A SHORT REVIEW ON PERSISTENT SCATTERER INTERFEROMETRY TECHNIQUES FOR SURFACE DEFORMATION MONITORING

Technology advancement has urged the development of Interferometric Synthetic Aperture Radar (InSAR) to be upgraded and transformed. The main contribution of the InSAR technique is that the surface deformation changes measurements can achieve up to millimetre level precision. Environmental problems such as landslides, volcanoes, earthquakes, excessive underground water production, and other phenomena can cause the earth's surface deformation. Deformation monitoring of a surface is vital as unexpected movement, and future behaviour can be detected and predicted. InSAR time series analysis, known as Persistent Scatterer Interferometry (PSI), has become an essential tool for measuring surface deformation. Therefore, this study provides a review of the PSI techniques used to measure surface deformation changes. An overview of surface deformation and the basic principles of the four techniques that have been developed from the improvement of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), which is Small Baseline Subset (SBAS), Stanford Method for Persistent Scatterers (StaMPS), SqueeSAR and Quasi Persistent Scatterer (QPS) were summarised to perceive the ability of these techniques in monitoring surface deformation. This study also emphasises the effectiveness and restrictions of each developed technique and how they suit Malaysia conditions and environment. The future outlook for Malaysia in realising the PSI techniques for structural monitoring also discussed in this review. Finally, this review will lead to the implementation of appropriate techniques and better preparation for the country's structural development.

Atmospheric phase delay correction of PS-InSAR to Monitor Land Subsidence in Surabaya

Atmospheric phase delay is one of the most significant errors limiting the accuracy of Interferometric Synthetic Aperture Radar (InSAR) results. In this research, we used the Generic Atmospheric Correction Online Service for InSAR (GACOS) data to correct the tropospheric delay modeling from the persistent scatterers’ InSAR monitoring. Eighty-one (81) Sentinel-1A images and tropospheric delay maps from GACOS monitored land subsidence in Surabaya city between 2017 and 2019. InSAR processing was carried out using the GMTSAR software, continued with StaMPS and TRAIN, which were used to correct the tropospheric delay of PSInSAR-derived deformation measurements. The results before and after the atmospheric phase delay correction using GACOS were confirmed and analyzed in the main subsidence area. The findings of the experiments reveal that the atmospheric phase affects the mean LOS velocity results to some extent. The average difference between PS-InSAR before and after tropospheric correction is 1.734 mm/year with a standard deviation of 0.550 mm/year. The significance test of the two variables, 95%, showed that the tropospheric correction with GACOS data could affect the PS-InSAR results. Furthermore, GACOS correction may increase the error at some points, which could be due to its turbulence data’s low accuracy.

Persistent Scatterer Interferometry for Pettimudi (India) Landslide Monitoring using Sentinel-1A Images

The continuous monitoring of land surface movement over time is of paramount importance for assessing landslide triggering factors and mitigating landslide hazards. This research focuses on measuring horizontal and vertical surface displacement due to a devastating landslide event in the west-facing slope of the Rajamala Hills, induced by intense rainfall. The landslide occurred in Pettimudi, a tea-plantation village of the Idukki district in Kerala, India, on August 6–7, 2020. The persistent-scatterer synthetic aperture radar interferometry (PSInSAR ) technique, along with the Stanford Method for Persistent Scatterers (StaMPS), was applied to investigate the land surface movement over time. A stack of 20 Sentinel-1A single-look complex images (19 interferograms) acquired in descending passes was used for PSInSAR processing. The line-of-sight (LOS ) displacement in long time series, and hence the average LOS velocity, was measured at each measurement-point location. The mean LOS velocity was decomposed into horizontal east–west (EW ) and vertical up–down velocity components. The results show that the mean LOS, EW, and up–down velocities in the study area, respectively, range from –18.76 to +11.88, –10.95 to +6.93, and –15.05 to +9.53 mm/y, and the LOS displacement ranges from –19.60 to +19.59 mm. The displacement values clearly indicate the instability of the terrain. The time-series LOS displacement trends derived from the applied PSInSAR technique are very useful for providing valuable inputs for disaster management and the development of disaster early-warning systems for the benefit of local residents.

Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, “Runway n.3” of the “Leonardo Da Vinci International Airport” in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data—interpolated through Ordinary Kriging—and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R2 = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs).

