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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.


Spectral relative response of: a) the three Canon EOS-350D, and b) the four Sony DSC-F828 channels.
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f3-sensors-08-03557: Spectral relative response of: a) the three Canon EOS-350D, and b) the four Sony DSC-F828 channels.

Mentions: The information about the CCD photodiodes spectral sensibility is not available from the manufacturer, neither the Bayer matrix to be able to estimate the spectral correspondence of each camera produced channel [40]. We were thus obliged to perform afterwards an optical characterization in the ONERA laboratories in Toulouse to get the resulting relative spectral response of the camera. Figure 3 shows the spectral sensitivity of the Canon EOS-350D and the Sony DSC-F828, respectively. Bandwidths are then derived at the half-height of the response curve, and given at Table 1. Considering common satellite sensors wavelength coverage (see Table 1), these bands are quite close to those generally used in remote sensing, although slightly (10 to 30 nm) shifted to smaller wavelengths. They are thus correctly located to characterize the vegetation, even if the red band fits the negative slope of the chlorophyll absorption band rather than its minimum.


Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
Spectral relative response of: a) the three Canon EOS-350D, and b) the four Sony DSC-F828 channels.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-08-03557: Spectral relative response of: a) the three Canon EOS-350D, and b) the four Sony DSC-F828 channels.
Mentions: The information about the CCD photodiodes spectral sensibility is not available from the manufacturer, neither the Bayer matrix to be able to estimate the spectral correspondence of each camera produced channel [40]. We were thus obliged to perform afterwards an optical characterization in the ONERA laboratories in Toulouse to get the resulting relative spectral response of the camera. Figure 3 shows the spectral sensitivity of the Canon EOS-350D and the Sony DSC-F828, respectively. Bandwidths are then derived at the half-height of the response curve, and given at Table 1. Considering common satellite sensors wavelength coverage (see Table 1), these bands are quite close to those generally used in remote sensing, although slightly (10 to 30 nm) shifted to smaller wavelengths. They are thus correctly located to characterize the vegetation, even if the red band fits the negative slope of the chlorophyll absorption band rather than its minimum.

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.