Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/28082
COMPARTIR / EXPORTAR:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

Using remote sensing for identification of late-season grass weed patches in wheat

AutorLópez Granados, Francisca CSIC ORCID ; Jurado-Expósito, Montserrat CSIC ORCID ; Peña Barragán, José Manuel CSIC ORCID CVN ; García Torres, Luis CSIC
Palabras claveHyperspectral
Multispectral
Site-specific weed management
Vegetation indices
Fecha de publicaciónmar-2006
EditorWeed Science Society of America
CitaciónWeed Science, 54:346–353. 2006
ResumenField research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R 2 G)/(R 1 G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.
Descripción8 pages; 3 figures; 3 tables
Versión del editorhttp://dx.doi.org/10.1043/0043-1745(2006)54[346:URSFIO]2.0.CO;2
URIhttp://hdl.handle.net/10261/28082
DOI10.1043/0043-1745(2006)54[346:URSFIO]2.0.CO;2
ISSN0043-1745 (Print)
1550-2759 (Online)
Aparece en las colecciones: (IAS) Artículos

Ficheros en este ítem:
Fichero Descripción Tamaño Formato
Digital CSIC.Nota.pdf153,65 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo

CORE Recommender

Page view(s)

327
checked on 27-mar-2024

Download(s)

359
checked on 27-mar-2024

Google ScholarTM

Check

Altmetric

Altmetric


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.