## 4 Discussion  
Studies analyzing the influence of land-use transitions on the variability of ecosystem valuation (EV) in mountainous areas with complex topographic characteristics can be efficiently performed using satellite land information and economic indices from existing markets [60; 61]. Some authors [50; 59] state that the historical detection of land-cover changes as a function of EV is still a challenging path, due to the lack of reliable and extrapolatable information on the economic value of environmental services (ESs); the scarcity of data causes complications in understanding the importance of natural resources from an economic perspective. Despite the difficulties in developing reliable ES studies, this study, as well as other research [25; 26], has shown that the use of remote-sensing tools and geographic information systems effectively facilitates the recognition of the trend of land-cover changes over time. The quantitative information of graphic transitions allows the association of economic values linked to the productive activities of the study site by means of soil trajectory analysis methods, as is the case of the opportunity cost methodology [24; 62]. For this case study, the land-cover classification was carried out in two phases: in the first phase, a boundary between water bodies and land systems was determined; in the second phase, the different land covers were detected and classified. In the determination of the extraction accuracy of water bodies, it was detected that accuracy problems can be relevant in areas where the background soils include areas of low albedo or shade. Shadows in satellite images cause errors due to reflectance patterns similar to those of water zones; this resemblance can reduce the extraction of aquatic systems. In low-reflectance zones where there are shaded areas without water, simple detection methodologies will not be able to satisfactorily classify pixels in images with water and without water [36; 63]. Therefore, three different water indices were included in the present study: MNDWI, NDWI and AWEI. By means of Pearson's \(r\), it was determined that the correlation between the indices was good; therefore, each index provided important information for the detection of water bodies. The index with the highest classification efficiency was the MNDWI, with an overall average accuracy of 91% and a Kappa of 89%. The application of zero as a threshold value can generate a more optimal accuracy in the extraction of water bodies. In the second phase of classification, the control zones were sufficient to successfully classify the coverages of the study area; the SVMs classifier used in this phase obtained an overall accuracy of 84.81% and a Kappa of 82.94%. The adaptation of the covariance matrix in the adjustment of the SVMs hyperparameter selection improved the efficiency of the model, as it allowed the development of a robust architecture of the algorithm in terms of automation. The information obtained through this classification allowed for understanding of the state of conservation and degradation of Andean ecosystems. From the analysis, it was possible to understand that factors linked to the economic growth of the inhabitants are transcendental to understand the degradation of the natural resource. Changes in land use in the Andean Mountain Range should be understood as socioecological and economic changes, as viable conservation strategies can only be generated from an environmental, social and economic approach [27; 64; 65]. The variability of the EV of the study area was manifested as follows: In 2000, the EV, as a function of carbon dioxide content avoided, was USD 8.00 \(\times\) 10\({}^{6}\). In 2010, it was USD 6.00 \(\times\) 10\({}^{6}\) and in 2020, it was USD 5.00 \(\times\) 10\({}^{6}\). The results per hectae in relation to carbon dioxide mitigated in 2000, 2010 and 2020 were USD 30, 226 and 188, respectively.