Depth, area, volume, and kinematics of slow-moving landslides from airborne synthetic aperture radar and mass conservation

Slow-moving, deep-seated landslides travel downslope at rates of only a few meters per year and can remain active for decades and possibly centuries. As a result, they transmit large quantities of sediment to the channel network and are a major natural hazard that impact transport corridors and infrastructure. However, because slow-moving landslides rarely fail catastrophically, it is challenging, and often infeasible to directly measure their thickness and volume, two key parameters required to quantify sediment flux and to model landslide motion. Here we use remote sensing data from the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to measure the 3-D surface velocity and geometry of over 90 slow-moving landslides in the California Coast Ranges. We then use mass conservation techniques to infer the thickness and volume of each landslide. These landslides have volumes that span between 10⁴ and 10⁷ m³, thicknesses between 3 and 90 m, and move at average annual rates < 5 m/yr. We also examined landslide depth-area and volume-area geometric scaling relations and compared our findings to a worldwide inventory of soil and bedrock landslides compiled by Larsen et al. (2010). We find that the landslide thickness, area, and volume are larger than soil landslides and smaller than bedrock landslides globally. Lastly, we estimate the subsurface geometry of the catastrophic Mud Creek landslide, central California Coast Ranges, during a period of slow motion that lasted at least 8 years before its ultimate failure. We find a volume of ~2.0 x 10⁶ m³, which is close to the post-catastrophic failure volume measured using Structure From Motion (~2.1 x 10⁶ m³) by Warrick et al. (2019). Therefore, in certain cases, it is possible to constrain landslide thickness and volume prior to catastrophic collapse. Our work shows how state-of-the-art remote sensing techniques can be used to better understand landslide processes and quantify their contribution to landscape evolution.