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WHEAT LODGING ASSESSMENT USING MULTISPECTRAL UAV DATA

Chauhan, S. ; Darvishzadeh, R. ; Lu, Y. ; Stroppiana, D. ; Boschetti, M. ; Pepe, M. ; Nelson, A.

International archives of the photogrammetry, remote sensing and spatial information sciences., 2019, Vol.XLII-2/W13, p.235-240 [Periódico revisado por pares]

Gottingen: Copernicus GmbH

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  • Título:
    WHEAT LODGING ASSESSMENT USING MULTISPECTRAL UAV DATA
  • Autor: Chauhan, S. ; Darvishzadeh, R. ; Lu, Y. ; Stroppiana, D. ; Boschetti, M. ; Pepe, M. ; Nelson, A.
  • Assuntos: Crop yield ; Farms ; High resolution ; Image acquisition ; Image segmentation ; Reflectance ; Remote sensing ; Resolution ; Spatial resolution ; Unmanned aerial vehicles ; Wheat
  • É parte de: International archives of the photogrammetry, remote sensing and spatial information sciences., 2019, Vol.XLII-2/W13, p.235-240
  • Descrição: Lodging is a major yield-reducing factors in wheat, causing reductions up to 80%. Timely detection of lodging can reduce its impacts and support proper decisions regarding expected yield, crop price or its insurance. Since the incidence of lodging is heterogeneous within a field, very high-resolution remote sensing data can be viable for accurate and prompt spatio-temporal assessment of lodging severity. As such unmanned aerial vehicles (UAVs) provide a versatile and cost-effective solution to monitor crops on a small scale with sub-centimetre spatial resolution. In this study, we analysed the spectral variability between different grades of lodging severity (non-lodged (NL), moderate (ML), severe (SL) and very severe (VSL)) and classified them using high-resolution UAV data. Multispectral orthomosaic UAV images with 5cm resolution and nine bands (covering the VIS-NIR spectrum with Sentinel-2 filters) were acquired in May 2018 for two wheat fields in Bonifiche Ferraresi farm, Jolanda di Savoia, Italy. Concurrent to the time of image acquisition, a field campaign was carried out in which crop characteristics and lodging related parameters were collected. The results showed that reflectance magnitude increased with lodging severity and demonstrated that the red-edge and NIR bands can be used to clearly discriminate between NL and lodged (all grades) wheat and to some extent between different lodging classes (ML, SL and VSL). The nearest neighbourhood classification performed using an object-based segmentation yielded optimal results with an overall accuracy of 90%, thus demonstrating the use of multispectral UAV data as a promising tool for wheat lodging assessment.
  • Editor: Gottingen: Copernicus GmbH
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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