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Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast
Seagrasses are regarded as indicators and first line of impact for anthropogenic activities affecting the coasts. The underlying mechanisms driving seagrass cover however have been mostly studied on small scales, making it difficult to establish the connection to seagrass dynamics in an impacted sea...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892891/ https://www.ncbi.nlm.nih.gov/pubmed/33603192 http://dx.doi.org/10.1038/s41598-021-83728-6 |
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author | Clarke, Kenneth Hennessy, Andrew McGrath, Andrew Daly, Robert Gaylard, Sam Turner, Alison Cameron, James Lewis, Megan Fernandes, Milena B. |
author_facet | Clarke, Kenneth Hennessy, Andrew McGrath, Andrew Daly, Robert Gaylard, Sam Turner, Alison Cameron, James Lewis, Megan Fernandes, Milena B. |
author_sort | Clarke, Kenneth |
collection | PubMed |
description | Seagrasses are regarded as indicators and first line of impact for anthropogenic activities affecting the coasts. The underlying mechanisms driving seagrass cover however have been mostly studied on small scales, making it difficult to establish the connection to seagrass dynamics in an impacted seascape. In this study, hyperspectral airborne imagery, trained from field surveys, was used to investigate broadscale seagrass cover and genus distribution along the coast of Adelaide, South Australia. Overall mapping accuracy was high for both seagrass cover (98%, Kappa = 0.93), and genus level classification (85%, Kappa = 0.76). Spectral separability allowed confident genus mapping in waters up to 10 m depth, revealing a 3.5 ratio between the cover of the dominant Posidonia and Amphibolis. The work identified the absence of Amphibolis in areas historically affected by anthropogenic discharges, which occasionally contained Posidonia and might be recovering. The results suggest hyperspectral imagery as a useful tool to investigate the interplay between seagrass cover and genus distribution at large spatial scales. |
format | Online Article Text |
id | pubmed-7892891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78928912021-02-23 Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast Clarke, Kenneth Hennessy, Andrew McGrath, Andrew Daly, Robert Gaylard, Sam Turner, Alison Cameron, James Lewis, Megan Fernandes, Milena B. Sci Rep Article Seagrasses are regarded as indicators and first line of impact for anthropogenic activities affecting the coasts. The underlying mechanisms driving seagrass cover however have been mostly studied on small scales, making it difficult to establish the connection to seagrass dynamics in an impacted seascape. In this study, hyperspectral airborne imagery, trained from field surveys, was used to investigate broadscale seagrass cover and genus distribution along the coast of Adelaide, South Australia. Overall mapping accuracy was high for both seagrass cover (98%, Kappa = 0.93), and genus level classification (85%, Kappa = 0.76). Spectral separability allowed confident genus mapping in waters up to 10 m depth, revealing a 3.5 ratio between the cover of the dominant Posidonia and Amphibolis. The work identified the absence of Amphibolis in areas historically affected by anthropogenic discharges, which occasionally contained Posidonia and might be recovering. The results suggest hyperspectral imagery as a useful tool to investigate the interplay between seagrass cover and genus distribution at large spatial scales. Nature Publishing Group UK 2021-02-18 /pmc/articles/PMC7892891/ /pubmed/33603192 http://dx.doi.org/10.1038/s41598-021-83728-6 Text en © The Author(s) 2021, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Clarke, Kenneth Hennessy, Andrew McGrath, Andrew Daly, Robert Gaylard, Sam Turner, Alison Cameron, James Lewis, Megan Fernandes, Milena B. Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast |
title | Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast |
title_full | Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast |
title_fullStr | Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast |
title_full_unstemmed | Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast |
title_short | Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast |
title_sort | using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892891/ https://www.ncbi.nlm.nih.gov/pubmed/33603192 http://dx.doi.org/10.1038/s41598-021-83728-6 |
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