## 2 Materials and Methods
### Workflow Description
#### 2.3.2 Satellite Data Preparation  
Satellite data acquisition (Table 2) and the generation of satellite information indices (Table 3) were conducted using the GEE tool. This Cloud Computing approach enabled multiple tasks to be performed on a high volume of data [54], facilitating the workflow. The data used were as follows: \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Multiclass Target**} & \multicolumn{4}{c}{**Binary Target**} \\
**Class Name** & **Code** & **Train** & **Validation** & **Total** & **Class Name** & **Code** & **Train** & **Validation** & **Total** \\ \hline _Polylepis_ & 1 & 2362 & 974 & 3336 & _Polylepis_ & 1 & 2362 & 974 & 3336 \\ \hline Native forest & 2 & 303 & 150 & 453 & & & & \\ Pine & 3 & 93 & 35 & 128 & No & & & \\ Shrub & 4 & 1674 & 700 & 2374 & & & & \\ vegetation & 5 & 9662 & 4228 & 13,890 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Forest classes and points (pixels) number for training and validation in multiclass and binary targets. Figure 3: Workflow diagram. * Sentinel-2 optical data (level 2A), radiometrically corrected to ground reflectance level, were used. There data correspond to the mosaic of tiles 17 MPS, 17 MPT, 17 MQS, and 17 MQT from 24 August 2020 to compose an image with the minimum presence of clouds. In GEE, the COPERNICUS/S2_SR_HARMONIZED repository was used in which the values of the scenes were harmonized so that the most recent data (after 25 January 2020) are in the same range as the oldest scenes [55]. * Synthetic Aperture Radar (SAR) data in Interferometric Wide (IW) mode and processed in Ground Range Detected (GRD) format were obtained from the Sentinel-1 sensor. This sensor offers two polarities: VV (vertical transmit/vertical receive) and VH (vertical transmit/horizontal receive). * Norway's International Climate and Forests Initiative (NICFI) provides free access to high-resolution PlanetScope imagery in Google Earth Engine (GEE). This dataset includes the red, green, blue, and near-infrared (NIR) bands, available as a mosaic spanning from June 2020 to August 2020, and resampled from 4.77 m to 5 m of pixel size. The textural features obtained from the NIR band were extracted via Gray-Level Co-occurrence Matrix (GLCM). The features generated include Max Corr. Coefficient (N_maxcorr), Angular Second Moment (N_asm), Entropy (N_ent), Cluster Prominence (N_prom), Difference Entropy (N_dent), Sum Entropy (N_sent), Variance (N_var), Inertia (N_inertia), Information Measure of Corr. 1 (N_imcorr1), Information Measure of Corr. 2 (N_imcorr2), Contrast (N_contrast), Difference Variance (N_dvar), Sum Variance (N_svar), Correlation (N_corr), Dissimilarity (N_diss), Shade (N_shade), and Inverse Difference Moment (N_idm). * Topography data were obtained from the Ecuador Sigtierras project, which provides data distributed with a pixel size of 3 m. This dataset was used to determine the terrain topography and derive slope and orientation, resampled to a pixel size of 3 m. The results of the experiments are shown in Figure 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Temporality** & **Spatial Resolution/Bands** & **Reference** \\ \hline Sentinel-2A MSI & Mean value August 2020 & 10 m (TCI_R, TCL_G, TCL_B, B2, B3, B4, B8) & [55] \\ Sentinel-1 SAR GRD & Mean value August 2020 & 20 m (B5, B6, B7, B8A, B11, B12) & [55] \\ Sentinel-1 SAR GRD & Mean value August 2020 & 10 m (VH, VV) & [23; 56] \\ PlanetScope normalized analytic &
\begin{tabular}{c} Mosaic June \\ 2020–August 2020 \\ \end{tabular} & 4.77 m (R, G, B, NIR) & [33] \\ \hline Topographic data & Photogrammetric fly 2010 & 3 m (Elevation, Slope, Aspect) & [56] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multispectral, SAR, and topographic datasets used in the study.