Abstract
Space-borne Differential Interferometric Synthetic Aperture Radar (DInSAR) has been extensively used in the last two decades to measure ground surface deformation, providing key information for the characterization and understanding of many natural and anthropogenic processes. However, conventional DInSAR technique measures only one component of the surface deformation (i.e. the satellite's line-of-sight (LOS)), causing the interpretation of DInSAR measurements to be challenging and potentially narrowing the understanding of the mechanisms and dynamics of the deformation processes at work. Presently available methods that estimate 3D surface deformation from DInSAR generally operate on individual interferograms and therefore do not produce 3D surface deformation time series. However, the availability of time series is essential for studying surface deformation processes, bringing information on, e.g., temporally and spatially-variable external forcing conditions and characteristics of future deformation patterns. The Multidimensional Small Baseline Subset (MSBAS) method was already able to produce 2D (east and vertical) surface deformation time series from multi-tracks and multi-sensors DInSAR data. Here we propose a novel version of the MSBAS (MSBAS-3D) method that can produce 3D (north, east, and vertical) surface deformation time series from ascending and descending DInSAR data. This new method proposes measuring the surface deformation for processes producing motion parallel to the surface, such as landslides and glacier flows, while conserving the DInSAR accuracy. The ability of MSBAS-3D to capture the full 3D deformation pattern of processes with a surface signature is illustrated for a large, slow-moving, deep-seated landslide, for which long DInSAR and dGNSS time series, as well as ground truth data, are available. Surface deformation is measured over a four-year period using 1D (LOS), 2D and 3D MSBAS methods, and the advantages and limitations of each approach are described. In this case, the novel MSBAS-3D technique produces superior results that greatly simplify interpretation of the processes at work. The MSBAS-3D software can be downloaded from http://insar.ca/.
Highlights
Surface deformation information is a key parameter for characterization of natural and anthropogenic processes such as earthquakes, volcanic eruptions, landslides, glacier flows, and subsidence and uplift due to resource exploitation (e.g. Colesanti and Wasowski, 2006; Wasowski and Bovenga, 2014; Elliott et al, 2016; Bayer et al, 2017; Dong et al, 2018; Li et al, 2019)
We propose a novel version of the Multidimensional Small Baseline Subset (MSBAS) (MSBAS-3D) method that can produce 3D surface deformation time series from ascending and descending Differential Interferometric Synthetic Aperture Radar (DInSAR) data
The ability of MSBAS-3D to capture the full 3D deformation pattern of processes with a surface signature is illustrated for a large, slowmoving, deep-seated landslide, for which long DInSAR and dGNSS time series, as well as ground truth data, are available
Summary
Surface deformation information is a key parameter for characterization of natural and anthropogenic processes such as earthquakes, volcanic eruptions, landslides, glacier flows, and subsidence and uplift due to resource exploitation (e.g. Colesanti and Wasowski, 2006; Wasowski and Bovenga, 2014; Elliott et al, 2016; Bayer et al, 2017; Dong et al, 2018; Li et al, 2019). Colesanti and Wasowski, 2006; Wasowski and Bovenga, 2014; Elliott et al, 2016; Bayer et al, 2017; Dong et al, 2018; Li et al, 2019) To this end, space-borne Differential Interferometric Synthetic Aperture Radar (DInSAR) allows the derivation of surface deformation with wide spatial coverage, millimetre precision, high temporal and spatial resolutions and all-day and all-weather working capabilities (Massonnet and Feigl, 1998; Rosen et al, 2000). Conventional DInSAR technique measures only one component of the surface deformation - in the satellite's line-of-sight (LOS) (Wright et al, 2004; Hu et al, 2014). This makes interpretation of DInSAR measurements challenging and narrows the understanding of the mechanisms and dynamics of the deformation processes at work
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