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dc.contributor.authorNemmaoui, Abderrahim
dc.contributor.authorAguilar, Manuel A.
dc.contributor.authorAguilar, Fernando J.
dc.contributor.authorNovelli, Antonio
dc.contributor.authorGarcía Lorca, Andrés
dc.date.accessioned2019-03-11T07:19:25Z
dc.date.available2019-03-11T07:19:25Z
dc.date.issued2018-11-30
dc.identifier.urihttp://hdl.handle.net/10835/6417
dc.description.abstractA workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary preclassification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/ES/MINECO/AGL2014-56017-R/ES/Identificación basada en objetos de cultivos hortícolas bajo invernadero a partir de estéreo imágenes del satélite Worldview-3 y series temporales de Landsat 8/IBOCHBIEISWSTLes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRemote Sensing, Agriculturees_ES
dc.titleGreenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/2072-4292/10/11/1751es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/rs10111751
dc.relation.projectIDAGL2014-56017-Res_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional