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Bridges play a crucial role in the development of the national economy and transportation industry, and their deformation monitoring is vital for ensuring their health. Therefore, it is necessary to conduct long-term monitoring of bridges’ deformation. This study monitored the deformation of the Wuhan Yangtze River Bridge using the SBAS-InSAR technology and Sentinel-1A data. The deformation results were analyzed in combination with bridge structure, human activity, temperature and stratigraphy. The results were as follows: (1) The vertical deformation rate of the bridge was between −15.6 and 10.7 mm/year, and part of the deformation belonged to rebound deformation; (2) The middle span deformation is the largest and the uplift and lowering alternate; (3) The reduction in human activity is the reason for the lower deformation amplitude from January to October 2020 compared to after October 2020; (4) A positive correlation between deformation and temperature was observed only along a portion of the bridge; (5) There is no direct correlation between observed lowering and stratigraphy under the bridge piers, as the sinking is presumably absorbed by the bridge structure.

In order to alleviate the conflict between populations and land resource, Tianjin adopted multi-phase reclamation projects to the formed large-scale artificial reclamation land. The reclamation areas, however, are prone to subsidence, which poses a significant threat to infrastructure as well as the safety and assets of the residents. The SBAS-InSAR was used to acquire surface deformation of Tianjin Binhai New Area from January 2017 to December 2022, analyze in depth the response relationship between land subsidence, reclamation project time, and land-use type. There is a strong correlation between surface deformation and reclamation time. Severe land subsidence occurred over newly reclaimed areas. In the offshore direction, the deformation values of the Nangang Industrial Zone, the Lingang Industrial Zone, and Hangu Harbor were −98 mm to −890 mm, 45 mm to −580 mm, and −140 mm to −290 mm, respectively. Significant differences in deformation were detected among different land-use types where reclamation projects were completed in the same time. Subsidence was positively correlated with surface load; in areas with higher surface loads, the surface settlement was also more severe. The average surface settlement for the heavy shipyard, with 67 grain storage tanks and 27 grain storage tanks, road, and bare land were −201 mm, −166 mm, −107 mm, −64 mm, and −43 mm, respectively. This study reveals significant differences of surface deformation in the reclamation completed at different times, and determines that the load is the main driving factor of settlement difference in the reclamation land completed at the same time. This has important guiding significance for preventing and controlling geological disasters in the reclamation area and later development planning.

Ground deformation poses an imminent threat to urban development. This study uses the multiscale geographically weighted regression (MGWR) model to investigate the spatial heterogeneity in factors influencing ground deformation, thereby elucidating the drivers behind regional variations in ground deformation patterns. To gain insights into the characteristics of ground deformation in Zhuhai, China, and its spatial relationship with natural and anthropogenic features, we initially utilized the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method to collect data on ground deformation and its distribution across the entire area. Concurrently, remote sensing imagery was used to identify the various mechanisms affecting ground deformation during the same period, including geotectonic conditions, geographic environment, and human activities. Subsequently, we used the MGWR model to quantitatively estimate the effects of these driving force factors on ground deformation in Zhuhai. Our findings reveal significant ground deformation in specific areas, including Baijiao Town (Doumen District), Hongqi Town (Jinwan District), the Gaolan Port Economic Zone, and the northern part of Hengqin Town, with peak deformation rates reaching 117 mm/y. Key drivers of ground deformation in Zhuhai include NDVI, groundwater extraction intensity, and soft soil thickness. The application of the MGWR model, with an R-sq value of 0.910, outperformed both the global regression model ordinary least squares (OLS), with an R-sq value of 0.722, and the local regression model geographically weighted regression (GWR), with an R-sq value of 0.770, in identifying driving forces. This study can provide valuable insights for government policies aimed at mitigating the disaster risks associated with urban ground deformation.

The study of the classification and failure modes of Yellow River terrace landslides under the influence of irrigation projects is of key importance to alleviate the paradox between the rapid evolution of terrace landscapes caused by landslides and the survival of local residents. However, such studies remain controversial, despite it being widely recognized that a rise in groundwater level caused by irrigation is a key factor associated with landslide failure modes. In this paper, we take the Heitai terrace as a case study. Using aerial images and field investigations, we classify landslides in the Heitai loess layer into type A landslides (not related to groundwater) and type B1 and B2 landslides (related to groundwater). We analyze the failure modes and disaster-causing characteristics of each type of landslide, and our results indicate that the attenuation in soil strength is a key factor common to both type A and type B landslides, based on which type A landslides with small volume and short sliding distance are able to block the previous spring discharge, causing a rise in localized groundwater, which further contributes to type B landslides; the location of previous type B1 landslides with a large volume and long sliding distance and type A landslides may be more susceptible to type B2 landslides with a small volume and short sliding distance, where there are low confining pressures during the lower soil shear process. Therefore, we believe that the inevitable interaction effects between the failure modes of landslides during landslide evolution, which govern the geomorphological evolution of the Heitai terrace, are unavoidable. Combining these data with numerical analyses, we further demonstrate that a rise in groundwater level and discontinuous attenuation of soil strength caused by changes in soil properties during irrigation together control terrace landslides and their failure modes. From the results of interferometric synthetic aperture radar time-series monitoring of Yellow River terrace activity with and without irrigation projects, and electrical resistivity tomography groundwater detection, we conclude that in the future, Heitai terrace will continue to experience a high intensity of landslide activity, and conditions for the most catastrophic type of landslide (type B1) will remain, including the high localized groundwater caused by previous landslides, and the discontinuous attenuation of soil strength caused by the deterioration in soil properties. In this context, we believe that slope-cutting engineering will be one of the most economical means to achieve future landslide-type transformation on the Heitai terrace; this will mitigate the process of geomorphological evolution and improve the human living environment.

