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Hyperspectral Aerial Imaging for Grassland Yield Estimation

Published online by Cambridge University Press:  01 June 2017

J. Geipel  and
A. Korsaeth
J. Geipel*
Affiliation:
Norwegian Institute of Bioeconomy Research, Pb 115, 1431 Ås, Norway
A. Korsaeth
Affiliation:
Norwegian Institute of Bioeconomy Research, Pb 115, 1431 Ås, Norway
*
†E-mail: jakob.geipel@nibio.no
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Abstract

In this study, we investigated the potential of airborne imaging spectroscopy for in-season grassland yield estimation. We utilized an unmanned aerial vehicle and a hyperspectral imager to measure radiation, ranging from 455 to 780 nm. Initially, we assessed the spectral signature of five typical grassland species by principal component analysis, and identified a distinct reflectance difference, especially between the erectophil grasses and the planophil clover leaves. Then, we analyzed the reflectance of a typical Norwegian sward composition at different harvest dates. In order to estimate yields (dry matter, DM), several powered partial least squares (PPLS) regression and linear regression (LR) models were fitted to the reflectance data and prediction performance of these models were compared with that of simple LR models, based on selected vegetation indices and plant height. We achieved the highest prediction accuracies by means of PPLS, with relative errors of prediction from 9.1 to 11.8% (329 to 487 kg DM ha−1) for the individual harvest dates and 14.3% (558 kg DM ha−1) for a generalized model.

Keywords

UAVhyperspectral imagingmultivariate statisticsgrasslandyield
Type
Precision Pasture
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 770 - 775
Copyright
© The Animal Consortium 2017 

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References

Bareth, G, Bolten, A, Hollberg, J, Aasen, H, Burkart, A and Schellberg, J 2015. Feasibility study of using non-calibrated UAV-based RGB imagery for grassland monitoring: Case study at the Rengen Long-term Grassland Experiment (RGE), Germany. In: Bridging Scales – Skalenübergreifende Nah- und Fernerkundungsmethoden, edited by TP Kersten, Publikationen der DGPF Band 24, München, Germany. pp 55–62.Google Scholar
Geipel, J, Link, J and Claupein, W 2014. Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System. Remote Sensing 6 (11), 10335–10355.Google Scholar
Guyot, G, Baret, F and Jacquemoud, S 1992. Imaging Spectroscopy for Vegetation Studies. In: Imaging Spectroscopy: Fundamentals and Prospective Applications, edited by F Toselli and J Bodechtel, Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 145–165.Google Scholar
Indahl, U 2005. A twist to partial least squares regression. Journal of Chemometrics 19 (1), 32–44.Google Scholar
Kusnierek, K and Korsaeth, A 2015. Simultaneous identification of spring wheat nitrogen and water status using visible and near infrared spectra and Powered Partial Least Squares Regression. Computers and Electronics in Agriculture 117, 200–213.Google Scholar
Rikola, 2017. Hyperspectral camera. Rikola Ltd. Oulu, FI http://www.rikola.fi/products/hyperspectral-camera. (retrieved 12/10/17).Google Scholar
Rouse, JW Jr, Haas, RH, Schell, JA and Deering, DW 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA SP–351, 309–317.Google Scholar
Schut, AGT, van der Heijden, GWAM, Hoving, I, Stienezen, MWJ, van Evert, FK and Meuleman, J 2006. Imaging Spectroscopy for On-Farm Measurement of Grassland Yield and Quality. Agronomy Journal 98 (5), 1318–1325.Google Scholar
Øvergaard, S, Isaksson, T, Kvaal, K and Korsæth, A 2010. Comparisons of two hand-held, multispectral field radiometers and a hyperspectral airborne imager in terms of predicting spring wheat grain yield and quality by means of powered partial least squares regression. Journal of Near Infrared Spectroscopy 18 (4), 247–261.Google Scholar