Stanford Method For Persistent Scatterers Research Articles

Аналіз деформацій земної поверхні у районі гірничих виробок рудника «Калуш» за даними радіолокаційного знімання 2018–2022

The purpose of this work is to analyze the current state of geodynamic activity of the earth’s surface in the territory of the city of Kalush based on satellite radar monitoring data for the period 2018-2022. Method. The input data for the study were 36 medium-resolution (C-band) radar images of the Sentinel-1A satellite. The method of persistent scatterers (PSInSAR), which is an advanced technology of differential interferometry (DInSAR), was used to process the results of radar acquisition. The PSI method was implemented using the Stanford Method for Persistent Scatterer (StaMPS) software package. The results. Maps of the average rates of deformation of the earth’s surface in the area of the mine fields of the “Kalush” mine were obtained. The rate of subsidence of the territories above the mining works reaches 4 mm/year. The accuracy of determining the deformation rates is an order of magnitude higher than their values, which indicates the reliability of the obtained results. The nature of subsidence, for most areas, has a linear model of deformation processes. The maximum rates of deformation of the earth’s surface are observed over the Khotyn sylvinite field, but the rates of subsidence have decreased significantly compared to the previous data of the monitoring carried out in 2016-2017. The scientific novelty consists in obtaining the updated results of the monitoring of the territories above the mining operations of the Kalush mine using the InSAR method for the period from June 2018 to December 2022. The practical significance of the research results is not only in determining the quantitative indicators of deformations, but also in obtaining data for further planning and improvement of the complex geodynamic monitoring system using ground and satellite measurements.

"Ankara Batıkent Mesa Bölgesinde Zemin Deformasyon Kontrolü: Metro Hattı Kontrolü

Subway systems in large cities play a crucial role in enhancing the sustainability and livability levels of urban areas by offering a solution to heavy traffic and transportation problems. Subway tunnels are critical transportation infrastructures that are constructed to serve residential areas, and thus, they have a direct relationship with residential areas in terms of construction and operation. This study is designed to generate precise data by analyzing ground deformation of metro systems using the Persistent Scatterer (PSI) Interferometry method and utilizing free dataThe Batıkent-Törekent metro line in Ankara is identified with the M3 code. At the location where M3 metro passes, ground deformation has been detected. In the analysis of the relevant location, both ascending and descending Sentinel-1 satellite datasets were used. The StaMPS (Stanford Method for Persistent Scatterers)/PSI (approach was utilized in the study. It was found that the results obtained from the ascending datasets were more sensitive. Observations were made in two different periods using ascending datasets: 2015-2016 and 2017-2022. Deformations were observed in the Line of Sight (LOS) direction in the locations where rail systems and tunnels were present. Eight months after the observation, due to the ground deformation in the area, passenger safety was at risk, and a ground strengthening work was carried out, resulting in a 16-day shutdown of the metro system in this location. The results of this study provide concrete contributions to the literature on the possibility of accurate ground measurement using free data

Parametric Test of the Sentinel 1A Persistent Scatterer- and Small Baseline Subset-Interferogram Synthetic Aperture Radar Processing Using the Stanford Method for Persistent Scatterers for Practical Landslide Monitoring

The landslide monitoring method that uses the Sentinel 1A Interferogram Synthetic Aperture Radar (InSAR) through the Stanford Method for Persistent Scatterers (StaMPS) method is a complimentary but complex procedure without exact guidelines. Hence, this paper delivered a parametric test by examining the optimal settings of the Sentinel 1A Persistent Scatterer (PS)- and Small Baseline Subset (SBAS)-InSAR using the StaMPS compared to the Global Navigation Satellite Systems (GNSS) in landslide cases. This study first revealed parameters with the suggested values, such as amplitude dispersion used to describe amplitude stability, ranging from 0.47 to 0.48 for PS and equal to or more than 0.6 for SBAS in WuWanZai, Ali Mt. The study further examined the suggested values for other factors, including the following: unwrap grid size to re-estimate the size of the grid; unwrap gold n win as the Goldstein filtering window to reduce the noise; and unwrap time win as the smoothing window (in days) for estimating phase noise distributions between neighboring pixels. Furthermore, the study substantiated the recommended settings in the Woda and Shadong landslide cases with the GNSS, inferring that the SBAS has adequate feasibility in practical landslide monitoring.

