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Numerous open-pit mines are scattered within the southern mountainous areas of China. Due to the complex mountainous terrain and abundant rainfall, surface disturbances caused by open-pit mining activities pose a serious risk of landslides. To identify potential landslide hazards in mountainous open-pit mining areas in advance, this study proposes a quantitative method that utilizes ascending and descending orbit Interferometric Synthetic Aperture Radar (InSAR) technology to accurately identify landslide hazards. We select the Xiaolongtan coal mining area in Yunnan, China, as a case study. Small Baseline Subset InSAR (SBAS-InSAR) technology was employed to obtain the Line of Sight (LOS) deformation of ascending and descending orbits from November 2019 to November 2021. Following this, two-dimensional deformations were calculated based on the obtained LOS deformations. Multiple remote sensing data sources, including Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Landsat 8 Operational Land Imager (OLI), were utilized to extract potential landslide points based on vertical deformation. A combined subjective and objective weighting method was then used to assess the landslide hazard in the study area, and an information quantity model was constructed for landslide hazards in the mining area. Finally, based on high-resolution remote sensing images from the study period, potential landslide hazards were identified in the study area. The results reveal that the vertical deformation rate in the mining area ranges from − 231.73 to 81.42 mm/year, indicating significant subsidence and uplift tendencies. A total of 2353 potential landslide points were identified, primarily located near the slopes of two open-pit mines and in areas with low vegetation coverage. The Xiaolongtan and Buzhaoba open-pit mines, along with the surrounding regions in the study area, were identified to exhibit relatively high landslide hazards. Among the three coal mine waste dumps, the Beipingba waste dump presents a higher landslide hazard. This study provides a scientific basis and practical reference for identifying landslide hazards in mountainous open-pit mining areas. [ABSTRACT FROM AUTHOR]

Catastrophic landslides are generally evolved from potential active landslides, and early identification of active landslides over an extensive region is vital to effective prevention and control of disastrous landslides in urban areas. Interferometric Synthetic Aperture Radar (InSAR) has immense potential in mapping active landslides. However, artificial interpretation of InSAR measurements and manual recognition of active landslides are very laborious and time-consuming, with a relatively high missing and false alarms. That hinders the application of InSAR technique and the identification of active landslides in wide areas. Automatic recognition of active landslides has always been a great challenge and has been relatively rarely investigated by previous studies. This work establishes comprehensive identification indices of geoenvironmental, disaster-triggering, and surface deformation features. Moreover, it suggests a novel deep learning algorithm of SDeepFM to conduct automatic identification of active landslides across a vast and landslide-serious area of Hualong County. Some new viewpoints are suggested as follows. (1) The identification indices consist of disaster-controlling, disaster-inducing, and active deformation characteristics and are constructed in terms of the cause characteristics of active landslides. Thus, it can effectively decrease the false alarms of active landslide identification. (2) The proposed SDeepFM algorithm features a spatial-perception ability and can adequately extract and fuse the low-level and high-level semantic features. It outperforms the classification and regression tree (CART), multi-layer perceptron (MLP), convolutional neural network (CNN), and deep neural network (DNN) algorithms. The test accuracy attains 0.91, 99.73%, 90.21%, 0.92, 0.96, and 0.91 in F1-score, Accuracy, Precision, Recall, AUC, and Kappa, respectively. (3) A total of 164 active landslides are exactly recognized, and 39 active landslides are newly identified in this work. (4) In Hualong County, the characteristics of slope deformation, spatial context, lithology, tectonic movement, human activity, and topography play important roles in active landslide identification. River distribution and rainfall also contribute to active landslide recognition. [ABSTRACT FROM AUTHOR]

The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China's largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence. [ABSTRACT FROM AUTHOR]

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately −16 mm/a over the 22 km section of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 °C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze–thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone. [ABSTRACT FROM AUTHOR]

The deformation of the No. 65 slope on the Shangsan Expressway poses a potential threat to road safety. In July 2021, the deformation rate of this slope accelerated significantly, leading to the implementation of reinforcement measures in 2022. To comprehensively analyze the historical deformation characteristics of the slope and evaluate the effectiveness of the reinforcement measures, this study employs Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to calculate and analyze the historical deformation characteristics of the slope and the adjacent hillside for two periods: from 10 January 2018 to 22 August 2021, and from 3 September 2021 to 22 December 2023. The SBAS-InSAR monitoring results were compared with in situ data from borehole inclinometers to verify the reliability of the calculations. The SBAS-InSAR results indicate that before reinforcement, the slope exhibited slow movement; however, after the implementation of the reinforcement measures, the displacement significantly decreased, demonstrating the success and effectiveness of the interventions. The consistency between the SBAS-InSAR results, borehole inclinometer data, and surface observations confirms the substantial potential of SBAS-InSAR technology for slope engineering monitoring. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies. [ABSTRACT FROM AUTHOR]

