Interferometric Synthetic Aperture Radar Technique Research Articles

Utilizing InSAR for Surface Stability Monitoring in Mining Sites: A Case Study of Sukari Gold Mine in Egypt

Abstract. The monitoring of mining sites is crucial as a result of the safety and environmental issues linked to mining activities. Traditional surveying and monitoring tools exhibit limitations in terms of spatial coverage and are associated with various concerns regarding time and cost efficiency. Hence, the mining industry has shown increased interest in the time-series analysis of interferometric synthetic aperture radar (InSAR). This is primarily due to its precision and wide spatial coverage advantages. This study employed an InSAR technology, persistent scatterer interferometry (PSI), to identify potential geohazards within the Sukari gold mine (SGM), located in Egypt. A total of 23 images were collected from the ascending orbit of Sentinel-1 sensor between January 7, 2022, and November 3, 2022, for the SGM mining location. Results revealed relatively stable measurements in non-mining areas of the site, with a surface displacement within ±10mm per year in the line-of-sight (LOS) direction. However, several active mining operation areas showed significant surface motion. Various sections of the open pit of the SGM exhibited displacement, including the southwestern edge (-16.8mm) and the northeastern edge (35.7mm), and the highest velocity was detected at a section of the western edge, which demonstrated an annual displacement of -64mm in the LOS. Additionally, notable surface motion was observed in some parts of the two tailings storage facilities at the site. The results of this study illustrate the importance of InSAR techniques in mining site-wide coverage stability monitoring.

Potential Landslide Identification in Baihetan Reservoir Area Based on C-/L-Band Synthetic Aperture Radar Data and Applicability Analysis

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.

InSAR-based landslide hazard mapping and identification of at-risk infrastructures in a selected district of North Shewa Zone, Ethiopia.

The devastating nature of landslides demands a thorough understanding of their spatial distribution and the risks they pose to human settlements and infrastructural assets. In this study, we employed a combination of Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information System (GIS) techniques to explore the western escarpment of the Main Ethiopian Rift, with a focus on selected districts within the northern Shewa Zone, Ethiopia. By analyzing the SAR data, we derived 28 displacement maps and utilized them to create a comprehensive landslide hazard zonation map. The results indicated significant ground displacement, particularly along the rift margins and areas characterized by rugged terrain. The hazard zones were classified based on their level of risk, with 44% classified as very low, 24% as low, 5% as moderate, 13% as high, and 14% as very high hazard zones. The accuracy of our results was evaluated using receiver operating characteristic (ROC) analysis, which was conducted utilizing landslide inventory data. The analysis demonstrated a remarkable area under the curve (AUC) value of 0.848, providing strong evidence for the validity of our findings. Additionally, our study involved a spatial and statistical assessment of major infrastructure, revealing that 20 to 28% of these properties were in hazard zones ranging from moderate to very high levels, which calls for efficient risk-reduction actions. Therefore, this finding enables stakeholders to identify high-risk areas, prioritize mitigation efforts, and minimize the impact of landslide disasters.

Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms.

Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development of an automated system to indicate ground displacements directly from the wrapped interferograms and coherence maps could be highly advantageous. Here, we compare different machine learning algorithms to evaluate the feasibility of achieving this objective. The inputs for the implemented machine learning models were pixels selected from the filtered-wrapped interferograms of Sentinel-1, using a coherence threshold. The outputs were the same pixels labeled as fast positive, positive, fast negative, negative, and undefined movements. These labels were assigned based on the velocity values of the measurement points located within the pixels. We used the Parallel Small Baseline Subset service of the European Space Agency's GeoHazards Exploitation Platform to create the necessary interferograms, coherence, and deformation velocity maps. Subsequently, we applied a high-pass filter to the wrapped interferograms to separate the displacement signal from the atmospheric errors. We successfully identified the patterns associated with slow and fast movements by discerning the unique distributions within the matrices representing each movement class. The experiments included three case studies (from Italy, Portugal, and the United States), noted for their high sensitivity to landslides. We found that the Cosine K-nearest neighbor model achieved the best test accuracy. It is important to note that the test sets were not merely hidden parts of the training set within the same region but also included adjacent areas. We further improved the performance with pseudo-labeling, an approach aimed at evaluating the generalizability and robustness of the trained model beyond its immediate training environment. The lowest test accuracy achieved by the implemented algorithm was 80.1%. Furthermore, we used ArcGIS Pro 3.3 to compare the ground truth with the predictions to visualize the results better. The comparison aimed to explore indications of displacements affecting the main roads in the studied area.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory Model.

