English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/127249
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 | DATACITE
Exportar a otros formatos:

Title

Evaluation of pixel- and object-based approaches for mapping wildoat (Avena sterilis) weed patches in wheat fields using QuickBirdimagery for site-specific management

AuthorsCastillejo González, Isabel L.; Peña Barragán, José Manuel ; Jurado-Expósito, Montserrat ; Mesas-Carrascosa, Francisco Javier; López Granados, Francisca
KeywordsHerbicide savings
Weeds
Remote sensing
Precision agriculture
Pixel- and object-based image analysis
Broad- and field-level weed mapping
Issue Date11-Jun-2014
PublisherElsevier
CitationEuropean Journal of Agronomy 59: 57- 66 (2014)
AbstractThis paper compares of pixel- and object-based techniques for mapping wild oat weed patches in wheatfields using multi-spectral QuickBird satellite imagery for site-specific weed management. The researchwas conducted at two levels: (1) at the field level, on 11 and 15 individual infested wheat fields in 2006 and2008, respectively, and (2) on a broader level, by analysing the entire 2006 and 2008 images. To evaluatethe wild oat patches mapping at the field level, both pixel- and object-based image analyses were testedwith six classification algorithms: Parallelepipeds (P), Mahalanobis Distance (MD), Maximum Likelihood(ML), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Decision Tree (DT). The resultsshowed that weed patches could be accurately detected with both analyses obtaining global accuraciesbetween 80% and 99% for most of the fields. The MD and SVM classifiers were the most accurate forboth the pixel- and object-based images from 2006 and 2008, respectively. In the broad-scale analysis,all of the wheat fields were identified in the imagery using a multiresolution hierarchical segmentationbased on two scales. The first segmentation scale was classified using the MD and ML algorithms todiscriminate wheat fields from other land uses. Accuracies greater than 85% were obtained for MD and88% for ML for both imagery. A hierarchical analysis was then performed with the second segmentationscale, increasing the accuracies to 93% and 91% for 2006 and 2008 imagery, respectively. Finally, based onthe most accurate results obtained in the field-level study, pixel-based classifications using the MD, MLand SVM algorithms were applied to the wheat fields identified. The results of these broad-level analysesshowed that wild oat patches were accurately discriminated in all the wheat fields present in the entireimages with accuracies greater than 91% for all the classifiers tested.
Publisher version (URL)http://dx.doi.org/10.1016/j.eja.2014.05.009
URIhttp://hdl.handle.net/10261/127249
DOIhttp://dx.doi.org/10.1016/j.eja.2014.05.009
Identifiersdoi: 10.1016/j.eja.2014.05.009
issn: 1161-0301
Appears in Collections:(IAS) Artículos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 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.