Monitoring project

This project aims at a thorough and accurate 4D monitoring of the Belvedere Glacier with photogrammetric approaches, exploiting different spatial (from centimetric to metric) and temporal resolution (from daily to 10-year periods) and with different platforms (UAVs, aerial photogrammetry, terrestrial time-lapse cameras).
Since 2015, an extensive and continuous monitoring activity was carried out with UAVs-based photogrammetry and in-situ GNSS measurements (Ioli et al, 2022). Every year, fixed-wing UAVs and quadcopters were used to remotely sense the glacier and build high-resolution photogrammetric models in order to estimate annual variations of ice volume and ice flow velocities.
The monitoring activity carried out on the Belvedere Glacier was designed and conducted jointly by the Department of Civil and Environmental Engineering (DICA) of Politecnico di Milano and the Department of Environment, Land and Infrastructure Engineering (DIIATI) of Politecnico di Torino. The DREAM projects (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring), involving teachers and students from Alta Scuola Politecnica (ASP) of Politecnico di Torino and Milano, contributed to the campaign from 2015 to 2017.
Moreover, to reconstruct the long-term evolution of the glacier, from 1977 up to now, we used historical images acquired for regional mapping purposes. Historic analog images were digitalized and processed with a modern photogrammetric approach to derive the glacier 3D morphology in 1977, 1991 and 2001 (De Gaetani et al., 2021).
Currently, two time-lapse cameras, permanently installed at the NW glacier terminus, are being used to derive daily 3D glacier movements and changes in the ice structures (such as ice falls and calving events).

Low-cost stereo-photogrammetry and Deep Learning for 4D monitoring of an alpine glacier

Time-lapse cameras are frequently used to retrieve information on glacial flows. However, only one camera is often employed, preventing photogrammetric reconstructions. To monitor sub-seasonal movements of the glacier, we designed a low-cost stereoscopic system composed of two time-lapse cameras for monitoring the north-west tongue of the Belvedere Glacier.
The stereoscopic system was realized in collaboration with the students of the Kuoleva Jäätikkö project of the Alta Scuola Politecnica (ASP).
Each monitoring station includes a DSLR camera, an Arduino microcontroller for camera triggering, and a Raspberry Pi Zero with a SIM card for sending images to a remote server. The instrumentation is enclosed in waterproof cases. During summer 2021, the two cameras were installed on each side of the Belvedere Glacier terminal ice cliff and they are currently taking daily images.
Due to the wide baseline (i.e., ∼260 m), traditional feature-based matching techniques (e.g., the ones implemented in software such as Agisoft Metashape and based on SIFT-like descriptors) fail to find enough homologous points for estimating the orientation of the cameras. State of-the-art deep learning matching methods, such a SuperGlue (Sarlin et al., 2020), outperform traditional feature based matching techniques.
Thefore, it was necessary to develop an ad-hoc pipeline for multi-temporal stereo reconstruction with wide camera baselines: ICEpy4D.
ICEpy4D is a multi-purpose Python package for 4D Image-based Continuos monitoring of glaciers’ Evolution with deep learning SfM and low-cost stereo-cameras and it is available at https://github.com/franioli/icepy4d The results of the daily monitoring of the Belvedere Glacier using this low-cost time-lapse camera system are published at https://link.springer.com/article/10.1007/s41064-023-00272-w