Landslides, widely happening across the world, have a severely negative impact on human activities and people's residential security. GNSS has been widely used in landslide disaster monitoring and warning, but there are still many technical bottlenecks in complex scenario monitoring and warning. First of all, this paper reviews the current research on landslip reduction technologies by GNSS such as monitoring receiver, high-precision positioning and multi-source heterogeneous data fusion monitoring, and lays special stress on analyzing the advantages, applicable range and latent problems of landslide-monitoring technologies of every kind. Furthermore, the technical methods suitable for releasing warning by GNSS are introduced from landslide displacement prediction, the prediction of sliding time and the implementation of early warning. Lastly, a comprehensive analysis on the challenges brought about by GNSS's real-time monitoring in complex scenarios is conducted, and some ideas are proposed for the future development and research of technologies monitoring and warning landslides through GNSS.

The development of InSAR technique and the tremendous amount of SAR data have made InSAR the fundamental tool in landslide monitoring.This paper begins with the data selection, the effects of different datasets and scenes in landslide monitoring by InSAR are introduced. We analyze the main limitations affecting the accuracy of landslide monitoring by InSAR, and demonstrate the corresponding solutions. Subsequently, the methods in retrieving landslide-induced 3D displacement using InSAR are then classified, and their advantages, disadvantages, and application scopes are also analyzed. Finally, the limitations in landslide monitoring with InSAR, the corresponding solutions, and future development trends are discussed.

Geoharzards occur frequently in China, and the losses are especially serious. A number of catastrophic geohazards have occurred in recent years, which indicate that the prevention of geohazard in mountainous areas covered by high vegetation and in some inaccessible areas restricted by topographic conditions is still a difficult problem for traditional methodology. Aerial remote sensing technology can quickly and efficiently reveal the spatial distribution characteristics and spatial and temporal evolution rules of geoharzard, which has played an important role in the field of geoharzard prevention. Based on the brief introduction of the airborne remote sensing technology, platform and sensor, this paper systematically summarizes the research and application of airborne remote sensing at home and abroad in the geoharzard identification, investigation and evaluation, long-term monitoring, emergency response, virtual reality displayed. Analyzes the challenges faced by the application of airborne remote sensing in geoharzard, and looks forward to the development of the application of airborne remote sensing in geoharzard research trend.

The analysis and evaluation of landslide disaster susceptibility is of great significance to the prevention and management of geological disasters. In view of the sample selection strategy and the unreasonable multi-feature mapping in single-kernel vector machine, this paper proposes the landslide susceptibility analysis and evaluation method of multiple kernel support vector machine (MKSVM) considering the sample optimization selection. To ensure sample balance and improve the plausibility of negative samples, using the relative frequency ratio (relative frequency, RF) comprehensively evaluate the importance degree of each state in the influence of landslide disaster susceptibility, the purpose is to realize the reasonable division of each evaluation factor state; Using the deterministic coefficient method (certainty factor, CF) calculates the sensitivity of each state of each evaluation factor, the weighted sum has obtained the landslide disaster susceptibility index of each grid cell, non-landslide disaster points consistent with the number of landslide disaster points were randomly selected in the very low and low landslide disaster prone index as the negative sample data. Then, multi-kernel learning is used to select the SVM optimal kernel function and to linear combine the optimal kernel functions in each feature space to avoid unreasonable mapping of a single kernel function, and it improve the classification accuracy and prediction accuracy of the model. Taking Xiangxi Tujia and Miao Autonomous Prefecture of Hunan province as the research area, MKSVM model of CF sample strategy, single-kernel SVM model of CF sample strategy, MKSVM model of random sample strategy and single-kernel SVM model of random sample strategy were compared analyzed from three aspects of landslide disaster prone zoning map, partition statistics and evaluation model accuracy. The results indicate that the subject operating characteristic curves of the four models (receiver operating characteristic, area under the ROC) (area under curve, AUC) were 0.859,0.809,0.798,0.766, the rationality and validity of the CF sample strategy and the reliability of the MKSVM model are verified.