An Adaptive Weighted Phase Optimization Algorithm Based on the Sigmoid Model for Distributed Scatterers

Distributed scatterers (DSs) have been widely used in the time series interferometric synthetic aperture radar technique, which compensates for the insufficient density of persistent scatterers (PSs) in nonurban areas. In contrast to PS, DS is vulnerable to temporal and geometric decorrelation effects. Thus, phase optimization processing for DS is essential for reliable deformation parameter estimation. Advanced research has revealed that the application of all possible interferometric pairs will be more conducive to the reduction in phase biases. However, the low-coherence pixels will inevitably increase the difficulty of phase optimization and introduce unpredictable negative effects, which will reduce the effect of phase optimization. Therefore, this study proposed an advanced adaptive weighted phase optimization algorithm (AWPOA). In the AWPOA, the adaptive weighting strategy based on the sigmoid model was first proposed to assign more reasonable weights to pixels of different quality, which can efficiently reduce the negative influence of low-coherence pixels and improve the optimization performance. Moreover, coherence bias correction based on the second-kind statistics and an efficient solution strategy based on eigenvalue decomposition were derived and applied to achieve optimal phase series retrieval. The experimental results validated against both simulated and two sets of TerraSAR-X data demonstrated the overall superiority of the AWPOA over traditional phase optimization algorithms (POAs). Specifically, the processing efficiency of the eigenvalue decomposition solution strategy used in AWPOA was nearly 20 times faster than that of the PTA iterative solution strategy under the case without bias correction. Although bias correction increased the processing time, the optimization effect was significantly improved. Moreover, in terms of the quantitative evaluation indexes with the residual and the sum of the phase difference, the mean value of the improvement percentage of the AWPOA was increased by more than 12%, and the standard deviation was reduced by more than 1% over the traditional POAs, indicating its superior optimization performance and noise robustness.

Analysis of the Deformation Behavior and Sinkhole Risk in Kerdabad, Iran Using the PS-InSAR Method

The unexpected collapse of land surface due to subsidence is one of the most significant geohazards that threatens human life and infrastructure. Kabudrahang and Famenin are two Iranian plains experiencing several sinkholes due to the characteristics of the underground soil layers and extreme groundwater depletion. In this study, space-based Synthetic Aperture Radar images are used to investigate the ground displacement behavior to examine the feasibility of Sentinel-1 data in detecting precursory deformation proceeding before the sinkhole formation. The selected sinkhole occurred in August 2018 in the vicinity of Kerdabad village in Hamedan province with a 40 m diameter and depth of ~40 m. Time series of the European constellation Sentinel-1 data, spanning from January 2015 to August 2018, is analyzed, and the results revealed a 3 cm annual subsidence (–3cm/year) along with the line-of-sight direction. Time-series analysis demonstrated that the driving mechanism of the sinkhole formation had a gradual process. Displacement of persistent scatterers (PSs) near the cave area had an acceleration by approaching the sinkhole formation date. In contrast, other areas that are far from the cave area show linear subsidence behavior over time. Additionally, the one-kilometer deformation profile over the cave area indicates a high subsidence rate precisely at the location where the sinkhole was formed later on 20 August 2018.

CONCEPT TO ANALYZE THE DISPLACEMENT TIME SERIES OF INDIVIDUAL PERSISTENT SCATTERERS

Persistent Scatterer Interferometry (PSInSAR) exploits a time series of Synthetic Aperture Radar (SAR) images to estimate the mean velocity with which the surface of the earth is deforming. However, most PSInSAR algorithms estimate the mean velocities using a linear regression model. Since some deformation phenomena can exhibit a more complex behavior over time, using a linear regression model leads to potentially wrong estimations for the mean velocity. For example, the velocity of a landslide moving down a steep slope can change depending on the water content of the material of the landslide, or an inactive landslide can reactivate due to an earthquake. Both scenarios would not result in a time series with a constant linear slope but in a piecewise linear time series.This paper presents a Matlab-based tool to analyze an individual Persistent Scatterer (PS) time series. The Persistent Scatterer Deformation Pattern Analysis Tool (PSDefoPAT) aims to build a mathematical model that sufficiently describes the time series trend and seasonal and noise components. The trend component is estimated using polynomial regression and piecewise linear models, while a sine function approximates the seasonal component. The goal is to identify the best fitting model for the displacement time series of a PS. PSDefoPAT is introduced by examine the time series of three different PS located in the region surrounding Patras, Greece. Based on the derived models, we discuss the nature of their deformation patterns.

