Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water Temperature
Lake surface water temperature (LSWT) is a crucial ecological indicator, impacting water quality, and aquatic life. Understanding its spatiotemporal trends and driving mechanisms is fundamental for lake water environment protection and management. Previous research has been limited by low-resolution...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10737462/ |
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| _version_ | 1846163837528571904 |
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| author | Dingpu Li Yi Luo Kun Yang Chunxue Shang Senlin Zhu Shuangyun Peng Anlin Li Rixiang Chen Zongqi Peng Xingfang Pei Yuanyuan Yin Qingqing Wang Changqing Peng Hong Wei |
| author_facet | Dingpu Li Yi Luo Kun Yang Chunxue Shang Senlin Zhu Shuangyun Peng Anlin Li Rixiang Chen Zongqi Peng Xingfang Pei Yuanyuan Yin Qingqing Wang Changqing Peng Hong Wei |
| author_sort | Dingpu Li |
| collection | DOAJ |
| description | Lake surface water temperature (LSWT) is a crucial ecological indicator, impacting water quality, and aquatic life. Understanding its spatiotemporal trends and driving mechanisms is fundamental for lake water environment protection and management. Previous research has been limited by low-resolution satellite data and numerical simulations, hindering in-depth understanding of LSWT. This article fills the research gap by reconstructing a high-resolution LSWT dataset spanning 2000 to 2020. Employing data fusion techniques, we combined moderate resolution imaging spectroradiometer (MODIS) and Landsat observations, achieving a spatial resolution of 30 m and a revisit cycle of eight days. Seven major lakes in Yunnan Province, China, varying in urbanization intensity, were selected to investigate the impacts and mechanisms of urbanization and climate change on LSWT. The results showed that: First, the high spatiotemporal LSWT dataset reconstructed on the ubESTARFM data fusion model outperformed the existing product datasets in terms of accuracy evaluation and spatial details. Over the past 20 years, all LSWT in the study area exhibited a warming trend in both temporal and spatial dimensions; lakes in basins with higher urbanization intensity had significantly higher warming rates than the warming rates of near-surface air temperature, and the lakes showed a global warming trend. Second, the warming trend of LSWT is not only related to lake morphology and climate change, but also closely associated with urbanization; higher spatiotemporal resolution LSWT data revealed better spatiotemporal correlations between urbanization and LSWT. Third, active ecological management and enhanced watershed vegetation coverage could effectively mitigate the rate of lake warming. |
| format | Article |
| id | doaj-art-6039b49fb5754f0c9ee262e6f16a31b2 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-6039b49fb5754f0c9ee262e6f16a31b22024-11-19T00:00:40ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117199551997110.1109/JSTARS.2024.348762310737462Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water TemperatureDingpu Li0Yi Luo1https://orcid.org/0000-0002-6256-4595Kun Yang2Chunxue Shang3Senlin Zhu4https://orcid.org/0000-0003-2803-5419Shuangyun Peng5Anlin Li6Rixiang Chen7Zongqi Peng8Xingfang Pei9Yuanyuan Yin10Qingqing Wang11Changqing Peng12Hong Wei13Faculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaCollege of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaFaculty of Geography, Yunnan Normal University, Yunnan, ChinaLake surface water temperature (LSWT) is a crucial ecological indicator, impacting water quality, and aquatic life. Understanding its spatiotemporal trends and driving mechanisms is fundamental for lake water environment protection and management. Previous research has been limited by low-resolution satellite data and numerical simulations, hindering in-depth understanding of LSWT. This article fills the research gap by reconstructing a high-resolution LSWT dataset spanning 2000 to 2020. Employing data fusion techniques, we combined moderate resolution imaging spectroradiometer (MODIS) and Landsat observations, achieving a spatial resolution of 30 m and a revisit cycle of eight days. Seven major lakes in Yunnan Province, China, varying in urbanization intensity, were selected to investigate the impacts and mechanisms of urbanization and climate change on LSWT. The results showed that: First, the high spatiotemporal LSWT dataset reconstructed on the ubESTARFM data fusion model outperformed the existing product datasets in terms of accuracy evaluation and spatial details. Over the past 20 years, all LSWT in the study area exhibited a warming trend in both temporal and spatial dimensions; lakes in basins with higher urbanization intensity had significantly higher warming rates than the warming rates of near-surface air temperature, and the lakes showed a global warming trend. Second, the warming trend of LSWT is not only related to lake morphology and climate change, but also closely associated with urbanization; higher spatiotemporal resolution LSWT data revealed better spatiotemporal correlations between urbanization and LSWT. Third, active ecological management and enhanced watershed vegetation coverage could effectively mitigate the rate of lake warming.https://ieeexplore.ieee.org/document/10737462/Data fusiondriving mechanismhigh spatiotemporal resolutionlake surface water temperature (LSWT) |
| spellingShingle | Dingpu Li Yi Luo Kun Yang Chunxue Shang Senlin Zhu Shuangyun Peng Anlin Li Rixiang Chen Zongqi Peng Xingfang Pei Yuanyuan Yin Qingqing Wang Changqing Peng Hong Wei Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water Temperature IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Data fusion driving mechanism high spatiotemporal resolution lake surface water temperature (LSWT) |
| title | Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water Temperature |
| title_full | Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water Temperature |
| title_fullStr | Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water Temperature |
| title_full_unstemmed | Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water Temperature |
| title_short | Effects of Climate Change and Urbanization on Spatiotemporal Variations of Lake Surface Water Temperature |
| title_sort | effects of climate change and urbanization on spatiotemporal variations of lake surface water temperature |
| topic | Data fusion driving mechanism high spatiotemporal resolution lake surface water temperature (LSWT) |
| url | https://ieeexplore.ieee.org/document/10737462/ |
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