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm-convolutional neural network-long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

Estimation of Co-Seismic Surface Deformation Induced by 24 September 2019 Mirpur, Pakistan Earthquake along an Active Blind Fault Using Sentinel-1 TOPS Interferometry

Surface deformation caused by an earthquake is very important to study for a better understanding of the development of geological structures and seismic hazards in an active tectonic area. In this study, we estimated the surface deformation due to an earthquake along an active blind fault using Sentinel-1 SAR data. On 24 September 2019, an earthquake with 5.6 Mw and 10 km depth stroke near Mirpur, Pakistan. The Mirpur area was highly affected by this earthquake with a huge collapse and the death of 34 people. This study aims to estimate the surface deformation associated with this earthquake in Mirpur and adjacent areas. The interferometric synthetic aperture radar (InSAR) technique was applied to study earthquake-induced surface motion. InSAR data consisting of nine Sentinel-1A SAR images from 11 August 2019 to 22 October 2019 was used to investigate the pre-, co- and post-seismic deformation trends. Time series investigation revealed that there was no significant deformation in the pre-seismic time. In the co-seismic time, strong displacement was observed and in post-seismic results, small displacements were seen due to 4.4 and 3.2 Mw aftershocks. Burst overlap interferometry and offset-tracking analysis were used for more sensitive measurements in the along-track direction. Comprehensive 3D displacement was mapped with the combination of LOS and along-track offset deformation. The major outcome of our results was the confirmation of the existence of a previously unpublished blind fault in Mirpur. Previously, this fault line was triggered during the 2005 earthquake and then it was activated on 24 September 2019. Additionally, we presented the co-seismically induced rockslides and some secondary faulting evidence, most of which occurred along or close to the pre-existing blind faults. The study area already faces many problems due to natural hazards where additional surface deformations, particularly because of the earthquake with activated blind fault, have increased its vulnerability.

Non-Linear PSInSAR Analysis of Deformation Patterns in Islamabad/Rawalpindi Region: Unveiling Tectonics and Earthquake-Driven Changes

The standard Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique, which is commonly used for surface motion analysis, assumes linear deformation velocities. While effective for monitoring urban subsidence over short periods, it falls short when dealing with non-linear, earthquake-related deformations over extended timeframes. To address this limitation, we use a non-linear PSInSAR technique, which is an enhancement of PSInSAR, to identify non-linear deformation patterns. We processed Sentinel-1A images from ascending and descending orbits in the Islamabad/Rawalpindi region from December 2015 to January 2023 using non-linear PSInSAR. By calculating the differences in deformation, we analyzed surface movements and assessed the impact of the 2017 earthquake on urban areas. Our findings reveal that the earthquake significantly increased the deformation in ascending and descending orbit tracks, with an average deformation of up to 70 mm/yr and a line-of-sight movement of up to 30 mm/yr. Our observations indicate that the deformation is directed towards the line of sight in the north and south of the deformed area, suggesting subsidence between the two uplifting faults, potentially linked to a concealed fault line along the deformation zone boundary. This contradicts previous arguments, suggesting that water extraction is the leading cause of deformation. Our analysis with non-linear PSInSAR demonstrates that tectonics play a significant role in deformation, providing valuable insights into tectonic-activity-induced deformations in urban areas over the long term.

