## 5 Discussion
If RF, for example, is used with unbalanced training data, it tends to focus on the prediction accuracy of prevailing classes which might lead to lower accuracies in less represented classes [21]. Furthermore, the tuning of hyperparameters has an impact on classification results and depends on the data used. The slightly better performance of SVM in our study might be due to the specific datasets as was also found for other studies [45; 46; 47]. Thus, with respect to only small performance differences, both algorithms are well-suited for the purpose of landcover classification in general and forest classification in particular as in this study. Compared to commercial and often expensive very high-resolution data, results using Sentinel-2 are very good, even though the spatial resolution is much lower. The high spectral resolution in the red edge area of Sentinel-2, however, is an advantage compared to other platforms such as Landsat and was tested extensively in this study. Our results showed, that the red edge part shows clearly visible differences in the spectral profiles, but higher resolution 10 m bands still reached better accuracies especially band 8. The lower spatial resolution (20 m) for the red edge region (bands 5 to 7) somewhat diminishes its capability to correctly classify vegetation on a species or even tree-patch level. However, for achieving good accuracies on a single-tree level, high spatial resolution as well as hyperspectral data are more adequate. For example, [PERSON] et al. [6], found that the fusion of very high geometrical resolution multi-/hyperspectral images and light detection and ranging (LiDAR) data can result in accuracies of up to 93% for macro classes using ML algorithms. Even tree species classification at individual tree crown level was shown to reach accuracies of 0.89 (kappa accuracy) for boreal forests using airborne hyperspectral data and LiDAR [48], making this kind of data more suitable for research on a single tree level, while Sentinel-2 data is more appropriate on a tree stands level in low-cost studies. Besides optical sensors, there are also studies on active systems only. [PERSON] et al. [49], for example, investigated the possibility to classify broad-leaved and coniferous tree types based on first and last pulses of a LiDAR methodology. They reached an overall accuracy of 89%, concluding that parameters like the branch structure influences the accuracy of the classification using LiDAR technology. Compared to our study with overall accuracies of more than 90%, we conclude that, depending on the study area and the scale, deca-metric-resolution optical sensors perform well for environments with low complexity compared to very high-resolution point-cloud data. However, for future studies, the use of additional data such as point cloud or radar data to benefit from the advantages of both, active and passive systems might help to further improve results was also suggested by [PERSON] et al. [50]. Data fusion can help to overcome problems that arise with more complex forest structures where trees intermingle. Data that captures different elevation levels and internal structures and morphologies can provide valuable additional information.