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dc.contributor.supervisor Staenz, Karl
dc.contributor.supervisor Rochdi, Nadia
dc.contributor.author Banting, James
dc.contributor.author University of Lethbridge. Faculty of Arts and Science
dc.date.accessioned 2016-05-19T20:57:25Z
dc.date.available 2016-05-19T20:57:25Z
dc.date.issued 2016
dc.identifier.uri https://hdl.handle.net/10133/4482
dc.description.abstract Oil and gas activities in Alberta require disturbing forested lands, among other ecosystems, in order to extract resources. Due to the number of oil and gas sites requiring reclamation, monitoring can be problematic. Remote sensing provides cost-effective, timely, and repeatable data of these areas in support of monitoring efforts. Support Vector Machine (SVM) and Multiple Endmember Spectral Mixture Analysis (MESMA) were tested in order to identify tree species around reclaimed and abandoned well sites near Cold Lake, Alberta using CHRIS satellite imagery with and without airborne LiDAR data. A hierarchical classification approach was employed, which achieved an accuracy of 83.4 % when using SVM together with CHRIS imagery and LiDAR. This positive result indicates the ability of remote sensing to support reclamation management and monitoring objectives within Alberta’s forested areas. en_US
dc.description.sponsorship Natural Science and Engineering Research Council of Canada (NSERC) CREATE scholarship (Advanced Methods, Education and Training in Hyperspectral Science and Technology; AMETHYST). Alberta Terrestrial Imaging Centre (ATIC). TECTERRA. Oil Sands Research and Information Network (OSRIN). Alberta Environment and Sustainable Resource Development (ESRD) en_US
dc.language.iso en_CA en_US
dc.publisher Lethbridge, Alta : University of Lethbridge, Dept. of Geography en_US
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science) en_US
dc.subject land cover monitoring en_US
dc.subject reclamation en_US
dc.subject remote sensing en_US
dc.subject spectral data en_US
dc.subject structural data en_US
dc.subject tree species mapping en_US
dc.title Tree species mapping around reclaimed oil and gas wells sites using hyperspectral and Light Detection and Ranging (LiDAR) remote sensing en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Geography en_US
dc.degree.level Masters en_US
dc.proquest.subject 0799 en_US
dc.proquest.subject 0366 en_US
dc.proquest.subject 0536 en_US
dc.proquestyes Yes en_US
dc.embargo No en_US


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