## 5 Discussion  
Results of this study indicate a high potential of Sentinel-2 data for forest classification using a hierarchical semi-automatic workflow. For the classification of coniferous and broad-leaved forest types a very simple combination of only three bands sufficed to obtain very good accuracies. Crucial bands were the red edge bands 6 and 7 and the infrared band 8. The best accuracy of 97% was obtained by either combining only the infrared bands 8 of all three months and not including any further bands or bands 8, 4 and 3 of the spring scenes. This result agrees with findings of [PERSON] et al. (2017) who found an overall accuracy of 86.2% using a RF classifier for coniferous vs. broad-leaved forest classification in the Mediterranean summer with bands 2, 8 and 7 being of most importance. Band 8 also shows highest factor loadings for the first and most important PC calculated from the images. The coarser spatial resolution of the red edge bands (20 m) in comparison to the 10 m resolution of bands 8, and 2-4 seems to be a great disadvantage for classification. It highlights, that even for rather patchy forests, besides spectral differences, spatial resolution is critical for the overall results. The classification of broad-leaved tree species gave results of 91% overall accuracy for the three classes beech, oak and other broad-leaved trees. This high accuracy was achieved in an object-based approach and an SVM classifier. This agrees with findings from [PERSON] et al. (2016) and others (e.g., [PERSON] et al. (2016), [PERSON] and [PERSON] (2017)) who also found that SVM learning schemes show good performance in OBIA. The segmentation was based on a mean shift algorithm using band 8 together with the green and blue bands of the May scene and therefore the flowering period of the respective trees. The SVM classifier included the August scene with 12 PCs in addition to the segmented image. The usage of principle components increased the accuracy by 1.8%, whereas including the NDVI decreased the outcome accuracy to 80.8%. Our results confirm the relevance of the NIR region that was proposed earlier by [PERSON] et al. (2016). The authors also explain the high relevance of the visible bands by the absorption of photosynthetic pigments of chlorophyll a and b. However, their analysis  
Figure 12: Ebersberger Forest Ground Imagery, Inventory Data Coniferous Tree Misclassified as Other Deciduous Tree. Photographed 15 June 2017.  
showed that it is not enough to only use the RGB bands. Instead, a combination of infrared and visible bands leads to high accuracies for broad-leaved tree classifications with Sentinel-2 imagery. These results could be confirmed and extended by the inclusion of PCs that were capable of increasing accuracies by several percent. The final classification result of this study with an overall accuracy of 88% and 85% does not reach the 96% overall accuracy reported for high-resolution imagery like World-View-2 [40] but is based on cost-free data. However, whereas [PERSON] et al. [41] encountered difficulties in mapping tree species based on deca-metric-resolution imagery, our results indicate that Sentinel-2 is at least capable of providing good results for forests with a less complex structure and intermingling of many species. [PERSON] et al. also refers to Landsat data that has a 30 m spatial resolution compared to the 10 m and 20 m resolution of the Sentinel-2 sensors. Thus, depending on the area, forest complexity and needed accuracy, data has to be chosen adequately. With respect to the different ML algorithms, we found that the SVM classifier performed slightly better than the RF classifier. The good performance of SVM classifiers was also found for other spectral classification studies from other fields and is confirmed in other studies [42; 43; 44]. However, there are several reasons for performance differences of ML algorithms: First of all, there is a dependence on the training data (e.g., quality, number of training samples per class, characteristics of the ground-truth...).