The black soil region experiences complex erosion due to natural processes and intense human activities, leading to soil degradation and adverse ecological and agricultural impacts. However, the complexities involved in quantifying regional erosion poses remarkable challenges in accurately assessing the current status of regional soil erosion for effective soil conservation. To solve this issue, we proposed a new method for monitoring soil erosion using Interferometric synthetic aperture radar (InSAR) technology and machine learning algorithms within the Google Earth Engine platform. The new method not only enables regional-scale monitoring, but also ensures high accuracy in measurement (millimeter-level). The erosion susceptibility of the study area (Yanshou County, Heilongjiang Province, Northeastern China) was also classified using random forest algorithms to refine the monitored and predicted soil erosion. The results indicate that the five-year (2016–2021) deformation in Yanshou County was −11.08 mm, with a significant mean cumulative deformation of −8.08 mm yr−1 occurring in 2017. The driving factor analysis shows that the region was subject to the compound effect of water and freeze–thaw erosion, closely related to crop phenological stages. The susceptibility analysis indicates that 73.3% of the region was susceptible to erosion, with a higher probability in river areas, at high altitudes, and on steep slopes. However, good vegetation cover can reduce the risk of soil erosion to some extent. This study offers a new perspective on monitoring regional soil erosion in the black soil region of China. The proposed method holds potential for future expansion to monitor soil erosion in a larger areas, thereby guiding the strategies development for protection of the agriculturally important black soil. [ABSTRACT FROM AUTHOR]

The adverse combination of excessive mining practices and the resulting land subsidence is a significant obstacle to the sustainable growth and stability of regions associated with mining activities. The Lakhra coal mines, which contain some of Pakistan's largest coal deposits, have been overlooked in land subsidence monitoring, indicating a considerable oversight in the region. Subsidence in mining areas can be spotted early when using Interferometric Synthetic Aperture Radar (InSAR), which can precisely monitor ground changes over time. This study is the first to employ the Small Baseline Subset (SBAS)-InSAR and stacking-InSAR techniques to identify land subsidence at the Lakhra coal mines. This research offers critical insights into subsidence mechanisms in the study area, which has never been previously investigated for ground deformation monitoring, by utilizing 150 Sentinel-1A (ascending) images obtained between January 2018 and September 2023. A total of 102 deformation spots were identified using SBAS-InSAR, while stacking-InSAR detected 73 deformation locations. The most extensive cumulative subsidence in the Lakhra coal mine was −114 mm, according to SBAS-InSAR, with a standard deviation of 6.63 mm. In comparison, a subsidence rate of −19 mm/year was reported using stacking-InSAR with a standard deviation of 1.17 mm/year. The rangeland covered 88.8% of the total area and exhibited the most significant deformation values, as determined by stacking and SBAS-InSAR techniques. Linear regression showed that there was not a strong correlation between subsidence and topographic factors. As detected by optical remote sensing data, the subsidence locations were near or above the mines in the research area, indicating that widespread mining in Lakhra coal mines was the cause of subsidence. Our findings suggest that SAR interferometric time series analysis is helpful for proactively identifying and controlling subsidence difficulties in mining regions by closely monitoring activities, hence reducing negative consequences on operations and the environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The southeastern coastal regions of China are characterized by typical hilly terrain with abundant rainfall throughout the year, leading to frequent geological hazards. To investigate the measurement accuracy of surface deformation and the effectiveness of error correction methods using the small baselines subset–interferometry synthetic aperture radar (SBAS-InSAR) method in identifying potential geological hazards in such areas, this study processes and analyzes 129 SAR images covering Ninghai County, China. By processing coherence coefficients using the Stacking technique, errors introduced by low-coherence images during phase unwrapping are mitigated. Subsequently, interferograms with high coherence are selected for time-series deformation analysis based on the statistical parameters of coherence coefficients. The results indicate that, after mitigating errors from low-coherence images, applying the SBAS-InSAR method to only high-coherence SAR datasets provides reliable surface deformation results. Additionally, when combined with field geological survey data, this method successfully identified landslide boundaries and potential landslides not accurately detected in previous geological surveys. This study demonstrates that using the SBAS-InSAR method and selecting high-coherence SAR images based on interferogram coherence statistical parameters significantly improves measurement accuracy and effectively identifies potential geological hazards. [ABSTRACT FROM AUTHOR]

