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Remote Sens. 2019, 11(7), 842; https://doi.org/10.3390/rs11070842

Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results

1
Wood Environment & Infrastructure Solutions, St. John’s, NL A1B 1H3, Canada
2
Department of Computer Science, Memorial University of Newfoundland, St John’s, NL A1B 3X5, Canada
3
The Canada Center for Mapping and Earth Observation, Ottawa, ON K1S 5K2, Canada
4
Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St John’s, NL A1B 3X5, Canada
5
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
6
University of Chinese Academy of Sciences, Beijing 100049, China
7
Environment Canada, National Wildlife Research Centre, Ottawa, ON K1A 0H3, Canada
8
Environmental Monitoring and Science Division, Alberta Environment and Parks, Lethbridge, AB T1J 4L1, Canada
9
Department of Geography, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
*
Author to whom correspondence should be addressed.
Received: 8 March 2019 / Revised: 2 April 2019 / Accepted: 4 April 2019 / Published: 8 April 2019
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Abstract

Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km2). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands. View Full-Text
Keywords: Canadian Wetland Inventory; Google Earth Engine; Landsat; remote sensing Canadian Wetland Inventory; Google Earth Engine; Landsat; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Amani, M.; Mahdavi, S.; Afshar, M.; Brisco, B.; Huang, W.; Mohammad Javad Mirzadeh, S.; White, L.; Banks, S.; Montgomery, J.; Hopkinson, C. Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results. Remote Sens. 2019, 11, 842.

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