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A new segmentation method for detection of rotational landslides from two epochs of Light Detection and Ranging System data (LiDAR) was developed. The developed method is based on outlier detection using Z scores of standard normal distribution. In this method, outlying height differences were obtained from two epochs of LiDAR data. The height differences were determined for each ith point uniquely by gridding the area along the steepest slope direction. Then, each profile containing the outlying height differences was tested for conformity to a possible rotational landslide and grouped into horizontal sets. Finally, the procedure was applied to horizontally grouped sets in the direction perpendicular to the steepest slope to determine each rotational landslide. To test the developed method, the area containing five different sizes of rotational landslides was scanned by the developed method. The results showed that the developed method segmented the area successfully identifying each rotational landslide.

The integration of data from different sources can be very helpful in understanding the mechanism, the geometry, the kinematic, and the area affected by complex instabilities, especially when the available geotechnical information is limited. In this work, the suitability of different techniques for the study of a deep-seated landslide affecting a bridge in Alcoy (Spain) is evaluated. This infrastructure presents such severe damage that has rendered the bridge unusable, which prevents normal access to an important industrial area. Differential SAR Interferometry (DInSAR) and terrestrial Light Detection and Ranging (LiDAR) remote sensing techniques have been combined with ground displacement monitoring techniques, such as inclinometers and conventional geological and geotechnical investigation, electrical-seismic tomography, damage, and topographic surveys, to determine the boundaries, mechanism, and kinematics of the landslide. The successful case study that is illustrated in this work highlights the potential and the need for integrating multi-source data for the optimal management of complex landslides and the effective design of remedial measurements. [ABSTRACT FROM AUTHOR]