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4. The Contribution of Spaceborne SAR Data in Linear Infrastructures Monitoring Activities

Author

Infante, D., Di Martire, D., Di Santolo, A. Scotto, Calcaterra, D., Ramondini, M., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Calvetti, Francesco, editor, Cotecchia, Federica, editor, Galli, Andrea, editor, and Jommi, Cristina, editor

Published
2020
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19. Landslide Hazard and Risk in the Campi Flegrei Caldera, Italy

Author

Calcaterra D., Di Martire D., Calcaterra, D., and Di Martire, D.

Abstract

This chapter deals with the main characteristics of the mass movements affecting the slopes of the Campi Flegrei Volcanic Field. To this aim, giving due credit to previous works, an overview of the Phlegraean landslides is presented, taking into account other ongoing researches. The prevailing landslide types depend upon the lithological characteristics of the rocks involved: slides and flows dominate in the loose pyroclastic deposits, while falls and topples characterise the slopes where tuffs and lavas crop out. All the Phlegraean landslides show a moderate mobility, with a maximum runout distance lower than 350 m, and a mean angle of reach equal to 39°. More than 15% of the whole territory of the six Phlegraean towns is prone to landslide hazard, though the distribution of at-risk areas among the six towns is uneven (e.g., Monte di Procida, 31.7%; Quarto, 5%). Due to the intense urbanisation of the area, such a hazard results in a quite high risk. In recent years, a number of structural countermeasures, including low-impact soil bioengineering techniques, have been introduced as to mitigate the landslide risk in the Neapolitan-Phlegraean area. However, a paradigm shift is strongly needed, due in part to the persistent economic crisis, which should induce to emphasise the importance of non-structural preventive solutions, among which the improvement of the urban communities’ societal resilience is of paramount importance.

Published
2022
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22. LANDSLIDE SUSCEPTIBILITY ASSESSMENT IN PYROCLASTIC SOILS: NUMERICAL ANALYSIS ON THE ROLE OF CAPILLARY BARRIERS

Author

Sepe C., Calcaterra D., Di Martire D., Ramondini M., Russo G., Vitale E., Pappalardo L., Sepe, C., Calcaterra, D., Di Martire, D., Ramondini, M., Russo, G., Vitale, E., and Pappalardo, L.

Subjects
Numerical analysi, Capillary barrier, Pyroclastic soils, Landslide susceptibility
Abstract

Landslides in pyroclastic soils are among the most calamitous natural phenomena causing damage to people and territory every year. The mechanisms underpinning such landslides can be controlled by multiple factors, including site conditions and features of pyroclastic soils. Spatial variations of textural characteristics and related hydro-mechanical features in the overlain pyroclastic layers can yield to relevant effects on water infiltration, with potential formation of capillary barriers at the interface between different soil layers. A better knowledge of the key triggering factors, based on engineering-geological modeling, may be carried out through a detailed field and laboratory measurements, as well as performing numerical simulations. The area surrounding Palma Campania (Napoli, Italy) is largely affected by fast-moving landslides involving pyroclastic deposits. The volcanoclastic sequence is constituted upward by (white and grey) vesicular pumices and (black) dense scoriae levels, associated with the Somma-Vesuvius “Pomici di Base” Plinian eruption (22 ka). Stratigraphic sections along the carbonate slopes of Vallone Lupici reveal failure surfaces along various pyroclastic contact layers (white/gray vesicular pumice and dark dense scoria layers). Representative samples were collected at different stratigraphic heights and analyzed to define structural and textural characteristics; the obtained data were used as input parameters to perform 2D numerical simulations to explore the propagation of hydraulic front under different conditions of water flow (flow rate) and textural features of rocks. The obtained results allow us to put constrains on capillary barriers development in function of soils characteristic and rain fall intensity.

Published
2021

23. PREDICTION OF DEFORMATION CAUSED BY LANDSLIDES BASED ON GRAPH CONVOLUTION NETWORKS ALGORITHM AND DINSAR TECHNIQUE.

Author

Khalili, M. A., Guerriero, L., Pouralizadeh, M., Calcaterra, D., and Di Martire, D.

Subjects
LANDSLIDES, MACHINE learning, SYNTHETIC aperture radar, ALGORITHMS, DEEP learning, K-nearest neighbor classification
Abstract

Around the world, the occurrence of landslides has become one of the greatest threats to human life, property, infrastructure, and natural environments. Despite extensive research and discussions on the spatiotemporal dependence of landslide displacements, there is still a lack of understanding concerning the factors that appear to control displacement distribution in landslides because of their significant variations. This paper implements a Graph Convolutional Network (GCN) to predict displacement following the Moio della Civitella landslide in southern Italy and identify factors that may affect the distribution of movement following the landslide. An interferometric technique, known as permanent scatter interferometry (PSI), has been developed based on Synthetic Aperture Radar (SAR) satellite imagery to derive permanent scatter points that can be used to represent the deformation of landslides. This study utilized the GCN regression model applied to PSs points and data reflecting geological and geomorphological factors to extract the interdependency between paired data points, resulting in an adjacency matrix of the interval [0, 0,8). The proposed model outperforms conventional machine learning and deep learning algorithms such as linear regression (LR), K-nearest neighbors (KNN), Support vector regression (SVR), Decision tree, lasso, and artificial neural network (ANN). The absolute error between the actual and predicted deformation is used to evaluate the proposed model, which is less than 2 millimeters for most test set points. [ABSTRACT FROM AUTHOR]

Published
2022
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27. Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques

Author

Novellino, A., Cesarano, M., Cappelletti, P., Di Martire, D., Di Napoli, M., Ramondini, M., Sowter, A., Calcaterra, D., Novellino, A., Cesarano, M., Cappelletti, P., Di Martire, D., Di Napoli, M., Ramondini, M., Sowter, A., and Calcaterra, D.

