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Concurrent datasets on land cover and river monitoring in Fukushima decontaminated catchment during 2013–2018
After the Fukushima nuclear accident, the Japanese government implemented extensive decontamination work in (137)Cs contaminated catchments for residents’ health and local revitalization. Whether dramatic land use changes in the upstream decontaminated regions affected river suspended sediment (SS)...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442352/ https://www.ncbi.nlm.nih.gov/pubmed/37604898 http://dx.doi.org/10.1038/s41597-023-02452-0 |
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author | Feng, Bin Onda, Yuichi Wakiyama, Yoshifumi Taniguchi, Keisuke Hashimoto, Asahi Zhang, Yupan |
author_facet | Feng, Bin Onda, Yuichi Wakiyama, Yoshifumi Taniguchi, Keisuke Hashimoto, Asahi Zhang, Yupan |
author_sort | Feng, Bin |
collection | PubMed |
description | After the Fukushima nuclear accident, the Japanese government implemented extensive decontamination work in (137)Cs contaminated catchments for residents’ health and local revitalization. Whether dramatic land use changes in the upstream decontaminated regions affected river suspended sediment (SS) and particulate (137)Cs discharge downstream remain unknown because of the poor quantification on land cover changes and long-term river SS dynamics. We here introduce a 6-year concurrent database of the Niida River Basin, a decontaminated catchment, including the first available vector decontamination maps, satellite images in decontaminated regions with a spatial resolution of 10 m, and long-term river monitoring datasets spanning decontamination (2013–2016) and subsequent natural restoration stages (2017–2018). These datasets allow us, for the first time, to directly link the transport dynamics of river SS (particulate (137)Cs) to land use changes caused by humans in real-time, which provide fundamental data for better understanding the river response of sediment to land use change. Moreover, the data obtained by interdisciplinary methods offer a template for land use change impact assessment in other river basins. |
format | Online Article Text |
id | pubmed-10442352 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104423522023-08-23 Concurrent datasets on land cover and river monitoring in Fukushima decontaminated catchment during 2013–2018 Feng, Bin Onda, Yuichi Wakiyama, Yoshifumi Taniguchi, Keisuke Hashimoto, Asahi Zhang, Yupan Sci Data Data Descriptor After the Fukushima nuclear accident, the Japanese government implemented extensive decontamination work in (137)Cs contaminated catchments for residents’ health and local revitalization. Whether dramatic land use changes in the upstream decontaminated regions affected river suspended sediment (SS) and particulate (137)Cs discharge downstream remain unknown because of the poor quantification on land cover changes and long-term river SS dynamics. We here introduce a 6-year concurrent database of the Niida River Basin, a decontaminated catchment, including the first available vector decontamination maps, satellite images in decontaminated regions with a spatial resolution of 10 m, and long-term river monitoring datasets spanning decontamination (2013–2016) and subsequent natural restoration stages (2017–2018). These datasets allow us, for the first time, to directly link the transport dynamics of river SS (particulate (137)Cs) to land use changes caused by humans in real-time, which provide fundamental data for better understanding the river response of sediment to land use change. Moreover, the data obtained by interdisciplinary methods offer a template for land use change impact assessment in other river basins. Nature Publishing Group UK 2023-08-21 /pmc/articles/PMC10442352/ /pubmed/37604898 http://dx.doi.org/10.1038/s41597-023-02452-0 Text en © The Author(s) 2023 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 | Data Descriptor Feng, Bin Onda, Yuichi Wakiyama, Yoshifumi Taniguchi, Keisuke Hashimoto, Asahi Zhang, Yupan Concurrent datasets on land cover and river monitoring in Fukushima decontaminated catchment during 2013–2018 |
title | Concurrent datasets on land cover and river monitoring in Fukushima decontaminated catchment during 2013–2018 |
title_full | Concurrent datasets on land cover and river monitoring in Fukushima decontaminated catchment during 2013–2018 |
title_fullStr | Concurrent datasets on land cover and river monitoring in Fukushima decontaminated catchment during 2013–2018 |
title_full_unstemmed | Concurrent datasets on land cover and river monitoring in Fukushima decontaminated catchment during 2013–2018 |
title_short | Concurrent datasets on land cover and river monitoring in Fukushima decontaminated catchment during 2013–2018 |
title_sort | concurrent datasets on land cover and river monitoring in fukushima decontaminated catchment during 2013–2018 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442352/ https://www.ncbi.nlm.nih.gov/pubmed/37604898 http://dx.doi.org/10.1038/s41597-023-02452-0 |
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