Evaluating the integrity of liquefied natural gas (LNG) storage tanks is essential for safety and environmental conservation. In this study, which centered on an LNG storage tank on a specific island, we utilized 37 interferometric wide (IW) swath mode VV polarization Sentinel-1 images from the European Space Agency (August 2020-December 2023). Deformation data were obtained using persistent scatterer synthetic aperture radar (SAR) interferometry (PSInSAR) and small-baseline subset SAR interferometry (SBAS-InSAR) techniques, supplemented by dense point cloud data for 3D and 2D analyses. Results showed consistent deformation rates in the tank, with peaks between October 2021 and August 2022. Point cloud analysis revealed millimeter-level localized deformations (0.1 to 2.6 mm), corroborating InSAR findings. This underscores the importance of PS-InSAR, SBAS-InSAR, and point cloud modeling in monitoring LNG tank deformations, and offers insights into managing the impact of climate change on LNG storage stability. [ABSTRACT FROM AUTHOR]

SBAS-InSAR technology is effective in obtaining surface deformation information and is widely used in monitoring landslides and mining subsidence. However, SBAS-InSAR technology is susceptible to various errors, including atmospheric, orbital, and phase unwrapping errors. These multiple errors pose significant challenges to precise deformation monitoring over large areas. This paper examines the spatial characteristics of these errors and introduces a spatially constrained SBAS-InSAR method, termed SSBAS-InSAR, which enhances the accuracy of wide-area surface deformation monitoring. The method employs multiple stable ground points to create a control network that limits the propagation of multiple types of errors in the interferometric unwrapped data, thereby reducing the impact of long-wavelength signals on local deformation measurements. The proposed method was applied to Sentinel-1 data from parts of Jining, China. The results indicate that, compared to the traditional SBAS-InSAR method, the SSBAS-InSAR method significantly reduced phase closure errors, deformation rate standard deviations, and phase residues, improved temporal coherence, and provided a clearer representation of deformation in time-series curves. This is crucial for studying surface deformation trends and patterns and for preventing related disasters. [ABSTRACT FROM AUTHOR]

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loess, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The atmospheric phase, which is the sum of vertical stratification and turbulent atmospheric phase, is a major challenge currently faced by small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements. Many previous studies have demonstrated that the former can be separated from the interferogram by establishing a functional model between it and the topography. Due to the high variability of the turbulent atmospheric phase (TAP) in the space and time domains, however, the TAP is difficult to model and remove. Recently, many stochastic models have been developed to reduce the influence of the TAP in SBAS-InSAR. To avoid the rank deficient in stochastic model method, we present a correction method using network-based variance estimation, interferogram stacking and ordinary kriging interpolation (NIO). There are three main steps in the proposed algorithm to ensure the accuracy of the correction result: (1) adaptively identify and select sufficient good-quality interferograms that contain less turbulent atmospheric noise to participate in deformation calculation; (2) further select the short temporal baseline interferogram and mask the corresponding deformation location to avoid the effect of deformation; and 3) take advantage of ordinary kriging interpolation to reduce the effects of TAP from the selected good-quality interferograms. The performance of the proposed method has been validated with a set of simulations and real Sentinel-1A SAR data in Southern California, USA. [ABSTRACT FROM AUTHOR]

