Small Baseline Subset Interferometric Synthetic Aperture Radar Research Articles

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.

Time-Series Analysis of Mining-Induced Subsidence in the Arid Region of Mongolia Based on SBAS-InSAR

Mongolia’s substantial mineral resources have played a pivotal role in its economic progress, with mining activities significantly contributing to this development. However, these continuous mining operations, particularly at the Oyu Tolgoi copper and gold mine, have induced land subsidence that threatens both production activities and poses risks of geological and other natural disasters. This study employs the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to monitor and analyze time-series surface subsidence using 120 Sentinel-1A datasets from 2018 to 2022. The findings reveal that the SBAS-InSAR method successfully captures the subsidence and its spatial distribution at Oyu Tolgoi, with the maximum cumulative subsidence reaching −742.01 mm and the highest annual average subsidence rate at −158.11 mm/year. Key drivers identified for the subsidence include variations in groundwater levels, active mining operations, and changes in surface stress. This research underscores the ongoing subsidence issue at the Oyu Tolgoi mining area, providing crucial insights that could aid in enhancing mining safety and environmental conservation in the region.

Deformation Monitoring Based on SBAS-InSAR and Leveling Measurement: A Case Study of the Jing-Mi Diversion Canal in China.

The Jing-Mi Diversion Canal is a large-scale water diversion project in Beijing. Routine monitoring is crucial for the reliability and stability of urban water supply. Compared with traditional monitoring methods, interferometric synthetic aperture radar (InSAR) has the advantages of large scale and high accuracy. Based on the small baseline subset InSAR, 187 ascending and 102 descending SAR images obtained from Sentinel-1 were used to detect the deformation along the diversion canal from 2017 to 2023. The results show that there was a sinking trend along the diversion canal. The subsidence was serious in the first half of the canal, and continued to sink from 2019 to 2020. The subsidence was alleviated in 2023. Combined with leveling measurements, the InSAR deformation monitoring results of important pumping station buildings were verified. The measurement accuracy of InSAR can reach the millimeter level. We extracted the groundwater level time series and subsidence for risky canal segments. Through pixel-by-pixel comparison, it was found that fluctuations in groundwater level would have some impact on surface deformation. Severe local subsidence or uplift deformation occasionally occurred. To ensure the safety of water diversion, the monitoring and maintenance of relevant pump station buildings in risky areas should be increased in the future.

Landslide Hazard Analysis Combining BGA-Net-Based Landslide Susceptibility Perception and Small Baseline Subset Interferometric Synthetic Aperture Radar in the Baige Section in the Upper Reaches of Jinsha River

The geological and topographic conditions in the upper reaches of the Jinsha River are intricate, with frequent occurrences of landslides. Landslide Susceptibility Prediction (LSP) in this area is a crucial aspect of geological disaster risk management. This study constructs an LSP model using a Convolutional Neural Network (CNN) based on a Bilateral Aggregation Guidance (BAG) strategy, termed BGA-Net. A comprehensive landslide hazard analysis, integrating static landslide susceptibility zonation with dynamic surface deformation monitoring, was therefore conducted. The study area selected was the upper reaches of the Jinsha River, particularly the site of the Baige landslide. The BGA-Net model was first proposed for LSP generation, achieving an accuracy exceeding 85%, while the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology was jointly applied to comprehensively analyze the dynamic geological hazard risk at a regional scale. The final results were presented in a lookup table format and mapped to delineate and grade each risk level. The results show the method is practical, with high feasibility. Compared with traditional machine learning methods, the BGA-strategy-oriented CNN model more effectively differentiated the extremely low- and extremely high-susceptibility areas, enhancing decision-making processes.

