Towards circumpolar mapping of infrastructure
The growth of settlements and the associated increase of the exploitation of natural resources is an ongoing trend in the Arctic. Buildings and other infrastructure are endangered by destabilization and collaps due to the climate change induced thawing of permafrost in northern regions. The majority...
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ftdatacite:10.5281/zenodo.3826134 2023-05-15T14:48:41+02:00 Towards circumpolar mapping of infrastructure Bartsch, Annett Pointner, Georg Ingeman-Nielsen, Thomas 2020 https://dx.doi.org/10.5281/zenodo.3826134 https://zenodo.org/record/3826134 en eng Zenodo https://zenodo.org/communities/nunataryuk https://dx.doi.org/10.5281/zenodo.3826133 https://zenodo.org/communities/nunataryuk Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Arctic Settlements Copernicus missions Landcover Text Poster article-journal ScholarlyArticle 2020 ftdatacite https://doi.org/10.5281/zenodo.3826134 https://doi.org/10.5281/zenodo.3826133 2021-11-05T12:55:41Z The growth of settlements and the associated increase of the exploitation of natural resources is an ongoing trend in the Arctic. Buildings and other infrastructure are endangered by destabilization and collaps due to the climate change induced thawing of permafrost in northern regions. The majority of human activity in the Arctic is located near permafrost coasts. Coastal settlements are additionally vulnerable because of coastal erosion, caused by rapid warming and thawing of coastal permafrost. The European Union (EU) Horizon2020 project “Nunataryuk” aims to assess the impacts of thawing land, coast and subsea permafrost on the climate and on local communities in the Arctic. One task of the project is to determine the impacts of permafrost thaw on coastal Arctic infrastructures and to provide appropriate adaptation and mitigation strategies. For that purpose, a circumpolar account of infrastructure is needed. The two polar-orbiting Sentinel-2 satellites of the Copernicus program of the EU are continuously providing multi-spectral images with high spatial and temporal resolution. Sentinel-2 data is of high value for mapping land cover. However, most traditional land cover classifications only contain one class for built-up areas. By using a multi-sensor approach, such as the combination of multispectral and Synthetic Aperture Radar (SAR) data, additional information can be derived that goes beyond the identification of built-up areas. Different types of infrastructure can be distinguished, as it is commonly needed. We assess the potential of combining Sentinel-2 multispectral data with Sentinel-1 (Synthetic Aperture Radar) data for mapping and characterizing Arctic infrastructure. Settlement characteristics (building properties, surface types) have been collected for sites in Greenland and Longyearbyen on Svalbard, Norway. First results based on machine learning methods show that the available resolution (10m) allows the identification of narrow features such as roads, which were not previously identifiable by commonly used data such as Landsat. Deep learning methods further improve the mapping with respect to errors of commission as well as distinguishing surface types. : Poster presentation at ASSW2020 https://arctic.ucalgary.ca/sites/default/files/webform/Bartsch_Annett_Towards%20circumpolar%20ma Still Image Arctic Climate change Greenland Longyearbyen permafrost Svalbard DataCite Metadata Store (German National Library of Science and Technology) Arctic Svalbard Longyearbyen Greenland Norway |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
Arctic Settlements Copernicus missions Landcover |
spellingShingle |
Arctic Settlements Copernicus missions Landcover Bartsch, Annett Pointner, Georg Ingeman-Nielsen, Thomas Towards circumpolar mapping of infrastructure |
topic_facet |
Arctic Settlements Copernicus missions Landcover |
description |
The growth of settlements and the associated increase of the exploitation of natural resources is an ongoing trend in the Arctic. Buildings and other infrastructure are endangered by destabilization and collaps due to the climate change induced thawing of permafrost in northern regions. The majority of human activity in the Arctic is located near permafrost coasts. Coastal settlements are additionally vulnerable because of coastal erosion, caused by rapid warming and thawing of coastal permafrost. The European Union (EU) Horizon2020 project “Nunataryuk” aims to assess the impacts of thawing land, coast and subsea permafrost on the climate and on local communities in the Arctic. One task of the project is to determine the impacts of permafrost thaw on coastal Arctic infrastructures and to provide appropriate adaptation and mitigation strategies. For that purpose, a circumpolar account of infrastructure is needed. The two polar-orbiting Sentinel-2 satellites of the Copernicus program of the EU are continuously providing multi-spectral images with high spatial and temporal resolution. Sentinel-2 data is of high value for mapping land cover. However, most traditional land cover classifications only contain one class for built-up areas. By using a multi-sensor approach, such as the combination of multispectral and Synthetic Aperture Radar (SAR) data, additional information can be derived that goes beyond the identification of built-up areas. Different types of infrastructure can be distinguished, as it is commonly needed. We assess the potential of combining Sentinel-2 multispectral data with Sentinel-1 (Synthetic Aperture Radar) data for mapping and characterizing Arctic infrastructure. Settlement characteristics (building properties, surface types) have been collected for sites in Greenland and Longyearbyen on Svalbard, Norway. First results based on machine learning methods show that the available resolution (10m) allows the identification of narrow features such as roads, which were not previously identifiable by commonly used data such as Landsat. Deep learning methods further improve the mapping with respect to errors of commission as well as distinguishing surface types. : Poster presentation at ASSW2020 https://arctic.ucalgary.ca/sites/default/files/webform/Bartsch_Annett_Towards%20circumpolar%20ma |
format |
Still Image |
author |
Bartsch, Annett Pointner, Georg Ingeman-Nielsen, Thomas |
author_facet |
Bartsch, Annett Pointner, Georg Ingeman-Nielsen, Thomas |
author_sort |
Bartsch, Annett |
title |
Towards circumpolar mapping of infrastructure |
title_short |
Towards circumpolar mapping of infrastructure |
title_full |
Towards circumpolar mapping of infrastructure |
title_fullStr |
Towards circumpolar mapping of infrastructure |
title_full_unstemmed |
Towards circumpolar mapping of infrastructure |
title_sort |
towards circumpolar mapping of infrastructure |
publisher |
Zenodo |
publishDate |
2020 |
url |
https://dx.doi.org/10.5281/zenodo.3826134 https://zenodo.org/record/3826134 |
geographic |
Arctic Svalbard Longyearbyen Greenland Norway |
geographic_facet |
Arctic Svalbard Longyearbyen Greenland Norway |
genre |
Arctic Climate change Greenland Longyearbyen permafrost Svalbard |
genre_facet |
Arctic Climate change Greenland Longyearbyen permafrost Svalbard |
op_relation |
https://zenodo.org/communities/nunataryuk https://dx.doi.org/10.5281/zenodo.3826133 https://zenodo.org/communities/nunataryuk |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.3826134 https://doi.org/10.5281/zenodo.3826133 |
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