The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 203–206, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-203-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 203–206, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-203-2019

  04 Jun 2019

04 Jun 2019

POTENTIAL OF NON-CALIBRATED UAV-BASED RGB IMAGERY FOR FORAGE MONITORING: CASE STUDY AT THE RENGEN LONG-TERM GRASSLAND EXPERIMENT (RGE), GERMANY

G. Bareth1, U. Lussem1, J. Menne1, J. Hollberg2, and J. Schellberg2 G. Bareth et al.
  • 1GIS & RS Group, Institute of Geography, University of Cologne, Germany
  • 2Institute of Crop Production (INRES), Bonn University, Germany

Keywords: UAS, RPAS, RGB, vegetation index, plant height, grassland, biomass, rising plate meter

Abstract. 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).