Comprehensive identification and discovery of potential landslide hazards has become a major practical demand of geological hazard prevention and control in China. At present, the application effect and applicability of the combination of InSAR technology and deep learning for the intelligent identification of geological hazards at large scale are still worthy of further exploration and research, this paper obtained the phase data of surface deformation based on stacking interferometric synthetic aperture radar (Stacking InSAR) technology, used deep learning to identify the location and distribution of the deforming landslide hazards, and determined the boundary of the significant deformation zone of potential landslide hazards. The above technical methods were exploratively applied to test and dynamic update data sets. The average identification precision, recall and F₁ score value of the test data set were 0.69, 0.67 and 0.67, respectively. The identification precision, recall and F₁ score value of the dynamic update data set were 0.85, 0.58 and 0.68, respectively. The results showed that the technical method used in this paper is feasible in the identification of potential landslide hazards in a wide area, and can provide theory and technical support for geological disaster monitoring and early warning.

The identification of hidden dangers is an important technical work to realize the transformation of potential geological hazards from post-disaster relief to pre-disaster prevention.This paper proposes a method for identifying the bedding rock landslide based on disaster pregnant mechanism and comprehensive remote sensing detection technology. Firstly, data analysis, remote sensing survey and field survey is used for identifying disaster-pregnant environment and establishing a disaster-pregnant index system; at the same time, typical hazard mode and the identification mark of integrated remote sensing are established. Then, key target areas and suspected hidden dangers of geological disasters are delineated. And geological hazard identification is realized relied on ground detailed assessments and professional identification. By using this set of technical methods, a total of 8 potential catastrophic geohazards have been identified in the work area, of which 5 are potential rockslides with hazard-pregnancy modes but not yet apparently deformed. The results show that this method can make up for the disadvantages of low accuracy or even failure mainly relying on remote sensing change detection. It is especially suitable for hidden and sudden geological hazard identification in areas with steep hills and dense vegetation.

Baihetan Dam is located in the high and steep mountain area of southwest China, which is an important part of national project “West to East power transmission” in China. The two banks of the reservoir are highly prone to geological disasters, and the traditional geological disaster investigation means have obvious shortcomings, such as low efficiency, high risk, and unable to cover potential hazards in upper slope. The application of space-borne synthetic aperture radar interferometry (InSAR) and unmanned aerial vehicle (UAV) aerial survey technology for the identification of potential landslides in wide-area is of great significance for identifying potential landslides, understanding their distribution characteristics and mitigating the disaster. In this paper, based on 142 Sentinel-1 satellite images, active slopes with clear displacements were identified on both banks of Baihetan Dam area before impoundment by using sequential InSAR technology. Meanwhile, UAV aerial survey technology was used to verify and analyze the active slopes. According to digital aerial photography products such as DEM, DOM and 3D model, and 32 potential landslides were identified. Combined with the factors of displacement magnitude, displacement area, whether the potential landslides are involved in water and the threating objects, the prevention and control suggestions were given, and a comprehensive and accurate database of potential landslides before impoundment was established. Finally, the advantages, application scope and the effective combination strategy of these two technologies are summarized based on this case. This paper reveals that during the combination of the two technologies on the potential landslides identification in wide area, not only can UAV survey verify the displacement area monitored by InSAR, but also InSAR could improve the flight efficiency of aerial survey. By combining the real-time and historical information, displacement and geological information provided by the two technologies, an efficient comprehensive space-air remote sensing strategy for potential landslides identification in wide dam area can be achieved.

Expansive soil can damage the civil engineering structures due to the swelling-shrinking characteristic when they are exposed to the water. Middle Route Project of the South-to-North Water Diversion is an important project for alleviating water shortages and optimizing water resource allocation in the North China Plain. One third of water channel in the Middle Route passes through the expansive soil region, threatening the safety of the South-to-North Water Diversion Project. It is urgent to monitor the ground deformation of expansive soil slope so that better understanding its engineering stability. In this context, 112 Sentinel-1A synthetic aperture radar (SAR) images with ascending orbit were processed by multi-temporal interferometric SAR (InSAR) technique to observe the time series deformation along the South-to-North Water Diversion Project. Meanwhile, the singular spectrum analysis (SSA) technique was used to decompose the time series deformation to analyze the deformation characteristics. The uplift deformation was observed in the excavating expansive soil slope, where the maximum deformation rate was 18 mm/a. This uplift deformation was mainly due to the soil unloading and rebound. In the filling region, the subsidence was observed due to the soil consolidation and the maximum subsiding rate was 15 mm/a. The magnitude of ground deformation was correlated with the excavating and filling thickness:the deeper excavation means the greater uplift, and the higher filling means the greater subsidence. The precipitation and temperature, which can cause the deformation temporal delay of 2~3 months, are two main external contributor to the expansive soil slope. This study will provide the evidence to the stability assessment of expansive soil slope along the South-to-North Water Diversion Project.