The accuracy of landslide susceptibility mapping is influenced by the quality of sample data, factor systems, and assessment methods. This study aims to enhance the representativeness and overall quality of the sample dataset through an effective sample expansion strategy, achieving greater precision and reliability in the landslide susceptibility model. An integrated interpretative framework for landslide susceptibility assessment is developed using the XGBoost-SHAP-PDP algorithm to deeply investigate the key contributing factors of landslides in karst areas. Firstly, 17 conditioning factors (e.g., surface deformation rate, land surface temperature, slope, lithology, and NDVI) were introduced based on field surveys, satellite imagery, and literature reviews, to construct a landslide susceptibility conditioning factor system in line with karst geomorphology characteristics. Secondly, a sample expansion strategy combining the frequency ratio (FR) with SBAS-InSAR interpretation results was proposed to optimize the landslide susceptibility assessment dataset. The XGBoost algorithm was then utilized to build the assessment model. Finally, the SHAP and PDP algorithms were applied to interpret the model, examining the primary contributing factors and their influence on landslides in karst areas from both global and single-factor perspectives. Results showed a significant improvement in model accuracy after sample expansion, with AUC values of 0.9579 and 0.9790 for the training and testing sets, respectively. The top three important factors were distance from mining sites, lithology, and NDVI, while land surface temperature, soil erosion modulus, and surface deformation rate also significantly contributed to landslide susceptibility. In summary, this paper provides an in-depth discussion of the effectiveness of LSM in predicting landslide occurrence in complex terrain environments. The reliability and accuracy of the landslide susceptibility assessment model were significantly improved by optimizing the sample dataset within the karst landscape region. In addition, the research results not only provide an essential reference for landslide prevention and control in the karst region of Southwest China and regional central engineering construction planning but also provide a scientific basis for the prevention and control of geologic hazards globally, showing a wide range of application prospects and practical significance. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The subsidence of the earth's surface in mining areas is characterized by fast speed and large gradients. Conventional small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) monitoring can significantly underestimate results, making it challenging to capture the surface's temporal subsidence features. In this context, this paper proposes a method for monitoring subsidence in mining areas. It utilizes a phase unwrapping network (PUNet) and a fused Weibull model within the SBAS-InSAR framework to address nonlinear and large-gradient subsidence. The basic principle of this method is to first process the SAR images using the small baseline method to obtain the differential interferogram, utilizing the PUNet to obtain reliable large-gradient unwrapped phases. Next, the Weibull model parameters of each pixel are calculated based on the unwrapped phase, and the temporal subsidence of each point on the surface is determined using the calculated parameters. This method introduces a nonlinear model into the SBAS-InSAR solution, which is more consistent with the subsidence characteristics of mining areas. Through experimentation in a backfilled mining working face, the proposed method in this paper yields superior monitoring results compared to conventional approaches. [ABSTRACT FROM AUTHOR]

Due to its unique geographical location and rapid urbanization, Xiamen is particularly susceptible to geological disasters. This study employs 80 Sentinel-1A SAR images covering Xiamen spanning from May 2017 to December 2023 for comprehensive dynamic monitoring of the land subsidence. PS-InSAR and SBAS-InSAR techniques were utilized to derive the surface deformation field and time series separately, followed by a comparative analysis of their results. SBAS-InSAR was finally chosen in this study for its higher coherence. Based on its results, we conducted cause analysis and obtained the following findings. (1) The most substantial subsidence occurred in Maluan Bay and Dadeng Island, where the maximum subsidence rate was 24 mm/yr and the maximum cumulative subsidence reached 250 mm over the course of the study. Additionally, regions exhibiting subsidence rates ranging from 10 to 30 mm/yr included Yuanhai Terminal, Maluan Bay, Xitang, Guanxun, Jiuxi entrance, Yangtang, the southeastern part of Dadeng Island, and Yundang Lake. (2) Geological structure, groundwater extraction, reclamation and engineering construction all have impacts on land subsidence. The land subsidence of fault belts and seismic focus areas was significant, and the area above the clay layer settled significantly. Both direct and indirect analysis can prove that as the amount of groundwater extraction increases, the amount of land subsidence increases. Significant subsidence is prone to occur after the initial land reclamation, during the consolidation period of the old fill materials, and after land compaction. The construction changes the soil structure, and the appearance of new buildings increases the risk of subsidence. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map. [ABSTRACT FROM AUTHOR]

Landslide hazards pose a significant threat to lives and infrastructure, especially in mountainous regions like the Three Gorges Reservoir area. While the mechanisms driving landslide initiation and progression in reservoir environments are not fully understood. This study aimed to leverage the capabilities of Sentinel-1 satellite imagery and the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to detect and monitor potential landslide deformations in Yunyang County, Chongqing, China. We utilized Sentinel-1 data acquired between January 1, 2020, and December 28, 2022, to generate deformation velocity maps. Twelve potential landslides were identified, primarily concentrated near residential areas and along the Yangtze River. Precipitation emerged as the primary driver of surface deformation and landslide initiation, with potential landslides in residential vicinities and along the river exhibiting significantly higher deformation rates during the wet season compared to the dry season. These sites are susceptible to slope failures and geological disasters upon reaching critical antecedent rainfall thresholds, highlighting the necessity for continuous monitoring. Other potential landslides maintained consistent deformation rates across seasons but experienced brief accelerations following heavy precipitation events. Notably, potential landslides adjacent to the Yangtze River experienced accelerated deformation during periods of significant river water level reductions, suggesting that the river's cyclical water level fluctuations influence slope stability. The study demonstrated the effectiveness of SBAS-InSAR in detecting millimetric deformations in incipient landslides, a crucial step in averting landslide disasters and ensuring public safety. [ABSTRACT FROM AUTHOR]

