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In recent years, the measurement of dam displacements has benefited from a great improvement of existing technology, which has allowed a higher degree of automation. This has led to data collection with an improved temporal and spatial resolution. Robotic total stations and GNSS (Global Navigation Satellite System) techniques, often in an integrated manner, may provide efficient solutions for measuring 3D displacements on precise locations on the outer surfaces of dams. On the other hand, remote-sensing techniques, such as terrestrial laser scanning, ground-based SAR (synthetic aperture radar) and satellite differential interferometric SAR offer the chance to extend the observed region to a large portion of a structure and its surrounding areas, integrating the information that is usually provided in a limited number of in-situ control points. The design and implementation of integrated monitoring systems have been revealed as a strategic solution to analyze different situations in a spatial and temporal context. Research devoted to the optimization of data processing tools has evolved with the aim of improving the accuracy and reliability of the measured deformations. The analysis of the observed data for the interpretation and prediction of dam deformations under external loads has been largely investigated on the basis of purely statistical or deterministic methods. The latter may integrate observation from geodetic, remote-sensing and geotechnical/structural sensors with mechanical models of the dam structure. In this paper, a review of the available technologies for dam deformation monitoring is provided, including those sensors that are already applied in routinary operations and some experimental solutions. The aim was to support people who are working in this field to have a complete view of existing solutions, as well as to understand future directions and trends.

Lakes are valuable water resources that support aquatic and terrestrial ecosystems and supply fresh water for the agricultural, industrial, and urban sectors worldwide. Although water levels should be tracked to monitor these services, conventional gauging is unfeasible in most lakes. This study applies Differential Interferometric Synthetic Aperture Radar (D‐InSAR) to estimate small water level changes, less than 2 cm, in Swedish lakes over 6‐day intervals. We validated the method across the shores of 30 Swedish lakes with gauged observations in 2019. We used Sentinel‐1A/B images with a 6‐day temporal separation to construct consecutive interferograms and accumulated the phase changes in pixels of high coherence to build a time series of water levels. We find that the accumulated phase change obtained by D‐InSAR replicates the magnitude of water levels in seven lakes in Southern Sweden, where water levels change slowly, less than 2 cm per 6‐day period, as validated by in‐situ gauges. In addition, this study demonstrates the application of D‐InSAR to estimate the long‐term direction of water level change (i.e., increase or decrease) in all 30 lakes. This work reveals the utility of high temporal resolution water level observations in support of other satellite water level instruments such as conventional altimeters and the recently launched Surface Water and Ocean Topography Mission. Key Points Small water level changes in seven lakes in South Sweden revealed by D‐InSAR Replicating the seasonal timing of high and low lake water levels by D‐InSAR

Underground coal mining often causes subsidence, goaf landslides, fissures, and even hazard chains, seriously damaging the ecological environment. To address the ecological vulnerability of mining areas is key to exploring the development characteristics and failure mechanisms of surface multi-hazards. Taking the Shuiliandong coal mine as an example, the mine deformation area was identified using differential interferometric synthetic aperture radar (D-InSAR) technology. We found that the spatial evolution of the deformation areas was controlled by the mining sequence. The impacts on the surface deformation and the mining-induced landslide were continuous and long term. Thus, the surface fissures and landslide were characterized using historical images. The fissures were clustered and short, and there was a negative exponential relationship between the length and the cumulative number of fissures. The fissure density decreased with increasing distance from the mine tunnel. In addition, the particle flow numerical simulation analysis method was used to simulate the subsidence-fissure-landslide hazard chain process. Three distinct stages were identified: initial stage, rapid development stage, and creep stage. The displacements at the different monitoring points exhibited a distinct S shape. The cumulative number of fissures developed same as the subsidence and landslide, exhibiting an S shape. The fissures played an important role in the hazard chain, accelerating the subsidence and landslide processes.

With advancements in radar sensors, communications, and computer technologies, alongside an increasing number of ground observation tasks, Synthetic Aperture Radar (SAR) remote sensing is transitioning from being theory and technology-driven to being application-demand-driven. Since the late 1960s, Interferometric Synthetic Aperture Radar (InSAR) theories and techniques have continued to develop. They have been applied significantly in various fields, such as in the generation of global topography maps, monitoring of ground deformation, marine observations, and disaster reduction efforts. This article classifies InSAR into repeated-pass interference and single-pass interference. Repeated-pass interference mainly includes D-InSAR, PS-InSAR and SBAS-InSAR. Single-pass interference mainly includes CT-InSAR and AT-InSAR. Recently, China has made significant progress in the field of SAR satellite development, successfully launching several satellites equipped with interferometric measurement capabilities. These advancements have driven the evolution of spaceborne InSAR systems from single-frequency to multi-frequency, from low Earth orbit to higher orbits, and from single-platform to multi-platform configurations. These advancements have supported high precision and high-temporal-resolution land observation, and promoted the broader application of InSAR technology in disaster early warning, ecological monitoring, and infrastructure safety.

