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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-2/W11
https://doi.org/10.5194/isprs-archives-XLII-2-W11-895-2019
© Author(s) 2019. 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-XLII-2-W11-895-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
05 May 2019
| 05 May 2019
GEOMATICS IN BRIDGE STRUCTURAL HEALTH MONITORING, INTEGRATING TERRESTRIAL LASER SCANNING TECHNIQUES AND GEOTECHNICAL INSPECTIONS ON A HIGH VALUE CULTURAL HERITAGE
F. Mugnai
DICEA - Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta 3, 50139 - Florence, Italy
European Commission, Joint Research Centre (JRC), Directorate A, Scientific Development Unit
L. Lombardi
Department of Earth Sciences, University of Florence, Largo Enrico Fermi 2, Arcetri, 50125, Florence, Italy
G. Tucci
DICEA - Dept. of Civil and Environmental Engineering, University of Florence, Via di S. Marta 3, 50139 - Florence, Italy
M. Nocentini
Department of Earth Sciences, University of Florence, Largo Enrico Fermi 2, Arcetri, 50125, Florence, Italy
G. Gigli
Department of Earth Sciences, University of Florence, Largo Enrico Fermi 2, Arcetri, 50125, Florence, Italy
R. Fanti
Department of Earth Sciences, University of Florence, Largo Enrico Fermi 2, Arcetri, 50125, Florence, Italy
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Short summary
Short summary
We improve the warning system (WS) used to forecast landslides in Emilia Romagna (Italy) by using averaged soil moisture estimates. We tested two approaches. The first (based on a soil moisture threshold under which the original WS is not used) is very simple, reduces false alarms and can be easily applied elsewhere. The second (integrating rainfall and soil moisture thresholds in the WS) is more complicated but reduces both false alarms and missed alarms.
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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.
Emanuele Intrieri, Federica Bardi, Riccardo Fanti, Giovanni Gigli, Francesco Fidolini, Nicola Casagli, Sandra Costanzo, Antonio Raffo, Giuseppe Di Massa, Giovanna Capparelli, and Pasquale Versace
Nat. Hazards Earth Syst. Sci., 17, 1713–1723, https://doi.org/10.5194/nhess-17-1713-2017, https://doi.org/10.5194/nhess-17-1713-2017, 2017
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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
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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
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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
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