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Bedding planes are a known factor that controls the type, abundance and pattern of landslides. Where layered rocks crop out, the geometrical relationships between the attitude of the bedding and the geometry of the terrain is crucial to understand landslide phenomena. Obtaining information on bedding attitude for large areas through field surveys is time-consuming, and resource intensive, hampering the possibility of quantitative investigations on the control of bedding planes on landslides. We propose a GIS-based method to extract information on bedding planes from the analysis of information captured through the visual interpretation of stereoscopic aerial photographs and a digital representation of the terrain. We tested the method in the Collazone study area, Umbria, Central Italy, where we used spatially distributed information on beddings and terrain information obtained from a 10 × 10-m DEM to determine morpho-structural domains. We exploited the morpho-structural terrain zonation, in combination with landslide information for the same area, to investigate the role of beddings in controlling the distribution and abundance of landslides in the study area. We found that beddings condition the location and abundance of relict and deep-seated landslides, most abundant in cataclinal slopes, and do not condition significantly the shallow landslides. We expect the method to facilitate the production of maps of morpho-structural domains in layered geological environments. This will contribute to a better understanding of landslide phenomena and to foster the preparation of advanced landslide susceptibility and hazard models. [ABSTRACT FROM AUTHOR]

Models for forecasting rainfall-induced landslides are mostly based on the identification of empirical rainfallthresholds obtained exploiting rain gauge data. Despite their increased availability, satellite rainfall estimatesare scarcely used for this purpose. Satellite data should be useful in ungauged and remote areas, or should providea significant spatial and temporal reference in gauged areas. In this paper, the analysis of the reliability of rainfallthresholds based on rainfall remote sensed and rain gauge data for the prediction of landslide occurrence is carriedout. To date, the estimation of the uncertainty associated with the empirical rainfall thresholds is mostlybased on a bootstrap resampling of the rainfall duration and the cumulated event rainfall pairs (D,E) characterizingrainfall events responsible for past failures. This estimation does not consider the measurement uncertaintyassociated with D and E. In the paper, we propose (i) a new automated procedure to reconstruct ED conditionsresponsible for the landslide triggering and their uncertainties, and (ii) three new methods to identify rainfallthreshold for the possible landslide occurrence, exploiting rain gauge and satellite data. In particular, the proposedmethods are based on Least Square (LS), Quantile Regression (QR) and Nonlinear Least Square (NLS) statisticalapproaches. We applied the new procedure and methods to define empirical rainfall thresholds and theirassociated uncertainties in the Umbria region (central Italy) using both rain-gauge measurements and satelliteestimates. We finally validated the thresholds and tested the effectiveness of the different threshold definitionmethodswith independent landslide information. The NLS method among the others performed better in calculatingthresholds in the full range of rainfall durations. We found that the thresholds obtained from satellite dataare lower than those obtained fromrain gaugemeasurements. This is in agreement with the literature,where satelliterainfall data underestimate the "ground" rainfall registered by rain gauges. [ABSTRACT FROM AUTHOR]

Abstract: A catalogue of 677 landslides of the slide type was selected from a global database of geometrical measurements of individual landslides, including landslide area (A L) and volume (V L). The measurements were used to establish an empirical relationship to link A L (in m2) to V L (in m3). The relationship takes the form of a power law with a scaling exponent α =1.450, covers eight orders of magnitude of A L and twelve orders of magnitude of V L, and is in general agreement with existing relationships published in the literature. The reduced scatter of the experiential data around the dependency line, and the fact that the considered landslides occurred in multiple physiographic and climatic environments and were caused by different triggers, indicate that the relationship between V L and A L is largely independent of the physiographical setting. The new relationship was used to determine the volume of individual landslides of the slide type in the Collazzone area, central Italy, a 78.9 km2 area for which a multi-temporal landslide inventory covering the 69-year period from 1937 to 2005 is available. In the observation period, the total volume of landslide material was V LT =4.78×107 m3, corresponding to an average rate of landslide mobilization φ L =8.8 mm yr−1. Exploiting the temporal information in the landslide inventory, the volume of material produced during different periods by new and reactivated landslides was singled out. The wet period from 1937 to 1941 was recognized as an episode of accelerated landslide production. During this 5-year period, approximately 45% of the total landslide material inventoried in the Collazzone area was produced, corresponding to an average rate of landslide mobilization φ L =54 mm yr−1, six times higher than the long term rate. The volume of landslide material in an event or period was used as a proxy for the magnitude of the event or period, defined as the logarithm (base 10) of the total landslide volume produced during the event, or period. With this respect, the new relationship to link A L and V L is a starting point for the adoption of a quantitative, process based landslide magnitude scale for landslide events. [Copyright &y& Elsevier]