Rock Glacier Inventory of the Southwestern Pamirs Supported by InSAR Kinematics

Although rock glaciers (RGs) are prevalent in the southwestern Pamirs, systematic studies on them are scarce. This article introduces the first inventory of RGs in the southwestern Pamirs, situated at the western edge of the High Mountain Asia region. The inventory, established through a combination of Google Earth optical imagery and Interferometric Synthetic Aperture Radar (InSAR) techniques, encompasses details on the locations, geomorphological parameters, and kinematic attributes of RGs. A total of 275 RGs were cataloged in an area of 55.52 km2 from 3620 to 5210 m in altitude. Our inventory shows that most RGs in this region are talus-connected (213 landforms), with the highest frequency facing northeast (23%). The distribution of RGs thins from west to east and is more abundant in higher altitudes. The Shakhdara range to the south hosts a denser and more active population of RGs than the Shughnon range to the north, highlighting the influence of topography and precipitation. Overall, RGs in the southwestern Pamirs exhibit high activity levels, with active RGs predominating (58%). A comparison between active and transitional RGs showed no significant differences in elevation, temperature, and slope. Glacier-connected and glacier forefield-connected RGs demonstrated higher line-of-sight (LOS) velocities than talus-connected and debris-mantled slope-connected RGs, underscoring the significant impact of precipitation and meltwater on their activity.

Ground Subsidence, Driving Factors, and Risk Assessment of the Photovoltaic Power Generation and Greenhouse Planting (PPG&GP) Projects in Coal-Mining Areas of Xintai City Observed from a Multi-Temporal InSAR Perspective

In recent years, photovoltaic power generation and greenhouse planting (PPG&GP) have become effective approaches for reconstructing and restoring the ecological environment of old coal-mining industry bases, such as Xintai City. However, the ecological impacts or improvements of the PPG&GP projects and their daily operations on the local environment are still unclear. To solve these problems, this study retrieved the ground deformation velocities and time series of the study region by performing the Small-Baseline Subset (SBAS)-Interferometric Synthetic Aperture Radar (InSAR) technique on the Advanced Land Observing Satellite (ALOS) PALSAR and Sentinel-1 SAR datasets. With these deformation results, the spatial analysis indicated that the area of the subsidence region within the PPG&GP projects reached 10.70 km2, with a magnitude of approximately −21.61 ± 12.10 mm/yr. Also, even though the ground deformations and their temporal changes were both visible in the construction and operation stages of the PPG&GP projects, the temporal analysis demonstrated that most observation points finally entered into the stationary phases in the late stage of the observation period. This phenomenon validated the effectiveness of the PPG&GP projects in enhancing the ground surface stability in coal-mining areas. Additionally, the precipitation, geological structure, increased coal-mining depths, and emergent agricultural modes were assumed to be the major impact factors controlling the ground deformation within the local PPG&GP projects. Finally, a novel risk assessment method with a designed index of IRA was utilized to classify the ground subsidence risks of the PPG&GP projects into three levels: Low (69.7%), Medium (16.9%), and High (9.4%). This study sheds a bright light on the ecological monitoring and risk management of the burgeoning industrial and agricultural infrastructures, such as the PPG&GP projects, constructed upon the traditional coal-mining areas in China from a multi-temporal InSAR perspective.

Optimization of InSAR based coseismic slip modeling for moderate earthquakes accounting for fore–aftershock sequence

Determination of coseismic slip distribution for moderate (moment magnitude, Mw≤ 6.5) earthquakes is challenging as the commonly-used interferometric synthetic aperture radar (InSAR) technique is unable to separate mainshock and fore–aftershocks due to its limited temporal resolution. In this study, we propose a new method of optimizing coseismic slip modeling by removing fore–aftershock contributions using earthquake relocation data and empirical earthquake source scaling relationships. The method is tested with a series of synthetic fore–main–aftershock sequences, through which we demonstrate that our method can largely recover the real slip distribution of the mainshock. The synthetic tests show that if the moment release by fore–aftershocks is ≥10% of the mainshock moment release, fore–aftershock corrections are necessary to improve the accuracy of coseismic slip model. We also test the proposed method with the 2021 Mw 6.3 northern Thessaly, Greece earthquake sequence, in which case we have independent InSAR measurements for both the mainshock and aftershocks. We successfully obtain the slip distribution of the mainshock from the main–aftershock-mixed InSAR measurements, and the result is highly consistent with that derived from the mainshock-only InSAR data. We also evaluate the robustness of the method using the northern Thessaly sequence through a series of tests, showing that the proposed method makes significant improvements even if the earthquake relocation error reaches 5 km. In the end, we apply the method to re-investigate the 2021 Mw 6.0 Yangbi earthquake sequence and find that previous studies may have overestimated the coseismic slip by 25% and the total seismic moment by 18%.

