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Automatically extracted Antarctic coastline using remotely-sensed data: an update

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Yu, Y, Zhang, Z, Shokr, M, Hui, F, Cheng, X, Chi, Z, Heil, P and Chen, Z 2019 , 'Automatically extracted Antarctic coastline using remotely-sensed data: an update' , Remote Sensing, vol. 11, no. 16 , pp. 1-19 , doi: 10.3390/rs11161844.

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Abstract

The temporal and spatial variability of the Antarctic coastline is a clear indicator of change in extent and mass balance of ice sheets and shelves. In this study, the Canny edge detector was utilized to automatically extract high-resolution information of the Antarctic coastline for 2005, 2010, and 2017, based on optical and microwave satellite data. In order to improve the accuracy of the extracted coastlines, we developed the Canny algorithm by automatically calculating the local low and high thresholds via the intensity histogram of each image to derive thresholds to distinguish ice sheet from water. A visual comparison between extracted coastlines and mosaics from remote sensing images shows good agreement. In addition, comparing manually extracted coastline, based on prior knowledge, the accuracy of planimetric position of automated extraction is better than two pixels of Landsat images (30 m resolution). Our study shows that the percentage of deviation (7 km2 (2005) to 1.3537 × 107 km2 (2010) and 1.3657 × 107 km2 (2017). We have found that the decline of the Antarctic area between 2005 and 2010 is related to the breakup of some individual ice shelves, mainly in the Antarctic Peninsula and off East Antarctica. We present a detailed analysis of the temporal and spatial change of coastline and area change for the six ice shelves that exhibited the largest change in the last decade. The largest area change (a loss of 4836 km2) occurred at the Wilkins Ice Shelf between 2005 and 2010.

Item Type: Article
Authors/Creators:Yu, Y and Zhang, Z and Shokr, M and Hui, F and Cheng, X and Chi, Z and Heil, P and Chen, Z
Keywords: Antarctica, digital coastline, SAR analysis, Antarctic coastline, coastline extraction, remote sensing, Canny algorithms, ice shelves
Journal or Publication Title: Remote Sensing
Publisher: MDPIAG
ISSN: 2072-4292
DOI / ID Number: 10.3390/rs11161844
Copyright Information:

Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

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