Abstract

This paper describes a novel methodology where Machine Learning Algorithms (MLAs) have been integrated to assess the landslide risk for slow moving mass movements, processes whose intermittent activity makes challenging any risk analysis worldwide. MLAs has been trained on datasets including Interferometric Synthetic Aperture Radar (InSAR) and additional remote sensing datasets such as aerial stereo photographs and LiDAR and tested in the Termini-Nerano landslides system (southern Apennines, Italy). The availability of such a wealth of materials allows also an unprecedented spatio-temporal reconstruction of the volume and the kinematic of the landslides system through which we could generate and validate the hazard map. Our analysis identifies fifteen slow-moving phenomena, traceable since 1955, whose total area amounts to 4.1 × 105 m2 and volume to ~1.4 × 106 m3. InSAR results prove that seven out of the fifteen slow-moving landslides are currently active and characterized by seasonal velocity patterns. These new insights on the dynamic of the landslides system have been selected as the main independent variables to train three MLAs (Artificial Neural Network, Generalized Boosting Model and Maximum Entropy) and derive the landslide hazard for the area. Finally, official population and buildings census data have been used to assess the landslide risk whose highest values are located in the crown area, south of Termini village, and nearby Nerano. This new methodology provides a different landslide risk scenario compared to the existing official documents for the study area and overall new insights on how to develop landslide risk management strategies worldwide based on a better understanding of slope processes thanks to the latest satellite technologies available.

Published
2021

39. Geomorphology of Cinque Terre National Park (Italy).

Author

Raso, E., Mandarino, A., Pepe, G., Calcaterra, D., Cevasco, A., Confuorto, P., Di Napoli, M., and Firpo, M.

Subjects
NATIONAL parks & reserves, TOPOGRAPHIC maps, AERIAL photographs, NATURAL landscaping, LAND management, GEOMORPHOLOGY, GLACIAL landforms
Abstract

This work presents the outcomes of the geomorphological investigation and mapping carried out within the Cinque Terre National Park (38 km2), an outstanding example of a human-modified landscape. Over the centuries, the natural landscape of Cinque Terre has been almost completely modified by slope terracing for agricultural purposes. Extensive field surveys, topographic maps examination and aerial photographs interpretation have led to the production, within a GIS environment, of a new geomorphological map at 1:18,000 scale which outlines the main genesis and related landforms and deposits: (i) gravity-induced features, (ii) fluvial and runoff features, (iii) coastal features and (iv) anthropogenic features. Special attention has been given to the mapping of terraced slopes, which at present are extremely vulnerable to gravity-driven processes and running water because of extensive farmland abandonment. The output map is a fundamental basis for future activities of hazard assessment and zonation and the definition of land management strategies. [ABSTRACT FROM AUTHOR]

Published
2021
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50. 25 years of satellite InSAR monitoring of ground instability and coastal geohazards in the archaeological site of Capo Colonna, Italy

Author

Notarnicola, C., Paloscia, S., Pierdicca, N., Mitchard, E., Cigna, F., Confuorto, P., Novellino, A., Tapete, D., Di Martire, D., Ramondini, M., Calcaterra, D., Plank, S., Ietto, F., Brigante, A., Sowter, A., Notarnicola, C., Paloscia, S., Pierdicca, N., Mitchard, E., Cigna, F., Confuorto, P., Novellino, A., Tapete, D., Di Martire, D., Ramondini, M., Calcaterra, D., Plank, S., Ietto, F., Brigante, A., and Sowter, A.

Abstract

For centuries the promontory of Capo Colonna in Calabria region, southern Italy, experienced land subsidence and coastline retreat to an extent that the archaeological ruins of the ancient Greek sanctuary are currently under threat of cliff failure, toppling and irreversible loss. Gas extraction in nearby wells is a further anthropogenic element to account for at the regional scale. Exploiting an unprecedented satellite Synthetic Aperture Radar (SAR) time series including ERS-1/2, ENVISAT, TerraSAR-X, COSMO-SkyMed and Sentinel-1A data stacks acquired between 1992 and 2016, this paper presents the first and most complete Interferometric SAR (InSAR) baseline assessment of land subsidence and coastal processes affecting Capo Colonna. We analyse the regional displacement trends, the correlation between vertical displacements with gas extraction volumes, the impact on stability of the archaeological heritage, and the coastal geohazard susceptibility. In the last 25 years, the land has subsided uninterruptedly, with highest annual line-of-sight deformation rates ranging between -15 and -20 mm/year in 2011-2014. The installation of 40 pairs of corner reflectors along the northern coastline and within the archaeological park resulted in an improved imaging capability and higher density of measurement points. This proved to be beneficial for the ground stability assessment of recent archaeological excavations, in an area where field surveying in November 2015 highlighted new events of cliff failure. The conceptual model developed suggests that combining InSAR results, geomorphological assessments and inventorying of wave-storms will contribute to unveil the complexity of coastal geohazards in Capo Colonna. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Published
2017

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