Title: First results from the Phenology-Based Synthesis classifier using Landsat 8 imagery
Authors: SIMONETTI DARIOSIMONETTI EdoardoSZANTOI ZOLTANLUPI ANDREAEVA Hugh
Citation: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS vol. 12 no. 7 p. 1496-1500
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Year: 2015
JRC N°: JRC95065
ISSN: 1545-598X
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7061922&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4357975)%26rowsPerPage%3D75
http://publications.jrc.ec.europa.eu/repository/handle/JRC95065
DOI: 10.1109/LGRS.2015.2409982
Type: Articles in periodicals and books
Abstract: A 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.
JRC Institute:Sustainable Resources

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