Remote monitoring of a concrete bridge using PSInSAR

AbstractStructural health monitoring (SHM) can support decision makers in structural integrity management by providing information about occurrence of damage and its evolution in time. Recently, due to new Synthetic Aperture Radar (SAR) missions, remote sensing through active satellites provides a promising methodology capable of monitoring infrastructure assets. This paper investigates the use of Multi‐Temporal Synthetic Aperture Radar Interferometry (MT‐InSAR) processing to monitor the structural health of bridges. The full interferometry processing chain is applied using Sentinel‐1 synthetic aperture radar data and a combination of open‐source routines from the Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS). Displacement time‐histories are derived from single look complex products by employing the Persistent Scatterer Interferometry (PSInSAR) technique. The PS‐InSAR technique is applied to study the behavior of a concrete bridge located over the Oglio river in Isola Dovarese, Italy, on which also GNSS receivers are installed. The GNSS data are then used to validate the PS‐InSAR measurements. The effects on PS‐InSAR results of the number of SAR images used as well of the target structure are investigated.

A new sequential homogeneous pixel selection algorithm for distributed scatterer InSAR

ABSTRACT Distributed scatterer interferometric synthetic aperture radar (DS InSAR) technology has been widely used in various fields. Homogeneous pixel selection is a crucial step in the use of DS InSAR, directly affecting the estimation precision and reliability of subsequent parameter calculations. The existing algorithms for selecting homogeneous pixels have inherent limitations, such as requiring many heterogeneous samples and strict requirements surrounding the required number of synthetic aperture radar (SAR) images. To address these problems, a new sequential selection algorithm for homogeneous pixels is proposed, based on the Baumgartner – Weiss–Schindler (BWS) test algorithm and dynamic interval estimation (DIE) theory. According to Monte Carlo simulation experiments, the average standard deviation (STD) of the mean of the rejection of the BWS-DIE algorithm under six sample conditions is 0.014. Compared with three existing algorithms, including the Kolmogorov‒Smirnov (KS), BWS and fast statistically homogeneous pixel selection (FaSHPS) algorithms, the BWS-DIE algorithm improves homogeneous pixel selection precision by 64.3%, 69.4% and 25.3%, respectively. In the real data experiment, 12 scenes of Advanced Land Observing Satellite-1 Phased Array type L-band Synthetic Aperture Radar (ALOS-1 PALSAR) data from February 2007 to March 2011 were used and the BWS-DIE multitemporal InSAR (MT InSAR) method based on the BWS-DIE algorithm was applied to surface subsidence monitoring in the western mining area of Xuzhou, Jiangsu Province, China. The experimental results show that, compared with the Stanford Method for Persistent Scatterers (StaMPS), the BWS-DIE MT InSAR method improves the ability to monitor the maximum subsidence by 12.3%, increases the point density by 5.7 times and decreases the root mean square error (RMSE) by 50%. In addition, new surface deformation patterns are found in the spatial-temporal evolution. The above experimental results show that the proposed BWS-DIE algorithm exhibits remarkable advantages in selection power and selection precision and is not limited by the number of SAR images. The proposed algorithm can further broaden the application scenarios for DS InSAR and provide high-quality and reliable monitoring data for subsequent scientific research.