The instability of saline soil foundation affects the safety of artificial construction, and may cause ground collapse, building destruction and road damage. It is fundamental to reduce the potential engineering geological disasters by exploring the dynamic evolution of saline soil. Most of the previous researches of saline soil were conducted by simulated experiments and traditional geodetic surveying methods. Restricted to the limited sampling points, the spatiotemporal evolution characteristics of saline soil were difficult to find out in a large scale. This paper exploited SBAS-InSAR method to extract the deformation of the Qarhan Salt Lake (in Qinghai, China) section along Qinghai-Tibet railway, based on 119 image data acquired by Sentinel-1A from 2015 to 2020. The results showed that the deformation trends varied frequently in this saline mud flat. Between the adjacent areas with different deformation trends along Qinghai-Tibet railway, discontinuities caused by uplift and subsidence were very obvious. Besides, among the areas with brine exploitation or artificial construction, the maximum subsidence rate reached50 mm/yr, and the maximum cumulative subsidence exceeded 320 mm in the latest 5 years. In the saline mud flat closed to the rivers and lakes, the deformation trend was continuous uplift. Nevertheless, there were obvious seasonal deformation characteristics in those areas far away from the water body. Further analysis found out a sharply subsidence caused by the salt collapsibility appeared in rainy seasons. While an uplift trend induced by the salt swelling or frost heave was remarkable in dry seasons. Subsequently, the relationships were analyzed between the time series deformation and external environmental factors. Furthermore, the deformation mechanism of saline soilin the Qarhan Salt Lake region was interpreted then. In general, this study provides complete spatiotemporal evolution information of saline soil, and demonstrates the deformation characteristics of saline soil in the Qarhan Salt Lake region successfully. Related results would contribute to the safety monitoring for large-scale infrastructure construction in the saline soil areas.

The construction of large reservoirs can address the problem of uneven distribution of rivers in time and space, thereby meeting the needs of human production and living. However, the huge elevation of the water level in some areas may modify the distribution of the groundwater level, causing geological disasters, such as surface deformation and landslides. The Yalong reservoir supplies water to the downstream area of Shannan, Tibet; however, since the reservoir started storing water in 2017, the government has discovered two ancient landslides. In this study, to monitor the deformation of the Yalong reservoir since its construction in 2014, we first used synthetic aperture radar (SAR) data and the multidimensional small baseline subset (MSBAS) method to obtain the deformation in the east–west and vertical directions. The result indicated the presence of three large, slow-moving landslides: Landslides I and II, located on the right bank of the Yalong reservoir, which are consistent with the results obtained by the actual survey, and a new discovery, Landslide III, located on the left side of the reservoir. Meanwhile, the experimental results indicated that the dam had undergone obvious deformation after impoundment, which should not be ignored. The global positioning system and interferometric SAR (InSAR) time-series deformation residual data were used to verify the accuracy of the InSAR method. The results also showed that the deformation caused by the three landslides had tended to accelerate after the reservoir's impoundment, and that the failure mode was retrogressive landslide. We found that InSAR plays a vital role in landslide detection and failure mode research around reservoirs, and assists in providing early warning for subsequent landslide disasters.

Tianjin is one of the inland cities with the most severe cases of subsidence hazard in China. The majority of the existing studies have mainly focused on Beijing-Tianjin-Hebei, and little attention has been given to Tianjin. In addition, these existing studies are short-term investigations, lacking long-term monitoring of surface subsidence. In the present study, we use the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to monitor the subsidence process in Tianjin between 2015 and 2020 and reveal its spatial and temporal variation. We divided the 44-view Sentinel-1A image data into three periods to avoid the effect of temporal and spatial decoherence by extracting the surface deformation field in Tianjin. We finally verified the accuracy and reliability of the inversion results using second-order leveling data. Results showed that the correlation coefficient r between the two reached 0.89, and the root mean square error was 4.84mm/y. Obvious subsidence funnels exist in Tianjin, mainly in the towns of Wangqingtuo and Shengfang. These subsidence funnels have a subsidence deformation rate of -136.2mm/y and a maximum cumulative settlement of -346.3mm within the study period. The subsidence area tends to extend to the southwest. The analysis of annual rainfall, groundwater resource extraction, spatial location distribution of industrial areas combined with SBAS-InSAR inversion results indicates that overextraction of groundwater resources is the main cause of land subsidence in the area. Therefore, strict control of groundwater extraction is the main approach to mitigate land subsidence effectively.

On April 6, 2021, the Baihetan dam launched impoundment, and the reservoir water surface elevation dramatically increased from 660 m to 812 m until October 2021, which may induce large-scale landslides in reservoir bank. Accurate landslides susceptibility evaluation during impoundment is crucial for controlling the possible disasters and taking early evacuation or disaster prevention measures. Although many traditional geological models can accurately evaluate the landslides susceptibility of the reservoir area, they are inadequate to make the prompt response to the quick condition changes of reservoir bank induced by impoundment, while InSAR technology can provide a dynamic observation to monitor the small displacement occurring in the reservoir bank. Thus, this study proposes a new approach to dynamically evaluate the landslides susceptibility on reservoir banks during impoundment integrating geological evaluation model and InSAR observation. The approach combined stability coefficient of the reservoir area calculated from Scoops 3D model and geotechnical parameters, with slope displacement from InSAR technology to evaluate the susceptibility of landslides. The comparison between before and during impoundment shows that when the reservoir water elevation increased by 150 m, the high risk area of geohazards increases by 14.26 km2. The field validation confirms that the approach provides an effective and accurate dynamic landslide susceptibility evaluation, which forms a timely response to the geoenvironment changes in reservoir bank caused by the 150-m water level increment during impoundment.

Ancient landslide has strong concealment and disturbance sensitivity due to its special geotechnical mechanical characteristics, and it is the potential hazard that cannot be ignored in human activities and major engineering planning. The quantitative assessment of ancient landslide reactivation risk has become more necessary for pre-disaster scientific warning. However, because the mechanisms of deformation and damage during the evolution of ancient landslides are quite complex, traditional landslide risk assessment methods only select the single-time scale and relatively stable environmental factors for analysis, lacking consideration of dynamic triggering factors such as rainfall. Focusing on the complexity, a quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism is proposed. First, on the basis of systematic analysis of the implicit genesis mechanism and explicit characterization, an evaluation system of the cross-time scale joint characteristics of ancient landslide reactivation is constructed. Then, XGBoost algorithm and SBAS-InSAR are used to establish the long-time scale developmental evolution mechanism model and the short-time scale dynamical trigger model, respectively. Subsequently, we propose a cross-time scale joint response mechanism. The information entropy weight method is applied to calculate the contribution degree of long-short time scale assessment models for ancient landslide reactivation based on the constraints of quantitative interval thresholds, and the assessment processes of different time scales are dynamically and quantitatively correlated. Finally, the updated optimization of the assessment of ancient landslide reactivation risk is achieved. In this research, experimental analysis was carried out for ancient landslide groups in a geological hazard-prone area in Fengjie County, Chongqing, a typical mountainous region of China. The results of the comparative analysis validate the superiority of the method in this paper. It helps to accurately assess the ancient landslide potential hazard in advance, providing scientific basis and technical support for the risk assessment of mountainous watershed geological hazards and major engineering projects.

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.)

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.)

