Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR

The Dadu River travels in the mountainous areas of southwestern China, one of regions with the most hazards that has long suffered from frequent geohazards. The early identification of landslides in this region is urgently needed, especially after the recent Luding earthquake (MS 6.8). While conventional ground-based monitoring techniques are limited by the complex terrain conditions in these alpine valley regions, space interferometric synthetic aperture radar (InSAR) provides an incomparable advantage in obtaining surface deformation with high precision and over a wide area, which is very useful for long-term and slow geohazard monitoring. In this study, more than 500 Sentinel-1 SAR images with four frames acquired during 2017~2022 were collected to detect the hidden landslide regions from the Jinchuan to Ebian Section along the Dadu River, based on joint-scatterer InSAR (JS-InSAR) and small baseline subset (SBAS) techniques. The results showed that our method could be successfully applied for landslide monitoring in complex mountainous regions. Furthermore, 143 potential landslide regions spreading over an 800 km area along the Dadu River were extracted by integrating the deformation measurements and optical images. Our study can provide a reference for large-scale geological hazard surveys in mountainous areas, and the InSAR technique will be encouraged for the local government in future long-term monitoring applications in the Dadu River Basin.

Monitoring Land Subsidence Using PS-InSAR Technique in Rawalpindi and Islamabad, Pakistan

Land subsidence is a major concern in vastly growing metropolitans worldwide. The most serious risks in this scenario are linked to groundwater extraction and urban development. Pakistan’s fourth-largest city, Rawalpindi, and its twin Islamabad, located at the northern edge of the Potwar Plateau, are witnessing extensive urban expansion. Groundwater (tube-wells) is residents’ primary daily water supply in these metropolitan areas. Unnecessarily pumping and the local inhabitant’s excessive demand for groundwater disturb the sub-surface’s viability. The Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) approach, along with Sentinel-1 Synthetic Aperture Radar (SAR) imagery, were used to track land subsidence in Rawalpindi-Islamabad. The SARPROZ application was used to study a set of Sentinel-1 imagery obtained from January 2019 to June 2021 along descending and ascending orbits to estimate ground subsidence in the Rawalpindi-Islamabad area. The results show a significant increase (−25 to −30 mm/yr) in subsidence from −69 mm/yr in 2019 to −98 mm/yr in 2020. The suggested approach effectively maps, detects, and monitors subsidence-prone terrains and will enable better planning, surface infrastructure building designs, and risk management related to subsidence.

Monitoring and Analysis of Ground Surface Settlement in Mining Clusters by SBAS-InSAR Technology

In this paper, we use the small baseline set technology and the early geological hazard identification method based on the selection of Permanent Scatter (PS) and Distributed Scatter (DS) points to carry out the research on surface deformation monitoring caused by underground activities in mining cluster areas. We adopted the Small Baseline Subset InSAR (SBAS-InSAR) technique to process Sentinel-1A SAR images over the research area from March 2017 to May 2021. The deformation estimation technology based on the robustness of PS points and DS points can be used for early identification of high-density surface subsidence in a large area of mines. The surface subsidence information can be obtained quickly and accurately, and the advantages of using InSAR technology to monitor long-time surface subsidence in complex mining cluster areas was explored in this study. By comparing the monitoring data of the Global Navigation Satellite System (GNSS) ground monitoring equipment, the accuracy error of large-scale surface settlement information is controlled within 8 mm, which has high accuracy. Meanwhile, according to the spatial characteristics of cluster mining areas, it is analyzed that the relationship between adjacent mining areas through groundwater easily leads to regional associated large-area settlement changes. Compared with the D-InSAR (Differential InSAR) technology applied in mine monitoring at the early stage, this proposed method can monitor a large range of long time series and optimize the problem of decoherence to some extent in mining cluster areas. It has important reference significance for early monitoring and early warning of subsidence disaster evolution in mining intensive areas.

A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring

This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices.

