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Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden
According to the EU Habitats directive, the Water Framework Directive, and the Marine Strategy Framework Directive, member states are required to map, monitor, and evaluate changes in quality and areal distribution of different marine habitats and biotopes to protect the marine environment more effe...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290658/ https://www.ncbi.nlm.nih.gov/pubmed/34270169 http://dx.doi.org/10.1002/ieam.4493 |
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author | Huber, Silvia Hansen, Lars B. Nielsen, Lisbeth T. Rasmussen, Mikkel L. Sølvsteen, Jonas Berglund, Johnny Paz von Friesen, Carlos Danbolt, Magnus Envall, Mats Infantes, Eduardo Moksnes, Per |
author_facet | Huber, Silvia Hansen, Lars B. Nielsen, Lisbeth T. Rasmussen, Mikkel L. Sølvsteen, Jonas Berglund, Johnny Paz von Friesen, Carlos Danbolt, Magnus Envall, Mats Infantes, Eduardo Moksnes, Per |
author_sort | Huber, Silvia |
collection | PubMed |
description | According to the EU Habitats directive, the Water Framework Directive, and the Marine Strategy Framework Directive, member states are required to map, monitor, and evaluate changes in quality and areal distribution of different marine habitats and biotopes to protect the marine environment more effectively. Submerged aquatic vegetation (SAV) is a key indicator of the ecological status of coastal ecosystems and is therefore widely used in reporting related to these directives. Environmental monitoring of the areal distribution of SAV is lacking in Sweden due to the challenges of large‐scale monitoring using traditional small‐scale methods. To address this gap, in 2020, we embarked on a project to combine Copernicus Sentinel‐2 satellite imagery, novel machine learning (ML) techniques, and advanced data processing in a cloud‐based web application that enables users to create up‐to‐date SAV classifications. At the same time, the approach was used to derive the first high‐resolution SAV map for the entire coastline of Sweden, where an area of 1550 km(2) was mapped as SAV. Quantitative evaluation of the accuracy of the classification using independent field data from three different regions along the Swedish coast demonstrated relative high accuracy within shallower areas, particularly where water transparency was high (average total accuracy per region 0.60–0.77). However, the classification missed large proportions of vegetation growing in deeper water (on average 31%–50%) and performed poorly in areas with fragmented or mixed vegetation and poor water quality, challenges that should be addressed in the development of the mapping methods towards integration into monitoring frameworks such as the EU directives. In this article, we present the results of the first satellite‐derived SAV classification for the entire Swedish coast and show the implementation of a cloud‐based SAV mapping application (prototype) developed within the frame of the project. Integr Environ Assess Manag 2022;18:909–920. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). |
format | Online Article Text |
id | pubmed-9290658 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92906582022-07-20 Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden Huber, Silvia Hansen, Lars B. Nielsen, Lisbeth T. Rasmussen, Mikkel L. Sølvsteen, Jonas Berglund, Johnny Paz von Friesen, Carlos Danbolt, Magnus Envall, Mats Infantes, Eduardo Moksnes, Per Integr Environ Assess Manag Special Series: The Future of Marine Environmental Monitoring and Assessment According to the EU Habitats directive, the Water Framework Directive, and the Marine Strategy Framework Directive, member states are required to map, monitor, and evaluate changes in quality and areal distribution of different marine habitats and biotopes to protect the marine environment more effectively. Submerged aquatic vegetation (SAV) is a key indicator of the ecological status of coastal ecosystems and is therefore widely used in reporting related to these directives. Environmental monitoring of the areal distribution of SAV is lacking in Sweden due to the challenges of large‐scale monitoring using traditional small‐scale methods. To address this gap, in 2020, we embarked on a project to combine Copernicus Sentinel‐2 satellite imagery, novel machine learning (ML) techniques, and advanced data processing in a cloud‐based web application that enables users to create up‐to‐date SAV classifications. At the same time, the approach was used to derive the first high‐resolution SAV map for the entire coastline of Sweden, where an area of 1550 km(2) was mapped as SAV. Quantitative evaluation of the accuracy of the classification using independent field data from three different regions along the Swedish coast demonstrated relative high accuracy within shallower areas, particularly where water transparency was high (average total accuracy per region 0.60–0.77). However, the classification missed large proportions of vegetation growing in deeper water (on average 31%–50%) and performed poorly in areas with fragmented or mixed vegetation and poor water quality, challenges that should be addressed in the development of the mapping methods towards integration into monitoring frameworks such as the EU directives. In this article, we present the results of the first satellite‐derived SAV classification for the entire Swedish coast and show the implementation of a cloud‐based SAV mapping application (prototype) developed within the frame of the project. Integr Environ Assess Manag 2022;18:909–920. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). John Wiley and Sons Inc. 2021-08-20 2022-07 /pmc/articles/PMC9290658/ /pubmed/34270169 http://dx.doi.org/10.1002/ieam.4493 Text en © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Special Series: The Future of Marine Environmental Monitoring and Assessment Huber, Silvia Hansen, Lars B. Nielsen, Lisbeth T. Rasmussen, Mikkel L. Sølvsteen, Jonas Berglund, Johnny Paz von Friesen, Carlos Danbolt, Magnus Envall, Mats Infantes, Eduardo Moksnes, Per Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden |
title | Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden |
title_full | Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden |
title_fullStr | Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden |
title_full_unstemmed | Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden |
title_short | Novel approach to large‐scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden |
title_sort | novel approach to large‐scale monitoring of submerged aquatic vegetation: a nationwide example from sweden |
topic | Special Series: The Future of Marine Environmental Monitoring and Assessment |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290658/ https://www.ncbi.nlm.nih.gov/pubmed/34270169 http://dx.doi.org/10.1002/ieam.4493 |
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