Integration of Sentinel-1 Interferometry and GNSS Networks for Derivation of 3-D Surface Changes

Bozsó, István; Bányai, László; Hooper, Andrew; Szűcs, Eszter; Wesztergom, Viktor

doi:10.1109/lgrs.2020.2984917

Cited by 5 publications

(9 citation statements)

References 6 publications

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“…In Slovenia, 3 works have been dedicated to the deformation monitoring of the Potoška Planina [128][129][130], while 2 peer-reviewed publications set in Malta deal with slow-moving coastal landslides [131] and landslide susceptibility modeling [132] of the Northwestern coast. Scientific contributions are also found investigating the Triesenberg-Triesen landslide in Liechtenstein [89], the Trifon Zarezan landslide in Bulgary [133], the ground deformation affecting the Choirokoitia UNESCO World Heritage site in Cyprus [134], the La Vallette landslide in France [135], the deformation phenomena that occurred in the Tatra Mountains in Slovakia [109], the ground deformation affecting Kulcs village in Hungary [136], and the investigation of the Grande da Pipa river basin in Portugal [67].…”

Section: Field Of Applicationmentioning

confidence: 99%

Review of Works Combining GNSS and InSAR in Europe

et al. 2021

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The Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) can be combined to achieve different goals, owing to their main principles. Both enable the collection of information about ground deformation due to the differences of two consequent acquisitions. Their variable applications, even if strictly related to ground deformation and water vapor determination, have encouraged the scientific community to combine GNSS and InSAR data and their derivable products. In this work, more than 190 scientific contributions were collected spanning the whole European continent. The spatial and temporal distribution of such studies, as well as the distinction in different fields of application, were analyzed. Research in Italy, as the most represented nation, with 47 scientific contributions, has been dedicated to the spatial and temporal distribution of its studied phenomena. The state-of-the-art of the various applications of these two combined techniques can improve the knowledge of the scientific community and help in the further development of new approaches or additional applications in different fields. The demonstrated usefulness and versability of the combination of GNSS and InSAR remote sensing techniques for different purposes, as well as the availability of free data, EUREF and GMS (Ground Motion Service), and the possibility of overcoming some limitations of these techniques through their combination suggest an increasingly widespread approach.

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Section: Field Of Applicationmentioning

confidence: 99%

Review of Works Combining GNSS and InSAR in Europe

et al. 2021

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“…In Slovenia, 3 works have been dedicated to the deformation monitoring of the Potoška Planina [128-130], while 2 peer-reviewed publications set in Malta deal with slow-moving coastal landslides [131] and landslide susceptibility modeling [132] of the Northwestern coast. Scientific contributions are also found investigating the Triesenberg-Triesen landslide in Liechtenstein [89], the Trifon Zarezan landslide in Bulgary [133], the ground deformation affecting the Choirokoitia UNESCO World Heritage site in Cyprus [134], the La Vallette landslide in France [135], the deformation phenomena that occurred in the Tatra Mountains in Slovakia [109], the ground deformation affecting Kulcs village in Hungary [136], and the investigation of the Grande da Pipa river basin in Portugal [67].…”

Section: Field Of Applicationmentioning

confidence: 99%

Ground Deformation Patterns Detection by InSAR and GNSS Techniques

2023

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“…Hu et al (2014) provide a systematic review of the methods for mapping 3-D displacements using InSAR measurements pointing out the strengths and weaknesses of each approach. The presented by Hu et al (2014) classification has three groups: (I) a combination of multi-pass LOS and azimuth measurements from at least two different geometries (e.g., Ng et al 2011;Yang et al 2017), (II) integration of GNSS and InSAR data (e.g., Bozso et al 2021;Liu et al 2019), and (III) prior information assisted approaches (e.g., Mohr et al 1998;Kumar et al 2011). The authors highlight that the methods from the first group are suitable for investigating the large displacements caused mostly by natural phenomena (e.g., earthquakes, volcanic eruptions).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, a new type of procedure to integrate Differential InSAR (DInSAR) and GNSS results was presented in the study of Bozso et al (2021). The paper shows a method for the estimation of 3-D deformation parameters and rates using Sentinel-1 SAR data and GNSS epoch observations converted to the Sentinel-1 LOS domain.…”

Section: Introductionmentioning

confidence: 99%

“…The Kalman filter is already widely used in the analysis of InSAR (Shirzaei and Walter 2010;Dalaison and Jolivet 2020) and GNSS (Pacione et al 2011;Bian et al 2014) time series; however, only a few studies (Bekaert et al 2016;Liu et al 2019;Bozso et al 2021) use it for the integration of these two types of measurement. The strength of properly parameterized Kalman filtering is the ability to estimate the state of a system with high accuracy, even when the measurements are noisy, incomplete, and determined in different spatiotemporal domains.…”

Section: Introductionmentioning

confidence: 99%

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Kalman filter-based integration of GNSS and InSAR observations for local nonlinear strong deformations

Tondaś,

Ilieva,

van Leijen

et al. 2023

J Geod

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The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition.

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Integration of Sentinel-1 Interferometry and GNSS Networks for Derivation of 3-D Surface Changes

Cited by 5 publications

References 6 publications

Review of Works Combining GNSS and InSAR in Europe

Review of Works Combining GNSS and InSAR in Europe

Ground Deformation Patterns Detection by InSAR and GNSS Techniques

Kalman filter-based integration of GNSS and InSAR observations for local nonlinear strong deformations

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