Identification of rock mass discontinuity and characterization of characteristic parameters are of fundamental significance to the study of rock mass behaviour and instability mechanism. In this paper, a photogrammetric system consisting of UAV aerial survey, GPS-RTK and close-range photogrammetry was employed to systematically investigate the rock mass discontinuities at different scales in Beishan area, Gansu province. The digital orthophoto map (DOM) of this site and 3D reconstructed digital surface model (DSM) of outcrops were established by using the ground object photos obtained from different perspectives. The effective interpretation of rock mass discontinuity information and characterization of characteristic parameters were realized by using digital identification and statistical methods. Studies of typical outcrops and areas show that the photogrammetric system can promote the fine investigation and identification of rock mass discontinuities from different dimensions and scales. According to the variation feature of discontinuity characteristic parameters, the fault affected zone of F31 fault in Beishan area were investigated. It can be preliminarily concluded that the affected range of F31 fault on hanging-wall rock mass integrity was approximately 150~200 m, and the influence form was negative exponential type.

Regional reference frames are the critical geodetic infrastructure for realizing high-accuracy crustal deformation and long-term landslide monitoring. According to the published results in active tectonic-block zonation and long-term GNSS observations, the authors divide China into seven “rigid-frame blocks” (or “rigid blocks”) and intend to establish the stable regional reference frame series covering the entire land and sea areas of China: Northeast, North, South, Northwest, Qinghai-Tibet, Sichuan-Yunnan, and South China Sea reference frames. This article introduces the method for realizing the coordinate transformation from the global reference frame (IGS14) to regional reference frames and exemplifies the applications of the North China Reference Frame (NChina20) and the South China Reference Frame (SChina20) in long-term landslide monitoring and initial slide automatic detection.

GNSS relative positioning technology is widely used in high-precision real-time landslide monitoring, and this high-precision positioning relies on continuous and stable reference station data. However, the data of the reference station is often interrupted due to reasons such as power supply and communication, which seriously affects the continuity and reliability of the landslide monitoring results. This paper proposed a switching method of GNSS landslide monitoring reference station considering the correction of the motion state. First, the motion status is determined according to the long-term monitoring time sequence of the new reference station. Then, a displacement model is established based on the landslide deformation evolution law and motion state, and the model is checked regularly. Finally, the displacement of each monitoring point is corrected based on the actual displacement of the new reference. This method was successfully applied to the switching of the reference station of Heifangtai landslide monitoring in Gansu. After correction, the displacement of each monitoring station was close to the real deformation. The improved tangent angle was calculated as the landslide warning criterion, and the monitoring displacement sequences before and after the correction were used for warning. If no corrections are made, it may lead to a misjudgment of the early warning. Aiming at the problem of data interruption in the reference station, this method obtains a continuous and reliable landslide monitoring sequence by switching a new reference and correcting errors, ensuring the continuity of monitoring and the timeliness of early warning.

