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dc.contributor.advisorHu, Baoxin
dc.creatorZhang, Kongwen
dc.date.accessioned2015-08-28T15:01:22Z
dc.date.available2015-08-28T15:01:22Z
dc.date.copyright2014-08-22
dc.date.issued2015-08-28
dc.identifier.urihttp://hdl.handle.net/10315/29915
dc.description.abstractThis dissertation describes three contributions in the characterization of vegetation canopies using remote sensing data, with a focus on hyperspectral and very high spatial resolution imagery. The new and innovative methods developed are: 1) integration of contribution theory into a model inversion approach to obtain high accuracy in canopy biophysical parameter estimation; 2) exploration and adoption of tree crown longitudinal profiles to achieve high accuracy in tree species classification; and 3) evaluation of canopy health state for Emerald Ash Borer (EAB) infestation assessment by intelligent combination of multi-sourced data.
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectRemote sensing
dc.subjectForestry
dc.titleExploring Hyperspectral and Very High Spatial Resolution Imagery in Vegetation Characterization
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.disciplineEarth & Space Science
dc.degree.namePhD - Doctor of Philosophy
dc.degree.levelDoctoral
dc.date.updated2015-08-28T15:01:22Z
dc.subject.keywordsRemote Sensing
dc.subject.keywordsHyperspectral
dc.subject.keywordsHigh spatial resolution imagery


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