Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis
Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent...
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ftunivwuerz:oai:opus.bibliothek.uni-wuerzburg.de:28195 2023-09-05T13:16:12+02:00 Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis Philipp, Marius Dietz, Andreas Ullmann, Tobias Kuenzer, Claudia 2022 application/pdf https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/28195 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-281956 https://doi.org/10.3390/rs14153656 https://opus.bibliothek.uni-wuerzburg.de/files/28195/remotesensing-14-03656-v2.pdf eng eng https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/28195 urn:nbn:de:bvb:20-opus-281956 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-281956 https://doi.org/10.3390/rs14153656 https://opus.bibliothek.uni-wuerzburg.de/files/28195/remotesensing-14-03656-v2.pdf https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess ddc:526 article doc-type:article 2022 ftunivwuerz https://doi.org/10.3390/rs14153656 2023-08-13T22:35:28Z Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments. Article in Journal/Newspaper Arctic Ice permafrost Sea ice Würzburg University: Online Publication Service Arctic Remote Sensing 14 15 3656 |
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Würzburg University: Online Publication Service |
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ftunivwuerz |
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English |
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ddc:526 Philipp, Marius Dietz, Andreas Ullmann, Tobias Kuenzer, Claudia Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis |
topic_facet |
ddc:526 |
description |
Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments. |
format |
Article in Journal/Newspaper |
author |
Philipp, Marius Dietz, Andreas Ullmann, Tobias Kuenzer, Claudia |
author_facet |
Philipp, Marius Dietz, Andreas Ullmann, Tobias Kuenzer, Claudia |
author_sort |
Philipp, Marius |
title |
Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis |
title_short |
Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis |
title_full |
Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis |
title_fullStr |
Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis |
title_full_unstemmed |
Automated extraction of annual erosion rates for Arctic permafrost coasts using Sentinel-1, Deep Learning, and Change Vector Analysis |
title_sort |
automated extraction of annual erosion rates for arctic permafrost coasts using sentinel-1, deep learning, and change vector analysis |
publishDate |
2022 |
url |
https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/28195 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-281956 https://doi.org/10.3390/rs14153656 https://opus.bibliothek.uni-wuerzburg.de/files/28195/remotesensing-14-03656-v2.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Ice permafrost Sea ice |
genre_facet |
Arctic Ice permafrost Sea ice |
op_relation |
https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/28195 urn:nbn:de:bvb:20-opus-281956 https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-281956 https://doi.org/10.3390/rs14153656 https://opus.bibliothek.uni-wuerzburg.de/files/28195/remotesensing-14-03656-v2.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs14153656 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
15 |
container_start_page |
3656 |
_version_ |
1776197871124611072 |