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dc.contributor.advisorWang, Cuizhenen
dc.contributor.authorZhou, Boen_US
dc.coverage.spatialMissouri -- Mark Twain National Forest
dc.date.issued2007eng
dc.date.submitted2007 Springen
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.en_US
dc.descriptionTitle from title screen of research.pdf file (viewed on November 9, 2007)en_US
dc.descriptionIncludes bibliographical references.en_US
dc.descriptionThesis (M.A.) University of Missouri-Columbia 2007.en_US
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Geography.en_US
dc.description.abstractWhen conservationists in Missouri realized that sericea lespedeza was taking its toll by threatening the healthy growth of economic vegetation, they decided to start controlling the invasion of this species. A major challenge encountered is to map the extent of its spatial spread. While satellite remote sensing and aerial photography have been available for many years, newer detection technologies such as hyperspectral sensors have made it possible to acquire large-scale laboratory-like spectra of sericea patches and surrounding natural grasses in the air. In this study, sericea was mapped using the Airborne Imaging Spectrometer for Application (AISA) sensor that records images at high spectral (9nm bandwidth, visible-infrared) and spatial (1̃m) resolution. Ground spectra were measured using the FieldSpecPro Full Range (FR) spectroradiometer from Analytical Spectral Devices (ASD, 2006). The study area is a grass field within the Mark Twain National Forest. The AISA images were processed with three different classification methods, and the results are validated based on field surveys. Major findings include: (1) the averaged sericea spectra is more accurate for mapping purposes; (2) moderate spectral response instead of strong spectral response is better in sericea mapping for they have less confusion with other classes; and (3) the MNF (Minimum Noise Fraction) and MTMF (Mixture Tuned Matched Filtering) approach is the best for mapping sericea.en_US
dc.identifier.merlin.b61277253en_US
dc.identifier.oclc180990540en_US
dc.identifier.otherZhouB-050407-T6737en_US
dc.identifier.urihttp://hdl.handle.net/10355/5051
dc.publisherUniversity of Missouri--Columbiaen_US
dc.relation.ispartof2007 Freely available theses (MU)en_US
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Theses. 2007 Theses
dc.subjecthyperspectral remote sensing.en_US
dc.subjecthyperspectral remote sensingen_US
dc.subject.lcshLespedeza cuneata -- Controlen_US
dc.subject.lcshLespedeza cuneata -- Remote sensingen_US
dc.titleApplication of hyperspectral remote sensing in detecting and mapping Sericea lespedeza in Missourien_US
dc.typeThesisen_US
thesis.degree.disciplineGeographyen_US
thesis.degree.disciplineGeographyeng
thesis.degree.grantorUniversity of Missouri--Columbiaen_US
thesis.degree.levelMastersen_US
thesis.degree.nameM.A.en_US


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