## 1 Introduction  
In the South American Andes, the _Polylepis_ forests hold significant conservation importance due to their roles in regulating water flow, facilitating soil regeneration, and retaining essential nutrients and sediments [1; 2; 3; 4]; however, they are some of the most threatened ecosystems [5] mainly due to anthropogenic activities such as deforestation [1; 2], agriculture [6; 7], large-scale cattle ranching, and demographic pressure [5; 8]. Therefore, detecting the location of _Polylepis_ stands is crucial for maintaining these ecosystems, particularly in Ecuador, which experiences high deforestation rates and significant forest loss along the Andean slopes [6]. For instance, the remnants of native forests have been reduced from approximately 68% to 56% between 1990 and 2018 [9]. _Polylepis_ is generally found in protected areas (near lagoons, in rocky areas, canyons, and stream banks) of the Andes Mountains [10; 11] in Venezuela, Colombia, Ecuador,Peru, Bolivia, Chile, and Argentina, in altitudinal ranges from 1800 to 5200 m above sea level (m.a.s.l. ), in isolated stands with limited surface area and difficult accessibility [10]. However, certain factors that influence the distribution of _Polylepis_ trees are still not known in depth [12]. On the other hand, remote sensing techniques have been used to identify, characterize, and assess forest species in biodiversity conservation and ecosystem management. For example, the review by [PERSON] et al. of _Populus tremula_ L., an ecologically important species in boreal forests, identified the importance of integrating ecological knowledge with remotely sensed information to understand the biodiversity patterns of this species, as well as its suitability for mapping [13]. In other studies, [PERSON] and [PERSON] used remote sensing to improve Species Distribution Models (SDMs) to determine the distribution of alpine plant species [14]. Similarly, but in the Qilian Mountains (China), [PERSON] et al. classified four tree species: _Sabina przewalskii_, _Picea crassifolia_, _Betula_, and _Populus_ using satellite imagery (specifically Sentinel-2 imagery) [15]. Remote sensing has also made it possible to assess spatiotemporal changes in forest cover, biomass, plant species composition, and health, thus improving the ability to estimate biomass at the level of individual trees [16]; classify forests into species groups [17]; assessing the health status of urban forests [18]; understanding succession patterns in tropical forests to monitor their biodiversity [19]; or analyzing forest recovery under diverse climatic conditions [20]. Using specific satellite images also makes identifying and characterizing species possible, as evidenced in the works cited above. Another important aspect refers to the Sentinel mission images. Sentinel is a satellite constellation part of the Copernicus program of the European Space Agency (ESA), providing free and medium-resolution satellite imagery. In this context, two of the missions are Sentinel-1 (S1) and Sentinel-2 (S2). The first (S1) has a Synthetic Aperture Radar (SAR) sensor that generates its energy wave and measures backscattered radiation from the ground [21] and can penetrate cloud cover and obtain data in different atmospheric conditions [22]. In addition, S1 mission data are used in the construction of forest masks [23], in the assessment of physical characteristics and properties such as the roughness, water content (moisture), and structural geometry of forest species [24], and in the detection of forest degradation and regeneration [25]. However, it can have substantial distortions due to the effect of topography [23]. S2 optical images are used in the detection and monitoring of land cover and other applications through the MultiSpectral Instrument (MSI). Thanks to the operation of two similar satellites, S2A and S2B, the image acquisition of the same area is achieved every five days.