Parts of sections in permafrost regions of the Qinghai-Tibet Railway (QTR) have been deformed due to permafrost degradation, the global warming and human activities. Therefore, monitoring the subgrade settlement of the QTR in permafrost regions has been a major working in QTR maintenance. For this reason, this paper investigates the subsidence characteristics and derivative mechanisms of railway subgrades in the permafrost regions of the QTR. Additionally, it analyses the impact mechanisms of surface temperature changes on the subsidence of permafrost subgrades. Building on this investigation, a C-I-GWO-BP neural network prediction model was developed to forecast subgrade settlement in permafrost regions. The findings reveal a substantial correlation (R2 = 0.834) between the deformation of railway subgrades in permafrost regions and changes in surface temperature. Meanwhile，with the intensification of global warming and human activities, the subsidence of permafrost subgrade has shown an increasing trend year by year. The primary areas of deformation are predominantly concentrated in ice-rich permafrost regions, including rivers, lakes, and snowmelt runoff areas. Additionally, when compared to traditional BP neural network models, the C-I-GWO-BP neural network prediction model proposed in this article showcases diminished prediction errors and heightened accuracy in identifying optimal weights and thresholds. This model offers technical support for the efficient, accurate, and automated prediction of railway subgrade subsidence in permafrost regions. [ABSTRACT FROM AUTHOR]

The realization of a harmonious relationship between the natural environment and economic development has always been the unremitting pursuit of traditional mineral resource-based cities. With rich reserves of iron and coal ore resources, Laiwu has become an important steel production base in Shandong Province in China, after several decades of industrial development. However, some serious environmental problems have occurred with the quick development of local steel industries, with ground subsidence and consequent secondary disasters as the most representative ones. To better evaluate possible ground collapse risk, comprehensive approaches incorporating the common deformation monitoring with small-baseline subset (SBAS)-synthetic aperture radar interferometry (InSAR) technique, environmental factors analysis, and risk evaluation are designed here with ALOS PALSAR and Sentinel-1 SAR observations. A retrospect on the ground deformation process indicates that ground deformation has largely decreased by around 51.57% in area but increased on average by around −5.4 mm/year in magnitude over the observation period of Sentinel-1 (30 July 2015 to 22 August 2022), compared to that of ALOS PALSAR (17 January 2007 to 28 October 2010). To better reveal the potential triggering mechanism, environmental factors are also utilized and conjointly analyzed with the ground deformation time series. These analysis results indicate that the ground deformation signals are highly correlated with human industrial activities, such underground mining, and the operation of manual infrastructures (landfill, tailing pond, and so on). In addition, the evaluation demonstrates that the area with potential collapse risk (levels of medium, high, and extremely high) occupies around 8.19 km2, approximately 0.86% of the whole study region. This study sheds a bright light on the safety guarantee for the industrial operation and the ecologically friendly urban development of traditional steel production industrial cities in China. [ABSTRACT FROM AUTHOR]

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east–west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities. [ABSTRACT FROM AUTHOR]

Le'an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town's terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le'an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le'an Town ranges from −10 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds −50 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value. [ABSTRACT FROM AUTHOR]

Many debris-covered glaciers are widely distributed on the Qinghai–Tibet Plateau. Glaciers are important freshwater resources and cause disasters such as glacier collapse and landslides. Therefore, it is of great significance to monitor the movement characteristics of large active glaciers and analyze the process of mass migration, which may cause serious threats and damage to roads and people living in surrounding areas. In this study, we chose a glacier with strong activity in Lulang County, Tibet, as the study area. The complete 4-year time series deformation of the glacier was estimated by using an improved small-baseline subset InSAR (SBAS-InSAR) technique based on the ascending and descending Sentinel-1 datasets. Then, the three-dimensional time series deformation field of the glacier was obtained by using the 3D decomposition technique. Furthermore, the three-dimensional movement of the glacier and its material migration process were analyzed. The results showed that the velocities of the Lulang glacier in horizontal and vertical directions were up to 8.0 m/year and 0.45 m/year, and these were basically consistent with the movement rate calculated from the historical optical images. Debris on both sides of the slope accumulated in the channel after slipping, and the material loss of the three provenances reached 6–9 × 103 m3/year, while the volume of the glacier also decreased by about 76 × 103 m3/year due to snow melting and evaporation. The correlation between the precipitation, temperature, and surface velocity suggests that glacier velocity has a clear association with them, and the activity of glaciers is linked to climate change. Therefore, in the context of global warming, the glacier movement speed will gradually increase with the annual increase in temperature, resulting in debris flow disasters in the future summer high-temperature period. [ABSTRACT FROM AUTHOR]