Prediction Method for Dynamic Subsidence Basin in Mining Area Based on SBAS-InSAR and Time Function

Dynamic predictions of surface subsidence are crucial for assessing ground damage and protecting surface buildings. Based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, a method for making dynamic predictions of large-scale surface subsidence in mining areas can be established; however, the problem of phase coherence loss in InSAR data makes it impossible to predict the complete dynamic subsidence basin. In this study, a method combining the WeiBull time function and the improved probabilistic integral method (IPIM) model was established based on the PIM model, and a method for predicting the dynamic subsidence basin in the mining area was proposed by integrating the IPIM and the combined WeiBull time function. Time-series subsidence data, obtained using SBAS-InSAR, were used as fitting data, and the parameters of the combined WeiBull function were inverted, pixel by pixel, to predict the dynamic subsidence of the working face in the study area. Based on the predicted surface subsidence results of a certain moment in the working face, the parameters of the IPIM model were inverted to predict the subsidence value in the incoherent region. The subsidence predictions of the combined WeiBull time function and the IPIM model were fused using inverse distance weighting (IDW) interpolation to restore the complete subsidence basin in the mining area. This method was tested at the Wannian Mine in Hebei, and the obtained complete subsidence basin was compared with the measured data, with an absolute error range of 0 to 10 mm. The results show that the dynamic subsidence basin prediction method for the SBAS-InSAR mining area, involving the combination of the IPIM model and the combined WeiBull model, can not only accurately fit the time series of surface observation points affected by mining but also accurately restore the subsidence data in the incoherent region to obtain complete subsidence basin information in the mining area.

Analysis of surface deformation and related factors over mining areas based on InSAR: A case study of Fengcheng mine

Abstract. Ground surface deformation in mines affects mining production, damages the ecological environment, and endangers human safety. Mastering the detailed time series deformation and related triggering factors can provide key information for the safety of the mining area. Therefore, the Fengcheng mining area, a large and ancient coal mine in Jiangxi Province, China, was selected as the study area in this work, and the following research was conducted: 1. The accuracy and applicability of the Stacking, Small-Baseline Subset InSAR (SBAS-InSAR), and Interferometric Point Target Analysis (IPTA) methods were preliminarily explored while monitoring the annual deformation rate based on Sentinel-1A data from October 2019 to November 2022. 2. The time-series deformation of the Fengcheng mining area was obtained with SBAS-InSAR technology, and the sedimentation was validated with leveling results. 3. The correlation factors of deformation, such as rainfall and land cover, were studied, and the relationship between the influencing factors, such as coal mining dip angle, digital elevation, coal mining elevation, and deformation, was quantitatively explored with the Grey correlation model and Pearson correlation analysis method. The following conclusions were drawn: The SBAS method has the best adaptability in the dense vegetation mining area, and the root-mean-square error of the difference between deformation results and leveling data does not exceed 4mm. The evolution process of deformation centers is mainly divided into the stages of initial deformation, constant velocity deformation, accelerated deformation, and stable condition. Compared with the natural factors, the settlement of the Fengcheng mining area is mainly affected by human-induced mining and construction of artificial facilities.

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.

Advanced risk assessment framework for land subsidence impacts on transmission towers in salt lake region

To ensure the continuous transmission of clean energy for electricity, transmission towers often traverse geologically challenging terrains. This is particularly evident in the Salt Lake region, where the convergence of unique geological attributes and environmental stressors leads to recurrent land subsidence incidents, jeopardizing the integrity of power lines. In light of the prohibitive costs associated with indiscriminate remedial actions, precision in risk assessment and management is imperative to streamline these interventions. The objective of this study is to propose an accurate and effective framework for assessing the risk of land subsidence for power transmission towers. To achieve this, by utilizing the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology and cross-validation from field investigations, representative sample disaster datasets of surface deformation points of transmission towers are obtained in the first. Two optimization approaches for susceptibility modeling were employed to further enhance the susceptibility model for land subsidence and obtain high-precision susceptibility results along the transmission towers. Then, combined on-site damage investigation with SBAS-InSAR technology to conduct subsidence vulnerability analysis of transmission towers. Finally, the ultimate outcome of this process was the development of a risk assessment matrix based on spatial probability (susceptibility) and damage scales (vulnerability), aimed at more accurately assessing land subsidence risk. This framework could identify the spatial distribution of subsidence susceptibility in transmission towers exposed to potential damage, which could be followed by a more detailed transmission tower-based vulnerability and risk assessment. The method has been successfully applied in the Salt Lake area, and the susceptibility results based on the Grey Wolf Optimization-Random Forest (GW0-RF) model have shown the best modeling effect, and the area under of receiver operating characteristic curve (AUC) = 0.95. The vulnerability results show that 105 towers have been most severely damaged by land subsidence The risk distribution map shows that the central part of the Salt Lake area is the core zone for future land subsidence risk management. A total of 101 towers are in the high and very high risk level. The insights garnered from this investigation furnish a comprehensive understanding of the risk landscape for transmission towers, underpinning future-focused design, planning, and management strategies.

