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Distributed physically based slope stability models usually provide outputs representing, on a pixel basis, the probability of failure of each cell. This kind of result, although scientifically sound, from an operational point of view has several limitations. First, the procedure of validation lacks standards. As instance, it is not straightforward to decide above which percentage of failure probability a pixel (or larger spatial units) should be considered unstable. Second, the validation procedure is a time-consuming task, usually requiring a long series of GIS operations to overlap landslide inventories and model outputs to extract statistically significant performance metrics. Finally, if model outputs are conceived to be used in the operational management of landslide hazard (e.g., early warning procedures), the pixeled probabilistic output is difficult to handle and a synthesis to characterize the hazard scenario over larger spatial units is usually required to issue warnings aimed at specific operational procedures. In this work, a tool is presented that automates the validation procedure for physically based distributed probabilistic slope stability models and translates the pixeled outputs in warnings released over larger spatial units like small watersheds. The tool is named DTVT (double-threshold validation tool) because it defines a warning criterion on the basis of two threshold values—the probability of failure above which a pixel should be considered stable (failure probability threshold, FPT) and the percentage of unstable pixels needed in each watershed to consider the hazard level widespread enough to justify the issuing of an alert (instability diffusion threshold, IDT). A series of GIS operations were organized in a model builder to reaggregate the raw instability maps from pixels to watershed; draw the warning maps; compare them with an existing landslide inventory; build a contingency matrix counting true positives, true negatives, false positive, and false negatives; and draw in a map the results of the validation. The DTVT tool was tested in an alert zone of the Aosta Valley (northern Italy) to investigate the high sensitivity of the results to the values selected for the two thresholds. Moreover, among 24 different configurations tested, we performed a quantitative comparison to identify which criterion (in the case of our study, there was an 85% or higher failure probability in 5% or more of the pixels of a watershed) produces the most reliable validation results, thus appearing as the most promising candidate to be used to issue alerts during civil protection warning activities.
Maria Alexandra Bulzinetti; Samuele Segoni; Giulio Pappafico; Elena Benedetta Masi; Guglielmo Rossi; Veronica Tofani. A Tool for the Automatic Aggregation and Validation of the Results of Physically Based Distributed Slope Stability Models. Water 2021, 13, 2313 .
AMA StyleMaria Alexandra Bulzinetti, Samuele Segoni, Giulio Pappafico, Elena Benedetta Masi, Guglielmo Rossi, Veronica Tofani. A Tool for the Automatic Aggregation and Validation of the Results of Physically Based Distributed Slope Stability Models. Water. 2021; 13 (17):2313.
Chicago/Turabian StyleMaria Alexandra Bulzinetti; Samuele Segoni; Giulio Pappafico; Elena Benedetta Masi; Guglielmo Rossi; Veronica Tofani. 2021. "A Tool for the Automatic Aggregation and Validation of the Results of Physically Based Distributed Slope Stability Models." Water 13, no. 17: 2313.
The influence of vegetation on mechanical and hydrological soil behavior represents a significant factor to be considered in shallow landslides modelling. Among the multiple effects exerted by vegetation, root reinforcement is widely recognized as one of the most relevant for slope stability. Lately, the literature has been greatly enriched by novel research on this phenomenon. To investigate which aspects have been most treated, which results have been obtained and which aspects require further attention, we reviewed papers published during the period of 2015–2020 dealing with root reinforcement. This paper—after introducing main effects of vegetation on slope stability, recalling studies of reference—provides a synthesis of the main contributions to the subtopics: (i) approaches for estimating root reinforcement distribution at a regional scale; (ii) new slope stability models, including root reinforcement and (iii) the influence of particular plant species, forest management, forest structure, wildfires and soil moisture gradient on root reinforcement. Including root reinforcement in slope stability analysis has resulted a topic receiving growing attention, particularly in Europe; in addition, research interests are also emerging in Asia. Despite recent advances, including root reinforcement into regional models still represents a research challenge, because of its high spatial and temporal variability: only a few applications are reported about areas of hundreds of square kilometers. The most promising and necessary future research directions include the study of soil moisture gradient and wildfire controls on the root strength, as these aspects have not been fully integrated into slope stability modelling.
Elena Masi; Samuele Segoni; Veronica Tofani. Root Reinforcement in Slope Stability Models: A Review. Geosciences 2021, 11, 212 .
AMA StyleElena Masi, Samuele Segoni, Veronica Tofani. Root Reinforcement in Slope Stability Models: A Review. Geosciences. 2021; 11 (5):212.
Chicago/Turabian StyleElena Masi; Samuele Segoni; Veronica Tofani. 2021. "Root Reinforcement in Slope Stability Models: A Review." Geosciences 11, no. 5: 212.
