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dc.contributor.authorNemmaoui, Abderrahim
dc.contributor.authorAguilar Torres, Manuel
dc.contributor.authorAguilar Torres, Fernando José
dc.contributor.authorNovelli, Antonio
dc.contributor.authorGarcía Lorca, Andrés Miguel
dc.date.accessioned2020-01-17T08:19:21Z
dc.date.available2020-01-17T08:19:21Z
dc.date.issued2018-11-06
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10835/7499
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 pre-classification 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.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLandsat 8es_ES
dc.subjectSentinel-2es_ES
dc.subjectWorldView-2es_ES
dc.subjecttime serieses_ES
dc.subjectobject-based classificationes_ES
dc.subjectgreenhouse mappinges_ES
dc.subjectcrop types classificationes_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


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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