Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts

Abstract

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 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.

Icepy4d: A Python Toolkit for Advanced Multi-Epoch Glacier Monitoring With Deep-Learning Photogrammetry

“…The software was successfully tested on the Belvedere Glacier, allowing for the derivation of daily volume variations at the glacier snout and glacier retreat. Although the glacier's debris cover and dirty ice terminal cliff provided favorable conditions for 3D reconstruction, due to the presence of distinct patterns in the images, we believe the method is applicable to other glaciers with different characteristics Previous studies have shown successful 3D reconstructions of debris-free glaciers using UAV and ground based SfM with traditional feature matching techniques (Gindraux et al, 2017, Belloni et al, 2023, Taylor et al, 2023 It is worth noting that these examples utilized traditional feature matching techniques. With state-of-the-art DL sparse and dense matching techniques, results can be further improved.…”

Proglacial lake evolution coincident with glacier dynamics in the frontal zone of Kvíárjökull, South‐East Iceland

“…Apart from airborne images, time-lapse photography has appeared as a powerful tool for analysing glacier-related processes in high temporal resolution (e.g. How et al, 2019;Mallalieu et al, 2017;Taylor et al, 2023).…”

“…Apart from airborne images, time‐lapse photography has appeared as a powerful tool for analysing glacier‐related processes in high temporal resolution (e.g. How et al, 2019; Mallalieu et al, 2017; Taylor et al, 2023). Oblique aerial or even terrestrial historic photographs might also be used for the reconstruction of early 20th‐century glacier characteristics where more appropriate image material is lacking (Bjork et al, 2012; Girod et al, 2018; Holmlund & Holmlund, 2019; Kavan, 2020).…”

A Cost-Effective Image-Based System for 3d Geomorphic Monitoring: An Application to Rockfalls

“…In contrast to those advancements, there has also been the emergence of very low-cost cameras like wild cameras or single board camera systems that are capable of capturing images and videos efficiently, albeit at a lower quality. For instance, very low-cost cameras such the Raspberry Pi Camera V2 (25€) from the Raspberry Pi foundation have become increasingly popular for deployment in remote study areas because of their simplicity of use, cost efficiency and low power consumption (Pagnutti et al, 2017;Eltner et al, 2018;Taylor et al, 2023). Such very low-cost require only basic programming skills to capture data, as they typically do not need to be integrated with other complex systems.…”

“…The application of 3D monitoring has been recently applied across different environmental contexts, further emphasizing its potential as a cost-effective and versatile solution. Some examples include: a) the monitoring of rockfalls (Blanch et al, 2020); b) the experimental system to study the stability in open-pit highwalls (Santise et al, 2017), c) the 3D modelling capabilities from a low-cost system mounted on a UAV (Piras et al, 2017) and d) the recent work by Taylor et al (2023) on 3D monitoring of glacier calving using Raspberry Pi cameras.…”