Multi-temporal InSAR Techniques Research Articles

Deformations monitoring in complicated-surface areas by adaptive distributed Scatterer InSAR combined with land cover: Taking the Jiaju landslide in Danba, China as an example

Multi-temporal InSAR (MT-InSAR) technique provides a powerful tool for measuring large-scale surface displacements at high precision. However, traditional MT-InSAR approaches always employ a single global threshold to select measurement points (MPs), making it difficult to identify enough MPs in complex land cover areas (e.g., mountainous areas). In this study, to increase the number and spatial distribution density of distributed scatterer (DS) detection while maintaining its quality, we proposed a new MT-InSAR method, named Adaptive Distributed Scatterer InSAR Combined with Land Cover (ADSI-CLC). The key ideas of ADSI-CLC include the introduction of land cover maps as the constraint condition for DS adaptive selection and the employment of coherence weighted spatial adaptive filtering method to estimate optimal phase. Of these, DS adaptive selection method as the most pivotal idea realizes the unification of mathematical, physical, and geographical mechanism in the process of statistically homogeneous pixels (SHP) identification and adaptively adjusts the DS selection thresholds in different land cover areas in the process of DS selection. ADSI-CLC significantly increases the number and spatial distribution density of DS by using the adaptive selection method, and purifies the SHPs populations, and improves the efficiency of SHPs identification through introducing land cover maps. To demonstrate the effectiveness of the ADSI-CLC method, a case study of the Jiaju landslide using 19 scenes ALOS-1 PALSAR-1 L-band data (2006–2011) and 31 scenes Sentinel-1 C-band data (2014–2016) was carried out. The comparison between deformation measurements retrieved by ADSI-CLC and classical StaMPS-SBAS method showed that their distribution pattern is similar, but the ADSI-CLC method can provide more deformation details due to much higher (nearly 10 times) DS density. Quantitative comparisons between the ADSI-CLC results from ALOS-1 data and GNSS measurements also showed that the accuracy of this method is about 11.7 mm/yr in terms of root mean square error (RMSE). These results proved the reliability and effectiveness of ADSI-CLC and indicated its great potential for displacements monitoring in complicated-surface areas with large topographic fluctuations (e.g., mountainous areas).

Application of InSAR satellite method for mapping of active landslides in Bulgaria – opportunities and perspectives

Abstract. Landslides are geological phenomena that are spread on Bulgarian territory mainly along the northern Black Sea coast and on the right banks of the Danube in the western part of the country. Mitigation of the negative effects of these destructive geological phenomena is the compilation of inventory maps of their distribution and registers with the main characteristics of the individual landslides. Conventional methods for making such maps are time-consuming and resource-intensive. Modern satellite, air and ground-based remote sensing technologies facilitate the production of landslide maps, reducing the time and resources required to compile and systematically update them. In this paper, we demonstrate the applicability of Differential Sentinel-1A satellite SAR interferometry (DInSAR) to assess the movement activity and use the information for further updating the national landslide inventories in Bulgaria. We perform several analyses based on multi-temporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides. The use of new opportunities for free access to satellite images, which can be applied in conjunction with other methods, greatly facilitates the processes of inventory, mapping and study of landslides.

Modified statistically homogeneous pixel selection for use with a non-local adaptive multilook approach for small SAR datasets

ABSTRACT Multitemporal interferometric synthetic aperture radar (MT-InSAR) technique is an important approach for surface deformation monitoring. Nonlocal adaptive multilooking approach has potential benefits for the filtering and coherence estimation in the data preprocessing steps of MT-InSAR technique. The kernel of nonlocal adaptive multilooking approach lies in the selection of statistically homogeneous pixels (SHPs). Various amplitude-based strategies, such as DespecKS and its variations, have been proposed for selecting SHPs. However, the detection rates of these methods are usually unsatisfactory in the case of small data sets. To overcome this limitation, SHPs are selected based on the adaptive joint data vector, which encompasses both time dimensional samples and spatial information. In this letter, the outliers of the time dimensional samples are removed first based on a revised boxplot method. Additionally, image segmentation and L1 norm are both introduced to adaptively construct joint data vector in the preset window. Experiments on five TSX images are used to verify the reliability and efficiency of the proposed algorithm.