Continuous exploitation in mining areas damages the surrounding environment and has various severe geological impacts. Hence, long-term monitoring of mining areas is crucial to reducing these impacts. Differential interferometric synthetic aperture radar (D-InSAR) is widely applied to monitor the subsidence in mining areas, but it cannot obtain accurate large-gradient subsidence result in the centre of the subsidence basin in mining areas due to the de-coherence phenomenon. The probability integral method (PIM) is a prediction method that can cooperate with D-InSAR (D-InSAR PIM, DPIM) to solve the problem. However, with this method, there is early convergence of the edge subsidence in narrow and long mining areas. Moreover, the PIM can only predict the spatial domain; it cannot achieve dynamic prediction. To address the above problems in the traditional DPIM data processing process, in this study, firstly, the traditional PIM was improved by adjusting the radius of the parameter and constructed an improved DPIM (IDPIM) method. The hybrid algorithm was applied to solve the parameters of the IDPIM method and then acquire subsidence results, thus solving the early convergence of edge subsidence problem characteristic of traditional PIM prediction in mining. Additionally, an area-weighting based fusion method was proposed to integrate the IDPIM results and the D-InSAR results (Area-weighting based fusion of the IDPIM and D-InSAR results, AFID) achieving whole-basin refined subsidence in mining areas. Secondly, based on a summary of subsidence laws in mining areas, the Hossfeld model was introduced and combined with the IDPIM method (IDPIM Hossfeld, IDPIM-H) to construct a subsidence dynamic prediction method. This achieved dynamic prediction of the subsidence in mining areas. A coal mine in Ordos was used as the study area, and the feasibility of the IDPIM method, the AFID method and the DPIM-H method was verified through a comparative analysis of leveling data. The results demonstrated that: (1) The results of the IDPIM method showed 8% and 66% improvement in RMSE along the striking and dip lines, respectively, over the D-InSAR results, improving the early convergence of the DPIM along the dip direction of the mine. (2) The results of the AFID method provide a 69% improvement in whole-basin RMSE over the D-InSAR results, which improves the lack of monitoring capacity in the D-InSAR technology center. (3) The results of the DPIM-H method provide a 35% improvement in basin-wide RMSE over the D-InSAR results, solving the problem of low temporal resolution of the D-InSAR technology and realizing the dynamic prediction with high temporal resolution. These findings provide a theoretical basis for future refined exploration of dynamic subsidence in mining areas

Landslide geological disasters, occurring globally, often result in significant loss of life and extensive economic damage. In recent years, the severity of these disasters has increased, likely due to the frequent occurrence of extreme rainstorms associated with global warming. This escalating trend emphasizes the urgent need for a simple and efficient method to identify hidden dangers related to landslide geological disasters. Areas experiencing seasonal heavy rainfall are particularly susceptible to such disasters, posing a serious threat to the lives and property of local residents. In response to the challenging characteristics of landslide geological hazards, such as their strong concealment and the high vegetation coverage in the Liupan Mountain area of the Loess Plateau, this study focuses on the integrated remote sensing identification and research of hidden landslide dangers in Longde County. The methodology combines differential interferometric synthetic aperture radar technology (D-InSAR) and high-resolution optical remote sensing. Surface deformation information of Longde County was obtained by analyzing 85 Sentinel-1A data from 2019 to mid-2020 using Stacking-InSAR, in conjunction with high-resolution optical remote sensing image data from GF-2 in 2019. Furthermore, the study conducted integrated remote sensing identification and field verification of landslide hazards throughout the entire county. This involved interpreting the shape and deformation marks of landslide hazards, identifying the disaster-bearing bodies, and expertly interpreting the environmental factors contributing to the hazards. As a result, 47 suspected landslide hazards and 21 field investigation points were identified, with 16 hazards verified with an accuracy of 76.19%. This outcome directly confirms the applicability and accuracy of the integrated remote sensing identification technology in the study area. The research results presented in this paper provide an effective scientific and theoretical basis for the monitoring and treatment of landslide geological disasters in the future stages. They also play a pivotal role in the prevention of such disasters.

