Predicting permafrost probability in a variable boreal environment utilizing a multiple logistic regression model, Whatì, NT, Canada

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dc.contributor.authorDaly, Seamus
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorBonnaventure, Philip
dc.date.accessioned2021-03-04T21:32:11Z
dc.date.available2021-03-04T21:32:11Z
dc.date.issued2021
dc.degree.levelMastersen_US
dc.description.abstractFor remote communities, access to permafrost information for hazard assessment is a considerable challenge. This study applies analytical methods illustrating a time- and cost-efficient method for conducting community-scale permafrost mapping in the community of Whatì, NT. A binary logistic regression model was created using a combination of field data, digital elevation model-derived variables and remotely sensed products. Independent variables included categorical inputs such as vegetation, topographic position index and elevation breaks. The dependent variable is sourced from 139 physical checks of permafrost presence/absence. Vegetation was shown to be the strongest predictor of permafrost. The model predicts 50.0 % of the vegetated area is underlain by permafrost with a model accuracy of 91.4 % and spatial agreement of 72.8 % when compared to ground-truth pits. Compared to existing permafrost products this value is on the lowest edge of Whatì’s current classification (extensive discontinuous) illustrating there could be less permafrost than presumed.en_US
dc.identifier.urihttps://hdl.handle.net/10133/5842
dc.language.isoen_USen_US
dc.proquest.subjectPhysical geography [0368]en_US
dc.proquest.subjectRemote sensing [0799]en_US
dc.proquest.subjectAtmospheric sciences [0725]en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Geography & Environmenten_US
dc.publisher.departmentDepartment of Geography & Environmenten_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectForest fire forecasting -- Northwest Territoriesen_US
dc.subjectLogistic regression analysisen_US
dc.subjectPermafrost -- Northwest Territoriesen_US
dc.subjectPermafrost -- Remote sensingen_US
dc.subjectTaigas -- Ecologyen_US
dc.subjectTaigas -- Northwest Territoriesen_US
dc.subjectDissertations, Academicen_US
dc.titlePredicting permafrost probability in a variable boreal environment utilizing a multiple logistic regression model, Whatì, NT, Canadaen_US
dc.typeThesisen_US
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