The analysis of slope stability over large areas is a demanding task for several reasons, such as the need for extensive datasets, the uncertainty of collected data, the difficulty of accounting for site-specific factors, and the considerable computation time required due to the size of investigated areas, which can pose major barriers, particularly in civil protection contexts where rapid analysis and forecasts are essential. However, as the identification of zones of higher failure probability is very useful for stakeholders and decision-makers, the scientific community has attempted to improve capabilities to provide physically based assessments. This study combined a transient seepage analysis of an unsaturated-saturated condition with an infinite slope stability model and probabilistic analysis through the use of a high-computing capacity parallelized platform. Both short- and long-term analyses were performed for a study area, and roles of evapotranspiration, vegetation interception, and the root increment of soil strength were considered. A model was first calibrated based on hourly rainfall data recorded over a 4-day event (December 14–17, 1999) causing destructive landslides to compare the results of model simulations to actual landslide events. Then, the calibrated model was applied for a long-term simulation where daily rainfall data recorded over a 4-year period (January 1, 2005–December 31, 2008) were considered to study the behavior of the area in response to a long period of rainfall. The calibration shows that the model can correctly identify higher failure probability within the time range of the observed landslides as well as the extents and locations of zones computed as the most prone ones. The long-term analysis allowed for the identification of a number of days when the slope factor of safety was lower than 1.2 over a significant number of cells. In all of these cases, zones approaching slope instability were concentrated in specific sectors and catchments of the study area. In addition, some subbasins were found to be the most recurrently prone to possible slope instability. Interestingly, the application of the adopted methodology provided clear indications of both weekly and seasonal fluctuations of overall slope stability conditions.
Veronica Tofani; Sabatino Cuomo; Elena Benedetta Masi; Mariagiovanna Moscariello; Guglielmo Rossi; Fabio Matano. Short and long term probabilistic slope stability analyses of a large area of unsaturated pyroclastic soils. 2021, 1 .
AMA StyleVeronica Tofani, Sabatino Cuomo, Elena Benedetta Masi, Mariagiovanna Moscariello, Guglielmo Rossi, Fabio Matano. Short and long term probabilistic slope stability analyses of a large area of unsaturated pyroclastic soils. . 2021; ():1.
Chicago/Turabian StyleVeronica Tofani; Sabatino Cuomo; Elena Benedetta Masi; Mariagiovanna Moscariello; Guglielmo Rossi; Fabio Matano. 2021. "Short and long term probabilistic slope stability analyses of a large area of unsaturated pyroclastic soils." , no. : 1.
Physically-based models employed for landslide forecasting are extremely sensitive to the use of geological information and a standard, universally accepted method to input maps containing information of geological interest into the models still has never been established. In this study, we used the information contained in a geo-database aimed to characterize the geotechnical and hydrological parameters of the hillslopes deposits in Tuscany, to find out how to organize and group the measurements to spatially create classes that mirror the distribution of the various types of bedrock lithology. Despite the deposits analysed are mainly consisting of well sorted silty sands, statistical analyses carried out on geotechnical and hydrological parameters highlighted that it is not possible to define a typical range of values with relation to the main mapped lithologies, because soil characteristics are not simply dependent on the bedrock typology from which the deposits originated. Instead, the analysis of the relationship of soil parameters with morphometric parameters (slope angle, profile curvature, planar curvature) shows that the highest correlation between the soil grain size class type (USCS classification) and morphometric attributes is with slope curvature, both profile and planar.
Veronica Tofani; Gabriele Bicocchi; Elena Benedetta Masi; Carlo Tacconi Stefanelli; Guglielmo Rossi; Filippo Catani. Characterization of Hillslope Deposits for Physically-Based Landslide Forecasting Models. Understanding and Reducing Landslide Disaster Risk 2020, 265 -272.
AMA StyleVeronica Tofani, Gabriele Bicocchi, Elena Benedetta Masi, Carlo Tacconi Stefanelli, Guglielmo Rossi, Filippo Catani. Characterization of Hillslope Deposits for Physically-Based Landslide Forecasting Models. Understanding and Reducing Landslide Disaster Risk. 2020; ():265-272.
Chicago/Turabian StyleVeronica Tofani; Gabriele Bicocchi; Elena Benedetta Masi; Carlo Tacconi Stefanelli; Guglielmo Rossi; Filippo Catani. 2020. "Characterization of Hillslope Deposits for Physically-Based Landslide Forecasting Models." Understanding and Reducing Landslide Disaster Risk , no. : 265-272.
