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An improved automated method to detect landfast ice edge off Zhongshan Station using SAR imagery

Li, X, Ouyang, L, Hui, F, Cheng, X, Shokr, M and Heil, P 2018 , 'An improved automated method to detect landfast ice edge off Zhongshan Station using SAR imagery' , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 12 , pp. 4737-4746 , doi: 10.1109/JSTARS.2018.2882602.

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Abstract

Landfast ice is an important component of the Antarctic sea ice. Its edge generally advances offshore to its annual maximum extent by mid-winter before retreating later in spring. This study presents an automated method to detect the seaward landfast ice edge (SLIE) at its maximum extent in the beginning in the austral spring (October) for a region northeast of the Amery Ice Shelf, East Antarctic. Here, the net gradient difference algorithm developed by Mahoney [1] has been extended to include the medium edge detection method to automatically delineate the SLIE using the sequential SAR data. The underlying method is to use a spatial gradient operation to identify potential edge pixels, before applying the noise removal using a baseline (2000-2008) SLIE, and a pixel connection technique to generate a contiguous edge. We show that in 2016, the SLIE extended 20 km (25%) further equatorward than in 2008. Good agreement has been achieved between the SLIE derived from our automated method and the manual SLIE extraction using the original SAR as well as a near-coincident Landsat-8 OLI image. The error in the automated approach is minimized when using three to four calibrated SAR images, all with the same incident angle and the maximum separation between them is less than 20 days. Our results confirm the potential of the method for operational application, and we expect it to promote the study of Antarctic landfast ice.

Item Type: Article
Authors/Creators:Li, X and Ouyang, L and Hui, F and Cheng, X and Shokr, M and Heil, P
Keywords: Antarctica, landfast ice edge, SAR data
Journal or Publication Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1939-1404
DOI / ID Number: 10.1109/JSTARS.2018.2882602
Copyright Information:

© 2018 IEEE.

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