Four‐dimensional imaging of moisture dynamics during landslide reactivation

Abstract

Landslides pose significant risks to communities and infrastructure, and mitigating these risks relies on understanding landslide causes and triggering processes. It has been shown that geophysical surveys can significantly contribute to the characterization of unstable slopes. However, hydrological processes can be temporally and spatially heterogeneous, requiring their related properties to be monitored over time. Geoelectrical monitoring can provide temporal and volumetric distributions of electrical resistivity, which are directly related to moisture content. To date, studies demonstrating this capability have been restricted to 2‐D sections, which are insufficient to capture the full degree of spatial heterogeneity. This study is the first to employ 4‐D (i.e., 3‐D time lapse) resistivity imaging on an active landslide, providing long‐term data (3 years) highlighting the evolution of moisture content prior to landslide reactivation and showing its decline post reactivation. Crucially, the time‐lapse inversion methodology employed here incorporates movements of the electrodes on the unstable surface. Although seasonal characteristics dominate the shallow moisture dynamics during the first 2 years with surficial drying in summer and wetting in winter, in the months preceding reactivation, moisture content increased by more than 45% throughout the slope. This is in agreement with independent data showing a significant rise in piezometric heads and shallow soil moisture contents as a result of prolonged and intense rainfall. Based on these results, remediation measures could be designed and early‐warning systems implemented. Thus, resistivity monitoring that can allow for moving electrodes provides a new means for the effective mitigation of landslide risk.

Brief communication: Landslide motion from cross correlation of UAV-derived morphological attributes

“…4a and 4b, as automatically determined after applying the NCC function implemented in CIAS. Spurious vectors at the edges of the back scarp, which were manually removed, were possibly generated due to rotational failures investigated by BGS (Uhlemann et al, 2017).…”

“…This demonstrates that image-cross-correlation performance is strongly related to the landslide movement type. For mixed types, such as the Hollin Hill landslide (a combination of rotational failures with earth flow, as shown in Uhlemann et al, 2017), the successful application of image cross-correlation is not entirely guaranteed.…”

“…After a trial and error procedure, a window size of 64 pixels × 64 pixels (3.84 m) with a step of 16 pixels × 16 pixels (0.96 m) and a patch of 20 pixels × 20 pixels (1.20 m) were chosen for this research. These settings ensured that the maximum imposed shift over Region B could be detected and was therefore chosen in line with a priori knowledge of the Hollin Hill landslide movement rates (Uhlemann et al, 2017). The computed displacements in easting and northing were combined to provide 2-D motion maps across successive epochs.…”

“…Manual cleaning to remove spurious vectors was also necessary, although this process could be automated by the application of various threshold parameters, if necessary. For instance, the sensitivity level could serve as a threshold to remove vectors of lengths lower than ±0.10 m. Based on previous knowledge of the Hollin Hill landslide (Uhlemann et al, 2017), a specific azimuth range could be used as an additional threshold to exclude vectors showing, for example, backward motion due to rotational failures.…”

“…The site has been monitored since 2009 by the British Geological Survey (BGS) using various methods, including terrestrial and airborne laser scanning, as well as ground-based geotechnical and geophysical investigations. BGS investigations have revealed that the landslide has a complex behaviour with seasonal surface variations and episodic failures mostly triggered by intensive rainfall and increased pore-water pressures within the constituent geological materials (Gunn et al, 2013;Uhlemann et al, 2017).…”