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Open AccessArticle

Wetland Mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR Data in Southern New Brunswick, Canada

1
Remote Sensing Laboratory, Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
2
Department of Forestry and Agro-Environmental Sciences, Università degli Studi di Padova, 35100 Padova, Italy
3
New Brunswick Department of Natural Resources and Energy Development, Fredericton, NB E3B 5A3, Canada
4
Canadian Wildlife Service, Environment and Climate Change Canada, Sackville, NB E3B 5A3, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2095; https://doi.org/10.3390/rs12132095
Received: 21 May 2020 / Revised: 16 June 2020 / Accepted: 24 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Environmental Mapping Using Remote Sensing)
Mapping wetlands with high spatial and thematic accuracy is crucial for the management and monitoring of these important ecosystems. Wetland maps in New Brunswick (NB) have traditionally been produced by the visual interpretation of aerial photographs. In this study, we used an alternative method to produce a wetland map for southern New Brunswick, Canada, by classifying a combination of Landsat 8 OLI, ALOS-1 PALSAR, Sentinel-1, and LiDAR-derived topographic metrics with the Random Forests (RF) classifier. The images were acquired in three seasons (spring, summer, and fall) with different water levels and during leaf-off/on periods. The resulting map has eleven wetland classes (open bog, shrub bog, treed bog, open fen, shrub fen, freshwater marsh, coastal marsh, shrub marsh, shrub wetland, forested wetland, and aquatic bed) plus various non-wetland classes. We achieved an overall accuracy classification of 97.67%. We compared 951 in-situ validation sites to the classified image and both the 2106 and 2019 reference maps available through Service New Brunswick. Both reference maps were produced by photo-interpretation of RGB-NIR digital aerial photographs, but the 2019 NB reference also included information from LiDAR-derived surface and ecological metrics. Of these 951 sites, 94.95% were correctly identified on the classified image, while only 63.30% and 80.02% of these sites were correctly identified on the 2016 and 2019 NB reference maps, respectively. If only the 489 wetland validation sites were considered, 96.93% of the sites were correctly identified as a wetland on the classified image, while only 58.69% and 62.17% of the sites were correctly identified as a wetland on the 2016 and 2019 NB reference maps, respectively. View Full-Text
Keywords: wetland; optical; SAR; Sentinel-1; Landsat 8 OLI; LiDAR; random forests; ALOS-1 PALSAR wetland; optical; SAR; Sentinel-1; Landsat 8 OLI; LiDAR; random forests; ALOS-1 PALSAR
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LaRocque, A.; Phiri, C.; Leblon, B.; Pirotti, F.; Connor, K.; Hanson, A. Wetland Mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR Data in Southern New Brunswick, Canada. Remote Sens. 2020, 12, 2095.

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