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.

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.

Study on erosion deformation of dry-red soil in Yuanmou dry-hot valley with different elevation gradients based on SBAS-InSAR technology

The Yuanmou dry-hot valley has been confirmed as a typical area subjected to severe soil erosion in southwestern China. The research on the soil erosion deformation exhibited by the dry-red soil that is extensively distributed in this region takes on critical significance in deepening the investigation of soil and water loss control efforts in the Yuanmou dry-hot valley. In this study, a time series of soil erosion deformation was established at different altitudinal gradients from March 2018 to October 2022 using Small Baseline Subset InSAR (SBAS-InSAR) technology to explore the deformation patterns exhibited by soil erosion in the dry-red soil of the Yuanmou dry-hot valley. Next, the time series of fractional vegetation cover (FVC) and monthly average rainfall in the identical period were analyzed comprehensively. The result of this study are presented as follows: 1) The dry-red soil regions in the Yuanmou dry-hot valley, which were observed in the line of sight (LOS) direction, attained the deformation rates ranging from −101.683 mm/yr to 30.57 mm/yr (Ascending),-79.658 mm/yr to 41.942 mm/yr (Descending). In general, areas with significant surface erosion were concentrated in the Longchuan River basin flowing through the north and south of Yuanmou County as well as in the river confluence zones. Uplifted areas have been more widely reported in the central and northern regions of Yuanmou (e.g., the Wudongde hydroelectric power station reservoir area). 2) A significant altitudinal gradient effect was exerted by soil erosion in the dry-red soil of the Yuanmou dry-hot valley. The valley-dam area and the medium and low mountain areas were subjected to the most severe soil erosion, and the maximum erosion reached over 80 mm. Erosion was mitigated in the low mountain areas around the dam and the medium and high mountain areas, and the maximum erosion reached 60 mm and 30 mm, respectively. At an altitude of 1,350 m, soil erosion in the dry-red soil was more significantly affected by rainfall. Nevertheless, at an altitude over 1,350 m, variations in FVC become the primary factor for soil erosion in the dry-red soil. The results of this study can scientifically support soil and water loss control efforts in the Yuanmou dry-hot valley.

Deformation Monitoring and Potential Risk Detection of In-Construction Dams Utilizing SBAS-InSAR Technology—A Case Study on the Datengxia Water Conservancy Hub

Deformation monitoring plays a pivotal role in assessing dam safety. Interferometric Synthetic Aperture Radar (InSAR) has the advantage of obtaining an extensive range of deformation, regardless of weather conditions. The Datengxia Water Conservancy Hub is the largest in-construction dam in China. To effectively assess the in-construction dam safety, the SBAS-InSAR (Small Baseline Subset-InSAR) technique and 86 Sentinel-1 images (from 11 February 2020, to 16 January 2023) have been employed in this study to monitor the deformation over the reservoir and its surrounding areas. The reliability of the SBAS-InSAR monitoring results over the study area was demonstrated by the in situ monitoring results. And the InSAR results show that the central section of the left dam exhibits the most substantial cumulative deformation, attributed to the maximal water pressure. This is closely followed by the left end of the dam, which reflects a similar but smaller deformation. However, the in-construction cofferdam facilities make the right-end section of the left dam more robust, and the deformation is the most stable. Additionally, significant deformation of the auxiliary dam slope has been identified. Moreover, the analysis indicated that the deformation of the four upstream slopes is closely related to the precipitation, which potentially poses a threat to the safety of the Datengxia Dam.

