A Framework for Studying Hydrology-Driven Landslide Hazards in Northwestern US Using Satellite InSAR, Precipitation and Soil Moisture Observations: Early Results and Future Directions

Abstract

The mountainous Pacific Northwest is prone to heavy winter rainfall, resulting in hundreds of landslides per year, human casualties, and billions of dollars of property damage. Precipitation is a major hydrologic trigger for landslides in the northwestern US and around the world. This paper reviews existing literature to outline a framework to study the linkage between precipitation and landslide hazards over the northwestern US using satellite remote sensing techniques including interferometric synthetic aperture radar (InSAR) methods, Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products, and Soil Moisture Active Passive (SMAP) satellite soil moisture data, along with correlation analysis and numerical modeling. InSAR time-series displacements provide an indication of landslide occurrence and extent, and help characterize the basal slip surface and slide-body volume based on the law of mass conservation. Precipitation and soil moisture sensed from the space and ground contribute to creating hydrogeological models associated with water infiltration. These crucial parameters are tracked through correlation and slope stability analysis to understand landslide dynamics. We highlight the results on mapping landslides over the state of Washington and analyses at a few select sites over southern Washington and southwestern Oregon. We conclude that satellite observations of landslide motions and the attributing hydrological variables from both radar and optical images improve our understanding of the inter-relationships between the hydrologic processes along with topographic and geologic settings, and the landslide kinematics and mechanisms inferred from time-series measurements and landslide modeling on a regional scale.

Kinematic Evolution of a Large Paraglacial Landslide in the Barry Arm Fjord of Alaska

“…Thus, lidar can provide more accurate values of cumulative displacement in this environment, which allows for average velocity calculations. Additionally, lidar provides accurate downslope (i.e., 3D) displacement values, whereas LOS displacement values provided by InSAR cannot be deconvolved to understand the horizontal and vertical components of motion without considering images in the opposite viewing mode (e.g., ascending vs. descending; Lu & Kim, 2021). Thus, a direct comparison in displacement magnitude cannot be made between these two techniques, nor can we compare pixel to pixel between methods due to both coherence loss in InSAR images (e.g., the Kite, Figure 7) and limited features that can be used for feature tracking in lidar (Figure 6).…”

Kinematics of Irrigation‐Induced Landslides in a Washington Desert: Impacts of Basal Geometry

“…Ground vegetation and surface modification (e.g., erosion, deposition, and soil moisture change) are the primary factors that cause decorrelation of SAR images, consequently decreasing the accuracy of InSAR measurements for monitoring landslide displacements in a regional scale (e.g., Lu & Kim, 2021; Xu, George, et al., 2021; Xu, Lu, et al., 2021). Such impacts are common in global climate regions abundant in precipitation, such as the Tropical Wet, Tropical Wet and Dry, Mediterranean, Marine West Coast, Humid Subtropical, and Humid Continental climates (Peel et al., 2007).…”

Satellite Interferometry Landslide Detection and Preliminary Tsunamigenic Plausibility Assessment in Prince William Sound, Southcentral Alaska

“…This method can correct baseline-induced artifacts, digital elevation model (DEM) errors, and atmospheric disturbances through spatiotemporal regression and filtering. This permits the creation of time series of subtle (on the order of 10s of millimeters) ground deformation (Ferretti and others, 2001;Hooper and others, 2004;Hooper, 2008;Lu and Kim, 2021) and is effective in identifying landslides in paraglacial regions (Kim and others, 2022).…”

“…Landslide velocity and changes in velocity also serve as critical inputs in landslide hazard assessments (for example, Hermanns and others, 2013). On a regional scale, satellite remote sensing that identifies deformation (for example, methods using synthetic aperture radar [SAR] data; Lu and Kim 2021;Kim and others, 2022) or detects change associated with movement (for example, methods using multispectral data; Nichol and Wong, 2005;Ramos-Bernal and others, 2018) can provide the spatial and temporal resolution required to better understand landslide initiation, kinematics, and triggering conditions.…”