InSAR monitoring of creeping landslides in mountainous regions: A case study in Eldorado National Forest, California

Abstract Monitoring surface deformation associated with geohazards is a prerequisite for geological disaster prevention. Interferometric synthetic aperture radar (InSAR) has the ability to capture ground deformation of landslides with high precision over a large area. However, in mountainous regions this capability is often limited by decorrelation noise and atmospheric phase artifacts. Over Eldorado National Forest, California, where many landslides need to be monitored and investigated, InSAR images are severely affected by atmospheric noise and the coherence is highly variable throughout the year, challenging InSAR techniques to effectively detect movement of active landslides. In order to obtain reliable measurements, we have designed an interferogram selection method and an InSAR segment processing (SP) technique to improve the deformation measurement. Compared with the traditional non-segment processing (NSP), the SP technique has demonstrated advantages in reducing the impact of atmospheric noise. Our results from both the ascending and descending InSAR datasets based on SP indicate that many landslides along the Highway 50 corridor were creeping at a rate of less than 10 cm/year during the investigation period. We have found that landslide movements in the study region present obvious seasonal patterns. The precipitation and pore-water measurements and our hydrogeological diffusion models suggest that the seasonal movements of these landslides are primarily driven by the pore-water pressures, and the peak deformation of the landslides may occur in the dry season (May to October) due to the time lag of precipitation infiltration. In addition, we have observed subtle upward movement of the landslides after the precipitation begins, which is likely caused by the swelling of clay-rich landslide body due to an increase in the pore pressure. Furthermore, several other localized unstable regions which may contain potential landslide hazards were also detected and mapped in the study area, and their dynamics need further investigation. We conclude that InSAR is capable of detecting slow landslide motions over difficult terrains if associated artifacts in the interferograms are suppressed. InSAR time-series measurements along with hydrogeological models enable us to characterize the time delay between peaks of landslide motions and precipitation.