Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard
Even though surge-type glaciers make up only a small percentage of all glaciers, related research contributes considerably to the general understanding of glacier flow mechanisms. Recent studies based on remote sensing techniques aimed to disentangle underlying processes related to glacier surges. T...
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ftuniverlangen:oai:ub.uni-erlangen.de-opus:22600 2023-06-06T11:53:57+02:00 Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard Koch, Moritz Seehaus, Thorsten Friedl, Peter Braun, Matthias 2023-03-11 application/pdf https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/22600 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-226004 https://doi.org/10.3390/rs15061545 https://opus4.kobv.de/opus4-fau/files/22600/remotesensing-15-01545.pdf eng eng https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/22600 urn:nbn:de:bvb:29-opus4-226004 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-226004 https://doi.org/10.3390/rs15061545 https://opus4.kobv.de/opus4-fau/files/22600/remotesensing-15-01545.pdf https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess ddc:550 article doc-type:article 2023 ftuniverlangen https://doi.org/10.3390/rs15061545 2023-04-17T00:37:49Z Even though surge-type glaciers make up only a small percentage of all glaciers, related research contributes considerably to the general understanding of glacier flow mechanisms. Recent studies based on remote sensing techniques aimed to disentangle underlying processes related to glacier surges. They have proven the possibilities yielded by combining high performance computing and earth observation. In addition, modelling approaches to surges have seen increasing popularity, yet large spatial and temporal data about timing of surge incites are missing. We aimed to develop an algorithm that not only detects surge type glaciers but also determines the timing of a surge onset, while being computationally inexpensive, transferable, and expandable in time and space. The algorithm is based on time series analyses of glacier surface velocity derived from Sentinel-1 data. After seasonal and trend decomposition, outlier detection is performed by the General Studentized Extreme Deviate Test, an iterative algorithm well suited for outlier detection in univariate time series. To determine surges, cluster analysis is performed to identify outlier clusters, which are linked to glacier surges. We demonstrate the viability on the Svalbard archipelago for the period 2015 to 2021 where we have identified 18 glacier surges and the timing of their active phase. Article in Journal/Newspaper glacier Svalbard OPUS FAU - Online publication system of Friedrich-Alexander-Universität Erlangen-Nürnberg Svalbard Svalbard Archipelago Remote Sensing 15 6 1545 |
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Open Polar |
collection |
OPUS FAU - Online publication system of Friedrich-Alexander-Universität Erlangen-Nürnberg |
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ftuniverlangen |
language |
English |
topic |
ddc:550 |
spellingShingle |
ddc:550 Koch, Moritz Seehaus, Thorsten Friedl, Peter Braun, Matthias Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard |
topic_facet |
ddc:550 |
description |
Even though surge-type glaciers make up only a small percentage of all glaciers, related research contributes considerably to the general understanding of glacier flow mechanisms. Recent studies based on remote sensing techniques aimed to disentangle underlying processes related to glacier surges. They have proven the possibilities yielded by combining high performance computing and earth observation. In addition, modelling approaches to surges have seen increasing popularity, yet large spatial and temporal data about timing of surge incites are missing. We aimed to develop an algorithm that not only detects surge type glaciers but also determines the timing of a surge onset, while being computationally inexpensive, transferable, and expandable in time and space. The algorithm is based on time series analyses of glacier surface velocity derived from Sentinel-1 data. After seasonal and trend decomposition, outlier detection is performed by the General Studentized Extreme Deviate Test, an iterative algorithm well suited for outlier detection in univariate time series. To determine surges, cluster analysis is performed to identify outlier clusters, which are linked to glacier surges. We demonstrate the viability on the Svalbard archipelago for the period 2015 to 2021 where we have identified 18 glacier surges and the timing of their active phase. |
format |
Article in Journal/Newspaper |
author |
Koch, Moritz Seehaus, Thorsten Friedl, Peter Braun, Matthias |
author_facet |
Koch, Moritz Seehaus, Thorsten Friedl, Peter Braun, Matthias |
author_sort |
Koch, Moritz |
title |
Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard |
title_short |
Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard |
title_full |
Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard |
title_fullStr |
Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard |
title_full_unstemmed |
Automated Detection of Glacier Surges from Sentinel-1 Surface Velocity Time Series—An Example from Svalbard |
title_sort |
automated detection of glacier surges from sentinel-1 surface velocity time series—an example from svalbard |
publishDate |
2023 |
url |
https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/22600 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-226004 https://doi.org/10.3390/rs15061545 https://opus4.kobv.de/opus4-fau/files/22600/remotesensing-15-01545.pdf |
geographic |
Svalbard Svalbard Archipelago |
geographic_facet |
Svalbard Svalbard Archipelago |
genre |
glacier Svalbard |
genre_facet |
glacier Svalbard |
op_relation |
https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/22600 urn:nbn:de:bvb:29-opus4-226004 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-226004 https://doi.org/10.3390/rs15061545 https://opus4.kobv.de/opus4-fau/files/22600/remotesensing-15-01545.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs15061545 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
6 |
container_start_page |
1545 |
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1767960316632629248 |