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dc.contributor.authorNemmaoui A
dc.contributor.authorAguilar M. A
dc.contributor.authorAguilar F.J
dc.contributor.authorNovelli A
dc.contributor.authorGarcia Lorca A
dc.date.accessioned2019-03-04T08:53:12Z
dc.date.available2019-03-04T08:53:12Z
dc.date.issued2018
dc.identifier.issn2072-4292
dc.identifier.urihttp://dx.doi.org/10.3390/rs10111751
dc.identifier.urihttps://www.mdpi.com/2072-4292/10/11/1751
dc.identifier.urihttp://hdl.handle.net/10863/8953
dc.description.abstract  A 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.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.relation
dc.rights
dc.titleGreenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)en_US
dc.typeArticleen_US
dc.date.updated2019-03-01T16:37:03Z
dc.language.isiEN-GB
dc.journal.titleRemote Sensing
dc.description.fulltextopenen_US


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