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A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images
High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses...
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/PMC10492804/ https://www.ncbi.nlm.nih.gov/pubmed/37689803 http://dx.doi.org/10.1038/s41597-023-02518-z |
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author | Sun, Genyun Li, Zheng Zhang, Aizhu Wang, Xin Yan, Kai Jia, Xiuping Liu, Qinhuo Li, Jing |
author_facet | Sun, Genyun Li, Zheng Zhang, Aizhu Wang, Xin Yan, Kai Jia, Xiuping Liu, Qinhuo Li, Jing |
author_sort | Sun, Genyun |
collection | PubMed |
description | High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016–2022. To accomplish this, we present a new machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surfaces. Furthermore, we also introduce a training sample migration strategy that eliminates the collection of additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 92.75% to 92.93% and 0.854 to 0.857, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban studies. |
format | Online Article Text |
id | pubmed-10492804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104928042023-09-11 A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images Sun, Genyun Li, Zheng Zhang, Aizhu Wang, Xin Yan, Kai Jia, Xiuping Liu, Qinhuo Li, Jing Sci Data Data Descriptor High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016–2022. To accomplish this, we present a new machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surfaces. Furthermore, we also introduce a training sample migration strategy that eliminates the collection of additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 92.75% to 92.93% and 0.854 to 0.857, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban studies. Nature Publishing Group UK 2023-09-09 /pmc/articles/PMC10492804/ /pubmed/37689803 http://dx.doi.org/10.1038/s41597-023-02518-z 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 Sun, Genyun Li, Zheng Zhang, Aizhu Wang, Xin Yan, Kai Jia, Xiuping Liu, Qinhuo Li, Jing A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images |
title | A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images |
title_full | A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images |
title_fullStr | A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images |
title_full_unstemmed | A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images |
title_short | A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images |
title_sort | 10-m resolution impervious surface area map for the greater mekong subregion from remote sensing images |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492804/ https://www.ncbi.nlm.nih.gov/pubmed/37689803 http://dx.doi.org/10.1038/s41597-023-02518-z |
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