**1. Introduction**

In Central Europe, semi-natural grasslands (SNGs) are an essential part of ancient cultural landscapes. They have developed over centuries of anthropogenic land use by grazing and mowing [1,2]. Until the 19th century, most European SNGs were used as pastures, whereas hay meadows developed mainly over the last 100 to 150 years [1]. The highest diversity of species and plant communities in grasslands was reached in the middle of the 19th century [2]. Increasing intensification of land use, however, has led to decreasing species richness, especially since the 1950s [3,4]. Furthermore, the area used as grasslands in Germany decreased continuously from the 1970s until 2013. Since then, a reform of the common agricultural policy of the European Union (EU) regulates the transformation of grasslands into arable land [5]. Furthermore, subsidies for biodiversityfriendly use of grasslands were included as *greening* in the subsidy scheme of the EU [1]. For example, in Lower Saxony subsidies were granted for low-intensity use of high-naturevalue grasslands [6]. This included a ban on mineral nitrogen fertilizers or pesticides and a prescribed earliest date for mowing.

Contrastingly, agriculturally improved grasslands are used, e.g., for dairy farming. Here, a high energy and protein concentration in the forage is required for increasing the milk production of the individual animal [7]. This is achieved by special grass cultivars

**Citation:** Pöttker, M.; Kiehl, K.; Jarmer, T.; Trautz, D. Convolutional Neural Network Maps Plant Communities in Semi-Natural Grasslands Using Multispectral Unmanned Aerial Vehicle Imagery. *Remote Sens.* **2023**, *15*, 1945. https:// doi.org/10.3390/rs15071945

Academic Editors: Kenji Omasa, Shan Lu and Jie Wang

Received: 20 February 2023 Revised: 30 March 2023 Accepted: 3 April 2023 Published: 6 April 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and fertilizer application, which increase the number of mowings possible per year. Yield from SNG is not always processed into silage for milk production but can also be cut once or twice a year to produce hay in the traditional way, which maintains species richness [8]. If this hay is not fed to cattle or sheep but to horses, special importance must be paid to its plant species composition. Horses do not tolerate some *Lolium* or *Festuca* species due to their high fructose content [9,10]. Furthermore, these grass species may contain endophytic fungi that make them highly resistant to environmental conditions [11] and are harmful to horses but not ruminants [12]. Apart from their usage as fodder for meat, dairy, and wool production, SNGs' multiple ecosystem services include good groundwater quality and quantity, water flow regulation, carbon storage, mitigation of greenhouse gas fluxes, and erosion prevention, as well as cultural and health values. [13]. Furthermore, they are a habitat for many plant and animal species [13]. Both ecosystem services and habitat conditions of grasslands cannot be determined by mapping land use or land cover type only, because of the spatial variability in the biophysical variables [14]. Ecosystem services can vary over land use or land cover types [15], as species abundance and diversity in grassland plant communities influence their provision [16]. The composition of plant communities can change due to spatiotemporal dynamics, like water balance in the soil, light availability, or management [17].

To monitor vegetation structure and species composition, field-based methods in the form of phytosociological relevés are commonly used but are rather time-consuming [18,19]. In contrast, remote sensing is a cost-effective and non-destructive alternative, which is increasingly applied to get vegetation data of large-scale areas or areas showing spatiotemporal dynamics [20–23]. On a large scale, various remote sensing systems can be used to classify plant communities in grasslands. The authors of [24,25] used spaceborne data as a combination of multispectral and/or radar time series, whereas [20] analyzed airborne LiDAR. Over the last years, UAVs are increasingly used for ecological tasks on a smaller scale [26]. As an example, they were used in grasslands for the estimation of biodiversity [27], species and vegetation functional groups classification [23,28,29], forage quality, and biomass prediction [30,31] as well as for the detection of weed plants [32,33]. Various methodological approaches are suitable for the classification of plant communities in remote sensing data. To use the influence of phenology, some studies use multitemporal data for species and plant community classification [23,29]. The authors of [24,29] used machine learning techniques such as support vector machine and random forest for the classification of species and plant communities in grasslands. The authors of [34,35] tested the suitability of convolutional neural networks (CNNs) for their classification of plant communities in shrublands and forests. Recently, CNNs have been increasingly applied for the analysis of remote sensing data [36], but also specifically in vegetation remote sensing [22]. CNNs are particularly suitable for the detection of spatial patterns. As plant communities in grasslands are formed by plants of different heights and shapes, the spatial pattern is, in addition to spectral information, a strong feature for separation.

In our study, plant communities in a semi-natural hay meadow in northwestern Germany were classified with UAV imagery using CNNs. The aim is to use CNNs (1) to analyze the spatial distribution of the plant community composition before the first and second cut of the grassland vegetation and (2) to map the distribution of weed species with low forage value. Thereby, (3) the usage of mono- and multitemporal data for the mapping of plant communities with respect to the phenological phases is compared.
