## 4 Results and Discussion  
### Most Suitable Classification Method  
Overall, three classification scenarios (i.e., single-date PS imagery with VI--CS1, multitemporal PS imagery with VI--CS2, and multitemporal PS imagery with VI and GLCM--CS3) were computed using RF, XGB, and MLP classifiers. Table 4 shows the OA, Kappa coefficients, F1 score, and FoM for each classification scenario (CS). For each CS, ten random splits were performed to obtain ten corresponding validations (Table 4). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Method** & **OA** & **K** & \multicolumn{2}{c}{**Ash**} & \multicolumn{2}{c}{**Birch**} & \multicolumn{2}{c}{**Plane tree**} & \multicolumn{2}{c}{**Other**} \\  & & & **F1** & **FoM** & **F1** & **FoM** & **F1** & **FoM** & **F1** & **FoM** & **F1** & **FoM** \\ \hline \multirow{4}{*}{\begin{tabular}{c} \(\text{F1}\) \\ \(\text{F1}\) \\ \end{tabular} } & RF & **52.17** & **0.36** & **0.49** & **0.34** & **0.60** & **0.39** & 0.57 & 0.38 & **0.43** & **0.32** \\  & XGB & 50.25 & 0.34 & 0.48 & 0.33 & 0.57 & 0.37 & 0.55 & 0.36 & 0.41 & 0.30 \\  & MLP & 49.36 & 0.33 & 0.37 & 0.29 & 0.53 & 0.35 & **0.58** & **0.39** & **0.43** & 0.31 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} \(\text{F1}\) \\ \(\text{F1}\) \\ \end{tabular} } & RF & 69.65 & 0.59 & 0.68 & 0.52 & 0.72 & 0.55 & 0.80 & 0.63 & **0.58** & **0.45** \\  & XGB & 70.27 & **0.60** & 0.68 & 0.52 & **0.73** & **0.56** & **0.82** & **0.66** & 0.57 & **0.45** \\  & MLP & **70.29** & **0.60** & **0.70** & **0.54** & 0.71 & 0.54 & **0.82** & 0.65 & **0.58** & **0.45** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} \(\text{F1}\) \\ \(\text{F1}\) \\ \(\text{F1}\) \\ \end{tabular} } & RF & 71.20 & 0.62 & 0.70 & 0.54 & **0.74** & 0.57 & 0.81 & 0.66 & 0.59 & 0.46 \\  & XGB & **73.13** & **0.64** & **0.72** & **0.56** & **0.74** & **0.59** & **0.85** & **0.71** & **0.60** & **0.48** \\ \cline{1-1}  & MLP & 72.07 & 0.63 & 0.71 & 0.55 & 0.73 & 0.57 & 0.84 & 0.69 & **0.60** & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative assessment results (i.e., overall accuracy, Kappa coefficient, F1 score, and figure of merit) for different classification scenarios (the assessment numbers in bold denote the best results among different classification scenarios). Interestingly, as shown in Table 4, different classifiers produced the best results depending on the classification scenario (i.e., CS1 used single-date PS imagery, CS2 used multitemporal (MT) PS imagery, and CS3 used MT PS imagery with ancillary data). In such a way, RF, MLP, and XGB performed the best in CS1, CS2, and CS3, respectively. These results confirm that an RF classifier produces solid results in complex areas mostly using single-date imagery and NDVI [29; 68], whereas the boosting classifier or neural network (i.e., XGB or MLP, respectively) require more training data. Similarly, [PERSON] et al. [69] achieved better results using an RF than an SVM classifier regardless of the classification scheme using a single date and bi-temporal Pleiades imagery. Furthermore, by increasing the number of training samples, MLP and RF achieved the best results for the classification of six tree species [70]. [PERSON] et al. [71] recommend more than 100 training samples for every tree species in order to properly train a SVM and RF classifiers. In our research, the classification scenario with the highest number of input features (i.e., CS3) demonstrated the strength of the XGBoost classifier. Although XGB is a relatively new classifier in the field of remote sensing and its application in tree species detection has been limited, an increasing trend towards its usage has been reported in tree species classification, especially when dealing with high-dimensional data [72]. These contrasting requirements and behaviors of RF, XGB, and MLP have important implications for our study. For research with limited data, RF might be the most effective choice.