Investigating front variations of Greenland glaciers using multi-temporal remote sensing images and deep learning =: 基於深度學習以及多時相遙感影像的格陵蘭島冰川前緣變化研究
Quantification of front variations of glaciers in Greenland is essential for under-standing ice-ocean interaction, glacier dynamics, and a more accurate projection of global sea-level rise. Most previous studies manually delineated ice fronts from remote sensing images. Such manual efforts, however,...
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| Format: | Text |
| Language: | English Chinese |
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2020
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| Online Access: | https://julac.hosted.exlibrisgroup.com/primo-explore/search?query=addsrcrid,exact,991040107923803407,AND&tab=default_tab&search_scope=All&vid=CUHK&mode=advanced&lang=en_US https://repository.lib.cuhk.edu.hk/en/item/cuhk-2651666 |
| Summary: | Quantification of front variations of glaciers in Greenland is essential for under-standing ice-ocean interaction, glacier dynamics, and a more accurate projection of global sea-level rise. Most previous studies manually delineated ice fronts from remote sensing images. Such manual efforts, however, are becoming more labor- intensive and even impractical for quantifying temporal changes of ice fronts at a large number of glaciers in a detailed manner, because the volume and diversity of satellite images have been increasing significantly. Automating the identification of glaciological features such as calving fronts therefore becomes necessary. In the first study, we applied U-Net, a deep learning architecture, to multi-temporal Synthetic Aperture Radar images taken by the TerraSAR-X satellite and automated the delineation of the calving front positions of Jakobshavn Isbræ from 2009 to 2015. The results are consistent with the manually delineated products generated by the Greenland Ice Sheet Climate Change Initiative project with the mean test errors of 38 meters ( 6 pixels). We show that the calving fronts of Jakobshavn’s two main branches retreated at mean rates of −117 ± 1 m yr⁻¹ and −157 ± 1 m yr⁻¹, respectively, during the years 2009 to 2015. We suggest that the retreat of the calving front into an overdeepened basin whose bed is retrograde may have accelerated the retreat after 2011, while the inland-uphill bed slope behind the bottom of the overdeepened basin has prevented the glacier from retreating further after 2012. Although we demonstrate the feasibility of deep-learning-based method, this study is limited to a specific study area (e.g., Jakobshavn Isbræ) and dataset (e.g., TerraSAR-X). The second study further improves the generalization and robustness of the deep- learning-based method to release its constraint of applicable region and data. The method can be applied an outlet glacier or remote sensing datasets that are not included in training, and we integrate seven remote sensing datasets ... |
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