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Terraced landscapes represent one of the most widespread human-induced/man-made transformations of hilly-mountainous environments. Slope terracing produces peculiar morphologies along with unusual soil textures and stratigraphic features, which in turn strongly influence slope hydrology. The investigation of the hydrological features of terraced soils is of fundamental importance for understanding the hydrological dynamics occurring in these anthropogenic landscapes, especially during rainfall events. To this purpose, the availability of extensive field monitoring data series and of information on subsoil properties and structure is essential. In this study, multi-sensor hydrological data were acquired over a period longer than 2 years in the experimental site of Monterosso al Mare, in the Cinque Terre National Park (Liguria region, Italy), one of the most famous examples of terraced landscape worldwide. Monitoring data were coupled with accurate engineering-geological investigations to achieve the hydro-mechanical characterization of backfill soils and to investigate their hydrological response at both the seasonal and the single rainstorm scale. The results indicated that the coarse-grained, and anthropically remolded texture of the soils favors the rapid infiltration of rainwater, producing sharp changes in both soil volumetric water content and pore water pressure. Furthermore, the pattern of hydrological parameters showed seasonal trends outlined by alternating phases of slow drying and fast wetting. The study outcomes provide useful insights on the short and long-term evolution of hydrological factors operating in agricultural terraces. These findings represent a useful basis for a better understanding of the time-dependent processes that guide water circulation in terraced systems, which have a key role in controlling the occurrence of erosion and landslide processes.

This study explores the innovative use of digital image processing (DIP) techniques, also named PhotoMonitoring, for analysing the triggering conditions of shallow landslides. The approach, based on the combination of optical and infrared thermographic imaging (IRT), was applied to a laboratory-scale slope, reproduced in a flume test apparatus. Three experiments were conducted to replicate rainfall-induced shallow landslides, applying change detection and digital image correlation analysis to both optical and thermal images. The method combines IRT’s ability to measure ground surface temperature changes with DIP’s capacity to track movement and displacement. Results showed the high reliability of the displacement time-series obtained through IRT-DIP with respect to the reference optical-DIP. The IRT-DIP technique also detects anomaly signals two minutes before landslide occurrence that can be regarded as a possible failure precursor. This study testifies to the potential of image analysis as a remote sensing technique, demonstrating the ability of DIP to capture the dynamics of shallow landslides, as well as the advantages of optical–IRT combinations to follow slope deformation processes during night-time. This approach, if properly adapted to real-scale scenarios, may contribute to a better understanding of landslide behaviour, improve landslide monitoring strategies, and promote more effective early warning systems (EWS).

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.