Articles | Volume 13, issue 7
https://doi.org/10.5194/nhess-13-1873-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-13-1873-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Non-interferometric GB-SAR measurement: application to the Vallcebre landslide (eastern Pyrenees, Spain)
O. Monserrat
Institute of Geomatics, Castelldefels, Barcelona, Spain
J. Moya
Dept. of Geotechnical Engineering and Geosciences, Technical University of Catalonia-BarcelonaTech, Barcelona, Spain
G. Luzi
Institute of Geomatics, Castelldefels, Barcelona, Spain
M. Crosetto
Institute of Geomatics, Castelldefels, Barcelona, Spain
J. A. Gili
Dept. of Geotechnical Engineering and Geosciences, Technical University of Catalonia-BarcelonaTech, Barcelona, Spain
J. Corominas
Dept. of Geotechnical Engineering and Geosciences, Technical University of Catalonia-BarcelonaTech, Barcelona, Spain
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Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
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S. M. Mirmazloumi, Á. F. Gambin, Y. Wassie, A. Barra, R. Palamà, M. Crosetto, O. Monserrat, and B. Crippa
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M. Crosetto, O. Monserrat, N. Devanthéry, M. Cuevas-González, A. Barra, and B. Crippa
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M. Crosetto, O. Monserrat, N. Devanthéry, M. Cuevas-González, A. Barra, and B. Crippa
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M. Crosetto, N. Devanthéry, M. Cuevas-González, O. Monserrat, and B. Crippa
Proc. IAHS, 372, 311–314, https://doi.org/10.5194/piahs-372-311-2015, https://doi.org/10.5194/piahs-372-311-2015, 2015
Short summary
Short summary
Persistent Scatterer Interferometry (PSI) is a remote sensing technique used to monitor land deformation from interferometric SAR images. The main products that can be derived using the PSI technique are the deformation maps and the time series of deformation. In this paper, an approach to apply the PSI technique to a stack of Sentinel-1 images is described. Sentinel-1 deformation maps and time series obtained over the metropolitan area of Mexico DF are discussed.
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Maria Carmelia Ramlie, Oriol Monserrat, Bruno Crippa, Paula Olea-Encina, and Michele Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 459–464, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-459-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-459-2024, 2024
M. Crosetto, S. Shahbazi, M. Cuevas-González, J. Navarro, and M. Mróz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1229–1234, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1229-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1229-2023, 2023
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023, https://doi.org/10.5194/nhess-23-1987-2023, 2023
Short summary
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Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
M. Crosetto, L. Solari, A. Barra, O. Monserrat, M. Cuevas-González, R. Palamà, Y. Wassie, S. Shahbazi, S. M. Mirmazloumi, B. Crippa, and M. Mróz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 257–262, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-257-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-257-2022, 2022
Q. Gao, M. Crosetto, O. Monserrat, R. Palama, and A. Barra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 271–276, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-271-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-271-2022, 2022
N. Kotulak, M. Mleczko, M. Crosetto, R. Palamà, and M. Mróz
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S. M. Mirmazloumi, Á. F. Gambin, Y. Wassie, A. Barra, R. Palamà, M. Crosetto, O. Monserrat, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 307–312, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-307-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-307-2022, 2022
J. A. Navarro, D. García, M. Crosetto, and O. Monserrat
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 313–320, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-313-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-313-2022, 2022
R. Palamà, M. Crosetto, O. Monserrat, A. Barra, B. Crippa, M. Mróz, N. Kotulak, M. Mleczko, and J. Rapinski
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 321–326, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-321-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-321-2022, 2022
S. Shahbazi, M. Crosetto, and A. Barra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 349–354, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-349-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-349-2022, 2022
Y. Wassie, Q. Gao, O. Monserrat, A. Barra, B. Crippa, and M. Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 361–366, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-361-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-361-2022, 2022
M. Crosetto, L. Solari, J. Balasis-Levinsen, L. Bateson, N. Casagli, M. Frei, A. Oyen, D. A. Moldestad, and M. Mróz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 141–146, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-141-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-141-2021, 2021
J. A. Navarro, A. Barra, O. Monserrat, and M. Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 163–169, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-163-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-163-2021, 2021
Y. Wassie, M. Crosetto, G. Luzi, O. Monserrat, A. Barra, R. Palamá, M. Cuevas-González, S. M. Mirmazloumi, P. Espín-López, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 177–182, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-177-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-177-2021, 2021
P. Olea, O. Monserrat, C. Sierralta, A. Barra, L. Bono, F. Fuentes, Z. Qiu, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 1–6, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-1-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-1-2020, 2020
M. Crosetto and L. Solari
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 1–6, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-1-2020, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-1-2020, 2020
O. Monserrat, C. Cardenas, P. Olea, V. Krishnakumar, and B. Crippa
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 137–142, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-137-2020, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-137-2020, 2020
J. A. Navarro, G. Luzi, O. Monserrat, and M. Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1685–1690, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1685-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1685-2020, 2020
M. Crosetto, G. Luzi, O. Monserrat, A. Barra, M. Cuevas-González, R. Palamá, V. Krishnakumar, Y. Wassie, S. M. Mirmazloumi, P. Espín-López, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 287–292, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-287-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-287-2020, 2020
M. Crosetto, L. Solari, J. Balasis-Levinsen, N. Casagli, M. Frei, A. Oyen, and D. A. Moldestad
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 293–298, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-293-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-293-2020, 2020
M. Crosetto, O. Monserrat, A. Barra, M. Cuevas-González, V. Krishnakumar, M. Mróz, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1921–1926, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1921-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1921-2019, 2019
Olga Mavrouli, Jordi Corominas, Iñaki Ibarbia, Nahikari Alonso, Ioseba Jugo, Jon Ruiz, Susana Luzuriaga, and José Antonio Navarro
Nat. Hazards Earth Syst. Sci., 19, 399–419, https://doi.org/10.5194/nhess-19-399-2019, https://doi.org/10.5194/nhess-19-399-2019, 2019
Short summary
Short summary
A methodology is proposed for the quantitative risk assessment of roadways subjected to rockfalls, retaining wall failures, and slow moving landslides. It includes the calculation of the probability of occurrence of each hazard with a given level, based on an extensive collection of field data, and its association with the consequences. The latter was assessed considering the road damage repair cost for each level in terms of a fixed unit cost.