An Investigation of Volcanic Ground Deformation Using InSAR Observations at Tendürek Volcano (Turkey)

Although approximately one-tenth of the world’s population lives near volcanoes, most of the 1500 active volcanoes are not monitored by ground-based instruments because of the cost and difficulty of access. Since the development of interferometric synthetic aperture radar (InSAR) in the 1990s, recent advances have allowed the near-real-time detection of surface deformations, one of the earliest markers of volcanic activity. According to the Global Volcanism Program, Turkey’s most recent eruption (involving gas and ash) occurred in the Tendürek volcano in 1885. An explosion in the Tendürek volcano, which continues to actively output gas and steam, would be a critical issue for the life and property of the people living nearby. In this context, we processed the Sentinel-1 data collected by the European Space Agency using the Stanford Method for Persistent Scatterers, and the surface deformations of the Tendürek volcano were investigated. In addition, we applied two different atmospheric correction approaches (linear phase-based tropospheric correction and the Generic Atmospheric Correction Online Service for InSAR) to reduce atmospheric effects and found that the linear phase-based tropospheric correction model produced lower standard deviation values. Subsequently, the mean deformation velocity maps, displacement time series, and deformation components in the line-of-sight direction were calculated. The results showed that the most severe subsidence was −11 mm/yr on the upper slopes of the Tendürek volcano. Although the lower slopes of the subsidence region have a lower settlement rate, the subsidence has a peak-caldera-centered location.

Present-Day Surface Deformation in North-East Italy Using InSAR and GNSS Data

Geodetic data can detect and estimate deformation signals and rates due to natural and anthropogenic phenomena. In the present study, we focus on northeastern Italy, an area characterized by ~1.5–3 mm/yr of convergence rates due to the collision of Adria-Eurasia plates and active subsidence along the coasts. To define the rates and trends of tectonic and subsidence signals, we use a Multi-Temporal InSAR (MT-InSAR) approach called the Stanford Method for Persistent Scatterers (StaMPS), which is based on the detection of coherent and temporally stable pixels in a stack of single-master differential interferograms. We use Sentinel-1 SAR images along ascending and descending orbits spanning the 2015–2019 temporal interval as inputs for Persistent Scatterers InSAR (PSI) processing. We apply spatial-temporal filters and post-processing steps to reduce unrealistic results. Finally, we calibrate InSAR measurements using GNSS velocities derived from permanent stations available in the study area. Our results consist of mean ground velocity maps showing the displacement rates along the radar Line-Of-Sight for each satellite track, from which we estimate the east–west and vertical velocity components. Our results provide a detailed and original view of active vertical and horizontal displacement rates over the whole region, allowing the detection of spatial velocity gradients, which are particularly relevant to a better understanding of the seismogenic potential of the area. As regards the subsidence along the coasts, our measurements confirm the correlation between subsidence and the geological setting of the study area, with rates of ~2–4 mm/yr between the Venezia and Marano lagoons, and lower than 1 mm/yr near Grado.

Integration of differential interferometric synthetic aperture radar and persistent scatterer interferometric approaches to assess deformation in enshi city, hubei, China

Land surface deformation can severely damage socioeconomic living conditions around the world. This study aimed to identify the Shaziba landslide and further assess deformation activities in Enshi city. For this purpose, the sentinel-1 C-bands data acquired in ascending directions were processed with Sentinel Application Platform and Stanford Method for Persistent Scatterers (StaMPS) software packages. Our results revealed the location of a landslide that occurred on 21 July 2020 in the Shaziba area, Enshi Prefecture. More interesting deformation results were found in Enshi city for the first time with a deformation range from −51.6 to 54.2 mm/year. We conducted a thorough observation of different urban infrastructures such as commercial and residential buildings, roads, bridges, and airports in Enshi city and along the Qingjiang River to evaluate land surface deformation. Observations revealed that there are a number of influencing factors contributing to disturbing the natural environment and resources in Enshi Prefecture. Of these influencing factors, intensive rainfall is a major cause as are the infiltration of rainfall into the subsurface Silurian strata together with the load of infrastructure in the study area. If this issue is not addressed it could lead to devastating geo-hazard disasters in the future. Scientific approaches to determine various causes of frequent geo-hazards in this region are of great significance for developing early warning systems for disasters and ensuring the safety of residents’ lives and property.