The contribution of coastal land subsidence to potential sea-level rise impact in data-sparse settings: The case of Ghana’s Volta delta

Deltas are highly valuable environmental systems, ensuring various livelihoods through their ecosystem services. However, human impact and climate change stressors are impacting deltas immensely. Consequently, many deltas, including Ghana's Volta Delta, are facing increasing risks, especially as hazards are increasing in magnitude and impacting coastal livelihoods. To provide a better understanding of coastal hazards in the Volta Delta, this study assessed the Delta's subsidence regime and its consequences for the potential impact of sea-level rise (SLR). Using the Interferometric Synthetic Aperture Radar (InSAR) technique and Global Navigation Satellite System (GNSS) surveys, vertical land motion (VLM) was documented. Interferograms of Sentinel-1 data from 2016 to 2020 indicated subsiding rates of up to −9.2 mm/yr. By combining local VLM information with recent SLR projections and elevation data, this study updates those projections and provides local assessments of potential Relative SLR (rSLR) impact. According to these locally improved scenarios, up to ∼45 % of the Delta will fall below local sea level by 2100, of which close to 10 % is explained by the integration of local VLM data alone. Depending on the climate change scenarios used, land subsidence will increase the deltaic area at risk by 4.31 % (96.27 km2) to 10.18 % (227.64 km2) and consequently exacerbate its exposure to coastal inundation. To avert the projections, the study recommends robust monitoring regimes; alternative freshwater sources to groundwater; reduced sediment trapping and river obstruction; and the need to stall ongoing oil and gas prospecting and subsequent extraction in the Voltain Basin.

TDFPI: A Three-Dimensional and Full Parameter Inversion Model and Its Application for Building Damage Assessment in Guotun Coal Mining Areas, Shandong, China

Subsidence prediction is essential for preventing and controlling geohazards in coal mining areas. However, the Interferometric Synthetic Aperture Radar (InSAR) technique is limited in deriving the goaf displacements with a large gradient and fast deformation rates, hindering its application for potential risk evaluation over the mining areas. In this study, we proposed a three-dimensional and full parameter inversion (TDFPI) model to derive the large-gradient subsidence and then investigate its application for building damage assessment over coal mining areas. By taking the Guotun coal mine as the case study, the TDFPI model was demonstrated to have effectively predicted the large-gradient deformation of the mining areas and successfully evaluated the house damage in Chelou village, which agrees well with our field investigations. Specifically, the predicted subsidence results were validated with high fitting accuracy against field measurements, with RMSE of 0.083 m and 0.102 m, respectively, on observation line A and line F. In addition, the classified damage levels are highly consistent with in situ field surveys for the house cracks in Chelou village, presenting its practicality and effectiveness for building damage evaluation, and thus can provide a useful tool for potential risk assessment and prevention over the mining areas.

Research on Time Series Monitoring of Surface Deformation in Tongliao Urban Area Based on SBAS-PS-DS-InSAR.

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were -90.78 mm and -83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are -35.38 mm/y and -30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning.

Satellite Synthetic Aperture Radar, Multispectral, and Infrared Imagery for Assessing Bridge Deformation and Structural Health—A Case Study at the Samuel de Champlain Bridge

A space-borne remote sensing method was applied, validated, and demonstrated in a case study on the Samuel de Champlain Bridge in Montreal, Canada. High-resolution C-band radar satellite imagery was analyzed using the Persistent Scatterer Interferometric Synthetic Aperture Radar technique to derive bridge displacements and compare them against theoretical estimates. Multispectral and long-wave thermal infrared satellite imagery acquired during the InSAR observation period and historical environmental data were analyzed to provide context for the interpretation and understanding of InSAR results. Thermal deformation measurements compared well with their theoretical estimates based on known bridge geometry and ambient temperature data. Non-thermal deformation measurements gave no evidence of settlement during the 2-year monitoring period, as would normally be expected for a newly constructed bridge with its foundation on bedrock. The availability of environmental data obtained from multispectral and thermal infrared satellite imagery was found to be useful in providing context for the bridge stability assessment. Ambient temperature measurements from thermal infrared satellite imagery were found to be a suitable alternative in cases where data from in situ temperature sensors or nearby weather stations are not available or not fit for purpose. No strong correlation was found between the river conditions and bridge deformation results from the InSAR analysis; this is partly due to the fact that most of these effects act along the river flow in the north–south direction, to which the satellite sensor is not sensitive.