Subsidence information is of paramount significance for the restoration and treatment of geological destruction in mining areas. However, the traditional interferometric synthetic aperture radar (InSAR) technique is incapable of detecting mining subsidence with large gradient deformation. To overcome this limitation, this study proposes a fine subsidence information extraction model to improve the subsidence detection ability. The model consists of the data integration, multi-source parameter inversion, and subsidence extraction method. The integration of small baseline subset InSAR (SBAS-InSAR) and leveling data allows the parameters to be accurately inversed and predict the subsidence funnel based on the probability integration method (PIM). By fusing the subsidence funnel and SBAS-InSAR data based on geographically weighted regression, the fine subsidence information is extracted. Model validation with a certain working face of coal mine in China shows that the proposed model is significantly efficient in detecting large deformation in the goaf center, as well as acquiring more detailed information for goaf boundary areas, indicating its superiority compared with SBAS-InSAR and PIM. The proposed model is not only a low-cost and practical way of parameter acquisition, but also a promising method for obtaining more accurate and reliable subsidence information, and is expected to provide decision support for geological disasters in mining areas. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) has become the primary remote sensing detection method for monitoring significant surface subsidence and deformation caused by underground coal mining. Time Series InSAR has been intensively researched in mining-caused deformation monitoring for its ability to provide surface deformation time series, with Small Baseline Subset InSAR (SBAS-InSAR) and Permanent Scatter InSAR (PS-InSAR) being two classical methods. To obtain the incline and curvature in mining areas and assess the building damage, this paper exploits the SBAS-InSAR and PS-InSAR techniques to estimate surface incline and curvature, following the principle of directional derivatives. The surface beneath the villages and the industrial square near the 7221 Grout-filled working face in the Huaibei mining area of Anhui Province, China, was analyzed. The estimated surface incline and curvature with SBAS-InSAR were within the threshold values specified for Class I damage, while PS-InSAR showed <1% of measurements that exceeded this threshold. This paper provides a method for monitoring the surface deformation beneath the buildings in the mine area. Meanwhile, the dynamic analysis of the damaged buildings provides a basis for determining the influence range with grout-filled working faces. [ABSTRACT FROM AUTHOR]

Landslides are one of the most dangerous natural disasters, which have affected national economic development and social stability. This paper proposes a method to indirectly monitor the deformation characteristics of landslides by extracting the abnormal vegetation information, especially for the inaccessible high-mountain landslides in southwestern China. This paper extracts the vegetation anomaly information in the Jizong Shed-Tunnel landslide which is located on the main traffic road to Tibet by the optical remote sensing Gaofen-1 (GF-1) data, and analyzes the temporal and spatial characteristics of the vegetation anomaly information through a time series. Then, we use the small baseline subsets interferometry synthetic aperture radar (SBAS-InSAR) technology to process Sentinel-1 data to obtain the time-series surface deformation information. Finally, we analyze and verify the results of the two methods. The results show that there is obvious vegetation coverage (VC) decline, with a maximum increasing percentage of 8.77% for the low and medium VC, and obvious surface deformation around the landslide, with the highest settlement rate of between 0 mm/year and 30 mm/year. Through the time-series analysis, we find that the change trends of the two methods are basically the same. This paper shows that the method of using abnormal vegetation information to monitor the Jizong Shed-Tunnel landslide has a certain degree of reliability and practicability. It can provide a new idea and effective supplement for landslide monitoring. [ABSTRACT FROM AUTHOR]

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.)

In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments. [ABSTRACT FROM AUTHOR]

Despite the profound roles of surface deformation monitoring techniques in observing permafrost surface stability, predetermining the approximate location and time of possibly occurring severe permafrost degradation before applying these techniques is extremely necessary, but has received little attention. Taking the oil tank collapse accident in the Norilsk region as a case, we explored this concern by analyzing the permafrost deformation mechanisms and determining early surface deformation signals. Regarding this case, we firstly applied the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to obtain its permafrost surface deformation rate, then utilized a sine model to decompose its interannual deformation and seasonal deformation, and finally compared the relationship between the topographic slope and deformation rate. Based on experimental results, we reveal that when the annual average temperature continuously increases at a rate of 2 ° C/year for 2∼3 consecutive years, permafrost areas with relatively large topographic slopes (>15 ° ) are more prone to severe surface deformation during the summer thaw period. Therefore, this paper suggests that permafrost areas with large topographic slopes (>15 ° ) should be taken as the key surveillance areas, and that the appropriate monitoring time for employing surface deformation monitoring techniques should be the summer thawing period after a continuous increase in annual average temperature at a rate of 2 ° C/year for 2∼3 years. [ABSTRACT FROM AUTHOR]

The extremely difficult area has the characteristics of extremely rugged terrain, complex geographical environment and sparse permanent scatterers. Therefore, an improved SBAS-InSAR technology is designed to monitor the surface deformation. In this paper, the candidate permanent scatterers are obtained from the coherence, amplitude dispersion index and deformation rate, and then the final permanent scatterers are selected by optical images, which are introduced into the SBAS-InSAR calculation process as orbit refining control points, and finally the surface deformation monitoring in the study area is completed. By comparing and analyzing the conventional PS-InSAR technology and SBAS-InSAR technology, the technology has good application value in extremely difficult areas.

Peatlands in Southeast Asia have been undergoing extensive and rapid degradation in recent years. Interferometric Synthetic Aperture Radar (InSAR) technology has shown excellent performance in monitoring surface deformation. However, due to the characteristics of high vegetation cover and large dynamic changes in peatlands, it is difficult for classical InSAR technology to achieve satisfactory results. Therefore, an adaptive high coherence temporal subsets (HCTSs) small baseline subset (SBAS)-InSAR method is proposed in this paper, which captures the high coherence time range of pixels to establish adaptive temporal subsets and calculates the deformation results in corresponding time intervals, combining with the time-weighted strategy. Ninety Sentinel-1 SAR images (2019–2022) in South Sumatra province were processed based on the proposed method. The results showed that the average deformation rate of peatlands ranged from approximately −567 to 347 mm/year and was affected by fires and the changes in land cover. Besides, the dynamic changes of peatlands’ deformation rate a long time after fires were revealed, and the causes of changes were analyzed. Furthermore, the deformation results of the proposed method observed 2 to 127 times as many measurement points as the SBAS-InSAR method. Pearson’s r (ranged from 0.44 to 0.75) and Root Mean Square Error (ranged from 50 to 75 mm/year) were calculated to verify the reliability of the proposed method. Adaptive HCTSs SBAS-InSAR can be considered an efficient method for peatland degradation monitoring, which provides the foundation for investigating the mechanisms of peatland degradation and monitoring it in broader regions.