EVALUATION OF PIXEL SELECTION METHODS FOR TRAFFIC INFRASTRUCTURE MONITORING USING SENTINEL-1 INSAR

Abstract. The Synthetic Aperture Radar (SAR) satellite Sentinel-1 provides excellent traffic infrastructure monitoring capabilities due to its short revisit time, wide-scale coverage and free of charge data policy. However, pixels from Sentinel-1 have a medium spatial resolution so that traffic infrastructure is only covered by a few pixels. Moreover, Interferometric Synthetic Aperture Radar (InSAR) yields deformation time series for coherent pixels only, thus limiting the number of pixels reporting the ground motion at traffic infrastructure. Although various InSAR time series methods have been successfully applied for traffic infrastructure monitoring, the selection of appropriate methods achieving a high pixel density has not yet been evaluated. In this study, we test whether we can improve the monitoring capabilities by combining different InSAR time series methods. On the one hand, we applied widely used Stanford Method for Persistent Scatterers (StaMPS) as a baseline method to retrieve the deformation time series. On the other hand, we enhanced the phase quality in a pre-processing step using the Phase-Linking (PL) approach and afterwards estimated the deformation time series also with StaMPS based on pixels selected by PL. We compared and evaluated the achieved pixel densities from both methods at main roads, highways and railways in a study area in Germany. We found that the InSAR time series methods selected complementary sets of pixels at all traffic infrastructure types. Moreover, the results indicate a great potential for railway monitoring using Sentinel-1 InSAR due to both high pixel density and homogeneous spatial distribution of pixels. Interestingly, coherent scatterers selected by StaMPS were observed to coincide with large traffic signs at highways which show a double-bounce scattering in the amplitude images. This study shows the benefits of combining PL and StaMPS for increased pixel density at traffic infrastructure and confirms Sentinel-1 data as a suitable data source for traffic infrastructure monitoring.

PROCESSING SENTINEL-1A IMAGES USING SENTINEL APPLICATION PLATFORM SOFTWARE TO GENERATE INTERFEROGRAM FOR MEASURE GROUND DEFORMATION IN KELANTAN

Land subsidence are deposition processes that lead to the earth's surface sinking slowly or suddenly as a result of movement of underground structures. It occurs when the amount of underground water has been pumped out from the certain types of sedimentary rock such as fine grains. Over-exploitation of subsurface water causes compression between clay and silt, resulting in land subsidence. Since 1935, Kelantan has relied on subsurface water as a daily requirement. There are 70% of the total domestic water supply in Kelantan is from groundwater. Consequently, the objective of this study is to monitor groundwater extraction-induced land deformation using Persistent Scatterer (PS) InSAR technique. The PSI method involves the use of large numbers of stack interferograms to determine and monitor deformation. The purpose of the study is to show how to generate interferograms from Sentinel-1A images using the SentiNel Application Platform (SNAP) software before exporting them to the Stanford Method for Persistent Scatterers (StaMPS). Furthermore, this study will focus on the state of Kelantan where the specific places involved in groundwater extractions are in Pintu Geng, Tanjung Mas and Tumpat. This paper will demonstrate how to use the SNAP software to process data.

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.

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

Abstract. 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.

