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dc.contributor.advisorEgbert, Stephen L.
dc.contributor.authorBishop, Christopher Ryan
dc.date.accessioned2011-01-03T02:21:36Z
dc.date.available2011-01-03T02:21:36Z
dc.date.issued2010-06-14
dc.date.submitted2010
dc.identifier.otherhttp://dissertations.umi.com/ku:10994
dc.identifier.urihttp://hdl.handle.net/1808/6972
dc.description.abstractIn many cases, when classifying satellite imagery, training sites and sample data are not available on a yearly basis. Therefore, it would be beneficial if accurate training data from a single year could be applied to other years.The objectives of this research were to: 1) utilize time-series MODIS 250m NDVI data to identify and map unique crop types for the state of Kansas and the surrounding Kansas River watershed and 2) test the level of accuracy when conducting cross-year classifications by applying a library of NDVI time-series curves to imagery from other years. MODIS 250m NDVI data were used to classify seven unique crop cover types for 2005. An overall accuracy of 82% was achieved. MODIS 250m time-series NDVI data and training and validation data from 2001 and 2005 were used to conduct the cross-year classifications. Overall accuracies were between 68% (2001) and 74% (2005) were achieved.
dc.format.extent132 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsThis item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
dc.subjectRemote sensing
dc.subjectGeographic information sciences
dc.subjectAgriculture
dc.subjectCropland
dc.subjectKansas
dc.subjectModis
dc.subjectNDVI
dc.subjectSatellite
dc.titleAn Evaluation of Cross-Year Crop Classification for the State of Kansas Using Time-Series MODIS 250m Vegetation Index Data
dc.typeThesis
dc.contributor.cmtememberSlocum, Terry A.
dc.contributor.cmtememberLi, Xingong
dc.thesis.degreeDisciplineGeography
dc.thesis.degreeLevelM.A.
kusw.oastatusna
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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