## 5 Conclusions  
This work demonstrates the effectiveness of combining Sentinel-2 imagery with topographic variables for the detection of _Polylepis_ stands. The integration of Sentinel-2 spectral bands and topographic data outperforms the use of higher spatial resolution data from PlanetScope, texture-based variables derived from the NIR spectral band of PlanetScope, or SAR data from Sentinel-1. The importance of spectral and topographic features over spatial resolution in this specific classification task is notably. Utilizing RFE with smileRandomForest within GEE proved to be effective, reducing the number of variables from 27 to 11 without compromising accuracy. The key variables selected include Sentinel-2 derived indices and bands (NDVI, MSR, MSI, B9, B2, CCCI, B12, and ARI) along with topographic features (elevation, aspect, and slope). The selected variables represent a balanced combination of spectral indices, specific Sentinel-2 bands, and topographic features, indicating that these features are essential for differentiating _Polylepis_ stands from other land cover types. The streamlined feature set includes critical indices sensitive to vegetation health, structure, and moisture content, such as NDVI, MSR, and CCCI, as well as elevation and slope, which are known to influence Polylepis distribution. By focusing on these key variables, the model achieves a high degree of accuracy in detecting Polylepis habitats, which are often found in challenging, high-altitude environments with unique topographic characteristics. The effectiveness of this variable reduction also underscores the value of the RFE process for refining remote sensing models, enabling a focus on the most informative features while reducing noise from less relevant data. Moreover, the success of smileRandomForest within GEE highlights the utility of cloud-based platforms for large-scale ecological classification tasks. GEE's extensive data library and computational power facilitate the integration of multispectral, SAR, and topographic datasets, making it possible to replicate and scale this approach for other regions or similar ecological studies. This streamlined approach not only improves the efficiency of data processing and analysis but also makes it feasible to apply the model across large or multi-temporal datasets, supporting long-term monitoring efforts. Random Forest (RF) has been demonstrated to be a powerful tool for managing imbalanced datasets, especially when distinguishing the _Polylepis_ class. Additionally, RF is highly resilient to overfitting, making it particularly effective in complex classification tasks. The results suggest that the identification of _Polylepis_ stands was more effective when combining spectral (S2) and topographic variables, with spectral resolution playing a more critical role than spatial resolution. Identifying _Polylepis_ forests using satellite data is essential as a baseline for applying advanced remote sensing techniques in Land-Use Change monitoring in the Andean region. This approach enables tracking changes over time with diverse data sources, including radar and spectral imagery, to improve classification accuracy and support the establishment of remote monitoring mechanisms. Accurately mapping these unique, high-altitude ecosystems enhances the precision of LULUCF inventories and identifies areas where _Polylepis_ forests significantly contribute to carbon sequestration. Developing robust detection methodologies and validating the reliability of remote sensing data for these stands can greatly support national and international reporting requirements. Conceptualization, D.P.-P. and L.A.R. ; methodology, D.P.-P.; investigation, D.P.-P. and E.B.-L.; writing--original draft preparation, D.P.-P. and E.B.-L.; supervision, L.A.R. All authors have read and agreed to the published version of the manuscript.