{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/43351"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/43351","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Towards Characterisation and Classification of Canadian Macrotidal Salt Marshes","abstract":"Despite interest in understanding the extent, distribution, and condition of tidal marshes in Canada, they have yet to be comprehensively mapped. Existing inventories show significant overestimation of Canadian tidal marsh extent, indicating that a regional model may be required. This thesis provides a review of freely available remote sensing data relevant to tidal marshes in the Bay of Fundy and uses freely available medium-resolution imagery to classify high and low tidal marsh extent in the Cumberland Basin for 2020 and 2023. Prediction maps are compared to five existing global and regional tidal marsh datasets and used to generate predicted change (activity) data. Assessment of prediction maps and activity data are then used as the basis to discuss the optimal sensor(s) and minimum requirements, the effects of model optimisation, and the potential for using medium-resolution imagery for the generation of activity data operationally for carbon inventories.","abstract_html":"Despite interest in understanding the extent, distribution, and condition of tidal marshes in Canada, they have yet to be comprehensively mapped. Existing inventories show significant overestimation of Canadian tidal marsh extent, indicating that a regional model may be required. This thesis provides a review of freely available remote sensing data relevant to tidal marshes in the Bay of Fundy and uses freely available medium-resolution imagery to classify high and low tidal marsh extent in the Cumberland Basin for 2020 and 2023. Prediction maps are compared to five existing global and regional tidal marsh datasets and used to generate predicted change (activity) data. Assessment of prediction maps and activity data are then used as the basis to discuss the optimal sensor(s) and minimum requirements, the effects of model optimisation, and the potential for using medium-resolution imagery for the generation of activity data operationally for carbon inventories.","abstract_has_math":false,"creators":["Richardson, Elisha Marie"],"institution":"Carleton University","degree_name":"Master of Science (M.Sc.)","degree_level":"Master's","degree_discipline":"Geography","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T01:34:24Z","subjects":[],"languages":["en"],"rights":["Copyright © 2024 the author(s). 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This thesis provides a review of freely available remote sensing data relevant to tidal marshes in the Bay of Fundy and uses freely available medium-resolution imagery to classify high and low tidal marsh extent in the Cumberland Basin for 2020 and 2023. Prediction maps are compared to five existing global and regional tidal marsh datasets and used to generate predicted change (activity) data. 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This thesis provides a review of freely available remote sensing data relevant to tidal marshes in the Bay of Fundy and uses freely available medium-resolution imagery to classify high and low tidal marsh extent in the Cumberland Basin for 2020 and 2023. Prediction maps are compared to five existing global and regional tidal marsh datasets and used to generate predicted change (activity) data. Assessment of prediction maps and activity data are then used as the basis to discuss the optimal sensor(s) and minimum requirements, the effects of model optimisation, and the potential for using medium-resolution imagery for the generation of activity data operationally for carbon inventories."],"dc:identifier.doi":["10.22215/etd/2024-16290"],"dc:identifier.uri":["https://hdl.handle.net/20.500.14718/43351"],"dc:language.iso":["en"],"dc:publisher":["Carleton University"],"dc:rights":["Copyright © 2024 the author(s). 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