**1. Introduction**

As is well known, vegetation cover plays a fundamental role in the protection of soil from erosive processes. Recent works [1–3] have developed research for the calculation of soil erosion with the Revised Universal Soil Loss Equation (RUSLE), using multispectral information in the visible bands and infrared mainly, for the analysis of vegetation cover. Additionally, [4, 5], used multispectral images of Landsat and Sentinel satellites to evaluate vegetation cover (C FACTOR RUSLE) through image classification processes, with spatial analysis tools for GIS software. The researchers conclude that the integration of remote sensors with GIS for the assessment of vegetation cover and soil properties represent suitable methods for forecasting changes in land use and accurately and easily measuring conditions that could lead to soil loss in the future.

With the current advances in Geospatial Technologies a number of airborne sensors are available in Unmanned Aerial Vehicles (UAVs) that dramatically improve the accuracy and resolution of information. Complex algorithms where used to detect topographical changes in agricultural surfaces with UAV images at different angles finding that vertical images are the most accurate to generate surface models that can be used in topographical evaluation, indispensable for the study of soil erosion [6]. On the other hand, the use of UAV has been reported to study the characteristics of the soil surface modified by the leveling, finding that these activities lead to a greater generation of runoff and sediment production [7].

There is a diversity of sensors for the use of UAVs in precision agriculture [8]. For the monitoring of crop and soil processes, the most commonly used sensors are those instrumented with multispectral cameras followed by thermal and hyperspectral camera and in the lastly RGB and infrared. Most works develop aerial monitoring processes that use machine learning or image processing techniques that include traditional indexes with multispectral bands.

The incorporation of multispectral sensors into UAV instruments, significantly increase costs, [9], and limit access to such technologies. For small producers with less economic resources it is proposed to use some alternative methods and indices, calculated and validated using conventional optics with visible bands [10–13]; accurate and reliable results are reported to analyze vegetation cover and its protective effect on soil. This possibility represents a strong competitive advantage for the processing of lowcost geospatial information relatively accessible to a larger number of users.

Permanent monitoring of vegetation cover is important to ensure sustainable management of agricultural activities, with a significant role in reducing water erosion. Beniaich used uncalibrated RGB images generated from a digital camera in an unmanned aerial vehicle (UAV), to assess 11 vegetation indices in the Bean and Mijo cycle study [11]. Vegetation indices with visible bands were effective tools for obtaining the soil coverage index compared to standard methods, resulting in these most practical and efficient rates in frequency and coverage area during the growing cycle.

In addition, orthomosaics in RGB have been used in multi-time periods to study physical and chemical characteristics of the soil. Better details were found for digital soil mapping, with multi-time-effective images and a classification overseen by the maximum likelihood method [5].

In summary, it shows the availability of a good variety of thematic indices within the visible range of the electromagnetic spectrum that offer the possibility of accurately and rapidly differentiating vegetation from another type of coverage on soil. For the analysis of this type of indexes it is very important to keep in mind that several of them do not respond to standardized formulas, therefore the resulting magnitudes can present a high fluctuation, and require processes of reclassification and interpretation of data according to each case.
