Kinematics of active earthflows revealed by Digital Image Correlation and DEM of Difference techniques applied to multi-temporal LiDAR data

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, with reference of a large flow-type landslide locatedin the northern Apennines of Italy, we explored the possibility to quantify displacement vectors on a spatially distributed basis and to quantify volumetric transfer at the slope scale with the application of digital image correlation (DIC) and digital elevation model difference (DEMoD) techniques to multitemporal airborne LiDAR surveys of 2006, 2007 and 2009. DIC was applied to greyscale slope gradient maps retrieved after precise co-registration of LiDAR surveys, and allowed for a reconstruction and quantification of movement patterns over various sectors of the landslide (up to 60 m in the upper and main track of the landslide and up to about 27 m at the landslide toe). 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 during reactivation events significant amounts of debris were actually eroded away from river erosion of the advancing toe. The combined analysis of results allowed discussing 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 evidenced the added value of high-resolution DEMs in the analysis earthflows kinematics toward a better understanding of their role in the geomorphic evolution of slopes

GB-InSAR survey of the Valoria earthflow during the 2009 reactivation (Northern Apennines, Italy)

A GB-InSAR is a valuable monitoring system for unstable slope. When LoSs of pixel coherence is low, it is capable to measure movements up to m/day in a spatial continuous way. These meas- urements can then integrated with spatially discrete monitoring data obtained for example by automated topographic survey stations. In this paper, the GB-InSAR technique was tested in a 3 day measuring campaign at the Valoria earth flow during a reactivation event in 2009. Results were compared and validated with measurements of a continuous topographic monitoring system that consists of a total station. For this purpose, total station data were projected into line of sight displacements concurring with the displacement direction of the GB-InSAR survey. The test proved that even in difficult conditions, i.e. high water content in the ground and significant displacement, the radar is capable of returning robust results that are comparable, and potentially complementary to that of the total station

Integrated displacement and activity analysis at the Valoria landslide (Italian Apennines) through automated topographic monitoring, image correlation velocimetry and surface roughness computation

ISBN 2-95183317-1-5At the Valoria landslide located in the Italian Apennines, surface monitoring data acquired by an automated total station were analyzed. The system provided short-term information on the behavior of the material during crisis and the duration of reactivations. Long-term displacements and morphometric properties were studied using a geographic information system (GIS). Results from monitoring show a strong correlation between rainfall related triggering of mass wasting in the crown zone and subsequent downslope reactiva-tions. We found that crown-to-toe zone reactivations generally occur within only 6 weeks. Further findings indicate that earth slide materials in the head zone fail almost instantaneous conversely to earth flow materials in the track channel. The findings are complemented by a digital image correlation analysis of multi temporal, high-resolution digital elevation models (DEM) acquired in 2006, 2007 and 2009. We utilized this technique to compute velocities and displacements of pixels in the DEMs between 2006 and 2009. It was found that both azimuth and magnitude of displacements could be reconstructed in for the crown zone, the main track channel and the landslide toe. The results from the image correlation compare well to displacements obtained from independent monitoring methods (GPS and interpreting of shaded reliefs maps). Based on the assump-tion that increased materials movements result in higher terrain roughness, we used LiDAR-derived to per-form a raster based roughness analysis. Different roughness calculation methods were applied to 0.5 m eleva-tion grids using different Kernel sizes. The performance of a supervised and an unsupervised approach was evaluated. Findings from the supervised approach showed that the difference between active and dormant landslides is evident in some cases but also that earth flows and dormant landslides tend to have a similar roughness patterns as stable areas. Results from the unsupervised approach demonstrated that landslide roughness is heterogeneous and that non-landslide areas may have a similar morphometric signature. In this study it was demonstrated that near- and remote sensing techniques are crucial, as well as complimentary in the analysis of landslide hazard. Applying both approaches can yield a more complete picture of the defor-mation history of landslide

Comparative analysis of surface roughness algorithms for the identification of active landslides

