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High-resolution global maps of tidal flat ecosystems from 1984 to 2019

Assessments of the status of tidal flats, one of the most extensive coastal ecosystems, have been hampered by a lack of data on their global distribution and change. Here we present globally consistent, spatially-explicit data of the occurrence of tidal flats, defined as sand, rock or mud flats that...

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Autores principales: Murray, Nicholas J., Phinn, Stuart P., Fuller, Richard A., DeWitt, Michael, Ferrari, Renata, Johnston, Renee, Clinton, Nicholas, Lyons, Mitchell B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9448797/
https://www.ncbi.nlm.nih.gov/pubmed/36068234
http://dx.doi.org/10.1038/s41597-022-01635-5
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author Murray, Nicholas J.
Phinn, Stuart P.
Fuller, Richard A.
DeWitt, Michael
Ferrari, Renata
Johnston, Renee
Clinton, Nicholas
Lyons, Mitchell B.
author_facet Murray, Nicholas J.
Phinn, Stuart P.
Fuller, Richard A.
DeWitt, Michael
Ferrari, Renata
Johnston, Renee
Clinton, Nicholas
Lyons, Mitchell B.
author_sort Murray, Nicholas J.
collection PubMed
description Assessments of the status of tidal flats, one of the most extensive coastal ecosystems, have been hampered by a lack of data on their global distribution and change. Here we present globally consistent, spatially-explicit data of the occurrence of tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation. More than 1.3 million Landsat images were processed to 54 composite metrics for twelve 3-year periods, spanning four decades (1984–1986 to 2017–2019). The composite metrics were used as predictor variables in a machine-learning classification trained with more than 10,000 globally distributed training samples. We assessed accuracy of the classification with 1,348 stratified random samples across the mapped area, which indicated overall map accuracies of 82.2% (80.0–84.3%, 95% confidence interval) and 86.1% (84.2–86.8%, 95% CI) for version 1.1 and 1.2 of the data, respectively. We expect these maps will provide a means to measure and monitor a range of processes that are affecting coastal ecosystems, including the impacts of human population growth and sea level rise.
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spelling pubmed-94487972022-09-08 High-resolution global maps of tidal flat ecosystems from 1984 to 2019 Murray, Nicholas J. Phinn, Stuart P. Fuller, Richard A. DeWitt, Michael Ferrari, Renata Johnston, Renee Clinton, Nicholas Lyons, Mitchell B. Sci Data Data Descriptor Assessments of the status of tidal flats, one of the most extensive coastal ecosystems, have been hampered by a lack of data on their global distribution and change. Here we present globally consistent, spatially-explicit data of the occurrence of tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation. More than 1.3 million Landsat images were processed to 54 composite metrics for twelve 3-year periods, spanning four decades (1984–1986 to 2017–2019). The composite metrics were used as predictor variables in a machine-learning classification trained with more than 10,000 globally distributed training samples. We assessed accuracy of the classification with 1,348 stratified random samples across the mapped area, which indicated overall map accuracies of 82.2% (80.0–84.3%, 95% confidence interval) and 86.1% (84.2–86.8%, 95% CI) for version 1.1 and 1.2 of the data, respectively. We expect these maps will provide a means to measure and monitor a range of processes that are affecting coastal ecosystems, including the impacts of human population growth and sea level rise. Nature Publishing Group UK 2022-09-06 /pmc/articles/PMC9448797/ /pubmed/36068234 http://dx.doi.org/10.1038/s41597-022-01635-5 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Murray, Nicholas J.
Phinn, Stuart P.
Fuller, Richard A.
DeWitt, Michael
Ferrari, Renata
Johnston, Renee
Clinton, Nicholas
Lyons, Mitchell B.
High-resolution global maps of tidal flat ecosystems from 1984 to 2019
title High-resolution global maps of tidal flat ecosystems from 1984 to 2019
title_full High-resolution global maps of tidal flat ecosystems from 1984 to 2019
title_fullStr High-resolution global maps of tidal flat ecosystems from 1984 to 2019
title_full_unstemmed High-resolution global maps of tidal flat ecosystems from 1984 to 2019
title_short High-resolution global maps of tidal flat ecosystems from 1984 to 2019
title_sort high-resolution global maps of tidal flat ecosystems from 1984 to 2019
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9448797/
https://www.ncbi.nlm.nih.gov/pubmed/36068234
http://dx.doi.org/10.1038/s41597-022-01635-5
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