Magnitudes and patterns of large-scale permafrost ground deformation revealed by Sentinel-1 InSAR on the central Qinghai-Tibet Plateau

Permafrost on the Qinghai-Tibet Plateau (QTP) undergoes significant thawing and degradation, which affects the hydrological processes, ecosystems and infrastructure stability. The ground deformation, a key indicator of permafrost degradation, can be quantified via geodetic observations, especially using multi-temporal InSAR techniques. The previous InSAR studies, however, either rely on data-driven models or Stefan-equation-based models, which are both lacking of consideration of the spatial-temporal variations of freeze-thaw processes. Furthermore, the magnitudes and patterns of the permafrost-related ground deformation over large scales (e.g., 1×105km2 or larger) is still insufficiently quantified or poorly understood. In this study, to account for the spatial heterogeneity of freeze-thaw processes, we develop a permafrost-tailored InSAR approach by incorporating a MODIS-land-surface-temperature-integrated ground deformation model to reconstruct the seasonal and long-term deformation. Utilizing the approach to Sentinel-1 SAR images on the vast regions of about 140,000km2 of the central QTP during 2014–2019, we observe widespread seasonal deformation up to about 80mm with a mean value of about 10mm and linear subsidence up to 20mm/year. We apply the geographical detector to determine the controlling factors on the permafrost-related deformation. We find that the slope angle is the primary controller on the seasonal deformation: strong magnitudes and variations of seasonal deformation are most pronounced in flat or gentle-slope regions. The aspect angle, vegetation and soil bulk density exhibit a certain correlation with seasonal deformation as well. Meanwhile, we find that a linear subsidence is higher in the regions with high ground ice content and warm permafrost. It indicates that warm and ice-rich permafrost regions are more vulnerable to extensive long-term subsidence. We also observe that the cold permafrost regions experience lower linear subsidence even with high ground ice content, which indicate ice loss is limited. Thus, we infer that under continuously warming, the transition from cold permafrost to warm permafrost may lead to more extensive ground ice melting. Moreover, the strong subsidence/uplift signals surrounding some lakes suggesting that the change of local hydrological conditions may induce localized permafrost degradation/aggradation. Our study demonstrates the capability of the permafrost-tailored InSAR approach to quantify the permafrost freeze-thaw dynamics as well as their spatial-temporal patterns over large scales in vast permafrost areas.

Assessing Urban Landslide Dynamics through Multi-Temporal InSAR Techniques and Slope Numerical Modeling

Landslides threaten more than before the urbanized areas and are a worldwide growing problem for the already affected communities and the local authorities committed to landslide risk management and mitigation. For this reason, it is essential to analyze landslide dynamics and environmental conditioning factors. Various techniques and instruments exist for landslide investigation and monitoring. Out of these, Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) techniques have been widely used in the last decades. Their capabilities are enhanced by the availability of the active Sentinel-1 mission, whose 6-day revisiting time enables near real-time monitoring of landslides. Interferometric results, coupled with ground measurements or other approaches such as numerical modeling, significantly improve the knowledge of the investigated surface processes. In this work, we processed the C-band SAR images of the available European Space Agency (ESA) satellite missions, using MT-InSAR methods to identify the surface deformations related to landslides affecting the Iași Municipality (Eastern Romania). The results (i.e., velocity maps) point out the most active landslides with velocities of up to 20 mm/year measured along the satellite Line of Sight (LOS). Following, we focused on the most problematic landslide that affects the Țicău neighborhood and is well-known for its significant implications that it had. To better understand its behavior and the sensitivity of the displacements to the environmental factors (i.e., rainfall), we carried out 2D numerical modeling using a finite difference code. The simulated displacement field is consistent with the InSAR displacements and reveals the most active sectors of the landslide and insights about its mechanism.

Integrated InSAR monitoring and structural assessment of tunnelling‐induced building deformations

Structural deformation monitoring is crucial for the identification of early signs of tunnelling-induced damage to adjacent structures and for the improvement of current damage assessment procedures. Satellite multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques enable measurement of building displacements over time with millimetre-scale accuracy. Compared to traditional ground-based monitoring, MT-InSAR can yield denser and cheaper building observations, representing a cost-effective monitoring tool. However, without integrating MT-InSAR techniques and structural assessment, the potential of InSAR monitoring cannot be fully exploited. This integration is particularly demanding for large construction projects, where big datasets need to be processed. In this paper, we present a new automated methodology that integrates MT-InSAR-based building deformations and damage assessment procedures to evaluate settlement-induced damage to buildings adjacent to tunnel excavations. The developed methodology was applied to the buildings along an 8-km segment of the Crossrail tunnel route in London, using COSMO-SkyMed MT-InSAR data from 2011 to 2015. The methodology enabled the identification of damage levels for 858 buildings along the Crossrail twin tunnels, providing an unprecedented number of high quality field observations for building response to settlements. The proposed methodology can be used to improve current damage assessment procedures, for the benefit of future underground excavation projects in urban areas.

Determining Suitable Spaceborne SAR Observations and Ground Control Points for Surface Deformation Study in Rugged Terrain With InSAR Technique

We aim to address two primary issues using the multitemporal (MT) spaceborne SAR observations and InSAR technique to monitor slope stability in rugged terrains. One is how to maximize the observation and application effectiveness based on the geometric distortion in an area of interest for determining appropriate ascending- and descending-orbit SAR observations. The other is how to optimize the selection of ground control points (GCPs) to correct orbital phase errors and reference in deriving valid ground surface displacements in the SBAS InSAR analysis. Four criteria were proposed to identify valid GCPs efficiently and effectively. The 2017 Xinmo landslide event, China, was showcased. The tear-away zone was 100% and ∼50% distorted when imaged by ascending- and descending-orbit Sentinel-1A/B SAR sensors. The descending SAR datasets were preferred. Then, we identified four spatially dispersed GCP clusters with the selection criteria using the SBAS InSAR technique and 45 descending-orbit MT SAR datasets. One GCP per cluster was chosen. With the four GCPs and SBAS analysis, we successfully detected the stable status in the landslide-free area and the prelandslide acceleration movement within the tear-away zone. Valid InSAR-derived results have been obtained. In turn, the GCP selection criteria have been validated, and the two issues resolved.

Multivariate Outlier Detection in Postprocessing of Multi-temporal PS-InSAR Results using Deep Learning

Abstract Multi-temporal InSAR (MT-InSAR) techniques proved to be very effective for deformation monitoring. However, decorrelation and other noise sources, can be limiting factors in MT-InSAR. The obtained observations (PS - Persistent scatterers) are usually very demanding from a computational perspective, as they can reach hundreds of thousands of observations. To simplify and speed up the classification process, in this study we present an approach based on Convolutional Neural Networks (CNN) classification models, for the detection of MT-InSAR outlying observations. For each PS, the corresponding MT-InSAR parameters, its neighbouring scatterers parameters and its relative position are considered. Tests in two independent PS datasets, covering the regions of Bratislava city and the suburbs of Prievidza, Slovakia, were performed. The results showed that such models are robust and reduced computation time method for the evaluation of MT-InSAR outlying observations. However, the applicability of these models is limited by the deformation pattern in which such models were trained.

Application of Time-Series Sentinel-1A for Land Deformation in Central Aceh, Indonesia

Several decorrelation phenomena of Interferometric Synthetic Aperture Radar (InSAR) have led researchers to develop various multitemporal InSAR (MT-InSAR) techniques with the application of time series/stack of images. In this study, we present the land surface movement monitoring using MT-InSAR techniques in the Central of Aceh-Indonesia, and focus on the temporal and spatial pattern of uplift and subsidence by using multi InSAR methods such as Quasi-Persistent Scatterer (Q-PS) and Small Baseline Subset (SBAS). A total of 18 scenes of Sentinel-1A(S-1A) and 14 scenes of ALOS PALSAR-1(PALSAR-1) images were acquired between 2018 and 2019, as well as 2007 and 2010, where then the multitemporal methods and techniques were applied sequentially to a set of those data. The results showed that the either the S-1A and PALSAR-1 velocity subsidence at Nunang and Musara Alun villages were range from 2.4 to 5.7 and 0.6 to 2.3 mm/year, respectively which corresponded to the results obtained by other research publication. The deformation in Central Aceh needs continuous monitoring using InSAR as the place is a landslide prone area because of the nature of the terrain.

