An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic
For regional environmental studies it is important to know the location of the fast ice edge which affects the coastal processes in the Arctic. The aim of this study is to develop a new automated method for fast ice delineation from SAR imagery. The method is based on a fine resolution hybrid sea ic...
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2021
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ftdoajarticles:oai:doaj.org/article:274c40fb0d734bdfb845964a97f7b1bf 2023-05-15T13:39:48+02:00 An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic Valeria Selyuzhenok Denis Demchev 2021-09-01T00:00:00Z https://doi.org/10.3390/rs13183783 https://doaj.org/article/274c40fb0d734bdfb845964a97f7b1bf EN eng MDPI AG https://www.mdpi.com/2072-4292/13/18/3783 https://doaj.org/toc/2072-4292 doi:10.3390/rs13183783 2072-4292 https://doaj.org/article/274c40fb0d734bdfb845964a97f7b1bf Remote Sensing, Vol 13, Iss 3783, p 3783 (2021) landfast sea ice stamukha SAR Arctic Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13183783 2022-12-31T15:06:53Z For regional environmental studies it is important to know the location of the fast ice edge which affects the coastal processes in the Arctic. The aim of this study is to develop a new automated method for fast ice delineation from SAR imagery. The method is based on a fine resolution hybrid sea ice tracking algorithm utilizing advantages of feature tracking and cross-correlation approaches. The developed method consists of three main steps: drift field retrieval at sub-kilometer scale, selection of motionless features and edge delineation. The method was tested on a time series of C-band co-polarized (HH) ENVISAT ASAR and Sentinel-1 imagery in the Laptev and East Siberian Seas. The comparison of the retrieved edges with the operational ice charts produced by the Arctic and Antarctic Research Institute (Russia) showed a good agreement between the data sets with a mean distance between the edges of <15 km. Thanks to the high density of the ice drift product, the method allows for detailed fast ice edge delineation. In addition, large stamukhas with horizontal size of tens of kilometers can be detected. The proposed method can be applied for regional fast ice mapping and large stamukhas detection to aid coastal research. Additionally, the method can serve as a tool for operational sea ice mapping. Article in Journal/Newspaper Antarc* Antarctic Arctic and Antarctic Research Institute Arctic laptev Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Antarctic Asar ENVELOPE(134.033,134.033,68.667,68.667) Remote Sensing 13 18 3783 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
landfast sea ice stamukha SAR Arctic Science Q |
spellingShingle |
landfast sea ice stamukha SAR Arctic Science Q Valeria Selyuzhenok Denis Demchev An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic |
topic_facet |
landfast sea ice stamukha SAR Arctic Science Q |
description |
For regional environmental studies it is important to know the location of the fast ice edge which affects the coastal processes in the Arctic. The aim of this study is to develop a new automated method for fast ice delineation from SAR imagery. The method is based on a fine resolution hybrid sea ice tracking algorithm utilizing advantages of feature tracking and cross-correlation approaches. The developed method consists of three main steps: drift field retrieval at sub-kilometer scale, selection of motionless features and edge delineation. The method was tested on a time series of C-band co-polarized (HH) ENVISAT ASAR and Sentinel-1 imagery in the Laptev and East Siberian Seas. The comparison of the retrieved edges with the operational ice charts produced by the Arctic and Antarctic Research Institute (Russia) showed a good agreement between the data sets with a mean distance between the edges of <15 km. Thanks to the high density of the ice drift product, the method allows for detailed fast ice edge delineation. In addition, large stamukhas with horizontal size of tens of kilometers can be detected. The proposed method can be applied for regional fast ice mapping and large stamukhas detection to aid coastal research. Additionally, the method can serve as a tool for operational sea ice mapping. |
format |
Article in Journal/Newspaper |
author |
Valeria Selyuzhenok Denis Demchev |
author_facet |
Valeria Selyuzhenok Denis Demchev |
author_sort |
Valeria Selyuzhenok |
title |
An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic |
title_short |
An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic |
title_full |
An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic |
title_fullStr |
An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic |
title_full_unstemmed |
An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic |
title_sort |
application of sea ice tracking algorithm for fast ice and stamukhas detection in the arctic |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13183783 https://doaj.org/article/274c40fb0d734bdfb845964a97f7b1bf |
long_lat |
ENVELOPE(134.033,134.033,68.667,68.667) |
geographic |
Arctic Antarctic Asar |
geographic_facet |
Arctic Antarctic Asar |
genre |
Antarc* Antarctic Arctic and Antarctic Research Institute Arctic laptev Sea ice |
genre_facet |
Antarc* Antarctic Arctic and Antarctic Research Institute Arctic laptev Sea ice |
op_source |
Remote Sensing, Vol 13, Iss 3783, p 3783 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/18/3783 https://doaj.org/toc/2072-4292 doi:10.3390/rs13183783 2072-4292 https://doaj.org/article/274c40fb0d734bdfb845964a97f7b1bf |
op_doi |
https://doi.org/10.3390/rs13183783 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
18 |
container_start_page |
3783 |
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1766124950967549952 |