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Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University 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.)

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]

Multi-epoch interferometric synthetic aperture radar (InSAR) is a highly effective technique for monitoring deformation in urban areas. However, interpreting InSAR deformation can be challenging due to various factors, including inherent geometric imaging distortion, the intricate structure and deformation properties of targets in urban scenes, and the multiple scattering of microwave signals between objects in urban scenes. This paper discusses the challenges involved in interpreting time-series InSAR deformation: (1) Precisely identifying the location of deformation signals and linking them to their corresponding objects, i. e., determining where the deformation signal occurs, (2) understanding the mechanisms and factors that cause the detected deformation signals, i.e., determining what the deformation signal represents, (3) establishing the connection among the detected deformation signals, the deformation events, and the scattering mechanisms. We suggest a parametric framework to improve the accurate interpretation of InSAR deformation. This framework includes several factors, including kinematic characteristics (deformation rate, cumulative deformation, deformation gradient, and deformation model), geometric parameters (position, size, structure, orientation, and roughness), semantic information (land cover type, terrain morphology, texture, and auxiliary information on natural and anthropogenic disturbance) and physical properties (scattering mechanism, penetrability, extensibility, conductivity, and thermal conductivity). Our approach aims to enhance the representation of coherent points for a better understanding of InSAR deformation. This paper offers a comprehensive overview of the advancements achieved in extracting parameters of InSAR coherent points and interpreting deformation based on geometric parameters, semantic information, and physical properties. High-precision 3D positioning is crucial for InSAR fine monitoring in urban areas. It helps determine the source of deformation signals and facilitates the analysis of deformation mechanisms. Semantic information, such as 3D models, high-resolution optical images, laser point cloud data, and land use data, can aid in interpreting InSAR deformation. By combining InSAR deformation data with a deep learning approach, there is an opportunity to interpret deformations effectively. In urban environments, the scattering mechanism of ground objects is complex. Multiple scattering signals can provide effective observations of deformation and information about the target's size. However, combining the scattering mechanism of synthetic aperture radar signals to carry out parameter inversion and deformation mechanism interpretation of urban target terrain remains a challenge. The framework, which considers the geometric parameters, semantic information, and physical attributes of InSAR coherent points, will be crucial for deformation interpretation and mechanism cognition. This framework will enable fine deformation monitoring, intelligent recognition, and application in future urban scenes. [ABSTRACT FROM AUTHOR]

Objectives: Co-seismic deformation monitoring is of great significance for the interpretation of co-seismic deformation characteristics and intuitive understanding of fault geometric characteristics. For large earthquakes with surface rupture, global navigation satellite system(GNSS) technique has a low spatial resolution, and interferometric synthetic aperture radar(InSAR) technology will cause phase decorrelation due to large deformation gradients, it is impossible to obtain specific deformation around the fault. Optical image correlation(OIC) and pixel offset tracking(POT) based on sub-pixel cross-correlation can solve these problems well. Methods: The main idea of the sub-pixel cross-correlation method is to use the pre-event image in the two images as a reference, and then compare the post-event image with it to evaluate the similarity between the two images, and then retrieve the displacement between the homonymous points. OIC can obtain east-west and north-south deformations, and POT can obtain range and azimuth deformations. In this paper, the deformation in each direction of the Kaikoura earthquake obtained from Sentinel-1 data and Sentinel-2 data is used to form different combinations, and the least square method is used to calculate the three-dimensional deformation. Results: The combination of OIC+POT_As_Des has the best constraint. The accuracy of the north-south deformation obtained by OIC+POT_Range is not as good as that of OIC+POT_As_Des. Compared with POT_As_Des, the north-south deformation precision obtained by it is higher, and the deformation performance on the right side of the Kekerengu fault and the left side of the Papatea fault is also better. In the northeast of the Kaikoura earthquake epicenter, very complex and huge surface deformation and multiple ruptures were detected in two close areas, and the vertical defoamation was mainly uplift. Conclusions: For Sentinel-1 and Sentinel-2, the combination of OIC+ POT_As_Des is most suitable for obtaining the three-dimensional deformation of the Kaikoura earthquake. This earthquake is a dextral strike-slip earthquake with reverse. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University 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 Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University 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.)

Objectives: Multi-temporal interferometric synthetic aperture radar (InSAR) technology is widely used in deformation monitoring over wide areas due to its characteristics of large-scale, high spatial-temporal resolution and the capability to monitor deformation signal with millimeter precision even submillimeter. For the time-consuming problem in acquiring image and data preprocessing over research area, this paper uses looking inside the continents from space with synthetic aperture radar (LiCSAR) products and takes a new time series analysis LiCSBAS method, which greatly improve the computational efficiency. Methods: The quality check and the closure error of the phase loop are used to detect the unwrapping error in the two-dimensional phase unwrapping, and the interferogram with low coherence and the large error pixels are excluded. To minimize atmospheric artifacts, utilizing the generic atmospheric correction online service for In‑ SAR (GACOS) products and based on this, a method for joint estimation of deformation and atmospheric phase is proposed. Taking the Chengdu Plain and western mountainous area as the study area, totally 90 [ABSTRACT FROM AUTHOR]

Complex image registration is a crucial step in interferometric synthetic aperture sonar (InSAS) signal processing. The registration quality directly influences the generation of interferogram and subsequent reconstruction of digital elevation model (DEM). Utilizing the geometric model of imaging, the rational function relationship between offset and slant range is deduced, thus this paper propose a new InSAS complex image registration method based on second order rational function surface fitting. Compared with the traditional polynomial surface fitting method, the proposed method in this paper has the advantages of higher precision and smaller calculation cost. The evaluation has been carried out using the root mean squared error(RMSE), correlation coefficient,residue numbers and computing time as the criteria. Simulation and real data experiments results indicate the validity of the proposed method. [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]

The theory and method of mining subsidence monitoring based on D-InSAR(differential interferometric synthetic aperture radar) technology is a hot topic in the current field of mine deformation monitoring. At present, three-dimensional monitoring model of mining subsidence has been constructed successfully by some scholars through integrating single line of sight to D-InSAR technology and mining subsidence law. However, the model is only suitable for the researches on mining subsidence of horizontal coal seam rather than inclined coal seam. In view of the above defects，according to the relationship between deformation of line of sight monitored by D-InSAR and three-dimensional deformation, considering the surface subsidence law of inclined coal seam mining, the surface subsidence monitoring method for inclined coal seam based on single line of sight to D-InSAR technology is proposed. The simulation experiment shows that the absolute relative error of the subsidence monitoring method is less than 9. 53%, with an average of 1. 31%. The absolute relative error of the horizontal movement monitoring along North-South (SN) and East-West (EW) is less than 9. 78%，with an ave-rage of 3. 71%, which meets the mining subsidence monitoring accuracy requirements. The surface movement and deformation caused by the mining of the 9310 working face of Nantun coal mine in yanzhou mining area, Shandong province China during 2012-01-27 - 2012-02-07 are successfully monitored by this method in this paper [ABSTRACT FROM AUTHOR]