## 3 Results
### Comparison of the Maps of the Best Scenarios  
Figure 6 presents a detailed example of the supervised classification results in a sub-area of the Toreadora Lagoon, which includes a variety of shrub classes. In this region, pixels classified as _Polylepis_ are shown in green. The scenario SC4, with a multiclass target and a spatial resolution of 5 m, also performs well with a high accuracy of 0.90 and Kappa of 0.78, indicating that different resolutions can yield similar results depending on the dataset combination. However, the evaluation of known areas reveals high separability between \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Target**} & \multirow{2}{*}{**Class**} & **Pixels** & \multirow{2}{*}{**SC4**} & \multirow{2}{*}{**SC6**} & \multirow{2}{*}{**SC10**} & \multirow{2}{*}{**SC12**} \\  & & **Number** & & & & \\ \hline \multirow{4}{*}{Multiclass} & _Polylepis_ & 974 & 0.88 & 0.88 & 0.90 & 0.89 \\  & Native forest & 150 & 0.49 & 0.45 & 0.37 & 0.25 \\ \cline{1-1}  & Pine & 35 & 0.06 & 0.00 & 0.48 & 0.37 \\ \cline{1-1}  & Shrub vegetation & 700 & 0.72 & 0.73 & 0.73 & 0.74 \\ \cline{1-1}  & Other classes & 4228 & 0.94 & 0.95 & 0.95 & 0.95 \\ \hline \multirow{2}{*}{Binary} & _Polylepis_ & 974 & 0.88 & 0.88 & 0.89 & 0.89 \\ \cline{1-1}  & No _Polylepis_ & 5113 & 0.98 & 0.98 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: F1-score comparison by best scenarios. Figure 4: Behavior of Accuracy (blue line), Kappa (orange line), UA (gray line), and PA (yellow line) metrics during the RFE process in the SC6 scenario. Peak values are obtained with 11 variables. _Polylepis_ and other vegetation classes in the 10 m SC10 and SC12 multiclass scenarios. In the multiclass target, red polygons indicate a Pine plantation. Although the Accuracy and Kappa metrics favor the SC4 scenario in the binary classification, the UA and PA for _Polylepis_, when evaluated together, were better in the SC10 multiclass scenario. This is reflected in an improved distinction of the _Polylepis_ class from other shrubs, as shown in Figure 6.  
The final scenario selected was SC10 with a multiclass target (Figure 7), showing _Polylepis_ located in the paramo zone. This was achieved using variables MSI, B9, B2, MSR, CCCI, B12, ARI, and NDVI of S2 in conjunction with aspect, slope, and elevation. The variable importance analysis (Figure 8) revealed that elevation and slope were the most  
Figure 5: Variables that were repeated (occurrences) in the best eight scenarios. S2 (Sentinel-2), S1 (Sentinel-1), TOPO (Topography), and PS (PlanetScope). Figure 6: Detailed view of the eight best classification scenarios in the Toreadora Lagoon region. significant factors, while the selected spectral indices and S2 bands had a similar contribution. In this map, another known area is Llaviuco Lagoon, where a clear distinction can be observed between _Polylepis_ stands and shrub vegetation despite both classes presenting similar spectral characteristics. The reports and confusion matrix of each scenario are available at [[https://gis.uazuay.edu.ec/papers/polylepis/](https://gis.uazuay.edu.ec/papers/polylepis/)]([https://gis.uazuay.edu.ec/papers/polylepis/](https://gis.uazuay.edu.ec/papers/polylepis/)) (accessed on 24 September 2024). The map in Figure 7 presents the spatial distribution of land cover classes in the study area, focusing on the classification of _Polylepis_ stands, native forest, pine plantations, shrub vegetation, and other vegetation types. _Polylepis_ stands are highlighted in light green,  
Figure 8: Feature importance of SC10 multiclass scenario. Figure 7: SC10 multiclass map was selected as the best scenario. Known regions such as Toreadora Lagoon (purple polygon) and Llaviuco Lagoon (red polygon) helped to visually interpret the quality of the classification models. Other land uses and coverages in the area are shown in white color. native forests in brown, pine plantations in red, shrub vegetation in dark green, and other classes in gray.