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Objectives: In 2015, the Mw 7.8 earthquake in Nepal caused landslides and seriously impacted Nepal and its surrounding areas. Methods: In this paper, the Sindhupalchowk area, which is close to the epicenter of the earthquake, is selected as the study area. First, based on the L-band ALOS-2 and C-band Sentinel-1A synthetic aperture radar data, the detection and identification of post-earthquake landslides are carried out by using stacking-InSAR(interferometric synthetic aperture radar) technology. Second, the two-dimensional time series deformation characteristics of a typical landslide are obtained by combining the ascending and descending orbit data and multi-dimensional small baseline subset-InSAR technology. Results: Combined with optical images, 14 hidden points of landslide are delineated. The main deformation of the typical landslide occurs in the horizontal east-west direction, and the maximum deformation rate is −69 mm/a. Conclusions: By analyzing the trend and periodic term deformation signals in time series of the typical landslide, it is found that the earthquake has a significant acceleration effect on the landslide movement, and the increase of rainfall makes the landslide displacement show a periodic change from August to November every year. This paper can provide a reference for post-earthquake landslide monitoring re⁃ search. [ABSTRACT FROM AUTHOR]

Objectives: The Sichuan Tibet Railway crosses the Jinsha River specifically the section from Xiongsong Town to Shadong Town, Gongjue County, Tibet Autonomous Region, in which exist strong topography variations and many potential geohazards, and hence it urgent to detect the potential geohazards in this region to ensure the railway safety. Methods: In this paper, 61 Sentinel -1 ascending, 53 Sentinel -1 descending and 7 advanced land observing satellite 2 (ALOS 2) ascending images are used to derive the annual mean deformation rates in the satellite radar line of sight (LOS) with two advanced InSAR (interferometric synthetic aperture radar) approaches, namely generic atmospheric correction online service for InSAR (GACOS) assisted InSAR Stacking and LiCSBAS. The three LOS annual mean deformation rate maps are then used to determine 2D surface movements (one along the slope and the other perpendicular to the slope) in the study region. Results: The comparison between GACOS assisted InSAR Stacking and LiCSBAS results show that they agree with each other with correlation coefficients over 0.92 for the LOS deformation rates and over 0.85 for the 2D deformation rates, suggesting the reliability of GACOS assisted InSAR Stacking. The maximum annual deformation rate of 163 mm/a can be observed in the slope direction and seven landslides (A, B, C, D, E, F, G) can be clearly identified, which in turn lays the foundation for future real-time monitoring. Based on the detailed analysis of the seven regions by using InSAR and optical interpretation results, it is found that the seven landslides had relatively obvious deformation. Landslides A, B, C and D are active landslides, which might cause the result of river blocking due to overall instability. At present, landslides E, F and G are still in the stage of slow deformation. Conclusions: This study also find that GACOS assisted InSAR Stacking can effectively remove long band and topographic related atmospheric delay error with the help of GACOS, which has the advantages of being simple, effective, fast, and easy to popularize and apply. GACOS assisted InSAR Stacking technology can be used to quickly identify potential landslide hazards. [ABSTRACT FROM AUTHOR]

Landslide evolution is a long‐term and complex process. Geo‐hazards have different characteristics in various data, and have certain applicability in different stages of landslide. This paper applies different characteristics and applicability of various data sources in landslide by the multi‐source data fusion method. The deformation and failure characteristics and temporal and spatial evolution law of Huangnibazi landslide during its dynamic evolution from pre‐sliding to mid‐sliding to post‐sliding are studied. The results show that the deformation and failure process of Huangnibazi landslide can be divided into four stages: startup stage, accelerated deformation (accelerated sliding) stage, front expansion (decelerated sliding) stage and gradual stabilization stage. Huangnibazi landslide is a creep ‐ pull ‐ split landslide formed under the influence of self‐weight, rainfall infiltration, earthquake and vibration caused by human engineering activities. The application of various data in different stages of landslide is summarized: interferometric synthetic aperture radar(InSAR) technology, optical image and topographic data are used to determine potential landslide body before sliding. Optical remote sensing image is used to observe the overall deformation and trend of landslide accumulation body during sliding. Global positioning system (GPS) is used to continuously observe local deformation after sliding. Field investigation is used to determine the characteristics of geological hazard body. [ABSTRACT FROM AUTHOR]

The pre-failure evolution of landslides is of great value for the analysis of landslide triggering factors and post-disaster stability assessment. At present, optical images are commonly employed to ana⁃ lyze the pre-failure evolution, but it is well-known that their data availability could be highly limited due to the presence of clouds. With the advance in radar remote sensing and interferometric synthetic aperture radar(InSAR), it could provide a new technical approach for landslide pre-failure detection. In this paper, the 2018 Nanyu landslide in Gansu Province is utilized to demonstrate the capability of InSAR to trace its pre-failure surface displacements using European Space Agency’s Sentinel-1 radar data with a temporal interval of 12 days in different tracks. The results show that the landslide began to deform in June of 2017, and the maximum cumulative deformation reached up to 77 mm in the 13 months before the occurrence of the landslide. The time series InSAR derive displacements and the rainfall data is consistent, suggesting that the rainfall should be one of the triggering factors for the landslide. The study demonstrates the potential of radar interferometry for landslide detection, which can provide insights on landslide triggering factors, landslide disaster prevention and mitigation, and even landslide monitoring and early warning work in the future. [ABSTRACT FROM AUTHOR]

In order to promote the wider use of ground-based synthetic aperture radar (GB-SAR) in the field of surface micro-deformation monitoring, this paper summarizes the main types of GB-SAR and introduces the working principles and important parameters of some representative systems. By taking the S-SAR (slope-synthetic aperture radar) ground-based radar independently developed by the China Academy of Safety and Production Science as an example，the key processing techniques utilized by the GB-SAR are introduced. Experiments verify the accuracy of GB-SAR. It also shows the application of GB-SAR in open pit mine slope monitoring，mine dumping site and landslide emergency rescue. Finally，the problems and suggestions of GB-SAR in monitoring are proposed. [ABSTRACT FROM AUTHOR]

Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously，which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements，and the geometric distortion introduced by the side-looking orbit. In this paper，from landslide detection and monitoring perspective，the four major challenges of satellite radar technologies are discussed：① The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar (SAR) imagery with a long radar wavelength (e. g. S-band or L-band)，a short temporal resolution，and/or a high spatial resolution (e. g. 1 m or even higher)，and/or advanced interferometric SAR (InSAR) time series，and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range splitspectrum interferometry techniques.② Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service (GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③ The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model (DEM) for medium-spatial-resolution SAR data； in contrast，for high-resolution SAR data，a machine learning approach can be used to identify water bodies，shadow and layover areas without a requirement of a high-spatial-resolution DEM.④ Residual topographic phase exhibits in areas with high buildings or steep slopes，which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e. g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development. [ABSTRACT FROM AUTHOR]

In China，traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space~air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar (InSAR) data from satellites are employed to obtain a global panorama of a region，highlighting these problematic locations； yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence，it makes the compilation of long displacement time-histories feasible，contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then，detailed assessments can be done through the development of unmanned aerial vehicles (UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging (LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system (GNSS) and crack gauges，validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention [ABSTRACT FROM AUTHOR]