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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9448797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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