Strong earthquakes induce a large number of secondary disasters, such as landslides, which bring serious challenges to post-disaster emergency rescue, and the rapid and accurate assessment of earthquake-induced landslide disasters is crucial for post-earthquake emergency rescue. This research aims to propose an emergency assessment model that is suitable for post-earthquake landslides, specifically targeting the first 72 h after an earthquake for emergency rescue guidance. The model combines remote sensing technology and the Newmark physical mechanics assessment model to form the InSAR Data–Newmark Physical Fusion Driver Model (IDNPM), which comprehensively considers the dynamic deformation of the ground surface and geological features. To validate the predictive performance of the IDNPM, the model is applied to the 5 September 2022 Luding earthquake event and the 8 August 2017 Jiuzhaigou earthquake event. The landslide qualitative evaluation, confusion matrix and Receiver Operating Characteristic (ROC) curve are utilized for quantitative assessment. The results show that the IDNPM can effectively reduce the false negative and false positive errors in landslide prediction by utilizing the SAR deformation information, and to a certain extent, it accounts for the dependence of the Newmark model on the accuracy of empirical formulas and geotechnical parameters. For the Luding earthquake event, the IDNPM shows an accuracy improvement of 10.296% compared to the traditional Newmark model. For the Jiuzhaigou earthquake event, there is also an improvement of 3.152%, with a promising generalization performance. The simplicity and ease of operation in constructing the model are accompanied by high reliability and accuracy. The research findings provide essential references for the development of post-earthquake landslide emergency prediction models and offer robust data support for emergency rescue and recovery efforts in earthquake-stricken areas in the future.

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 Chenab basin is located in northwest India and eastern Pakistan. Glaciers in Chenab basin, as local freshwater resources, are very important to the regional ecological environment and development. At the same time, SBAS InSAR can monitor the deformation of the ground for a long time, the monitoring accuracy can reach mm level, and can obtain the time series change of deformation, whcih provides a new idea and method for glacier detection. In this study, the deformation from SBAS InSAR was combined with glacial area data to study the glacial changes in Chenab Basin. Sentinel-1 and Landsat series images were used to obtain the deformation and change in the area of glaciers by SBAS InSAR and the semi-automated method. The results showed that glaciers in the Chenab basin retreated rapidly, especially in the past ten years. The glacier area decreased by 88.05 km2 in 1990∼2000 and 118.86 km2 in 2000∼2010, and the glacier area decreased by 236.01 km2 in 2010∼2020, which was the largest rate of change of 9.49%. Moreover, glacial deformation decreased in 2020, and the deformation rate ranged from -146.3 mm/a to 119.52 mm/a. Most glaciers had deformation rates between -50 mm/a and 50 mm/a. Influenced by precipitation and temperature, glacial deformation in the Chenab basin started to change in August, the maximum accumulation was 6,828.43 mm, and the minimum ablation was -20,656.41 mm. [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.)

Landslide susceptibility maps (LSM) are often used by government departments to carry out land use management and planning, which supports decision makers in urban and infrastructure planning. The accuracy of conventional landslide susceptibility maps is often affected by classification errors. Consequently, they become less reliable, which makes it difficult to meet the needs of decision-makers. Therefore, it is proposed in this paper to reduce classification errors and improve LSM reliability by integrating the Small Baseline Subsets-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique and LSM. By using the logistic regression model (LR) and the support vector machine model (SVM), experiments were conducted to generate LSM in the Dongchuan district. It was classified into five classes: very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. Then, the surface deformation rate of the Dongchuan area was obtained through the ascending and descending orbit sentinel-1A data from January 2018 to January 2021. To correct the classification errors, the SBAS-InSAR technique was integrated into LSM under the optimal model by constructing the contingency matrix. Finally, the LSMs obtained before and after correction were compared. Moreover, the correction results were validated and analyzed by combining remote sensing images, InSAR deformation results, and field surveys. According to the research results, the susceptibility class of 66,094 classification error cells (59.48 km2) was significantly improved in the LSM after the integration of the SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images are highly consistent with the trends of InSAR cumulative deformation and the results of field investigation. It is suggested that integrating SBAS-InSAR and LSM is effective in correcting classification errors and further improving the reliability of LSM for landslide prediction. The LSM obtained by using this method plays an important role in guiding local government departments on disaster prevention and mitigation, which is conducive to eliminating the risk of landslides. [ABSTRACT FROM AUTHOR]

In order to solve the problem that SBAS-InSAR technology cannot accurately estimate the deformation value of coal mine settlement center, differential InSAR was integrated with synthetic aperture radar. D-Insar and SBAS-InSAR techniques were used to study the monitoring method of surface deformation in mining area. Based on 25-scene Sentinel-1 data, the D-InSAR method based on coherence coefficient and the PS-InSAR were used. The SBAS-InsAR method of PS-InSAR respectively generated deformation maps of the study area. With the help of Kriging interpolation method, the results of the former method were filled in the deformation results of the latter method to improve the vacancy value of deformation in the mining area, so as to estimate the surface deformation results of the mining area more accurately. In addition, the deformation results of typical mining areas were analyzed in detail along the transverse and longitudinal sections of the mining face, and the surface deformation profile curve was drawn to analyze the evolution process of the subsidence funnel in the mining area. Finally, in order to quantitatively illustrate the reliability of this method, the errors of deformation results in mining area and leveling data were compared, and the standard deviation and root mean square difference were taken as the evaluation indexes of deformation accuracy in mining area. The results show that the overall quality of the coherence coefficient diagram is good, the maximum cumulative settlement can reach 176.39 mm in the study area, and the maximum annual average settlement rate can reach-103.82 mm/a. The deformation results are basically consistent with the leveling data, and the errors are all less than 13 cm. The monitoring accuracy meets the technical requirements of coal mining related measurement. [ABSTRACT FROM AUTHOR]

Cities in the northern Gulf of Mexico, such as Houston, have experienced one of the fastest rates of subsidence, with groundwater/hydrocarbon withdrawal being considered the primary cause. This work reports substantial ground subsidence in a few parts of Greater Houston and adjoining areas not reported before. Observation of surface deformation using interferometric synthetic aperture radar (InSAR) data obtained from Sentinel-1A shows total subsidence of up to 9 cm in some areas from 2016 to 2020. Most of the area within the Houston city limits shows no substantial subsidence, but growing suburbs around the city, such as Katy in the west, Spring and The Woodlands in the north and northwest, and Fresno in the south, show subsidence. In this study, we performed emerging hot spot analysis on InSAR displacement products to identify areas undergoing significant subsidence. To investigate the contributions of groundwater to subsidence, we apply optimized hot spot analysis to groundwater level data collected over the past 31 years from over 71,000 water wells and look at the correlation with fault surface deformation patterns. To evaluate the contribution of oil/gas pumping, we applied optimized hot spot analysis to known locations of oil and gas wells. The high rate of water pumping in the suburbs is the main driver of subsidence, but oil/gas withdrawal plays an important role in areas such as Mont Belvieu. Displacement time series shows that the Clodine, Hockley, and Woodgate faults are active, whereas the Long Point Fault shows no motion, although it was once very active. [ABSTRACT FROM AUTHOR]