Long-Term SAR Data Analysis for Subsidence Monitoring and Correlation Study at Beijing Capital Airport

Land subsidence, resulting from natural or human activities, is a global environmental geological disaster. The Interferometric Synthetic Aperture Radar (InSAR) time-series analysis technique offers high spatial and continuous temporal resolution, providing data and a foundation for investigating regional land subsidence and its evolution mechanism. Beijing Capital International Airport (BCIA) has experienced uneven land subsidence since 1935, together with severe fissures significantly affecting its normal operations. In this study, the time-series InSAR method was successfully applied to monitor the gradual increase in uneven local subsidence and ground fissures activity at BCIA from June 2003 to March 2023. Initially, ENVISAT-ASAR, Cosmo-SkyMed, and Sentinel-1 data were processed by time-series InSAR techniques to generate deformation rate maps and time series for the airport area. Subsequently, a comparison was made between the displacement time series from InSAR and ground leveling measurements to assess the accuracy of InSAR-derived measurements. Through a comprehensive analysis of the distribution characteristics of land subsidence at the airport, a long-standing ground fault was located within the airport was identified. A preliminary discussion on the development status of this ground fissure was carried out based on the visual interpretation of optical images. Lastly, the inducing factors and evolutionary conditions of land subsidence were discussed. This case demonstrates the applicability of InSAR technology in identifying and monitoring geological processes such as land subsidence and ground fissure activities. It provides a scientific approach to exploring and studying the causes and formation mechanisms of land subsidence and ground fissures in the Beijing Capital Airport area.

Analyzing gully erosion and deposition patterns in loess tableland: Insights from small baseline subset interferometric synthetic aperture radar (SBAS InSAR)

The fragile Loess Plateau of China suffers substantial gully erosion. It is imperative to elucidate gully erosion patterns for implementing effective erosion control strategies. However, high spatiotemporal resolution quantification of gully dynamics remains limited across the Loess Plateau landscape. We utilized the small baseline subset interferometric synthetic aperture radar (SBAS InSAR) technique to investigate the phenomenon of gully erosion and deposition on the Dongzhiyuan tableland, which sits within the vast expanse of the Loess Plateau in China, over the period spanning 2020–2022. The tableland edges subsided while gully bottoms uplifted due to sedimentation. Low elevations underwent active deformation. Slope, aspect, and curvature modulated uplift and subsidence patterns by affecting runoff and sediment transport. Gentle downstream slopes displayed enhanced sedimentation. Southern gullies showed pronounced uplift compared to northern gullies. Heavy rainfall triggered extensive erosion followed by rapid uplift, reflecting an adaptive oscillation between erosion and deposition. Basin hydrology correlated with spatial patterns of deformation. Vegetation cover above 60 % of the maximum substantially increased InSAR error. Our study reveals intricate spatiotemporal behaviors of erosion and deposition in loess gullies using time-series InSAR. The findings provide new insights into gully geomorphology and evolution, and our study quantifies gully erosion and deposition patterns at high spatiotemporal resolution, enabling identification of the most vulnerable areas and prioritization of conservation efforts.

