A Workflow for Monitoring Ground Deformations through Spaceborne Optical Offset Tracking

Landslides are among the most destructive geohazards and commonly interact with other hazards, amplifying impacts and leading to cascading and compounding events (Shugar et al., 2021). For example, earthquakes can trigger widespread landsliding, and landslides can induce floods by failing into lakes (e.g. GLOFs) or damming rivers. Identifying the location of potential landslides pre-failure can help understand and mitigate these multihazard events. One possible approach is tracking the precursory failure signals, such as subtle ground displacement, that many landslides exhibit pre-collapse. Enhancing the identification of unstable areas and monitoring their displacement over space and time is therefore critical to understanding their role in multi-hazard chains and mitigating their impacts.

Spatially resolved ground motion monitoring over large areas is only possible with remote sensing techniques, with radar interferometry (InSAR) being the most widely used method. While InSAR is sensitive to small deformations (millimetres to centimetres), it struggles to capture rapid ground motions and is less reliable in regions with dense vegetation. Offset tracking techniques offer an alternative for monitoring faster ground velocities and remain applicable in heavily vegetated areas and for NS-oriented displacements.

In this abstract, we introduce an open-source, cloud-based, end-to-end optical offset tracking tool for ground motion monitoring. Building on previous implementations (Provost et al., 2022; Van Wyk de Vries et al., 2024), the tool leverages Google Earth Engine and Sentinel-2 imagery, allowing users to interactively define the area of interest, automatically download and pre-process satellite data, and compute displacements using different offset tracking techniques. Outputs include velocity maps and time series, with customizable filters to refine results for different use cases and scales. The tool can operate entirely in the Google Colaboratory cloud environment. Hence, it removes the need for local computational resources, avoids software conflicts, and is accessible even to those with limited experience in Python programming. We validated the tool on cases with independent displacement measurements, including the Slumgullion landslide, showing that its results are consistent with existing estimates.

Owing to its ease of use and versatility, the tool is a valuable resource for the multihazard and landslide research communities, complementing InSAR for monitoring surface motion in space and time. The tool can estimate motion in near real time, making it an asset for early warning systems that rely on velocity thresholds or predictive modelling of future motion. Furthermore, its ability to identify unstable slopes can guide targeted, detailed investigations into landslide dynamics, enhancing situational awareness and supporting proactive risk mitigation.

References:

Shugar, D. H., et al. (2021). A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science, 373(6552), 300-306.

Van Wyk de Vries, M., et al. (2024). Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery. Earth Surface Processes and Landforms, 49(4), 1397-1410.

Provost, F., et al. (2022). Terrain deformation measurements from optical satellite imagery: The MPIC-OPT processing services for geohazards monitoring. Remote Sensing of Environment, 274, 112949.