InSAR Research Articles

Enhancing infrastructure management with InSAR monitoring

Over the last few years, the management of multi-asset infrastructure over wide regions has experienced a significant evolution due to novel technologies or accessibility to cheaper sensors combined with the Internet of Things (IoT). Interferometric Synthetic Aperture Radar (InSAR) data has proven to be the most cost-effective technology to meet users’ requests. However, InSAR’s large volume of data presents challenges for end-users. Data aggregation and visualisation are needed to streamline operations and information transfer between teams. This proposed methodology seeks to address the challenges associated with InSAR data by integrating InSAR observations with the digital equivalent of existing structures in a repeatable and scalable manner. To achieve this, deformation profiles are generated adaptively, considering the asset’s characteristics, such as width and orientation, as well as the satellite acquisition geometry. In parallel, a two-dimensional approach is followed to associate InSAR information with each structure element and thus organise the information spatially. It should be noted that, even when using high-resolution radar data, it is sometimes challenging to associate radar returns with specific asset segments due to the complex interaction of microwaves with particular targets. Consequently, a key feature of this methodology is to classify InSAR data according to a precise geocoding of each scatterer, including an accurate estimation of its elevation. This methodology makes it possible to analyse many different types of infrastructure, which can be time-consuming for large highway or railway operators. The coverage of the InSAR results on a regional scale, and the semi-automated method gives decision-makers an overview of their structures to send maintenance teams, saving time and costs. This methodology allows for InSAR-derived product analysis, making the results more readable, accessible, and impactful. This type of infrastructure monitoring reflects a new maturity of InSAR in this field, demonstrated in real case studies.

Integrated Remote Sensing Investigation of Suspected Landslides: A Case Study of the Genie Slope on the Tibetan Plateau, China

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the current status of the surface deformation of this slope, this study used high-resolution optical remote sensing, airborne light detection and ranging (LiDAR), and interferometric synthetic aperture radar (InSAR) technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with differential InSAR (D-InSAR) analysis of ALOS-1 radar images from 2007 to 2008 or with Stacking-InSAR and small baseline subset InSAR (SBAS-InSAR) processing of Sentinel-1A radar images from 2017 to 2020. This study verified the credibility of the InSAR results using the standard deviation of the phase residuals, as well as in-borehole displacement monitoring data. A conceptual model of the slope was developed by combining field investigation, borehole coring, and horizontal exploratory tunnel data, and the results indicated that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the trailing edges of the slope were caused by historical shallow toppling. Unlike previous remote sensing studies of deformed landslides, this paper argues that remote sensing results with reliable accuracy are also applicable to the study of undeformed slopes and can help make preliminary judgments about the stability of unexplored slopes. The study demonstrates that the long-term consistency of InSAR results in integrated remote sensing can serve as an indicator for assessing slope stability.

Utilising InSAR for monitoring infrastructure at national scales

Interferometric SAR (InSAR) is now a widely used tool for the monitoring of infrastructure, in particular mines, dams, and oil and gas facilities. The technology is also widely used for the monitoring of construction activities such as tunnelling. With the growth of globally available and continuously updated SAR datasets, the monitoring of entire regional and national infrastructure networks is now feasible. Here we show how continuously updated, network-wide, InSAR datasets can be used to proactively manage large infrastructure networks such as roads, railways, and water pipelines. In the UK continuously updated InSAR data (derived from Sentinel-1 SAR data) is being effectively used across the entire railway network to proactively monitor potential embankment failures, large-scale landslides, and potential hazards outside of the boundary fence. A challenge of using InSAR data in this way is the huge volume of data that is available, as such custom risk metrics and machine learning algorithms are applied to highlight some of the areas at heightened risk. InSAR is also being applied to tackle the issue of water pipe leakage caused by natural ground movements, such as soil shrink-swell. In this case, regularly updated InSAR data has been used in the UK and Australia to detect areas at elevated risk of pipeline failures based on ground movement patterns, and by combining historical movement data and known failure events it is possible to refine such models. With expanding global SAR constellations proactive, nationwide, monitoring of critical assets is now possible, with the challenge being how we derive insight from these very large datasets.

