Small Baseline Subset Technique Research Articles

Simulation of time-series surface deformation to validate a multi-interferogram InSAR processing technique

A refined, small baseline subset (SBAS) interferometric synthetic aperture (InSAR) method has been developed to estimate time-series surface deformation through fusion of multi-interferogram processing. Using a synthetic data set that takes into account two time-varying deformation sources, topography-induced errors, atmospheric delay anomalies, orbital errors and temporal decorrelation, all of which are based on realistic ERS-1/ERS-2 SAR image acquisition dates and baseline configuration over the Seguam volcano, Alaska, we assess the accuracy of the refined SBAS technique. Detailed comparison between SBAS-derived products, including time-series deformation maps, atmospheric delays and baseline errors with those of synthetic values, attest the robustness of the refined SBAS technique. The root mean square error of the mean deformation rate between the simulated and SBAS-retrieved is about 0.66 mm year–1. Thus, the measurement accuracy of the refined SBAS method would be around 1 mm year–1.

Long-term ERS/ENVISAT deformation time-series generation at full spatial resolution via the extended SBAS technique

We extend the small baseline subset (SBAS) differential synthetic aperture radar (SAR) interferometry (DInSAR) approach to allow the generation of deformation time-series by processing, at the full spatial resolution scale, long sequences of European Remote Sensing (ERS-1 and ERS-2) and Environmental Satellite (ENVISAT) SAR data acquired with the same illumination geometry. In particular, we avoid the generation of ERS/ENVISAT cross-interferograms, which are severely affected by noise phenomena due to the carrier frequency separation of the two SAR systems, and we focus on single-platform interferograms only (i.e. ERS/ERS and ENVISAT/ENVISAT interferograms) that are properly combined by applying the singular value decomposition (SVD)-based SBAS approach. Moreover, we exploit the Doppler centroid variations of the post-2000 acquisitions of the ERS-2 sensor and the carrier frequency difference between the ERS-1/2 and the ENVISAT systems, in order to maximize the number of investigated SAR pixels and to improve their geocoding. The presented results, achieved on two data sets relevant to the Napoli Bay area and to the Murge region, both located in southern Italy, confirm the effectiveness of the extended SBAS technique and demonstrate the relevance of deformation analysis carried out at the scale of single buildings or human-made structures with more than 15 years of ERS and ENVISAT acquisitions.

The Stripmap–ScanSAR SBAS Approach to Fill Gaps in Stripmap Deformation Time Series With ScanSAR Data

We present a simple approach to jointly exploit stripmap and ScanSAR acquisitions to generate differential synthetic aperture radar interferometry (DInSAR) time series. In particular, we extend the capability of the Small BAseline Subset (SBAS) approach to compute deformation time series from a set of stripmap images by filling possible temporal gaps in the available SAR data sequence with ScanSAR acquisitions. The starting point of our approach is the raw data focusing step, which is properly carried out to align the characteristics of the ScanSAR images to those of the stripmap ones. To achieve this task, we exploit stripmap processing codes to focus both SAR data types, the ScanSAR ones being processed on a burst-by-burst basis, accounting also for possible differences of the pulse repetition frequency with respect to that of the stripmap data. The coherent combination of the focused bursts generates phase-preserved ScanSAR images with the same output geometry and pixel spacing as the stripmap ones. This allows a straightforward implementation of the next steps of the SBAS processing chain, including the interferogram generation operation. In this case, we concentrate on a selection of small baseline (SB) stripmap-stripmap multilook interferograms identified through a Delaunay triangulation, which are complemented with a set of hybrid SB stripmap-ScanSAR interferograms. This interferogram selection permits us to develop an effective phase unwrapping algorithm based on a two-step processing strategy. Finally, the whole data set of unwrapped interferograms is inverted through the SBAS technique to retrieve the final deformation time series, including both stripmap and ScanSAR data. The proposed stripmap-ScanSAR SBAS process ing approach is particularly attractive because it is very easy to implement since it requires only limited modifications with respect to the conventional stripmap-based SBAS algorithm. Our approach has been applied to descending and ascending hybrid stripmap-ScanSAR data sets of Envisat/ASAR C-band acquisitions from the Big Island of Hawaii. In spite of not including any common-band azimuthal filtering, which would account for the ScanSAR burst spectral properties at the expense of the algorithm simplicity, the presented results show that we may retrieve DInSAR time series with an accuracy ranging between 5 and 10 mm, consistent with previous C-band data analyses using only stripmap data.