Two decades of settlement of Hong Kong International Airport measured with multi-temporal InSAR

The Hong Kong International Airport (HKIA) was constructed on a platform mainly reclaimed from the sea. The platform has experienced some significant settlement since its operation in 1998 due mainly to consolidation of the materials used in the land reclamation. Although some studies have been carried out to measure the settlement with techniques such as Interferometric Synthetic Aperture Radar (InSAR), the past studies have each only covered some limited time periods. Therefore, a complete history of settlement since the operation of the Airport has never been available to aid the understanding of the spatiotemporal behavior of the land settlement. We attempt for the first time to make full use of most of the available SAR data from multiple SAR sensors (ERS-2, ENVISAT ASAR, COSMO-SkyMed (CSK), and Sentinel-1A) to generate a settlement time series of the HKIA over 1998–2018 with an improved multi-temporal InSAR technique. In order to fill the time gaps between the different SAR datasets, a settlement model is developed based on the InSAR measurements. The results reveal both the spatial and temporal variations of the land settlement. They also show for the first time that the accumulative settlement reaches up to 40 cm over the past two decades. The settlement is largely associated with the material types used in the landfill works and the underlying alluvial sediments as some earlier research has indicated, and the stages of the reclamation works. The results are validated through cross-validation between the datasets and with leveling and GPS measurements on the Airport platform.

An overview of the land subsidence phenomena occurring in Greece, triggered by the overexploitation of the aquifers for irrigation and mining purposes

Abstract. Land subsidence caused by overexploitation of aquifers manifests with an increasing frequency in several regions of Greece. The first signs of land subsidence have been identified since 1965 at the west side of Thessaloniki (broader Kalochori village region), in the form of a progressive marine invasion. Since then several areas have been investigated and proved to be affected mainly by the overexploitation of the aquifers. The Multidisciplinary nature of the subsidence mechanism combining the geological, hydrogeological and morphological setting of the areas with the human activities and the land use data makes their study complicated and require the intervention of multiple scientific specialties. Furthermore, at several sites, beside ground truth data, the land subsidence trends and extends have been also identified via multi-temporal InSAR techniques, such as PSI and SBAS techniques. Among the sites to be presented in the current paper is the wider area of Kalochori village in the eastern part of Thessaloniki plain, the east and west Thessaly plain in central Greece and the region extending to the west–southwest of the Amyntaio opencast coalmine in West Macedonia.

Sequential InSAR Time Series Deformation Monitoring of Land Subsidence and Rebound in Xi’an, China

Interferometric synthetic aperture radar (InSAR) time series deformation monitoring plays an important role in revealing historical displacement of the Earth’s surface. Xi’an, China, has suffered from severe land subsidence along with ground fissure development since the 1960s, which has threatened and will continue to threaten the stability of urban artificial constructions. In addition, some local areas in Xi’an suffered from uplifting for some specific period. Time series deformation derived from multi-temporal InSAR techniques makes it possible to obtain the temporal evolution of land subsidence and rebound in Xi’an. In this paper, we used the sequential InSAR time series estimation method to map the ground subsidence and rebound in Xi’an with Sentinel-1A data during 2015 to 2019, allowing estimation of surface deformation dynamically and quickly. From 20 June 2015 to 17 July 2019, two areas subsided continuously (Sanyaocun-Fengqiyuan and Qujiang New District), while Xi’an City Wall area uplifted with a maximum deformation rate of 12 mm/year. Furthermore, Yuhuazhai subsided from 20 June 2015 to 14 October 2018, and rebound occurred from 14 October 2018 to 17 July 2019, which can be explained as the response to artificial water injection. In the process of artificial water injection, the rebound pattern can be further divided into immediate elastic recovery deformation and time-dependent visco-elastic recovery deformation.

