Using C/X-band SAR interferometry and GNSS measurements for the Assisi landslide analysis

Abstract

This work presents an analysis of the applicability of synthetic aperture radar (SAR) interferometry to landslide monitoring. This analysis was carried out by using different interferometric approaches, different spaceborne SAR data (both in the C-band and in the X-band), and in situ global navigation satellite system (GNSS) measurements. In particular, we investigated both the reliability of displacement monitoring and the issues of the cross-comparison and validation of the interferometric synthetic aperture radar (InSAR) results. The work was focused on the slow-moving landslide that affects a relevant part of the urban area of the historical town of Assisi (Italy).A C-band ENVISAT advanced synthetic aperture radar (ENVISAT ASAR) dataset acquired between 2003 and 2010 was processed by using two different interferometric techniques, to allow cross-comparison of the obtained displacement maps. Good correspondence between the results was found, and a deeper analysis of the movement field was possible. Results were further compared to a set of GNSS measurements with a 7 year overlap with SAR data. A comparison was made for each GNSS marker with the surrounding SAR scatterers, trying to take into account local topological effects, when possible.Further, the high-resolution X-band acquired on both ascending and descending tracks by the COSMO-SkyMed (CSK) constellation was processed. The resultant displacement fields show good agreement with C-band and GNSS measurements and a sensible increase in the density of measurements.

Monitoring soil creep landsliding in an urban area using persistent scatterer interferometry (El Papiol, Catalonia, Spain)

“…As mentioned above, the challenge of analysing urban areas like El Papiol is that the boundary of the area affected by soil creep cannot be defined using geomorphologic criteria alone. Similar examples are described in Bovenga et al (2013), Cigna et al (2013), Wasowski and Bovenga (2014), Peduto et al (2016) and Gullà et al (2017). An advantage of PSI is its high sensitivity to small displacements, which allows us to detect extremely slow landslides, characterised by displacement rates of a few mm/year.…”

“…8) has a precision of the order of 1 mm/year (see Crosetto et al (2016)). The transformation from the LOS values into displacements parallel to the slope involves the multiplication by a factor, which amplifies the errors; see Cascini et al (2010), Bovenga et al (2013) and Wasowski and Bovenga (2014). Finally, the displacement time series are usually noisier than the velocity maps: the single LOS displacement values can have error values of several millimetres.…”

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

“…The Ivancich landslide has been studied by using different SAR datasets to detect surface displacements and to carry out their long-term monitoring [18,26,30]. In this work, we benefit from the images acquired by the Cosmo-SkyMed (CSK) constellation.…”

“…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.…”

High resolution satellite multi-temporal interferometry for monitoring infrastructure instability hazards

“…The quality of SPINUA processing products has already been tested and confirmed in different types of applications. In particular, the results obtained through SPINUA have been cross-compared with those derived using other MTI techniques [18,20], as well as validated through in situ measurements using GNSS and precision leveling [6].…”