Using deep learning and multi-source remote sensing images to map landlocked lakes in Antarctica
Anyao Jiang,Xin Meng,Yan Huang,and Guitao Shi
Anyao Jiang
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences and State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Xin Meng
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences and State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Yan Huang
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences and State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences and State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai, 200241, China
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Total article views: 2,373 (including HTML, PDF, and XML)
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Total article views: 1,731 (including HTML, PDF, and XML)
Thereof 1,687 with geography defined
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Total article views: 642 (including HTML, PDF, and XML)
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Landlocked lakes are crucial to the Antarctic ecosystem and sensitive to climate change. Limited research on their distribution prompted us to develop an automated detection process using deep learning and multi-source satellite imagery. This allowed us to accurately determine the landlocked lake open water (LLOW) area in Antarctica, generating high-resolution time series data. We find that the changes in positive and negative degree days predominantly drive variations in the LLOW area.
Landlocked lakes are crucial to the Antarctic ecosystem and sensitive to climate change. Limited...