Nonparametric Estimation of DEM Error in Multitemporal InSAR

Isolating phase residuals due to inaccurate external digital elevation model (DEM) is important in retrieval and interpretation of deformation behavior from interferometric synthetic aperture radar (InSAR) observations. Multitemporal InSAR (MTInSAR), by taking DEM error as a parameter, can make the isolation possible. However, due to the presence of atmospheric artifacts in observations and improper deformation model employed in the observation system, accurate retrieval of DEM error cannot be guaranteed in current MTInSAR techniques. Considering that the DEM error has a fixed spatial pattern and its contribution to interferometric phase-only changes with spatial baselines, we propose here a nonparametric method that can estimate the DEM error in a more robust way. To retrieve signals having a fixed spatial pattern from unwrapped MTInSAR measurements, the independent component analysis (ICA) is used. Experiments with synthetic and real data sets indicate the proposed method is able to estimate DEM error with no $a$ priori information about deformation. Moreover, experiments also show that the method can provide a more robust estimation when the observed phase observations are affected by atmospheric delays and/or the number of interferograms used is limited.

Minimizing Height Effects in MTInSAR for Deformation Detection Over Built Areas

Removing the topographic component in the interferometric synthetic aperture radar (InSAR) phase is conventionally conducted using an external digital elevation model (DEM). However, with an increasing spatial resolution of SAR data, the external DEM is becoming less qualified for this purpose, resulting in notable phase residues and even decorrelation in differential interferograms. Although topographic residuals can be parameterized and estimated by multi-temporal InSAR (MTInSAR) techniques, its accuracy is limited by several factors. Instead of providing accurate height information, shortening the length of baselines is an alternative for DEM phase mitigation. We propose here an MTInSAR processing framework that can retrieve the deformation time series without the estimation of topographic residuals. Within the framework, we generate a set of pseudo interferograms with near-zero baselines by integer combination and take these pseudo interferograms as observations of MTInSAR model, where deformation becomes the only signal that needs to be parameterized. The deformation time series is then retrieved directly from wrapped phases by ridge estimation with an integer ambiguity detector. It is noted that although atmospheric artifacts might be magnified during the combination, their differential components at arcs constructed with neighboring points that are not significantly enlarged. The proposed method is particularly suitable for infrastructure deformation monitoring in urban areas where no accurate external DEM is available. It also has promising potential for retrieving deformation from SAR data stacks with short acquisition intervals since the combination can enlarge the signal of interests in pseudo-observations. Semisynthetic and real data tests indicate that the proposed method has satisfied performance on DEM error mitigation and deformation time series estimation.

Ground Deformations Controlled by Hidden Faults: Multi-Frequency and Multitemporal InSAR Techniques for Urban Hazard Monitoring

This work focuses on the study of land subsidence processes by means of multi-temporal and multi-frequency InSAR techniques. Specifically, we retrieve the long-term evolution (2003–2018) of the creeping phenomenon producing ground fissuring in the Ciudad Guzmán (Jalisco state, Mexico) urban area. The city is located on the northern side of the Volcan de Colima area, one of the most active Mexican volcanoes. On September 21 2012, Ciudad Guzmán was struck by ground fissures of about 1.5 km of length, causing the deformation of the roads and the propagation of fissures in adjacent buildings. The field surveys showed that fissures follow the escarpments produced during the central Mexico September 19 1985 Mw 8.1 earthquake. We extended the SAR (Synthetic Aperture Radar) interferometric monitoring starting with the multi-temporal analysis of ENVISAT and COSMO-SkyMed datasets, allowing the monitoring of the observed subsidence phenomena affecting the Mexican city. We processed a new stack of Sentinel-1 TOPSAR acquisition mode images along both descending and ascending paths and spanning the 2016–2018 temporal period. The resulting long-term trend observed by satellites, together with data from volcanic bulletin and in situ surveys, seems to suggest that the subsidence is due to the exploitation of the aquifers and that the spatial arrangement of ground deformation is controlled by the position of buried faults.

