Integrationof robotic total station and digital image correlation to assess the three-dimensional surface kinematics of a landslide

In the field of landslide monitoring, the assessment of the spatially-distributed three-dimensional surfacedisplacement is crucial to understand the underlying mechanisms. Nevertheless, available technologies andtechniques that provide such a datum are few and often suffer spatio-temporal resolution, logistic and/orfinancial limitations. In this framework, we developed a methodology that merges the three-dimensional measurementsat specific points, acquired by a robotic total station (RTS), and the spatially-distributed data obtainedwith digital image correlation (DIC) of time-lapse camera photographs, to achieve the spatially-distributed threedimensionalsurface displacement. The integration method follows this procedure: i) the DIC results areorthorectified on an existing digital elevation model; ii) the RTS data are rototranslated into the camera coordinatesystem; iii) the ratio ? between displacement vertical component and module measured by the RTS iscalculated and interpolated across the region of interest; iv) the orthorectified DIC results are rescaled accordingto ?, obtaining the three surface displacement components; v) the displacement vector is rototranslated into thegeographical coordinate system. The sensitivity analysis respect to ? revealed that the integration method can besuccessfully applied even with a limited number of RTS measurement points. The developed methodology hasbeen applied to the Mont de La Saxe rockslide case study, during a phase of strong acceleration. In this period,the displacement magnitudes varied between 0.1 m and 10 m, thus providing a stress-test input for methodologydevelopment and validation. The results have been compared with independent ground-based interferometricradar measurements, obtaining 0.99 linear correlation coefficient and median absolute deviation of 0.086 m,which is comparable with the DIC measurement uncertainty. The proposed method is based on the use of lowcostportable and commonly used field equipment, thus it can be easily implemented in existing monitoringnetworks without additional financial costs.