## 2 Materials and Methods
### Classification and Accuracy Assessment
For the object-based approaches, we used the mean shift algorithm ([PERSON] et al., 2016) as segmentation algorithm that replaces each discrete point with a finitely bounded continuous kernel of density, and then groups points, according to a global density estimator ([PERSON] et al., 2016). The kernel can be manually adapted by the user. Spectral detail can be set in a range between 1.0 and 20.0, with a higher value being appropriate for features that should be classified separately but have somewhat similar spectral characteristics. Another parameter is the spatial detail setting. Small values produce smooth outcomes between clustered areas whereas higher values are more appropriate when small objects are observed and should be matched together. As we are dealing with small objects, high values were chosen for both parameters. The spectral detail value found to work well in this study was 15.5, the spatial detail value 15. The first classification step consisted in separating broad-leaved and coniferous trees. Spectral profiles were analyzed with respect to absorption features and variability between the respective classes. Mean class spectra are shown in Figure 4. Significant differences in the reflectance values can be recognized in the red edge portion and the infrared region of the spectral signatures (most pronounced differences in bands 7-9). Figure 5 shows a multitemporal spectral signature (May, August and September) and it is obvious that differences are strongest in May, the flowering period of broad-leaved trees (May et al., 2016). Based on the spectral characteristics, different band-combinations were tested on the seasonal input images. Furthermore, OBIA vs. PB performance was tested and all 14 runs are summarized in Table 3.  
25 training regions and 10 independent test areas for accuracy assessment were defined based on the preprocessed inventory data. From these areas, samples were extracted by collecting the spectral signatures of each pixel contained in the circles. As only pixels completely within the circles were considered, the number of pixels per area training/test varies slightly with four samples per area on average. The confusion matrix for the first classification step is highlighted in the results section (Table 4). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy** & **Method** & **Classifier** & **Segmented Image** & **Segmentation Settings** \\ \hline
95.2 & OBIA & SVM & May22/Bands 6 7 8 & DS \\
86.8 & OBIA & RF & May22/Bands 6 7 8 & DS \\
92.3 & PB & SVM & May22/Bands 6 7 8 & DS \\
90.2 & OBIA & SVM & May 22/Bands 3 4 8 & DS \\
74 & OBIA & RF & May 22/Bands 3 4 8 & DS \\
97 & PB & SVM & May 22/Bands 3 4 8 & DS \\
87 & OBIA & SVM & May 22/Bands 3 4 8 & SegSize 5 \\
85 & OBIA & SVM & Multitemporal/May Band 3, & DS \\  & & Aug Band 8, Sept Band 7 & DS \\
86.6 & OBIA & RF & Multitemporal/May Band 3, & DS \\  & & Aug Band 8, Sept Band 7 & DS \\
83 & PB & SVM & Multitemporal/May Band 3, & DS \\
**97** & **OBIA** & **SVM** & **Multitemporal/May Band 8,** & **DS** \\  & & & Aug Band 8, Sept Band 8 & \\
92.4 & OBIA & SVM & Multitemporal/May Band 8, & SegSize 5 \\
81.6 & OBIA & RF & Multitemporal/May Band 8, & \\  & & Aug Band 8, Sept Band 8 & DS \\
89.8 & PB & SVM & Multitemporal/May Band 8, & DS \\ \hline OBIA = Object Based Image Analysis; PB = Pixel based; SVM = Support Vector Machine; RF = Random Forest; DS = Default Segmentation Settings (Spectral detail = 15.5, Spatial detail = 15, segment size = 20), SegSize 5 = Minimum Segment Size in Pixels equals 5. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results from the first classification step separating coniferous and broad-leaved trees (Ebersberger Forest). Figure 4: Mean spectral profile Ebersberger Forest May 22—all tree-types. (Inventory Data@Bayerische Staatsforsten). Based on the first classification step, a polygon mask for the broad-leaved trees was used to extract the area for the second step of the classification.