On May 25, 2016, an artificial riverbank of the Arno River collapsed just upstream from the famous Ponte Vecchio bridge in the city of Florence, Italy, a UNESCO World Heritage Site. An analysis of the failure was performed to identify the damage condition of the involved structures, to define the causes of the failure, and preserve the site. This study was based on borehole integration and geotechnical characterization, terrestrial laser scanner (TLS) and digital photogrammetry (DP), bathymetric and geophysical surveys, riverbank stability analysis, and wall seismic vibrations assessment. The TLS survey results were used to characterize the three-dimensional (3D) wall deformations pattern, the landslide geometry, and to define the involved volumes. The riverbank stability analysis demonstrates that the lower safety factor was obtained in the case of complete saturation of filling materials and low river level in accordance with the major cause of collapse being attributed to the loss of water from subterranean pipes.
S. Morelli; V. Pazzi; Luca Tanteri; M. Nocentini; L. Lombardi; G. Gigli; V. Tofani; N. Casagli. Characterization and Geotechnical Investigations of a Riverbank Failure in Florence, Italy, UNESCO World Heritage Site. Journal of Geotechnical and Geoenvironmental Engineering 2020, 146, 05020009 .
AMA StyleS. Morelli, V. Pazzi, Luca Tanteri, M. Nocentini, L. Lombardi, G. Gigli, V. Tofani, N. Casagli. Characterization and Geotechnical Investigations of a Riverbank Failure in Florence, Italy, UNESCO World Heritage Site. Journal of Geotechnical and Geoenvironmental Engineering. 2020; 146 (10):05020009.
Chicago/Turabian StyleS. Morelli; V. Pazzi; Luca Tanteri; M. Nocentini; L. Lombardi; G. Gigli; V. Tofani; N. Casagli. 2020. "Characterization and Geotechnical Investigations of a Riverbank Failure in Florence, Italy, UNESCO World Heritage Site." Journal of Geotechnical and Geoenvironmental Engineering 146, no. 10: 05020009.
Information regarding the shape and depth of a landslide sliding surface (LSS) is fundamental for the estimation of the volume of the unstable masses, which in turn is of primary importance for the assessment of landslide magnitude and risk scenarios as well as in refining stability analyses. To assess an LSS is not an easy task and is generally time-consuming and expensive. In this work, a method existing in the literature, based on the inclination of movement vectors along a cross-section to estimate the depth and geometry LSSs, is used for the first time while exploiting satellite interferometric data. Given the advent of satellite interferometric data and the related increasing availability of spatially dense and accurate measurements, we test the effectiveness of this method—here named the vector inclination method (VIM)—to four case landslides located in Italy characterized by different types of movement, kinematics and volume. Geotechnical and geophysical information of the LSS is used to validate the method. Our results show that each of the presented cases provides useful insight into the validity of VIM using satellite interferometric data. The main advantages of VIM applied to satellite interferometry are that it enables estimation of the LSS with a theoretical worldwide coverage, as well as with no need for onsite instrumentation or even direct access; however, a good density of measurement points in both ascending and descending geometry is necessary. The combined use of VIM and traditional investigations can provide a more accurate LSS model.
Emanuele Intrieri; William Frodella; Federico Raspini; Federica Bardi; Veronica Tofani. Using Satellite Interferometry to Infer Landslide Sliding Surface Depth and Geometry. Remote Sensing 2020, 12, 1462 .
AMA StyleEmanuele Intrieri, William Frodella, Federico Raspini, Federica Bardi, Veronica Tofani. Using Satellite Interferometry to Infer Landslide Sliding Surface Depth and Geometry. Remote Sensing. 2020; 12 (9):1462.
Chicago/Turabian StyleEmanuele Intrieri; William Frodella; Federico Raspini; Federica Bardi; Veronica Tofani. 2020. "Using Satellite Interferometry to Infer Landslide Sliding Surface Depth and Geometry." Remote Sensing 12, no. 9: 1462.