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Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Time-series monitoring of the land subsidence in the Yangtze River coastal area is crucial for maintaining river stability and early warning of disasters. This study employed PS-InSAR and SBAS-InSAR techniques to monitor the land subsidence along the Yangtze River in Nanjing, using a total of 42 Sentinel-1A images obtained between April 2015 and November 2021. The accuracy of both methods was compared and validated, while a comprehensive analysis was conducted to ascertain the spatial distribution characteristics and underlying causes of land subsidence. The maximum deviation between the two methods and six leveling point data did not exceed ±5 mm. Within the 5 km buffer zone on either side of the Yangtze River in Nanjing, four subsidence funnels were identified. Analysis of the factors contributing to land subsidence in this area indicates that underground engineering construction and operation, increasing ground building area, and geological condition all have certain correlations to the land subsidence. The results obtained through PS-InSAR and SBAS-InSAR technologies revealed a high degree of consistency in monitoring outcomes, and the latter method exhibited superior monitoring accuracy than the former one in this area. This study holds significant implications for guiding the scientific management of urban geohazards along the Yangtze River. [ABSTRACT FROM AUTHOR]

Changsha City is the political, economic and cultural centre of Hunan Province. In recent years, because of large-scale urban construction and special geological structure, land subsidence has become one of the main geological disasters in Changsha City, which seriously threatens the health of buildings and infrastructure. To solve this problem, this paper monitored the land subsidence in Changsha City from 2015 to 2021 using the Point Target (PT) based Small Baseline Subset InSAR (SBAS-InSAR), revealed the spatial distribution characteristics of land subsidence in the study area, studied the causes of subsidence, and carried out geological disaster risk assessment. The results show that the overall stability of Changsha City is dominated by uneven subsidence. The subsidence areas are mainly distributed in the north of Kaifu District, Moon Island, Malan Mountain Chuangzhi Park and the periphery of the main urban area. The maximum settlement rate is about 87 mm/year, and the cumulative settlement reaches 250 mm. The settlement monitoring results are basically consistent with the actual situation at the same time, which is mainly caused by engineering construction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Strong earthquakes can frequently trigger a large number of co-seismic landslides. Obtaining an accurate susceptibility map of co-seismic landslides is crucial for post-disaster rescue and reconstruction efforts. In this study, the pre-seismic average annual surface deformation rate of the study area was obtained using Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. Furthermore, the landslide hazards prior to the earthquake in multiple locations within the study area were analyzed. Subsequently, the deformation data was combined with 11 evaluation factors, including the distance to the fault and the Peak Ground Acceleration (PGA), to model the susceptibility of landslides. We constructed four models to evaluate the susceptibility of landslides in the study area: the Bayesian optimization random forest (BO-RF) model, the random forest model (RF), the logistic regression model (LR), and the support vector machine model (SVM). The BO-RF model outperformed the other models, achieving an AUC value of 0.984, an accuracy of 0.952, a precision of 0.953, a recall of 0.952, and an F1 score of 0.953. Furthermore, incorporating pre-seismic deformation features in the evaluation of co-seismic landslide susceptibility can effectively improve the reliability of model predictions, as compared to the evaluation model results without incorporating deformation factors. The obtained research results provide valuable data support for the rescue and management of disaster-stricken areas.

The Raniganj coalfield, which is the oldest coal mine in India, is susceptible to ground subsidence. For the purpose of detecting surface deformation, this work makes use of field surveys, interferometric synthetic aperture radar (InSAR), and Differential GPS (DGPS). The study utilised Sentinel-1 InSAR data spanning from 2017 to 2023. PS-InSAR was used for both ascending and descending datasets, while SBAS-InSAR was used for descending datasets alone. Records show a maximum subsidence rate of −21.18 mm/year. Three surface deformation maps were generated from time-series assessments of individual locations, revealing regions undergoing significant changes. Known mining collapse locations and continuing deformation zones were designated with four differential GPS stations. By analysing the DGPS data with the GAMIT/GLOBK software, we were able to measure surfaces that were undergoing rapid deformation. Using Google Earth Engine (GEE), we generated thermal maps to delve deeper into the coal fire activity. We found coal bed methane (CBM) mining causing substantial subsidence in Kataberia and the surrounding areas. Shyamsundarpur and New Kenda are impacted by mining voids and coal fire-induced subsidence, respectively. The results of this study give the Raniganj region’s decision-making procedures more credibility in terms of minimising and controlling geological dangers.

