Automating riparian health assessment using high-resolution remotely sensed imagery

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dc.contributor.authorLeo, Gabrielle Marie
dc.contributor.examiningcommitteeBaydack, Rick (Environment and Geography) Koper, Nicola (Natural Resources Institute)en_US
dc.contributor.supervisorWalker, David (Environment and Geography)en_US
dc.date.accessioned2015-03-31T21:09:57Z
dc.date.available2015-03-31T21:09:57Z
dc.date.issued2015-03-31
dc.degree.disciplineEnvironment and Geographyen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractRiparian areas are ecologically and economically critical habitats in the Canadian Prairies. An estimated 80% of riparian zones in North America are threatened by anthropogenic development. While riparian conservation is integrated into agricultural, watershed, and forestry best management practices across Canada, existing riparian health assessments are reliant on resource-intensive field surveys. The objective of this thesis was to develop a riparian health assessment using high-resolution remotely sensed imagery. Riparian health surveys were conducted along the La Salle River. High-resolution imagery and LiDAR data were integrated into an object-based image analysis of vegetation. Topographic analysis was conducted using a high-resolution DEM. These data were input into a linear discriminant classifier to model riparian health. Riparian health models containing both vegetation and topographic variables, and only vegetation variables, produced good agreement with field assessments. LiDAR data and the object-based image analysis method were successfully used to develop a remote riparian health assessment.en_US
dc.description.noteMay 2015en_US
dc.identifier.urihttp://hdl.handle.net/1993/30323
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectriparianen_US
dc.subjectOBIAen_US
dc.subjectLiDARen_US
dc.subjecthealthen_US
dc.subjectmodellingen_US
dc.titleAutomating riparian health assessment using high-resolution remotely sensed imageryen_US
dc.typemaster thesisen_US
local.subject.manitobayesen_US
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