Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022

The Arctic coast is facing rapid changes due to thawing permafrost and melting glaciers and sea ice. Communities all around the Arctic urgently need local-scale information on coastal change. This study aimed at developing a scalable and transferable procedure for mapping shoreline displacement in A...

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Main Authors: Nylén, Tua, Calle, Mikel, Gonzales-Inca, Carlos
Format: Text
Language:English
Published: 2023
Subjects:
Ice
Online Access:https://doi.org/10.5194/egusphere-2023-1399
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1399/
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spelling ftcopernicus:oai:publications.copernicus.org:egusphere112766 2023-10-09T21:48:16+02:00 Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022 Nylén, Tua Calle, Mikel Gonzales-Inca, Carlos 2023-09-12 application/pdf https://doi.org/10.5194/egusphere-2023-1399 https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1399/ eng eng doi:10.5194/egusphere-2023-1399 https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1399/ eISSN: Text 2023 ftcopernicus https://doi.org/10.5194/egusphere-2023-1399 2023-09-18T16:24:17Z The Arctic coast is facing rapid changes due to thawing permafrost and melting glaciers and sea ice. Communities all around the Arctic urgently need local-scale information on coastal change. This study aimed at developing a scalable and transferable procedure for mapping shoreline displacement in Arctic conditions by using an archive of satellite images. Our approach utilizes cloud computing in Google Earth Engine to process a large number of open satellite images for large areas and a long period of time (here 39 years). The procedure was iteratively developed in two contrasting study areas in Arctic Norway. It applies data fusion (including sensor fusion, algorithm fusion, and decision fusion) to improve classification accuracy and processing efficiency. For one 2 500 km 2 area of interest, the procedure utilizes c. 600 satellite images to create coastal land cover and shoreline time series in less than one hour. Data fusion reduces problems related to the low availability and quality of satellite data in the Arctic before 2013 and reduces the impacts of noise and short-term changes. However, low data availability tends to create local gaps in the time series. Validation in the Tanafjorden and north-western Svalbard coasts indicates an overall classification accuracy of more than 99 % (against an independent sample of 2000 coastal points) and a median shoreline error distance of less than 15 m (against manually digitized shoreline) in 2019–2022. We exemplify how the method produces new information for identifying coastal change hotspots and examining long-term trends and local scale processes. We give examples of glacier retreat, spit migration, and delta development. This procedure is scalable and transferable to any coastal area demonstrating potential for producing the first circumpolar dataset of shoreline displacement. Text Arctic glacier glacier Ice permafrost Sea ice Svalbard Copernicus Publications: E-Journals Arctic Norway Svalbard Tanafjorden ENVELOPE(28.391,28.391,70.747,70.747)
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description The Arctic coast is facing rapid changes due to thawing permafrost and melting glaciers and sea ice. Communities all around the Arctic urgently need local-scale information on coastal change. This study aimed at developing a scalable and transferable procedure for mapping shoreline displacement in Arctic conditions by using an archive of satellite images. Our approach utilizes cloud computing in Google Earth Engine to process a large number of open satellite images for large areas and a long period of time (here 39 years). The procedure was iteratively developed in two contrasting study areas in Arctic Norway. It applies data fusion (including sensor fusion, algorithm fusion, and decision fusion) to improve classification accuracy and processing efficiency. For one 2 500 km 2 area of interest, the procedure utilizes c. 600 satellite images to create coastal land cover and shoreline time series in less than one hour. Data fusion reduces problems related to the low availability and quality of satellite data in the Arctic before 2013 and reduces the impacts of noise and short-term changes. However, low data availability tends to create local gaps in the time series. Validation in the Tanafjorden and north-western Svalbard coasts indicates an overall classification accuracy of more than 99 % (against an independent sample of 2000 coastal points) and a median shoreline error distance of less than 15 m (against manually digitized shoreline) in 2019–2022. We exemplify how the method produces new information for identifying coastal change hotspots and examining long-term trends and local scale processes. We give examples of glacier retreat, spit migration, and delta development. This procedure is scalable and transferable to any coastal area demonstrating potential for producing the first circumpolar dataset of shoreline displacement.
format Text
author Nylén, Tua
Calle, Mikel
Gonzales-Inca, Carlos
spellingShingle Nylén, Tua
Calle, Mikel
Gonzales-Inca, Carlos
Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022
author_facet Nylén, Tua
Calle, Mikel
Gonzales-Inca, Carlos
author_sort Nylén, Tua
title Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022
title_short Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022
title_full Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022
title_fullStr Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022
title_full_unstemmed Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022
title_sort arctic shoreline displacement with open satellite imagery and data fusion: a pilot study 1984–2022
publishDate 2023
url https://doi.org/10.5194/egusphere-2023-1399
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1399/
long_lat ENVELOPE(28.391,28.391,70.747,70.747)
geographic Arctic
Norway
Svalbard
Tanafjorden
geographic_facet Arctic
Norway
Svalbard
Tanafjorden
genre Arctic
glacier
glacier
Ice
permafrost
Sea ice
Svalbard
genre_facet Arctic
glacier
glacier
Ice
permafrost
Sea ice
Svalbard
op_source eISSN:
op_relation doi:10.5194/egusphere-2023-1399
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1399/
op_doi https://doi.org/10.5194/egusphere-2023-1399
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