In February 2010 a large landslide affected the Maierato municipality (Calabria, Italy). The landslide, mainly caused by a period of prolonged and intense rainfalls, produced a mass displacement of about 5 million m³ and several damages to farmlands, houses and infrastructures. In the aftermath several conventional monitoring actions were carried out. In the current post emergency phase, the monitoring was resumed by carrying out unmanned aerial vehicles (UAV) flights in order to describe the recent behavior of the landslide and to assess residual risk. Thanks to the potentialities of the structure from motion algorithms and the availability of post emergency reconnaissance photos and a previous 3D dataset, the three-dimensional evolution of the area was computed. Moreover, an experimental multispectral flight was carried out and its results supported the interpretation of local phenomena. The dataset allowed to quantify the elevation losses and raises in several peculiar sectors of the landslide. The obtained results confirm that the UAV monitoring and the structure from motion approach can effectively contribute to manage residual risk in the medium and long term within an integrated geotechnical monitoring network.

We present a new method for near-real-time monitoring of surface displacements due to landslide phenomena, namely ADVanced dIsplaCement monitoring system for Early warning (ADVICE). The procedure includes: (i) data acquisition and transfer protocols; (ii) data collection, filtering, and validation; (iii) data analysis and restitution through a set of dedicated software; (iv) recognition of displacement/velocity threshold, early warning messages via SMS and/or emails; (v) automatic publication of the results on a dedicated webpage. We show how the system evolved and the results obtained by applying ADVICE over three years into a real early warning scenario relevant to a large earthflow located in southern Italy. ADVICE has speed-up and facilitated the understanding of the landslide phenomenon, the communication of the monitoring results to the partners, and consequently the decision-making process in a critical scenario. Our work might have potential applications not only for landslide monitoring but also in other contexts, as monitoring of other geohazards and of complex infrastructures, as open-pit mines, buildings, dams, etc.

This paper presents a methodology taking advantage of the GPOD-SBAS service to study the surface deformation information over high mountain regions. Indeed, the application of the advanced DInSAR over the arduous regions represents a demanding task. We implemented an iterative selection procedure of the most suitable SAR images, aimed to preserve the largest number of SAR scenes, and the fine-tuning of several advanced configuration parameters. This method is aimed at minimizing the temporal decorrelation effects, principally due to snow cover, and maximizing the number of coherent targets and their spatial distribution. The methodology is applied to the Valle d’Aosta (VDA) region, Northern Italy, an alpine area characterized by high altitudes, complex morphology, and susceptibility to different mass wasting phenomena. The approach using GPOD-SBAS allows for the obtainment of mean deformation velocity maps and displacement time series relative to the time period from 1992 to 2000, relative to ESR-1/2, and from 2002 to 2010 for ASAR-Envisat. Our results demonstrate how the DInSAR application can obtain reliable information of ground displacement over time in these regions, and may represent a suitable instrument for natural hazards assessment.

In this work, we present the results of a low-cost optical monitoring station designed for monitoring the kinematics of glaciers in an Alpine environment. We developed a complete hardware/software data acquisition and processing chain that automatically acquires, stores and co-registers images. The system was installed in September 2013 to monitor the evolution of the Planpincieux glacier, within the open-air laboratory of the Grandes Jorasses, Mont Blanc massif (NW Italy), and collected data with an hourly frequency. The acquisition equipment consists of a high-resolution DSLR camera operating in the visible band. The data are processed with a Pixel Offset algorithm based on normalized cross-correlation, to estimate the deformation of the observed glacier. We propose a method for the pixel-to-metric conversion and present the results of the projection on the mean slope of the glacier. The method performances are compared with measurements obtained by GB-SAR, and exhibit good agreement. The system provides good support for the analysis of the glacier evolution and allows the creation of daily displacement maps.

Landslide inventories provide the knowledge basis for many geomorphological applications and also planning and emergency management. Detailed landslide inventories should also be prepared where pre-existing inventories are available, as knowledge updates. In this paper, we present a new geomorphological landslide inventory for an area of the High Agri Valley, Southern Italian Apennines. The map was prepared through systematic interpretation of historical aerial photographs testing extensive use of anaglyph glasses in StereoPhoto Maker freeware. A total of 2124 landslides were classified based on the type of movement, estimated depth, estimated relative age and three levels of uncertainty, providing landslide attributes and map constraints useful for land planning and hazard studies. The map also documents the relationships between landslides and fluvial landforms of different generations, recording important information to investigate the geomorphological evolution of the area further. We expect that landslide mapping in similar environments will benefit from the workflow here presented.

In the field of geo-hazards and geo-engineering, monitoring networks represent a key element for the geological risk assessment and the design and management of large infrastructures construction. In the last decade, we have observed a strong development on remote sensing techniques but just small changes in the subsoil observations. However, this type of measurement is very important to have a three-dimensional representation of the studied area, since the surface measurements often represent a sum of deformations that develop in a complex way in the subsoil. In this paper, we present a robotic inclinometer system developed to acquire deep-seated ground deformations in boreholes. This instrumentation combines advantages offered by manual inclinometer measurements with a robotized approach that improves the results in term of accuracy, revisiting time, and site accessibility. The Automated Inclinometer System (AIS) allows one to explore automatically all the length of the monitored borehole using just one inclinometer probe with a semi-wireless system. The paper presents the system and a detailed dataset of measurements acquired on three inclinometer tubes installed for the monitoring of the construction phase of the new Line C Metro of Rome. The dataset was acquired in real monitored site and undisturbed conditions and can represent a benchmark for modern inclinometer measurements.