Bayesian Data Fusion for Enhanced Monitoring of Bridge Displacements Using Satellite InSAR and Topographic Techniques

This paper investigates the effectiveness of satellite Interferometric Synthetic Aperture Radar (InSAR) technology to remotely monitor the structural health of road bridges and detect possible unexpected behaviours. The study is based on an Italian multi-span prestressed concrete bridge and a dataset of eight years of X-band SAR images collected by the COSMO-SkyMed satellite constellation. The method implemented for this study is as follows. First, SAR images are analysed using the Multi-Temporal InSAR technique to extract displacement time series of reflective targets on the bridge decks. Then, the extracted displacements are interpreted to validate their consistency with the bridge's expected responses to environmental loads, such as air temperature and river water flow variations, and detect possible unexpected behaviours. Eventually, the correlation with the air temperature is investigated to identify different bridge spans based only on monitoring data. The findings from this study advance the knowledge on using satellite InSAR-based monitoring for early detection of anomalous bridge behaviours on a large scale, providing valuable insights for the maintenance and safety of critical infrastructure.

Structural evaluation of urban bridges in Amsterdam through InSAR-based displacement data

Thousands of bridges worldwide face growing risks due to aging materials, increased traffic loads, and climate change-induced weather extremes. Managing these assets is financially demanding, necessitating prioritisation strategies for interventions. Consequently, innovative approaches are urgently required to evaluate the structural conditions of these bridges continuously and regularly. Recent advancements in space-borne Interferometric Synthetic Aperture Radar (InSAR) technology offer cost-effective remote monitoring capabilities, ensuring extensive coverage and high spatial resolution. Multi Temporal (MT) InSAR techniques enable the reconstruction of millimetre-scale deformation measurements for a large number of assets, opening opportunities for long-term regional-scale monitoring of bridge deformations. However, a major challenge in utilising MT-InSAR-based displacement data operationally is that MT-InSAR analysis reconstructs only the projection of displacements along the satellite Line of Sight (LOS) direction. Due to the typical availability of only two satellite viewing geometries, in most cases the three-dimensional displacement field cannot be fully reconstructed. Consequently, without accounting for the anticipated motion of a given structure and its alignment with respect to the satellite flight path, the actual asset movement is likely to be underestimated, leading to erroneous interpretation. In this paper, we propose a method using the bridge typologies and their associated likely failure mechanisms to derive assumptions regarding expected displacement directions. Then, the information on bridge alignments with respect to the satellite flight direction is used to assess the MT-InSAR sensitivity to the expected displacement directions and define ad-hoc damage indicators. We tested the proposed method on urban bridges in Amsterdam, the Netherlands, using deformation measurements derived from TerraSAR-X data spanning 2011 to 2020. Findings have potential to enhance current procedures for the structural evaluation of bridges.

Correlation analysis of InSAR displacement data and inspection data towards multiple bridge monitoring in transportation network

This paper presents a study on multiple bridge monitoring in a wide area of transportation networks using satellite Synthetic Aperture Radar (SAR) technology. SAR has been expected to have potential to analyze displacements of multiple large-scale structures in wide areas and in long-term without installing any sensors on them. This study shows quantitative analysis of InSAR (interferometric SAR) data on multiple bridges regarding to structural condition using their periodic inspection data. The target bridges are seventeen bridges located at a region in Japan, Ibaraki prefecture. They are the bridges that has lengths over 100 m selected from ones operated by the rural-road office of MLIT, the transport ministry of Japan, where periodic inspection data are all opened and available. Their displacements are then obtained using Sentile1 satellite SAR data. The time period for the analysis is seven years from 2016 to 2022, with time interval of 12 days. Using those data, line-of-sight (LoS) displacements of each bridge are derived as a time-series data through interferometric processing. The LoS displacement time-series data are then analyzed for each bridge by principal component analysis (PCA) to extract significant components of time-series deformation behavior. The extracted principle components, air temperature, and inspection records are compared and analyzed. As a result, it is found that a stable correlation between the principle component and air temperature could be one of explanatory factors of bridge conditions. This may indicate the bridges having stable correlation between LoS displacements and air temperature are in a nominal and undamaged condition although further investigation to more bridges is required. Moreover, visualizations of time-series InSAR bridge deflection and extracted feature components and its pattern analysis in multiple bridges are shown to have high potential to contribute on multiple bridge operations in whole transportation network.

