Unsteady sediment discharge in earth flows: A case study from the Mount Pizzuto earth flow, southern Italy

Guerriero, Luigi; Bertello, Lara; Cardozo, Néstor; Berti, Matteo; Grelle, Gerardo; Revellino, Paola

doi:10.1016/j.geomorph.2017.07.011

Cited by 32 publications

(23 citation statements)

References 60 publications

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“…The displacement field of Figure 4b depicts a typical condition for this kind of landslide with velocity variations related to the width of the landslide corridor and to the distance from the toe. Specifically, as the width of the landslide corridor decreases, velocity increase and vice versa [40]. In addition, the proposed coded procedure seems to be able to recognize (lateral) spreading occurring at the toe of the slide and velocity reduction at cross-section edges.…”

Section: Discussion and Validationmentioning

confidence: 93%

“…Once the displacement field is obtained, this is validated using a comparison with available local velocity data and considering the expected earthflow and glacier spatial kinematics [16,30,[39][40][41].…”

Section: Methodsmentioning

confidence: 99%

“…As observed, the density of recognized features in the moving zone is much higher than in the non-moving zone and no outliers (displacement vectors related to inconsistently tracked objects) are visually recognizable. Image rectification, completed using the coordinates of image corners, indicates a rectification RMSE of~1 m. Once the displacement field is obtained, this is validated using a comparison with available local velocity data and considering the expected earthflow and glacier spatial kinematics [16,30,[39][40][41].…”

Section: Slumgullion Landslidementioning

confidence: 99%

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Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation

et al. 2020

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An increasing number of satellite platforms provide daily images of the Earth’s surface that can be used in quantitative monitoring applications. However, their cost and the need for specific processing software make such products not often suitable for rapid mapping and deformation tracking. Google Earth images have been used in a number of mapping applications and, due to their free and rapid accessibility, they have contributed to partially overcome this issue. However, their potential in Earth’s surface displacement tracking has not yet been explored. In this paper, that aspect is analyzed providing a specific procedure and related MATLAB™ code to derive displacement field maps using digital image correlation of successive Google Earth images. The suitability of the procedure and the potential of such images are demonstrated here through their application to two relevant case histories, namely the Slumgullion landslide in Colorado and the Miage debris-covered glacier in Italy. Result validation suggests the effectiveness of the proposed procedure in deriving Earth’s surface displacement data from Google Earth images.

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Section: Discussion and Validationmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Slumgullion Landslidementioning

confidence: 99%

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Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation

et al. 2020

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“…In these conditions, the combination of multiple perspectives derived by the analysis of aerial imagery and the direct observation in the field is often the key to accurately report a landslide and its mesoscopic features in a map (e.g. Ardizzone et al, 2012;Calista et al, 2016;Guerriero et al, 2013;Guerriero et al, 2016;Guerriero et al, 2017a;Revellino, Grelle, Donnarumma, & Guadagno, 2010;Santangelo, Gioia, Cardinali, Guzzetti, & Schiattarella, 2015). Conversely, in urban areas, landslide identification is not always possible due to the masking action of urban settlements.…”

Section: Introductionmentioning

confidence: 99%

PS-driven inventory of town-damaging landslides in the Benevento, Avellino and Salerno Provinces, southern Italy

et al. 2019

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The Apennine provinces of Campania Region (southern Italy), Benevento, Avellino and Salerno, are known for their 'unstable towns' suffering periodic damage from landslides. Their identification and mapping are very challenging tasks, since boundary mapping under urban settlements is not always possible without time-consuming field analysis of building damage and/or expensive mid-term diffuse ground-surface deformation monitoring. To overcome this problem, an inventory of town-damaging landslides, guided by available Permanent Scatterers (PS) ground-deformation data, was prepared. It provides an updated tool suitable to guide future land planning and historical site restoration in the Apennine provinces of Campania Region. Our fourteen Map Sheets show active and local reactivation of suspended/ dormant landslides. Overall, 356 landslides were identified, amongst which 162 were identified as flows, 101 as slides, 1 as a spreads and 92 as complex landslides. To supplement our maps, a simplified distribution analysis based on major landslide morphometric characteristics was completed. ARTICLE HISTORY

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“…On 15 October 2015, a destructive overflow of the Tammaro and Calore rivers hit the town of Benevento in southern Italy, and the central sector of its province, already known for other natural hazards like landslides and earthquakes (e.g. Guerriero et al, 2016;Guerriero et al, 2017;Maresca, Castellano, De Matteis, Saccorotti, & Vaccariello, 2003;Revellino, Grelle, Donnarumma, & Guadagno, 2010). The event caused two deaths and severe damage to infrastructure, buildings and local agriculture.…”

Section: Introductionmentioning

confidence: 99%

Flood hazard of major river segments, Benevento Province, Southern Italy

Guerriero

Focareta²,

Fusco

et al. 2018

Journal of Maps

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On 15 October 2015, a storm-induced flood hit the central sector of Benevento Province (southern Italy) causing two deaths and severe damage to infrastructure, buildings and local agriculture. This area has a long history of similar events and since 1924 its major river segments have been monitored with several hydrometric stations. We used data from two of these stations and a LiDAR derived high-resolution topography to develop a flood hazard map. For map computation, we first derived a flood inundation map from topography. Subsequently we estimated the probability of exceedance of each specific fluvial stage from the combination of a Generalized Extreme Value and a Gamma fits of available hydrometric data. As boundary condition, we considered a reference scenario corresponding to an estimated 500 year flood. The hazard maps provide an overview of the flood hazard in the central sector of Benevento Province and floodplains zonation in flood perspective. ARTICLE HISTORY

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Unsteady sediment discharge in earth flows: A case study from the Mount Pizzuto earth flow, southern Italy

Cited by 32 publications

References 60 publications

Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation

Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation

PS-driven inventory of town-damaging landslides in the Benevento, Avellino and Salerno Provinces, southern Italy

Flood hazard of major river segments, Benevento Province, Southern Italy

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