Roberta Bonì

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.

From ERS-1/2 to Sentinel-1: two decades of subsidence monitored through A-DInSAR techniques in the Ravenna area (Italy)

Land subsidence due to underground resources exploitation is a well-known problem that affects many cities in the world, especially the ones located along the coastal areas where the combined effect of subsidence and sea level rise increases the flooding risk. In this study, 25 years of land subsidence affecting the Municipality of Ravenna (Italy) are monitored using Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) techniques. In particular, the exploitation of the new Sentinel-1A SAR data allowed us to extend the monitoring period till 2016, giving a better understanding of the temporal evolution of the phenomenon in the area. Two statistical approaches are applied to fully exploit the informative potential of the A-DInSAR results in a fast and systematic way. Thanks to the applied analyses, we described the behavior of the subsidence during the monitored period along with the relationship between the occurrence of the displacement and its main driving factors.

Integration of Multi-sensor A-DInSAR Data for Landslide Inventory Update

A systematic and reproducible methodology to analyze multi-sensors advanced satellite radar differential interferometry (A-DInSAR) data for identifying ground motion areas and for updating landsides inventories is proposed. We apply the methodology in a wide area of north-western Italy, corresponding to Piedmont region that is affected by different landslides. We use satellites images acquired, in ascending and descending acquisition geometry, by C-band (ERS ½ and ENVISAT, RADARSAT) and X-band (COSMO-SkyMed) sensors and processed using SqueeSAR™, PSInSAR™ and PSP-IfSAR techniques. Landslides characterized by linear and non-linear behavior were recognized.

Groundwater and Subsidence Modeling Combining Geological and Multi-Satellite SAR Data over the Alto Guadalentín Aquifer (SE Spain)

In the current context of climate change, improving groundwater monitoring and management is an important issue for human communities in arid environments. The exploitation of groundwater resources can trigger land subsidence producing damage in urban structures and infrastructures. Alto Guadalentin aquifer system in SE Spain has been exploited since 1960 producing an average piezometric level drop of 150 m. This work presents a groundwater model that reproduces groundwater evolution during 52 years with an average error below 10%. The geometry of the model was improved introducing a layer of less permeable and deformable soft soils derived from InSAR deformation and borehole data. The resulting aquifer system history of the piezometric level has been compared with ENVISAT deformation data to calculate a first-order relationship between groundwater changes, soft soil thickness, and surface deformation. This relationship has been validated with the displacement data from ERS and Cosmo-SkyMed satellites. The resulting regression function is then used as an empirical subsidence model to estimate a first approximation of the deformation of the aquifer system since the beginning of the groundwater extraction, reaching 1 to 5.5 m in 52 years. These rough estimations highlight the limitations of the proposed empirical model, requiring the implementation of a coupled hydrogeomechanical model.

A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data

This paper presents a methodology to exploit the Persistent Scatterer Interferometry (PSI) time series acquired by Sentinel-1 sensors for the detection and characterization of uplift phenomena in urban areas. The methodology has been applied to the Tower Hamlets Council area of London (United Kingdom) using Sentinel-1 data covering the period 2015–2017. The test area is a representative high-urbanized site affected by geohazards due to natural processes such as compaction of recent deposits, and also anthropogenic causes due to groundwater management and engineering works. The methodology has allowed the detection and characterization of a 5 km2 area recording average uplift rates of 7 mm/year and a maximum rate of 18 mm/year in the period May 2015–March 2017. Furthermore, the analysis of the Sentinel-1 time series highlights that starting from August 2016 uplift rates began to decrease. A comparison between the uplift rates and urban developments as well as geological, geotechnical, and hydrogeological factors suggests that the ground displacements occur in a particular geological context and are mainly attributed to the swelling of clayey soils. The detected uplift could be attributed to a transient effect of the groundwater rebound after completion of dewatering works for the recent underground constructions.