Long-term InSAR, borehole inclinometer, and rainfall records provide insight into the mechanism and activity patterns of an extremely slow urbanized landslide

New radar satellites provide global coverage and the possibility of long-term, regular frequency (days-weeks) surface displacement measurements through the application of high precision multi-temporal InSAR (Synthetic Aperture Radar Interferometry) techniques. This represents an excellent opportunity to investigate and improve our understanding of the behavior of extremely slow landslides, as well as of the long- to short-term controls of their activity. In urban settings, such landslides deserve special attention, as their cumulative movements can cause significant socio-economic damage. Here, we re-examine the case of a long-lived, deep-seated landslide in the Apennine Mountains (Italy) which was urbanized between the late 1970s and early 2000s. The case provides a rare opportunity to highlight the benefits of the integrated analysis of long-term (several years) borehole inclinometer measurements with 15 years of multi-temporal InSAR displacement data. We present evidence of the landslide composite nature and asymmetry, and draw attention to the recent period of accelerated movement that coincided with the foot failure event. This helps constraining the interpretation of the borehole and InSAR data and demonstrating the predominantly rotational landslide mechanism. We show how a detailed analysis of sparse inclinometer and more spatially continuous InSAR measurements, when combined with local rainfall records, can reveal long- to short-term patterns of temporal variability in landslide motions and allow anticipating the consequences of future landslide activity.

Identify and Monitor Growth Faulting Using InSAR over Northern Greater Houston, Texas, USA

Growth faults are widely distributed in the Greater Houston (GH) region of Texas, USA, and the existence of faulting could interrupt groundwater flow and aggravate local deformation. Faulting-induced property damages have become more pronounced over the last few years, necessitating further investigation of these faults. Interferometric synthetic aperture radar (InSAR) has been proved to be an effective way for mapping deformations along and/or across fault traces. However, extracting short-wavelength small-amplitude creep signal (about 10–20 mm/yr) from long time span interferograms is extremely difficult, especially in agricultural or vegetated areas. This study aims to position, map and monitor the rate, extent, and temporal evolution of faulting over GH at the highest spatial density using Multi-temporal InSAR (MTI) technique. The MTI method, which maximizes usable signal and correlation, has the ability to identify and monitor faulting and provide accurate and detailed depiction of active faults. Two neighboring L-band Advanced Land Observing (ALOS) tracks (2007–2011) are utilized in this research. Numerous areas of sharp phase discontinuities have been discerned from MTI-derived velocity map. InSAR measurements allow us to position both previously known faults traces as well as nucleation of new fractures not previously revealed by other ground/space techniques. Faulting damages and surface scarps were evident at most InSAR-mapped fault locations through our site investigations. The newly discovered fault activation appears to be related to excessive groundwater exploitation from the Jasper aquifer in Montgomery County. The continuous mining of groundwater from the Jasper aquifer formed new water-level decline cones over Montgomery County, corroborating the intensity of new fractures. Finally, we elaborate the localized fault activities and evaluate the characteristics of faulting (locking depth and slip rate) through modeling MTI-derived deformation maps. The SW–NE-oriented faults pertain to normal faulting with an average slip rate of 7–13 mm/yr at a shallow locking depth of less than 4 km. Identifying and characterizing active faults through MTI and deformation modeling can provide insights into faulting, its causal mechanism and potential damages to infrastructure over the GH.

Land Subsidence and Ground Fissures in Beijing Capital International Airport (BCIA): Evidence from Quasi-PS InSAR Analysis

