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Forage monitoring in grassland is an important task to support management decisions. Spatial data on (i) yield,(ii) quality, and (iii) floristic composition are of interest. The spatio-temporal variability in grasslands is significant and requires fast and low-cost methods for data delivery. Therefore, the overarching aim of this contribution is the investigation of low-cost and non-calibrated UAV-derived RGB imagery for forage monitoring. Study area is the Rengen Grassland Experiment (RGE) in Germany which is a long-term field experiment since 1941. Due to the experiment layout, destructive biomass sampling during the growing period was not possible. Hence, non-destructive Rising Plate Meter (RPM) measurements, which are a common method to estimate biomass in grasslands, were carried out. UAV campaigns with a Canon Powershot 110 mounted on a DJI Phantom 2 were conducted in the first growing season in 2014. From the RGB imagery, the RGB vegetation index (RGBVI) and the Grassland Index (GrassI) introduced by Bendig et al. (2015) and Bareth et al. (2015), respectively, were computed. The RGBVI and the GrassI perform very well against the RPM measurements resulting in R2 of 0.84 and 0.9, respectively. These results indicate the potential of low-cost UAV methods for grassland monitoring and correspond well to the studies of Viljanen et al. (2018) and Näsi et al. (2018).

Non-destructive methods to derive spatial information on the development of forage mass are of key importance in managed grasslands. Established methods are rising plate meter (RPM) and rapid pasture meter, which both require in-field work, are rather time consuming, and do not provide spatially continuous data. Therefore, the overall objective of this study is to investigate low-cost unmanned aerial vehicle (UAV)-based RGB image acquisition for grassland monitoring. The idea of this paper is to transfer the successfully introduced approach of crop surface models (CSMs) for ultrahigh resolution analysis of plant height to managed grasslands. The study area is the Rengen Long-term Grassland Experiment, Germany, which is a two-cut experiment and was established in 1941. We conducted RPM and UAV-based data acquisition over six growth periods in 2014, 2015, and 2016. In 3 years, 26 RPM and 46 UAV campaigns were conducted under varying weather conditions (cloudy/sunny). The UAV-based RGB imagery was photogrammetrically processed with Structure from Motion and Multi-view Stereopsis techniques, producing multi-temporal CSMs for grassland sward height analysis. The regression analysis of UAV-derived sward height (CSM-SH) against RPM-measured sward height (RPM-SH) resulted in R2 of 0.91, 0.87, and 0.83 for 2014, 2015, and 2016, respectively. The pooling of the data for all 3 years resulted in an R2 of 0.86. These findings prove the successful transfer of the CSM approach for grassland monitoring and the potential of UAV-based monitoring to replace manual or in-field measurements with RPM or rapid pasture meter.

Monitoring the spectral response of intensively managed grassland throughout the growing season allows optimizing fertilizer inputs by monitoring plant growth. For example, site-specific fertilizer application as part of precision agriculture (PA) management requires information within short time. But, this requires field-based measurements with hyper- or multispectral sensors, which may not be feasible on a day to day farming practice. Exploiting the information of RGB images from consumer grade cameras mounted on unmanned aerial vehicles (UAV) can offer cost-efficient as well as near-real time analysis of grasslands with high temporal and spatial resolution. The potential of RGB imagery-based vegetation indices (VI) from consumer grade cameras mounted on UAVs has been explored recently in several. However, for multitemporal analyses it is desirable to calibrate the digital numbers (DN) of RGB-images to physical units. In this study, we explored the comparability of the RGBVI from a consumer grade camera mounted on a low-cost UAV to well established vegetation indices from hyperspectral field measurements for applications in grassland. The study was conducted in 2014 on the Rengen Grassland Experiment (RGE) in Germany. Image DN values were calibrated into reflectance by using the Empirical Line Method (Smith & Milton 1999). Depending on sampling date and VI the correlation between the UAV-based RGBVI and VIs such as the NDVI resulted in varying R2 values from no correlation to up to 0.9. These results indicate, that calibrated RGB-based VIs have the potential to support or substitute hyperspectral field measurements to facilitate management decisions on grasslands.

International audience; ᅟThe critical nitrogen concentration (CNC) is a simple yet robust relationship that describes the changes in crop N during growth. In Reyes et al. (Agron Sustain Dev 35:1561–1570, 2015), we applied the concept of CNC to calculate N uptake across various cutting regimes. While it is well-established that decreasing cutting frequency changes growth rates, the parameters of the CNC equations may need to be adjusted based on the grass physiological age (i.e., vegetative versus reproductive stage). We do not question the validity of the CNC during vegetative growth; however, we recognize that there is research needed to test the effects of cutting date on composition and internal partitioning of N as it relates to the CNC. Moreover, we focus on the applicability of the CNC for use in process-based models for better understanding of the CNC for different crop ages and management considerations.