An Overview of the Small BAseline Subset Algorithm: a DInSAR Technique for Surface Deformation Analysis

Abstract

We present an overview of the Differential SAR Interferometry algorithm referred to as Small BAseline Subset (SBAS) technique, that allows us to detect surface deformation and to analyze their space-time characteristics. Following the description of the main theoretical aspects of the algorithm, we present several results obtained by applying the SBAS approach in selected case studies relevant to phenomena affecting volcanic areas (Campi Flegrei caldera and Somma-Vesuvio complex, Italy), aquifers (Santa Clara area within the San Francisco bay, California) and landslides (Maratea Valley, Italy). The overall analysis is carried out by using multilook DInSAR interferograms with a spatial resolution of the order of 100 · 100 m, computed from SAR data acquired by the ERS-1 and ERS-2 sensors. In particular, we highlight the peculiarities of the SBAS technique and its surface deformation retrieval capability for what concerns both large-scale deformation phenomena and more localized displacement effects.

A simple solution to mitigate noise effects in time-redundant sequences of small baseline multi-look DInSAR interferograms

“…The presented DInSAR analysis exploits a sequence of SB time-redundant differential interferograms, selected by limiting the maximum perpendicular baseline and the relevant time span of the SAR data pairs to 400 m and 2000 days, respectively. Note that these threshold values have been derived through extensive experimental analyses carried out in several geophysical contexts by exploiting the SBAS approach (Lanari et al 2007 and references therein, Manzo et al 2012 and references therein). As a result, we retrieved a network of 664 differential SAR interferograms (see figure 1), which were computed by performing a complex multi-look operation with 4 looks in the range direction and 20 looks in the azimuth one (Bamler and Hartl 1998, Rosen et 2000, Lanari et al 2007), leading to a resulting pixel dimension of about 100 m × 100 m. For the interferogram generation (Gabriel et al 1989), the topographic phase contributions were removed by using precise satellite orbit information and a threearcsecond shuttle radar topography mission (SRTM) digital elevation model (DEM) of the region.…”

“…Note that these threshold values have been derived through extensive experimental analyses carried out in several geophysical contexts by exploiting the SBAS approach (Lanari et al 2007 and references therein, Manzo et al 2012 and references therein). As a result, we retrieved a network of 664 differential SAR interferograms (see figure 1), which were computed by performing a complex multi-look operation with 4 looks in the range direction and 20 looks in the azimuth one (Bamler and Hartl 1998, Rosen et 2000, Lanari et al 2007), leading to a resulting pixel dimension of about 100 m × 100 m. For the interferogram generation (Gabriel et al 1989), the topographic phase contributions were removed by using precise satellite orbit information and a threearcsecond shuttle radar topography mission (SRTM) digital elevation model (DEM) of the region. The multi-look interferograms were also pre-filtered by applying the well-known approach described in Goldstein and Werner (1998), and subsequently processed by following the lines of the filtering approach described in the previous section.…”

“…simply selected by imposing thresholds on the maximum allowed temporal and perpendicular baseline values of the SAR data pairs (Berardino et al 2002, Lanari et al 2007. Furthermore, the computed interferograms can be profitably filtered by using one of the currently available noise-filtering approaches based on space and/or frequency analyses Werner 1998, Baran et al 2003).…”

“…w k in equation (2) also represents a weighted version of the temporal coherence factor originally proposed in Pepe and Lanari (2006), and widely used in Small BAseline Subset (SBAS) (Berardino et al 2002) analyses (Lanari et al 2007, Manzo et al 2012) as a quality index for the retrieved deformation time-series. Note also that the optimal values of Λ (·) in equation (2) can be adopted as a reliability measure of the phase reconstruction.…”

DInSAR estimation of land motion using intermittent coherence with application to the South Derbyshire and Leicestershire coalfields

“…The Small Baseline Subset (SBAS) method (Berardino et al, 2002;Lanari et al, 2007) is a small baseline technique that uses stacks of differential interferometric synthetic aperture radar (DInSAR) observations of the same location to estimate slow linear and non-linear motion of the land surface to millimetric precision. The method, especially when using multilooked, low-pass interferograms, is relatively easy to implement and is excellent for the investigation of large spatial scale distortions.…”