Land subsidence is a global environmental geological hazard caused by natural or human activities. The high spatial resolution and continuous time coverage of interferometric synthetic aperture radar (InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Beijing, the capital city of China, has suffered from land subsidence for decades since it was first recorded in the 1950s. It was reported that uneven ground subsidence and fractures have seriously affected the normal operation of the Beijing Capital International Airport (BCIA) in recent years before the overhaul of the middle runway in April 2017. In this study, InSAR time series analysis was successfully used to detect the uneven local subsidence and ground fissure activity that has been gradually increasing in BCIA since 2010. A multi-temporal InSAR (MT-InSAR) technique was performed on 63 TerraSAR-X/TanDem-X (TSX/TDX) images acquired between 2010 and 2017, then deformation rate maps and time series for the airport area were generated. Comparisons of deformation rate and displacement time series from InSAR and ground-leveling were carried out in order to evaluate the accuracy of the InSAR-derived measurements. After an integrated analysis of the distribution characteristics of land subsidence, previous research results, and geological data was carried out, we found and located an active ground fissure. Then main cause of the ground fissures was preliminarily discussed. Finally, it can be conducted that InSAR technology can be used to identify and monitor geological processes, such as land subsidence and ground fissure activities, and can provide a scientific approach to better explore and study the cause and formation mechanism of regional subsidence and ground fissures.

Ground Deformation Analysis of Bolvadin (W. Turkey) by Means of Multi-Temporal InSAR Techniques and Sentinel-1 Data

Surface deformations were observed in Bolvadin, located in the province of Afyon (western Turkey) in the last decade which occurred without any destructive earthquakes. In this study, ground deformation of the Bolvadin region is analyzed by means of multi-temporal interferometric synthetic aperture radar (InSAR) technique with Sentinel-1 synthetic aperture radar (SAR) data. Sentinel-1 data acquired in ascending and descending orbits between October 2014 and October 2018 are processed with the Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) open source software tools. Deformation velocity maps and line-of-sight (LOS) displacement time series are produced and compared with geology, groundwater level and the water surface area of Eber Lake nearby. Deformation velocity maps reveal significant subsidence in most of the town and surrounding regions, which is confirmed by field observations that show severe damage to the settlements and infrastructure of the town. The most severe subsidence is observed to be in the southern part of Bolvadin with rates up to 35 mm/year, which is characterized by the presence of soft alluvial deposits. Composed of slope debris/talus and conglomerate, the northeastern part of the deforming region experiences a relatively lower rate of subsidence. A strong correlation between LOS displacement time series and groundwater level exists both in the short and long term. Moreover, short term variations in LOS displacement time series also show high similarity with seasonal variations in the water surface area of Eber Lake located a few km southeast of the town. We conclude that the primary cause of subsidence is most probably the overexploitation of groundwater and hydrological changes because of (1) the strong correlation of subsidence with lithological units, (2) the similarity between deformation rate and groundwater level changes, (3) the correspondence of seasonal variations in water surface area and short-term deformation rate oscillations, and (4) the absence of InSAR velocity contrast across the active faults.

Pixel-Wise MTInSAR Estimator for Integration of Coherent Point Selection and Unwrapped Phase Vector Recovery

Coherent point (including persistent and distributed scatterers) selection and phase ambiguity treatment (or parameter estimation) are the key tasks involved in multitemporal InSAR (MTInSAR) algorithms, which are usually conducted separately with empirical thresholds. It is not rare to see that due to the discrepancies on threshold setting, even for the same MTInSAR technique with the same data sets, it will raise different (sometimes quite notable) results and affect the applicability of InSAR techniques. We propose here an integrated MTInSAR estimator that combines the coherent point selection and phase vector unwrapping into a single step. Essentially, the estimator aims to recover the unwrapped phase vector at coherent points. Therefore, it could serve as an alternative solution of spatial-temporal phase unwrapping problem. In the estimator, wrapped phase at all pixels in short baseline interferograms are taken as observations. Starting from the phase differences at arcs of a fully connected network of pixels, based on the residual analysis and spatial closure of phase triangularity, the estimator can detect and delete the arcs having unacceptable phase noise and phase ambiguities. By integrating the phase differences at the remained arcs, the unwrapped phase at coherent points in consecutive acquisition intervals can be obtained. Impressively, the estimator is immune to the bias raised by improper deformation model. The performance of the proposed estimator is evaluated via semisynthetic and real data tests. Considering that the phase enhancement algorithms (e.g., phase-linking and Extended Minimum Cost Flow-Small BAseline Subset) that can reconstruct high-quality wrapped phases are gaining popularity, the proposed estimator can also be implemented as a postprocessing module of these algorithms for retrieval of unwrapped phase vectors at coherent points.