Comparison of anomalous displacement detectability of bridges between X and C band InSAR data: Case study on a collapse of water pipe bridge

This paper presents a feasibility of detecting anomalous displacements on bridges where collapses have occurred using C-band satellite synthetic aperture radar (SAR). Recently, the number of aging bridges due to damage and corrosion has been increased in many countries. Although there are various maintenance and management systems, the authors have focused on satellite SAR, one of the remote sensing technologies, to establish a wide-area multiple bridge monitoring technology. In a previous study, the authors analyzed high-resolution X-band interferometric SAR (InSAR) data acquired by the Italian satellite COSMO-SkyMed during the two years before the accident for the MUSOTA water pipe bridge in Japan, which collapsed on October 2021. As a result, it was shown that there was an anomalous displacement about one year before the accident, which seemed to be a sign of the collapse. On the other hand, the utilization of Sentinel-1 (C-band) data, which has lower resolution than X-band data, but is freely available, covers a wide area, and has dense time-series SAR images, would be beneficial for the practical application of this technology. In this study, InSAR data acquired by Sentinel-1 for the MUSOTA water pipe bridge from 2016 to 2022 are analyzed. Then, we compare the line-of-sight (LOS) displacement anomaly before the collapse with the results analyzed by the X-band satellite. As a result of statistical analysis of the displacement between the collapsed span and the adjacent spans with similar structural type assumed to be intact, it is shown that Sentinel-1 also captured differences which seemed to be the signs of the collapse about one year before the accident. This result supports the analysis of the X-band satellite data and shows potential for utilization in bridge anomaly detection systems, even for low-resolution C-band data.

Surface deformation analysis due to the Poso earthquake on May 29, 2017 using the DInSAR method

Abstract Deformation can be a significant parameter in determining the presence and impact of an earthquake. The amount of deformation can be obtained from SAR image data using the InSAR and DinSAR methods. The DinSAR (Differential Interferometric Synthetic Aperture Radar) method is a well-developed method for observing subsidence and uplift with high accuracy in centimeters (cm). In this study, SAR imagery is used to detect surface deformation caused by the earthquake was happened on May 29, 2017, at 21:35:23 WIB in the Poso Regency, Central Sulawesi. The data used is Sentinel-1 satellite imagery data in single-look complex (SLC) format consisting of Master image recorded on May 19, 2017 (10 days before the main earthquake occurred) and Slave image recorded on May 31, 2017 (2 days after the main earthquake occurred). The interferogram formed during the Poso earthquake is around the main earthquake directions from the Southwest, South, to the Southeast. The amount of deformation generated in the deformation phase (wrapped phase) is in the form of negative and positive phase values. Negative phase values indicate areas experiencing deformation in the form of subsidence. The positive phase values indicate areas experiencing uplift. The results of unwrapping the SAR imagery obtained a maximum uplift deformation value of 0.0663 meters, spread over the areas of North Pesisir Poso District, Lore Peore District, Central Lore District, South Coastal Poso District, and East Lore District. The maximum subsidence deformation values (0.076 meters) that are quite large are spread in the North Coastal Poso District, Poso Coastal District, Lage District, and Lore Tengah District.

MT-InSAR Optimisation for Structural Health Monitoring

The implementation of effective and sustainable Structural Health Monitoring (SHM) systems for the evaluation of infrastructure conditions is critical to address the deterioration and damage experienced by structures worldwide. Given the vast number of structures involved, resorting to traditional in-situ visual inspections and data gathering methods is becoming increasingly unfeasible. Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has recently gained attention as a viable solution for long-term SHM. This remote sensing technique combines multiple satellite radar images to measure changes in the Earth’s surface over time. Unlike conventional techniques, MT-InSAR does not require in-situ installations and offers extensive coverage, enabling observations across diverse location and structures. However, the applicability of MT-InSAR monitoring depends on the relatively unpredictable distribution and location of permanent scatterers (PSs), which are influenced by surface characteristics and vegetation changes. Evaluating the reliability and capacity of MT-InSAR is therefore crucial to enhance its effectiveness in assessing the location and extent of structural damage. In this study, we present an effective approach to determine the optimal number and position of PSs for detecting different structural damage mechanisms. The approach is exemplified through a case study of a quay wall in Amsterdam, with data inputs simulated using the Finite Element Method. The proposed method has the potential to evaluate the feasibility of MT-InSAR for a broader range of scenarios, enabling to detect specific structural conditions.

