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Article

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

1
Grazing Land Systems/Remote Sensing Centre, Queensland Department of Environment and Science, Eco-sciences Precinct, Dutton Park, Brisbane 4102, Queensland, Australia
2
Joint Remote Sensing Research Centre, University of Queensland, St Lucia, Brisbane 4072, Queensland, Australia
3
International Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba 4350, Queensland, Australia
*
Author to whom correspondence should be addressed.
Received: 27 September 2020 / Revised: 24 October 2020 / Accepted: 30 October 2020 / Published: 5 November 2020
(This article belongs to the Special Issue Digital Agriculture: Latest Advances and Prospects)
The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring. View Full-Text
Keywords: UAV; structure from motion; photogrammetry; crude protein; acid detergent fibre; hyperspectral sensing UAV; structure from motion; photogrammetry; crude protein; acid detergent fibre; hyperspectral sensing
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MDPI and ACS Style

Barnetson, J.; Phinn, S.; Scarth, P. Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands. AgriEngineering 2020, 2, 523-543. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2040035

AMA Style

Barnetson J, Phinn S, Scarth P. Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands. AgriEngineering. 2020; 2(4):523-543. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2040035

Chicago/Turabian Style

Barnetson, Jason, Stuart Phinn, and Peter Scarth. 2020. "Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands" AgriEngineering 2, no. 4: 523-543. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering2040035

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