Earthquake-induced landslides can cause severe surface damage and casualties, posing a serious threat to the overall ecological environment and social stability. Traditional landslide susceptibility prediction (LSP) techniques often suffer from low effectiveness and precision, necessitating the exploration of remote sensing technology. However, this research in this area is limited, and the development of high-performance prediction models remains a pressing scientific issue. This study focuses on the Ms7.0 earthquake in Jiuzhaigou on 8 August 2017. To investigate the optimal integration of remote sensing technology with traditional LSP techniques, the study applies collaborative factor analysis and contingency matrix methods to create four new coupling models (SVM-I, SVM-II, RF-I, RF-II), followed by a comprehensive performance evaluation of these models. The results indicate that the integration of SAR-derived surface deformation data significantly enhances the accuracy of Landslide Susceptibility Mapping (LSM). Comparing the model performance with the receiver operating characteristic curve and landslide density, the reliability and prediction performance of the RF-I model are outstanding, reflecting that the improved method based on the InSAR collaborative machine learning model with shape variables along the slope direction can optimize the accuracy of the LSM, and has better performance and robustness in earthquake landslide susceptibility evaluation.

Accurately extracting surface deformation is essential for the prevention and control of geological hazards caused by underground coal mining. By taking a working face in Guotun Coal Mine, Shandong Province, as the case study, this paper first obtains 18 Sentinel-1A satellite images during the extraction period of the working face (July 31, 2017, to May 3, 2018), and derives the surface deformation of the goaf area based on SBAS-InSAR technology. Then, driven by InSAR observations, the functional projection relationships for the three-dimensional parameters between the probability integration method (PIM) and line-of-sight (LOS) deformation derived by SBAS-InSAR are deduced, and a three-dimensional and full-parameter inversion model based on genetic algorithm with random error elimination (GAREE) is proposed. Based on this model, the subsidence parameters inside the study area are accurately retrieved with the deviation for each parameter less than 3% compared with the empirical parameters. Finally, by using the retrieved parameters, PIM is employed to predict the whole goad deformation with the predicted results highly consistent with the field leveling data. The root mean square errors (RMSE) on observation line A and line F are 0.083 m and 0.102 m, respectively, and the mean absolute errors (MAE) are 0.068 m and 0.089 m, respectively. Results show that the parameter inversion model proposed by this study can effectively obtain the subsidence information for the whole basin of a mining goaf in a low-cost way, providing scientific and significant importance for engineering application and potential disaster predictions in coal mining areas.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The surface deformation of Yangshuo County is extracted by small baselines subset interferometry synthetic aperture radar(SBAS-InSAR) and persistent scatterer interferometry synthetic aperture radar(PS-InSAR) based on 30 Sentinel-1A views covering Yangshuo County from January 2018 to December 2019. The results show that the deformation rate monitored by SBAS is from -20. 1 to 16. 7 mm / a, and that monitored by PS is from -24. 4 to 24. 6 mm / a. The results obtained by the two methods are consistent. Landslide hazards are mainly distributed in highway side slope and rural mountains which are exploited by human beings. The subside is mostly affected by human activities such as production and construction. Because of the geological characteristics of complex structure in Guangxi, the influence of continuous rainfall may accelerate the subsidence. This case can provide effective data and technical support for disaster prevention and reduction in Yangshuo, and provide ideas and reference for landslide monitoring and analysis in hills and mountainous areas. It also proves that interferometric synthetic aperture radar, as a new radar measurement means, can accurately and efficiently identify landslide hazards in the area. [ABSTRACT FROM AUTHOR]

