27 research outputs found
PSI-based methodology to land subsidence mechanism recognition
Abstract. A methodology based on Persistent Scatterer Interferometry (PSI) is proposed in order to disentangle the contribution of different processes that act at different spatio-temporal scales in land subsidence (i.e. vadose zone processes as swelling/shrinkage of clay soils, soil consolidation and fluid extraction). The methodology was applied in different Italian geological contexts characterized by natural and anthropic processes (i.e. a Prealpine valley and the Po Plain in northern Italy)
Application of multi-sensor advanced DInSAR analysis to severe land subsidence recognition: Alto GuadalentĂn Basin (Spain)
Multi-sensor advanced DInSAR analyses have been performed and compared with
two GPS station measurements, in order to evaluate the land subsidence
evolution in a 20-year period, in the Alto GuadalentĂn Basin where the
highest rate of man-induced subsidence (> 10 cm yr−1) of
Europe had been detected. The control mechanisms have been examined
comparing the advanced DInSAR data with conditioning and triggering factors
(i.e. isobaths of Plio-Quaternary deposits, soft soil thickness and
piezometric level)
Satellite based radar interferometry to mapping and monitoring swelling/shrinking clay soils
A methodology for the study of swelling/shrinkage through the satellite radar images acquired from 1992 to 2010 by different sensors (ERS, RADARSAT) and processed by means of the SqueeSAR™ technique is proposed. The methodology aimed at improving the understanding of the kinematical behavior of swelling/shrinking processes, mapping these soils, monitoring the soil volume change and enabling also non-expert users to handle DInSAR data. The methodology has been applied and validated in an area of the Northern Italy where swelling/shrinking soils are frequent. The map of the ground displacements was compared with the geological model of the subsurface and with the distribution of the damaged buildings. The obtained results are helpful in land use planning to identify and quantify the swelling/ shrinkage of clay soils
Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis
Recent improvement to Advanced Differential Interferometric SAR (A-DInSAR) time
series quality enhances the knowledge of various geohazards. Ground motion studies need an
appropriate methodology to exploit the great potential contained in the A-DInSAR time series.
Here, we propose a methodology to analyze multi-sensors and multi-temporal A-DInSAR data for
the geological interpretation of areas affected by land subsidence/uplift and seasonal movements.
The methodology was applied in the plain area of the Oltrepo Pavese (Po Plain, Italy) using ERS-1/2
and Radarsat data, processed using the SqueeSAR™algorithm, and covering time spans, respectively,
from 1992 to 2000 and from 2003 to 2010. The test area is a representative site of the Po Plain, affected
by various geohazards and characterized by moderate rates of motion, ranging from 10 to 4 mm/yr.
Different components of motion were recognized: linear, non-linear, and seasonal deformational
behaviors. Natural and man-induced processes were identified such as swelling/shrinkage of clayey
soils, land subsidence due to load of new buildings, moderate tectonic uplift, and seasonal ground
motion due to seasonal groundwater level variations
PSI-based methodology to land subsidence mechanism recognition
A methodology based on Persistent Scatterer Interferometry (PSI) is proposed in order to disentangle the contribution of different processes that act at different spatio-temporal scales in land subsidence (i.e. vadose zone processes as swelling/shrinkage of clay soils, soil consolidation and fluid extraction). The methodology was applied in different Italian geological contexts characterized by natural and anthropic processes (i.e. a Prealpine valley and the Po Plain in northern Italy)
PSI-based methodology to land subsidence mechanism recognition
A methodology based on Persistent Scatterer Interferometry (PSI) is proposed in order to disentangle the contribution of different processes that act at different spatio-temporal scales in land subsidence (i.e. vadose zone processes as swelling/shrinkage of clay soils, soil consolidation and fluid extraction). The methodology was applied in different Italian geological contexts characterized by natural and anthropic processes (i.e. a Prealpine valley and the Po Plain in northern Italy)
A novel method for landslides investigation through A-DINSAR time series
In recent years, Advanced Differential Interferometric Synthetic-Aperture
Radar A-DInSAR technique has advanced rapidly for detecting and
monitoring ground surface deformations due to landslides. Identification
of the areas affected by ground motion through A-DInSAR data is generally
based on visual inspection and hotspot or cluster analysis of average
displacement rates. However, interpreting A-DInSAR time series of a
particular area provides a better indication of the real trend of displacement
of a landslide, while identifying the possible moment of acceleration of the
deformation process as well. A novel methodology is then proposed for
identifying different typologies of ground motion areas mainly related to
landslide phenomena at a regional scale, by means of A-DinSAR data at high
spatial and temporal resolutions. This methodological approach was tested
and validated in Piedmont region northern Italy, by means of RADARSAT
and COSMO-SkyMed satellite data, in both ascending and descending
modes. Linear constant in time displacement and non-linear acceleration
or deceleration in the displacement rate trends were recognised, allowing
characterisation of the kinematic pattern of a landslide or a portion of it. Local
and site-specific scale analyses, performed in an Alpine valley and in two
hillslopes representative of the main geological/geomorphological contexts
of the study area, validated the results obtained at the regional scale. This
supported the interpretation of the driving mechanism for such known
landslides, or other geological processes which can cause ground motion
along slopes. The developed procedure can allow one to specify priority
areas for prevention activities, in order to optimise the costs and benefits
of designing a plan to monitor instability phenomena at regional and sitespecific
scales. Moreover, ground motion areas identified by different sensors
in the same landslide help in the characterization of the state of activity of
this phenomenon, identifying also possible moments of re-activation
Multi-sensor SAR data for landslide inventory updating: the case study of Piemonte Region
In the last decade, satellite radar differential interferometry (DInSAR) has been used in updating the landslides
inventories. The technique allows to monitor the deformation patterns over large areas, in order to verify and/or modify
the landslide boundaries. Moreover, it contributes to define the state of activity of a phenomenon. The improvements of
the SAR data, guaranteed by the COSMO-SkyMed satellites and by the future ESA Sentinel missions, that act at higher
spatio-temporal resolution, require appropriate methodologies for analyzing large datasets of points of measures.
