Chaoying Zhao

Time-series deformation monitoring over mining regions with SAR intensity-based offset measurements

Underground mining can induce large vertical displacements that often lead to the loss of coherence in repeat-pass interferometric synthetic aperture radar (SAR) images. Using SAR intensity images, this paper employs the image offset tracking method to map SAR slant range changes due to ground deformation over areas of mining. The rationale of slant range offset measurement with respect to the vertical deformation is analysed and the prerequisite of applying the slant range offset method to monitor vertical deformation is discussed. Results from the slant range offset method are used to produce time-series of cumulative ground displacements via least square estimate. We use six Advanced Land Observation Satellite (ALOS)-Phased Array L-band SAR (PALSAR) images over two coalfields in Inner Mogolia, China, to illustrate the proposed method and its effectiveness. We achieve deformation measurements with a precision of ∼0.2 m, with the maximum vertical displacement over the mining sites reaching ∼4.5 m. Finally, we use time-series results to outline common features identified in mining-induced deformation. Our results are supported by in situ investigations.

Monitoring mine collapse by D-InSAR

Abstract For harmful ground collapse and its special deformation characteristics, which causes SAR images to lose coherence, InSAR technology cannot be applied in monitoring surface collapse in mining areas. We took the Shenmu mining area in northern Shaanxi province as an example to study subsidence in mining areas and proposed an interpolated multi-view processing method. The results show that this method can improve the detectable deformation gradient to a certain extent and can become a good reference value for monitoring large scale gradient deformation. We also analyzed the rules for temporal decorrelation in mining.

Mining collapse monitoring with SAR imagery data: a case study of Datong mine, China

Abstract A mining-induced collapse is often characterized by large deformation gradient, spatial discontinuity, and temporal nonlinearity, resulting in the loss of interferometric SAR (InSAR) coherence and consequently subsidence information in areas of steep deformation gradients. In this study, we present different SAR deformation monitoring schemes to map the mining-induced subsidence and collapse. First, SAR data with different wavelengths, including C-band ERS-1, C-band Envisat ASAR, and X-band TerraSAR-X data, are used to highlight three mining subsidence stages and their temporal evolutions over Datong mine (China) in the past 20 years. Mining-induced subsidence over a region can be delimited from InSAR deformation maps, where InSAR coherence is lost over the area of peak subsidence. Second, in order to monitor the large-gradient surface deformation caused by underground mining activities, three SAR deformation monitoring schemes are proposed, including a full-resolution interferogram method, a “remove-restore” phase unwrapping method, and a fusion of phase and offset measurements. Then, taking the Datong coalfield as an example, we demonstrate the capabilities of these methods on mapping large-gradient deformation. Finally, we have found that over 80% of coalfields have deformed during the past 20 years. We conclude that the fusion of the InSAR phase and offset measurements can provide a reliable estimate of large-gradient mining-induced deformation.

An iterative Goldstein SAR interferogram filter

This article presents a new modified Goldstein synthetic aperture radar (SAR) interferogram filter algorithm, named the iterative Goldstein filter. The main idea of this approach is to iteratively filter the SAR interferogram, by determining the filtering parameter alpha adaptively with respect to the pseudo-correlation value of the original and/or last filtered interferograms several times. The filter can be stopped automatically by pre-setting the threshold of mean value and the improvement of pseudo-correlation in given filter windows. Experimental results with both a simulated digital elevation model (DEM) interferogram and real SAR deformation interferogram show an improvement in the new algorithm results compared with those using the Goldstein filter, and its enhanced version, the Baran filter. In addition, from a pseudo-correlation map of the iteratively filtered interferogram, some valuable information can also be abstracted based on the signal residues.

An improved SAR interferogram denoising method based on principal component analysis and the Goldstein filter

ABSTRACT Interferogram filtering is an important data processing step in Interferometric synthetic aperture radar (InSAR) applications, which has a direct impact on the accuracy of the phase unwrapping and digital elevation model (DEM) or deformation results retrieval. An improved synthetic aperture radar (SAR) interferogram denoising method based on principal component analysis and the Goldstein filter is proposed, which can improve the coherence of interferogram remarkably and get more coherent targets. First, homogeneous pixels are identified with stacks of SAR amplitude data, which can obtain the unbiased coherence estimation. Then, the noise phase of one resolution unit is suppressed based on the principal component analysis of multi-baseline InSAR coherence stacks by considering the relationship between pixel size and scattering mechanism. Finally, the remaining noise is smoothed with the iterative Goldstein filter over spatial domain. The proposed method is tested over one deformed and low-coherence region to verify the better performance in the terms of noise reduction and coherence increase.

