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Mapping Pan-Canadian Wetlands Using Multi-Source Earth Observations: The Third Generation of 10m Canadian Wetland Inventory Map.

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Mahdianpari, Masoud, Brisco, Brian, Granger, Jean Elizabeth, Mohammadimanesh, Fariba, Salehi, Bahram, Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356 et Bourgeau-Chavez, Laura (2021). Mapping Pan-Canadian Wetlands Using Multi-Source Earth Observations: The Third Generation of 10m Canadian Wetland Inventory Map. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14 . p. 8789-8803. DOI: 10.1109/JSTARS.2021.3105645.

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Résumé

Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some over-estimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average overall accuracy obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an overall accuracy increase of 2% or greater and individual ecozone overall accuracy results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area over-estimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multi-source data for large-scale wetland mapping.

Type de document: Article
Mots-clés libres: wetlands; remote sensing; earth; vegetation mapping; training data; artificial satellites; Arctic
Centre: Centre Eau Terre Environnement
Date de dépôt: 15 oct. 2021 17:35
Dernière modification: 04 nov. 2022 13:51
URI: https://espace.inrs.ca/id/eprint/11958

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