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dc.contributor.advisorBender, Darren
dc.contributor.authorYuen, Jeffrey Lin
dc.date.accessioned2005-08-16T17:35:29Z
dc.date.available2005-08-16T17:35:29Z
dc.date.issued2004
dc.identifier.citationYuen, J. L. (2004). An integrated predictive vegetation modelling approach: combining remote sensing, GIS, and biological theory (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/22066en_US
dc.identifier.isbn0612934152en
dc.identifier.urihttp://hdl.handle.net/1880/42162
dc.descriptionBibliography: p. 53-59en
dc.description.abstractMany diverse approaches have developed in order to characterize the spatial distribution of landscape features (particularly vegetation pattern). While technically variable, the data used in landscape characterization can be assigned into one of two categories - those that are spectrally derived from remotely sensed data and those that are ecologically relevant resource gradients. The remote sensing approach uses empirical relationships between land cover classes and spectral reflectance to classify a landscape, whereas the biological approach uses physiological and ecological relationships in defining geographic suitability for different species. Remote sensing approaches are limited in their ability to distinguish vegetation classes at the species scale, while biological approaches are limited in their ability to identify nonvegetated features on the landscape. In this thesis, an integrated method that combines these complimentary approaches is developed and compared to the individual spectral and biological approaches. The result is an accurate land cover pattern distribution that is able to resolve tree cover at the species level, while retaining the ability to define disturbed and non-vegetated areas.
dc.format.extentvii, 64 leaves : ill. ; 30 cm.en
dc.language.isoeng
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.titleAn integrated predictive vegetation modelling approach: combining remote sensing, GIS, and biological theory
dc.typemaster thesis
dc.publisher.institutionUniversity of Calgaryen
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/22066
thesis.degree.nameMaster of Science
thesis.degree.nameMS
thesis.degree.nameMSc
thesis.degree.disciplineGeography
thesis.degree.grantorUniversity of Calgary
dc.identifier.lccAC1 .T484 2004 Y84en
dc.publisher.placeCalgaryen
ucalgary.thesis.notesUARCen
ucalgary.thesis.uarcreleaseyen
ucalgary.item.requestcopytrue
ucalgary.thesis.accessionTheses Collection 58.002:Box 1549 520492066


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University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.