Spatio-temporal Kalman filter can be used for spatio-temporal data denoising, interpolation and deformation prediction. In order to use the spatio-temporal Kalman filter model for spatio-temporal deformation analysis, the performance and applicability of three typical spatio-temporal Kalman filter models, namely Kriged Kalman filter (KKF), space time Kalman filter (STKF) and spatio-temporal mixed effects (STME), are compared and analyzed from the aspects of principles and experiments. The results show that: in theory, the three spatio-temporal Kalman filter models are based on the combination of spatial basis function and dynamic model to describe the spatio-temporal correlation. The main difference lies in the expression of spatial data, such as trend term, fine-scale variation, observation noise and spatial basis function. In terms of applicability, the KKF model is more suitable for the spatio-temporal deformation analysis of sparse stations, while the STKF model and STME model are more suitable for the spatio-temporal deformation analysis of massive stations. In terms of application effects of spatio-temporal deformation analysis, the three spatio-temporal Kalman filter models have high-precision effect in denoising, data interpolation and deformation prediction performance. The average improvement rate of denoising results compared with ordinary Kalman model is 21.1%, the average improvement rate of interpolation results compared with Hermite time interpolation results is 42.4%, the average improvement rate of its spatio-temporal prediction results relative to Kriging spatial interpolation results is 65.3%, the average improvement rate of its spatio-temporal prediction results for observation stations relative to the time prediction results of ordinary Kalman filter is 20.6%, and the average improvement rate of its spatio-temporal prediction results for non-observation stations relative to the prediction results of Kalman filter+Kriging model is 20.5%.

Ground-based arc-scanning synthetic aperture radar realizes landslide warning through deformation measurement, and it is an important remote sensing method for landslide disaster monitoring. The atmospheric phase affects the accuracy of deformation value measurement, and the correction of the atmospheric phase is a key technology for long-term stable monitoring of the area of interest. In this paper, a two-stage atmospheric phase correction method based on grid division is proposed. This method obtains permanent scattering points through feature extraction and classification, realizes automatic screening of control points, and estimates atmospheric phase based on the grid. It effectively reduces the amount of computation, improves computational efficiency, and ensures the accuracy of atmospheric phase estimation by combining spatial and temporal filtering. The experimental results show the effectiveness of the proposed method in atmospheric phase correction.

In view of the multi-objective optimization problem of landslide parameter inversion, and to compensate for the lack of sparse landslide displacement monitoring point, a landslide multi-objective weighted displacement back analysis method synthesizing ground and underground displacement monitoring data is proposed. Firstly, the multi-objective weighted displacement back analysis model is constructed by ground and underground displacement information. Secondly, the robust post-test random model of various observations is calculated by the robust Helmert variance component estimation method, and then it is used to optimize the inversion model. Finally, the equivalent mechanical parameters are solved by iteration computation. Experimental results show that insufficient amount of underground displacement information will lead to serious deviations in the displacement back analysis results, and the inversion results that integrate ground and underground displacement information are more accurate; the multi-objective weighted displacement back analysis method based on robust Helmert variance component estimation can not only reasonably determine the weight of different types of observation data, but also effectively resist the influence of abnormal gross errors on the inversion results, and improve the inversion calculation accuracy.

Landslide deformation monitoring data is the direct basis for understanding the evolution law of landslide deformation, and the deep mining of this data is a powerful guarantee to realize the early warning and prediction of landslide disaster. The existing landslide prediction models are mostly limited to the time-series displacement prediction of a single monitoring point and do not consider the spatial correlation among monitoring points. To address the above problems, this paper proposes a spatio-temporal prediction model for landslide displacement based on deep learning: Firstly, the weighted adjacency matrix expressing the spatial correlation among all points in the interpretation is constructed; Secondly, the external influences are introduced to strengthen the attribute feature matrix in order to construct the graph structure data; Finally, this model of ensemble graph convolutional network (GCN) and gate recurrent unit (GRU) is used, and the optimal hyper-parameters are found through multiple sets of experiments .Compared with the comparison model, the root mean square error(RMSE) of the proposed model is 4.429 mm, which is at least 47.3% lower. The ablation experiment results also show that the attribute augmentation with the introduction of external influences can further improve the prediction performance of the model, and the RMSE is reduced by 28.4% compared with the results without attribute augmentation. The results suggest that the method can be used for spatio-temporal prediction of landslide displacements or other observed quantities in geological hazards that also have spatio-temporal correlation properties.

