Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites
This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult...
Published in: | Earth Surface Processes and Landforms |
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ftsubggeo:oai:e-docs.geo-leo.de:11858/8506 2024-06-09T07:44:26+00:00 Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites Anders, Katharina Marx, Sabrina Boike, Julia Herfort, Benjamin Wilcox, Evan James Langer, Moritz Marsh, Philip Höfle, Bernhard 1 3D Geospatial Data Processing Group (3DGeo) Institute of Geography, Heidelberg University 69120 Heidelberg Germany 3 Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research 14473 Potsdam Germany 5 Cold Regions Research Centre Wilfrid Laurier University Waterloo N2L 3C5 Canada 2020-02-21 https://doi.org/10.1002/esp.4833 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8506 eng eng doi:10.1002/esp.4833 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8506 This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. ddc:551 change analysis 3D geoinformation ground surface displacement permafrost monitoring multitemporal LiDAR doc-type:article 2020 ftsubggeo https://doi.org/10.1002/esp.4833 2024-05-10T04:58:51Z This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert‐driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1‐year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web‐based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area‐based assessment. Using the expert‐driven quantification as reference, we validate the other methods, including in‐situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert‐driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in‐situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark‐based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert ... Article in Journal/Newspaper Arctic Ice permafrost Tundra GEO-LEOe-docs (FID GEO) Arctic Earth Surface Processes and Landforms 45 7 1589 1600 |
institution |
Open Polar |
collection |
GEO-LEOe-docs (FID GEO) |
op_collection_id |
ftsubggeo |
language |
English |
topic |
ddc:551 change analysis 3D geoinformation ground surface displacement permafrost monitoring multitemporal LiDAR |
spellingShingle |
ddc:551 change analysis 3D geoinformation ground surface displacement permafrost monitoring multitemporal LiDAR Anders, Katharina Marx, Sabrina Boike, Julia Herfort, Benjamin Wilcox, Evan James Langer, Moritz Marsh, Philip Höfle, Bernhard 1 3D Geospatial Data Processing Group (3DGeo) Institute of Geography, Heidelberg University 69120 Heidelberg Germany 3 Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research 14473 Potsdam Germany 5 Cold Regions Research Centre Wilfrid Laurier University Waterloo N2L 3C5 Canada Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites |
topic_facet |
ddc:551 change analysis 3D geoinformation ground surface displacement permafrost monitoring multitemporal LiDAR |
description |
This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert‐driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1‐year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web‐based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area‐based assessment. Using the expert‐driven quantification as reference, we validate the other methods, including in‐situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert‐driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in‐situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark‐based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert ... |
format |
Article in Journal/Newspaper |
author |
Anders, Katharina Marx, Sabrina Boike, Julia Herfort, Benjamin Wilcox, Evan James Langer, Moritz Marsh, Philip Höfle, Bernhard 1 3D Geospatial Data Processing Group (3DGeo) Institute of Geography, Heidelberg University 69120 Heidelberg Germany 3 Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research 14473 Potsdam Germany 5 Cold Regions Research Centre Wilfrid Laurier University Waterloo N2L 3C5 Canada |
author_facet |
Anders, Katharina Marx, Sabrina Boike, Julia Herfort, Benjamin Wilcox, Evan James Langer, Moritz Marsh, Philip Höfle, Bernhard 1 3D Geospatial Data Processing Group (3DGeo) Institute of Geography, Heidelberg University 69120 Heidelberg Germany 3 Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research 14473 Potsdam Germany 5 Cold Regions Research Centre Wilfrid Laurier University Waterloo N2L 3C5 Canada |
author_sort |
Anders, Katharina |
title |
Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites |
title_short |
Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites |
title_full |
Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites |
title_fullStr |
Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites |
title_full_unstemmed |
Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites |
title_sort |
multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at arctic permafrost monitoring sites |
publishDate |
2020 |
url |
https://doi.org/10.1002/esp.4833 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8506 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Ice permafrost Tundra |
genre_facet |
Arctic Ice permafrost Tundra |
op_relation |
doi:10.1002/esp.4833 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8506 |
op_rights |
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
op_doi |
https://doi.org/10.1002/esp.4833 |
container_title |
Earth Surface Processes and Landforms |
container_volume |
45 |
container_issue |
7 |
container_start_page |
1589 |
op_container_end_page |
1600 |
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1801373177100632064 |