POTENTIAL OF NON-CALIBRATED UAV-BASED RGB IMAGERY FOR FORAGE MONITORING: CASE STUDY AT THE RENGEN LONG-TERM GRASSLAND EXPERIMENT (RGE), GERMANY
| Autor: | J. Menne, Ulrike Lussem, Georg Bareth, J. Schellberg, Jens L. Hollberg |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
lcsh:Applied optics. Photonics
geography Biomass (ecology) geography.geographical_feature_category 010504 meteorology & atmospheric sciences lcsh:T Field experiment Sampling (statistics) Growing season lcsh:TA1501-1820 Forage 04 agricultural and veterinary sciences 01 natural sciences lcsh:Technology Grassland lcsh:TA1-2040 040103 agronomy & agriculture 0401 agriculture forestry and fisheries RGB color model Environmental science lcsh:Engineering (General). Civil engineering (General) Spatial analysis 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W13, Pp 203-206 (2019) |
| ISSN: | 2194-9034 1682-1750 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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