Parameters correlated to surface roughness are quite commonly used to describe landslide activity in quantitative geomorphology. Previous studies proved that topographic roughness is closely related to both landslide mechanics and features. A number of different techniques have emerged over the years to describe quantitatively the great variety of landforms and processes that affect unstable slopes. In this work we perform a comparative analysis of severalmethods used in literature to compute surface roughness (root mean square applied to elevation and slope grids, eigenvalue ratios, semivariance, discrete Fourier transform, continuous wavelet transform and wavelet lifting scheme) in order to evaluate quantitatively which algorithms are best suited to discriminate active landslides and to predict them for automated mapping purposes. A first test was carried out on artificial surfaces simulating different roughness patterns encountered in nature, so to highlight advantages and limits in controlled conditions. Then, the algorithms were applied to LiDAR datasets of two earth flow case studies in the Northern Apennines, Italy. Results obtained by using “effect-size” statistical test to objectively quantify the capability of the different algorithms of discriminating active landslide slopes from other slope types showed that most algorithms perform reasonablywell and that simple techniques (RMS-based and wavelet lifting scheme) achieve equal or sometimes even better results thatmore complex ones. Results fromthe use of roughness indexes for the prediction of landslide slopes in automated mapping showed that non-forested active slopes could be predicted bymostmethods with an accuracy greater than 85% and that most methods had a 15% drop in prediction accuracy in forested active slopes. Results also proved that increasing the size of the moving window has minor beneficial effects in predictive capability, suggesting that small size of pixels and moving windows should be used to retain a full resolution of surface conditions in slopes

CERN Past performance and future prospects

ESRC End of Grant ReportSIGLEAvailable from British Library Document Supply Centre- DSC:8318.1714F(ESRC-G--00/23/0076)fiche / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The Relevance of Early-Warning Systems and Evacuations Plans for Risk Management

Garcia et al. (Chap. 13) analyse different types of Early-Warning Systems with the aim to connect scientific advances in hazard/risk assessment with local management strategies and practical demands of stakeholders/end-users. An Integrated People-Centred Early Warning System (IEWS) is presented, which is mainly based on prevention as a key element for disaster risk reductio

Innovative Techniques for the Detection and Characterization of the Kinematics of Slow-Moving Landslides

The chapter introduces several innovative remote-sensing techniques to monitor and analyse the kinematics of slow moving to moderately moving landslides. These are illustrated in three case studies in Italy and Franc

Applications of a numerical model for slow moving landslides to the Valoria landslide in the Italian Apennines, and the Super Sauze mudslide in the French Alps

ISBN 2-95183317-1-5This research demonstrates the applicationof the dynamic SLOWMOVE model onthe Valoria case study located in the northern Apennines (Italy) and the Super-Sauze landslide in the southwestern Alps (France). The SLOWMOVE model is based on the Navier-Stokes equations. The landslide is modeled as a one-phase material with homogeneous and constant rheological properties. The slow movements of landlside materials allow for the cancellation of the inertia term from the momentum equation. At the Valoria case study site a 3.5 km long earth slide/flow with materials comprised of disaggregated Flysch, Marl and Clay-stones resumed activity in 2001 after a suspended phase. The landslide is characterized as earth slides in the upper slope, and as earth flows in the main track following complete disaggregation of the materials in the source areas. During reactivations, the earth flows can reach velocities up to 10 m·h-1. Characteristic for the landslide activity are repeated acceleration events ascribed to seasonal climatic inputs. Through continuous activity since 2001, more than 15 million cubic meters of material have been transferred down-slope. The Su-per-Sauze landslide is triggered in Callovo-Oxfordian black marls and is composed of a silty-sand matrix mixed with moraine debris. It extends over a horizontal distance of 850 m with an average slope of 25° impli-cating a volume of 560,000 m³. The complex paleo-topography covered by the landslide is made by succes-sions of crests and gullies which play an essential role in the mudslide behavior by creating sections with dis-tinct kinematical, mechanical and hydrological characteristics. The mudslide kinematics is characterized by a spatially heterogeneous displacement field with velocities ranging from 0.01 to 0.40 m day-1. For the Valoria landslide, the performance of the 1D approach of SLOWMOVE was analyzed on a representative landslide cross-section from the main track zone down to the toe zone. Multi-temporal Lidar surveys in conjunction with a large set of surface displacement data obtained from continuous monitoring since March 2008 was used to calibrate and evaluate the SLOWMOVE model. The model is capable to simulate realistic velocities and displacements but failed to achieve an accurate topographic reconstruction of the morphologic changes between 2003 and 2007. For the Super Sauze mudslide, the 2.5 D approach is used to simulate the heteroge-neous displacement field of the mudslide. The performance of the model is evaluated on multi-temporal and spatially distributed datasets of landslide displacements for the period of summer 200

Innovative Techniques for the Characterization of the Morphology, Geometry and Hydrological Features of Slow-Moving Landslides

highlight the interest of combining different techniques obtained from numerous developments in remote-sensing, near-surface geophysics, \ufb01eld instrumentation and data processing. In a number of case studies, they show signi\ufb01cant advances in characterizing the landslide morphology and internal structur