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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Multidisciplinary Digital Publishing Institute (MDPI)
2021
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Online Access: | https://epic.awi.de/id/eprint/54434/ https://epic.awi.de/id/eprint/54434/1/Kaiser_et_al_2021_RemSen.pdf https://doi.org/10.3390/rs13142802 https://hdl.handle.net/10013/epic.e48de366-a88d-48b2-ae4c-d33583ebbd13 |
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ftawi:oai:epic.awi.de:54434 2024-03-24T08:55:35+00:00 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 https://epic.awi.de/id/eprint/54434/ https://epic.awi.de/id/eprint/54434/1/Kaiser_et_al_2021_RemSen.pdf https://doi.org/10.3390/rs13142802 https://hdl.handle.net/10013/epic.e48de366-a88d-48b2-ae4c-d33583ebbd13 unknown Multidisciplinary Digital Publishing Institute (MDPI) https://epic.awi.de/id/eprint/54434/1/Kaiser_et_al_2021_RemSen.pdf Kaiser, S. orcid:0000-0001-8179-5084 , Grosse, G. orcid:0000-0001-5895-2141 , Boike, J. orcid:0000-0002-5875-2112 and Langer, M. orcid:0000-0002-2704-3655 (2021) Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery , Remote Sensing, 13 (14), p. 2802 . doi:10.3390/rs13142802 <https://doi.org/10.3390/rs13142802> , hdl:10013/epic.e48de366-a88d-48b2-ae4c-d33583ebbd13 EPIC3Remote Sensing, Multidisciplinary Digital Publishing Institute (MDPI), 13(14), pp. 2802-2802, ISSN: 2072-4292 Article isiRev 2021 ftawi https://doi.org/10.3390/rs13142802 2024-02-27T09:55:26Z 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 myr^-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 Arctic north slope permafrost Alaska Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Arctic Arctic Lake ENVELOPE(-130.826,-130.826,57.231,57.231) Remote Sensing 13 14 2802 |
institution |
Open Polar |
collection |
Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
op_collection_id |
ftawi |
language |
unknown |
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 myr^-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 Grosse, Guido Boike, Julia Langer, Moritz |
spellingShingle |
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 |
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 |
Multidisciplinary Digital Publishing Institute (MDPI) |
publishDate |
2021 |
url |
https://epic.awi.de/id/eprint/54434/ https://epic.awi.de/id/eprint/54434/1/Kaiser_et_al_2021_RemSen.pdf https://doi.org/10.3390/rs13142802 https://hdl.handle.net/10013/epic.e48de366-a88d-48b2-ae4c-d33583ebbd13 |
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 Arctic north slope permafrost Alaska |
genre_facet |
Alaska North Slope Arctic Arctic north slope permafrost Alaska |
op_source |
EPIC3Remote Sensing, Multidisciplinary Digital Publishing Institute (MDPI), 13(14), pp. 2802-2802, ISSN: 2072-4292 |
op_relation |
https://epic.awi.de/id/eprint/54434/1/Kaiser_et_al_2021_RemSen.pdf Kaiser, S. orcid:0000-0001-8179-5084 , Grosse, G. orcid:0000-0001-5895-2141 , Boike, J. orcid:0000-0002-5875-2112 and Langer, M. orcid:0000-0002-2704-3655 (2021) Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery , Remote Sensing, 13 (14), p. 2802 . doi:10.3390/rs13142802 <https://doi.org/10.3390/rs13142802> , hdl:10013/epic.e48de366-a88d-48b2-ae4c-d33583ebbd13 |
op_doi |
https://doi.org/10.3390/rs13142802 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
14 |
container_start_page |
2802 |
_version_ |
1794405212544303104 |