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 Guadalentín 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 Guadalentín 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