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Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

View Article: PubMed Central

ABSTRACT

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

No MeSH data available.


Relationship between micro-plot mean NDVI and crop mean leaf area index (LAI)
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f8-sensors-08-03557: Relationship between micro-plot mean NDVI and crop mean leaf area index (LAI)

Mentions: The best relationships were obtained between, on one hand, the leaf area index (LAI) and the normalized difference vegetation index (NDVI) (Figure 8), and, on the other hand, the total nitrogen uptake per square metre (QN) and the green normalized difference vegetation index (GNDVI) (Figure 9):(2)LAI=0.011exp(11.756×NDVI)+0.827(3)QN=17.8exp(5.2×GNDVI)−82.4Root Square Error (RSE) of the determination of these relationships were respectively 0.57 for LAI and 19 for QN, corresponding respectively to a mean relative error of 17% and 13%. This error falls down to 8% for higher LAI values (∼6 m2/m2) and 6% for higher QN values (∼300 Kg/ha). A very good correlation was found between the values calculated with these relationships and the values measured in the fields, respectively of 0.82 for LAI and 0.92 for QN.


Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
Relationship between micro-plot mean NDVI and crop mean leaf area index (LAI)
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675559&req=5

f8-sensors-08-03557: Relationship between micro-plot mean NDVI and crop mean leaf area index (LAI)
Mentions: The best relationships were obtained between, on one hand, the leaf area index (LAI) and the normalized difference vegetation index (NDVI) (Figure 8), and, on the other hand, the total nitrogen uptake per square metre (QN) and the green normalized difference vegetation index (GNDVI) (Figure 9):(2)LAI=0.011exp(11.756×NDVI)+0.827(3)QN=17.8exp(5.2×GNDVI)−82.4Root Square Error (RSE) of the determination of these relationships were respectively 0.57 for LAI and 19 for QN, corresponding respectively to a mean relative error of 17% and 13%. This error falls down to 8% for higher LAI values (∼6 m2/m2) and 6% for higher QN values (∼300 Kg/ha). A very good correlation was found between the values calculated with these relationships and the values measured in the fields, respectively of 0.82 for LAI and 0.92 for QN.

View Article: PubMed Central

ABSTRACT

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

No MeSH data available.