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Since 2009, the Guobu landslide has been very active, posing a safety threat to the Laxiwa Hydropower Station, located only several hundred meters downstream along the upper Yellow River in China. To investigate the current state of this landslide, we analyzed two stacks of X-band TerraSAR-X (TSX) High-resolution Spotlight (HS) mode images acquired from September 2015 to April 2017 in descending orbits with different look angles. A new time-series point-like target offset tracking (TS-PTOT) method is proposed to retrieve time-series surface displacements at point-like targets (PTs) from SAR image pairs properly combined with large temporal baselines and small spatial baselines. According to an evaluation of the standard deviation of time-series displacements at stable points, the TS-PTOT method increased the measurement precision of offset tracking by about 25% over the results using a single master. Our TSX-HS TS-PTOT results manifested a spatial pattern and magnitude of displacements highly similar to DInSAR result produced with an ALOS-2 PALSAR-2 image pair. The maximum displacement rate at the upper part of the slope during the study period was around 80 cm/yr in the line-of-sight (LOS) direction, which is much lower than the displacement rate measured in 2010. Furthermore, three-dimensional (3D) displacements at those identified homologous PTs were estimated by combining two-dimensional (2D) displacements measured by TS-PTOT from the two SAR data stacks. The 3D deformation pattern of the Guobu landslide properly verified its toppling-sliding deformation mechanism.

Arch bridges are important transportation infrastructures widely distributed in China, but they are prone to structural defects due to aging without routine inspection and maintenance. Therefore, Structural Health Monitoring (SHM) of these bridges is urgently needed by civil engineers to effectively reduce the risk of bridge damage or collapse on public safety. An essential method for SHM, the modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, can detect subtle deformation of bridges at relatively low costs. Nevertheless, identifying dense point-like targets (PTs) on such partially coherent arch bridges in SAR image is more difficult than that for other man-made objects, owing to their complex structures and backscattering behaviors. Furthermore, the complex mechanical properties of arch bridges, due to the varying arch-beam interactions, make it hard to separate the surface deformation and thermal dilation accurately, and the lack of specific structural knowledge, that can help to understand the deformation evolution process, limits the global structural risk assessment. Aiming at these problems, we developed a structure-driven multi-temporal DInSAR approach for arch bridge-specific SHM. By introducing three structure-driven steps, i.e. backscattering geometrical interpretation, linear thermal dilation estimation and validation, and Deformation Feature Points (DFPs) based risk assessment, into the traditional DInSAR method, the reliability of PTs identification, thermal dilation separation, and structural risk assessment for arch bridges are significantly improved. The effectiveness of our approach was fairly presented by two case studies of the Rainbow and Lupu bridges, and the experimental results were verified by leveling benchmark validation, cross-sensor comparison, as well as structural-reliability assessment. Our results revealed that arch bridges exhibit a similar pattern of PTs distribution that is dense around piers and sparse on the spans, as well as a symmetrical progressive pattern of surface deformation with the subsidence increasing from piers and reaching a peak at the central spans. In contrast, magnitudes and mechanisms of thermal dilation are different, and highly dependent on the materials and structural characteristics of specific bridges.

InSAR technology provides a powerful tool to detect potentially unstable slopes across wide areas and to monitor surface displacements of a single landslide. However, conventional time series InSAR methods such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) can rarely identify sufficient measurement points (MPs) in mountainous areas due to decorrelations caused by steep terrain and vegetation coverage. In this study, we developed a new InSAR approach, coherent scatterer InSAR (CSI), to map landslide surface displacements in the radar line-of-sight (LOS) direction by combining persistent scatterers (PS) and distributed scatterers (DS). The key ideas of CSI include the employment of the generalized likelihood ratio (GLR) test for the identification of statistically homogeneous pixels (SHPs) and the use of the phase linking algorithm to estimate optimal phase for each DS pixel. The joint exploitation of PS and DS targets dramatically increases the spatial density of MPs, which makes the phase unwrapping more reliable. To demonstrate the effectiveness of the CSI approach, we applied it to retrieve the historical displacements of the Jiaju landslide in Danba County of southwest China using 19 L-band ALOS PALSAR images (2006–2011) and nine C-band ENVISAT ASAR images (2007–2008). Multiple comparisons clearly illustrated the big advantages of CSI over PSI and SBAS in mapping landslide displacements with more details owing to much higher (> 10 times) MP density. Furthermore, the superiority of L-band SAR data over C-band for landslide investigation in rural environments was confirmed. Quantitative validation of the CSI results for PALSAR data against in-situ GPS measurements suggested an accuracy of about 10.5 millimeters per year (mm/year) in terms of root mean square error (RMSE). Afterwards, the spatial-temporal characteristics of the Jiaju landslide surface displacements were summarized, with a new upper boundary for the active northern part delineated. Particularly, the northern part of the landslide moved faster than the southern part, exhibiting a maximum LOS displacement rate of around 120 mm/year. Subsequently, the fluvial erosion by the Dajinchuan River was identified as the predominant impact factor for the instability of the Jiaju landslide. Finally, the major problems and challenges for the application of CSI method were discussed, and the conclusions were given.

