## 3 Results
Most information of the Sentinel-2 image can thus be represented in only a few principal components. For all PCAs, band 8 contributes most to the first PC, highlighting its importance for classification. The statistics for the PCAs can be found as additional online material. In general, we found that OBIA methods outperformed PB methods, especially for the combination of the infrared band with the green and blue bands. Furthermore, even by including indices such as the NDVI or PCs, the PB approach had slightly lower accuracies than the OBIA results. However, this is probably due to the patchy nature of the forest structure and the overall setting with endmembers being rather similar. PB methods perform best if there are enough sample points for all classes that do not show too much colinearity. From the ML perspective, slightly better results were acquired using the SVM. From all settings tested, 18 classifications resulted in accuracies higher than 80%, all of them using a SVM classifier and the OBIA approach. The final classification result for this step is displayed in Figure 8.  
Table 6 shows the confusion matrix for the best result based on 19 independent test samples. Beech trees reach a 94% UA and a 79% PA, whereas oak trees reach a 100% accuracy for UA and PA. Other broad-leaved trees are represented with an 81% UA and a 94% PA. The Kappa value is 0.87 and indicates a very good classification result ([37] recommendation for a detailed discussion about the suitability of the Kappa value). The final classification result was achieved by merging the tree species classes with the coniferous forest class. The result is shown in Figure 9 and includes all four tree species groups (Beech, Oak,  
Figure 8: Classification result of single tree species within the broad-leaved class. Other broad-leaved trees and Coniferous trees). We performed another validation on the whole dataset (Table 7) with independent test samples from the inventory dataset (samples which were not used before). The overall accuracy is 88% with a Kappa value of 0.83. For beech trees, the UA is 94%, the PA only 71% with two samples being assigned to the coniferous forest and 4 samples to the general \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class Name** & **Beech Trees** & **Oak Trees** & **Other Broad-Leaved** & **Total** & **UserAccuracy** & **Kappa** \\ \hline
**Beech Trees** & 15 & 0 & 1 & 16 & 0.94 & 0 \\
**Oak Trees** & 0 & 21 & 0 & 21 & 1 & 0 \\
**Other Broad-Leaved** & 4 & 0 & 17 & 21 & 0.81 & 0 \\
**Total** & 19 & 21 & 18 & 58 & 0 & 0 \\
**ProducerAccuracy** & 0.79 & 1 & 0.94 & 0 & 0.91 & 0 \\
**Kappa** & 0 & 0 & 0 & 0 & 0 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validation Result Accuracy Assessment Broad-Leaved Forest Subset Classification (Ebersberger Forest). Results are shown for the classification approach highlighted in Table 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class Name** & **Beech Trees** & **Oak Trees** & **Other Broad-Leaved** & **Coniferous Forest** & **Total** & **UserAccuracy** & **Kappa** \\ \hline
**Beech Trees** & 15 & 0 & 1 & 0 & 16 & 0.94 & 0 \\
**Oak Trees** & 0 & 21 & 0 & 0 & 21 & 1 & 0 \\
**Other Broad-Leaved** & 4 & 0 & 17 & 0 & 21 & 0.81 & 0 \\
**Oak Trees** & 0 & 21 & 0 & 0 & 21 & 1 & 0 \\
**Other Broad-Leaved** & 4 & 0 & 17 & 0 & 21 & 0.81 & 0 \\
**Coniferous Forest** & 2 & 0 & 3 & 20 & 25 & 0.8 & 0 \\
**Total** & 21 & 21 & 21 & 20 & 83 & 0 & 0 \\
**ProducerAccuracy** & 0.71 & 1 & 0.81 & 1 & 0 & 0.88 & 0 \\
**Kappa** & 0 & 0 & 0 & 0 & 0 & 0 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Final Confusion Matrix for Final 4 Classification Tree Species (Ebersberger Forest). This result was obtained by validating the final merged classification (Figure 9) with an independent test set. Figure 9: Final Classification with Four Classes, Broad-Leaved Trees and Coniferous Forest, Ebersberger Forest.