## 3 Results
### Random Forest (RF) Using Combined Sets of Attributes in Two Classification Scenarios  
Despite its challenging and complex nature, the ability to discriminate among the five classes of pasture degradation (first classification scenario) is appealing. Areas characterized by low-to-moderate intensity degradation (LID and MID) can be strategically chosen, in terms of funding allocation, as priority areas for renewal or recovery efforts compared to severely degraded regions. However, the RF classification results presented in Table 4 using combined sets of attributes revealed that the highest F1-scores were achieved for the two severely degraded classes (0.71 for SAD and 0.88 for SBD). Consequently, notable levels of confusion were evident between non-degraded pastures (NDP) and pastures at initial or moderate stages of degradation (LID and MID). These areas exhibited high variability in vegetation characteristics on their surfaces, such as plant height and composition, making their discrimination more challenging. While SBD stood out as the most distinctive class due to soil exposure, some degree of confusion with SAD was also noted, as indicated by Table 4.  
When compared with the classification using only the reflectance attribute (Figure 11), the results from Table 4, utilizing the combination of metrics, showed an improvement in overall classification accuracy from 0.66 to 0.69. In terms of accuracy, notable differences were observed in the SAD and SBD classes, suggesting that utilizing all attributes reduces the likelihood of commission errors in these classes. Conversely, Recall was significantly higher in the classification using the combined attributes than in the one using individual attributes, particularly in the SBD class, achieving 0.9. This outcome indicates less confusion in the classification of this class with fewer false negatives. Figure 11: Variations in Precision, Recall, F1-score, and Overall Accuracy (OA) resulting from the Random Forest (RF) classification of five classes of pasture degradation (NDP, LID, MID, SAD, and SBD). The classifier utilized individual attributes, including the reflectance of the eight SuperDove bands, five vegetation indices (EVI, GRND, MPRI, NDVI, and REND), and four-endmember fractions from Spectral Mixture Analysis (SMA) (GV1, GV2, soil, and shade). Results from GLCM texture metrics were excluded for enhanced graphical representation. The reported results refer to the validation dataset. Among the entire set of attributes, the top three most crucial variables for discriminating the classes were EVI, NDVI, and GRND. Soil fraction from SMA and the reflectance of the red and NIR bands of SuperDove followed closely in terms of importance (Figure 12). In comparison, GLCM texture data associated with the NIR band held lower importance. Overall, concerning importance, vegetation indices accounted for 43.93% of the classification performance, while the reflectance of the SuperDove bands and the SMA fraction images contributed 28.51% and 21.33%, respectively. Texture metrics contributed only 6.23% to the discriminatory power, with a notable concentration in texture mean and variance (Figure 12). Figure 13 presents various examples of RF classification for different study sites. A comparison with the ground truth map (Figure 13a) indicates that the RF classification, utilizing all five classes, demonstrated superior performance in non-degraded and severely degraded classes, represented by the green, orange, and red colors (NDP, SAD, and SBD in Figure 13b). However, notable confusions were evident in the low and moderate degradation classes, represented by light green and yellow colors (LID and MID, respectively). The former was frequently misclassified as NDP, and the latter showed confusion with NDP and SAD. In Figure 13c, the uncertainty map reveals higher uncertainties in areas identified as SBD that are in close proximity to non-degraded pasture. Additionally, uncertainties are