Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts
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, su...
| Main Authors: | , , |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://doi.org/10.5194/nhess-2022-201 https://nhess.copernicus.org/preprints/nhess-2022-201/ |
| Summary: | 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. |
|---|