Detecting glacial lake water quality indicators from RGB surveillance images via deep learning
Global warming has accelerated glacier retreat, subsequently leading to the formation of glacial lakes in high-altitude mountainous regions. These lakes represent emerging ecological water systems and could potentially pose significant hazards. Observations of these systems are constrained by their...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000391 |
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| author | Zijian Lu Xueyan Zhu Jinfeng Li Mingyue Li Jie Wang Wenqiang Wang Yili Zheng Qianggong Zhang |
| author_facet | Zijian Lu Xueyan Zhu Jinfeng Li Mingyue Li Jie Wang Wenqiang Wang Yili Zheng Qianggong Zhang |
| author_sort | Zijian Lu |
| collection | DOAJ |
| description | Global warming has accelerated glacier retreat, subsequently leading to the formation of glacial lakes in high-altitude mountainous regions. These lakes represent emerging ecological water systems and could potentially pose significant hazards. Observations of these systems are constrained by their remote locations and the lack of cost-effective monitoring methods, resulting in limited understanding of their dynamics. In this study, we synchronized surveillance monitoring with in-situ water quality measurements at a typical high-altitude glacial lake on the Qinghai-Tibet Plateau. We aim to use images from surveillance cameras to estimate the turbidity parameter, a key indicator of changes in the water environment and the impacts of climate change on high-altitude ecosystems. We segmented RGB images and applied regression modeling with field-measured water turbidity data, and then used deep learning models to accurately estimates turbidity levels and their changes. Our study demonstrates the potential of RGB imagery and deep learning for the long-term, continuous, and high-resolution monitoring of water quality in remote glacial lakes. It presents a novel and cost-effective approach for monitoring these newly emerged and swiftly changing water systems at high altitudes, offering a significant advancement in tracking environmental changes in these critical high mountain regions. |
| format | Article |
| id | doaj-art-b789603bfc7e4dd9a07ef2f5f56ebfe6 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-b789603bfc7e4dd9a07ef2f5f56ebfe62025-02-03T04:16:38ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104392Detecting glacial lake water quality indicators from RGB surveillance images via deep learningZijian Lu0Xueyan Zhu1Jinfeng Li2Mingyue Li3Jie Wang4Wenqiang Wang5Yili Zheng6Qianggong Zhang7State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101 China; University of Chinese Academy of Sciences, Beijing 100049 ChinaSchool of Technology, Beijing Forestry University, Beijing 100083 ChinaSchool of Technology, Beijing Forestry University, Beijing 100083 ChinaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101 China; University of Chinese Academy of Sciences, Beijing 100049 ChinaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101 China; University of Chinese Academy of Sciences, Beijing 100049 ChinaCenter for the Pan‑Third Pole Environment, Lanzhou University, Lanzhou 730000 China; Chayu Monsoon Corridor Observation and Research Station for Multi-Sphere Changes, Xizang Autonomous Region, Chayu 860600 ChinaSchool of Technology, Beijing Forestry University, Beijing 100083 ChinaState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101 China; University of Chinese Academy of Sciences, Beijing 100049 China; Lhasa Earth System Multi-Dimension Observatory Network (LEMON), Lhasa 850000 China; Corresponding author at: Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, CAS, Beijing 100101, China.Global warming has accelerated glacier retreat, subsequently leading to the formation of glacial lakes in high-altitude mountainous regions. These lakes represent emerging ecological water systems and could potentially pose significant hazards. Observations of these systems are constrained by their remote locations and the lack of cost-effective monitoring methods, resulting in limited understanding of their dynamics. In this study, we synchronized surveillance monitoring with in-situ water quality measurements at a typical high-altitude glacial lake on the Qinghai-Tibet Plateau. We aim to use images from surveillance cameras to estimate the turbidity parameter, a key indicator of changes in the water environment and the impacts of climate change on high-altitude ecosystems. We segmented RGB images and applied regression modeling with field-measured water turbidity data, and then used deep learning models to accurately estimates turbidity levels and their changes. Our study demonstrates the potential of RGB imagery and deep learning for the long-term, continuous, and high-resolution monitoring of water quality in remote glacial lakes. It presents a novel and cost-effective approach for monitoring these newly emerged and swiftly changing water systems at high altitudes, offering a significant advancement in tracking environmental changes in these critical high mountain regions.http://www.sciencedirect.com/science/article/pii/S1569843225000391Glacial lakeWater qualityQinghai-Tibet PlateauSurveillance camerasDeep learning |
| spellingShingle | Zijian Lu Xueyan Zhu Jinfeng Li Mingyue Li Jie Wang Wenqiang Wang Yili Zheng Qianggong Zhang Detecting glacial lake water quality indicators from RGB surveillance images via deep learning International Journal of Applied Earth Observations and Geoinformation Glacial lake Water quality Qinghai-Tibet Plateau Surveillance cameras Deep learning |
| title | Detecting glacial lake water quality indicators from RGB surveillance images via deep learning |
| title_full | Detecting glacial lake water quality indicators from RGB surveillance images via deep learning |
| title_fullStr | Detecting glacial lake water quality indicators from RGB surveillance images via deep learning |
| title_full_unstemmed | Detecting glacial lake water quality indicators from RGB surveillance images via deep learning |
| title_short | Detecting glacial lake water quality indicators from RGB surveillance images via deep learning |
| title_sort | detecting glacial lake water quality indicators from rgb surveillance images via deep learning |
| topic | Glacial lake Water quality Qinghai-Tibet Plateau Surveillance cameras Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000391 |
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