A Quantitative Assessment of DInSAR Measurements of Interseismic Deformation: The Southern San Andreas Fault Case Study

We investigate the capabilities and limitations of the Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, in particular of the Small BAseline Subset (SBAS) approach, to measure surface deformation in active seismogenetic areas. The DInSAR analysis of low-amplitude, long-wavelength deformation, such as that due to interseismic strain accumulation, is limited by intrinsic trade-offs between deformation signals and orbital uncertainties of SAR platforms in their contributions to the interferometric phases, the latter being typically well approximated by phase ramps. Such trade-offs can be substantially reduced by employing auxiliary measurements of the long-wavelength velocity field. We use continuous Global Positioning System (GPS) measurements from a properly distributed set of stations to perform a pre-filtering operation of the available DInSAR interferograms. In particular, the GPS measurements are used to estimate the secular velocity signal, approximated by a spatial ramp within the azimuth-range radar imaging plane; the phase ramps derived from the GPS data are then subtracted from the available set of DInSAR interferograms. This pre-filtering step allows us to compensate for the major component of the long-wavelength range change that, within the SBAS procedure, might be wrongly interpreted and filtered out as orbital phase ramps. With this correction, the final results are obtained by simply adding the pre-filtered long-wavelength deformation signal to the SBAS retrieved time series. The proposed approach has been applied to a set of ERS-1/2 SAR data acquired during the 1992–2006 time interval over a 200 × 200 km area around the Coachella Valley section of the San Andreas Fault in Southern California, USA. We present results of the comparison between the SBAS and the Line Of Sight (LOS)—projected GPS time series of the USGC/PBO network, as well as the mean LOS velocity fields derived using SBAS, GPS and stacking techniques. Our analysis demonstrates the effectiveness of the presented approach and provides a quantitative assessment of the accuracy of DInSAR measurements of interseismic deformation in a tectonically active area.

Long-term deformation analysis of historical buildings through the advanced SBAS-DInSAR technique: the case study of the city of Rome, Italy

Monitoring of deformation phenomena affecting urban areas and man-made structures is of key relevance for the preservation of the artistic, archaeological and architectural heritage. The differential SAR interferometry (DInSAR) technique has already been demonstrated to be an effective tool for non-invasive deformation analyses over large areas by producing spatially dense deformation maps with centimetre to millimetre accuracy. Moreover, by exploiting long sequences of SAR data acquired by different sensors, the advanced DInSAR technique referred to as the small baseline subset (SBAS) approach allows providing long-term deformation time series, which are strategic for guaranteeing the monitoring of urban area displacements. In this work, we investigate the effectiveness of the two-scale multi-sensor SBAS-DInSAR approach to detect and monitor displacements affecting historical and artistic monuments. The presented results, achieved by applying the full resolution SBAS technique to a huge set of ERS-1/2 and ENVISAT data, spanning the 1992–2010 time interval and relevant to the city of Rome (Italy), show the capability of this approach to detect and analyse the temporal evolution of possible deformation phenomena affecting historical buildings and archaeological sites. Accordingly, our analysis demonstrates the effectiveness of the full resolution multi-sensor SBAS approach to operate as a surface deformation tool for supporting the study and conservation strategies of the historical, cultural and artistic heritage.

Deformation Time-Series Generation in Areas Characterized by Large Displacement Dynamics: The SAR Amplitude Pixel-Offset SBAS Technique

We exploit the amplitude information of a sequence of synthetic aperture radar (SAR) images, acquired at different times, in order to generate displacement time-series in areas characterized by large and/or rapid deformation, the size of which is on the order of the image's pixel dimensions. We follow the same rationale of the Small BAseline Subset (SBAS) differential SAR interferometry (DInSAR) approach, by coupling the available SAR images into pairs characterized by a small separation between the acquisition orbits. We exploit the amplitudes of the selected image pairs in order to calculate the relative across-track (range) and along-track (azimuth) pixel-offsets (PO). Finally, we apply the SBAS inversion strategy to retrieve the range and azimuth displacement time-series. This approach, referred to as pixel-offset (PO-) SBAS technique, has been applied to a set of 25 ENVISAT SAR observations of the Sierra Negra caldera, Galapagos Islands, spanning the 2003-2007 time interval. The retrieved deformation time-series show the capability of the technique to detect and measure the large displacements affecting the inner part of the caldera that, in correspondence to the October 2005 eruption, reached several meters. Moreover, by comparing the PO-SBAS results to continuous GPS measurements, we estimate that the accuracy of the PO-SBAS time-series is on the order of 1/30th of a pixel for both range and azimuth directions.

Time series analysis of subsidence at Tauhara and Ohaaki geothermal fields, New Zealand, observed by ALOS PALSAR interferometry during 2007–2009

We present a modification of the Small Baseline Subset (SBAS) technique and apply it to L-band ALOS PALSAR data for time series analysis of subsidence at Tauhara and Ohaaki geothermal fields in the Taupo Volcanic Zone, North Island, New Zealand. We noticed that residual topographic noise presented in most ALOS PALSAR interferograms that have been acquired with large perpendicular baselines is a significant limiting factor that complicates their interpretation. We modified the original SBAS technique in such a way that the residual topographic contribution is accurately estimated and removed. For testing purposes we acquired 12 ALOS PALSAR images between 13 January 2007 and 18 January 2009 and created 33 differential interferograms for use in SBAS processing. The calculated time series of deformation at both geothermal fields show steady subsidence with rates of 5–6 cm/year that is clearly mapped with the proposed technique. A comparison of results calculated with the proposed technique and with the original SBAS algorithm shows that the proposed approach is more accurate and produces results of higher quality. The technique presented in this paper can be used for time series analysis of any SAR data; however, it is most appropriate for L-band ALOS PALSAR that acquires coherent images with large spatial baselines that also correlate with the time of acquisition.

