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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: [email protected]
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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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