Detection of sinkhole activity in West-Central Florida using InSAR time series observations

Sinkhole activity in Florida is a major hazard for people and property. Its increasing frequency is often related to an accelerated use of ground-water and land resources in the region. In this work, we use a combined approach of radar interferometry and spatial clustering analysis over three selected sites in West-Central Florida to identify localized deformation that may be caused by sinkhole activity. The region of West-Central Florida is a densely active sinkhole region, where sinkholes tend to be small and land cover is mixed resulting in variable interferometric coherence that complicates Interferometric Synthetic Aperture Radar (InSAR) surveys. In this work, we present a combined methodology implementing InSAR and a Density-Based Spatial Clustering Analysis (DBSCAN) algorithm to detect unknown sinkhole activity and to alert to possible precursors of sinkhole collapse. The data used for the study consist of acquisitions from three TerraSAR-X frames covering time spans of ~1.7 and 2.5 years with spatial resolutions ranging from 25 cm up to 1 m. We applied the Persistent Scatterer Interferometry (PSI) technique using the Stanford Method for Persistent Scatterers (StaMPS) and confirmed the observed deformation signals by also processing the data using the SAR PROcessing tool (SARPROZ). Results show several areas of localized subsidence, from which the cluster with highest rates for each site was selected for detailed inspection. Locations of selected clusters were found in buildings with sizes ranging from 300 m2 to nearly 2000 m2, with subsidence trends ranging from −3 to −6 mm/yr. Results were compared with in-situ observations such as ground penetrating radar (GPR), electrical resistivity tomography (ERT) surveys, visual structural inspection and public county archive documents to help as ground truthing; subsiding locations were found to be related to sinkhole presence or development.

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.

Monitoring surface deformations of the reclamation site using PS interferometry: Senol Gunes sport complex (Turkey)

Recently, structures, built on reclamation sites in the Eastern Black Sea region are quite remarkable. As one of them, Şenol Güneş Medical Park Arena Sport Complex, which is located in Akyazı town of Trabzon city, is one of the rare areas in Turkey. It is very important to monitor surface deformations on this type of unfamiliar and very expensive constructions for protecting human life and property and for taking necessary precautions in advance. Persistent Scatterer Interferometry (PSI), based on the analysis of time series of multiple Synthetic Aperture Radar (SAR) data, is an invaluable geodetic tool to measure surface deformations. So, in this study, it is aimed to monitor the surface deformations on the reclamation site between the dates 20/12/2016–28/01/2020 by StaMPS (Stanford Method for Persistent Scatterers)-PSI technique using 96 Sentinel-1 SAR images. For processing, integration of European Space Agency (ESA) SentiNel Application Platform (SNAP) and StaMPS programs are used. After processing, surface deformations are determined as annual velocities (millimeter(mm)/year(y)) of PS points in the reclamation area along the line-of-sight (LOS) direction between the values −9.7 mm/y and 3.9 mm/y with an average standard deviation of 0.7 mm/y. From the results, it is understood that the area demonstrates very heterogeneous deformations rates, which occur on both horizontal and vertical directions because of the diversity and being on the reclamation site of the structures.

The Use of InSAR for Monitoring Deformation of Offshore Platforms

Fluid extraction or injection from reservoirs, which corresponds to subsidence or uplift, can cause offshore platform surface deformation. Extremely large deformation of offshore platforms often lead to production losses, threat to the structure integrity and loss of life. Therefore, preventing severe deformation incidents is important by monitoring surface deformation caused by oil and gas production activities. In this study, the A1 and B1 offshore platforms have been selected as research study areas to detect surface deformation caused by production activities. A total of 12 radar images from TerraSAR-X satellite were obtained from 24th August 2018 to 22nd August 2019 to derive surface deformation through the Stanford Method for Persistent Scatterers (StaMPS) method. The maximum amounts of subsidence observed on A1 and B1 platforms were -4 mm/yr and -6.3 mm/yr, respectively. This study provides an important insight the use of InSAR technology for the monitoring deformation of offshore platforms.

