Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site and Field Sampling
2.2. UAS Imagery Acquisition and Processing
2.3. Validation of UAS Sward Height
2.4. Modeling Biomass of Pasture with UAS Imagery
2.5. Mapping Sward Height Differences and Biomass with UAS
3. Results
3.1. Validation of UAS Sward Height
3.2. Modeling Biomass of Pasture with UAS Imagery
3.3. Mapping Sward Height Differences and Biomass with UAS
4. Discussion
4.1. Modeling Biomass of Pasture with UAS Imagery
4.2. Mapping Sward Height Differences and Biomass with UAS
4.3. Operational Recommendation
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Reference | R Squared | RMSE | MAE |
---|---|---|---|
Model 1 | 0.62 | 0.04 | 0.03 |
Model 2 | 0.33 | 0.11 | 0.09 |
Model 3 | 0.40 | 0.10 | 0.08 |
Model 4 | 0.52 | 0.09 | 0.08 |
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Vegetation Index | Formula | Parameter | Reference |
---|---|---|---|
Normalized Difference Vegetation Index (NDVI) | (NIR − RED)/(NIR + RED) | Photosynthetic activity, plant stress | [35] |
Normalized Difference Red Edge (NDRE) | (NIR − REDEDGE)/(NIR + REDEDGE) | Chlorophyll and nitrogen content | [36] |
Green NDVI (GNDVI) | (NIR − GREEN)/(NIR + GREEN) | More sensitive to chlorophyll-a concentration, monitoring of plant stress | [37] |
Green Ratio Vegetation Index (GRVI) | NIR/GREEN | Photosynthetic activity | [38] |
Name | Type | Ground Sampling Distance of the Layer (m) | Sensor |
---|---|---|---|
NDVI | Vegetation index (VI) | 0.05 | Multispectral (Sequoia) |
NDRE | Vegetation index (VI) | 0.05 | Multispectral (Sequoia) |
GNDVI | Vegetation index (VI) | 0.05 | Multispectral (Sequoia) |
GRVI | Vegetation index (VI) | 0.05 | Multispectral (Sequoia) |
Red (R) | Reflectance | 0.05 | Multispectral (Sequoia) |
Green (G) | Reflectance | 0.05 | Multispectral (Sequoia) |
Near Infra-Red (NIR) | Reflectance | 0.05 | Multispectral (Sequoia) |
Red-Edge (RE) | Reflectance | 0.05 | Multispectral (Sequoia) |
Sward Height Model (SHM) | 3D | 0.025 | RGB (Sony RX100) |
Rel. Importance (%) | Regression Coefficients | Pr (>|t|) | ||
---|---|---|---|---|
UAS height (SHM) | 45 | 1.1 | 0.00105 | ** |
GRVI | 27 | −0.1 | 0.00139 | ** |
GNDVI | 17 | 6.5 | 0.01162 | * |
NDRE | 11 | 0.8 | 0.04675 | * |
Intercept | / | −4.2 | 0.01725 | * |
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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. https://doi.org/10.3390/rs11050473
Michez A, Lejeune P, Bauwens S, Herinaina AAL, Blaise Y, Castro Muñoz E, Lebeau F, Bindelle J. Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System. Remote Sensing. 2019; 11(5):473. https://doi.org/10.3390/rs11050473
Chicago/Turabian StyleMichez, Adrien, Philippe Lejeune, Sébastien Bauwens, Andriamandroso Andriamasinoro Lalaina Herinaina, Yannick Blaise, Eloy Castro Muñoz, Frédéric Lebeau, and Jérôme Bindelle. 2019. "Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System" Remote Sensing 11, no. 5: 473. https://doi.org/10.3390/rs11050473