Abstract
Interferometric Synthetic Aperture Radar (InSAR) capability to detect slow deformation over terrain areas is limited by temporal decorrelation, geometric decorrelation and atmospheric artefacts. Multitemporal InSAR methods such as Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS) have been developed to deal with various aspects of decorrelation and atmospheric problems affecting InSAR observations. Nevertheless, the applicability of both PS-InSAR and SBAS in mountainous regions is still challenging. Correct phase unwrapping in both methods is hampered due to geometric decorrelation in particular when using C-band SAR data for deformation analysis. In this paper, we build upon the SBAS method implemented in StaMPS software and improved the technique, here called ISBAS, to assess tectonic and volcanic deformation in the center of the Alborz Mountains in Iran using both Envisat and ALOS SAR data. We modify several aspects within the chain of the processing including: filtering prior to phase unwrapping, topographic correction within three-dimensional phase unwrapping, reducing the atmospheric noise with the help of additional GPS data, and removing the ramp caused by ionosphere turbulence and/or orbit errors to better estimate crustal deformation in this tectonically active region. Topographic correction is done within the three-dimensional unwrapping in order to improve the phase unwrapping process, which is in contrast to previous methods in which DEM error is estimated before/after phase unwrapping. Our experiments show that our improved SBAS approach is able to better characterize the tectonic and volcanic deformation in the center of the Alborz region than the classical SBAS. In particular, Damavand volcano shows an average uplift rate of about 3 mm/year in the year 2003–2010. The Mosha fault illustrates left-lateral motion that could be explained with a fault that is locked up to 17–18 km depths and slips with 2–4 mm/year below that depth.
Highlights
Monitoring of ground deformation due to natural and anthropogenic hazards provides valuable information for various stages involved in disaster cycle response, from pre-disaster risk reduction to mapping the effects of an event for post-disaster management
We present an algorithm to improve Small Baseline Subset (SBAS) method that has been implemented in the StaMPS [12,34] in order to better estimate slow deformation in the center of the Alborz Mountains, Iran
In StaMPS, the specific identification of pixels for the time-series analysis is done based on single-look interferograms that are coherent in time [19]
Summary
Monitoring of ground deformation due to natural and anthropogenic hazards provides valuable information for various stages involved in disaster cycle response, from pre-disaster risk reduction to mapping the effects of an event for post-disaster management. There are three broad categories of multi-temporal methods that deal with decorrelation phenomena in InSAR observations These are PS-InSAR, SBAS, and SAR tomography techniques. The SBAS algorithm uses all possible SAR image combinations with a small temporal and spatial baseline to reduce spatial decorrelation and the effect of residual phase due to uncompensated topography [23,24,25,26,27,28]. We applied an improved multi-temporal method to both Envisat and ALOS data dealing with several stages in time-series analysis. These are filtering prior to phase unwrapping, topography correction within 3D unwrapping (equipped 3D-unwrapping to topographic correction), and correcting for atmospheric artifacts using GPS data.
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