Preprints
https://doi.org/10.5194/nhess-2022-201
https://doi.org/10.5194/nhess-2022-201
 
09 Aug 2022
09 Aug 2022
Status: this preprint is currently under review for the journal NHESS.

Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts

Liam S. Taylor, Duncan J. Quincey, and Mark W. Smith Liam S. Taylor et al.
  • School of Geography and water@leeds, University of Leeds, Leeds, UK

Abstract. 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 downstream communities of the threat of hazards, such as glacial lake outburst floods (GLOFs). Timelapse 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 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 represent 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 photogrammetry of glacier calving fronts for deployment as an early warning system to calving-triggered GLOFs.

Liam S. Taylor et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-201', Karen Anderson, 08 Sep 2022 reply

Liam S. Taylor et al.

Data sets

Dataset for: Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts Liam Taylor, Duncan Quincey, Mark Smith https://doi.org/10.5281/zenodo.6786740

Liam S. Taylor et al.

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Short summary
Hazards from glaciers are becoming more likely as the climate warms, which poses a threat to communities living beneath them. We have developed a new camera system which can capture regular, high-quality 3D models to monitor small changes in glaciers which could be indicative of a future hazard. This system is far cheaper than more typical camera sensors, yet produces very similar quality data. We suggest that deploying these cameras near glaciers could assist in warning communities of hazards.
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