Two-dimensional deformation monitoring over Qingxu (China) by integrating C-, L- and X-bands SAR images

Multi-band synthetic aperture radar (SAR) data sets with different imaging parameters enrich the ground deformation monitoring by interferometric synthetic aperture tadar (InSAR) technique. It is desirable to integrate of these images to produce the high-precision three-dimensional (3-D) or two-dimensional (2-D) deformation. In this study, high-precision east–west horizontal and vertical deformation rate over Qingxu region (Shanxi province, China), where serious deformation associated with the excessive pumping of ground water is documented, is produced through integrating of 22 C-band ENVISAT/ASAR, 16 L-band ALOS/PALSAR and 11 X-band TerraSAR images. The results show that the vertical and east–west deformation rate reach to −16.68 and −4.6 cm year−1, respectively, indicating the severe surface deformation in our study area. The comparison between our observed 2-D deformation and global position system (GPS) observations displays that the standard deviations of east–west deformation and vertical deformation are about 4.7 and 3.8 mm year−1, respectively, demonstrating the reliability of our result. This study should help us to better understand the formation and development of the deformation over the research area and provide scientific evidence on a sound management of ground water pumping to mitigate potential damages on infrastructures and environments.

Remote Sensing of Landslides—A Review

Triggered by earthquakes, rainfall, or anthropogenic activities, landslides represent widespread and problematic geohazards worldwide. In recent years, multiple remote sensing techniques, including synthetic aperture radar, optical, and light detection and ranging measurements from spaceborne, airborne, and ground-based platforms, have been widely applied for the analysis of landslide processes. Current techniques include landslide detection, inventory mapping, surface deformation monitoring, trigger factor analysis and mechanism inversion. In addition, landslide susceptibility modelling, hazard assessment, and risk evaluation can be further analyzed using a synergic fusion of multiple remote sensing data and other factors affecting landslides. We summarize the 19 articles collected in this special issue of Remote Sensing of Landslide, in the terms of data, methods and applications used in the papers.

Small-scale loess landslide monitoring with small baseline subsets interferometric synthetic aperture radar technique—case study of Xingyuan landslide, Shaanxi, China

Abstract. Small baseline subsets interferometric synthetic aperture radar technique is analyzed to detect and monitor the loess landslide in the southern bank of the Jinghe River, Shaanxi province, China. Aiming to achieve the accurate preslide time-series deformation results over small spatial scale and abrupt temporal deformation loess landslide, digital elevation model error, coherence threshold for phase unwrapping, and quality of unwrapping interferograms must be carefully checked in advance. In this experience, land subsidence accompanying a landslide with the distance <1  km is obtained, which gives a sound precursor for small-scale loess landslide detection. Moreover, the longer and continuous land subsidence has been monitored while deformation starting point for the landslide is successfully inverted, which is key to monitoring the similar loess landslide. In addition, the accelerated landslide deformation from one to two months before the landslide can provide a critical clue to early warning of this kind of landslide.

Application of InSAR Techniques to an Analysis of the Guanling Landslide

On the afternoon of 28 June 2010, an enormous landslide occurred in the Gangwu region of Guanling County, Guizhou Province. In order to better understand the mechanism of the Guanling landslide, archived ALOS/PALSAR data was used to acquire the deformation prior to the landslide occurrence through stacking and time-series InSAR techniques. First, the deformation structure from InSAR was compared to the potential creep bodies identified using the optical remote sensing data. A strong consistency between the InSAR detected deformed regions and the creep bodies detected from optical remote sensing images was achieved. Around 10 creep bodies were suffering from deformation. In the source area, the maximum pre-slide mean deformation rate along the slope direction reached 160 mm/year, and the uncertainty of the deformation rates ranged from 15 to 34 mm/year. Then, the pre-slide deformation at the source area was analyzed in terms of the topography, geological structure, and historical rainfall records. Through observation and analysis, the deformation pattern of one creep body located within the source area can be segmented into three sections: a creeping section in the front, a locking section in the middle, and a cracking section in the rear. These sections constitute one of the common landslide modes seen in the south-west of China. This study concluded that a sudden shear failure in the locking segment of one creeping body located within the source area was caused by a strong rainstorm, which triggered the Guanling landslide.

Landslide detection and monitoring with insar technique over upper reaches of jinsha river, china

Three InSAR products and DEM data are involved to detect the potential landslides over Wudongde Hydropower Station Section, Jinsha River. More than ten known and unknown landslides are successfully detected. Meanwhile, Small Baseline Subsets (SBAS) InSAR technique is applied to calculate the time-series deformation of the Jinpingzi landslide. Then, in-situ georobot measurements are used for point-wise comparison. The precision in slide direction is 1.8 cm by comparing with in-situ georobot measurements. At last, the Erpingcun landslide is taken as an example to calculate the vertical and horizontal deformation components by fusing the ascending and descending line-of-sight results.