Enhanced landslide investigations through advanced DInSAR techniques: The Ivancich case study, Assisi, Italy

Calò, Federica; Ardizzone, Francesca; Castaldo, Raffaele; Lollino, Piernicola; Tizzani, Pietro; Guzzetti, Fausto; Lanari, R.; Angeli, Maceo-Giovanni; Pontoni, Fabrizio; Manunta, Michele

doi:10.1016/j.rse.2013.11.003

Cited by 132 publications

(76 citation statements)

References 48 publications

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“…In the last few years, the DInSAR technique has been used to detect, study and monitor the surface displacements related to mass movement and slope instabilities [11][12][13] through the availability of several advanced DInSAR algorithms able to retrieve deformation time series and velocity maps [14][15][16][17]. Among the DInSAR techniques currently available, the Small BAseline Subset (SBAS) approach [16,17] has well demonstrated its capability in monitoring the deformations related to mass movement phenomena with high spatial density of measure points [18]. The SBAS DInSAR measurements have already used the implement optimization procedure for the FE model of a landslide process, mainly in two-dimensional domains [8].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, the area has been deeply investigated over the last 20 years and extensively monitored through in situ inclinometers, piezometers and GPS measurements [23][24][25]. At the same time, remote sensing techniques have been exploited in order to achieve dense spatial information of the unstable mass [18,26]. Such a large availability of measurements and in situ data makes the Ivancich landslide a real-scale laboratory in order to test and validate new approaches and techniques based on the integration of multi-source data.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys

et al. 2016

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Abstract:In this paper, we propose an advanced methodology to perform three-dimensional (3D) Finite Element (FE) modeling to investigate the kinematical evolution of a slow landslide phenomenon. Our approach benefits from the effective integration of the available geological, geotechnical and satellite datasets to perform an accurate simulation of the landslide process. More specifically, we fully exploit the capability of the advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) technique referred to as the Small BAseline Subset (SBAS) approach to provide spatially dense surface displacement information. Subsequently, we analyze the physical behavior characterizing the observed landslide phenomenon by means of an inverse analysis based on an optimization procedure. We focus on the Ivancich landslide phenomenon, which affects a residential area outside the historical center of the town of Assisi (Central Italy). Thanks to the large amount of available information, we have selected this area as a representative case study highlighting the capability of advanced 3D FE modeling to perform effective risk analyses of slow landslide processes and accurate urban development planning. In particular, the FE modeling is constrained by using the data from 7 litho-stratigraphic cross-sections and 62 stratigraphic boreholes; and the optimization procedure is carried out using the SBAS-DInSAR retrieved results by processing 39 SAR images collected by the Cosmo-SkyMed (CSK) constellation in the 2009-2012 time span. The achieved results allow us to explore the spatial and temporal evolution of the slow-moving phenomenon and via comparison with the geomorphological data, to derive a synoptic view of the kinematical activity of the urban area affected by the Ivancich landslide.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys

et al. 2016

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“…So far, most deformation studies using DInSAR data were focused on the spatial analysis of ground movements using mainly the average rates of the displacements. Only in recent years, thanks to the improvement in processing techniques and the possibility to infer non-linear ground motions, it was also possible to take advantage of the capability of DInSAR techniques in describing the long-term evolution of natural processes (CALÒ et al, 2014). Furthermore, novel works were conducted in order to identify different trends in Time Series (TS) and to detect different phases of temporal evolution of a natural process: acceleration and deceleration, seasonality, sudden change in motion, trend inversion (MILONE AND SCEPI, 2011;CIGNA et al, 2012;BERTI et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…3). SAR datasets characterized by high to very high ground range resolution (see Table 8) ease the detection of a larger number of measurement points, so that very spatially dense deformation maps can be produced (CALÒ et al, 2014). We applied the proposed quality index SDQI to SAR datasets acquired by different satellites, at various frequency bands and spatial resolutions, over different study areas (Table 1 resumes all the parameters).…”

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confidence: 99%

A User-Oriented Methodology for DInSAR Time Series Analysis and Interpretation: Landslides and Subsidence Case Studies

Notti

Calò

Cigna

et al. 2015

Pure Appl. Geophys.

