Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Acquisition
2.2. RPAS, Satellite and Field Data
2.2.1. RPAS Data Capture
2.2.2. Planet Dove Satellite Image Data Imagery
2.2.3. Destructive Pasture Sampling
2.3. Methods
2.3.1. RPAS Pasture Height Modelling
2.3.2. RPAS Pasture Yield Modelling
2.3.3. Time-Series Measure of Persistent Green Pasture
Missing Values
Filtering
Growth Period
Persistence Measure
2.3.4. Planet Dove Satellite Image Data-Based Pasture Yield Modelling
3. Results
3.1. RPAS Pasture Height
3.2. RPAS Height Estimates for Estimation of Pasture Yield
3.3. Satellite Estimation of Pasture Yield
3.3.1. Satellite Measures of Pasture Persistence and Correlation with RPAS Predicted Yield
3.3.2. Planet Dove Satellite Image Data-Based Predictions of Pasture Yield
4. Discussion
5. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Esk | Samford | |
---|---|---|
Location (latitude/longitude) | −27.253, 152.396 | −27.388, 152.878 |
Pasture types | Sub-Tropical Woodland (STW) | Sub-Tropical Grassland (STG) |
Common pasture species | Heteropogen contortus, Imperata cylindrica Melinis repens, Chloris gayana and Chrysopogon sp’s. | Paspalum mandiocanum, Chloris gayana and Imperata cylindrica |
Associated vegetation | Eucalyptus maculata and E. crebra | Various forbs and ephemeral grasses |
Soil types | Shallow red earthy loam | Gleyed soils in local alluvium |
No. of field plots | 2 | 2 |
Sampling dates | 1 December 2020, 16 December 2020, 28 January 2021, 22 February 2021, 16 March 2021, 29 March 2021 | 5 November 2020, 3 December 2020, 5 January 2021, 9 February 2021, 26 February 2021, 8 March 2021 |
Equipment | |
Aircraft | DJI M600ProTM |
Gimbal | DJI Ronin-MXTM |
Camera and Photogrammetry parameters | |
Camera | Sony Alpha 7R IITM |
Trigger | intelliGTM wifi sync trigger |
Lens | 24 mm; aperture priority |
Sensor | 42 mpx; 35 mm full frame; RGB |
Image format | JPEG file format; 15 MB per image; bit depth RGB (14) |
Flying height | ∼50 m |
Image size | 9–10 mm ground sampling; 75 × 50 m footprints |
Ground speed | 1.5–2 m/s |
Image count | ∼150 per site at nadir |
Image forward and side overlap | 90% |
Flight time | 20–30 min |
Flight pattern | Grid in north/south alignment; stop and turn at ends |
Pix4D SfM photogrammetry processing parameters | |
Keypoints image scale | Full |
Point cloud densification | Original image size |
Minimum no. of matches | 3 |
Point density | Optimal |
Resolution | 1 × GSD |
Specifications | ||
Product | PSScene4BandTM-analytic | |
Corrections | Surface reflectance | |
Ground sample resolution | 3–3.5 m | |
Bands | Blue (450–515 nm), Green (500–590 nm), Red (590–670 nm), NIR (780–860 nm) | |
Camera dynamic range | 12-bit | |
Images per month | Grassland | Woodland |
October | 2 | 7 |
November | 10 | 9 |
December | 3 | 3 |
January | 1 | 4 |
February | 2 | 3 |
March | 2 | 4 |
Totals | 20 | 30 |
Index | Equation | Source |
---|---|---|
Modified Soil-Adjusted Vegetation Index (MSAVI) | [31] | |
Modified Triangular Vegetation Index (MTVI) | [31] | |
Normalised Difference Vegetation Index (NDVI) | [32] | |
Re-normalised Difference Vegetation Index (RDVI) | [33] | |
Transformed Vegetative Index (TVI) | [32] |
Short Pasture Model | Tall Pasture Model | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Calibration | Validation | Calibration | Validation | |||||||||
Date | N * | R2 | MAE ** (t/ha) | N * | R2 | MAE ** (t/ha) | N * | R2 | MAE ** (t/ha) | N * | R2 | MAE ** (t/ha) |
Grassland | ||||||||||||
March 2021 | 69 | 0.7 | 1.29 | 30 | 0.6 | 1.8 | 255 | 0.2 | 1.2 | 109 | <0.1 | 1.1 |
Woodland | ||||||||||||
February–March 2021 | 43 | 0.7 | 1.1 | 19 | 0.7 | 1.3 | 646 | <0.1 | 0.7 | 277 | <0.1 | 0.9 |
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Barnetson, J.; Phinn, S.; Scarth, P. Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield. AgriEngineering 2021, 3, 681-702. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030044
Barnetson J, Phinn S, Scarth P. Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield. AgriEngineering. 2021; 3(3):681-702. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030044
Chicago/Turabian StyleBarnetson, Jason, Stuart Phinn, and Peter Scarth. 2021. "Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield" AgriEngineering 3, no. 3: 681-702. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030044