Deploying four optical UAV-based sensors over grassland: challenges and limitations

• Published in: Biogeosciences Vol. 12; no. 1; pp. 163 - 175
• Main Authors: von Bueren, S. K, Burkart, A, Hueni, A, Rascher, U, Tuohy, M. P, Yule, I. J
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 09-01-2015; Copernicus Publications
• Subjects: Comparative analysis; Sensors; Unmanned aerial vehicles; PSE, New Zealand
• ISSN: 1726-4189; 1726-4170; 1726-4189
• DOI: 10.5194/bg-12-163-2015

Unmanned aerial vehicles (UAVs) equipped with lightweight spectral sensors facilitate non-destructive, near-real-time vegetation analysis. In order to guarantee robust scientific analysis, data acquisition protocols and processing methodologies need to be developed and new sensors must be compared with state-of-the-art instruments. Four different types of optical UAV-based sensors (RGB camera, converted near-infrared camera, six-band multispectral camera and high spectral resolution spectrometer) were deployed and compared in order to evaluate their applicability for vegetation monitoring with a focus on precision agricultural applications. Data were collected in New Zealand over ryegrass pastures of various conditions and compared to ground spectral measurements. The UAV STS spectrometer and the multispectral camera MCA6 (Multiple Camera Array) were found to deliver spectral data that can match the spectral measurements of an ASD at ground level when compared over all waypoints (UAV STS: R2=0.98; MCA6: R2=0.92). Variability was highest in the near-infrared bands for both sensors while the band multispectral camera also overestimated the green peak reflectance. Reflectance factors derived from the RGB (R2=0.63) and converted near-infrared (R2=0.65) cameras resulted in lower accordance with reference measurements. The UAV spectrometer system is capable of providing narrow-band information for crop and pasture management. The six-band multispectral camera has the potential to be deployed to target specific broad wavebands if shortcomings in radiometric limitations can be addressed. Large-scale imaging of pasture variability can be achieved by either using a true colour or a modified near-infrared camera. Data quality from UAV-based sensors can only be assured, if field protocols are followed and environmental conditions allow for stable platform behaviour and illumination.