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Recent advances in multi-temporal Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) have greatly improved our capability to monitor geological processes. Ground motion studies using DInSAR require both the availability of good quality input data and rigorous approaches to exploit the retrieved Time Series (TS) at their full potential. In this work we present a methodology for DInSAR TS analysis, with particular focus on landslides and subsidence phenomena. The proposed methodology consists of three main steps: (1) pre-processing, i.e., assessment of a SAR Dataset Quality Index (SDQI), (2) post-processing, i.e., application of empirical/stochastic methods to improve the TS quality, and (3) trend analysis, i.e., comparative implementation of methodologies for automatic TS analysis.Tests were carried out on TS datasets retrieved from processing of SAR imagery acquired by different radar sensors (i.e., ERS-1/2 SAR, RADARSAT-1, ENVISAT ASAR, ALOS PALSAR, TerraSAR-X, COSMO-SkyMed) using advanced DInSAR techniques (i.e., SqueeSAR TM , PSInSAR TM , SPN and SBAS). The obtained values of SDQI are discussed against the technical parameters of each data stack (e.g., radar band, number of SAR scenes, temporal coverage, revisiting time), the retrieved coverage of the DInSAR results, and the constraints related to the characterization of the investigated geological processes. Empirical and stochastic approaches were used to demonstrate how the quality of the TS can be improved after the SAR processing, and examples are discussed to mitigate phase unwrapping errors, and remove regional trends, noise and anomalies. Performance assessment of recently developed methods of trend analysis (i.e., PS-Time, Deviation Index and velocity TS) was conducted on two selected study areas in Northern Italy affected by land subsidence and landslides. Results show that the automatic detection of motion trends enhances the interpretation of DInSAR data, since it provides an objective picture of the deformation behaviour recorded through TS and therefore contributes to the understanding of the on-going geological processes.

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“…On the contrary, the Satellite Synthetic Aperture Radar (SAR) Interferometry (InSAR) technique has evolved as a powerful tool to assess the slope deformation related to the activation of deep-seated landslides [3][4][5][6]. With the increasing availability of suitable radar data, advanced time-series analysis techniques have been developed, which allow for the quantitative derivation of spatially variable deformation rates with increasing improvements in the temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan

et al. 2015

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This study focuses on evaluating the potential of ALOS/PALSAR time-series data to analyze the activation of deep-seated landslides in the foothill zone of the high mountain Alai range in the southern Tien Shan (Kyrgyzstan). Most previous field-based landslide investigations have revealed that many landslides have indicators for ongoing slow movements in the form of migrating and newly developing cracks. L-band ALOS/PALSAR data for the period between 2007 and 2010 are available for the 484 km 2 area in this study. We analyzed these data using the Small Baseline Subset (SBAS) time-series technique to assess the surface deformation related to the activation of landslides. We observed up to ±17 mm/year of LOS velocity deformation rates, which were projected along the local steepest slope and resulted in velocity rates of up to −63 mm/year. The obtained rates indicate very slow movement of the deep-seated landslides during the observation time. We also compared these movements with precipitation and earthquake records. The results suggest that the deformation peaks correlate with rainfall in the 3 preceding months and with an earthquake event. Overall, the results of this study indicated the great potential of L-band InSAR time series analysis for OPEN ACCESS Remote Sens. 2015, 7 8974 efficient spatiotemporal identification and monitoring of slope activations in this region of high landslide activity in Southern Kyrgyzstan.

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Enhanced landslide investigations through advanced DInSAR techniques: The Ivancich case study, Assisi, Italy

Cited by 132 publications

References 48 publications

Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys

Advanced Three-Dimensional Finite Element Modeling of a Slow Landslide through the Exploitation of DInSAR Measurements and in Situ Surveys

A User-Oriented Methodology for DInSAR Time Series Analysis and Interpretation: Landslides and Subsidence Case Studies

ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan

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