LADI: Landslide displacement interpolation through a spatial-temporal Kalman filter

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Monitoring landslide displacement is critical towards understanding kinematics and evaluating risk. Both remote sensing technology and in-situ sensors are currently used for monitoring purposes, each with their own spatio-temporal advantages. However, the majority of current workflows analyze these data independently, missing a synergistic opportunity to combine complementary datasets to enhance analyses both spatially and temporally. This work presents the first landslide specific displacement interpolation technique using a novel approach, LADI (Landslide Displacement Interpolation), which creates a high-spatial, high-temporal resolution interpolation of landslide surface displacement by combining remotely-sensed observation data with and in-situ sensor data. At a minimum, LADI requires only a single in-situ monitoring station (control point) and a single high-resolution displacement dataset (calibration data) to function. LADI implements a series of Kalman filters, where interpolation is first performed spatially, then optionally in the temporal domain. LADI is demonstrated on both simulated and real-world landslide data to showcase the behavior, effectiveness, and applicability of the method. Results are compared to common spatial interpolation methods, which demonstrate that LADI achieves superior performance (average EPE = 0.018 m and 0.031 m of LADI and the best performing common spatial interpolation method respectively when number of interpolation control points = 4) in the real-world test sparse displacement dataset when the number of spatial control points is less than sixteen. Additionally, the performance gain of utilizing LADI is further enhanced over common interpolation methods when the interpolation control points are not equally distributed geographically (average EPE = 0.016 m and 0.049 m respectively) as is usually the case with in-situ landslide displacement monitoring stations.