Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts
<p>Glacier calving fronts are highly dynamic environments that are becoming ubiquitous as glaciers recede and, in many cases, develop proglacial lakes. Monitoring of calving fronts is necessary to fully quantify the glacier ablation budget and to warn nearby communities of the threat of hazard...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/23/329/2023/nhess-23-329-2023.pdf |
| _version_ | 1811176987543207936 |
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| author | L. S. Taylor D. J. Quincey M. W. Smith |
| author_facet | L. S. Taylor D. J. Quincey M. W. Smith |
| author_sort | L. S. Taylor |
| collection | DOAJ |
| description | <p>Glacier calving fronts are highly dynamic environments that are becoming
ubiquitous as glaciers recede and, in many cases, develop proglacial lakes.
Monitoring of calving fronts is necessary to fully quantify the glacier
ablation budget and to warn nearby communities of the threat of hazards,
such as glacial lake outburst floods (GLOFs), tsunami waves, and iceberg
collapses. Time-lapse camera arrays, with structure-from-motion
photogrammetry, can produce regular 3D models of glaciers to monitor changes
in the ice but are seldom incorporated into monitoring systems owing to the
high cost of equipment. In this proof-of-concept study at Fjallsjökull,
Iceland, we present and test a low-cost, highly adaptable camera system
based on Raspberry Pi computers and compare the resulting point cloud data
to a reference cloud generated using an unoccupied aerial vehicle (UAV). The
mean absolute difference between the Raspberry Pi and UAV point clouds is
found to be 0.301 m with a standard deviation of 0.738 m. We find that
high-resolution point clouds can be robustly generated from cameras
positioned up to 1.5 km from the glacier (mean absolute difference 0.341 m,
standard deviation 0.742 m). Combined, these experiments suggest that for
monitoring calving events in glaciers, Raspberry Pi cameras are an
affordable, flexible, and practical option for future scientific research.
Owing to the connectivity capabilities of Raspberry Pi computers, this opens
the possibility for real-time structure-from-motion reconstructions of
glacier calving fronts for deployment as an early warning system to
calving-triggered GLOFs.</p> |
| first_indexed | 2024-04-10T20:00:57Z |
| format | Article |
| id | doaj.art-f18c1906f35045a9ab8cdd9320e1770a |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-04-10T20:00:57Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-f18c1906f35045a9ab8cdd9320e1770a2023-01-27T06:32:07ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812023-01-012332934110.5194/nhess-23-329-2023Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving frontsL. S. TaylorD. J. QuinceyM. W. Smith<p>Glacier calving fronts are highly dynamic environments that are becoming ubiquitous as glaciers recede and, in many cases, develop proglacial lakes. Monitoring of calving fronts is necessary to fully quantify the glacier ablation budget and to warn nearby communities of the threat of hazards, such as glacial lake outburst floods (GLOFs), tsunami waves, and iceberg collapses. Time-lapse camera arrays, with structure-from-motion photogrammetry, can produce regular 3D models of glaciers to monitor changes in the ice but are seldom incorporated into monitoring systems owing to the high cost of equipment. In this proof-of-concept study at Fjallsjökull, Iceland, we present and test a low-cost, highly adaptable camera system based on Raspberry Pi computers and compare the resulting point cloud data to a reference cloud generated using an unoccupied aerial vehicle (UAV). The mean absolute difference between the Raspberry Pi and UAV point clouds is found to be 0.301 m with a standard deviation of 0.738 m. We find that high-resolution point clouds can be robustly generated from cameras positioned up to 1.5 km from the glacier (mean absolute difference 0.341 m, standard deviation 0.742 m). Combined, these experiments suggest that for monitoring calving events in glaciers, Raspberry Pi cameras are an affordable, flexible, and practical option for future scientific research. Owing to the connectivity capabilities of Raspberry Pi computers, this opens the possibility for real-time structure-from-motion reconstructions of glacier calving fronts for deployment as an early warning system to calving-triggered GLOFs.</p>https://nhess.copernicus.org/articles/23/329/2023/nhess-23-329-2023.pdf |
| spellingShingle | L. S. Taylor D. J. Quincey M. W. Smith Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts Natural Hazards and Earth System Sciences |
| title | Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts |
| title_full | Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts |
| title_fullStr | Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts |
| title_full_unstemmed | Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts |
| title_short | Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts |
| title_sort | evaluation of low cost raspberry pi sensors for structure from motion reconstructions of glacier calving fronts |
| url | https://nhess.copernicus.org/articles/23/329/2023/nhess-23-329-2023.pdf |
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