Landslides usually occur suddenly and cause great damage, often causing serious life safety accidents and property losses. The monitoring and prediction methods of landslide deformation with high reliability, high precision and anti-difference performance are of practical significance to the needs of national disaster prevention and mitigation. Interferometric synthetic aperture radar(InSAR) technology is a monitoring method capable of all-day and all-weather observation, obtaining images with high spatial resolution and wide coverage, and capturing dynamic changes of spatio-temporal dimensions with high sensitivity. However, at present, the landslide prediction based on InSAR time series image is very rare. This paper presents a landslide prediction method based on deep learning, which can effectively solve the problem of medium- and short-term landslide prediction by exploiting multi-temporal InSAR observations. Neural basis expansion analysis (N-BEATS) network model was used to predict the landslide in the Xinpu area, the Three Gorges. The landslide prediction was completed with an accuracy (root mean square error) of 1.1 mm. The results are analyzed by the regularity of data structure, comparison to traditional methods, evaluation of the tolerance and estimation of the confidence interval. The results show that the proposed prediction method has outstanding advantages of high precision, high reliability and certain robust estimation ability.

In addition to the displacement caused by its own gravity, the landslide body is also affected by rainfall, but usually the effect of rainfall on the displacement of the landslide has a hysteresis. In order to analyze and predict the impact of rainfall on landslide displacement, this paper proposes a dynamic optimization time-lag time-lag GM(1,2) landslide displacement prediction model that takes into account the impact of rainfall. First, use EMD (empirical mode decomposition) to decompose the displacement sequence and reconstruct the periodic displacement sequence and the trend displacement sequence through the time sequence. Perform time lag analysis and correlation analysis on the rainfall data and the landslide periodic displacement sequence, determine the time lag and the degree of influence, and establish an optimization based on the background value. The dynamic time-lag GM(1,2) model predicts the cyclic displacement change of the landslide caused by the change of rainfall. At the same time, a threshold autoregressive model is established to predict the trend displacement of the landslide tending to natural changes. Finally, the landslide prediction displacement taking into account the influence of rainfall is obtained through time series superposition. Established a dynamic optimization time lag time GM(1,2) combined forecasting method that takes into account the rainfall factor. The paper uses the monitoring data of Funing Bachimen landslide and Zigui county Bazimen landslide as examples to verify the accuracy of the dynamic optimization time-lag GM(1,2) model, and compares and analyzes the prediction results with other models. The experimental results show that the dynamic the optimized time-lag time series GM(1,2) combined forecasting model can accurately predict the landslide displacement changes caused by rainfall, and the forecasting effect is better, the combined model has certain reference value for the early warning and prevention of landslide disasters.

Landslide displacement prediction is an important part of real-time monitoring and early warning of landslide disasters. A good landslide displacement prediction model is helpful to predict the occurrence of geological disasters. The deformation of the landslide is affected by a variety of external factors and presents the characteristics of randomness and nonlinearity. Among the existing landslide displacement prediction methods, machine learning methods have been widely used in landslide displacement prediction. Prediction of landslide displacement is the superposition of trend displacement and periodic displacement. In this study, the displacement extraction method of landslide trend term and period term based on integrated empirical mode decomposition (EEMD) and the support vector regression (SVR) model were used to predict the landslide displacement. The construction process and prediction performance of the model are introduced in detail, and the root mean square error (RMSE), average absolute error (MAE), average absolute percentage error (MAPE) and coefficient of determination (R²) are used as the predictive performance indicators of the evaluation model. The EEMD-SVR, SVR, and Elman models are used to predict the displacement of a landslide in the karst mountainous area of Guizhou province. The results showed that the RMSE, MAPE and R² values of EEMD-SVR model were 0.648 mm, 0.518% and 0.996 8, respectively. This model can provide higher and more reliable landslide displacement prediction accuracy, and has certain reference value for the displacement prediction of similar landslide.

