## 4 Discussion  
In this work, we compared twelve scenarios resulting from combinations of Sentinel-2 (S2), Sentinel-1 (S1), PlanetScope (PS), texture GLCM for NIR PS band, and topographic data (elevation, slope, and aspect), using two target types (multiclass or binary) for the classification of _Polylepis_ stands. Among all scenarios, four (SC4, SC6, SC10, and SC12) demonstrated the highest reliability in Accuracy and Kappa. Although in several scenarios, the metrics did not show significant differences, selecting known review areas allowed for a better detection of the best scenario by _Polylepis_ discrimination, providing a qualitative assessment of each scenario's validity. The combination of Sentinel-2 and topographic data (S2 and TOPO) provides the most effective scenario for achieving high classification accuracy in a multiclass context. [PERSON] et al. (2022) [31] obtained the highest reliability with this same combination of variables using Random Forest. However, their study focused on discriminating against land use and land cover in the Andes, while ours specifically targeted the identification of _Polylepis_. Discrimination between _Polylepis_ and pine was more effective in a 10 m scenario (SC10 and SC12) despite the fact that the metrics Accuracy and the Kappa of scenarios SC4 and SC6 do not have a significant difference. This makes us think that a greater range of radiometric spectra promotes the distinction of _Polylepis_. This finding challenges the assumption that higher spatial resolution automatically translates to better classification results. The superior performance of the multiclass approach, as opposed to binary, suggests that capturing the variation within _Polylepis_ stands, rather than simply the presence or absence, is crucial for accurate mapping. By distinguishing between multiple classes, the model can account for the ecological diversity within _Polylepis_ habitats. This approach enables a more detailed understanding of _Polylepis_ distribution, which is essential for effective conservation and management. The stands of these forests often exhibit substantial heterogeneity due to environmental gradients, such as altitude, slope, and soil type. The reduction in variables using RFE is the best scenario (SC10); it was possible to reduce the number of variables from 27 to 11 (8 spectral and 3 topographic) without significantly affecting accuracy and Kappa. The selected variables were MSI, B9, B2, MSR, CCCI, B12, ARI, and NDVI of S2, in conjunction with aspect, slope, and elevation, characterized by the inclusion of elevation and slope as key variables, findings consistent with prior studies [11; 23; 24; 35]. Conversely, scenarios with the lowest reliability (SC1, SC3, and SC7) omitted topographic factors. This underscores the sensitivity of _Polylepis_ to altitude (typically above 3000 m) and slope (favoring drainage and sunlight exposure), highlighting the critical role of these variables in its growth. In classifying _Polylepis_ stands, the moisture content of vegetation, indicated by various spectral indices, plays a crucial role in distinguishing these areas. The Moisture Stress Index (MSI), alongside Sentinel-2 bands B9 and B12, is particularly sensitive to water content in the high canopy moisture. Band B2, while mainly used for detecting chlorophyll content, can also provide insight into vegetation health and indirectly reflect moisture levels. Additionally, indices like the Modified Simple Ratio (MSR) and the Anthocyanin Reflectance Index (ARI) highlight specific vegetation properties, with ARI being useful for detecting stress in plants, which can signal varying moisture content. The Canopy Chlorophyll Content Index (CCCI) integrates red-edge bands, useful for capturing subtle changes in chlorophyll and moisture, which estimates chlorophyll content variations within the canopy, offering insights into plant health and vitality. Previous studies, such as [PERSON] et al.