*4.2. Comparison of Mono- and Multitemporal Data for Plant Community Mapping*

In comparison of the mono- and multitemporal VU classification, it was noticeable that larger homogeneous areas are found in both multitemporal classifications. Furthermore, class boundaries could be better delimited in the multitemporal results, and the transition areas were smaller. This could be explained by the expanded feature space of the multitemporal training data. As described in Section 3.2, both the flowering aspect and the occurrence of individual species changed with the phenological phases. It could therefore be assumed that the flowering aspect and the change in vegetation structure had a positive influence on the multitemporal classification, as they should vary the same or similar within a plant community over the vegetation period. However, the validation showed that the monotemporal model for *G*<sup>1</sup> had a higher accuracy on the independent plot data

(82.75% to 68.97%, Table 3). For *G*2, the multitemporal model had a higher accuracy on the independent plot data (88.57% to 71.43%, Table 3).

The authors of [23] showed an improvement of 5–10% in the accuracy of the classification of vegetation functional groups by using multitemporal data. The influence of shadows and flowering was reduced when using data of different phenological stages. In our work, this improvement was only visible in the validation of independent plot data of *G*2, but in general, the multitemporal models showed a weaker overall accuracy than the monotemporal models. It is possible that the multitemporal models could be improved with extended training data. These models have more input neurons than the monotemporal models and therefore need more data to properly learn the relevant features. The classifier of the multitemporal classification of *G*<sup>1</sup> showed problems, especially in the detection of *Rumex obtusifolius*. This plant is small and barely detectable at early observation dates of *G*<sup>1</sup> and later overgrown by tall grass, whereas it was present in *G*<sup>2</sup> from the beginning of the observation. The multitemporal classification of *G*<sup>1</sup> showed problems in the detection of the *Alopecurus pratensis*-community. At early dates, this class was dominated by *Alopecurus pratensis*, but at later dates the flowering of *Holcus lanatus* was also visible, especially in the transition areas to the other plant communities. Possibly, these plants caused a decreased accuracy in the multitemporal classification because the borders of the plant communities were less clear at *G*1*T*3. Some plots in the northwest of the study site lay in the transition area between the *Lolium perenne*- and the *Alopecurus pratensis*-community, which influences the separability.

Object identification showed good results in the monotemporal models (97.72% accuracy, Table 3). In the multitemporal models it was not necessary, because most objects (e.g., molehills) were not temporally stable. Areas that were not classified in the monotemporal models are replaced by the surrounding VU in the multitemporal models (see subfigures of Figure 4). So, areas removed by the object identification did not affect the applicability and interpretability of the result map.
