Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands
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 thro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/2/4/35 |
| _version_ | 1797548777252323328 |
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| author | Jason Barnetson Stuart Phinn Peter Scarth |
| author_facet | Jason Barnetson Stuart Phinn Peter Scarth |
| author_sort | Jason Barnetson |
| collection | DOAJ |
| description | 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 (<inline-formula><math display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) 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<sup>−1</sup> in grasslands and 1.3 t ha<sup>−1</sup> 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 R<sup>2</sup> = 0.9 and CP R<sup>2</sup> = 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. |
| first_indexed | 2024-03-10T15:04:31Z |
| format | Article |
| id | doaj.art-dadae7afe7e04f969855c5d9df749f35 |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-03-10T15:04:31Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-dadae7afe7e04f969855c5d9df749f352023-11-20T19:55:30ZengMDPI AGAgriEngineering2624-74022020-11-012452354310.3390/agriengineering2040035Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s RangelandsJason Barnetson0Stuart Phinn1Peter Scarth2Grazing Land Systems/Remote Sensing Centre, Queensland Department of Environment and Science, Eco-sciences Precinct, Dutton Park, Brisbane 4102, Queensland, AustraliaJoint Remote Sensing Research Centre, University of Queensland, St Lucia, Brisbane 4072, Queensland, AustraliaJoint Remote Sensing Research Centre, University of Queensland, St Lucia, Brisbane 4072, Queensland, AustraliaThe 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 (<inline-formula><math display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) 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<sup>−1</sup> in grasslands and 1.3 t ha<sup>−1</sup> 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 R<sup>2</sup> = 0.9 and CP R<sup>2</sup> = 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.https://www.mdpi.com/2624-7402/2/4/35UAVstructure from motionphotogrammetrycrude proteinacid detergent fibrehyperspectral sensing |
| spellingShingle | Jason Barnetson Stuart Phinn Peter Scarth Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands AgriEngineering UAV structure from motion photogrammetry crude protein acid detergent fibre hyperspectral sensing |
| title | Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands |
| title_full | Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands |
| title_fullStr | Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands |
| title_full_unstemmed | Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands |
| title_short | Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands |
| title_sort | estimating plant pasture biomass and quality from uav imaging across queensland s rangelands |
| topic | UAV structure from motion photogrammetry crude protein acid detergent fibre hyperspectral sensing |
| url | https://www.mdpi.com/2624-7402/2/4/35 |
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