• The Journal of Engineering Geology
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• v.32 no.2
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• pp.271-280
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• 2022

Landfast sea ice forms near coastlines in polar regions. Continuous monitoring of this sea ice is important, as it plays a key role in the marine ecosystem and affects the operation of nearby research stations. This study detected landfast sea ice around Jang Bogo research station in East Antarctica by stacking interferometric coherence images of Sentinel-1 synthetic aperture radar (SAR) data with 6-, 12- and 18-day temporal baselines. A total of 50 landfast sea ice maps were generated covering July 2017 to June 2018. The time series revealed regional differences in the timing of the maximum extent as well as growth rate of landfast sea ice. Overall, detecting landfast sea ice using interferometric SAR coherence seems promisingly feasible; however, limitations remain owing to low backscattering coefficients from new and smooth sea ice surfaces and subtle movements of sea ice in contact with the Campbell Glacier Tongue.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.589-593
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• 2013

Remote sensing of SAR images is suitable for sea ice observations to obtain the sea ice data if clouds or weather conditions change. There are various types of sea ice, classification results can be seen more easily to detect the change by types of sea ice. In this study, we classified the image by supervised classification method, which is minimum distance was used. Also, we compared the overall accuracy when compared to the results with classification result of SAR images and the result of texture images. When using Radarsat-2 texture images, the overall accuracy was the highest, generally, when using the SAR images had higher overall accuracy.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.1-9
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• 2013

To classify the landfast ice in the north of the Greenland, observation data, multifrequency Synthetic Aperture Radar (SAR) images and texture images were used. The total four types of sea ice are first year ice, highly deformed ice, ridge and moderately deformed ice. The texture images that were processed by K-means algorithm showed higher accuracy than the ones that were processed by SAR images; however, overall accuracy of maximum likelihood algorithm using texture images did not show the highest accuracy all the time. It turned out that when using K-means algorithm, the accuracy of the multi SAR images were higher than the single SAR image. When using the maximum likelihood algorithm, the results of single and multi SAR images are differ from each other, therefore, maximum likelihood algorithm method should be used properly.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.501-512
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• 2015

Landfast sea ice (LFI) in Terra Nova Bay, East Antarctica where the Jangbogo Antarctic Research Station is located, has significant influences on marine ecosystem and the sailing of an icebreaker. Therefore, it is essential to analyze the spatio-temporal variation of the LFI in Terra Nova Bay. In this study, we chose interferometric pairs with the temporal baseline from 1 to 9 days out of a total of 62 COSMO-SkyMed synthetic aperture radar (SAR) images over Terra Nova Bay obtained from December 2010 to January 2012, and then constructed the coherence image of each pair. The LFI showed coherence values higher than 0.3 even in the interferometric SAR (InSAR) pairs of up to 9-days of temporal baseline. This was because the LFI was fixed at coastline and thus showed low temporal phase decorrelation. Based on the characteristics of the coherence on LFI, We defined the areas of LFI that show spatially homogeneous coherence values higher than 0.5. Pack ice (PI) and open water showed low coherence values due to large temporal phase decorreation caused by current and wind. Distinguishing PI from open water in the coherence images was difficult due to their similarly low coherence values. PI was identified in SAR amplitude images by investigating cracks on the ice. The extents of the LFI and PI were estimated from the coherence and SAR amplitude images and their temporal variations were analyzed. The extent of the LFI increased from March to July (maximum extent of $170.7km^2$) and decreased from October. The extent of the PI increased from February to May and decreased from May to July when the LFI increases dramatically. The extent of the LFI and air temperature showed an inverse correlation with a time lag of about 2 months, i.e., the extent of the LFI decreases after 2 months of the increase in the air temperature. Meanwhile the correlation between wind speed and the extent of the LFI was very low. This represents that the extent of LFI in Terra Nova Bay are influenced more by the air temperature than wind speed.