M. Crosetto, A. Budillon, A. Johnsy, G. Schirinzi, N. Devanthéry, O. Monserrat, and M. Cuevas-González
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 235–238, https://doi.org/10.5194/isprs-archives-XLII-3-235-2018, https://doi.org/10.5194/isprs-archives-XLII-3-235-2018, 2018
V. Krishnakumar, O. Monserrat, M. Crosetto, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 741–744, https://doi.org/10.5194/isprs-archives-XLII-3-741-2018, https://doi.org/10.5194/isprs-archives-XLII-3-741-2018, 2018
O. Monserrat, A. Barra, G. Herrera, S. Bianchini, C. Lopez, R. Onori, P. Reichenbach, R. Sarro, R. M. Mateos, L. Solari, S. Ligüérzana, and I. P. Carralero
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W4, 351–355, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, 2018
Q. Huang, M. Crosetto, O. Monserrat, and B. Crippa
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 457–463, https://doi.org/10.5194/isprs-annals-IV-2-W4-457-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-457-2017, 2017
M. Crosetto, O. Monserrat, G. Luzi, N. Devanthéry, M. Cuevas-González, and A. Barra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 593–596, https://doi.org/10.5194/isprs-archives-XLII-2-W7-593-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-593-2017, 2017
M. Crosetto, O. Monserrat, N. Devanthéry, M. Cuevas-González, A. Barra, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 597–600, https://doi.org/10.5194/isprs-archives-XLII-2-W7-597-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-597-2017, 2017
Marco Mulas, Jordi Corominas, Alessandro Corsini, and Jose Moya
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2016-253, https://doi.org/10.5194/nhess-2016-253, 2016
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Short summary
In this work, the Cross-Correlation Function is used in order to quantitatively investigate the time-lagged correlation between high frequency monitoring data on rainfall, piezometric and displacement with the objective to evidence hydro-mechanical processes in the Vallcebre landslide (Eastern Pyrenees, Spain). The analysis highlighted and constrained in time a dual triggering mechanism in which factors controlling movement change from the upper to the lower part of the landslide.
M. Crosetto, O. Monserrat, N. Devanthéry, M. Cuevas-González, A. Barra, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 835–839, https://doi.org/10.5194/isprs-archives-XLI-B7-835-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-835-2016, 2016
M. Crosetto, N. Devanthéry, M. Cuevas-González, O. Monserrat, and B. Crippa
Proc. IAHS, 372, 311–314, https://doi.org/10.5194/piahs-372-311-2015, https://doi.org/10.5194/piahs-372-311-2015, 2015
Short summary
Short summary
Persistent Scatterer Interferometry (PSI) is a remote sensing technique used to monitor land deformation from interferometric SAR images. The main products that can be derived using the PSI technique are the deformation maps and the time series of deformation. In this paper, an approach to apply the PSI technique to a stack of Sentinel-1 images is described. Sentinel-1 deformation maps and time series obtained over the metropolitan area of Mexico DF are discussed.
C. Abancó, M. Hürlimann, and J. Moya
Nat. Hazards Earth Syst. Sci., 14, 929–943, https://doi.org/10.5194/nhess-14-929-2014, https://doi.org/10.5194/nhess-14-929-2014, 2014
M. Crosetto, J. A. Gili, O. Monserrat, M. Cuevas-González, J. Corominas, and D. Serral
Nat. Hazards Earth Syst. Sci., 13, 923–933, https://doi.org/10.5194/nhess-13-923-2013, https://doi.org/10.5194/nhess-13-923-2013, 2013
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