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• W3174162783 endingPage "2429" @default.
• W3174162783 startingPage "2429" @default.
• W3174162783 abstract "Glaciers and numerous glacial lakes that are produced by glacier melting are key indicators of climate change. Often overlooked, supra-glacial lakes develop in the melting area in the low-lying part of a glacier and appear to be highly variable in their size, shape, and location. The lifespan of these lakes is thought to be quite transient, since the lakes may be completely filled by water and burst out within several weeks. Changes in supra-glacial lake outlines and other surface features such as supra-glacial rivers and crevasses on the glaciers are useful indicators for the direct monitoring of glacier changes. Synthetic aperture radar (SAR) is not affected by weather and climate, and is an effective tool for study of glaciated areas. The development of the Chinese GaoFen-3 (GF-3) SAR, which has high spatial and temporal resolution and high-precision observation performance, has made it possible to obtain dynamic information about glaciers in more detail. In this paper, the classical Canny operator, the variational B-spline level-set method, and U-Net-based deep-learning model were applied and compared to extract glacial lake outlines and other surface features using different modes and Chinese GF-3 SAR imagery in the Mount Everest Region of the Himalayas. Particularly, the U-Net-based deep-learning method, which was independent of auxiliary data and had a high degree of automation, was used for the first time in this context. The experimental results showed that the U-Net-based deep-learning model worked best in the segmentation of supra-glacial lakes in terms of accuracy (Precision = 98.45% and Recall = 95.82%) and segmentation efficiency, and was good at detecting small, elongated, and ice-covered supra-glacial lakes. We also found that it was useful for accurately identifying the location of supra-glacial streams and ice crevasses on glaciers, and quantifying their width. Finally, based on the time series of the mapping results, the spatial characteristics and temporal evolution of these features over the glaciers were comprehensively analyzed. Overall, this study presents a novel approach to improve the detection accuracy of glacier elements that could be leveraged for dynamic monitoring in future research." @default.
• W3174162783 created "2021-07-05" @default.
• W3174162783 creator A5053307621 @default.
• W3174162783 date "2021-06-22" @default.
• W3174162783 modified "2023-10-05" @default.
• W3174162783 title "Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images" @default.
• W3174162783 cites W1541872020 @default.
• W3174162783 cites W1961672453 @default.
• W3174162783 cites W1966980409 @default.
• W3174162783 cites W1967436512 @default.
• W3174162783 cites W1978081629 @default.
• W3174162783 cites W1981878534 @default.
• W3174162783 cites W1982526103 @default.
• W3174162783 cites W2004459073 @default.
• W3174162783 cites W2007338973 @default.
• W3174162783 cites W2010613594 @default.
• W3174162783 cites W2024578125 @default.
• W3174162783 cites W2034770444 @default.
• W3174162783 cites W2038851050 @default.
• W3174162783 cites W2065785581 @default.
• W3174162783 cites W2078877578 @default.
• W3174162783 cites W2084477822 @default.
• W3174162783 cites W2085683568 @default.
• W3174162783 cites W2086139388 @default.
• W3174162783 cites W2088840575 @default.
• W3174162783 cites W2094757617 @default.
• W3174162783 cites W2094801462 @default.
• W3174162783 cites W2095208500 @default.
• W3174162783 cites W2101178628 @default.
• W3174162783 cites W2102369545 @default.
• W3174162783 cites W2106097225 @default.
• W3174162783 cites W2115535975 @default.
• W3174162783 cites W2123194201 @default.
• W3174162783 cites W2137117880 @default.
• W3174162783 cites W2138963913 @default.
• W3174162783 cites W2142894544 @default.
• W3174162783 cites W2143523928 @default.
• W3174162783 cites W2145023731 @default.
• W3174162783 cites W2164816089 @default.
• W3174162783 cites W2234850500 @default.
• W3174162783 cites W2283265276 @default.
• W3174162783 cites W2292149690 @default.
• W3174162783 cites W2345393156 @default.
• W3174162783 cites W2398968891 @default.
• W3174162783 cites W2467909254 @default.
• W3174162783 cites W2526838437 @default.
• W3174162783 cites W2550521585 @default.
• W3174162783 cites W2557132287 @default.
• W3174162783 cites W2559757886 @default.
• W3174162783 cites W2590081250 @default.
• W3174162783 cites W2604844733 @default.
• W3174162783 cites W2617083732 @default.
• W3174162783 cites W2622632592 @default.
• W3174162783 cites W2698424090 @default.
• W3174162783 cites W2733271701 @default.
• W3174162783 cites W2733955274 @default.
• W3174162783 cites W2742040830 @default.
• W3174162783 cites W2765498694 @default.
• W3174162783 cites W2767405965 @default.
• W3174162783 cites W2789607755 @default.
• W3174162783 cites W2797972929 @default.
• W3174162783 cites W2810418258 @default.
• W3174162783 cites W2891191426 @default.
• W3174162783 cites W2895165409 @default.
• W3174162783 cites W2921300998 @default.
• W3174162783 cites W2945957599 @default.
• W3174162783 cites W2965521953 @default.
• W3174162783 cites W2986456964 @default.
• W3174162783 cites W3000094388 @default.
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• W3174162783 cites W3023390435 @default.
• W3174162783 cites W3036506870 @default.
• W3174162783 cites W3045982802 @default.
• W3174162783 cites W3087929000 @default.
• W3174162783 cites W3090844588 @default.
• W3174162783 cites W3091800860 @default.
• W3174162783 cites W3106860957 @default.
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• W3174162783 cites W3118807321 @default.
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• W3174162783 doi "https://doi.org/10.3390/rs13132429" @default.
• W3174162783 hasPublicationYear "2021" @default.
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