Intensive and large-scale underground coal mining has caused geological disasters such as local ground subsidence, cracks and collapse in the Datong coalfield, China, inducing serious threats to local residents. Interferometric synthetic aperture radar (InSAR) has the capability of surface deformation detection with high precision in vast mountainous areas. DInSAR, stacking-InSAR and SBAS-InSAR are commonly used InSAR-related deformation analysis methods. They can provide effective support for mine ecological security monitoring and prevent disasters. We use the three methods to conduct the deformation observation experiments in the Datong coalfield. Sentinel-1A data from November 2020 to October 2021 are used. As a result, a total of 256 deformations in the Datong coalfield were successfully detected by the three methods, of which 218 are mining deformations, accounting for 85% of the total deformations. By comparing the results of the three methods, we found that DInSAR, stacking-InSAR, and SBAS-InSAR detected 130, 256, and 226 deformations in the Datong coalfield, respectively, while the deformations caused by coal mining were 128, 218, and 190. DInSAR results with long spatiotemporal baselines are seriously incoherent. SBAS-InSAR results of displacement rate are more precise than stacking-InSAR, and the mean standard deviation is 1.0 mm/a. However, for areas with lush vegetation or low coherence, SBAS-InSAR has poor performance. The detection deformation area results of DInSAR and SBAS-InSAR are subsets of stacking-InSAR. The displacement rates obtained by stacking-InSAR and SBAS-InSAR are consistent; the mean difference in the displacement rate between the two methods is 2.7 mm/a, and the standard deviation is 5.1 mm/a. The mining deformation locations and their shapes in the study area can be identified with high efficiency and power by stacking-InSAR. Therefore, with a comprehensive understanding of the advantages and limitations of the three methods, stacking-InSAR can be an effective and fast method to identify the level, location and range of mining deformation in lush mountainous areas. [ABSTRACT FROM AUTHOR]

In the InSAR solution, the uneven distribution of permanent scatterer candidates (PSCs) or slowly decoherent filtering phase (SDFP) pixel density in a region of variable radar reflection feature can cause local low accuracy in single interferometry. PSCs with higher-order coherence in Permanent Scatter InSAR (PS-InSAR) are generally distributed in those point targets of urban built-up areas, and SDFP pixels in Small Baseline Subset InSAR (SBAS-InSAR) are generally distributed in those distributed targets of countryside vegetation areas. According to the respective reliability of PS-InSAR and SBAS-InSAR for different radar reflection features, a new land subsidence monitoring method is proposed, which combines PS-SBAS InSAR by data fusion of different interferometry in different radar reflection regions. Density-based spatial clustering of applications with noise (DBSCAN) clustering analysis is carried out on the density of PSCs with higher-order coherence in PS-InSAR processing to zone the region of variable radar reflection features for acquiring the boundary of data fusion. The vector monitoring data of PS-InSAR is retained in the dense region of PSCs with higher-order coherence, and the vector monitoring data of SBAS-InSAR is used in the sparse region of PSCs with higher-order coherence. The vertical displacements from PS-InSAR and SBAS-InSAR are integrated to obtain the optimal land subsidence. The verification case of 38 SAR images acquired by the Sentinel-1A in Suzhou city indicates that the proposed method can automatically choose a matched interferometry technique according to the variability of radar reflection features in the region and improve the accuracy of using a single interferometry method. The integrated method of the combined field is more representative of overall subsidence characteristics than the PS-InSAR-only or SBAS-InSAR-only results, and it is better suited for the assessment of the impact of land subsidence over the study area. The research results of this paper can provide a useful comprehensive reference for city planning and help decrease land subsidence in Suzhou. [ABSTRACT FROM AUTHOR]

The freeze–thaw (F-T) cycle of the active layer (AL) causes the "frost heave and thaw settlement" deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology. [ABSTRACT FROM AUTHOR]

Land subsidence is a common geological hazard. Rapid urban expansion has led to different degrees of ground subsidence within Wuhan in the past few years. The novel coronavirus outbreak in 2020 has seriously impacted urban construction and people's lives in Wuhan. Land subsidence in Wuhan has changed greatly with the resumption of work and production. We used 80 Sentinel-1A Synthetic Aperture Radar (SAR) images covering Wuhan to obtain the land subsidence change information of Wuhan from July 2017 to September 2021 by using the small baseline subset interferometric SAR technique. Results show that the subsidence in Wuhan is uneven and concentrated in a few areas, and the maximum subsidence rate reached 57 mm/yr during the study period. Compared with land deformation before 2017, the land subsidence in Wuhan is more obvious after 2020. The most severe area of subsidence is located near Qingling in Hongshan District, with a maximum accumulated subsidence of 90 mm, and obvious subsidence funnels are observed in Qiaokou, Jiangan, Wuchang and Qingshan Districts. The location of subsidence centers in Wuhan is associated with building intensity, and most of the subsidence funnels are formed in connection with urban subway construction and building construction. Carbonate belt and soft ground cover areas are more likely to lead to karst collapse and land subsidence phenomena. Seasonal changes are observed in the land subsidence in Wuhan. A large amount of rainfall can replenish groundwater resources and reduce the rate of land subsidence. The change in water level in the Yangtze River has a certain impact on the land subsidence along the rivers in Wuhan, but the overall impact is small. An obvious uplift is observed in Caidian District in the south of Wuhan, and the reason may be related to the physical and chemical expansion effects of the expansive clay. [ABSTRACT FROM AUTHOR]

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Dongchuan District is one of the most densely distributed regions of geological disasters in the world. Geological disasters such as debris flows and landslides are serious threats to the lives and property safety of local people. Slope deformation monitoring and early identification of geological disasters in debris flow source areas are of great significance for disaster prevention and reduction. In this study, the time-series Synthetic Aperture Radar Interferometry (InSAR) technique was used to detect the deformation in the source area of the Baini river debris flow. The deformation rate in the middle part of the three-sided mountain is the largest, the average deformation rate is about 30 mm/year. The deformation rate of the lower and upper part of the mountain is slightly lower, but the deformation is creeping constantly, and obvious tensile cracks faced the river valley in the upper. The production and living areas of residents in 8 villages around the debris flow source area are constantly deformed, and there are different degrees of landslide risk. This study provides a scientific basis and reference for early identification, deformation analysis, disaster prevention and mitigation of geological disasters in the Xiaojiang Basin.