The Terrain Observation by Progressive Scans (TOPS) acquisition mode of the Sentinel-1 mission provides a wide coverage per acquisition with resolutions of 5 m in range and 20 m in azimuth, which makes this acquisition mode attractive for glacier velocity monitoring. Here, we retrieve surface velocities from the southern Ellesmere Island ice caps (Canadian Arctic) using both offset tracking and Differential Interferometric Synthetic Aperture Radar (D-InSAR) techniques and combining ascending and descending passes. We optimise the offset tracking technique by omitting the azimuth offsets. By doing so, we are able to improve the final resolution of the velocity product, as Sentinel-1 shows a lower resolution in the azimuth direction. Simultaneously, we avoid the undesired ionospheric effect manifested in the data as azimuth streaks. The D-InSAR technique shows its merits when applied to slow-moving areas, while offset tracking is more suitable for fast-moving areas. This research shows that the methods used here are complementary and the use of both to determine glacier velocities is better than only using one or the other. We observe glacier surface velocities of up to 1200 m year − 1 for the fastest tidewater glaciers. The land-terminating glaciers show typical velocities between 12 and 33 m year − 1 , though with peaks up to 150 m year − 1 in narrowing zones of the confining valleys.

An approach to study the mechanism of mining-induced subsidence, using a combination of phase-stacking and sub-pixel offset-tracking methods, is reported. In this method, land subsidence with a small deformation gradient was calculated using time-series differential interferometric synthetic aperture radar (D-InSAR) data, whereas areas with greater subsidence were calculated by a sub-pixel offset-tracking method. With this approach, time-series data for mining subsidence were derived in Yulin area using 11 TerraSAR-X (TSX) scenes from 13 December 2012 to 2 April 2013. The maximum mining subsidence and velocity values were 4.478 m and 40 mm/day, respectively, which were beyond the monitoring capabilities of D-InSAR and advanced InSAR. The results were compared with the GPS field survey data, and the root mean square errors (RMSE) of the results in the strike and dip directions were 0.16 m and 0.11 m, respectively. Four important results were obtained from the time-series subsidence in this mining area: (1) the mining-induced subsidence entered the residual deformation stage within about 44 days; (2) the advance angle of influence changed from 75.6° to 80.7°; (3) the prediction parameters of mining subsidence; (4) three-dimensional deformation. This method could be used to predict the occurrence of mining accidents and to help in the restoration of the ecological environment after mining activities have ended.

Ground surface subsidence is a universal phenomenon in coal mining areas which can cause serious damage to the surrounding environment. In this paper, we consider the use of differential interferometric synthetic aperture radar (D-InSAR), multi-temporal InSAR (MT-InSAR), and the pixel offset tracking technique to monitor the surface deformation of a coal mining area. In this study, we use the two-pass D-InSAR method to generate 19 interferometric image pairs from 20 TerraSAR-X SpotLight images. The results show that D-InSAR can be used to obtain high accuracy surface deformation in the mining areas where there is no high gradient deformation, and the pixel offset tracking method offers advantages in those areas where high gradient deformation is found, but its performance is not stable. This means that the unilateral use of these technologies cannot obtain reliable subsidence information in mining areas. Therefore, it is essential to find a new way to integrate the respective advantages of these different methods. In this paper, a new fusion method combining the D-InSAR result with the offset tracking result based on a spatial decorrelation distribution map is proposed to obtain the subsidence results in a mining area. To ensure the reliability of the results, a decision rule is proposed for the spatial decorrelation distribution map, which is generated manually by union analysis in ArcGIS. In the experiments, the mean absolute error of the fusion result is 0.0748 m, while that of D-InSAR is 0.1890 m, and that of offset tracking is 0.1358 m. It is therefore clear that the proposed fusion method is more reliable and more accurate than the use of individual methods, and it may be able to serve as a reference in mining subsidence monitoring.

It is of great significance to obtain timely and accurate information of surface subsidence caused by mining. The probability integral method (PIM) model is more suitable for mining subsidence prediction in China and has been widely used. However, the PIM model has the question on too fast convergence in predicting at the edge of subsidence basin. In recent years, many scholars have studied a lot of subsidence monitoring methods in the coal mine area using the technical advantages of differential interferometry synthetic aperture radar (D-InSAR). But, serious incoherence of interferometry phase occurs because of the large gradient subsidence of mining area, which leads to the inability to accurately obtain large gradient subsidence of surface. Meanwhile, PIM model is more suitable for static prediction of mining subsidence, and has certain defects in dynamic prediction in the process of mining subsidence. In view of the above shortcomings, the improved PIM (IPIM) prediction model was first introduced through improving the PIM model in the paper, and the IPIM-G dynamic prediction model was constructed based on the PIM model and the Gompertz time function for mine-area mining. Then, a method was used to invert the parameters of the IPIM-G dynamic prediction model using time-series superposition results of surface subsidence monitored by the D-InSAR technology, and then the parameters obtained by inversion were applied to the IPIM-G model for mining subsidence prediction. The model was applied to a coal mine in Huaibei mining area, Anhui Province, its average RMSE is 138 mm and the average RMSE at the edge is 2.8 mm. The accuracy of IPIM-G dynamic prediction model is 88% higher than the monitoring results of the D-InSAR technology in obtaining the gradient subsidence information of the subsidence basin in the mining area. The results show that the model proposed in this paper can provide theoretical support for mining and production planning in the mining area.