Estimation of active surface deformation using PSInSAR technique of the Central Himalayan region

The Kumaun Himalaya is considered as the most active part of the Central Seismic gap in the Indian Sub-continent. In this paper, we presented active surface deformation rates of the Kumaun region from February 2017 to February 2021 using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) technique. The cumulative displacement that occurred during the span of 4 years is ±55 mm, whereas the Line of Sight (LOS) deformation velocity rate ranges is ±7 mm/yr. Apart from PSI, we also estimated the b-value of the Kumaun region from 1803 to 2021 (399 events) and its value is 0.54 ± 0.03. A distinct NE-SW trend of b-value is observed where earthquakes with M > 6 occurred towards NE of this trend. The PSI-derived deformation reveals that the central part of the Inner Lesser Himalaya along with the Main Central Thrust (MCT) zone is dominated by uplift. The zone between the Munsiari Thrust (MT) and MCT in the central region shows the maximum uplift ranging 5–7 mm/yr which exactly lies above the mid-crustal ramp of the Main Himalayan Thrust (MHT). Our results are well corroborated with available observations of geodetic strain and peak ground acceleration values. However, the deformation patterns and high-velocity rates in the central part of the study area between MT and MCT indicate the accumulation of high stress.

Trend Classification of InSAR Displacement Time Series Using SAE–CNN

Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention.

A Statistical Approach for the Integration of Multi-Temporal InSAR and GNSS-PPP Ground Deformation Measurements.

Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between -4.86 mm/yr and -23.58 mm/yr and -9.50 and -27.77 mm/yr for InSAR and GNSS, respectively.

INVESTIGATIONS ON INSAR DATA PROCESSING STANDARD FOR VOLCANO ISLAND MONITORING IN INDONESIA

Abstract. In critical situations such as disaster and humanitarian relief, the reliable geospatial data will present an appropriate decision on a timely manner. As fundamental datasets, large-scale topographic maps are are mandatory in order to perform geospatial analysis for many societal challenges. Indeed, Large Scale Topographic Mapping in Indonesia shall be accelerated in an innovative and productive way to consider the production efficiency, especially for the geospatial data acquisition as its primary source. Interferometric Synthetic Aperture Radar (InSAR) data acquisition presents some advantages, especially in conjunction with the security clearance and global weather as some main constraints for the geospatial data acquisition. In terms of disaster preparedness and emergency response, radar interferometry techniques can play an important role by generating a Digital Elevation Model (DEM) as an input to Decision Support Systems (DSS). In addition, the techniques of differential Interferometric Synthetic Aperture Radar (D-InSAR) can provide the earth surface deformation from time series Radar Datasets. A set of standards related to the InSAR data acquisition are essential, especially with regards to the improved sensors, and processing methods implemented by the worldwide space agencies nowadays. Hence, the precise definition of a standard is fundamental in order to prepare the InSAR data as one potential source of geospatial data and information, especially for LSTM data production. Generated time series of Digital Elevation Model (DEM) can identify the potential cause of 2018’s Sunda Strait tsunami (Tampubolon, 2019). It was related with the increasing eruption activity of Anak Krakatau (Child of Krakatoa). From DEM analysis, the Volcanic mudflow material avalanche has been detected before the tsunami hit the coastal surrounding area. In this paper, more advanced techniques was investigated by the combination between Time Series DEM Analysis and Ground Displacement detection. The focus on the integration between reliable TanDEM-X and up-to-date Sentinel 1A/B was proposed on another volcano island of Gamalama in Ternate. As TanDEM-X uses X-band as its medium wave, it can produce more reliable DEM as a reference data. On the other hand, Sentinel 1 uses C-band that can contribute to detect the ground deformation over the aforementioned volcano island. TanDEM-X uses a more robust and consistent approach to scan the earth surface by single data acquisition scheme, which is generally more accurate and reliable for the DEM generation. However, many factors influence the selection of a platform that can be best suited for radar interferometry. Initially, the German TanDEM-X Coregistered Singlelook Slant-range Complex (CoSSC) using bi-static InSAR approach has been implemented in order to generate DEMs comply with the LSTM specification in Indonesia. The basic principle is performing a simultaneous measurement of the same scene and identical doppler spectrum by using 2 sensors, thereby avoiding temporal decorrelation (DLR, 2012). Subsequently, the differential InSAR techniques by using Sentinel 1A/B has been performed in order to detect the ground deformation. Finally, tt can be shown that the reliable TanDEM-X data has increased the ground deformation resolution and accuracy in order to provide potential solution for disaster preparedness.