Detection and Mechanical Interpretation of Surface Displacement by Volcanic and Anthropogenic Interactions around Bandung Basin, through Multi Temporal Interferometric SAR (MT-InSAR) Technique

Abstract This study aims to evaluate capability of MT-InSAR (Multi Temporal interferometric synthetic aperture radar) technique for measuring the surface displacement caused by tectonic, volcanic, and anthropogenic activity around the Bandung Basin, West Java, Indonesia. For this, persistent scatterer (PS), small baseline subset (SBAS), and combined PS and SBAS techniques are adopted. The results reveal different displacement patterns within two areas anthropogenic-caused subsidence in the middle Bandung city and geothermal production field, and volcanic-caused uplift in the northern area around the Tangkuban Parahu volcano. In addition, each InSAR tehnique shows unique spatial deformation. A significant pattern is located around the Wayang Windu geothermal field that large displacements has been occurred around injection, production wells. Those detected displacements are crucial for mitigating geological hazards as well as geothermal production monitoring.

Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China

The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models.

Developing a remote-sensing-based indicator for peat soil vertical displacement. A case study in the Biebrza Valley, Poland

Peatlands play a crucial role in carbon storage, but drainage and climate change-induced hydrological changes drivers degrade peat soils, followed by negative vertical displacement of the soil surface, referred to in the literature as peat subsidence. Assessing peatland subsidence is an important indicator of peatland status that allows the development of peatlands to be revealed over a given period. However, traditional methods are limited in their applicability to large and inaccessible peatlands. In this study, we introduce a remote sensing framework for easily indicating peat subsidence at a large scale. Our framework utilizes the Alaska Satellite Facility (ASF) Interferometry Synthetic Aperture Radar (InSAR) with on-demand cloud computing, employing an optimization process including a Small Baseline Set technique and seasonal-annual search approach. By implementing this approach in the Biebrza Valley, Poland, covering the period from April 2015 to April 2022, we unveiled a tragedy, an annual subsidence rate of 1.44 cm. It means a peat loss between 58.1 and 89.6 million cubic meters over seven years, an annual loss of 86.4 to 132.5 (average 109) m3, approximately 46 tons, of dry matter peat per hectare. This finding was verified through field surveys, with high accuracy. An R2 value of 0.7, and an RMSE value of 0.23 cm determine the reliability of this approach in estimating the vertical displacement. The potential of this tool as a robust method to assess large-scale peatlands would allow for the assessment of average water levels as well as greenhouse gas emissions over large areas of peatlands, even at the continental scale.

Separation of Water Level Change From Atmospheric Artifacts Through Application of Independent Component Analysis to InSAR Time Series

AbstractIn recent years, synthetic aperture radar (SAR) interferometry (InSAR) has emerged as a valuable tool for measuring water level change (WLC) to study hydrodynamic processes in coastal wetlands. However, the highly dynamic wet atmosphere conditions common in these areas have a significant impact on InSAR observations, producing errors in the derived values. Standard methods for estimating atmospheric noise in InSAR time series lack the spatial or temporal resolution needed to adequately correct for wet tropospheric delays. In this study, we utilize the Independent Component Analysis (ICA) signal decomposition technique to identify the likely WLC signal and eliminate atmospheric noise in a time series derived from rapid repeat measurements made with the L‐band uninhabited aerial vehicle synthetic aperture radar airborne instrument. The method compares in‐situ water level measurements with the independent components (IC) to identify the ICA components corresponding to WLC. The signal‐to‐noise ratio between the WLC after the ICA‐based filtering and in situ water level gauges used for validation reaches 16 dB compared to an average of 2.6 dB before filtering. The excluded IC are used to generate maps showing a time series of likely atmospheric features. The identified features in the maps generally correspond to atmospheric features identifiable in Next Generation Weather Radar (NEXRAD) S‐band ground weather radar reflectivity maps collected during the SAR acquisitions. The method is sufficiently general to be applied to any InSAR‐derived surface displacement time series.