Sentinel-1A for monitoring land subsidence of coastal city of Pakistan using Persistent Scatterers In-SAR technique

Karachi is located in the southern part of Pakistan along the Arabian Sea coast. Relevant institutions are concerned about the possibility of ground subsidence in the city, contributing to the comparative sea-level rise. So yet, no direct measurement of the subsidence rate and its relation to city submergence danger has been made. SAR (Synthetic Aperture Radar) interferometry is a powerful method for obtaining millimeter-accurate surface displacement measurements. The Sentinel-1 satellite data provide extensive geographical coverage, regular acquisitions, and open access. This research used the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technology with Sentinel-1 SAR images to monitor ground subsidence in Karachi, Pakistan. The SARPROZ software was used to analyze a series of Sentinel-1A images taken from November 2019 to December 2020 along ascending and descending orbit paths to assess land subsidence in Karachi. The cumulative deformation in Line of Sight (LOS) ranged from - 68.91 to 76.06 mm/year, whereas the vertical deformation in LOS ranged from - 67.66 to 74.68 mm/year. The data reveal a considerable rise in subsidence from 2019 to 2020. The general pattern of subsidence indicated very high values in the city center, whereas locations outside the city center saw minimal subsidence. Overall, the proposed technique effectively maps, identifies, and monitors land areas susceptible to subsidence. This will allow for more efficient planning, construction of surface infrastructure, and control of subsidence-induced risks.

Measuring Coastal Subsidence after Recent Earthquakes in Chile Central Using SAR Interferometry and GNSS Data

Coastal areas concentrate a large portion of the country’s population around urban areas, which in subduction zones commonly are affected by drastic tectonic processes, such as the damage earthquakes have registered in recent decades. The seismic cycle of large earthquakes primarily controls changes in the coastal surface level in these zones. Therefore, quantifying temporal and spatial variations in land level after recent earthquakes is essential to understand shoreline variations better, and to assess their impacts on coastal urban areas. Here, we measure the coastal subsidence in central Chile using a multi-temporal differential interferometric synthetic aperture radar (MT-InSAR). This geographic zone corresponds to the northern limit of the 2010 Maule earthquake (Mw 8.8) rupture, an area affected by an aftershock of magnitude Mw 6.8 in 2019. The study is based on the exploitation of big data from SAR images of Sentinel-1 for comparison with data from continuous GNSS stations. We analyzed a coastline of ~300 km by SAR interferometry that provided high-resolution ground motion rates from between 2018 and 2021. Our results showed a wide range of subsidence rates at different scales, of analyses on a regional scale, and identified the area of subsidence on an urban scale. We identified an anomalous zone of subsidence of ~50 km, with a displacement <−20 mm/year. We discuss these results in the context of the impact of recent earthquakes and analyze the consequences of coastal subsidence. Our results allow us to identify stability in urban areas and quantify the vertical movement of the coast along the entire seismic cycle, in addition to the vertical movement of coast lands. Our results have implications for the planning of coastal infrastructure along subduction coasts in Chile.

Urban growth and land subsidence: Multi-decadal investigation using human settlement data and satellite InSAR in Morelia, Mexico

Limited attention is typically paid to the cause-effect relationship between land subsidence due to aquifers overexploitation in expanding metropolises and urban growth models and patterns. This paper implements an integrated urban and satellite Interferometric Synthetic Aperture Radar (InSAR) approach to investigate subsidence, multi-decadal urban growth and peopling trends in the Metropolitan Area of Morelia (ZMM) in the Mexican state of Michoacán. Stacking of JRC's Global Human Settlement Layer, DLR's World Settlement Footprint and INEGI's National Geostatistical Framework datasets reveals a predominant edge-expansion growth model, with urban densification in 1975-2020 and some sprawling in 1990-2000. Population of the ZMM doubled in the last 30 years, reaching over 1 million inhabitants. The ENVISAT and Sentinel-1 InSAR analysis confirms that subsidence is structurally-controlled by the main normal faults within the Cuitzeo half-graben. Differential sinking and ground discontinuities are aligned with buried tectonic faults and contrasting compressible sediment thickness. Non-linearly deforming subsidence bowls develop at extraction wells in both old and newly urbanized sectors of the ZMM. Maximum vertical displacement velocities increased from -2.5 cm/year in 2003-2010 to -9.0 cm/year in 2014-2021, with subsidence migrating towards recently urbanized zones. More than 250 new groundwater wells were added to the public registry since 2000, many of which within new urban sectors. Time-lapse InSAR reveals a 4 km² rapidly subsiding bowl that formed at the largest social housing neighbourhood of Villas del Pedregal, as building lots were progressively completed and sold, and new wells registered. With angular distortions due to the differential subsidence reaching 0.12% in 2014-2021, new buildings and roads are exposed to fracturing and surface faulting risk of comparable level as the city historic building blocks located along the main faults. By providing useful insights into the relationship between urban growth and land subsidence in the ZMM, the approach proves valuable for application to other metropolises worldwide.

