Limits...
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.


Date-specific relationships between micro-plot mean GNDVI and crop mean nitrogen uptake per square meter (QN), compared to the generic relationship.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3675559&req=5

f11-sensors-08-03557: Date-specific relationships between micro-plot mean GNDVI and crop mean nitrogen uptake per square meter (QN), compared to the generic relationship.

Mentions: No converging exponential model of the form (Eq.1) was found to relate either LAI to NDVI, or QN to GNDVI at a specific given date. Indeed, in the small range of values occurring for each date, these relationships are rather linear (Figure 10, Figure 11), of the form:(4)K=A×I+BA and B coefficients are given for each date in Table 4, together with the corresponding model errors and the coefficient of correlation between estimated and ground truth parameters. These results clearly show that either LAI or QN estimations are loosing in quantitative accuracy while using date-specific relationships. Indeed, the mean relative error in QN estimation is increasing from 13% with the generic relationship to 16-18% with the date-specific one. LAI estimation depends on the considered date: for the two middle dates, close to the flowering phenological stage, the error is a little bit lower (15-16% against 17%), while it increases up to 23% for the earlier and later dates. This effect is often observed in the literature (e.g. 22, 69, 70, 71), and gives some confidence in the validity of the observed trends.


Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
Date-specific relationships between micro-plot mean GNDVI and crop mean nitrogen uptake per square meter (QN), compared to the generic relationship.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-08-03557: Date-specific relationships between micro-plot mean GNDVI and crop mean nitrogen uptake per square meter (QN), compared to the generic relationship.
Mentions: No converging exponential model of the form (Eq.1) was found to relate either LAI to NDVI, or QN to GNDVI at a specific given date. Indeed, in the small range of values occurring for each date, these relationships are rather linear (Figure 10, Figure 11), of the form:(4)K=A×I+BA and B coefficients are given for each date in Table 4, together with the corresponding model errors and the coefficient of correlation between estimated and ground truth parameters. These results clearly show that either LAI or QN estimations are loosing in quantitative accuracy while using date-specific relationships. Indeed, the mean relative error in QN estimation is increasing from 13% with the generic relationship to 16-18% with the date-specific one. LAI estimation depends on the considered date: for the two middle dates, close to the flowering phenological stage, the error is a little bit lower (15-16% against 17%), while it increases up to 23% for the earlier and later dates. This effect is often observed in the literature (e.g. 22, 69, 70, 71), and gives some confidence in the validity of the observed trends.

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.