## 3 Results and Discussion
### Evaluation of the Vegetation Regeneration in Fire-Affected and Treated Areas Using VARI  
The comparison of the mean VARI values under the four studied land conditions between the two remote images shows that, at the catchment scale, VARI was higher (0.009 \(\pm\) 0.046) under CFDs and lower (\(-\)0.034 \(\pm\) 0.035) in UB areas. This appears again unrealistic, further confirming the misleading meaning of land cover images captured by UAV. Moreover, the differences among the four land conditions were not significant (\(p\) = 0.724) (Figure 8a).  
Figure 7: Correlations between VARI and vegetation cover at catchment (**a**), and hillslope (**b**) scales surveyed by LANDSAT8 and UAV images (2016) in Sierra de Los Donceles forest (Castilla-La Mancha, Spain).  
In contrast, when analyzing the images by UAV, the highest VARI (4.578 \(\pm\) 0.230) was found in the UB areas (indicating the most extensive vegetation cover), while the lowest value was detected in the areas burned but not treated (VARI of 3.024 \(\pm\) 0.853). However, in contrast with field surveys, vegetation regeneration was higher in the land treated with CFDs (mean VARI of 4.270 \(\pm\) 0.350) compared to the areas with LEBs (3.889 \(\pm\) 0.416) (Figure 8b). As for field surveys, the differences in VARI were significant (_p_ < 0.05) at this smaller scale using the UAV. Lack of correspondence between the post-fire land treatments suggests that UAV is a viable technique in detecting the variability of vegetation cover only when the difference in VARI mean values is noticeable (e.g., between burned and unburned areas); conversely, the contrasts between areas with similar VARIs may be quite misleading, for instance, when the effectiveness of a treatment has to be evaluated. This result is in close accordance with the indications by [PERSON] et al. (2016), who suggested the use of aerial and satellite imagery characterized by high or, better, very high spatial resolution for an effective support to post-fire management (burned area mapping, fire severity assessment, post-fire vegetation monitoring).  
Comparisons to other published data about evaluations of post-fire vegetation recovery using remote sensing and greenness indices reveal that SAR images perform better compared to optical images in estimating forest regeneration, particularly when object-based classification procedure is applied (nearly 90% of accuracy) ([PERSON] et al., 2016). Moreover, [PERSON] et al. (2016) found a better reliability of NDVI (Normalized Difference Vegetation Index) in capturing the diverse vegetation regeneration in both natural and managed areas as well as before and after fire occurrence compared to NBR index, thanks to the data processing using detrended fluctuation analysis (DFA). LANDSAT time series analysis with NBR application was a useful means of describing and analyzing post-fire vegetation recovery across mixed-severity wildfire extents ([PERSON] et al., 2016), while, in contrast, [PERSON] et al. (2016) reported that dNBR is an imperfect indicator of post-fire effects on vegetation and soil, when the burned areas are affected by different burn severities and are of a highly variable patch size. Regarding the use of drone images, [PERSON] et al. (2016) highlighted the more detailed spatial information provided by the drone orthomosaic compared to WorldView-2 satellite imagery in vegetation regeneration of heterogeneous burned areas.  
Overall, the use of UAV systems for vegetation recovery monitoring is still under development, since these systems have a great potential not yet fully valorized; however,  
Figure 8: Mean values of VARI surveyed by LANDSAT8 (**a**) and UAV (**b**) images (2016) among four land conditions after the wildfire of 2012 in Sierra de Los Donceles forest (Castilla-La Mancha, Spain).  
this use is still limited by some constraints, such as battery life and availability of cameras with suitable spectral range, as well as cost [22].