English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/28082
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

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

AuthorsLópez Granados, Francisca ; Jurado-Expósito, Montserrat ; Peña Barragán, José Manuel ; García Torres, Luis
KeywordsHyperspectral
Multispectral
Site-specific weed management
Vegetation indices
Issue DateMar-2006
PublisherWeed Science Society of America
CitationWeed Science, 54:346–353. 2006
AbstractField 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.
Description8 pages; 3 figures; 3 tables
Publisher version (URL)http://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)
Appears in Collections:(IAS) Artículos
Files in This Item:
File Description SizeFormat 
Digital CSIC.Nota.pdf153,65 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.