A temporary soil dump settlement and landslide risk analysis using the improved small baseline subset-InSAR and continuous medium model

Abandoned soil disposal in China has become increasingly challenging, which may pose significant safety hazards such as soil settlement and landslides. Interferometry Synthetic Aperture Radar (InSAR) is effective in surface deformation monitoring and Small Baseline Subset-InSAR (SBAS-InSAR) can obtain sufficient coherent point density from the bare soil surface. This study investigated Chishi soil dump in the Shenzhen-Shanwei Special Cooperation Zone (SSSCZ), and a total of 91 Sentinel-1 images from 2019 to 2022 was processed. Accordingly, an improved SBAS-InSAR method was utilized to analyze the soil dump’s time-series deformation, and multi-source remote sensing data were used for auxiliary interpretation. Our results indicate that precipitation, high temperature, and construction vibrations may cause instability of the soil dump. Therefore, a depth-integrated continuous medium model was introduced to analyze the potential landslide risk of the soil dump, which demonstrate that a landslide will likely occur, harm personnel, and damage the buildings surrounding the Chishi soil dump when it was saturated (λ ≥ 0.50). This research can provide a vital case reference for the analysis, interpretation, disaster prevention, and control evaluation of similar soil dumps.

Prediction of airport runway settlement using an integrated SBAS-InSAR and BP-EnKF approach

The prediction of surface settlement occupies a crucial role in achieving effective catastrophe prevention and mitigation, as well as facilitating the maintenance of airport runways. Given the challenges associated with the manual collection of settlement data and the suboptimal timeliness of the Back Propagation (BP) technique in neural networks, this study proposes an integrated prediction method that combines the Ensemble Kalman Filter (EnKF) with BP. This study focuses on the processing of Synthetic Aperture Radar (SAR) pictures obtained from the ascending orbit of the Sentinel-1A satellite. The Small Baseline Subset Interferometric synthetic aperture radar (SBAS-InSAR) technology is employed to derive the settlement time series on the runway of Kangding Airport. Moreover, three sites with high coherence within the primary settlement region are employed to assess the dependability of the model after the expansion of the data by cubic spline interpolation. The findings of the study indicate that both the BP-EnKF and BP models exhibit favorable outcomes in predicting airport runway settlement. However, following the alteration of data caused by external environmental influences, the BP-EnKF model has superior adaptability to variations in data. It has been shown that the BP-EnKF model exhibits a prediction accuracy that surpasses the BP model by 9.25%.

Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022

Abstract A 6.9 magnitude earthquake hit Menyuan County, Haibei Prefecture, Qinghai Province, at 01:45 PM Beijing time on January 8, 2022 (17:45 PM GMT time on January 7, 2022). To explore the magnitude of the earthquake deformation and the affected area, this work combined optical remote sensing interpretation, interferometric synthetic aperture radar (InSAR) coseismic deformation extraction, and field surveys for research and analysis. Relying on the high-resolution Earth observation system of the Qinghai Remote Sensing Center for Natural Resources, high-resolution GF1D, GF2, and TRIPLESAT optical remote sensing images were acquired immediately after the earthquake. The airborne triangulation encryption method was used to carry out orthographic correction, fusion, and mosaic processing of digital orthophoto map (DOM) and digital surface model (DSM) images, and first-hand optical remote sensing images of the disaster areas were obtained. Based on differential InSAR (D-InSAR), small baseline subset InSAR (SBAS-InSAR) and lifting rail fusion methods, the coseismic deformation field and deformation rate of the lifting rail direction were obtained by using Sentinel-1A data processing before and after the earthquake. Combined with optical interpretation, InSAR deformation, and field investigation, the results show that the deformation trend of the line of sight (LOS) images to the north and south of the ascending and descending orbits show an obvious opposite trend. The surface shape variables are −50 to 45 cm and −65 to 72 cm, respectively, and the deformation rate before the earthquake reached 25 cm/year. The deformation field characteristics show that the earthquake was mainly due to thrust, and the coseismic deformation field fractured along the WNW‒ESE direction with a length of approximately 33 km. The areas affected by 10 mm, 20 cm, and 50 cm deformation magnitudes in the whole earthquake area were 975.14, 321.10, and 38.55 km2, respectively. Within 20 km, there were two main affected townships, namely, Sujitan Township and Huangcheng Mongolian Township. Within 50 km, there were four main affected towns and townships, namely, Sujitan Township, Mongolian Township of the Imperial city, Qingshizui town, and Haomen town.

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.

