Monitoring the transformation of Arctic landscapes
Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery...
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ftubpotsdam:oai:kobv.de-opus4-uni-potsdam:63286 2024-05-12T07:52:16+00:00 Monitoring the transformation of Arctic landscapes Kaiser, Soraya (Dr.) Grosse, Guido (Prof. Dr.) Boike, Julia (Dr.) Langer, Moritz (Dr.) 2021-07-16 https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63286 https://doi.org/10.3390/rs13142802 eng eng https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63286 https://doi.org/10.3390/rs13142802 https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/closedAccess ddc:550 ddc:620 Institut für Geowissenschaften article doc-type:article 2021 ftubpotsdam https://doi.org/10.3390/rs13142802 2024-04-17T14:03:34Z Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40-0.56 m yr(-1) for lake sizes of 0.10 ha-23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations. Article in Journal/Newspaper Alaska North Slope Arctic north slope permafrost Alaska University of Potsdam: publish.UP Arctic Arctic Lake ENVELOPE(-130.826,-130.826,57.231,57.231) Remote Sensing 13 14 2802 |
institution |
Open Polar |
collection |
University of Potsdam: publish.UP |
op_collection_id |
ftubpotsdam |
language |
English |
topic |
ddc:550 ddc:620 Institut für Geowissenschaften |
spellingShingle |
ddc:550 ddc:620 Institut für Geowissenschaften Kaiser, Soraya (Dr.) Grosse, Guido (Prof. Dr.) Boike, Julia (Dr.) Langer, Moritz (Dr.) Monitoring the transformation of Arctic landscapes |
topic_facet |
ddc:550 ddc:620 Institut für Geowissenschaften |
description |
Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40-0.56 m yr(-1) for lake sizes of 0.10 ha-23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations. |
format |
Article in Journal/Newspaper |
author |
Kaiser, Soraya (Dr.) Grosse, Guido (Prof. Dr.) Boike, Julia (Dr.) Langer, Moritz (Dr.) |
author_facet |
Kaiser, Soraya (Dr.) Grosse, Guido (Prof. Dr.) Boike, Julia (Dr.) Langer, Moritz (Dr.) |
author_sort |
Kaiser, Soraya (Dr.) |
title |
Monitoring the transformation of Arctic landscapes |
title_short |
Monitoring the transformation of Arctic landscapes |
title_full |
Monitoring the transformation of Arctic landscapes |
title_fullStr |
Monitoring the transformation of Arctic landscapes |
title_full_unstemmed |
Monitoring the transformation of Arctic landscapes |
title_sort |
monitoring the transformation of arctic landscapes |
publishDate |
2021 |
url |
https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63286 https://doi.org/10.3390/rs13142802 |
long_lat |
ENVELOPE(-130.826,-130.826,57.231,57.231) |
geographic |
Arctic Arctic Lake |
geographic_facet |
Arctic Arctic Lake |
genre |
Alaska North Slope Arctic north slope permafrost Alaska |
genre_facet |
Alaska North Slope Arctic north slope permafrost Alaska |
op_relation |
https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63286 https://doi.org/10.3390/rs13142802 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.3390/rs13142802 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
14 |
container_start_page |
2802 |
_version_ |
1798850532630069248 |