4D Multi-View Stereo Reconstruction for High-Resolution Calving Monitoring of Glacier Perito Moreno: A Basis for Dynamic Analysis and Prediction

In this study, we present a high temporal resolution (30-minute) multi-view stereo (MVS) photogrammetric dataset capturing the front of the lake-terminating Perito Moreno Glacier during daylight over more than one year. We aim to provide novel data to investigate glacier dynamics, which are strongly affected by climate change and pose increasing risks to ecosystems and human infrastructure. The images for the photogrammetric reconstruction are automatically inspected and prepared to create high-precision 4D (3D + time) point clouds, which are then compared per epoch to detect surface changes along an approx. 300 m wide study reach.

The MVS system comprises eight DSLR cameras and uses 4G connectivity for daily data transfer, aiming for near-real-time monitoring. This has resulted in more than 75,000 images stored on a central server, which allows the calving analysis based on more than 5,000 models. To isolate image regions relevant for the generation of dense clouds with maximum precision across as many epochs as possible, the images are segmented using the artificial intelligence (AI)-based image segmentation model SAM2. The relevant regions are assessed using blur metrics, which identify low contrast caused by harsh glacier conditions, such as moisture or water droplets, thereby reducing the risk of including images of low quality that interfere with image correlation success.

The results of our image pre-processing demonstrate that lighting conditions have the greatest impact on image segmentation performance. In contrast, the final model quality of the 4D point cloud reconstructions, which are based on a multi-epoch multi-imagery (MEMI) strategy, is mostly affected by the presence of adjacent dynamically changing regions, such as floating ice on the lake, highlighting the need for masking these regions. Applying masking further seems to improve the robustness of detecting subtle glacier surface changes, which is essential for pre-failure deformation analysis, providing valuable input for future calving prediction efforts.

The point cloud sequences are analyzed using the Multiscale Model to Model Cloud Comparison (M3C2) algorithm to quantify surface changes. Although calibration parameters of focal length and principal point exhibit temporal variability, a constant calibration strategy is examined to ensure consistent alignment across all epochs, which furthermore enables the observation of glacier flow velocities in both horizontal and vertical directions. Initial results for a test period during a week in summer indicate that a constant calibration does not adversely affect model generation, suggesting that calibration stability may be sufficient under favorable summer conditions, while ongoing analyses will assess the robustness during winter months. Current efforts focus on refining dense cloud quality by separating glacier surfaces from points reconstructed on stable rock areas, which were intentionally retained during point cloud generation to provide stable reference regions for the MEMI workflow.

Our introduced workflow enables the creation of a reliable calving inventory, exceeding the spatio-temporal resolution of conventional glacier monitoring techniques. In a next step, we aim to combine the created calving inventory with associated dynamic parameters, such as glacier velocity, and with environmental variables to support the development of AI-based calving prediction models.