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dc.contributor.authorSIMONETTI DARIOen_GB
dc.contributor.authorSIMONETTI Edoardoen_GB
dc.contributor.authorSZANTOI ZOLTANen_GB
dc.contributor.authorLUPI ANDREAen_GB
dc.contributor.authorEVA Hughen_GB
dc.date.accessioned2016-01-09T01:24:40Z-
dc.date.available2015-04-13en_GB
dc.date.available2016-01-09T01:24:40Z-
dc.date.created2015-03-17en_GB
dc.date.issued2015en_GB
dc.date.submitted2015-03-03en_GB
dc.identifier.citationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS vol. 12 no. 7 p. 1496-1500en_GB
dc.identifier.issn1545-598Xen_GB
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7061922&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4357975)%26rowsPerPage%3D75en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC95065-
dc.description.abstractA fully automatic phenology based synthesis classification (PBS) algorithm was developed to map land cover based on medium spatial resolution satellite data using Google Earth Engine (GEE) cloud computing platform. Vegetation seasonality, especially in the tropical dry regions, can lead conventional algorithms based on a single date image classification to “misclassify” land cover types, as the selected date might reflect only a particular stage of the natural phenological cycle. The PBS classifier operates with occurrence rules applied to a selection of single date image classifications (SDC) of the study area to assign the most appropriate land cover class. Since the launch of Landsat 8 2013, it has been possible to acquire imagery at any point on the Earth every 16 days with exceptional radiometric quality. The relatively high global acquisition frequency and the open data policy allow near real time land cover mapping and monitoring with automated tools such as the PBS classifier. We mapped four protected areas and their 20km buffer zones from different ecoregions in Sub-Saharan Africa using the PBS classifier to present its first results. Accuracy assessment was carried out through a visual interpretation of very high resolution images using a WEB-GIS interface. The combined overall accuracy was over 90% which demonstrates the potential of the classifier and the power of cloud computing in geospatial sciences.en_GB
dc.description.sponsorshipJRC.H.3-Forest Resources and Climateen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.ispartofseriesJRC95065en_GB
dc.titleFirst results from the Phenology-Based Synthesis classifier using Landsat 8 imageryen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1109/LGRS.2015.2409982en_GB
JRC Directorate:Sustainable Resources

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