Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery
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...
Published in: | Remote Sensing |
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Language: | English |
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Humboldt-Universität zu Berlin
2021
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Online Access: | http://edoc.hu-berlin.de/18452/24015 https://nbn-resolving.org/urn:nbn:de:kobv:11-110-18452/24015-7 https://doi.org/10.3390/rs13142802 https://doi.org/10.18452/23366 |
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fthuberlin:oai:edoc.hu-berlin.de:18452/24015 2023-12-03T10:08:42+01:00 Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery Kaiser, Soraya Grosse, Guido Boike, Julia Langer, Moritz 2021-07-16 application/pdf http://edoc.hu-berlin.de/18452/24015 https://nbn-resolving.org/urn:nbn:de:kobv:11-110-18452/24015-7 https://doi.org/10.3390/rs13142802 https://doi.org/10.18452/23366 eng eng Humboldt-Universität zu Berlin http://edoc.hu-berlin.de/18452/24015 urn:nbn:de:kobv:11-110-18452/24015-7 doi:10.3390/rs13142802 http://dx.doi.org/10.18452/23366 2072-4292 (CC BY 4.0) Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ change detection shoreline movement rate arctic water bodies permafrost lowlands automated monitoring North Slope very high resolution imagery 910 Geografie und Reisen ddc:910 article doc-type:article publishedVersion 2021 fthuberlin https://doi.org/10.3390/rs1314280210.18452/23366 2023-11-05T23:36:31Z 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. Bundesministerium für Bildung und Forschung Peer Reviewed Article in Journal/Newspaper Alaska North Slope Arctic north slope permafrost Alaska Open-Access-Publikationsserver der Humboldt-Universität: edoc-Server Arctic Arctic Lake ENVELOPE(-130.826,-130.826,57.231,57.231) Remote Sensing 13 14 2802 |
institution |
Open Polar |
collection |
Open-Access-Publikationsserver der Humboldt-Universität: edoc-Server |
op_collection_id |
fthuberlin |
language |
English |
topic |
change detection shoreline movement rate arctic water bodies permafrost lowlands automated monitoring North Slope very high resolution imagery 910 Geografie und Reisen ddc:910 |
spellingShingle |
change detection shoreline movement rate arctic water bodies permafrost lowlands automated monitoring North Slope very high resolution imagery 910 Geografie und Reisen ddc:910 Kaiser, Soraya Grosse, Guido Boike, Julia Langer, Moritz Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery |
topic_facet |
change detection shoreline movement rate arctic water bodies permafrost lowlands automated monitoring North Slope very high resolution imagery 910 Geografie und Reisen ddc:910 |
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. Bundesministerium für Bildung und Forschung Peer Reviewed |
format |
Article in Journal/Newspaper |
author |
Kaiser, Soraya Grosse, Guido Boike, Julia Langer, Moritz |
author_facet |
Kaiser, Soraya Grosse, Guido Boike, Julia Langer, Moritz |
author_sort |
Kaiser, Soraya |
title |
Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery |
title_short |
Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery |
title_full |
Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery |
title_fullStr |
Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery |
title_full_unstemmed |
Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery |
title_sort |
monitoring the transformation of arctic landscapes: automated shoreline change detection of lakes using very high resolution imagery |
publisher |
Humboldt-Universität zu Berlin |
publishDate |
2021 |
url |
http://edoc.hu-berlin.de/18452/24015 https://nbn-resolving.org/urn:nbn:de:kobv:11-110-18452/24015-7 https://doi.org/10.3390/rs13142802 https://doi.org/10.18452/23366 |
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 |
http://edoc.hu-berlin.de/18452/24015 urn:nbn:de:kobv:11-110-18452/24015-7 doi:10.3390/rs13142802 http://dx.doi.org/10.18452/23366 2072-4292 |
op_rights |
(CC BY 4.0) Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs1314280210.18452/23366 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
14 |
container_start_page |
2802 |
_version_ |
1784258955058348032 |