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Open AccessArticle

Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System

1
TERRA Research and Teaching Center—Forest Is life, Gembloux Agro Bio-Tech, University of Liege, 5030 Gembloux, Belgium
2
Biotechnologie et Gestion des Agents Pathogènes en agriculture (BIOGAP), Yncréa Hauts-de-France, Institut Supérieur d’Agriculture, 48 boulevard Vauban, 59046 Lille Cedex, France
3
AgroBioChem/TERRA, Precision Livestock and Nutrition Unit, Gembloux Agro Bio-Tech, University of Liege, 5030 Gembloux, Belgium
4
Universidad Central del Ecuador, Facultad de Ciencias Agrícolas. Jerónimo Leiton y Av. La Gasca s/n. Ciudadela Universitaria, 170521 Quito, Ecuador
5
Lebeau Frederic ITAP, University Montpellier, Irstea, Montpellier SupAgro, 34196 Montpellier, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 473; https://doi.org/10.3390/rs11050473
Received: 24 January 2019 / Revised: 20 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Special Issue Progress on the Use of UAS Techniques for Environmental Monitoring)
The tools available to farmers to manage grazed pastures and adjust forage demand to grass growth are generally rather static. Unmanned aerial systems (UASs) are interesting versatile tools that can provide relevant 3D information, such as sward height (3D structure), or even describe the physical condition of pastures through the use of spectral information. This study aimed to evaluate the potential of UAS to characterize a pasture’s sward height and above-ground biomass at a very fine spatial scale. The pasture height provided by UAS products showed good agreement (R2 = 0.62) with a reference terrestrial light detection and ranging (LiDAR) dataset. We tested the ability of UAS imagery to model pasture biomass based on three different combinations: UAS sward height, UAS sward multispectral reflectance/vegetation indices, and a combination of both UAS data types. The mixed approach combining the UAS sward height and spectral data performed the best (adj. R2 = 0.49). This approach reached a quality comparable to that of more conventional non-destructive on-field pasture biomass monitoring tools. As all of the UAS variables used in the model fitting process were extracted from spatial information (raster data), a high spatial resolution map of pasture biomass was derived based on the best fitted model. A sward height differences map was also derived from UAS-based sward height maps before and after grazing. Our results demonstrate the potential of UAS imagery as a tool for precision grazing study applications. The UAS approach to height and biomass monitoring was revealed to be a potential alternative to the widely used but time-consuming field approaches. While reaching a similar level of accuracy to the conventional field sampling approach, the UAS approach provides wall-to-wall pasture characterization through very high spatial resolution maps, opening up a new area of research for precision grazing. View Full-Text
Keywords: unmanned aerial vehicles; unmanned aerial systems; drone; precision grazing; pasture biomass modeling; sward height; pasture height unmanned aerial vehicles; unmanned aerial systems; drone; precision grazing; pasture biomass modeling; sward height; pasture height
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Michez, A.; Lejeune, P.; Bauwens, S.; Herinaina, A.A.L.; Blaise, Y.; Castro Muñoz, E.; Lebeau, F.; Bindelle, J. Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System. Remote Sens. 2019, 11, 473.

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