The 25 scenes of Sentinel-1A data taken in 2020 were processed by SBAS-InSAR, and the land subsidence information in Jiangzhou District of Chongzuo is obtained. The causes of deformation are compared and analyzed by high-resolution remote sensing images from the same period of time. The results show that in terms of deformation rate, the maximum deformation rate in a small area of Jiangzhou District is -40. 96 mm / a, the maximum deformation rate in central area is -30. 00 mm / a and presents a large-scale and continuous deformation, and the maximum deformation rate in northern part of the city is 10. 00 mm / a and has a rising trend in some areas. Compared with measurements of CORS, the maximum difference between inversion results of SBASInSAR and settlement amount of CORS station is 2 mm, and the minimum difference is -3 mm. The results obtained by the two methods are basically consistent, which verifies the feasibility of SBAS-InSAR for ground settlement in karst areas and can effectively monitor ground settlement. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The upper Jinsha River, located in a high-mountain gorge with complex geological features, is highly prone to large-scale landslides, which could result in the formation of dammed lakes. Analyzing the movement characteristics of the typical Xiaomojiu landslide in this area contributes to a better understanding of the dynamics of landslides in the region, which is of great significance for landslide risk prediction and analysis. True displacement data on the surface of landslides are crucial for understanding the morphological changes in landslides, providing fundamental parameters for dynamic analysis and risk assessment. This study proposes a method for calculating the actual deformation of landslide bodies based on multi-track Interferometric Synthetic Aperture Radar (InSAR) deformation data. It iteratively solves for the optimal true deformation vector of the landslide on a per-pixel basis under a least-squares constraint based on the assumption of consistent displacement direction among adjacent points on the landslide surface. Using multi-track Sentinel data from 2017 to 2023, the line of sight (LOS) accumulative de-formation of the Xiaomojiu landslide was obtained, with a maximum LOS deformation of −126 mm/year. The true surface displacement of the Xiaomojiu landslide after activation was calculated using LOS deformation. The development of two rotational sub-slipping zones on the landslide body is inferred based on the distribution of actual displacements along the central profile line. Analysis of temporal changes in water body area data revealed that the Xiaomojiu landslide was activated after a barrier lake event and continuously moved due to the influence of higher water levels' in the river channel. In conclusion, the proposed method can be applied to calculate the true surface displacement of landslides with complex mechanisms for analyzing the movement status of landslide bodies. Furthermore, the spatiotemporal analysis of the Xiaomojiu landslide characteristics can support analyzing the mechanisms of similar landslides in the Jinsha River Basin. [ABSTRACT FROM AUTHOR]

In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction. [ABSTRACT FROM AUTHOR]

The upstream Jinsha River, located in the eastern Tibetan Plateau, has been experiencing intense geological hazards characterized by a high density of ancient landslides, significant deformation and reactivation challenges. In this study, remote sensing interpretation, field investigations, and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technologies have been employed. Along a 17 km stretch of the Jinsha River, specifically in the Xiongba-Sela segment, 16 large-scale ancient landslides were identified, 9 of which are currently undergoing creeping deformation. Notably, the Sela and Xiongba ancient landslides exhibit significant deformation, with a maximum deformation rate of −192 mm/yr, indicating a high level of sliding activity. The volume of the Sela ancient landslide is estimated to be 1.8 × 108 to 4.5 × 108 m3, and characterized by extensive fissures and long-term creeping deformation. The SBAS-InSAR results revealed significant spatial variations in the deformation of the Sela ancient landslide, generally displaying two secondary zones of intense deformation, and landslide deformation exhibits nonlinear behavior with time. Between January 2016 and February 2022, Zone III1 on the southwest side of the Sela ancient landslide, experienced a maximum cumulative deformation of −857 mm, with a maximum deformation rate of −108 mm/yr. Zone III2, on the northeast side of the Sela ancient landslide, the maximum cumulative deformation was −456 mm, with a maximum deformation rate of −74 mm/yr; among these, the H2 and H4 secondary bodies on the south side of III1 are in the accelerative deformation stage and at the Warn warning level. We propose that the large-scale flood and debris flow disasters triggered by the Baige landslide-dammed lake-dam broken disaster chain in Tibetan Plateau during October and November 2018 caused severe erosion at the foot of downstream slopes. This far-field triggering effect accelerated the creep of the downstream ancient landslides. Consequently, the deformation rate of Zone III2 of the Sela ancient landslide increased by 6 to 8 times, exhibiting traction-type style reactivation. This heightened activity raises concerns about the potential for large-scale or overall reactivation of the landslide, posing a risk of damming the Jinsha River and initiating a dam-break disaster chain. Our research on the reactivation characteristics and mechanisms of large ancient landslides in high deep-cut valleys provides valuable guidance for geological hazard investigation and risk prevention. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)