## 1 Introduction
The remote sensing techniques (using satellites and drones) are new tools to make land surveys quicker and cheaper [22]. Remote sensing is usually applied in fire-affected areas for fire risk and fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire monitoring vegetation recovery [23]. The remote sensing techniques using satellite images are able to evaluate post-fire regeneration with increasingly improved spatial, temporal and radiometric resolutions [24]. In the last decade, the appearance of unmanned aerial vehicles has increased the use of remote sensing application to agro-forestry [25; 26]. UAV technology is promising for monitoring vegetation regrowth, since the spatial resolution and temporal intervals of surveys are not dependent on satellite orbits [23]. Despite their versatility and low cost, UAVs are not widely used to survey wide areas due to legal and technical limitations (e.g., autonomy, payload capacity) [27]. The assessment of wildfire effects and post-fire regeneration using remote sensing has been done through the calculation of spectral vegetation indices [28]. These indices are calculated using the electromagnetic wave reflectance data of vegetation using passive sensors [29]. Several studies have successfully developed and applied vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI; the Composite Burn Index, CBI; the Differenced Normalized Burn Ratio, DNBR, and the Visible Atmospherically Resistant Index, VARI [24]. In this regard, [PERSON] et al. [25] published a comprehensive review about remote sensing of post-fire vegetation recovery and highlighted the important role of remote sensing in the future as well as the need to perform more studies in Mediterranean areas. [PERSON] et al. [26] reported that UAVs provide data at fine resolution with the desired temporal resolution, which make them cost-effective and efficient data collectors. Regarding satellite images, [PERSON] et al. [29] was able to compare different multispectral satellite data to assess vegetation cover in abandoned lands and rewetted peatlands. [PERSON] et al. [30] analyzed post-wildfire recovery in different coniferous forest types in North America using the Normalized Burn Ratio (NBR) derived from LandTrendr images. The same index was used in the same environment by [PERSON] et al. [31] to monitor post-fire burn severity and vegetation response following eight large wildfires. In the Mediterranean Basin, post-fire vegetation recovery was assessed by [PERSON] et al. [32] in Portugal (using the VEGETATION sensor), [PERSON] et al. [33] in Italy (using satellite MODIS data), and [PERSON] et al. [34] in Greece (using optical and SAR data [35]. Regarding VARI, several applications have been carried out in different research fields. For example, [PERSON] et al. [36] showed that VARI outperforms other indexes in distinguishing historical wildfire data in southern California. In the same environment, [PERSON] et al. [37] demonstrated better performances of VARI compared to another normalized index (Normalized Difference Water Index, NDWI) in monitoring chaparral moisture content. [PERSON] et al. [38] found that VARI was more effective in estimating the fraction of vegetation cover and recognizing the different land use compared to NDVI index, observing a standard error lower than 8%. From these studies, the feasibility of VARI to estimate variable levels of land cover emerges. However, applications of this index for evaluation of vegetation regeneration in fire-affected areas are very scarce. Only [PERSON] and [PERSON] [39] calculated four greenness indices (among them VARI) to analyze post-fire regeneration of Mediterranean forests. [PERSON] et al. [40] proposed a comparative evaluation of restoration practices using remote sensing and GIS in naturally-regenerating and reforested areas of Greece. No research is available about the estimation of vegetation recovery after hillslope stabilization techniques using VARI applied to satellite or UAV surveys.