Long-term InSAR techniques, such as Persistent Scatterer Interferometry and Distributed Scatterer Interferometry, are effective approaches able to detect slow-moving landslides with millimeter precision. This study presents a novel approach of optimized hot spot analysis (OHSA) on persistent scatterers (PS) and distributed scatterers (DS), and evaluates its performance on detection of landslides across the Volterra area in central Tuscany region of Italy. 1625 ascending and 2536 descending PS processed from eight years (2003–2010) of ENVISAT images were produced by the PS-InSAR technique. In addition, 16,493 ascending and 9746 descending PS/DS measurement points (MP) processed from four years (2011–2014 for ascending orbits and 2010–2013 for descending orbits) of COSMO-SkyMed images were collected by the SqueeSAR approach. The OHSA approach was then implemented on the derived PS and DS through the analysis of incremental spatial autocorrelation and the Getis-Ord Gi* statistics. As a result of OHSA, PS and DS MP that are statistically significant with velocity >|±2| mm/year, p-value < 0.01 and z-score >|±2.58| were recognized as hot spots (HS). Meanwhile, a landslide inventory covering the Volterra area was manually prepared as the reference data for accuracy assessment of landslide detection. The results indicate that, in terms of OHSA-derived ENVISAT HS, the detection accuracy can be improved from 23.3% to 25.3% and from 50.7% to 66.4%, with decreased redundancy from 5.3% to 3.7% and from 5.3% to 2.4%, for ascending and descending orbits, respectively. In addition, for OHSA-derived Cosmo-SkyMed HS, the detection accuracy can be improved from 57.7% to 70.3% and from 73.8% to 81.5%, with decreased redundancy from 3.1% to 1.7% and from 3.4% to 2.1%, for ascending and descending orbits, respectively. Compared to traditional HS analysis such as Persistent Scatterers Interferometry Hot Spot and Cluster Analysis (PSI-HCA), OHSA has the significant advantage that the scale distance used for the Getis-Ord Gi* statistics can be automatically determined by the analysis of incremental spatial autocorrelation and accordingly no manual intervention or additional digital terrain model (DTM) is further needed. The proposed method is very succinct and can be easily implemented in diverse geographic information system (GIS) platforms. To the best of our knowledge, this is the first time that OHSA has been applied to PS and DS.
Ping Lu; Shibiao Bai; Veronica Tofani; Nicola Casagli. Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 156, 147 -159.
AMA StylePing Lu, Shibiao Bai, Veronica Tofani, Nicola Casagli. Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 156 ():147-159.
Chicago/Turabian StylePing Lu; Shibiao Bai; Veronica Tofani; Nicola Casagli. 2019. "Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers." ISPRS Journal of Photogrammetry and Remote Sensing 156, no. : 147-159.
Nicola Casagli; Veronica Tofani. Department of Earth Sciences, University of Florence. Landslides 2019, 16, 1809 -1813.
AMA StyleNicola Casagli, Veronica Tofani. Department of Earth Sciences, University of Florence. Landslides. 2019; 16 (9):1809-1813.
Chicago/Turabian StyleNicola Casagli; Veronica Tofani. 2019. "Department of Earth Sciences, University of Florence." Landslides 16, no. 9: 1809-1813.
Veronica Tofani; Filippo Catani; Kaoru Takara. EGU 2019 Sergey Soloviev Medal Lecture. Landslides 2019, 16, 1613 -1617.
AMA StyleVeronica Tofani, Filippo Catani, Kaoru Takara. EGU 2019 Sergey Soloviev Medal Lecture. Landslides. 2019; 16 (8):1613-1617.
Chicago/Turabian StyleVeronica Tofani; Filippo Catani; Kaoru Takara. 2019. "EGU 2019 Sergey Soloviev Medal Lecture." Landslides 16, no. 8: 1613-1617.
The 6-days repeatability of Sentinel-1 constellation allows building up an interferometric stack with unprecedented velocity. Easily updatable hot-spot analyses, frequently repeated following the update of Sentinel-1 images, represent very useful tools for MTInSAR (Multi-Temporal Interferometric Synthetic Aperture Radar) data analysis. Mountain regions are a challenging environment for interferometric analyses because of their climatic, morphological and land cover characteristics. In this context, MTInSAR data can retrieve reliable information over wide areas, with high cost-benefits ratio and where the installation of ground-based devices is not feasible. Considering the well-known limitations of interferometric techniques (such as fast motions or temporal and spatial decorrelation), hot-spot analyses are a viable solution for semi-automatic ground movements extraction over mountain regions. In this work, we present an example of a hot-spot analysis applied to a large stack of MTInSAR products generated by means of SqueeSAR technique over an alpine region (Valle d’Aosta, north-western Italy). The obtained outputs allow detecting 277 moving areas connected to landslides and mass wasting processes in general. These products are intended to be implemented in the landslide risk management chain of the region, focusing on landslide state of activity definition and landslide mapping.
Lorenzo Solari; Matteo Del Soldato; Roberto Montalti; Silvia Bianchini; Federico Raspini; Patrick Thuegaz; Davide Bertolo; Veronica Tofani; Nicola Casagli. A Sentinel-1 based hot-spot analysis: landslide mapping in north-western Italy. International Journal of Remote Sensing 2019, 40, 7898 -7921.
AMA StyleLorenzo Solari, Matteo Del Soldato, Roberto Montalti, Silvia Bianchini, Federico Raspini, Patrick Thuegaz, Davide Bertolo, Veronica Tofani, Nicola Casagli. A Sentinel-1 based hot-spot analysis: landslide mapping in north-western Italy. International Journal of Remote Sensing. 2019; 40 (20):7898-7921.
