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Census Parcels Cropping System Classification from Multitemporal Remote Imagery: A Proposed Universal Methodology

AutorGarcía Torres, Luis ; Caballero Novella, Juan José ; Gómez-Candón, David ; Peña Barragán, José Manuel
Palabras claveCROPCLASS
Fecha de publicación17-feb-2015
EditorPublic Library of Science
CitaciónPLoS ONE 10(2): e0117551 (2015)
ResumenA 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%.
Versión del editorhttp://dx.doi.org/10.1371/journal.pone.0117551
URIhttp://hdl.handle.net/10261/121368
DOI10.1371/journal.pone.0117551
ISSN1932-6203
E-ISSN1932-6203
ReferenciasGarcía 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
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
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
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