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Abstract Catastrophic landslides are often preceded by slow, progressive, accelerating deformation that differs from the persistent motion of slow‐moving landslides. Here, we investigate the motion of a landslide that damaged 12 homes in Rolling Hills Estates (RHE), Los Angeles, California on 8 July 2023, using satellite‐based synthetic aperture radar interferometry (InSAR) and pixel tracking of satellite‐based optical images. To better understand the precursory motion of the RHE landslide, we compared its behavior with local precipitation and with several slow‐moving landslides nearby. Unlike the slow‐moving landslides, we found that RHE was a first‐time progressive failure that failed after one of the wettest years on record. We then applied a progressive failure model to interpret the failure mechanisms and further predict the failure time from the pre‐failure movement of RHE. Our work highlights the importance of monitoring incipient slow motion of landslides, particularly where no discernible historical displacement has been observed.
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Multi-sensor remote sensing captures geometry and slow-to-fast sliding transition of the 2017 Mud Creek landslide
Abstract Landslides pose a significant hazard worldwide. Despite advances in landslide monitoring, predicting their size, timing, and location remains a major challenge. We revisit the 2017 Mud Creek landslide in California using radar interferometry, pixel tracking, and elevation change measurements from satellite and airborne radar, lidar, and optical data. Our analysis shows that pixel tracking of optical imagery captured the transition from slow motion to runaway acceleration starting ~ 1 month before catastrophic failure—an acceleration undetected by satellite InSAR alone. Strain rate maps revealed a new slip surface formed within the landslide body during acceleration, likely a key weakening mechanism. Failure forecast analysis indicates the acceleration followed a hyperbolic trend, suggesting failure time could have been predicted at least 6 days in advance. We also inverted for the landslide thickness during the slow-moving phase and found variations from < 1 to 36 m. While thickness inversions provide important first-order information on landslide size, more work is needed to better understand how landslide subsurface properties and deforming volumes may evolve during the transition from slow-to-fast motion. Our findings underscore the need for integrated remote sensing techniques to improve landslide monitoring and forecasting. Future advancements in operational monitoring systems and big data analysis will be critical for tracking slope instability and improving regional-scale failure predictions.
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- Award ID(s):
- 2026099
- PAR ID:
- 10660063
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41598-025-11399-8