Chicago/Turabian StyleLorenzo Solari; Matteo Del Soldato; Roberto Montalti; Silvia Bianchini; Federico Raspini; Patrick Thuegaz; Davide Bertolo; Veronica Tofani; Nicola Casagli. 2019. "A Sentinel-1 based hot-spot analysis: landslide mapping in north-western Italy." International Journal of Remote Sensing 40, no. 20: 7898-7921.
Kyoji Sassa; Khang Dang; Fausto Guzzetti; Nicola Casagli; Binod Tiwari; Matjaž Mikoš; Vit Vilimek; Peter Bobrowsky; Kazuo Konagai; Željko Arbanas; Snježana Mihalić Arbanas; Ping Lu; Katsuo Sasahara; Irasema Alcantara-Ayala; Alexander Strom; Michael Hendry; Hiromitsu Yamagishi; Veronica Tofani; Sabatino Cuomo; Faisal Fathani; Jan Klimeš; Fawu Wang; Paola Reichenbach; Candan Gokceoglu; Daisuke Higaki; Tomofumi Koyama. Invited and accepted speakers of the Fifth World Landslide Forum in Kyoto, 2020. Landslides 2019, 16, 431 -446.
AMA StyleKyoji Sassa, Khang Dang, Fausto Guzzetti, Nicola Casagli, Binod Tiwari, Matjaž Mikoš, Vit Vilimek, Peter Bobrowsky, Kazuo Konagai, Željko Arbanas, Snježana Mihalić Arbanas, Ping Lu, Katsuo Sasahara, Irasema Alcantara-Ayala, Alexander Strom, Michael Hendry, Hiromitsu Yamagishi, Veronica Tofani, Sabatino Cuomo, Faisal Fathani, Jan Klimeš, Fawu Wang, Paola Reichenbach, Candan Gokceoglu, Daisuke Higaki, Tomofumi Koyama. Invited and accepted speakers of the Fifth World Landslide Forum in Kyoto, 2020. Landslides. 2019; 16 (2):431-446.
Chicago/Turabian StyleKyoji Sassa; Khang Dang; Fausto Guzzetti; Nicola Casagli; Binod Tiwari; Matjaž Mikoš; Vit Vilimek; Peter Bobrowsky; Kazuo Konagai; Željko Arbanas; Snježana Mihalić Arbanas; Ping Lu; Katsuo Sasahara; Irasema Alcantara-Ayala; Alexander Strom; Michael Hendry; Hiromitsu Yamagishi; Veronica Tofani; Sabatino Cuomo; Faisal Fathani; Jan Klimeš; Fawu Wang; Paola Reichenbach; Candan Gokceoglu; Daisuke Higaki; Tomofumi Koyama. 2019. "Invited and accepted speakers of the Fifth World Landslide Forum in Kyoto, 2020." Landslides 16, no. 2: 431-446.
Nicola Casagli; Veronica Tofani. Establishment of ICL Italian network. Landslides 2018, 15, 1907 -1908.
AMA StyleNicola Casagli, Veronica Tofani. Establishment of ICL Italian network. Landslides. 2018; 15 (10):1907-1908.
Chicago/Turabian StyleNicola Casagli; Veronica Tofani. 2018. "Establishment of ICL Italian network." Landslides 15, no. 10: 1907-1908.
In this work, we apply a physically based model, namely the HIRESSS (HIgh REsolution Slope Stability Simulator) model, to forecast the occurrence of shallow landslides at the regional scale. HIRESSS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions during a rainfall event. The modeling software is made up of two parts: hydrological and geotechnical. The hydrological model is based on an analytical solution from an approximated form of the Richards equation, while the geotechnical stability model is based on an infinite slope model that takes the unsaturated soil condition into account. The test area is a portion of the Aosta Valley region, located in the northwest of the Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400 m a.s.l. on the Dora Baltea River's floodplain to 4810 m a.s.l. at Mont Blanc. In the study area, the mean annual precipitation is about 800–900 mm. These features make the territory very prone to landslides, mainly shallow rapid landslides and rockfalls. In order to apply the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslide formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed using 12 survey points. The data collected contributed to the generation of an input map of parameters for the HIRESSS model. In order to consider the effect of vegetation on slope stability, the soil reinforcement due to the presence of roots was also taken into account; this was done based on vegetation maps and literature values of root cohesion. The model was applied using back analysis for two past events that affected the Aosta Valley region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, provided good results and a good prediction accuracy for the HIRESSS model from both a temporal and spatial point of view.
Teresa Salvatici; Veronica Tofani; Guglielmo Rossi; Michele D'Ambrosio; Carlo Tacconi Stefanelli; Elena Benedetta Masi; Ascanio Rosi; Veronica Pazzi; Pietro Vannocci; Miriana Petrolo; Filippo Catani; Sara Ratto; Hervè Stevenin; Nicola Casagli. Application of a physically based model to forecast shallow landslides at a regional scale. Natural Hazards and Earth System Sciences 2018, 18, 1919 -1935.
