Multi-temporal Multi-sensor Data Fusion

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dc.contributorVirginia Tech GIS & Remote Sensing 2014 Research Symposiumen
dc.contributor.authorGhannam, Sherinen
dc.contributor.authorAbbott, A. Lynnen
dc.date.accessioned2014-11-04T19:39:23Zen
dc.date.available2014-11-04T19:39:23Zen
dc.date.issued2014en
dc.description.abstractLandsat data offered a great help in mapping a lot of vegetation parameters at 30 m spatial resolution but unfortunately does not provide daily coverage (it has a 16 day revisit cycle). This is a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time. MODIS, on the other hand, offers daily coverage but with a coarser resolution; 250m or coarser. The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This would be helpful for many purposes including crop monitoring and investigating landscape disturbances. This study tries to make benefit of the multi-resolution analysis offered by data transforms to adopt a fusion technique for estimating missing Landsat data with the help of MODIS data. Results should be compared to the known STARFM algorithm.en
dc.description.sponsorshipVirginia Tech. Office of Geographical Information Systems and Remote Sensingen
dc.identifier.urihttp://hdl.handle.net/10919/50690en
dc.language.isoenen
dc.rightsIn Copyrighten
dc.rights.holderGhannam, Sherinen
dc.rights.holderAbbott, Lynn A.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectLandsat dataen
dc.subjectVegetationen
dc.subjectData fusionen
dc.subjectLandscape disturbanceen
dc.subjectVegetation mappingen
dc.titleMulti-temporal Multi-sensor Data Fusionen
dc.typeAbstracten
dc.type.dcmitypeTexten

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