and no Action; sin fuego = unburned.  
The regression equations for the correlation VARI versus vegetal cover (VC) measured in field was the following:  
\[\text{VC}=16.90\text{ VARI}+9.15 \tag{2}\]  
The vegetation cover distribution in classes according to the VARI classification by UAV is reported in Table 2 and confirms the spatial differences among the land conditions. By visually comparing the vegetation cover distribution according to the two VARI classifications (satellite and UAV, Table 2), no overlay among the same class was detected. This lack of correspondence further confirms the higher reliability of VARI estimated from UAVs to reproduce land cover compared to estimations by satellite, due to unsuitability of image resolution.  
The use of VARI to evaluate vegetation regeneration from UAVs has been widely studied in literature. To cite the most recent studies, VARI was used to analyze vegetation on different land uses (e.g., [36; 37; 38; 39; 56]), monitoring vegetation into water bodies (e.g., [57], and preparation of the Digital Elevation Model (DEM) [58]). This index shows significant correlation with crop height and yield [59; 60]. In burned lands, [PERSON] and [PERSON] [39] calculated VARI for analysis of post fire regeneration of Mediterranean forests. However, these authors reported that this index underperformed compared to other greenness indexes, such as the Excess green index (ExGI) and green chromatic coordinate (GCC) index, and the same was found for the green red vegetation index (GRVI). Despite this, these authors have demonstrated that low-cost UAVs may improve forest monitoring after disturbance, even in those habitats and situations where resource limitation is an issue. In general, VARI shows a minimal sensitivity to atmospheric effects [61; 62; 26].