AMA StyleTeresa Salvatici, Veronica Tofani, Guglielmo Rossi, Michele D'Ambrosio, Carlo Tacconi Stefanelli, Elena Benedetta Masi, Ascanio Rosi, Veronica Pazzi, Pietro Vannocci, Miriana Petrolo, Filippo Catani, Sara Ratto, Hervè Stevenin, Nicola Casagli. Application of a physically based model to forecast shallow landslides at a regional scale. Natural Hazards and Earth System Sciences. 2018; 18 (7):1919-1935.
Chicago/Turabian StyleTeresa Salvatici; Veronica Tofani; Guglielmo Rossi; Michele D'Ambrosio; Carlo Tacconi Stefanelli; Elena Benedetta Masi; Ascanio Rosi; Veronica Pazzi; Pietro Vannocci; Miriana Petrolo; Filippo Catani; Sara Ratto; Hervè Stevenin; Nicola Casagli. 2018. "Application of a physically based model to forecast shallow landslides at a regional scale." Natural Hazards and Earth System Sciences 18, no. 7: 1919-1935.
According to the United Nations Educational, Scientific and Cultural Organization (UNESCO) agency, the World Heritage Sites (WHS) inscribed in the World Heritage List (WHL) must be safeguarded with an adequate protection system, in order to guarantee their integrity and authenticity. Currently, many UNESCO sites are threatened by geohazards, but the safeguard of these sites does not seem to be wide-ranging. Looking at the standard list of factors affecting the Outstanding Universal Value (OUV) of WHS, which has been adopted by the World Heritage Committee in 2008, it seems that only “sudden geological events” are considered as factors that undermine the protection of the properties. Furthermore, it is well known that slow-kinematic phenomena can also threaten cultural and natural heritage. This study proposes a satellite InSAR-based procedure to identify and monitor the temporal and spatial evolution of ground deformation related to slow-kinematic geohazards (slow-moving landslides and ground-subsidence). This procedure, applied in this work on the Tuscany Region (Italy), simplify the InSAR products interpretation, making them easily exploitable by the local WHS managers for long-term geohazards monitoring and conservation strategies. These activities, thanks to the main characteristics of the recent Sentinel-1 data (short revisit time, free availability without any restrictions and worldwide coverage), can be defined for each UNESCO site of the world.
Laura Pastonchi; Anna Barra; Oriol Monserrat; Guido Luzi; Lorenzo Solari; Veronica Tofani. Satellite Data to Improve the Knowledge of Geohazards in World Heritage Sites. Remote Sensing 2018, 10, 992 .
AMA StyleLaura Pastonchi, Anna Barra, Oriol Monserrat, Guido Luzi, Lorenzo Solari, Veronica Tofani. Satellite Data to Improve the Knowledge of Geohazards in World Heritage Sites. Remote Sensing. 2018; 10 (7):992.
Chicago/Turabian StyleLaura Pastonchi; Anna Barra; Oriol Monserrat; Guido Luzi; Lorenzo Solari; Veronica Tofani. 2018. "Satellite Data to Improve the Knowledge of Geohazards in World Heritage Sites." Remote Sensing 10, no. 7: 992.
We propose a methodology to couple rainfall thresholds and susceptibility maps for dynamic landslide hazard assessment at regional scale. Both inputs are combined in a purposely-built hazard matrix to get a spatially and temporally variable definition of landslide hazard: while statistical rainfall thresholds are used to accomplish a temporal forecasting with very coarse spatial resolution, landslide susceptibility maps provide static spatial information about the probability of landslide occurrence at fine spatial resolution. The test site is the Northern part of Tuscany (Italy), where a recent landslide susceptibility map and a set of recently updated rainfall thresholds are available. These products were modified and updated to meet the requirements of the proposed procedure: the susceptibility map was reclassified and the threshold set was expanded defining additional thresholds. The hazard matrix combines three susceptibility classes (S1, low susceptibility; S2 medium susceptibility; S3 high susceptibility) and three rainfall rate classes (R1, R2, R3), defining five hazard classes, from H0 (null hazard) to H4 (high hazard). A key passage of the procedure is the appropriate calibration and validation of the matrix, letting the hazard classes have a precise meaning in terms of expected consequences and hazard management. The employ of the proposed procedure in a regional warning system brings two main advantages: (i) it is possible to better hypothesize when and where landslide are expected and with which hazard degree, thus fostering a more effective hazard and risk management (e.g., setting priorities of intervention); (ii) the spatial resolution of the regional scale warning system is markedly refined because from time to time the areas where landslides are expected represent only a fraction of the alert zone.
