Coupling terrestrial laser scanning and UAV photogrammetry with geoelectrical data for better time-lapse hydrological characterisation of an active landslide

Landslides are complex geological hazards that affect all globally settled areas; hence the necessity to understand this hazard for purposes of studying failure mechanisms and managing risk levels. Numerous methods have been explored for characterising the geomorphology and geology of active landslides. In this study we characterise and monitor a well understood field site, Hollin Hill (situated in Lias Group rocks in the southern Howardian Hills, UK), using geomatics (UAV and LiDAR surveys), near-surface geophysics and petrophysical relationships. Time-lapse electrical resistivity tomography (ERT) is an effective tool for monitoring hydrological processes, given that the Hollin Hill landslide is moisture-induced, the field site is instrumented with a permanent (shallow buried) 3D ERT monitoring array. However, monitoring active landslides poses specific challenges regarding time-lapse geophysical methods as the surface topography is distorted with slope movements, which in this case are expressed as centimetre to metre scale lateral and vertical movements that complicate time-lapse resistivity processing. To compensate for the changing slope topography, we incorporate terrestrial LiDAR and aerial photogrammetry surveys to capture the changing slope surface through time. Additionally, lateral movements are periodically recorded with RTK corrected GNSS surveys. For each geophysical survey the topography and positions of the electrodes are interpolated using thin plate splines, and a modelling mesh with unique surface topography is created for each time step in the time-lapse ERT scheme (which uses a baseline constraint). Hence, we develop a time-lapse geophysical model spanning approximately 8 years, which captures both changes in the electrical properties of Hollin Hill and the slope’s geomorphology.

To further understand the hydrological state of the landslide, we observe a direct relationship between electrical conductivity (the inverse of resistivity), gravimetric moisture content and soil suction for the relevant lithologies present at Hollin Hill. The resistivity models are partitioned into different lithologies using k-means clustering, and subsequently resistivity is converted to matric suction via a petrophysical relationship. Areas of consistently low resistivity, and by extension high moisture content and low suction, correspond to areas on the landslide which exhibit the most movement. Furthermore, the movements of electrodes are used to estimate the depth of the landslide surface via the balanced cross section method (after Bishop). Low soil suctions occur at the location of the likely slip surface, thus offering insights into the failure mechanisms occurring at the Hollin Hill landslide. This suggests that a combination of the techniques demonstrated in this study could be used to assess active landslide dynamics and hence improve our capacity to forecast movements on unstable slopes.