Kinematics of active earthflows revealed by digital image correlation and DEM subtraction techniques applied to multi‐temporal LiDAR data

Abstract

Earthflow‐type landslides are persistent natural hazards having deep socio‐economic and environmental consequences. They have significantly contributed to the geomorphic evolution of mountainous slopes in Europe since the Late Glacial. An understanding of their complex kinematics is crucial to better constrain the processes governing their occurrence and mobility. In this work we explored the possibility to quantify displacement vectors on a spatially distributed basis and to quantify volumetric transfer at the slope scale with regard to a large flow‐type landslide located in the northern Apennines of Italy. For this purpose we applied digital image correlation (DIC) and digital elevation model differencing (DEMoD) techniques to multi‐temporal airborne LiDAR surveys of 2006, 2007 and 2009. The DIC was applied to greyscale slope gradient maps retrieved after precise co‐registration of LiDAR surveys. Thereby, movement patterns over various sectors of the landslide were reconstructed and quantified, most notably up to 20 m in the head zone, up to 51 m in the lower main track, and up to about 27 m at the landslide toe. The DEMoD analysis revealed significant mass transfer from the source to the tracks and toe zone, with the upper flow tracks acting as temporal storage of large amounts of material. The mass balance indicated that significant amounts of advancing landslide debris were eroded by a local stream. An integrated analysis of DEMoD and DIC results allowed for a discussion of governing processes, such as the transition from slide to flow, the influence of underlying topography on earthflow mobility, and the role of undrained loading as a mechanism of toe zone reactivation. In conclusion, the successful application of DIC and DEMoD to the case study underlines the added value of high‐resolution DEMs in the analysis of earthflow kinematics toward a better understanding of their role in the geomorphic evolution of slopes. Copyright © 2012 John Wiley & Sons, Ltd.

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

“…This occurred as features with similar/repetitive patterns within the vicinity of the specified window sizes generated false displacements (Travelletti et al, 2014;Fey et al, 2015). Hence, the choice of the function's parameters is usually based on the required spatial resolution, the computational effort and the displacement magnitude (Daehne and Corsini, 2013;Travelletti et al, 2014;Fey et al, 2015).…”

“…The differencing of successive co-registered DEMs constitutes a standard approach to estimate ground accumulation and depletion in monitoring applications (Daehne and Corsini, 2013;Travelletti et al, 2014). Moreover, the application of image-cross-correlation functions to optical imagery has long been successfully implemented for the quantification of surface planimetric movement in the context of landslides, glaciers, etc.…”

“…Nevertheless, the application of image-cross-correlation functions to UAV-derived orthomosaics can increase noise due to variations in illumination conditions (Lucieer et al, 2014). Recent studies have demonstrated that the implementation of image-cross-correlation functions with DEM morphological derivatives can automatically determine the movement of surface features that preserve their structural patterns over time (Daehne and Corsini, 2013;Lucieer et al, 2014;Travelletti et al, 2014;Fey et al, 2015). Among these, Lucieer et al (2014) and Turner et al (2015) found the UAV-derived morphological attribute of "shaded relief", combined with image-cross-correlation functions, to provide better surface displacement estimation of a landslide than single bands from the corresponding orthomosaic.…”

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

“…Most of the previous comparative studies on multi-period DTM data have adopted ground control points, e.g., the discussion of landslide and river terrain variation by comparing the DTM data from ground measurements and those from aerial photogrammetry and ALS measurements [10,[17][18][19], analysis of river terrain variation and slope earthflows by contrasting between two-period [20,21] or three-period [22] ALS data and ground measurement data, evaluation of earthflow terrain variation using airborne and ground LiDAR data, and error assessment of these two techniques [23], using InSAR-derived DTM to discuss terrain changes before and after the landslide event [7], and error assessment based on the two LiDAR datasets of Taiwan [24]. All of these studies mentioned above were conducted based on accessible ground measurement points.…”

Geomorphological Change Detection Using an Integrated Method: A Case Study on the Taan River Watershed, Taiwan

“…The changes can then be derived from these correspondences among multi-temporal images. Meanwhile, Digital elevation model (DEM) subtraction, that is, computing the difference between two DEMs of the same site but at different times, is a common approach for assessing topographic changes (Dewitte and Demoulin 2005;Dewitte et al 2008;Corsini et al 2009;Prokop and Panholzer 2009;DeLong et al 2012;Daehne and Corsini 2013). This study proposes an integrated method combining PIV and digital surface model (DSM) subtraction.…”