###### Abstract
The classification by minimum distance determines the types of coverage from the distance between the different pixels, where each pixel will be assigned to a class with respect to its minimum distance. This method is not very reliable because it overdclassifies the image, i.e., no pixel is left unclassified [19; 20]. Other researchers have used the parallelepiped method, which establishes limits for each class based on the maximum and minimum reflectivity values of each of the bands of a series of rectangles. The drawback of this method is that it does not allow modeling the dispersion of the control zones, due to the high correlation between the bands [21]. Artificial neural networks have also been used in coverage monitoring. This type of methodology has been developed from the weighted sum of the data of the input variables to the network; by means of a threshold function, an output signal is produced, and the results are adjusted by means of back propagation algorithms. Some of the most commonly used neural networks are random forest (RF) and the classification and regression tree (CART). The disadvantages of neural networks are that in the regressions predictions cannot be made beyond the range of values of the training set, the predictions are not continuous in nature and the predictions do not allow constant control of the model [22]. The support vector machines (SVMs) methodology bases its land-use recognition technique on supervised learning classification and identifies the best hyperplane to achieve a clear separation between a set of data. Some researches [23; 24; 25] have focused on analyzing the performance of SVMs against other classifiers such as RF, CART and the parallelepiped method. The authors claim that the SVMs methodology outperforms classifiers. However, its performance is limited by the quality of its hyperparameter settings. In relation to the analysis of ecosystem valuation based on land-use transitions, several studies have used methodologies that combine remote-sensing tools and monetary indices. For example, the contingent valuation method assigns an economic value to natural resources through the simulation of a hypothetical market by interviewing consumers, in which they state their maximum willingness to pay for a given good [26]. The drawback of this method is that the responses of the interviewees do not always represent reality. Other studies have determined the monetary value of environmental services using the revealed preferences methodology, which is based on observable data from actual choices in markets. This approach analyzes the value of ecosystem goods and services based on people's choices according to value-theoretic principles. This means that the ES included will belong to a subset of the total value of the service [27]. The scope of this method does not include non-use values. Researchers have also used the valuation method considering the cadastral appraisal and the opportunity cost methodology. The valuation method considering cadastral valuation calculates the value of the natural asset using the physical and geographical characteristics of the property that refer to the existence value of the land as a resource of agricultural potential. It is common to consider that land with poor soil quality is equally distributed, and moving to subsequent categories increases inequity [28]. The opportunity cost (OC) methodology assumes that the cost of using a resource can be approximated by the income foregone from other uses of the resource, i.e., it estimates how much income must be sacrificed and decides whether it is worthwhile [28]. For the land-cover study, this study used the SVMs method, due to its efficiency in land-use recognition, and made efforts to reduce the margin of error in the performance of the method based on evolutionary algorithms and effective validation error rates. Ecosystem valuation based on land-cover changes was determined using the opportunity cost methodology, including the relationships between the direct and indirect ESs of land use.