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Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses.

Caturegli L, Corniglia M, Gaetani M, Grossi N, Magni S, Migliazzi M, Angelini L, Mazzoncini M, Silvestri N, Fontanelli M, Raffaelli M, Peruzzi A, Volterrani M - PLoS ONE (2016)

Bottom Line: Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits.The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%.UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management.

View Article: PubMed Central - PubMed

Affiliation: Department of Agriculture, Food and Environment, University of Pisa, Pisa, Italy.

ABSTRACT
Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits. The objectives of the trial were i) to compare the spectral reflectance of 3 turfgrasses acquired via UAV and by a ground-based instrument; ii) to test the sensitivity of the 2 data acquisition sources in detecting induced variation in N levels. N application gradients from 0 to 250 kg ha-1 were created on 3 different turfgrass species: Cynodon dactylon x transvaalensis (Cdxt) 'Patriot', Zoysia matrella (Zm) 'Zeon' and Paspalum vaginatum (Pv) 'Salam'. Proximity and remote-sensed reflectance measurements were acquired using a GreenSeeker handheld crop sensor and a UAV with onboard a multispectral sensor, to determine Normalized Difference Vegetation Index (NDVI). Proximity-sensed NDVI is highly correlated with data acquired from UAV with r values ranging from 0.83 (Zm) to 0.97 (Cdxt). Relating NDVI-UAV with clippings N, the highest r is for Cdxt (0.95). The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%. UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management. For relatively small green areas, a hand-held crop sensor can be a less expensive and more practical option.

No MeSH data available.


Related in: MedlinePlus

The RGB image of the turfgrass fields acquired by Tetracam ADCMicro mounted on the unmanned aerial vehicle.
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pone.0158268.g002: The RGB image of the turfgrass fields acquired by Tetracam ADCMicro mounted on the unmanned aerial vehicle.

Mentions: A radiometric calibration has been also performed with a three steps normalization procedure: i) estimation of across-track illumination variability by using smoothed (20 m kernel) band-averaged response, ii) normalization of band-averaged response upon mean value of across-track Regions Of Interest, and iii) inversion of band-averaged rescaling to produce a normalization layer. The derived normalization layer was multiplied by the original digital number to derived normalized data in the green, red and NIR (Near InfraRed) channels. Normalized digital number channels were then calibrated to surface reflectance values using empirical line regression and in situ spectral measurements. Reflectance is measured in 3 multi-spectral bands: Green 520–600 nm; Red 630–690 nm; Near Infrared (NIR) 760–900 nm. The Normalized Difference Vegetation Index (NDVI) was derived from the normalized and calibrated reflectance of the three sensor’s channels (Green channel, Red channel, NIR channel). Every pixel (0.05 x 0.05 m) of the image contained coordinates and an NDVI value (Fig 2). Pixel NDVI values were extracted using ENVI software (RSI Inc., Boulder, CO, USA). Thus, the plots were identified and NDVI values were obtained in the same position where the ground NDVI readings were performed [2].


Unmanned Aerial Vehicle to Estimate Nitrogen Status of Turfgrasses.

Caturegli L, Corniglia M, Gaetani M, Grossi N, Magni S, Migliazzi M, Angelini L, Mazzoncini M, Silvestri N, Fontanelli M, Raffaelli M, Peruzzi A, Volterrani M - PLoS ONE (2016)

The RGB image of the turfgrass fields acquired by Tetracam ADCMicro mounted on the unmanned aerial vehicle.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920401&req=5

pone.0158268.g002: The RGB image of the turfgrass fields acquired by Tetracam ADCMicro mounted on the unmanned aerial vehicle.
Mentions: A radiometric calibration has been also performed with a three steps normalization procedure: i) estimation of across-track illumination variability by using smoothed (20 m kernel) band-averaged response, ii) normalization of band-averaged response upon mean value of across-track Regions Of Interest, and iii) inversion of band-averaged rescaling to produce a normalization layer. The derived normalization layer was multiplied by the original digital number to derived normalized data in the green, red and NIR (Near InfraRed) channels. Normalized digital number channels were then calibrated to surface reflectance values using empirical line regression and in situ spectral measurements. Reflectance is measured in 3 multi-spectral bands: Green 520–600 nm; Red 630–690 nm; Near Infrared (NIR) 760–900 nm. The Normalized Difference Vegetation Index (NDVI) was derived from the normalized and calibrated reflectance of the three sensor’s channels (Green channel, Red channel, NIR channel). Every pixel (0.05 x 0.05 m) of the image contained coordinates and an NDVI value (Fig 2). Pixel NDVI values were extracted using ENVI software (RSI Inc., Boulder, CO, USA). Thus, the plots were identified and NDVI values were obtained in the same position where the ground NDVI readings were performed [2].

Bottom Line: Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits.The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%.UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management.

View Article: PubMed Central - PubMed

Affiliation: Department of Agriculture, Food and Environment, University of Pisa, Pisa, Italy.

ABSTRACT
Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits. The objectives of the trial were i) to compare the spectral reflectance of 3 turfgrasses acquired via UAV and by a ground-based instrument; ii) to test the sensitivity of the 2 data acquisition sources in detecting induced variation in N levels. N application gradients from 0 to 250 kg ha-1 were created on 3 different turfgrass species: Cynodon dactylon x transvaalensis (Cdxt) 'Patriot', Zoysia matrella (Zm) 'Zeon' and Paspalum vaginatum (Pv) 'Salam'. Proximity and remote-sensed reflectance measurements were acquired using a GreenSeeker handheld crop sensor and a UAV with onboard a multispectral sensor, to determine Normalized Difference Vegetation Index (NDVI). Proximity-sensed NDVI is highly correlated with data acquired from UAV with r values ranging from 0.83 (Zm) to 0.97 (Cdxt). Relating NDVI-UAV with clippings N, the highest r is for Cdxt (0.95). The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%. UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management. For relatively small green areas, a hand-held crop sensor can be a less expensive and more practical option.

No MeSH data available.


Related in: MedlinePlus