Landslides pose huge challenges to the economic activities in mountainous areas at present, while large numbers of landslide disasters have developed in the Hengduan Mountains area in the eastern part of the Tibetan Plateau. Accurate landslide susceptibility mapping (LSM) serves as a critical measure to predict the serious risks that may be encountered in engineering activities. However, previous landslide susceptibility assessment can only play a limited role in the real-time analysis of current activities of slopes. In this study, the deformation rates of the slopes along the Lancang River were determined using the SBAS-InSAR technique. Meanwhile, the landslide susceptibility along the north Lancang River was assessed using the frequency ratio (FR), random forest and FR-RF models, and the precision of the assessment results was verified according to receiver operating characteristic curves (ROCs). Finally, a refined landslide susceptibility map was developed by integrating the deformation rates and landslide susceptibility indexes (LSIs) using a contingency matrix. As indicated by the deformation rates calculated using the SBAS-InSAR technique according to ascending and descending data show that the RADARSAT-2 descending data yielded more precise deformation results. The area-under-the-curve (AUC) values of the three assessment models were 0.866, 0.897, and 0.916, respectively, indicating that the assessment results obtained with the FR-RF model are the most precise. In the upgraded landslide susceptibility map, the areas with high and very high landslide susceptibility increased by 2.97%. Meanwhile, a total of 563,430 grid cells showed an increase in landslide susceptibility, accounting for 11.15% of all the grid cells. Most especially, the Xueru and Ritong areas exhibited a significant increase in landslide susceptibility, and it has been verified by remote sensing images and field surveys that both areas are subject to landslide risks. Therefore, the upgraded landslide susceptibility map has a better prediction performance and can provide valuable support for the decision making in the construction of major engineering facilities and the prevention and remediation of landslides.

The high-precision digital elevation model (DEM) is of great significance for improving the accuracy of InSAR deformation monitoring. In today’s free opening of multi-source DEM, there is no consensus on how to select suitable DEMs to assist InSAR in deformation monitoring for different landforms. This article introduces five types of DEMs: ALOS12.5, SRTM-1, ASTER V3, AW3D30, and Copernicus 30, and uses SBAS-InSAR technology to analyze the applicability of deformation monitoring in the Qinghai Tibet Plateau and Central China Plain regions. The coverage, average value, standard deviation, and unwrapping efficiency of the phase unwrapping results, the temporal deformation rate curves of six random deformation points in the key deformation area, as well as the consistency with the second-level data and the comparative analysis of RMSE of all deformation points, show that in the Qinghai Tibet Plateau region, Copernicus 30 is the best, followed by ASTER V3, AW3D30, and SRTM-1 having low accuracy, and ALOS12.5 is the worst. In the Central China Plain region, AW3D30 is the best, followed by Copernicus 30, SRTM-1, and ASTER V3 having low accuracy, and ALOS12.5 is still the worst. Although ALOS12.5 has the highest resolution, it is not recommended for deformation monitoring based on its worst performance in plateau and plain areas. It is recommended to use Copernicus 30 in plateau areas and AW3D30 for deformation monitoring in plain areas.

It is difficult for single time-series Interferometric Synthetic Aperture Radar (InSAR) processing to guarantee the accuracy and efficiency of continuous track monitoring in regions of differential subsidence. This paper proposes a new method, integrating the Persistent Scatterer InSAR (PS-InSAR) with high precision and the Small Baseline Subset InSAR (SBAS-InSAR) with high efficiency for continuous track monitoring in regions of differential land subsidence rates. Based on PS-InSAR processing, the Iterative Self-Organizing Data Analysis Techniques (ISODATA) algorithm is adopted to search the boundary of differential subsidence between slow and fast subsidence rates. The SBAS-InSAR processing with high frequency is used to continuously track and monitor the regions with fast subsidence rates incorporating original data and newly added data into small data sets from time to time according to SAR data updating, the monitoring results of which are obtained from the weighted average of the added results of SBAS-InSAR processing and the original results of PS-InSAR processing. The impact of SAR data updating on the slow subsidence rate region is so weak that it is not necessary to simultaneously update the corresponding monitoring results to improve global efficiency. If the slow subsidence rates region must be remeasured in relation to its previous subsidence, or the proportion of new data capacity alters compared with the original data set, PS-InSAR processing is used to analyze the whole monitoring region again using the complete data set. A case study performed on the west region of the Qinhuai River in Nanjing, China, indicates that the density of monitoring points in the fast-subsidence region is greatly improved, increasing from 711 points/km2 to 2760 points/km2—an increase of 288.2%.

Most dams in China have been operating for a long time and are products of the economic and technical limitations at the time of construction. Due to decades of aging engineering and ancillary problems, these reservoirs pose great threats to the safety of local people and the development of the surrounding economy. In this study, the surface deformation information for the Banqiao Reservoir is monitored with the small baseline subset–synthetic aperture radar interferometry (SBAS-InSAR) method using 80 Sentinel-1A images acquired from 3 January 2020 to 20 August 2022. Additionally, ground measurements from the BeiDou ground-based deformation monitoring stations were collected to validate the InSAR results. Based on the InSAR results, the spatiotemporal deformation features of the dam were analyzed in detail. The results show that the deformation in most areas, including the dam in the study area, is relatively stable, and the regional deformation velocity of the Banqiao Reservoir dam and other hydraulic engineering facilities varies between −1 mm/y and −4 mm/y. The Ru River area has a relatively obvious subsidence trend, and the maximum subsidence velocity reaches 30 mm/y. The InSAR monitoring results are consistent with the change trend in the BeiDou ground-based deformation measurement results. The monitoring results for the reservoir dam area provide a reference for local sustainable development and geological disaster prevention.

