## 3 Results  
### Variable Reduction Using RFE and Comparison of Classification Metrics for Different Scenarios  
The reliability values obtained from the various classification scenarios (GEE) are compared in Table 5. This table reports the total number of variables and the variables selected by the RFE process, considering their importance in the RF algorithm. The initial variable set (N column) is significantly reduced in most scenarios through the Recursive Feature Elimination (RFE) process, underscoring its effectiveness in filtering out less informative features. For example, in SC6, the original 28 variables are pared down to 11 post-RFE (Figure 4). Conversely, RFE retains a smaller subset of features in scenarios that start with fewer variables (e.g., SC1 and SC7), suggesting that these scenarios either have inherently simpler data structures or initially contain fewer irrelevant features. The scenarios that produced the best accuracy and Kappa values are SC4 (0.90 and 0.78) for the 5 m datasets. In the 10 m datasets for the multiclass approach, SC10 (0.91 and 0.80) and SC12 (0.91 and 0.80) were the best. For the binary target, SC6 (0.96 and 0.86) and SC10 (0.96 and 0.87) were the best. On the other hand, the scenarios that evaluated the datasets without multispectral information (SC1, SC3, SC7, and SC9) had lower reliability compared to the other scenarios that included these types of variables. In scenarios using 10 m resolution data (SC7-SC12), the models exhibit strong predictive performance across both multiclass and binary classification tasks, with particularly notable gains when Sentinel-2 imagery is combined with topographic data (TOPO). Meanwhile, in scenarios employing 5 m resolution data, the integration of PlanetScope imagery with additional layers, such as topographic or texture-based features (e.g., SC4, SC5, and SC6), consistently improves classification accuracy. These findings imply that the \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**Datasets**} & \multicolumn{6}{c}{**Multiclass Target**} & \multicolumn{6}{c}{**Binary Target**} \\  & & & **N * & **RFE** & **Acc** & **Kappa** & **UA** & **PA** & **RFE** & **Acc** & **Kappa** & **UA** & **PA** \\ \hline SC1 & & TOPO & 3 & 3 & 0.73 & 0.37 & 0.46 & 0.30 & 3 & 0.83 & 0.26 & 0.45 & 0.29 \\ SC2 & & PS & 7 & 7 & 0.86 & 0.69 & 0.75 & 0.79 & 7 & 0.93 & 0.72 & 0.80 & 0.74 \\ SC3 & & GLCM NIR & 18 & 5 & 0.68 & 0.12 & 0.36 & 0.12 & 18 & 0.84 & 0.08 & 0.42 & 0.07 \\ SC4 & & TOPO + PS & 10 & 9 & 0.90 & 0.78 & 0.87 & 0.89 & 9 & 0.96 & 0.86 & 0.89 & 0.87 \\ SC5 & & TOPO + GLCM NIR & 21 & 7 & 0.76 & 0.41 & 0.64 & 0.25 & 6 & 0.85 & 0.29 & 0.59 & 0.25 \\ SC6 & & PS + TOPO + NIR & 28 & 11 & 0.90 & 0.78 & 0.88 & 0.88 & 7 & 0.96 & 0.86 & 0.90 & 0.87 \\  & & GLCM & & & & & & & & & & & \\ \hline SC7 & & S1 & 2 & 2 & 0.68 & 0.24 & 0.29 & 0.20 & 2 & 0.82 & 0.09 & 0.31 & 0.12 \\ SC8 & & S2 & 24 & 8 & 0.88 & 0.74 & 0.85 & 0.85 & 8 & 0.95 & 0.81 & 0.87 & 0.81 \\ SC9 & & S1 + TOPO & 5 & 5 & 0.82 & 0.60 & 0.74 & 0.66 & 4 & 0.88 & 0.53 & 0.69 & 0.54 \\ SC10 & & S2 + TOPO & 27 & 11 & 0.91 & 0.80 & 0.90 & 0.89 & 7 & 0.97 & 0.87 & 0.90 & 0.88 \\ SC11 & & S2 + S1 & 26 & 13 & 0.89 & 0.76 & 0.85 & 0.86 & 10 & 0.95 & 0.82 & 0.88 & 0.82 \\ SC12 & & S2 + S1 + TOPO & 29 & 7 & 0.91 & 0.80 & 0.89 & 0.90 & 9 & 0.96 & 0.86 & 0.90 & 0.87 \\ \hline \hline \end{tabular}  
* The column N represents the initial number of variables for each scenery. This is reduced by the RFE process and presents the new number of variables in column RFE.  
\end{table} 
Table 5: _Polylepis_ classification scenarios: spatial resolution of input features, number of features produced and selected, Accuracy (Acc), and Kappa coefficient after the RFE reduction process for binary and multiclass targets. UA and PA refer to the _Polylepis_ class.