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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-M-2-2023
https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-273-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-273-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
24 Jun 2023
| 24 Jun 2023
VIRTUAL INSPECTION BASED ON 3D SURVEY SUPPORTING RISKS DETACHMENT ANALYSIS IN PIETRAFORTE STONE BUILT HERITAGE
V. Bonora
GeCO Lab, Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta, 3 - 50139 Florence, Italy
I. Centauro
LAM Lab, Department of Earth Sciences, University of Florence, Via A. La Pira 4, 50125 Florence, Italy
L. Fiorini
GeCO Lab, Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta, 3 - 50139 Florence, Italy
La Sapienza University of Rome, P.le A. Moro, 5, 00185 Rome, Italy
A. Conti
GeCO Lab, Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta, 3 - 50139 Florence, Italy
T. Salvatici
LAM Lab, Department of Earth Sciences, University of Florence, Via A. La Pira 4, 50125 Florence, Italy
S. Calandra
LAM Lab, Department of Earth Sciences, University of Florence, Via A. La Pira 4, 50125 Florence, Italy
Department of Chemistry Ugo Schiff, University of Florence, Via della Lastruccia, 13 - 50019 Sesto Fiorentino (FI), Italy
R. Raffa
GeCO Lab, Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta, 3 - 50139 Florence, Italy
E. Intrieri
LAM Lab, Department of Earth Sciences, University of Florence, Via A. La Pira 4, 50125 Florence, Italy
C. A. Garzonio
LAM Lab, Department of Earth Sciences, University of Florence, Via A. La Pira 4, 50125 Florence, Italy
G. Tucci
GeCO Lab, Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta, 3 - 50139 Florence, Italy
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 45–50, https://doi.org/10.5194/isprs-archives-XLII-5-45-2018, https://doi.org/10.5194/isprs-archives-XLII-5-45-2018, 2018
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In this paper, we present the application of the physically based HIRESSS model (High Resolution Stability Simulator) to forecast the occurrence of shallow landslides in a portion of the Aosta Valley region (Italy). An in-depth study of the geotechnical and hydrological properties of the hillslopes controlling shallow landslides formation was conducted, in order to generate an input map of parameters. The main aim of this study is to set up a regional landslide early warning system.
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Short summary
Short summary
A local scale GB-InSAR system was implemented for mapping and monitoring slope landslide residual deformations and for early warning purposes in case of landslide reactivations, with the aim of assuring the safety of the valley inhabitants and the personnel involved in the post-event recovery phase. The here presented methodology could represent a useful contribution to a better understanding of landslide phenomena and decision making process during the post-emergency management activities.
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Short summary
Short summary
Landslides are a threat not only to people but also to important infrastructure, like highways. Nowadays there are several monitoring systems that are able to detect slope displacements in order to give prompt alarms. On the other hand, such instruments produce a huge amount of information, which is often not totally used and which can also represent an issue for data storage and transmission. In this paper we explain how we dealt with the large quantity of data provided by one of these tools.
G. Tucci, V. Bonora, A. Conti, and L. Fiorini
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W5, 679–686, https://doi.org/10.5194/isprs-archives-XLII-2-W5-679-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W5-679-2017, 2017
G. Tucci, V. Bonora, A. Conti, and L. Fiorini
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W5, 687–693, https://doi.org/10.5194/isprs-archives-XLII-2-W5-687-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W5-687-2017, 2017
G. Bitelli, C. Balletti, R. Brumana, L. Barazzetti, M. G. D’Urso, F. Rinaudo, and G. Tucci
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W5, 83–90, https://doi.org/10.5194/isprs-archives-XLII-2-W5-83-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W5-83-2017, 2017
Federica Ferrigno, Giovanni Gigli, Riccardo Fanti, Emanuele Intrieri, and Nicola Casagli
Nat. Hazards Earth Syst. Sci., 17, 845–860, https://doi.org/10.5194/nhess-17-845-2017, https://doi.org/10.5194/nhess-17-845-2017, 2017
Short summary
Short summary
This paper represents one of the main outcomes of a 3-year PhD program at the Earth Sciences Department of the University of Firenze (Centre of Competence of the Italian Civil Protection for geohazards). The main objectives of this paper were to investigate the landslide kinematics through the monitoring activity using GB-InSAR technology and to validate the stabilization works effectiveness using the coupled action of the GB-InSAR and the observational method (OM).
V. Tesi, G. Tucci, V. Bonora, L. Fiorini, and A. Conti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 343–349, https://doi.org/10.5194/isprs-archives-XLII-5-W1-343-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-343-2017, 2017
C. Mastrodicasa, S. Montecchi, G. Tucci, A. Conti, and L. Fiorini
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 515–518, https://doi.org/10.5194/isprs-archives-XLII-5-W1-515-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-515-2017, 2017
G. Tucci and V. Bonora
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W1, 1–1, https://doi.org/10.5194/isprs-archives-XLII-5-W1-1-2017, https://doi.org/10.5194/isprs-archives-XLII-5-W1-1-2017, 2017
Emanuele Intrieri and Giovanni Gigli
Nat. Hazards Earth Syst. Sci., 16, 2501–2510, https://doi.org/10.5194/nhess-16-2501-2016, https://doi.org/10.5194/nhess-16-2501-2016, 2016
Short summary
Short summary
Forecasting a landslide collapse is a key element in risk reduction, but it is also a very difficult task due to scientific difficulties in predicting a complex natural event and the severe social repercussions caused by a false or missed alarm.
In order to help decision makers, we propose a method of increasing the confidence when making landslide predictions. This study also helps understand how geology affects landslide predictability by introducing a predictability index.
G. Tucci, V. Bonora, L. Fiorini, and A. Conti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 977–984, https://doi.org/10.5194/isprs-archives-XLI-B5-977-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-977-2016, 2016
E. Intrieri, G. Gigli, N. Casagli, and F. Nadim
Nat. Hazards Earth Syst. Sci., 13, 85–90, https://doi.org/10.5194/nhess-13-85-2013, https://doi.org/10.5194/nhess-13-85-2013, 2013
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