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http://hdl.handle.net/10261/121368
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Campo DC | Valor | Lengua/Idioma |
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dc.contributor.author | García Torres, Luis | es_ES |
dc.contributor.author | Caballero Novella, Juan José | es_ES |
dc.contributor.author | Gómez-Candón, David | es_ES |
dc.contributor.author | Peña Barragán, José Manuel | es_ES |
dc.date.accessioned | 2015-08-24T12:45:22Z | - |
dc.date.available | 2015-08-24T12:45:22Z | - |
dc.date.issued | 2015-02-17 | - |
dc.identifier.citation | PLoS ONE 10(2): e0117551 (2015) | es_ES |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | http://hdl.handle.net/10261/121368 | - |
dc.description.abstract | A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%. | es_ES |
dc.description.sponsorship | Funded by Spanish Council for Scientific Research (CSIC)(https:/www.csic.es), Ministry of Economy and Competitivity (https://sede.micinn.gob. es/) through the project AGL-2010-15506. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Public Library of Science | es_ES |
dc.relation.isversionof | Publisher's version | es_ES |
dc.relation.isreferencedby | García Torres, Luis; Gómez-Candón, David; Caballero Novella, Juan José; Peña Barragán, José Manuel; López Granados, Francisca; Jurado-Expósito, Montserrat. CROPCLASS-2.0 software for census parcel cropping systems classification from multitemporal remote imagery. http://hdl.handle.net/10261/121360 | es_ES |
dc.relation.isreferencedby | García Torres, Luis; Caballero Novella, Juan José; Gómez-Candón, David; López Granados, Francisca. Automatic image processing for agriculture through specific ENVI modules (add-on). http://hdl.handle.net/10261/121363 | es_ES |
dc.relation.isreferencedby | Caballero Novella, Juan José; García Torres, Luis; Gómez-Candón, David. Procedimiento CROPCLASS® de clasificación de cultivos en imágenes remotas a nivel parcela para su uso en el censo agrícola. http://hdl.handle.net/10261/121367 | es_ES |
dc.rights | openAccess | es_ES |
dc.subject | CROPCLASS | es_ES |
dc.title | Census Parcels Cropping System Classification from Multitemporal Remote Imagery: A Proposed Universal Methodology | es_ES |
dc.type | artículo | es_ES |
dc.identifier.doi | 10.1371/journal.pone.0117551 | - |
dc.description.peerreviewed | Peer reviewed | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1371/journal.pone.0117551 | es_ES |
dc.identifier.e-issn | 1932-6203 | - |
dc.rights.license | http://creativecommons.org/licenses/by/4.0/ | es_ES |
dc.contributor.funder | Consejo Superior de Investigaciones Científicas (España) | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | es_ES |
dc.relation.csic | Sí | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100003339 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100003329 | es_ES |
dc.identifier.pmid | 25689830 | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.openairetype | artículo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
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Census_parcels_cropping_system_GarciaTorres.pdf | 13,19 MB | Adobe PDF | Visualizar/Abrir |
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