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Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis
This paper presents a novel reference dataset for North Norfolk, UK, that demonstrates the value of harmonising coastal field-based topographic and remotely sensed datasets at local scales. It is hoped that this reference dataset and the associated methodologies will facilitate the use of topographi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486598/ https://www.ncbi.nlm.nih.gov/pubmed/31028259 http://dx.doi.org/10.1038/s41597-019-0044-3 |
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author | Pollard, James A. Brooks, Susan M. Spencer, Tom |
author_facet | Pollard, James A. Brooks, Susan M. Spencer, Tom |
author_sort | Pollard, James A. |
collection | PubMed |
description | This paper presents a novel reference dataset for North Norfolk, UK, that demonstrates the value of harmonising coastal field-based topographic and remotely sensed datasets at local scales. It is hoped that this reference dataset and the associated methodologies will facilitate the use of topographic and remotely sensed coastal datasets, as demonstrated here using open-access UK Environment Agency datasets. Two core methodologies, used to generate the novel reference dataset, are presented. Firstly, we establish a robust approach to extracting shorelines from vertical aerial photography, validated against LiDAR (Light Detection and Ranging) and coastal topography surveys. Secondly, we present a standard methodology for quantifying sediment volume change from spatially continuous LiDAR elevation datasets. As coastal systems are monitored at greater spatial resolution and temporal frequency there is an unprecedented opportunity to determine how and why coastal systems have changed in the past with a view to informing future forecasting. With revelation of trends that suggest increasing coastal risk, coastal change research is needed to inform the management and protection of coasts. |
format | Online Article Text |
id | pubmed-6486598 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64865982019-04-29 Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis Pollard, James A. Brooks, Susan M. Spencer, Tom Sci Data Data Descriptor This paper presents a novel reference dataset for North Norfolk, UK, that demonstrates the value of harmonising coastal field-based topographic and remotely sensed datasets at local scales. It is hoped that this reference dataset and the associated methodologies will facilitate the use of topographic and remotely sensed coastal datasets, as demonstrated here using open-access UK Environment Agency datasets. Two core methodologies, used to generate the novel reference dataset, are presented. Firstly, we establish a robust approach to extracting shorelines from vertical aerial photography, validated against LiDAR (Light Detection and Ranging) and coastal topography surveys. Secondly, we present a standard methodology for quantifying sediment volume change from spatially continuous LiDAR elevation datasets. As coastal systems are monitored at greater spatial resolution and temporal frequency there is an unprecedented opportunity to determine how and why coastal systems have changed in the past with a view to informing future forecasting. With revelation of trends that suggest increasing coastal risk, coastal change research is needed to inform the management and protection of coasts. Nature Publishing Group UK 2019-04-26 /pmc/articles/PMC6486598/ /pubmed/31028259 http://dx.doi.org/10.1038/s41597-019-0044-3 Text en © The Author(s) 2019 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Pollard, James A. Brooks, Susan M. Spencer, Tom Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis |
title | Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis |
title_full | Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis |
title_fullStr | Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis |
title_full_unstemmed | Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis |
title_short | Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis |
title_sort | harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486598/ https://www.ncbi.nlm.nih.gov/pubmed/31028259 http://dx.doi.org/10.1038/s41597-019-0044-3 |
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