Monitoring of Land Subsidence and Ground Fissure Activity within the Su-Xi-Chang Area Based on Time-Series InSAR

Serious land subsidence and ground fissure (GF) disasters have brought huge economic losses to the Su-Xi-Chang area (China) and threatened the safety of its residents. To better understand the development of these disasters, it is urgent to carry out long-term and large-scale deformation monitoring in this region. In this study, based on time-series interferometric synthetic aperture radar (InSAR) technology, ground deformation characteristics were obtained at different periods. Meanwhile, Fast Lagrangian Analysis of Continua in Three Dimensions (FLAC3D) version 5.00 was used to study the stress, seepage field, and displacement changes in the soil layers caused by pumping activities at the bedrock bulge. The results showed that three subsidence centers were located in Suzhou, Wuxi, and Changzhou from 2007 to 2010. The ground fissures in Guangming village had obvious differential settlements and intense activities. The land subsidence in the Su-Xi-Chang area was under control from 2018 to 2021, while there was a relative rebound in most areas. Combined with numerical simulation and geological data, we demonstrated that pumping activities would accelerate and intensify the land subsidence process, and differential subsidence was prone to occur at the buried hill, which in turn led to the formation of ground fissures. By comparing the characteristics of ground deformation in different periods, it was proven that banning groundwater exploitation is an effective measure for preventing and controlling such disasters.

Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol’s Coastal Front

In the last five years, the urban development of the city of Limassol has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development arises several concerns about the aggravation of potential land subsidence in the Limassol coastal front. Forty six Copernicus Sentinel-1 acquisitions from 2017 to 2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). A case study for the identification and analysis of the persistent scatterers (PS) in pixels in a series of interferograms and the quantity of the land displacements in the line of sight of the Limassol coastal front is presented in this research, with subsidence rates up to about (−5 to 4 mm/year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, considering that there is a significant number of skyscrapers planned or currently under construction, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol.

Tracking hidden crisis in India's capital from space: implications of unsustainable groundwater use

National Capital Region (NCR, Delhi) in India is one of the fastest-growing metropolitan cities which is facing a severe water crisis due to increasing water demand. The over-extraction of groundwater, particularly from its unconsolidated alluvial deposits makes the region prone to subsidence. In this study, we investigated the effects of plummeting groundwater levels on land surface elevations in Delhi NCR using Sentinel-1 datasets acquired during the years 2014-2020. Our analysis reveals two distinct subsidence features in the study area with rates exceeding 11 cm/year in Kapashera-an urban village near IGI airport Delhi, and 3 cm/year in Faridabad throughout the study period. The subsidence in these two areas are accelerating and follows the depleting groundwater trend. The third region, Dwarka shows a shift from subsidence to uplift during the years which can be attributed to the strict government policies to regulate groundwater use and incentivizing rainwater harvesting. Further analysis using a classified risk map based on hazard risk and vulnerability approach highlights an approximate area of 100 square kilometers to be subjected to the highest risk level of ground movement, demanding urgent attention. The findings of this study are highly relevant for government agencies to formulate new policies against the over-exploitation of groundwater and to facilitate a sustainable and resilient groundwater management system in Delhi NCR.

Accuracy of Sentinel-1 PSI and SBAS InSAR Displacement Velocities against GNSS and Geodetic Leveling Monitoring Data

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.

FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service

The purpose of the ForM@Ter LArge-scale multi-Temporal Sentinel-1 InterferoMetry service (FLATSIM) is the massive processing of Sentinel-1 data using multi-temporal interferometric synthetic aperture radar (InSAR) over large areas, i.e., greater than 250,000 km2. It provides the French ForM@ter scientific community with automatically processed products using a state of the art processing chain based on a small baseline subset approach, namely the New Small Baseline (NSBAS). The service results from a collaboration between the scientific team that develops and maintains the NSBAS processing chain and the French Spatial Agency (CNES) that mirrors the Sentinel-1 data. The proximity to Sentinel-1 data, the NSBAS workflow, and the specific optimizations to make NSBAS processing massively parallel for the CNES high performance computing infrastructure ensures the efficiency of the chain, especially in terms of input and output, which is the key for the success of such a service. The FLATSIM service is made of a production module, a delivery module and a user access module. Products include interferograms, surface line of sight velocity, phase delay time series and auxiliary data. Numerous quality indicators are provided for an in-depth analysis of the quality and limits of the results. The first national call in 2020 for region of interest ended up with 8 regions spread over the world with scientific interests, including seismology, tectonics, volcano-tectonics, and hydrological cycle. To illustrate the FLATSIM capabilities, an analysis is shown here on two processed regions, the Afar region in Ethiopa, and the eastern border of the Tibetan Plateau.

Integrated Analysis of the Combined Risk of Ground Subsidence, Sea Level Rise, and Natural Hazards in Coastal and Delta River Regions

Non-climate-related anthropogenic processes and frequently encountered natural hazards exacerbate the risk in coastal zones and megacities and amplify local vulnerability. Coastal risk is amplified by the combination of sea level rise (SLR) resulting from climate change, associated tidal evolution, and the local sinking of land resulting from anthropogenic and natural hazards. In this framework, the authors of this investigation have actively contributed to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon IV initiative through a project (ID. 32294) that was explicitly designed to address the issue of monitoring coastal and delta river regions through Earth Observation (EO) technologies. The project’s primary goals were to provide a complete characterization of the changes in target scenes over time and provide estimates of future regional sea level changes to derive submerged coastal areas and wave fields. Suggestions are also provided for implementing coastal protection measures in order to adapt and mitigate the multifactor coastal vulnerability. In order to achieve these tasks, well-established remote sensing technologies based on the joint exploitation of multi-spectral information gathered at different spectral wavelengths, the exploitation of advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques for the retrieval of ground deformations, the realization of geophysical analyses, and the use of satellite altimeters and tide gauge data have effectively been employed. The achieved results, which mainly focus on selected sensitive regions including the city of Shanghai, the Pearl River Delta in China, and the coastal city of Saint Petersburg in Europe, provide essential assets for planning present and future scientific activities devoted to monitoring such fragile environments. These analyses are crucial for assessing the factors that will amplify the vulnerability of low-elevation coastal zones.

Spatio-Temporal Distribution of Ground Deformation Due to 2018 Lombok Earthquake Series

Lombok Island in Indonesia was hit by four major earthquakes (6.4 Mw to 7 Mw) and by at least 818 earthquakes between 29 July and 31 August 2018. The aims of this study are to measure ground deformation due to the 2018 Lombok earthquake series and to map its spatio-temporal distribution. The application of DinSAR was performed to produce an interferogram and deformation map. Time series Sentinel-1 satellite imageries were used as master and slave for each of these four major earthquakes. The spatio-temporal distribution of the ground deformation was analyzed using a zonal statistics algorithm in GIS. It focused on the overlapping area between the raster layer of the deformation map and the polygon layer of six observation sites (Mataram City, Pamenang, Tampes, Sukadana, Sembalun, and Belanting). The results showed that the deformation includes uplift and subsidence. The first 6.4 Mw foreshock hitting on 29 July 2018 produces a minimum uplift effect on the island. The 7.0 Mw mainshock on 5 August 2018 causes extreme uplift at the northern shore. The 6.2 Mw Aftershock on 9 August 2018 generates subsidence throughout the study area. The final earthquake of 6.9 Mw on 19 August 2018 initiates massive uplift in the study area and extreme uplift at the northeastern shore. The highest uplift reaches 0.713 m at the northern shore, while the deepest subsidence is measured −0.338 m at the northwestern shore. Dominant deformation on the northern area of Lombok Island indicates movement of Back Arc Trust in the north of the island. The output of this study would be valuable to local authorities to evaluate existing earthquake’s impacts and to design mitigation strategies to face earthquake-induced ground displacement.

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.