Remote sensing monitoring of influence of underground mining in the area of the S3 Express Road

Land subsidence is strongly associated with the activities of underground mines. Direct influence of exploitation lead to the formation of subsidence troughs, which are a common phenomenon in the Legnica-Glogow Copper Belt, in southwest Poland, where copper ore is currently intensively mined. As a result, the process of creating troughs may cause significant deformations in the surrounding urban infrastructure, including highways, bridges and railways. Satellite radar interferometry (InSAR), as a remote sensing method, appears to be a useful tool for detecting this type of extensive terrain surface change. Aim of this research was to detect the occurrence of displacements of the S3 Express Road section between the Glogow West Node and the Glogow South Node. For this purpose 29 images provided by European Space Agency (ESA) over descending orbit 22 was obtained from the Sentinel-1A satellite for the period from May 14, 2019 to April 26, 2020. Open source softwares has been used – GMTSAR to generate differential interferograms using the Persistent Scatterer Interferometry (PSI) method and Stanford Method for Persistent Scatterers (StaMPS) to process Persistent Scatterers (PS). The express road, as an anthropogenic object, is characterized by relatively high radar backscatter, thanks to which subsidence of the area are clearly noticeable and indicate the trend of long-term deformations.

A Workflow Based on SNAP–StaMPS Open-Source Tools and GNSS Data for PSI-Based Ground Deformation Using Dual-Orbit Sentinel-1 Data: Accuracy Assessment with Error Propagation Analysis

This paper discusses a full interferometry processing chain based on dual-orbit Sentinel-1A and Sentinel-1B (S1) synthetic aperture radar data and a combination of open-source routines from the Sentinel Application Platform (SNAP), Stanford Method for Persistent Scatterers (StaMPS), and additional routines introduced by the authors. These are used to provide vertical and East-West horizontal velocity maps over a study area in the south-western sector of the Po Plain (Italy) where land subsidence is recognized. The processing of long time series of displacements from a cluster of continuous global navigation satellite system stations is used to provide a global reference frame for line-of-sight–projected velocities and to validate velocity maps after the decomposition analysis. We thus introduce the main theoretical aspects related to error propagation analysis for the proposed methodology and provide the level of uncertainty of the validation analysis at relevant points. The combined SNAP–StaMPS workflow is shown to be a reliable tool for S1 data processing. Based on the validation procedure, the workflow allows decomposed velocity maps to be obtained with an accuracy of 2 mm/yr with expected uncertainty levels lower than 2 mm/yr. Slant-oriented and decomposed velocity maps provide new insights into the ground deformation phenomena that affect the study area arising from a combination of natural and anthropogenic sources.

Monitoring the Vertical Land Motion of Tide Gauges and Its Impact on Relative Sea Level Changes in Korean Peninsula Using Sequential SBAS-InSAR Time-Series Analysis

The relative sea-level changes from tide gauges in the Korean peninsula provide essential information to understand the regional and global mean sea-level changes. Several corrections to raw tide gauge records are required to account for coastal vertical land motion (VLM), regional and local coastal variability. However, due to the lack of in-situ measurements such as leveling data and the Global Navigation Satellite System (GNSS), making precise assessments of VLM at the tide gauges is still challenging. This study aims to address the above limitation to assess the VLM in the Korean tide gauges using the time-series Interferometric Synthetic Aperture Radar (InSAR) technique. For 10 tide gauges selected in the Korean peninsula, we applied the Stanford Method for Persistent Scatterers (StaMPS)—Small Baseline Subset (SBAS) method to C-band Sentinel-1 A/B Synthetic Aperture Radar (SAR) data acquired during 2014/10–2020/05, with the novel sequential interferograms pair selection approach to increase the slowly decorrelating filtered phase (SDFP) pixels density near the tide gauges. Our findings show that overall the tide gauges in the Korean peninsula are stable, besides the largest VLM observed at Pohang tide gauge station (East Sea) of about −26.02 mm/year; also, higher rates of uplift (>1 mm/year) were observed along the coast of Yellow Sea (Incheon TG and Boryeong TG) and higher rates of subsidence (<−2 mm/year) were observed at Jeju TG and Seogwipo TG. Our approach estimates the rate of VLM at selected tide gauges with an unprecedented spatial and temporal resolution and is applicable when the in-situ and GNSS observations are not available.

Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data

Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas.