*4.3. CNNs for Plant Community Classification in Grasslands*

The spectral classes of the VUs could not be separated linearly. Although there were correlations between class membership and spectral information (see Figure 3), these were not sufficient for a separation. The samples of *Rumex obtusifolius* extended across the other VUs and had no distinctive spectral signature. However, due to their size and structure in rosettes [55], they could be easily distinguished from the surrounding grasses and herbs. The detection of *Rumex obtusifolius* in grasslands with CNNs was already shown by [32]; the authors achieved an accuracy of over 91% on a monotemporal model. The accuracy of the identification of *Rumex obtusifolius* with the models presented here varies. The multitemporal model for *G*<sup>1</sup> achieved the worst accuracy with 79.71% on the test set (0% on the plot data). The best accuracy was achieved by the monotemporal model of *G*<sup>1</sup> with 98.04% on the test set (100% on the plot data). The other classes are characterized not only by different spectral values but also by a distinctive spatial structure. The *Alopecurus pratensis* community is dominated by tall grasses, which are no longer upright because of wind at later observation dates. Thus, a wavy structure becomes visible, which is less apparent in the *Lolium perenne*-community, where mainly herbs and low grasses are found (see Appendix A).

It was shown by other studies [34,35] that CNNs are suitable for the classification of different plant communities. In this work, individual plants of the species *Rumex obtusifolius* were identified in addition to the *Lolium perenne*-, *Alopecurus pratensis*-, and *Bromus hordeaceus*-community. Different requirements for classifications of VUs show the great potential of CNNs. A single network can infer and combine multiple spatial and spectral nonlinear features. In this complex problem, good accuracies in separating multiple plant communities and individual plants could be achieved. Even though only a single study site was observed in two growing periods within this study, it can be assumed that the presented methodology can be used in other grasslands with different or differently separated plant communities. For this, a database should be created from grasslands in various expressions at the same or similar phenological phases. With this database, plant communities in various grasslands could be classified with little effort and no deep ecological and botanical knowledge.
