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dc.contributor.supervisor Peddle, Derek Roland
dc.contributor.author Parshakov, Ilia
dc.contributor.author University of Lethbridge. Faculty of Arts and Science
dc.date.accessioned 2021-02-26T20:39:59Z
dc.date.available 2021-02-26T20:39:59Z
dc.date.issued 2021
dc.identifier.uri https://hdl.handle.net/10133/5840
dc.description.abstract Unsupervised Classification to Change (UC-Change) is a new remote sensing approach for mapping areas affected by logging and wildfires. It addresses the main limitations of existing image time-series change detection techniques, such as limited multi-sensor capabilities, use of purely spectral-based forest recovery metrics, and poor detection of salvage harvesting. UC Change detects disturbances and tracks forest recovery by analyzing changes in the spatial distribution of spectral classes over time. The algorithm detected approximately 85% and 70% of reference cutblock and fire scar pixels at a ±2-year temporal agreement, respectively, consistently outperforming existing algorithms across different biogeoclimatic zones of British Columbia, Canada. The results indicate an upper estimate of 7.5 million ha of forest cleared between 1984 and 2014, which is above estimates based on existing maps and databases (6.3 – 6.7 million ha). Also presented is a new framework for using open-access data for validation of change detection results. en_US
dc.description.sponsorship Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) grant entitled Advanced Methods, Education and Training in Hyperspectral Science and Technology (AMETHYST). Financial code: KS-NSERC2 Staenz 40307-4185-8001 en_US
dc.language.iso en_US en_US
dc.publisher Lethbridge, Alta. : University of Lethbridge, Dept. of Geography and Environment en_US
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science) en_US
dc.subject remote sensing en_US
dc.subject change detection en_US
dc.subject environmental monitoring en_US
dc.subject forest disturbance en_US
dc.subject machine learning en_US
dc.subject forestry en_US
dc.subject time series en_US
dc.subject Landsat en_US
dc.subject Sentinel-2 en_US
dc.subject UC-Change en_US
dc.subject cutblocks en_US
dc.subject clearcuts en_US
dc.subject forest harvest en_US
dc.subject Forest monitoring -- British Columbia en_US
dc.subject Forests and forestry -- British Columbia -- Remote sensing en_US
dc.subject Forests and forestry -- British Columbia -- Forecasting -- Mathematical models en_US
dc.subject Forests and forestry -- British Columbia -- Measurement -- Data processing en_US
dc.subject Forest mapping -- British Columbia en_US
dc.subject Time series analysis -- Data processing en_US
dc.subject Clearcutting -- British Columbia -- Measurement en_US
dc.subject Cutover lands -- British Columbia -- Measurement en_US
dc.subject Logging -- British Columbia -- Measurement en_US
dc.subject Wildfires -- British Columbia -- Measurement en_US
dc.subject Image analysis -- Data processing en_US
dc.subject Remote sensing images -- Data processing en_US
dc.subject Dissertations, Academic en_US
dc.title An unsupervised classification-based time series change detection approach for mapping forest disturbance en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Geography and Environment en_US
dc.degree.level Ph.D en_US
dc.proquest.subject 0799 en_US
dc.proquest.subject 0768 en_US
dc.proquest.subject 0478 en_US
dc.proquestyes Yes en_US


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