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ABSTRACTWe present the geomorphological map of the upper sector of the Roncovetro active landslide (Enza Valley, Emilia-Romagna, Italy). The 1:1500 scale map provides an accurate picture of the landslide in October 2014. The map is mainly based on the data collected during an airborne LiDAR survey. The capability of LiDAR to ‘penetrate’ the vegetation cover makes these data the most complete and accurate topographic dataset of this landslide. The map shows that the upper sector of the Roncovetro landslide consists of gravity- and water runoff-related forms. Gravitational features are linked to sliding and flowing movements that characterize the short- and long-term behaviour of the landslide. By comparing the 2014 LiDAR-Digital Elevation Model (DEM) with the 1973 DEM provided by the Emilia-Romagna Region, we calculated that 6.2 ± 0.8 × 105 m3 of material has moved from the top of the Roncovetro landslide in about 40 years.

Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

The city of Antananarivo is located in the central highlands of Madagascar, and is the largest urban center of the island. Considering the frequent heavy cyclonic rains frequently affecting the area, its geomorphological context is particularly prone to geo-hydrological hazards, such as landslides and flash floods, as recently proved during the disastrous events of the winters of 2015 and 2018. Field data and high-resolution remote sensing data interpretation (DEMs and satellite orthophotos) were combined in order to produce detailed hydrographic and geomorphological maps. The aim was to understand the processes acting in the Analamanga hill area, with special regards to the effect of human activity in modeling the natural landforms and exacerbating the geo-hydrological hazards. The obtained maps will provide management-planning tools to be used as a first step towards a risk reduction strategy in the Antananarivo historical urban center.

In the last decade satellite remote sensing has become an effective tool for monitoring geo-hazard-induced ground motions, and has been increasingly used by the scientific community. Direct and indirect costs due to geo-hazards are currently rising, causing serious socio-economics and casualty losses. Therefore, creating a priority list turns out to be essential to highlight the most relevant ground deformations and to better focus risk management practices at regional scale. The Sentinel-1 constellation, thanks to the 6-days repeatability and the free availability of the data, allows to easily update the geo-hazard-induced ground motions, compared to other kind of satellite sensors. In this work, a hot-spot-like method is presented by filtering a large stack of Sentinel-1 images processed by means of the SqueeSAR algorithm. Three periods, with six months repetitiveness, have been analysed in order to evaluate the behaviour and evolution of deformation clusters. The target area is Tuscany Region, located in the central part of Italy and affected by a wide gamma of geohazards, ranging from landslides to large subsidence areas. The final output is a geo-database of ground motions that can be used by regional authorities to prioritize and to effectively plan local risk reduction actions.

The historical centre of Antananarivo represents one of the most important cultural heritage sites in Madagascar. During 2015, the whole city area was severely affected by geo-hydrological hazards due to cyclonic rain, resulting in severe flooding, and in widespread shallow landslides along the hillslopes. This event proved the vulnerability to geo-hydrological risk of the Upper town’s historical buildings. In October 2017, a geo-hydrological hazard mapping was performed in the Upper Town by combining field surveys, remote sensing and geomatic data analysis in a GIS environment. This provided detailed products such as a geological-geomorphological map, a map of the hydrographic network and of the creek basins, and a geodatabase to be used for detecting areas prone to erosion and geo-hydrological hazards. The final aim was to understand the geological, geomorphological and hydrographic features of the Upper Town, in order locate the more critical areas and to recommend priority works to be carried out, as a first step toward a risk management strategy and a conservation/valorization plan for building the resilience of the site. The results highlighted that a Cultural Heritage site protection strategy can be planned also in case of limited data, which is a frequent condition in developing countries.

Landslide detection and mapping are essential issues for reducing impact of such natural disasters, and for improving the future built-up expansion and planning strategies, especially in developing countries where a reasonable land-use design is an important concern for sustainable growth and environmental management. Armenia is a landlocked country and its urban development is strongly tied to the improvement of infrastructures, which must takes into account the environmental setting and the slope instability of the area, in order to identify risks and possible damages to settlements and economic activities. The use of satellite-based Earth Observation data has advanced significantly in the last decade and has turned out to be very useful for measuring and monitoring slow-moving surface deformation phenomena with millimetric precision. In this framework, this study aims at providing a remote sensing-based Landslide Inventory Map (LIM) and a Landslide Susceptibility Map (LSM) over Dilijan (Armenia) area, performed within the Secondary Cities Urban Development in Armenia project. In particular, LIM and LSM in the study area were produced by using ground deformation measurements derived from satellite Synthetic Aperture Radar (SAR) data, acquired by ALOS and ENVISAT sensors from 2003 up to 2010, and integrated with photo-interpretation of recent optical images and morphological analysis of Digital Elevation Model (DEM). Given the extensive presence of vegetation in the area of interest, satellite SAR images were processed to produce both SqueeSAR™ and Temporary Coherent Scatterers data, which are PSI (Persistent Scatterer Interferometry) data conceived as evolution of PSInSAR™ approach and particularly suited for non-urban and rural areas characterized by low density of coherent terrain benchmarks over time. Landslide mapping produced through this work identifies the most hazardous landslide-affected and landslide-prone areas around Dilijan city, and can be used for further estimating environmental risks for urban infrastructure development in the area.

The accurate evaluation of landslide-induced damage is a necessity for planning of proper and effective mitigation measures. It requires the implementation of field investigations to identify structural failures to more effectively trace landslide boundaries. Many methods have been proposed to classify landslide-induced damage of buildings. The existing methods demonstrate several advantages and drawbacks depending on the parameters considered, as lack of some important features and difficulties in applicability. A new classification approach of landslide-induced damage of facilities is proposed, which specifically focuses on assessing of damage degree and its relationship to the ground motion intensity and impact severity. The new approach is designed in two steps: a chart utilized during surveys to quantify cracks on structures and ground surface; an a posteriori ranking of structures performed using a cell-grid matrix. Furthermore, a damage recording scheme useful for field surveying is proposed. This approach considers several parameters derived from different existing methodologies by smoothing out drawbacks and homogenizing the considered features. The resulting approach provides a new procedure of landslide-induced damage assessment adoptable in case of private dwellings, as it does not require internal accessibility, and it is exploitable for different landslide events and for different kinds of structures and facilities.

Nowadays many developing countries need effective measures to reduce the disaster related risks. Structural interventions are the most effective to achieve these aims. Nevertheless, in the absence of adequate financial resources different low-cost strategies can be used to minimize losses. The purpose of this paper is to demonstrate that the disaster risk reduction can be gathered building a community coping capacity. In the case study, flood and seismic analyses have been carried out using relatively simple and low-cost technologies, fundamental for governments and research institutions of poorly developed countries. In fact, through the acquisition and dissemination of these basic information, a reduction of vulnerability and risk can be achieved. In detail, two methodologies for the evaluation of hydraulic and seismic vulnerability were tested in the Dajç municipality (Northern Albania), a high-seismicity region that is also severely affected by floods. Updated bathymetric, topographic and hydraulic data were processed with HEC-RAS software to identify sites potentially affected by dykes overflowing. Besides, the soil-structure interaction effects for three strategic buildings were studied using microtremors and the Horizontal to Vertical Spectral Ratio method. This flood and seismic vulnerability analysis was then evaluated in terms of costs and ease of accessibility in order to suggest the best use both of the employed devices and the obtained information for designing good civil protection plans and to inform the population about the right behaviour in case of threat.