## 4 Transferability Study to the Freisinger Forest  
Transferability is a crucial topic in image classification if automation and applicability of an approach should be guaranteed with a certain workflow. As one of our goals was to provide tools capable of scaling and distributed processing, we performed a transferability study of the final classification workflow to the test area, the Freisinger Forest. The same parameters as for the Ebersberger Forest were used. Table 8 shows the accuracies for the final classification of the Freisinger Forest. The overall accuracy is 85% with a Kappa value of 79. Beech trees have an UA of 56% and a PA of 71%. Oak trees reach a 100% UA and a 79% PA. Other broad-leaved trees are classified with an accuracy of 75% for the UA and 86% for the PA. The coniferous tree group achieves a 100% accuracy for both, the UA and the PA. Results are slightly worse than for the Ebersberger forest but still very good. More misclassification within the broad-leaved classes occurs and is probably due to the spectral similarity of the respective classes.  
Figure 10 shows the final classification for the Freisinger Forest. The good results from the transferability study suggest that the classification results for the Ebersberger Forest are not only based on specific characteristics of the Ebersberger Forest. The workflow can be at least transferred to closely related forest regions with similar characteristics such as species presence/absence and distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class Name** & **Beech Trees** & **Oak Trees** & \begin{tabular}{c} **Other** \\ **Broad-Leaved** \\ \end{tabular} & \begin{tabular}{c} **Converious** \\ **Forest** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **UserAccuracy** \\ \end{tabular} &
\begin{tabular}{c} **Kappa** \\ \end{tabular} \\ \hline
**Beech Trees** & 5 & 3 & 1 & 0 & 9 & 0.56 & 0 \\
**Oak Trees** & 0 & 11 & 0 & 0 & 11 & 0 \\
**Other Broad-Leaved** & 2 & 0 & 6 & 0 & 8 & 0.75 & 0 \\
**Coniferous Forest** & 0 & 0 & 0 & 11 & 11 & 0 \\
**Total** & 7 & 14 & 7 & 11 & 39 & 0 & 0 \\
**ProducerAccuracy** & 0.71 & 0.79 & 0.86 & 1 & 0 & 0.85 & 0 \\
**Kappa** & 0 & 0 & 0 & 0 & 0 & 0 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Final Confusion Matrix for the final Classification of Tree Species, Freisinger Forest (compare Figure 10).  
The transferability study was based on a semi-automatic workflow created in ArcGIS Pro in model builder and as a chain of raster functions for distributed processing. It is a ready-to-use toolset, which can be applied to new datasets (Figure 11).  
Additionally, as described in the validation section, we created a mobile application using Collector for ArcGIS to validate some misclassified samples in the field. The mobile application synchronizes with the classification results as well as a predefined layer to collect data in the field that allows attaching photos. The app was especially useful to collect data for misclassified samples in  
Figure 11: Semi-automatic workflow (ArcGIS Pro) for the classification process of single tree species.  
Figure 10: Final Classification for the Freisinger Forest (compare Table 8).  
the field but can also be used to update inventory data. One example of misclassification is shown in Figure 12 where an inventory data point of the class coniferous was wrongly classified as \"Other Broad-leaved Trees\". When rechecking the point, the error is directly visible. There are indeed several coniferous trees, but also the occurrence of maple trees and other broadleaf trees in the undergrowth.