Abstract
Rockfalls pose significant challenges and hazards to railways and recognizing and monitoring landslide-prone areas is crucial for effective disaster mitigation. Small Baseline and Subset (SBAS), is an advanced subset of the Interferometric Synthetic Aperture Radar (InSAR) technique, that can measure small displacements and effectively detect landslides. However, relying solely on a single-point deformation reading increases result uncertainty and the potential for incorrect judgments in landslide recognition. This paper introduces two novel approaches to address this concern: SBAS threshold stacking and SBAS timeline analysis. SBAS threshold stacking involves stacking multiple SBAS analyses that exceed a displacement threshold, thereby identifying areas with substantial activity. SBAS timeline analysis, which monitors regions using a set number of satellite images, this approach provides effective tools for rockfall recognition.
These approaches were tested in a rockfall event in Sandstone, WV, and validated in a rockfall event in Maupin, OR. In both cases, the SBAS threshold stacking technique identified two distinct clusters highlighting areas with significant activity leading to the rockfall. The SBAS timeline analysis showed a sudden increase in displacement activity before the rockfall event. Results from both sites showed consistent results demonstrating the potential of satellite-based monitoring for rockfall early warning systems.
