Monitoring landslide displacement is critical towards understanding kinematics and evaluating risk. Landslide displacement varies across both space and time and can therefore be regarded as a spatio-temporal continuous problem. Spatially, landslide movement varies as a result of multiple factors, including but not limited to: geology, rheology, groundwater and surface morphology. Over time, magnitude and patterns of landslide displacement can vary due to factors such as precipitation, seismic activity, temperature, surface erosion, etc. Due to the complex nature of these processes, interpolating landslide movement, either spatially or temporally is an ill-posed problem since there is no unique model that can be used to extrapolate existing measurements.
By Andrew Senogles et al.
Both remote sensing technology and in-situ sensors are currently used for monitoring purposes, each with their own spatiotemporal 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.
LADI utilizes a Kalman filter inspired approach for both the spatial and temporal components of interpolation with the assumption that the spatial pattern of landslide displacement remains relatively constant over short time intervals between sequential spatial surveys (Figure 1). This assumption is applicable to slow-moving, non-evacuative, deep-seated, landslides.
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. LADI is demonstrated to accurately interpolate landslide displacement over a 73-day period using a single in-situ RTK-GNSS station, a control point with an RMS of 0.01 m on a second RTK-GNSS station used as a checkpoint (Figure 2).
An implementation of LADI in the Python programming language is available here
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