A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China
Brine shrimp are vital inhabitants of saltwater lakes, contributing significantly to economic and ecological systems. With increasing threats from environmental degradation and overharvesting, effective monitoring is urgently needed. Traditional field sampling methods are limited in scope and effici...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-04-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2025.2490407 |
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| author | Jingchen He Zhenyu Tan Junli Li Bo Jiang Hongtao Duan |
| author_facet | Jingchen He Zhenyu Tan Junli Li Bo Jiang Hongtao Duan |
| author_sort | Jingchen He |
| collection | DOAJ |
| description | Brine shrimp are vital inhabitants of saltwater lakes, contributing significantly to economic and ecological systems. With increasing threats from environmental degradation and overharvesting, effective monitoring is urgently needed. Traditional field sampling methods are limited in scope and efficiency, necessitating a reliable remote sensing-based approach. However, Ebinur Lake’s complex spectral environment, characterized by poor water quality and diverse suspended particulates, poses challenges for satellite remote sensing accuracy. To overcome these issues, we developed a novel multi-rule extraction model based on Landsat data, leveraging the distinct short-wave infrared signatures of brine shrimp to enhance detection accuracy. We evaluated and validated this method using Landsat 8 and Sentinel-2 datasets, achieving a classification accuracy of 94.5% and a kappa coefficient of 0.88, surpassing existing methods. Additionally, our analysis of a decade of Landsat data in Ebinur Lake via Google Earth Engine revealed a correlation between brine shrimp distribution and lake surface area. Our model demonstrates high accuracy and scalability in mapping brine shrimp, making it a valuable tool for long-term, large-scale assessments in saline lakes. This capability holds significant potential for advancing fisheries research and informing conservation strategies. |
| format | Article |
| id | doaj-art-bfd969d482564ced87e99194e24fb509 |
| institution | Kabale University |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-bfd969d482564ced87e99194e24fb5092025-08-20T03:27:51ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172025-04-019229832010.1080/20964471.2025.2490407A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, ChinaJingchen He0Zhenyu Tan1Junli Li2Bo Jiang3Hongtao Duan4School of Urban and Environmental Sciences, Northwest University, Xi’an, Shaanxi, ChinaSchool of Urban and Environmental Sciences, Northwest University, Xi’an, Shaanxi, ChinaXinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, ChinaSchool of Information Science and Technology, Northwest University, Xi’an, Shaanxi, ChinaSchool of Urban and Environmental Sciences, Northwest University, Xi’an, Shaanxi, ChinaBrine shrimp are vital inhabitants of saltwater lakes, contributing significantly to economic and ecological systems. With increasing threats from environmental degradation and overharvesting, effective monitoring is urgently needed. Traditional field sampling methods are limited in scope and efficiency, necessitating a reliable remote sensing-based approach. However, Ebinur Lake’s complex spectral environment, characterized by poor water quality and diverse suspended particulates, poses challenges for satellite remote sensing accuracy. To overcome these issues, we developed a novel multi-rule extraction model based on Landsat data, leveraging the distinct short-wave infrared signatures of brine shrimp to enhance detection accuracy. We evaluated and validated this method using Landsat 8 and Sentinel-2 datasets, achieving a classification accuracy of 94.5% and a kappa coefficient of 0.88, surpassing existing methods. Additionally, our analysis of a decade of Landsat data in Ebinur Lake via Google Earth Engine revealed a correlation between brine shrimp distribution and lake surface area. Our model demonstrates high accuracy and scalability in mapping brine shrimp, making it a valuable tool for long-term, large-scale assessments in saline lakes. This capability holds significant potential for advancing fisheries research and informing conservation strategies.https://www.tandfonline.com/doi/10.1080/20964471.2025.2490407Brine shrimpremote sensingmulti-rule indexmachine learningEbinur Lake |
| spellingShingle | Jingchen He Zhenyu Tan Junli Li Bo Jiang Hongtao Duan A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China Big Earth Data Brine shrimp remote sensing multi-rule index machine learning Ebinur Lake |
| title | A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China |
| title_full | A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China |
| title_fullStr | A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China |
| title_full_unstemmed | A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China |
| title_short | A multi-rule index to extract brine shrimp from satellite imagery: a case study in Ebinur Lake, China |
| title_sort | multi rule index to extract brine shrimp from satellite imagery a case study in ebinur lake china |
| topic | Brine shrimp remote sensing multi-rule index machine learning Ebinur Lake |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2025.2490407 |
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