The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure
Ground subsidence and erosion processes caused by permafrost thaw pose a high risk to infrastructure in the Arctic. Climate warming is increasingly accelerating the thawing of permafrost, emphasizing the need for thorough monitoring to detect damages and hazards at an early stage. The use of unoccup...
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MDPI AG
2022
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Online Access: | https://doi.org/10.3390/rs14236107 https://doaj.org/article/871390d7076b4ce2916462b91c38b6cc |
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ftdoajarticles:oai:doaj.org/article:871390d7076b4ce2916462b91c38b6cc 2023-05-15T15:02:03+02:00 The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure Soraya Kaiser Julia Boike Guido Grosse Moritz Langer 2022-12-01T00:00:00Z https://doi.org/10.3390/rs14236107 https://doaj.org/article/871390d7076b4ce2916462b91c38b6cc EN eng MDPI AG https://www.mdpi.com/2072-4292/14/23/6107 https://doaj.org/toc/2072-4292 doi:10.3390/rs14236107 2072-4292 https://doaj.org/article/871390d7076b4ce2916462b91c38b6cc Remote Sensing, Vol 14, Iss 6107, p 6107 (2022) permafrost degradation consumer-grade unoccupied aerial vehicle North Slope Alaska land surface displacement point cloud alignment structure from motion Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14236107 2022-12-30T20:59:30Z Ground subsidence and erosion processes caused by permafrost thaw pose a high risk to infrastructure in the Arctic. Climate warming is increasingly accelerating the thawing of permafrost, emphasizing the need for thorough monitoring to detect damages and hazards at an early stage. The use of unoccupied aerial vehicles (UAVs) allows a fast and uncomplicated analysis of sub-meter changes across larger areas compared to manual surveys in the field. In our study, we investigated the potential of photogrammetry products derived from imagery acquired with off-the-shelf UAVs in order to provide a low-cost assessment of the risks of permafrost degradation along critical infrastructure. We tested a minimal drone setup without ground control points to derive high-resolution 3D point clouds via structure from motion (SfM) at a site affected by thermal erosion along the Dalton Highway on the North Slope of Alaska. For the sub-meter change analysis, we used a multiscale point cloud comparison which we improved by applying (i) denoising filters and (ii) alignment procedures to correct for horizontal and vertical offsets. Our results show a successful reduction in outliers and a thorough correction of the horizontal and vertical point cloud offset by a factor of 6 and 10, respectively. In a defined point cloud subset of an erosion feature, we derive a median land surface displacement of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>0.35</mn></mrow></semantics></math> m from 2018 to 2019. Projecting the development of the erosion feature, we observe an expansion to NNE, following the ice-wedge polygon network. With a land surface displacement of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>0.35</mn></mrow></semantics></math> m and an alignment root mean square error of <math ... Article in Journal/Newspaper Arctic Ice north slope permafrost wedge* Alaska Directory of Open Access Journals: DOAJ Articles Arctic Vertical Point ENVELOPE(-131.621,-131.621,52.900,52.900) Remote Sensing 14 23 6107 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
permafrost degradation consumer-grade unoccupied aerial vehicle North Slope Alaska land surface displacement point cloud alignment structure from motion Science Q |
spellingShingle |
permafrost degradation consumer-grade unoccupied aerial vehicle North Slope Alaska land surface displacement point cloud alignment structure from motion Science Q Soraya Kaiser Julia Boike Guido Grosse Moritz Langer The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure |
topic_facet |
permafrost degradation consumer-grade unoccupied aerial vehicle North Slope Alaska land surface displacement point cloud alignment structure from motion Science Q |
description |
Ground subsidence and erosion processes caused by permafrost thaw pose a high risk to infrastructure in the Arctic. Climate warming is increasingly accelerating the thawing of permafrost, emphasizing the need for thorough monitoring to detect damages and hazards at an early stage. The use of unoccupied aerial vehicles (UAVs) allows a fast and uncomplicated analysis of sub-meter changes across larger areas compared to manual surveys in the field. In our study, we investigated the potential of photogrammetry products derived from imagery acquired with off-the-shelf UAVs in order to provide a low-cost assessment of the risks of permafrost degradation along critical infrastructure. We tested a minimal drone setup without ground control points to derive high-resolution 3D point clouds via structure from motion (SfM) at a site affected by thermal erosion along the Dalton Highway on the North Slope of Alaska. For the sub-meter change analysis, we used a multiscale point cloud comparison which we improved by applying (i) denoising filters and (ii) alignment procedures to correct for horizontal and vertical offsets. Our results show a successful reduction in outliers and a thorough correction of the horizontal and vertical point cloud offset by a factor of 6 and 10, respectively. In a defined point cloud subset of an erosion feature, we derive a median land surface displacement of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>0.35</mn></mrow></semantics></math> m from 2018 to 2019. Projecting the development of the erosion feature, we observe an expansion to NNE, following the ice-wedge polygon network. With a land surface displacement of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>−</mo><mn>0.35</mn></mrow></semantics></math> m and an alignment root mean square error of <math ... |
format |
Article in Journal/Newspaper |
author |
Soraya Kaiser Julia Boike Guido Grosse Moritz Langer |
author_facet |
Soraya Kaiser Julia Boike Guido Grosse Moritz Langer |
author_sort |
Soraya Kaiser |
title |
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure |
title_short |
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure |
title_full |
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure |
title_fullStr |
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure |
title_full_unstemmed |
The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure |
title_sort |
potential of uav imagery for the detection of rapid permafrost degradation: assessing the impacts on critical arctic infrastructure |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14236107 https://doaj.org/article/871390d7076b4ce2916462b91c38b6cc |
long_lat |
ENVELOPE(-131.621,-131.621,52.900,52.900) |
geographic |
Arctic Vertical Point |
geographic_facet |
Arctic Vertical Point |
genre |
Arctic Ice north slope permafrost wedge* Alaska |
genre_facet |
Arctic Ice north slope permafrost wedge* Alaska |
op_source |
Remote Sensing, Vol 14, Iss 6107, p 6107 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/23/6107 https://doaj.org/toc/2072-4292 doi:10.3390/rs14236107 2072-4292 https://doaj.org/article/871390d7076b4ce2916462b91c38b6cc |
op_doi |
https://doi.org/10.3390/rs14236107 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
23 |
container_start_page |
6107 |
_version_ |
1766334041076793344 |