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How can Sentinel‐2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters?

How can Sentinel‐2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? Seagrass meadows are one of the most important benthic habitats in the Baltic Sea. Nevertheless, spatially continuous mapping data of Zostera marina, the predominant seagrass species in the Baltic Sea, are lacking in the shallow coastal waters. Sentinel‐2 turned out to be valuable for mapping coastal benthic habitats in clear waters, whereas knowledge in turbid waters is rare. Here, we transfer a clear water mapping approach to turbid waters to assess how Sentinel‐2 can contribute to seagrass mapping in the Western Baltic Sea. Sentinel‐2 data were atmospherically corrected using ACOLITE and subsequently corrected for water column effects. To generate a data basis for training and validating random forest classification models, we developed an upscaling approach using video transect data and aerial imagery. We were able to map five coastal benthic habitats: bare sand (25 km²), sand dominated (16 km²), seagrass dominated (7 km²), dense seagrass (25 km²) and mixed substrates with red/ brown algae (3.5 km²) in a study area along the northern German coastline. Validation with independent data pointed out that water column correction does not significantly improve classification results compared to solely atmospherically corrected data (balanced overall accuracies ~0.92). Within optically shallow waters (0–4 m), per class and overall balanced accuracies (>0.82) differed marginally depending on the water depth. Overall balanced accuracy became worse (<0.8) approaching the border to optically deep water (~ 5 m). The spatial resolution of Sentinel‐2 (10–20 m) allowed delineating detailed spatial patterns of seagrass habitats, which may serve as a basis to retrieve spatially continuous data for ecologically relevant metrics such as patchiness. Thus, Sentinel‐2 can contribute unprecedented information for seagrass mapping between 0 and around 5 m water depths in the Western Baltic Sea. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing in Ecology and Conservation Wiley

How can Sentinel‐2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters?

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References (59)

Publisher
Wiley
Copyright
© 2022 Published by John Wiley & Sons Ltd.
ISSN
2056-3485
eISSN
2056-3485
DOI
10.1002/rse2.246
Publisher site
See Article on Publisher Site

Abstract

Seagrass meadows are one of the most important benthic habitats in the Baltic Sea. Nevertheless, spatially continuous mapping data of Zostera marina, the predominant seagrass species in the Baltic Sea, are lacking in the shallow coastal waters. Sentinel‐2 turned out to be valuable for mapping coastal benthic habitats in clear waters, whereas knowledge in turbid waters is rare. Here, we transfer a clear water mapping approach to turbid waters to assess how Sentinel‐2 can contribute to seagrass mapping in the Western Baltic Sea. Sentinel‐2 data were atmospherically corrected using ACOLITE and subsequently corrected for water column effects. To generate a data basis for training and validating random forest classification models, we developed an upscaling approach using video transect data and aerial imagery. We were able to map five coastal benthic habitats: bare sand (25 km²), sand dominated (16 km²), seagrass dominated (7 km²), dense seagrass (25 km²) and mixed substrates with red/ brown algae (3.5 km²) in a study area along the northern German coastline. Validation with independent data pointed out that water column correction does not significantly improve classification results compared to solely atmospherically corrected data (balanced overall accuracies ~0.92). Within optically shallow waters (0–4 m), per class and overall balanced accuracies (>0.82) differed marginally depending on the water depth. Overall balanced accuracy became worse (<0.8) approaching the border to optically deep water (~ 5 m). The spatial resolution of Sentinel‐2 (10–20 m) allowed delineating detailed spatial patterns of seagrass habitats, which may serve as a basis to retrieve spatially continuous data for ecologically relevant metrics such as patchiness. Thus, Sentinel‐2 can contribute unprecedented information for seagrass mapping between 0 and around 5 m water depths in the Western Baltic Sea.

Journal

Remote Sensing in Ecology and Conservation – Wiley

Published: Jun 1, 2022

Keywords: Baltic sea; benthic habitat mapping; eelgrass; random forest; Sentinel‐2; submerged aquatic vegetation

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