## 1 Introduction  
The paramos are high mountain ecosystems of volcanic origin, located between 3500 and 5000 m.a.s.l, and extend from the Andean Mountain range of Merida in Venezuela, through Colombia and Ecuador to northern Peru. Ecuadorian paramo ecosystems cover an area of 1,250,000 ha and extend from the northern border with Colombia to the southern border with Peru, through the Andean corridor [1]; the Andean zones harbor a large number of alpine ecosystems, with about 628 varieties of endemic plant species, equivalent to 15% of the vegetation of the area and 4% of the total vegetation of the Ecuadorian territory [2]. The environmental services (ES) of the paramos are fundamental for the development and equilibrium of their environment and are characterized by their high capacity for carbon sequestration, water regulation, and high levels of biodiversity [3]. Runoff from paramos is estimated to be between 506 and 933 mm of water per year. This value is equivalent to 2/3 of the annual precipitation of the Altiplano region. The paramo can retain up to 400 t/ha of carbon [4]. Population growth in the paramo zones is accompanied by economic activities that cause changes in the soils; the primary economic activities in the study area are based on agriculture and livestock, while the secondary activities are based on mining and forestry. Most of the settlers engaged in agriculture plant crops without any planning system that included fallow periods and soil spatial logistics [5]. Cattle ranchers burn Andean vegetation to fertilize the soil to increase pasture productivity [6]. At first glance, silvicultural activities favor ecosystem stability by planting trees in the paramo; however, this is not true, since tree planting eliminates native vegetation in the area, causing alterations in the ecosystem [7]. The environmental impacts caused by economic activities are associated with water availability and soil fertility. Studies of the causes that contribute to the degradation of ecosystem valuation (EV) related to paramo soils are key to identifying the moment when the ecosystem will move from a stable to a critical state [7; 8]. In relation to land use assessment, several studies recommend the use of remote sensing data and geographic information systems (GIS) to monitor areas that are difficult to access [9; 10]. According to [PERSON] et al. [11] significant progress has been made in satellite image classification due to the emergence of machine learning methodologies. There is a large body of research focused on the study of land cover classification techniques from satellite information [12]. Several studies have evaluated land use transitions using multi-criteria analysis (MCA) to assign weights to various factors based on input from decision-makers. This method facilitates the evaluation of the relative effects of each criterion in land use modeling. However, the subjectivity of the input values for the weights represents a limitation of the model [13]. Other studies have used methods such as k-means and support vector machines. The k-means method uses MODIS data that have a high temporal observation frequency but limited geographic resolution. This methodology is suitable for the analysis of natural disasters due to its short tracking interval. Support vector machines (SVMs), base their technique on supervised learning classification and identify the best hyperplane to achieve a clear separation between a dataset. Unfortunately, the practical use of SVMs is limited by the quality of their hyperparameter settings, which have a direct impact on their performance [14]. The maximum likelihood algorithm, which is also used in soil analysis, assumes that the data follow a normal distribution function to assign the probability that any pixel belongs to each of the classes. However, the hypothesis that reflectivity data follow a normal distribution is not always satisfied and must always be verified [15].