A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study

On December 3, 2013, a large complex landslide was triggered SW of the town of Montescaglioso (Southern Italy), causing the destruction of roads, commercial buildings and private dwellings, as well as several direct and indirect economic losses. A set of interferometric ground measurements acquired by the Cosmo-SkyMed satellite constellation and processed by means of the SqueeSAR algorithm was used to study the pre-event slope displacements in the entire Montescaglioso municipal area. Data span from January 30, 2012, to December 2, 2013, and show average line-of-sight velocities of 1–10 mm/year in the slope sector ultimately affected by the collapse. In retrospect, a time series analysis of the radar targets was performed in order to identify and characterize all the slope instabilities in proximity of the town. This was based on the setup of characteristic thresholds of displacement. The procedure permitted to locate several areas which recurrently exceeded these previously established thresholds, in consistency with the amount of precipitation. In particular, the major source of potential hazard in the area was indeed found where the December 3, 2013, landslide eventually occurred. The results of this quick data processing technique were validated through comparison with two independently developed landslide maps. This simple method, which is not supposed to diminish the importance of geomorphologic field surveys, could improve both the accuracy and the update rate of landslide susceptibility maps. Not relying on arbitrary or empirically derived approaches, it has the advantage of computing statistically based thresholds specific for each time series. By indicating the slope sectors in higher need of deeper in situ investigation, more support could be provided to administrative bodies for the processes of risk assessment and management.     

Hydrological Regime and Renewal Capacity of the Micro-tidal Lesina Lagoon,Italy

A multidisciplinary approach that combines field measurements, artificial neural networks, water balance analyses and hydrodynamic modelling was developed to investigate the water budget and renewal capacity of semi-closed coastal systems. The method was applied to the Lesina Lagoon, a micro-tidal lagoon in the southern Adriatic Sea (Italy). Surface water flux between the lagoon and the sea was determined by neural network prediction and used as input in the analysis. Strong seasonal variations in the water budget equation were predicted. Fresh water inputs estimated by the water balance analysis were used as forcing by a calibrated finite element model to describe the water circulation and transport time scale of the lagoon’s surface waters. The model highlighted the spatial heterogeneity of the renewal behaviour of the system, with a strong east–west water renewal time gradient. Knowledge of spatial distribution of water renewal times is crucial for understanding the lagoon’s renewal capacity and explaining the high spatial variability of the biogeochemistry of the Lesina Lagoon.