Automatically delineating the calving front of Jakobshavn Isbræ from multitemporal TerraSAR-X images: a deep learning approach
Publication in refereed journal

Times Cited
Altmetrics Information

Other information
AbstractThe calving fronts of many tidewater glaciers in Greenland have been undergoing strong seasonal and interannual fluctuations. Conventionally, calving front positions have been manually delineated from remote sensing images. But manual practices can be labor-intensive and time-consuming, particularly when processing a large number of images taken over decades and covering large areas with many glaciers, such as Greenland. Applying U-Net, a deep learning architecture, to multitemporal synthetic aperture radar images taken by the TerraSAR-X satellite, we here automatically delineate the calving front positions of Jakobshavn Isbrae from 2009 to 2015. Our results are consistent with the manually delineated products generated by the Greenland Ice Sheet Climate Change Initiative project. We show that the calving fronts of Jakobshavn's two main branches retreated at mean rates of -117 +/- 1 and -157 +/- 1 m yr(-1), respectively, during the years 2009 to 2015. The interannual calving front variations can be roughly divided into three phases for both branches. The retreat rates of the two branches tripled and doubled, respectively, from phase 1 (April 2009-January 2011) to phase 2 (January 2011-January 2013) and then stabilized to nearly zero in phase 3 (January 2013-December 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. Demonstrating through this successful case study on Jakobshavn Isbrae and due to the transferable nature of deep learning, our methodology can be applied to many other tidewater glaciers both in Greenland and else-where in the world, using multitemporal and multisensor remote sensing imagery.
Acceptance Date06/06/2019
All Author(s) ListEnze Zhang, Lin Liu, Lingcao Huang
Journal nameCryosphere
Volume Number13
Issue Number6
PublisherEuropean Geosciences Union (EGU) / Copernicus Publications
Place of PublicationBahnhofsallee 1e 37081 Göttingen Germany
Pages1729 - 1741
LanguagesEnglish-United States
KeywordsGreenland, Deep Learning, Glacier, Remote Sensing

Last updated on 2020-08-07 at 01:35