Surface deformation of active volcanic areas retrieved with the SBAS-DInSAR technique: an overview

This paper presents a comprehensive overview of the surface deformation retrieval capability of the Differential Synthetic Aperture Radar Interferometry (DInSAR) algorithm, referred to as Small BAseline Subset (SBAS) technique, in the context of active volcanic areas. In particular, after a brief description of the algorithm some experiments relevant to three selected case-study areas are presented. First, we concentrate on the application of the SBAS algorithm to a single-orbit scenario, thus considering a set of SAR data composed by images acquired on descending orbits by the European Remote Sensing (ERS) radar sensors and relevant to the Long Valley caldera (eastern California) area. Subsequently, we address the capability of the SBAS technique in a multipleorbit context by referring to Mt. Etna volcano (southern Italy) test site, with respect to which two different ERS data set, composed by images acquired both on ascending and descending orbits, are available. Finally, we take advantage of the capability of the algorithm to work in a multi-platform scenario by jointly exploiting two different sets of SAR images collected by the ERS and the Environment Satellite (ENVISAT) radar sensors in the Campi Flegrei caldera (southern Italy) area. The presented results demonstrate the effectiveness of the algorithm to investigate the deformation field in active volcanic areas and the potential of the DInSAR methodologies within routine surveillance scenario.

SBAS-DInSAR Analysis of Very Extended Areas: First Results on a 60 000-$\hbox{km}^{2}$ Test Site

We present the results of the first experiment to survey the temporal evolution of the deformation affecting very large areas using the small baseline subset (SBAS) differential synthetic aperture radar interferometry (DInSAR) algorithm. In particular, we have analyzed a set of 264 descending European Remote Sensing (ERS) SAR data frames from 1992 to 2000; these data are relevant to an area in central Nevada (U.S.) that extends for about 600times100 km. The starting point of our study has been the generation of an appropriate set of small baseline multilook interferograms computed from long SAR image strips, which were obtained by jointly focusing six contiguous raw data frames. Following their generation, the selected interferograms, which are computed on a spatial grid of 160times160 m, have been inverted via the SBAS technique to retrieve, for each coherent pixel, the displacement time series and the corresponding mean deformation velocity. The presented results are, to our knowledge, the first ones with such an extended multitemporal SAR data set, and they demonstrate the effectiveness of the approach to analyze the deformation of the investigated zone.

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

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 quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data

We investigate in this work the performance of the Small BAseline Subset (SBAS) approach that is a Differential Synthetic Aperture Radar Interferometry (DInSAR) algorithm allowing the generation of mean deformation velocity maps and displacement time series from a data set of subsequently acquired SAR images. In particular, we have carried out a quantitative assessment of the SBAS procedure performance by processing SAR data acquired by the European Remote Sensing Satellite (ERS) sensors and comparing the achieved results with geodetic measurements that are assumed as reference. The analysis has been focused on the Napoli bay (Italy) and Los Angeles (California) test areas where different deformation phenomena are present and, at the same time, a large amount of ERS SAR data is available as well as geometric leveling (in the Napoli zone) and continuous GPS (in the Los Angeles zone) measurements, to be used for our performance analysis. Moreover, due to the presence of large urbanized zones, the selected test sites are also characterized by extended, highly coherent areas in the DInSAR maps. The presented study shows that the SBAS technique provides an estimate of the mean deformation velocity with a standard deviation of about 1 mm/year for a typical ERS data set including between 40 and 60 images. Moreover, the single displacement measurements, computed with respect to a reference point of known motion, show a sub-centimetric accuracy with a standard deviation of about 5 mm, consistently in both the SAR/leveling and SAR/GPS comparisons; we also show that there is an increase of this standard deviation value as we move away from the reference SAR pixel, with an estimated spatial variation value of about 0.05 mm/km.

A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms

This paper presents a differential synthetic aperture radar (SAR) interferometry (DIFSAR) approach for investigating deformation phenomena on full-resolution DIFSAR interferograms. In particular, our algorithm extends the capability of the small-baseline subset (SBAS) technique that relies on small-baseline DIFSAR interferograms only and is mainly focused on investigating large-scale deformations with spatial resolutions of about 100/spl times/100 m. The proposed technique is implemented by using two different sets of data generated at low (multilook data) and full (single-look data) spatial resolution, respectively. The former is used to identify and estimate, via the conventional SBAS technique, large spatial scale deformation patterns, topographic errors in the available digital elevation model, and possible atmospheric phase artifacts; the latter allows us to detect, on the full-resolution residual phase components, structures highly coherent over time (buildings, rocks, lava, structures, etc.), as well as their height and displacements. In particular, the estimation of the temporal evolution of these local deformations is easily implemented by applying the singular value decomposition technique. The proposed algorithm has been tested with data acquired by the European Remote Sensing satellites relative to the Campania area (Italy) and validated by using geodetic measurements.