Assessing Current Seismic Hazards in Irpinia Forty Years after the 1980 Earthquake: Merging Historical Seismicity and Satellite Data about Recent Ground Movements

Recently, a new strain rate map of Italy and the surrounding areas has been obtained by processing data acquired by the persistent scatterers (PS) of the synthetic aperture radar interferometry (InSAR) satellites—ERS and ENVISAT—between 1990 and 2012. This map clearly shows that there is a link between the strain rate and all the shallow earthquakes (less than 15 km deep) that occurred from 1990 to today, with their epicenters being placed only in high strain rate areas (e.g., Emilia plain, NW Tuscany, Central Apennines). However, the map also presents various regions with high strain rates but in which no damaging earthquakes have occurred since 1990. One of these regions is the Apennine sector, formed by Sannio and Irpinia. This area represents one of the most important seismic districts with a well-known and recorded seismicity from Roman times up to the present day. In our study, we merged historical records with new satellite techniques that allow for the precise determination of ground movements, and then derived physical dimensions, such as strain rate. In this way, we verified that in Irpinia, the occurrence of new strong shocks—forty years after one of the strongest known seismic events in the district that occurred on the 23 November 1980, measuring Mw 6.8—is still a realistic possibility. The reason for this is that, from 1990, only areas characterized by high strain rates have hosted significant earthquakes. This picture has been also confirmed by analyzing the historical catalog of events with seismic completeness for magnitude M ≥ 6 over the last four centuries. It is easy to see that strong seismic events with magnitude M ≥ 6 generally occurred at a relatively short time distance between one another, with a period of 200 years without strong earthquakes between the years 1732 and 1930. This aspect must be considered as very important from various points of view, particularly for civil protection plans, as well as civil engineering and urban planning development.

Sentinel-1 InSAR survey to constrain subsidence-induced surface faulting and quantify its induced risk in major cities of central Mexico

<p>Several major cities in central Mexico suffer from aquifer depletion and land subsidence driven by overexploitation of groundwater resources to address increasing water demands for domestic, industrial and agricultural use. Ground settlement often combines with surface faulting, fracturing and cracking, causing damage to urban infrastructure, including private properties and public buildings, as well as transport infrastructure and utility networks. These impacts are very common and induce significant economic loss, thus representing a key topic of concern for inhabitants, authorities and stakeholders. This work provides an Interferometric Synthetic Aperture Radar (InSAR) 2014-2020 survey based on parallel processing of Sentinel-1 IW big data stacks within ESA’s Geohazards Exploitation Platform (GEP), using hosted on-demand services based on multi-temporal InSAR methods including Small BAseline Subset (SBAS) and Persistent Scatterers Interferometry (PSI). Surface faulting hazard is constrained based on differential settlement observations and the estimation of angular distortions that are produced on urban structures. The assessment of the E-W deformation field and computation of horizontal strain also allows the identification of hogging (tensile strain or extension) and sagging (compression) zones, where building cracks are more likely to develop at the highest and lowest elevations, respectively. Sentinel-1 observations agree with in-situ observations, static GPS surveying and continuous GNSS monitoring data. The distribution of field surveyed faults and fissures compared with maps of angular distortions and strain also enables the identification of areas with potentially yet-unmapped and incipient ground discontinuities. A methodology to embed such information into the process of surface faulting risk assessment for urban infrastructure is proposed and demonstrated for the Metropolitan Area of Mexico City [1], one of the fastest sinking cities globally (up to 40 cm/year subsidence rates), and the state of Aguascalientes [2], where a structurally-controlled fast subsidence process (over 10 cm/year rates) affects the namesake valley and capital city. The value of this research lies in the demonstration that InSAR data and their derived parameters are not only essential to constrain the deformation processes, but can also serve as a direct input into risk assessment to quantify (at least, as a lower bound) the percentage of properties and population at risk, and monitor how this percentage may change as land subsidence evolves.</p><p>[1] Cigna F., Tapete D. 2021. Present-day land subsidence rates, surface faulting hazard and risk in Mexico City with 2014–2020 Sentinel-1 IW InSAR. <em>Remote Sens. Environ.</em> 253, 1-19, doi:10.1016/j.rse.2020.112161</p><p>[2] Cigna F., Tapete D. 2021. Satellite InSAR survey of structurally-controlled land subsidence due to groundwater exploitation in the Aguascalientes Valley, Mexico. <em>Remote Sens. Environ.</em> 254, 1-23, doi:10.1016/j.rse.2020.112254</p>