Forest attributes from multi-angle multi-date remotely sensed data

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dc.contributor.authorDyk, Andrew
dc.contributor.supervisorNiemann, K. O.
dc.date.accessioned2010-08-30T20:56:07Z
dc.date.available2010-08-30T20:56:07Z
dc.date.copyright2010en
dc.date.issued2010-08-30T20:56:07Z
dc.degree.departmentDepartment of Geography
dc.degree.levelMaster of Science M.Sc.en
dc.description.abstractMulti-Angle, Multi-Date, Hyperspectral imagery of forests have been used to provide accurate estimates of the canopy characteristics. This thesis investigated the influence of various forest attributes on the spectral reflectance over time and view direction. The Compact High Resolution Imaging Spectrometer (CHRIS) is aboard the ESA PROBA satellite. The revisits of the CHRIS multi-angle images have been used to improve the accuracies of forest species recognition and stand densities compared to a nadir view only. Multi-angle data for CHRIS analysis of forest species produced higher accuracy and were easier to obtain than multi-date date. 5-Scale, a radiative transfer model, and CHRIS data have been compared as inputs into Partial Least Squares (PLS), a fullspectrum analytical method that offers relations between forest stand parameters and the resulting spectra. The resulting coefficients highlight where (view angle and spectral regions) within the multi-angle spectra contributed to estimating the various forest parameters. Methodology of collecting spectral calibration data in the field and the unique pre-processing challenges have been described.en
dc.identifier.urihttp://hdl.handle.net/1828/2997
dc.languageEnglisheng
dc.language.isoenen
dc.rightsAvailable to the World Wide Weben
dc.subjectRemote Sensingen
dc.subjectHyperspectralen
dc.subjectForestsen
dc.subjectBidirectional Reflectanceen
dc.subject.lcshUVic Subject Index::Sciences and Engineering::Earth and Ocean Sciences::Physical geographyen
dc.subject.lcshUVic Subject Index::Sciences and Engineering::Agriculture::Forests and forestryen
dc.titleForest attributes from multi-angle multi-date remotely sensed dataen
dc.typeThesisen

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