Characterizing the spatial structure and aliasing effect of ocean tide loading on InSAR measurements

Ocean tide loading (OTL) displacements, shown as long-wavelength errors in Interferometric Synthetic Aperture Radar (InSAR), must be considered in large-scale applications. Despite efforts to explore the impacts of OTL on InSAR, most studies use individual interferograms and simple metrics, which fail to characterize the spatial structure of OTL. Moreover, the OTL contribution to InSAR time series remains relatively unexplored. The aliasing effect and related biases due to OTL, which are common to space-geodetic time series, are primarily theoretical with few practical observations for InSAR. This study comprehensively explores the statistical properties of OTL and their impacts on InSAR measurements, using the Southwest United Kingdom and Northwest France as study areas. Spatially, OTL artifacts on interferograms exhibit an escalating magnitude along the principal direction that aligns with the coastline's orientation. Temporally, the aliasing effect originating from OTL introduces periodic signals with prominent 15/64-day cycles into the Sentinel-1 InSAR time series, causing high velocity biases (up to ∼1 cm/yr) and uncertainties (up to ∼5 mm/yr) for short time spans. Applying OTL correction mitigates the noise level in the displacement time series, leading to a 16% improvement in accuracy, as validated against the Global Navigation Satellite System (GNSS). The study proposes the “overlapping effect” concept, which links InSAR tropospheric delay errors and OTL effects. It underscores the importance of accurate error assessment and removal. Neglecting this interaction may result in a 13% underestimation of the tropospheric error correction efficacy.

Enhancing the groundwater storage estimates by integrating MT-InSAR, GRACE/GRACE-FO, and hydraulic head measurements in Henan Plain (China)

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to scarcity of surface water and over-pumping of groundwater. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed an enhanced methodology for estimating Groundwater Storage change (GWSC). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment (and Follow-on)/Global Land Data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparisons based on well water levels and residual statistics of equivalent water thickness (EWT) validate the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSC, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSC significantly enhances the equal lon-lat tile of 1/4-degree (∼27 km) from CSR GRACE/GRACE-FO Mascon solution (CSR-M) to within 1 km in aquifers with known prior hydrogeological information. Furthermore, the geographic patterns analysis of GWSC in HNP using standard deviational ellipse (SDE) reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction.

Precise orbit determination of the LuTan satellite using GPS, BDS-2, and BDS-3 signals

Abstract The LuTan satellite is the first formation-flying mission of China enabling synthetic aperture radar interferometry in the L-band. As a key mission requirement, the absolute position of each satellite must be determined with an accuracy of 5 cm in three dimensions (3D). To fulfill this demand, each satellite carries a Global Positioning System/Beidou Navigation Satellite System (GPS/BDS) dual-constellation Global Navigation Satellite System receiver that can simultaneously track the GPS L1 and L2 signals, the BDS-2 B1I and B3I signals (BD2), and the BDS-3 B1C and B2a signals (BD3). In this study, we assess the performances of precise orbit determination with different signals and their combinations. For that purpose, we produce a total of five sets of orbit solutions, i.e., GPS-based, BD2-based, BD3-based, GPS/BD2 combined, and GPS/BD3 combined solutions. To evaluate the orbit solutions, comparisons in the orbital overlaps and with the GPS-based orbit are adopted. The orbital overlap analysis shows that the GPS-based solution has the smallest root mean square (RMS) of overlap orbit differences than other single-constellation solutions with 3.7 mm in 3D. The BD2- and BD3-based orbits are suffered from large errors and the overlap RMS differences are 24.7 and 18.9 mm, respectively. Furthermore, all of the combined orbits (both 3.1 mm for the GPS/BD2 and GPS/BD3 combined orbits) based on the variance component estimation method can obtain improvement than GPS-based solutions. For orbit comparisons, the GPS-based solution is selected to serve as the reference. The RMS values of the orbit differences are 29.9 and 15.4 mm for the BD2- and BD3-based solutions, respectively. For the GPS/BD2 and GPS/BD3 combined solutions, the values are 4.1 and 4.8 mm. In general, the results agree with those obtained in the overlap analysis. These results indicate that the BD3-based orbit is superior to the BD2-based orbit, while both of them are inferior to the GPS-based orbit. We show that inaccurate ephemerides of the BDS satellites can partly explain the degraded performance of the BDS-derived orbits. Nevertheless, the orbits still fulfill the 5 cm accuracy demand and can be readily used for subsequent precise baseline determination.

