## 3 Results and Discussion
Similar results were also received in [18], where for a RF model the obtained Cohen's Kappa varies between 0.85 and 0.88, depending on the satellite map used. Similarly, in [24], a coefficient of determination between the predicted and observed values of R\({}^{2}\) = 0.79 was obtained for the pixel-based analysis of grassland degradation, which is slightly lower compared to our results. Nevertheless, when performing such analyses, one should consider that the other studies used different classification categories, so no direct comparison could be made. Considering the high imbalance in the training data between the different classes, it is worth going deeper into the confusion matrices of the object-based RT and SVM classifications, as the best-performing algorithms (Tables 3 and 4, respectively). If compared, it can be seen that their precision and recall when identifying grasses and shrubs are almost identical. There is some difference when it comes to the identification of stones. It can be seen that the RT classification has 0.568 precision, compared to 0.401 of the SVM one, which indicates that the latter misclassified more than half of the stones as shrubs. On the other hand, the SVM algorithm returns fewer false positives, as its recall is 0.812, compared to 0.787 of the RT one. Once again, the false positives come mostly from misclassifying stones as shrubs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Classification Algorithm** & **Cohen’s Kappa** \\ \hline
1 & Optimal object-based MxL & 0.53 \\
2 & Optimal object-based RT & 0.86 \\
3 & Optimal object-based SVM & 0.82 \\
4 & Pixel-based MxL & 0.54 \\
5 & Pixel-based RT & 0.41 \\
6 & Pixel-based SVM & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the different algorithms’ overall accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Reference**} & **Precision** & **Recall** & **F-Score** \\ \cline{3-8}  & & **Grass** & **Stones** & **Soil** & **Shrubs** & & \\ \hline \multirow{4}{*}{Results} & Grass & 3052 & 0 & 10 & 343 & 0.896 & 0.993 & 0.942 \\ \cline{2-8}  & Stones & 0 & 251 & 0 & 191 & 0.568 & 0.787 & 0.660 \\ \cline{2-8}  & Soil & 0 & 0 & 47 & 89 & 0.346 & 0.825 & 0.487 \\ \cline{2-8}  & Shrubs & 21 & 68 & 0 & 5922 & 0.985 & 0.905 & 0.943 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for object-based RT classification. Significantly different is the situation when it comes to the identification of soil. The SVM algorithm performs significantly better, and its precision reaches 0.929, compared to 0.346 for RT. The reason for this is that the RT algorithm identifies many shrubs as soil. On the other hand, SVM returns more false positives and the recall reaches 0.684, compared to 0.825 for the RT algorithm. Examples of the misclassification of stones and shrubs are presented in Figure 7. In the upper part of the image, a large area of different shrubs (Shrubs 1) is selected, some of which were falsely classified as stones by the SVM algorithm. False-positive stones are also classified with the RT algorithm, though the areas are smaller. If we zoom in, it can be seen that the main reason for this is the color of the shrubs, as some of them are peeling. This allows us to speculate that if the investigation of the pasture degradation is made one month later when all the bushes and trees have completely turned green, the accuracy of both algorithms will increase. In the bottom part of Figure 7 are shown samples for misclassifying stones as shrubs. The first marked stones area (Stones 1) is characterized by small white stones with a black background, and the second area (Stones 2) is characterized by small white stones surrounded by green background (grass and/or shrubs). It can be seen that both algorithms fail to correctly identify large parts of the area \"Stones 1\". For area \"Stones 2\", the SVM algorithm performs slightly better than the RT one.