Temporal data fusion approaches to remote sensing-based wetland classification

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dc.contributor.authorMontgomery, Joshua
dc.contributor.supervisorHopkinson, Christopher
dc.date.accessioned2018-01-22T16:04:52Z
dc.date.available2018-01-22T16:04:52Z
dc.date.issued2017
dc.degree.levelMastersen_US
dc.descriptionen_US
dc.description.abstractThis thesis investigates the ecology of wetlands and associated classification in prairie and boreal environments of Alberta, Canada, using remote sensing technology to enhance classification of wetlands in the province. Objectives of the thesis are divided into two case studies, 1) examining how satellite borne Synthetic Aperture Radar (SAR), optical (RapidEye & SPOT) can be used to evaluate surface water trends in a prairie pothole environment (Shepard Slough); and 2) investigating a data fusion methodology combining SAR, optical and Lidar data to characterize wetland vegetation and surface water attributes in a boreal environment (Utikuma Regional Study Area (URSA)). Surface water extent and hydroperiod products were derived from SAR data, and validated using optical imagery with high accuracies (76-97% overall) for both case studies. High resolution Lidar Digital Elevation Models (DEM), Digital Surface Models (DSM), and Canopy Height Model (CHM) products provided the means for data fusion to extract riparian vegetation communities and surface water; producing model accuracies of (R2 0.90) for URSA, and RMSE of 0.2m to 0.7m at Shepard Slough when compared to field and optical validation data. Integration of Alberta and Canadian wetland classifications systems used to classify and determine economic value of wetlands into the methodology produced thematic maps relevant for policy and decision makers for potential wetland monitoring and policy development.en_US
dc.description.sponsorshipFunding for this thesis was provided by the NSERC CREATE AMETHYST Program, and the Government of Alberta (Economic Development and Trade, Environment and Parks), Campus Alberta Innovates Program.en_US
dc.embargoNoen_US
dc.identifier.urihttps://hdl.handle.net/10133/5002
dc.language.isoen_USen_US
dc.proquest.subject0329en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Geographyen_US
dc.publisher.departmentDepartment of Geographyen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectSynthetic aperture radaren_US
dc.subjectWetlandsen_US
dc.subjecthydroperioden_US
dc.subjecttime seriesen_US
dc.subjectfrequency analysisen_US
dc.subjectdata fusionen_US
dc.subjectOptical radaren_US
dc.titleTemporal data fusion approaches to remote sensing-based wetland classificationen_US
dc.typeThesisen_US
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