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dc.creatorCiriza Labiano, Raqueles_ES
dc.creatorSola Torralba, Iones_ES
dc.creatorAlbizua, Lourdeses_ES
dc.creatorÁlvarez-Mozos, Jesúses_ES
dc.creatorGonzález de Audícana Amenábar, Maríaes_ES
dc.date.accessioned2017-11-15T09:28:42Z
dc.date.available2017-11-15T09:28:42Z
dc.date.issued2017
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/2454/26175
dc.description.abstractPermanent crops, such as olive groves, vineyards and fruit trees, are important in European agriculture because of their spatial and economic relevance. Agricultural geographical databases (AGDBs) are commonly used by public bodies to gain knowledge of the extension covered by these crops and to manage related agricultural subsidies and inspections. However, the updating of these databases is mostly based on photointerpretation, and thus keeping this information up-to-date is very costly in terms of time and money. This paper describes a methodology for automatic detection of uprooted orchards (parcels where fruit trees have been eliminated) based on the textural classification of orthophotos with a spatial resolution of 0.25 m. The textural features used for this classification were derived from the grey level co-occurrence matrix (GLCM) and wavelet transform, and were selected through principal components (PCA) and separability analyses. Next, a Discriminant Analysis classification algorithm was used to detect uprooted orchards. Entropy, contrast and correlation were found to be the most informative textural features obtained from the co-occurrence matrix. The minimum and standard deviation in plane 3 were the selected features based on wavelet transform. The classification based on these features achieved a true positive rate (TPR) of over 80% and an accuracy (A) of over 88%. As a result, this methodology enabled reducing the number of fields to photointerpret by 60–85%, depending on the membership threshold value selected. The proposed approach could be easily adopted by different stakeholders and could increase significantly the efficiency of agricultural database updating tasks.en
dc.description.sponsorshipThis study was funded by the Spanish National Institute for Agricultural and Food Research and Technology (INIA) through its training program for researchers.en
dc.format.extent22 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherMDPIen
dc.relation.ispartofRemote sensing, 2017, 9(5), 492en
dc.rights© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectOrchard detectionen
dc.subjectImage analysisen
dc.subjectTexture featureen
dc.subjectGLCMen
dc.subjectWavelet transformen
dc.subjectDiscriminant analysisen
dc.subjectParcel level classificationen
dc.titleAutomatic detection of uprooted orchards based on orthophoto texture analysisen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Proyectos e Ingeniería Rurales_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Landa Ingeniaritza eta Proiektuak Sailaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.3390/rs9050492
dc.relation.publisherversionhttps://dx.doi.org/10.3390/rs9050492
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen


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© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.