UAV PhenoFly

Main content

Mission Statement

The ETHZ Crop Science Lab PhenoFly project aims to develop sensing systems and analysis procedures that deliver meaningful, scientific grade data beyond pretty pictures to capture the status of vegetation. 

In particular, we focus on the detection of plant traits important for field phenotyping and precision agriculture applications to facilitate of a more sustainable use of resources.

We aim to bridge the gap between different observational scales by integrating data from proximal ground measurement, the ETHZ field phenotyping platform (FIP) and airborne observations with data from your UAVs sensing systems.

People

Dr. Helge Aasen
(project lead)

Dr. Frank Liebisch
(project lead)

Key publications

Roth, L., Streit, B., 2017. Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach. Precision Agriculture. doi:10.1007/s11119-017-9501-1

Aasen, H., 2016. Influence of the viewing geometry on hyperspectral data retrieved from UAV snapshot cameras, in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Presented at the XXIII congress of the International Society for Photogrammetry and Remote Sensing, Prague, Czech Republic.

Aasen, H., Burkart, A., Bolten, A., Bareth, G., 2015. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing 108, 245–259. doi:10.1016/j.isprsjprs.2015.08.002

Burkart, A., Aasen, H., Alonso, L., Menz, G., Bareth, G., Rascher, U., 2015. Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer. Remote Sensing 7, 725–746. doi:10.3390/rs70100725

Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L., Bareth, G., 2015. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation 39, 79–87. doi:10.1016/j.jag.2015.02.012

Liebisch, F., Kirchgessner, N., Schneider, D., Walter, A., Hund, A., 2015. Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11, 9. doi:10.1186/s13007-015-0048-8

Tilly, N., Aasen, H., Bareth, G., 2015. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sensing 7, 11449–11480. doi:10.3390/rs70911449

Aasen, H., Gnyp, M.L., Miao, Y., Bareth, G., 2014. Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with HyperCor. Photogrammetric Engineering & Remote Sensing 80, 785–795. doi:10.14358/PERS.80.8.785

 
 
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Sun Apr 30 02:35:38 CEST 2017
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