Since the impoundment in 2003, more than 5000 landslides or potential landslides have been identified in the Three Gorges Reservoir area, and these hazards seriously threaten the continued operation of the Three Gorges Reservoir and the safety of dams, waterways, and residents. By studying the deformation characteristics, inducing triggering factors, and instability mechanism of landslides, it is helpful to evaluate the stability of landslides and construct early warning and prediction models. Taking the Bazimen Landslide in the Three Gorges Reservoir Area as an example, the deformation characteristics and instability mechanism of the landslide are studied by comprehensively analyzing the data of rainfall, reservoir water level, manual and automatic GNSS monitoring, combined with the field macro inspection and exploration data, and the reasonable early warning criterion and threshold are determined. The research shows that: ① The overall deformation of the Bazimen landslide is obvious and in the creep deformation stage. The landslide deformation is mainly concentrated in May to September every year, and the landslide accumulation curve presents typical “step-like” deformation characteristics. ② The deformation of landslides is controlled by slope structure, lithology, and other factors. The decline of reservoir water level is the main driving factor of landslide deformation and is positively correlated with the decline rate of reservoir water. In addition, severe rainstorms and continuous rainfall will promote landslide deformation in the stage of water-level decline, reservoir low water level operation, and water level rise, which is the second driving factor of landslide. ③ The threshold displacement rate for step-like deformation of Bazimen Landslide is 4.6 mm/d, the threshold of 7d cumulative rainfall is 60 mm, the threshold of reservoir water level is 159 m, and the threshold of reservoir water level decline rate is 0.4 m/d, obtained by refined data analysis and improved tangent angle method.

The prediction of regional rainfall-induced landslides mainly relies on the rainfall threshold. However, given the physical mechanism of rainfall-induced landslides, in addition to the changes in soil water content caused by rainfall infiltration, the soil water content before rainfall infiltration is also an important factor affecting slope instability. The inability to consider the soil moisture condition before rainfall infiltration is regarded as the main reason for the poor performance of the rainfall threshold in landslide predictions. Aiming at this problem, this study takes Dujiangyan district of Sichuan province as the experimental area, and proposes the idea of predicting regional rainfall-induced landslides by considering the antecedent soil moisture condition. Through statistical analysis of historical landslide records, we constructed a hydro-meteorological threshold based on the antecedent soil moisture and recent rainfall conditions, in which the antecedent soil moisture condition is described by the modified antecedent precipitation index, and the recent rainfall condition is characterized by the recent cumulated rainfall. Results show that the hydro-meteorological threshold performs well in terms of hit rate and false alarm rate in predicting rainfall-induced landslides at the experimental area. The constructed hydro-meteorological threshold can simultaneously consider the role of the antecedent soil moisture and recent rainfall in the occurrence of landslides, and has advantages of requiring fewer data, easy operation due to the simple method, and good prediction performances, which is suitable for application and promotion in the prediction of regional rainfall-induced landslides.

Slope stability analysis is an efficient method for landslide risk reduction. However, it is still a challenging task owing to the unilaterally assessment approach applied to practical issues. To overcome this problem, a new stability evaluation of loess landslide based on integration of engineering geological data (high-precision DEM, groundwater level, drilling information in engineering geology and irrigation information) constrained by external high-precision GNSS monitoring information is proposed. Firstly, a fine 3D geological model of landslide which can reflect the real nature characteristics is constructed by using Arcgis-Rhinoceros-Griddle based on high-resolution image, high-precision multi-source monitoring information, engineering geological borehole information, groundwater level information and field investigation. Then, a comprehensive evaluation model for landslide stability by coupling the landslide displacement evolution process with the dynamic disaster mechanism, which can ascertain the cause of landslide failure and the catastrophe mechanism, is constructed with constraints of the high-precision GNSS monitoring data from the outside of the landslide. Data obtained from monitoring before instability of three landslides in Dangchuan, Heifang, Gansu province are selected for model verification. The results show that HF06/07 GNSS monitoring site first becomes unstable, followed by HF09 GNSS monitoring site, and HF05 GNSS monitoring site is the last one that slides. It indicates that compared with the numerical simulation method of engineering geology, the results obtained by the developed technique in this chapter are better consistent with the actual monitoring situation. And the sliding sequence of landslide is also in high agreement with the actual sequence. All the obtained achievements in this paper demonstrate the organic coupling of the external high-precision monitoring information and the internal physical and mechanical evaluation model provides a new idea and perspective for the stability evaluation of loess landslide, and helps to well understand the deformation evolution mechanism of loess landslide.