Early Identification of Geological Disasters in Loess Hilly Area of Ningnan Based on InSAR Technology: A case study of Yuanzhou District, China

Using small baseline subset InSAR (SBAS-InSAR) technology and multi-temporal synthetic aperture radar data, multitemporal phase and long-term sequential deformation monitoring and early identification of geological hazards were carried out in the loess hilly area of southern Ningnan-Yuanzhou area. Based on the analysis of radar visibility in the loess hilly area of southern Ningxia. The Sentinel-1A historical archived data of 36 periods from January 2019–December 2021 and descending orbit data from January 2019–May 2021 were used to carry out monitoring of surface deformation in the river basin, and a total of 61 hidden danger. 49 new concealed high risk points were identified by comprehensive optical remote sensing interpretation. The annual average deformation rate of the radar line-of-sight direction of the ascent data was -47.6746–71.6472mm/a, and the cumulative deformation variable was -81.20–121.61mm. There were 23 new concealed high risk points identified. The annual average deformation rate was -72.1574–51.6028mm/a, and the cumulative deformation variable was -165.6–112.4 mm. During the descending orbit, there were 21 obvious deformations and 16 newly discovered geological hazards. Taking a group of ascending orbit deformation zones in Yuanzhou District as an example, which proved that the application of SBAS-InSAR technology in the loess hilly area of Ningnan was feasible.

Impact of precipitation on Beishan landslide deformation from 1986 to 2023

Investigating the response of landslide activity to climate change is crucial for understanding the disastrous effects of climate change on high mountains. However, the lack of long-term, spatial–temporal consistent measurement of landslide activity prohibits the study of this relationship. In this work, we used two methods to derive the time series of a landslide’s deformation and study its relationship with precipitation in the northeastern Tibetan Plateau. The small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) method with Sentinel-1A images is first applied to derive time series of the landslide’s deformation from 2020 to 2021. A recently developed method to derive cumulative deformations of optical images was used with Landsat 5 and Sentinel-2 images to derive the long-term deformation time series from 1986 to 2023. Centimeter-scale deformations detected by using the InSAR method are mainly located in the upper and eastern parts of the landslide, whereas meter-scale deformations detected by using the optical method are in the middle of the landslide. Time-series results from both methods show that intra-annual initiations of the landslide’s deformation occurred in rainy months (from July to October). Although there seems to be no direct relations between inter-annual deformations and precipitation, significant displacements since 2020 occurred after exceptionally wet years from 2018 (with a record-breaking precipitation year in 2020). With optical images, we found that the maximum cumulative deformation of the landslide has been >35 m since 1986 with major deformations (>20 m) found after 2020, which may indicate an imminent risk to the Lijie town near the toe of the landslide. With climate change, increased precipitation is expected in future, which may trigger more similar landslides in the vicinity of this region. This work demonstrates an executable framework to assess landslide hazard risk under climate change.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region.

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

Linking ground ice and glacier melting to lake volume change in the Dogai Coring watershed on the Tibetan Plateau

The Dogai Coring Lake on the Tibetan Plateau, with widespread permafrost and glaciers in the watershed, has expanded rapidly between 2014 and 2020. However, it remains unclear how much melting water from permafrost and glaciers contributes to the increase in lake volume. This study quantifies the potential amount of water released by the melt of permafrost and glaciers, and assesses the fraction of water storage gains in the lake offset by the loss from the cryosphere (i.e., ground ice and glaciers). The small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method was used to derive the spatiotemporal changes in surface deformation, which then assisted the estimation of the potential water release from ground ice melting. The glacier mass balance was estimated using a geodetic method with ASTER DEMs. Results show that 51.3 % of the region experienced subsidence with rates in the range of 5–20 mm∙a−1 during 2014–2020. Ground ice melting released water at a rate of 35.1 × 106 m3∙a−1, accounting for ∼19.3 % of the increase in lake volume. The glacier mass loss rate was estimated to be − 0.69 ± 0.04 m w.e. yr−1 (113.6 ± 7.3 × 106 m3∙a−1), contributing ∼62.5 % to the lake volume increase. Our results show that the water storage loss in the cryosphere has largely (∼80 %) offset the water storage gain in the lake, suggesting that cryospheric meltwater is an important factor regulating water budget change of the lake watershed.