Samuele Segoni; Veronica Tofani; Ascanio Rosi; Filippo Catani; Nicola Casagli. Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale. Frontiers in Earth Science 2018, 6, 1 .
AMA StyleSamuele Segoni, Veronica Tofani, Ascanio Rosi, Filippo Catani, Nicola Casagli. Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale. Frontiers in Earth Science. 2018; 6 ():1.
Chicago/Turabian StyleSamuele Segoni; Veronica Tofani; Ascanio Rosi; Filippo Catani; Nicola Casagli. 2018. "Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale." Frontiers in Earth Science 6, no. : 1.
This paper presents the preliminary results of the IPL project 196 “Development and applications of a multi-sensor drone for geohazards monitoring and mapping.” The objective of the project is to test the applicability of a multi-sensor drone for the mapping and monitoring of different types of geohazards. The Department of Earth Sciences of the University of Florence has developed a new type of drone airframe. Several survey campaigns were performed in the village of Ricasoli, in the Upper Arno river Valley (Tuscany, Italy) with the drone equipped with an optical camera to understand the possibility of this rising technology to map and characterize landslides. The aerial images were combined and analyzed using Structure-from-Motion (SfM) software. The collected data allowed an accurate reconstruction and mapping of the detected landslides. Comparative analysis of the obtained DTMs also permitted the detection of some slope portions being prone to failure and to evaluate the area and volume of the involved mass.
Guglielmo Rossi; Luca Tanteri; Veronica Tofani; Pietro Vannocci; Sandro Moretti; Nicola Casagli. Multitemporal UAV surveys for landslide mapping and characterization. Landslides 2018, 15, 1045 -1052.
AMA StyleGuglielmo Rossi, Luca Tanteri, Veronica Tofani, Pietro Vannocci, Sandro Moretti, Nicola Casagli. Multitemporal UAV surveys for landslide mapping and characterization. Landslides. 2018; 15 (5):1045-1052.
Chicago/Turabian StyleGuglielmo Rossi; Luca Tanteri; Veronica Tofani; Pietro Vannocci; Sandro Moretti; Nicola Casagli. 2018. "Multitemporal UAV surveys for landslide mapping and characterization." Landslides 15, no. 5: 1045-1052.
In this study, the main focus is the application and improvement of four empirical models, which account for the pyroclastic cover deposit thickness (PCDT) spatial distribution with respect to the bedrock surrounding the Somma-Vesuvius volcano (Campania, southern Italy). Three models, which are already known in the literature, link the depth to bedrock to the morphological features of a slope. An original model called SEPT (slope exponential pyroclastic thickness) is presented in this manuscript and combines the initial total thickness of ash-fall pyroclastic cover with the slope gradient. All models were applied and validated using field measurements derived from this and preceding studies in the study area.
Matteo Del Soldato; Veronica Pazzi; Samuele Segoni; Pantaleone De Vita; Veronica Tofani; Sandro Moretti. Spatial modeling of pyroclastic cover deposit thickness (depth to bedrock) in peri-volcanic areas of Campania (southern Italy). Earth Surface Processes and Landforms 2018, 43, 1757 -1767.
AMA StyleMatteo Del Soldato, Veronica Pazzi, Samuele Segoni, Pantaleone De Vita, Veronica Tofani, Sandro Moretti. Spatial modeling of pyroclastic cover deposit thickness (depth to bedrock) in peri-volcanic areas of Campania (southern Italy). Earth Surface Processes and Landforms. 2018; 43 (9):1757-1767.
Chicago/Turabian StyleMatteo Del Soldato; Veronica Pazzi; Samuele Segoni; Pantaleone De Vita; Veronica Tofani; Sandro Moretti. 2018. "Spatial modeling of pyroclastic cover deposit thickness (depth to bedrock) in peri-volcanic areas of Campania (southern Italy)." Earth Surface Processes and Landforms 43, no. 9: 1757-1767.
The Island of Ischia is located in the Tyrrhenian Sea, approximately 30 km WSW from the city of Naples in Southern Italy. The Island is a debris-flow prone area due to its steep slopes covered by loose volcanic lithologies. On April 30th 2006, following several hours of rainfall, four soil slips were triggered on the slopes of Mt. Vezzi (about 400 m a.s.l.) in the SE portion of the island. The soil slips changed quickly into debris flows that reached the inhabited at the foot of the hill, causing four victims, destroying several buildings and forcing the evacuation of 250 inhabitants. This work presents the analysis of the triggering and propagation phase of the phenomena. In particular, to model the triggering conditions, a finite element analysis was used to reconstruct the variations in pore water pressure during the event in transient conditions. The limit equilibrium slope-stability method was then applied using the temporal pore water pressure distributions derived from the seepage analysis. The dynamic modeling of the propagation phase was carried out by means of two dynamic codes DAN-W and FLO2D, with the aim of evaluating the residual hazard linked to other potential debris flows recognized in the same area. Once the DAN-W and FLO2D models satisfactorily reproduced the 30th April events, the simulations were extended to a larger area, whose susceptibility to future landslide events has been determined through a detailed geomorphological survey and a following GIS analysis.
