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Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013
Accurate and timely information describing urban wetland resources and their changes over time, especially in rapidly urbanizing areas, is becoming more important. We applied an object-based image analysis and nearest neighbour classifier to map and monitor changes in land use/cover using multi-temp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5943268/ https://www.ncbi.nlm.nih.gov/pubmed/29743666 http://dx.doi.org/10.1038/s41598-018-25823-9 |
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author | Hu, Tangao Liu, Jiahong Zheng, Gang Li, Yao Xie, Bin |
author_facet | Hu, Tangao Liu, Jiahong Zheng, Gang Li, Yao Xie, Bin |
author_sort | Hu, Tangao |
collection | PubMed |
description | Accurate and timely information describing urban wetland resources and their changes over time, especially in rapidly urbanizing areas, is becoming more important. We applied an object-based image analysis and nearest neighbour classifier to map and monitor changes in land use/cover using multi-temporal high spatial resolution satellite imagery in an urban wetland area (Hangzhou Xixi Wetland) from 2000, 2005, 2007, 2009 and 2013. The overall eight-class classification accuracies averaged 84.47% for the five years. The maps showed that between 2000 and 2013 the amount of non-wetland (urban) area increased by approximately 100%. Herbaceous (32.22%), forest (29.57%) and pond (23.85%) are the main land-cover types that changed to non-wetland, followed by cropland (6.97%), marsh (4.04%) and river (3.35%). In addition, the maps of change patterns showed that urban wetland loss is mainly distributed west and southeast of the study area due to real estate development, and the greatest loss of urban wetlands occurred from 2007 to 2013. The results demonstrate the advantages of using multi-temporal high spatial resolution satellite imagery to provide an accurate, economical means to map and analyse changes in land use/cover over time and the ability to use the results as inputs to urban wetland management and policy decisions. |
format | Online Article Text |
id | pubmed-5943268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-59432682018-05-14 Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013 Hu, Tangao Liu, Jiahong Zheng, Gang Li, Yao Xie, Bin Sci Rep Article Accurate and timely information describing urban wetland resources and their changes over time, especially in rapidly urbanizing areas, is becoming more important. We applied an object-based image analysis and nearest neighbour classifier to map and monitor changes in land use/cover using multi-temporal high spatial resolution satellite imagery in an urban wetland area (Hangzhou Xixi Wetland) from 2000, 2005, 2007, 2009 and 2013. The overall eight-class classification accuracies averaged 84.47% for the five years. The maps showed that between 2000 and 2013 the amount of non-wetland (urban) area increased by approximately 100%. Herbaceous (32.22%), forest (29.57%) and pond (23.85%) are the main land-cover types that changed to non-wetland, followed by cropland (6.97%), marsh (4.04%) and river (3.35%). In addition, the maps of change patterns showed that urban wetland loss is mainly distributed west and southeast of the study area due to real estate development, and the greatest loss of urban wetlands occurred from 2007 to 2013. The results demonstrate the advantages of using multi-temporal high spatial resolution satellite imagery to provide an accurate, economical means to map and analyse changes in land use/cover over time and the ability to use the results as inputs to urban wetland management and policy decisions. Nature Publishing Group UK 2018-05-09 /pmc/articles/PMC5943268/ /pubmed/29743666 http://dx.doi.org/10.1038/s41598-018-25823-9 Text en © The Author(s) 2018 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/. |
spellingShingle | Article Hu, Tangao Liu, Jiahong Zheng, Gang Li, Yao Xie, Bin Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013 |
title | Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013 |
title_full | Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013 |
title_fullStr | Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013 |
title_full_unstemmed | Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013 |
title_short | Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013 |
title_sort | quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5943268/ https://www.ncbi.nlm.nih.gov/pubmed/29743666 http://dx.doi.org/10.1038/s41598-018-25823-9 |
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