Discontinuous GBSAR deformation monitoring

https://doi.org/10.1016/j.isprsjprs.2014.04.002Get rights and content

Abstract

This paper is focused on deformation monitoring using the Ground-Based SAR (GBSAR) technique and a particular data acquisition configuration, which is called discontinuous GBSAR (D-GBSAR). In the most commonly used GBSAR configuration, the radar is left installed in situ, acquiring data periodically, e.g. every few minutes. Deformations are estimated by processing sets of GBSAR images acquired during several weeks or months, without moving the system. By contrast, in the D-GBSAR the radar is installed and dismounted at each measurement campaign, revisiting a given site periodically. This configuration is useful to monitor slow deformation phenomena. This paper outlines the D-GBSAR data analysis procedure implemented by the authors. This is followed by a discussion of some specific aspects of D-GBSAR monitoring. Two successful examples of D-GBSAR monitoring are discussed: one concerns an urban area, while the second one involves a rural area where the monitoring requires the use of artificial corner reflectors.

Introduction

Ground-Based SAR (GBSAR) interferometry is a radar-based terrestrial remote sensing technique (Tarchi et al., 1999), which can be used for digital elevation model generation or deformation monitoring (Monserrat et al., 2014). This paper is focused on the latter application. GBSAR deformation monitoring can be performed using two types of acquisition modes: the continuous (C-GBSAR) and the discontinuous (D-GBSAR). In the C-GBSAR the radar is left installed in situ, acquiring data periodically, with a period that typically ranges from a few minutes to a few hours. This is the most commonly used configuration, which can provide a near real-time deformation monitoring (Casagli et al., 2003, Tarchi et al., 2003a, Tarchi et al., 2005) and which offers the best performances in terms of density, precision and reliability of deformation measurements (Monserrat et al., 2014). The most relevant C-GBSAR applications include slope monitoring in open pit mines for operational early warning systems (Noon and Harries, 2007, Mecatti et al., 2010, Farina et al., 2012); slope instability monitoring related to rockslides (Tarchi et al., 2005), landslides (Tarchi et al., 2003b, Luzi et al., 2006, Barla et al., 2010) or volcanoes (Casagli et al., 2010); urban monitoring (Pieraccini et al., 2004, Pipia et al., 2013); structure monitoring (Tarchi et al., 1997); dam monitoring (Tarchi et al., 1999); dike monitoring (Monserrat, 2012); and glacier monitoring (Noferini et al., 2009, Strozzi et al., 2012).

In the D-GBSAR configuration the radar is installed and dismounted at each campaign, revisiting a given site periodically, e.g. monthly, yearly, etc. depending on the kinematics of the deformation phenomenon at hand and the monitoring requirements. This configuration, which is useful to monitor slow deformation phenomena, is usually adopted by many other deformation monitoring techniques. Its main advantage is the reduced monitoring cost by using the same instrument over several sites. Its drawbacks include a more complex data processing and, generally speaking, reduced density, precision and reliability of deformation measurements. In the literature there are only a few works that describe D-GBSAR applications, e.g. see Noferini et al., 2008, Luzi et al., 2010, Wujanz et al., 2013.

The paper starts with a discussion of the specific aspects of the D-GBSAR processing chain implemented by the authors. This is followed by the description of a D-GBSAR deformation monitoring of an urban area: the village of Barberà de la Conca (Catalonia, Spain). Then a more complex example is illustrated, which concerns a rural area close to the village of Canillo (Andorra). In this case the monitoring was performed using artificial Corner Reflectors (CRs). Both case studies were monitored using a Ku-band GBSAR: the IBIS-L by IDS Spa (www.idscorporation.com). Finally, the conclusions summarize the main results of this work.

Section snippets

D-GBSAR data processing and analysis

This section outlines the D-GBSAR data processing procedure implemented by the authors. It has several points in common with the processing chain used with C-GBSAR data. In addition, both processing chains share common tools with the procedure to process satellite-based SAR interferometric data. The flow chart of D-GBSAR data processing and analysis procedure is shown in Fig. 1, see Monserrat, 2012, Monserrat et al., 2014. The flow chart assumes that N images are acquired, typically at N

First case study: urban area

This section describes the D-GBSAR deformation monitoring of an urban area: the village of Barberà de la Conca (Catalonia, Spain). This village has experienced deformations since 2011 that have caused cracks in the church and several buildings. Four D-GBSAR campaigns were performed (14/11/11, 19/12/11, 08/05/12 and 20/03/13), covering a total observation period of about 16 months. The radar was installed outside the village at an average distance of 500 m, see a photograph of the imaged scene in

Second case study: rural area

This section describes the monitoring of the “Forn de Canillo” landslide, located near the village of Canillo (Andorra). The area of interest, seen from the radar viewpoint, is shown in Fig. 6. As it is discussed in a previous section, see Fig. 2, the D-GBSAR measurement density in this area is insufficient to cover the deformation phenomenon of interest. For this reason, 15 artificial CRs (Fig. 7) were installed at each of the three D-GBSAR campaigns, which were carried out the 29/09/2009,

Conclusions

In this paper the deformation monitoring based on the discontinuous GBSAR (D-GBSAR) configuration has been addressed. This represents an acquisition mode that is useful to monitor slow deformation phenomena and that has been rarely described in the literature. It offers the advantage of reduced monitoring costs by using the same instrument over several sites. However, it requires a more complex data processing and, generally speaking, yields reduced measurement density, precision and

Acknowledgements

This work has been partially funded by the Government of Catalonia, through the SAXA project (2010CTP00048) of the Comunitat de Treball dels Pirineus (www.ctp.org), and by the Spanish Ministry of Science and Innovation, through the XLIDE project (IPT-2011-1287-370000) in the framework of the INNPACTO programme.

References (24)

  • G. Luzi et al.

    Advances in ground based microwave interferometry for landslide survey: a case study

    Int. J. Remote Sens.

    (2006)
  • Luzi, G., Monserrat, O., Crosetto, M., Copons, R., Altimir, J., 2010. Ground-Based SAR interferometry applied to...
  • Cited by (46)

    • Review of cutting-edge sensing technologies for urban underground construction

      2021, Measurement: Journal of the International Measurement Confederation
      Citation Excerpt :

      Besides photogrammetry, another remote sensing technique has received a significant attention: Persistent Scatterer Interferometry, a radar-based method, which belongs to the group of differential interferometric Synthetic Aperture Radar (SAR) [66]. In particular, IN-SAR and GB-SAR technologies can be applied in surface settlement measurements [67–69]. Researchers developed a photogrammetric system using DIC techniques to monitor the deformation of a structure based on an escalator barrel in an underground station affected by a shotcrete lining excavation with high precision [50].

    • Modelling of instrument repositioning errors in discontinuous Multi-Campaign Ground-Based SAR (MC-GBSAR) deformation monitoring

      2019, ISPRS Journal of Photogrammetry and Remote Sensing
      Citation Excerpt :

      In practice, Ground-Based Synthetic Aperture Radar (GBSAR) data acquisition can be performed either in a continuous or discontinuous mode, depending on the rate of change in any particular case study, or the environment for instrument deployment (e.g. Caduff et al., 2015a; Crosetto et al., 2014a; Crosetto et al., 2017; Monserrat et al., 2014).

    • An Atmospheric Correction Method for Ground-Based Radar Under Complex Environment

      2023, Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University
    View all citing articles on Scopus
    View full text