Massimiliano Nocentini; Veronica Tofani; Giovanni Gigli; Francesco Fidolini; Nicola Casagli. TXT-tool 4.039-3.3: Debris Flows Modeling for Hazard Mapping. Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools 2018, 761 -770.
AMA StyleMassimiliano Nocentini, Veronica Tofani, Giovanni Gigli, Francesco Fidolini, Nicola Casagli. TXT-tool 4.039-3.3: Debris Flows Modeling for Hazard Mapping. Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools. 2018; ():761-770.
Chicago/Turabian StyleMassimiliano Nocentini; Veronica Tofani; Giovanni Gigli; Francesco Fidolini; Nicola Casagli. 2018. "TXT-tool 4.039-3.3: Debris Flows Modeling for Hazard Mapping." Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools , no. : 761-770.
The current availability of advanced remote sensing technologies in the field of landslide analysis allows rapid and easily updatable data acquisitions, improving the traditional capabilities of detection, mapping and monitoring, optimizing field work, and allowing to investigate hazardous and inaccessible areas while granting at the same time the safety of the operators. In the recent years in particular, ground-based remote sensing techniques have undergone a significant increase of usage, thanks to their technological development and quality data improvement, offering advantages with respect to air- or spaceborne remote sensing techniques, in terms of data spatial resolution and accuracy, fast measurement and processing times, and portability and cost-effectiveness of the acquiring instruments. These advantages can be highlighted in the framework of landslide emergency management, when it is often urgently necessary to minimize survey time when operating in dangerous environments and gather all the required information as fast as possible. In this paper, the potential of some ground-based remote sensing techniques and the effectiveness of their synergic use is explored in several case studies, analyzing different slope instability processes at different scales of emergency or post-emergency management. Thanks to them and to the support of existing bibliography, the most common fields of application are suggested for all the considered ground-based sensor technologies and their level of effectiveness is evaluated in relation to the dynamics of landslide types.
Nicola Casagli; Stefano Morelli; William Frodella; Emanuele Intrieri; Veronica Tofani. TXT-tool 2.039-3.2 Ground-Based Remote Sensing Techniques for Landslides Mapping, Monitoring and Early Warning. Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools 2017, 255 -274.
AMA StyleNicola Casagli, Stefano Morelli, William Frodella, Emanuele Intrieri, Veronica Tofani. TXT-tool 2.039-3.2 Ground-Based Remote Sensing Techniques for Landslides Mapping, Monitoring and Early Warning. Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools. 2017; ():255-274.
Chicago/Turabian StyleNicola Casagli; Stefano Morelli; William Frodella; Emanuele Intrieri; Veronica Tofani. 2017. "TXT-tool 2.039-3.2 Ground-Based Remote Sensing Techniques for Landslides Mapping, Monitoring and Early Warning." Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools , no. : 255-274.
In this work the application of remote sensing for landslide detection and mapping is described. Among Earth Observation (EO) techniques optical and radar images are very effective tools for these applications since very high spatial resolution obtained by optical systems (currently in the order of tens of centimeters) and by the launching of Synthetic Aperture Radar (SAR) sensors, purposely built for interferometric applications with revisiting times of few days. In this paper the potentiality of both satellite optical and radar data is explored in a selected case study, analyzing different slope instability processes at different scales. Thanks to them and to the support of existing bibliography, the main advantages and disadvantages are highlighted as well as some suggestions are proposed concerning the main fields of applications.
Nicola Casagli; Veronica Tofani; Andrea Ciampalini; Federico Raspini; Ping Lu; Stefano Morelli. TXT-tool 2.039-3.1: Satellite Remote Sensing Techniques for Landslides Detection and Mapping. Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools 2017, 235 -254.
AMA StyleNicola Casagli, Veronica Tofani, Andrea Ciampalini, Federico Raspini, Ping Lu, Stefano Morelli. TXT-tool 2.039-3.1: Satellite Remote Sensing Techniques for Landslides Detection and Mapping. Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools. 2017; ():235-254.
Chicago/Turabian StyleNicola Casagli; Veronica Tofani; Andrea Ciampalini; Federico Raspini; Ping Lu; Stefano Morelli. 2017. "TXT-tool 2.039-3.1: Satellite Remote Sensing Techniques for Landslides Detection and Mapping." Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools , no. : 235-254.