The source region of the Yellow River (SRYR) is situated on the permafrost boundary in the northeast of the Qinghai-Tibet Plateau (QTP), which is an area highly sensitive to climate change. As a result of increasing global temperatures, the permafrost in this region has undergone significant degradation. In this study, we utilized Sentinel-1 to obtain ground surface deformation data in the SRYR from June 2017 to January 2022. We then analyzed the differences in terrain deformation under various environmental conditions. Our findings indicated an overall subsidence trend in the SRYR, with a long-term deformation velocity of −4.2 mm/a and seasonal deformation of 8.85 mm. Furthermore, the results showed that terrain deformation varied considerably from region to region, and that the Huanghe’ yan sub-basin with the highest permafrost coverage among all sub-basins significantly higher subsidence rates than other regions. Topography strongly influenced ground surface deformation, with flat slopes exhibiting much higher subsidence rates and seasonal deformation. Moreover, the ground temperature and ground ice richness played a certain role in the deformation pattern. This study also analyzed regional deformation details from eight boreholes and one profile line covering different surface conditions, revealing the potential for refining the permafrost boundary. Overall, the results of this study provide valuable insights into the evolution of permafrost in the SRYR region.

The ground surface deformation (GSD) caused by oil shale in-situ mining poses a threat to land resources and human's lives and property. This study, for the first time, conducts a full stratum simulation of the Fuyu oil shale in-situ pyrolysis pilot base, analyzing the evolution characteristics of the temperature field, stress field, and deformation field of the entire stratum profile during the heating and cooling processes of convective heating mining. Considering the changes in the pore structure, thermophysical, and mechanical properties of the stratum, the environmental geological effects of rock deformation during in-situ mining were identified. Simulation results show that after heating, the temperature within 80 cm of the heating well reaches above the initial pyrolysis temperature of 350 °C for oil shale organic matter, and there is a significant stress concentration near the heat source. In the simulation, ground displacement rises in a wave-like manner during heating, quickly subsides after cooling, and finally stabilizes. Eventually, the entire stratum exhibited subsidence, with a subsidence amount of 0.59 cm. The spatiotemporal deformation trend obtained from SBAS-InSAR real-time monitoring results is similar to the simulation results. By comparing the monitoring results with the simulation results, the synergistic deformation mechanism of underground and ground surface co-deformation during in-situ mining of geochemical reactions in the study area was analyzed. The deformation rate is determined by the thermal hysteresis phenomenon and the temperature difference between the heated fluid and the rock layer. This provides scientific support for the geological effect evaluation of oil shale in-situ mining, which helps to improve mining safety and efficiency. • First-time analysis of the entire heating and cooling process in convective mining • Simulating the whole strata change of an oil shale in-situ mining case • Analyzing deformation using microstructures and macro-physical mechanics properties • The first use of SBAS-InSAR for displacement monitoring in oil shale in-situ mining • Revealing the oil shale in-situ mining's subsurface and surface co-deformation law [ABSTRACT FROM AUTHOR]

Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information.

Yunnan Province, China, has complex topography and geomorphology, many ravines and valleys, and frequent landslide geological disasters and is of great significance in the assessment of regional landslide geological disasters in Yunnan Province for disaster prevention and mitigation. In this study, Yunnan Province was selected as the research area, and the average annual deformation rate of radar line-of-sight in Yunnan Province over four years from 2018 to 2021 was obtained with SBAS-InSAR technology, which was used as one of the index factors for the susceptibility evaluation of Yunnan Province. The deformation rate reflects the slow movement of the land surface. In addition, elevation, slope, aspect, lithological classification, geological structure, rainfall, distance from roads, distance from rivers, topographic undulation, and NDVI were selected as evaluation index factors and combined with the annual mean deformation rate. A random forest model was used to evaluate and accurately analyze landslide geological disasters in Yunnan Province. The results showed that as an important index factor, the annual mean deformation rate of Yunnan Province can be added to the random forest model to improve the prediction accuracy. The area with high susceptibility accounted for 10% of the entire province, and the number of landslides in the region accounted for 68% of the province. Additionally, the results for prone zoning were highly correlated with the landslide distribution. The accuracy of the random forest model prediction was 0.80, and the AUC value was 0.87, indicating that the random forest model was a highly accurate and reliable evaluation method for studying landslide geological disasters. It is very suitable for the evaluation of landslide susceptibility in Yunnan Province.

The superimposed effects of sea level rise caused by global warming and land subsidence seriously threaten the sustainable development of coastal cities. In recent years, an important coastal city in China, Zhuhai, has been suffering from severe and widespread land subsidence; however, the characteristics, triggers, and vulnerability assessment of ground subsidence in Zhuhai are still unclear. Therefore, we used the SBAS-InSAR technique to process 51 Sentinel-1A images to monitor the land subsidence in Zhuhai during the period from August 2016 to June 2019. The results showed that there was extensive land subsidence in the study area, with a maximum rate of −109.75 mm/yr. The surface had sequentially undergone a process of minor uplift and decline fluctuation, sharp settlement, and stable subsidence. The distribution and evolution of land subsidence were controlled by tectonic fractures and triggered by the thickness of soft soil, the intensity of groundwater development, and the seasonal changes of atmospheric precipitation. The comprehensive index method and the analytic hierarchy process were applied to derive extremely high subsidence vulnerability in several village communities and some traffic arteries in Zhuhai. Our research provides a theoretical basis for urban disaster prevention in Zhuhai and the construction planning of coastal cities around the world.

Overexploitation of underground fluid resources is an important factor causing land subsidence, seriously threatening the development of coastal cities and the personal and property safety of residents. Due to the unreasonable exploitation of groundwater for a long time, Guangrao has become the most serious area of land subsidence in Shandong Province. The study analysed spatial and temporal evolution characteristics of settlement in the region during the last 5 years (2017–2021) and discussed the response patterns of land subsidence to groundwater level changes in different aquifers. Sentinel-1 satellite data and the short baseline (SBAS) method were used to reveal the sedimentation evolution pattern in Guangrao from 2017 to 2021. MODFLOW was used to simulate dynamic changes in groundwater flow fields to obtain more accurate and reliable gridded water level data. Geographically weighted regression (GWR) model was used to explore the spatial correlation between land subsidence and groundwater level changes in each aquifer in Guangrao. The results showed that the maximum cumulative land subsidence in the study area was 502 mm, with two subsidence centers. The GWR model of the study area was established by studying the effects of the first confined aquifer (AⅠ), the second confined aquifer (AⅡ), and the third confined aquifer (AⅢ) on different settlement zones and their spatial correlation. Positive and negative correlations existed between the water level changes of each aquifer and different subsidence zones. The changes of water level in AⅠ and AⅢ were the primary controlling factors, affecting most of the subsidence areas in Guangrao. The research results not only quantify the spatial response pattern between aquifers and land subsidence, but also provide a basis for the rational allocation of water resources and scientific prevention and control of land subsidence in coastal areas. [Display omitted] [ABSTRACT FROM AUTHOR]

