Bedding control on landslides: A methodological approach for computer-aided mapping analysis

Litho-structural control on the spatial and temporal evolution of landslides is one of the major typical aspects on slopes constituted of structurally complex sequences. Mainly focused on instabilities of the earth flow type, a semi-quantitative analysis has been developed with the purpose of identifying and characterizing litho-structural control exerted by bedding on slopes and its effects on landsliding. In quantitative terms, a technique for azimuth data interpolation, Non-continuous Azimuth Distribution Methodological Approach (NADIA), is presented by means of a GIS software application. In addition, processed by NADIA, two indexes have been determined: (i) Δ, aimed at defining the relationship between the orientation of geological bedding planes and slope aspect, and (ii) C, which recognizes localized slope sectors in which the stony component of structurally complex formations is abundant and therefore operates an evolutive control of landslide masses. Furthermore, some Litho-Structural Models (LSMs) of slopes are proposed aiming at characterizing recurrent forms of structural control in the source, channel and deposition areas of gravitational movements. In order to elaborate evolutive models controlling landslide scenarios, LSMs were qualitatively related and compared with Δ and C; quantitative indexes. The methodological procedure has been applied to a lithostructurally complex area of Southern Italy where data about azimuth measurements and landslide mapping were known. It was found that the proposed methodology enables the recognition of typical control conditions on landslides in relation to the LSMs. Different control patterns on landslide shape and on style and distribution of the activity resulted for each LSM. This provides the possibility for first-order identification to be made of the spatial evolution of landslide bodies. © Author(s) 2011

Remote sensing monitoring of the Pietrafitta earth flows in Southern Italy. An integrated approach based on multi-sensor data

Earth flows are complex gravitational events characterised by a heterogeneous displacement pattern in terms of scale, style, and orientation. As a result, their monitoring, for both knowledge and emergency purposes, represents a relevant challenge in the field of engineering geology. This paper aims to assess the capabilities, peculiarities, and limitations of different remote sensing monitoring techniques through their application to the Pietrafitta earth flow (Southern Italy). The research compared and combined data collected during the main landslide reactivations by different ground-based remote sensors such as Robotic Total Station (R-TS), Terrestrial Synthetic Aperture Radar Interferometry (T-InSAR), and Terrestrial Laser Scanner (TLS), with data being derived by satellite-based Digital Image Correlation (DIC) analysis. The comparison between R-TS and T-InSAR measurements showed that, despite their different spatial and temporal resolutions, the observed deformation trends remain approximately coherent. On the other hand, DIC analysis was able to detect a kinematic process, such as the expansion of the landslide channel, which was not detected by the other techniques used. The results suggest that, when faced with complex events, the use of a single monitoring technique may not be enough to fully observe and understand the processes taking place. Therefore, the limitations of each different technique alone can be solved by a multi-sensor monitoring approach

Earth-flow deformation from GPS surveys, Mount Pizzuto earth flow, southern Italy

We reconstruct the deformational pattern of the Mount Pizzuto earth flow using displacement data derived by successive GPS measurements of a network of 99 monitoring points. 2D strain was computed using displacement data of sets of 4 to 6 points. Results from our analysis indicate that deformation varies along the earth flow with several zones of longitudinal stretching and shortening corresponding to areas where the earth-flow displacement/velocity increase and decrease, respectively. Dilatation and orientation of principal strain axes are consistent with the geometry of deformational structures at the flow surface. © Società Geologica Italiana, Roma 2016

Calculating Economic Flood Damage through Microscale Risk Maps and Data Generalization: A Pilot Study in Southern Italy

In recent decades, floods have caused significant loss of human life as well as interruptions in economic and social activities in affected areas. In order to identify effective flood mitigation measures and to suggest actions to be taken before and during flooding, microscale risk estimation methods are increasingly applied. In this context, an implemented methodology for microscale flood risk evaluation is presented, which considers direct and tangible damage as a function of hydrometric height and allows for quick estimates of the damage level caused by alluvial events. The method has been applied and tested on businesses and residential buildings of the town of Benevento (southern Italy), which has been hit by destructive floods several times in the past; the most recent flooding occurred in October 2015. The simplified methodology tries to overcome the limitation of the original method-the huge amounts of input data-by applying a simplified procedure in defining the data of the physical features of buildings (e.g., the number of floors, typology, and presence of a basement). Data collection for each building feature was initially carried out through careful field surveys (FAM, field analysis method) and subsequently obtained through generalization of data (DGM, data generalization method). The basic method (FAM) allows for estimating in great detail the potential losses for representative building categories in an urban context and involves a higher degree of resolution, but it is time-consuming; the simplified method (DGM) produces a damage value in a shorter time. By comparison, the two criteria show very similar results and minimal differences, making generalized data acquisition most efficient