To address to these problems, we present a guiding procedure to analyze multi-sensors SAR dataset with the aim of
updating landslides inventories. We applied the methodology in Piemonte region, a wide area of north-western Italy
affected by a big amount of different types of landslides. We use satellites images acquired, in ascending and descending
acquisition geometry, by C-band (ERS ½, ENVISAT, RADARSAT) and X-band (COSMO-SkyMed) sensors and
processed using SqueeSARTM, PSInSARTM and PSP-IfSAR techniques. The project was carried out in collaboration with
ARPA Piemonte and a part of the interferometric data were provided by the Italian Ministry of Environment in the frame
of the “Extraordinary Plan of Environmental Remote Sensing” (PST-A).
The developed methodology consists of three main steps: 1) post-processing elaborations of the SAR data, for
removing possible errors which could affect the dataset; 2) identification of the ground motion areas characterized by
different deformation style (i.e. lowering, uplift and non-linear trend) by the use of automatic and semi-automatic
statistical analysis, based on Principal Component Analysis, on the displacement time series; 3) analysis between the
identified ground motion areas and the landslides distribution (The Piemonte Landslide inventory–SIFRAP) both at
regional scale and at local scale, thanks to detailed in situ analysis for the most interesting sites.
Integrating multi-sensor SAR data collected for a continuous period of 24 years (from 1992 to 2015) provided
important information on the landslides detection at regional and local scales, in the different geological,
geomorphological and environmental contexts of the Piemonte region. Three study areas, where SAR images of all the
considered sensors were available, were selected for representing the main contexts of Piemonte region: the Susa (528
km2 wide) and the Orco-Lanzo (996 km2 wide) valleys, representative of the Alps domain; the western Turin hill (404
km2 wide), representative of the Turin hill context. The availability of the large archive of SAR data allowed the backmonitoring
of the time evolution of different phenomena. In particular, different phases of activation, re-activation,
acceleration or stabilization of the phenomena were recognized. In addition we have assessed the performance of the
multi-sensors SAR data for monitoring different landslides types
Multi-sensor SAR data for landslide inventory updating: the case study of Piemonte Region
In the last decade, satellite radar differential interferometry (DInSAR) has been used in updating the landslides
inventories. The technique allows to monitor the deformation patterns over large areas, in order to verify and/or modify
the landslide boundaries. Moreover, it contributes to define the state of activity of a phenomenon. The improvements of
the SAR data, guaranteed by the COSMO-SkyMed satellites and by the future ESA Sentinel missions, that act at higher
spatio-temporal resolution, require appropriate methodologies for analyzing large datasets of points of measures.
To address to these problems, we present a guiding procedure to analyze multi-sensors SAR dataset with the aim of
updating landslides inventories. We applied the methodology in Piemonte region, a wide area of north-western Italy
affected by a big amount of different types of landslides. We use satellites images acquired, in ascending and descending
acquisition geometry, by C-band (ERS ½, ENVISAT, RADARSAT) and X-band (COSMO-SkyMed) sensors and
processed using SqueeSARTM, PSInSARTM and PSP-IfSAR techniques. The project was carried out in collaboration with
ARPA Piemonte and a part of the interferometric data were provided by the Italian Ministry of Environment in the frame
of the “Extraordinary Plan of Environmental Remote Sensing” (PST-A).
The developed methodology consists of three main steps: 1) post-processing elaborations of the SAR data, for
removing possible errors which could affect the dataset; 2) identification of the ground motion areas characterized by
different deformation style (i.e. lowering, uplift and non-linear trend) by the use of automatic and semi-automatic
statistical analysis, based on Principal Component Analysis, on the displacement time series; 3) analysis between the
identified ground motion areas and the landslides distribution (The Piemonte Landslide inventory–SIFRAP) both at
regional scale and at local scale, thanks to detailed in situ analysis for the most interesting sites.
Integrating multi-sensor SAR data collected for a continuous period of 24 years (from 1992 to 2015) provided
important information on the landslides detection at regional and local scales, in the different geological,
geomorphological and environmental contexts of the Piemonte region. Three study areas, where SAR images of all the
considered sensors were available, were selected for representing the main contexts of Piemonte region: the Susa (528
km2 wide) and the Orco-Lanzo (996 km2 wide) valleys, representative of the Alps domain; the western Turin hill (404
km2 wide), representative of the Turin hill context. The availability of the large archive of SAR data allowed the backmonitoring
of the time evolution of different phenomena. In particular, different phases of activation, re-activation,
acceleration or stabilization of the phenomena were recognized. In addition we have assessed the performance of the
multi-sensors SAR data for monitoring different landslides types