## 3 Results  
Results for the first classification step, the separation of broad-leaved and coniferous trees, are summarized in Table 3.  
A total of 14 different classification methods were tested and, except for one accuracy of only 74%, all other accuracies were above 80%, eight of them better than 90%. Comparing the results from OBIA and PB approaches, we do not find performance differences. However, a comparison of performance between the two machine-learning algorithms shows slightly better results for SVM than for the RF classifier. The confusion matrix for this classification step is presented in Table 4.  
The best overall accuracy of 97% was reached in an object-based approach using the SVM classifier on only band 8 of each temporal image (22 May 2016, 9 August 2016, 29 September 2016). Similar results are also achieved by using bands 8, 4 and 3 of the spring scenes, the 10 m spatial resolution bands. Three spectral bands suffice to get high classification accuracies and using more bands does not result in better results. This is due to significant differences in the blooming and autumn period  
Figure 6: Spectral Profile Beech Trees vs. Oak Trees Ebersberger and Freisinger Forest 22 May Level 2A. (Inventory Data@Bayerische Staatsforsten). between broad-leaved and coniferous trees in band 8 and the higher spatial resolution of band 8 (and bands 3 and 4) in comparison to other spectral bands. A combination of the infrared band with visible bands (2, 3 and 4) as well as a combination with the red edge region bands (in this case 6 and 7) did not result in an accuracy as high as only using the infrared band 8. Combining two red edge bands (6 and 7) with the infrared band also resulted in a high accuracy of over 95%. These results show, that a very simple and therefore reproducible approach, is ideal for achieving very good classification results for coniferous vs. broad-leaved trees. There is no need for including many bands and therefore many variables that might even reduce accuracies. Figure 7 shows the final classification result for the separation of coniferous and broad-leaved trees. Results from the second classification step separating single tree species (oak, beech, other broad-leaved trees) within the broad-leaved class are summarized in Table 5 and shown in Figure 8. For the OBIA approach, the evaluation of changing segmentation parameters and bands showed, that best results were achieved by using the May 22 scene with a band combination of the infrared band (8) and the green (3) and blue (2) band and a maximum number of pixels per segment of five and high values for spectral and spatial detail (15.5 and 15 respectively). The attributes for segmentation were mean digital value and active chromaticity color. Further parameters such as compactness or rectangularity did not result in better segmentation and therefore this segmentation was used for all OBIA classification approaches. Figure 7: Classification Result Coniferous Forest vs. Broad-Leaved Forest (Ebersberger Forest). Furthermore, areas for training and testing samples are shown that were randomly split. The result for this first step of our hierarchical classification is shown in Figure 7 and the confusion matrix [36] that was calculated on 28 independent test samples derived from the inventory circles is shown in Table 4.  
In contrast to the classification results for only coniferous vs. broad-leaved trees, the use of principal components 1-12 of the August scene achieved the highest overall accuracy of 91%. The use of principal components increased the accuracy by 2% compared to the untransformed August scene. The use of only the first five principal components for the same scene resulted in a similar accuracy of 90.5%, highlighting, that the original Sentinel-2 image contains a lot of \"noise\" and/or redundant information. This is also indicated by the eigenvalues and factor loadings of the PCs. For all three times of the year, the eigenvalues of the first PC range between 87 and more than 90%.