The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1215–1219, 2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1215–1219, 2018

  30 Apr 2018

30 Apr 2018


U. Lussem1, A. Bolten1, M. L. Gnyp2, J. Jasper2, and G. Bareth1,3 U. Lussem et al.
  • 1Institute of Geography, GIS & Remote Sensing Group, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany
  • 2Research Center for Crop Nutrition Hanninghof, Yara International ASA, 48249 Dülmen, Germany
  • 3ZFL – Center for Remote Sensing of Land Surfaces, University of Bonn, Genscherallee 3, 53113 Bonn, Germany

Keywords: RGB, vegetation indices, unmanned aerial vehicle, biomass, yield, grassland, pasture

Abstract. Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGB-based VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGB-based VIs such as the NGRDI with R2 values of 0.62.