Landslides are geological events that frequently cause major disasters. Research on landslides is essential, but current studies mostly use historical landslide data and do not reflect dynamic, real-time research results. In this study, landslide deformations and land-use changes were used to analyze the landslide distribution in Fengjie County and Wushan County in the Three Gorges Reservoir Area (TGRA) by using interferometric and polarimetric SAR. In this study, the mean annual rate of landslide deformations was obtained using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the ALOS-2 (2014–2019) data. Land-use changes were based on the 2007 and 2017 land-use results from dual-polarization ALOS-1 and ALOS-2 data, respectively. To address the problem of classification accuracy reduction caused by geometric distortion in mountainous areas, we first used texture maps and pseudocolor maps synthesized with dual-polarization intensity maps to perform classification with random forest (RF), and then we used coherence and slope maps to run the K-Means algorithm (KMA). We named this the secondary classification method. It is an improvement on the single classification method, exhibiting a 94% classification accuracy, especially in rugged areas. Combined with land-use changes, GIS spatial analysis was used to analyze the spatial distribution of landslides, and it was found that the landslide rate was significantly correlated with the type after change, with a correlation coefficient of 0.7. In addition, land-use types associated with human activities, such as cultivated vegetation, were more likely to cause landslide deformation, which can be used to guide local land-use planning.

Reservoir bank landslides are a frequent phenomenon, and the stability of these landslides is affected by two essential factors: rainfall and reservoir level changes. Studying the response patterns of reservoir bank landslide movements to these variables is crucial in preventing their occurrence and mitigating their effects. To this end, this study employed 103 European Space Agency (ESA) Copernicus Sentinel-1 images and the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) technique to obtain a time series of the Guobu slope deformation from September 2015 to December 2019. The Guobu slope showed significant toppling damage. The satellite line of sight (LOS) detected a maximum subsidence rate of −447 mm/y (the negative sign indicates movement away from the satellite, i.e., subsidence) in the upper section of the slope. Subsequently, three wavelet tools were used to quantitatively analyze the effect of rainfall and reservoir water level on the deformation of the Guobu slope. The results demonstrate a positive correlation between rainfall and the deformation of the Guobu slope. Moreover, the deformation lags behind the rainfall by approximately 70 days. In contrast, the reservoir water level and the deformation of the Guobu slope exhibit an inverse relationship. The deformation of the leading edge of the slope body lags behind the reservoir level by approximately 19 days, while the middle and upper sections of the slope body, which have the most significant rate of variability, lag by about 80 days. Among these factors, rainfall plays a dominant role in the deformation of the Guobu slope, while reservoir levels play a synergistic role. The findings of this study highlight the importance of monitoring and understanding the impact of changes in rainfall and reservoir water levels on the stability of reservoir bank landslides. This understanding is crucial in preventing the occurrence of such landslides and minimizing their impact. The use of remote sensing techniques, together with wavelet analysis, enables the accurate and timely monitoring of the deformation of the Guobu slope, providing valuable insights for disaster warnings and disaster prevention and reduction efforts.

Landslide identification in alpine and canyon areas is difficult due to the terrain limitations. The main objective of this research was to explore the method of combining small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), multi-temporal optical images and field surveys to identify potential landslides in the human-modified alpine and canyon area of the Niulan River in southwestern China based on terrain visibility analysis. The visibility of the terrain is analyzed using the different incident and heading angles of the Sentinel satellite’s ascending and descending orbits. Based on the SAR image data of Sentinel-1A satellites from 2016 to 2019, the SBAS-InSAR method was used to identify landslides, and then multi-temporal optical images were used to facilitate landslide identification. Field surveys were carried out to verify the identification accuracy. A total of 28 landslides were identified, including 13 indicated by SBAS-InSAR, 8 by optical imaging and 7 by field investigation. Many landslides were induced by the impoundment and fluctuation of reservoir water. The comparison and verification of typical landslide monitoring data and reservoir water fluctuations revealed that a sudden drop of reservoir water had a great influence on landslide stability. These research results can facilitate a comprehensive understanding of landslide distribution in the reservoir area and guide the follow-up landslide risk management.

Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed lands. Therefore, it is crucial to monitor the land subsidence and predict the future deformation trend to mitigate the damage and take measures for the land reclamation and any infrastructure. In this paper, a total of 125 SAR images acquired by the C-band Sentinel-1A satellite between June 2017 and September 2021 are collected. The small baseline subsets (SBAS) SAR interferometry (InSAR) method is first conducted to detect the land deformation in Xiamen and Zhangzhou cities of Fujian Province, China, and the distributed scatterers (DS)-InSAR method is used to recover the complete deformation history of some typical areas including Xiamen Airport in Dadeng Island and Shuangyu Island. Then, the sequential estimation and the geotechnical model are jointly applied to demonstrate the current and future evolution of land subsidence of the constructed roads on Shuangyu Island. The results show that the maximum cumulative deformation reaches 425 mm of Xiamen Xiang'an Airport and 626 mm of Shuangyu Island, and the maximum deformation is predicted to be as large as 1.1 m by 2026 of Shuangyu Island. This research will provide important guidelines for the design and construction of Xiamen Xiang'an Airport and Shuangyu Island to prevent and control land subsidence. [ABSTRACT FROM AUTHOR]

Shuicheng county is located in the west of Guizhou province. Under the comprehensive effect of natural environment and human activities，landslides occur frequently in Shuicheng county，which has the characteristics of strong concealment，high suddenness and strong destructiveness，which threatens the safety of local people′s life and property. It is urgent to identify potential landslides in Shuicheng county by effective means，so as to provide strong information support for disaster prevention and mitigation. In view of the fact that Shuicheng county is mostly mountainous and canyon areas，and traditional manual investigation methods are difficult to implement，this paper proposes an early landslide identification method based on interferometry synthetic aperture radar technology. In this method，the effective deformation points in the slope direction of Shuicheng county are obtained by the improved small baseline set interferometry technology. The spatial outliers of these deformation points are analyzed and clustered by Anselin Local Moran′s I index，and get the low value gathering area. Finally，combined with the surface features of Google Image，the potential landslide is identified.The results show that：the surface deformation rate of Shuicheng county is uneven，and there are multiple deformation centers，and the maximum deformation rate of slope direction reaches 142.3mm/a. Through the method of this paper，the potential landslides in Shuicheng county are identified，and combined with the field survey data，14 potential landslides are successfully identified， and 8 landslides are located in the invisible area of radar，so the identification is not successful [ABSTRACT FROM AUTHOR]

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