Ground Deformation Monitoring Using InSAR and Meteorological Time Series and Least-Squares Wavelet Software: A Case Study in Catania, Italy

Abstract. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced satellite remote sensing technique which allows an effective monitoring of ground movement. In this work, PS-InSAR time series as well as precipitation and temperature time series in a region in Catania, Italy are utilized during 2018–2022, and their possible interconnections with land subsidence/uplift due to groundwater level change are investigated. First, the potential jumps in the displacement time series are removed, and then the Sequential Turning Point Detection (STPD) is applied to estimate the times when the velocity of the displacement time series changes. The results show a significant correlation between the frequency of turning points in displacement time series and precipitation trend change, particularly during the winter season. Furthermore, the Least-Squares Cross Wavelet Analysis (LSCWA) is applied to estimate the coherency and phase delay between the displacement and weather cycles in the time-frequency domain. The annual cycles of displacement and temperature show more coherency than the ones of displacement and precipitation across the study region. The results presented herein are important for infrastructure and water management planning.

Lembang Fault Deformation Study Using Sentinel-1A Image with Ps-Insar Method

Badan Nasional Penanggulangan Bencana (BNPB) states that the earthquakes that have occurred in the territory of Indonesia during 2018 were 11,418. Nearly 95 percent of earthquakes occur in fault areas and plate boundaries. In West Java, especially in the Bandung area there is a fault structure called the Lembang Fault which is mostly occupied by community settlements. The Lembang Fault is an active fault, therefore it is necessary to conduct research related to monitoring the Lembang Fault for disaster mitigation purposes. One of parameter to determine the level of activity of a fault is deformation. The amount of deformation can be analyzed through the remote sensing methods, one of them is the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) method. In this study the main data used is Sentinel-1A image data consist of eight scenes with acquisition time from January 2018 to March 2019. The results of the study concluded that the average deformation that occurred along the Lembang Fault was 2,485 mm with a graph that tends to rise. The area most affected by deformation is Cisarua Subdistrict with Line-of-Sight velocity of deformation of 28.4 - 48.9 mm / year. While the smallest deformation occurred in Lembang District with Line-of-Sight velocity of deformation of -2.3 - 19.2 mm / year.

EGMS-toolkit: a set of Python scripts for improved access to datasets from the European Ground Motion Service

Continental-scale, open-access datasets of ground surface displacement in all countries of the European Union, plus Norway, United Kingdom, and Iceland, are now available from the European Ground Motion Service (EGMS). Under the European Union’s Copernicus program, the EGMS has been available since the end of 2022 and will continue for the foreseeable future. The EGMS data are presently derived from Interferometric Synthetic Aperture Radar (InSAR) processing of the Sentinel-1 SAR satellite imagery, which has been collected from 2015 to date. While EGMS data can be visualised and obtained through an online platform (EGMS Explorer), the data access arrangements are inefficient for large-scale analysis of ground surface displacements due to the volume of data, the tile-formatting of datasets and some server limitations. Here we present a Python-based toolkit, named EGMS-toolkit, to provide a unified and more efficient workflow for accessing EGMS datasets. The toolkit can automatically detect and download EGMS datasets based on a Region of Interest provided by users, then it can merge, clip, and crop the results to that region regardless of its scale. The toolkit then produces files of EGMS ground surface motions in GIS-ready formats for further analysis.

Identification and Analysis of the Geohazards Located in an Alpine Valley Based on Multi-Source Remote Sensing Data.

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work.