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dc.contributor.authorHOJAS GASCÓN LORENAen_GB
dc.contributor.authorBELWARD Alanen_GB
dc.contributor.authorEVA Hughen_GB
dc.contributor.authorCECCHERINI GUIDOen_GB
dc.contributor.authorHAGOLLE Olivieren_GB
dc.contributor.authorGARCIA J.en_GB
dc.contributor.authorCERUTTI Paoloen_GB
dc.date.accessioned2015-07-25T00:06:20Z-
dc.date.available2015-07-24en_GB
dc.date.available2015-07-25T00:06:20Z-
dc.date.created2015-07-23en_GB
dc.date.issued2015en_GB
dc.date.submitted2015-05-11en_GB
dc.identifier.issn1682-1750en_GB
dc.identifier.urihttp://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/417/2015/isprsarchives-XL-7-W3-417-2015.htmlen_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC95914-
dc.description.abstractThe forthcoming European Space Agency’s Sentinel-2 mission promises to provide high (10 m) resolution optical data at higher temporal frequencies (5 day revisit with two operational satellites) than previously available. CNES, the French national space agency, launched a program in 2013, ‘SPOT4 take 5’, to simulate such a dataflow using the SPOT HRV sensor, which has similar spectral characteristics to the Sentinel sensor, but lower (20m) spatial resolution. Such data flow enables the analysis of the satellite images using temporal analysis, an approach previously restricted to lower spatial resolution sensors. We acquired 23 such images over Tanzania for the period from February to June 2013. The data were analysed with aim of discriminating between different forest cover percentages for landscape units of 0.5 ha over a site characterised by deciduous intact and degraded forests. The SPOT data were processed by one extracting temporal vegetation indices. We assessed the impact of the high acquisition rate with respect to the current rate of one image every 16 days. Validation data, giving the percentage of forest canopy cover in each land unit were provided by very high resolution satellite data. Results show that using the full temporal series it is possible to discriminate between forest units with differences of more than 40% tree cover or more. Classification errors fell exclusively into the adjacent forest canopy cover class of 20% or less. The analyses show that forestation mapping and degradation monitoring will be substantially improved with the Sentinel-2 programen_GB
dc.description.sponsorshipJRC.H.3-Forest Resources and Climateen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_GB
dc.relation.ispartofseriesJRC95914en_GB
dc.titlePotential improvement for forest cover and forest degradation mapping with the forthcoming sentinel-2 programen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.5194/isprsarchives-XL-7-W3-417-2015en_GB
JRC Directorate:Sustainable Resources

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