In this study, on the basis of the image correlation technique and the time-series images of Landsat-8 (L8), Sentinel-2 (S2), and GaoFen-2 (GF2), a systematic technical process is designed to investigate the precursory displacement evolution of two successive slope failures occurred in Baige Village, China on Oct. 11, 2018 and Nov. 3, 2018. An innovative fusion strategy is proposed to investigate the displacement history of the Oct. 11, 2018 Baige landslide, which has two steps: (1) selecting the optimal correlation window size and search step to eliminate the inconsistency of image correlation for different sensors, and (2) inverting the fused displacement time-series from correlation results to enhance the temporal sampling density. The normalized displacement velocity indicates that the Oct. 11, 2018 Baige landslide is characterized by high-speed sliding (28 m/yr) and high shear outlet with an average elevation difference of around 82 m. The fused displacement time series indicates that the whole phase transformation can be from the secondary stage to the tertiary stage on Mar. 26, 2017. Furthermore, the displacement velocity shows four quiescence phases in the secondary stage and two acceleration phases in the tertiary stage. The seasonal precipitation is assumed as the main external triggering factor, and it combined with the brittle geological material attributes to control the precursory landslide displacement evolution and caused the catastrophic slope failure on Oct. 11, 2018. The precursory displacement signals with a magnitude above 5 m/day of the second landslide (Nov. 3, 2018) is quantified by the S2 image correlation. This study highlights the prospects of optical observation time series with medium-/high-resolution in detecting and quantifying the spatio-temporal evolution characteristics of long-term creeping landslides which may play a significant role in the early warning of the catastrophic slope failure.

Landslides and resultant barrier lakes are significant threats to human lives and infrastructures. Three-dimensional (3D) surface displacements can give vital clues to the exploration of internal structure of landslides, but they are difficult to be retrieved from spaceborne Synthetic Aperture Radar (SAR) observations due to the intrinsic limitation of SAR imaging geometry. Meanwhile, studies on predicting slope failure based on SAR-measured displacements are rarely seen. Here, we used SAR pixel offset tracking to investigate the Baige landslide before the collapse on 10 October 2018. 3D surface displacements retrieved by combining satellite SAR and optical observations revealed heterogeneous spatial patterns within the landslide complex. We observed linear secondary creep and accelerating tertiary creep prior to the failure from multi-sensor SAR data. The possibility of forecasting the failure was demonstrated by applying an inverse velocity method to the time-series displacements measured by Sentinel-1 during the tertiary creep, which is valuable for risk evaluation and disaster early warning.

Investigation of the spatio-temporal patterns of dune migration in a large-scale area with optical remote sensing techniques can help us to better understand aeolian phenomena and mitigate sand-dust disasters. With the rapid growth in data volume, extracting more accurate dune displacement time series and rates from optical observations has become possible; however, the method is not yet fully fledged. To address this issue, we propose an extended algorithm for the mature optical imagery cross-correlation (OICC) technique based on Landsat-8 (L8) and Sentinel-2 (S2) acquisitions. The main innovative points of this algorithm are: 1) the proposed pairing strategy for the OICC processing; 2) the modularized post-processing procedures for noise removal; and 3) the introduction of singular value decomposition (SVD) time-series inversion of the redundant optical observations to quantify the dune migration. To test the effectiveness of this algorithm, it was applied in the study of dune migration near Minqin Oasis in northwestern China, using enriched L8 and S2 images collected between April 2013 and April 2018. Compared with the original OICC results in stable areas, the post-processing and inversion of the proposed algorithm reduce the uncertainty by around 22–35% and 3–5% for L8, 29–48% and 5–12% for S2, respectively. The cross-comparison between the L8- and S2-derived displacement time series shows high consistency, and presents a lower uncertainty than the result of the traditional no-inversion method. Furthermore, the derived displacement rates show spatial patterns that are similar to those of the manually digitized results obtained with historical Google™ Earth (GE) images. These comparisons show the advantage of the proposed algorithm in automatically and accurately quantifying dune migration. Taking into account these measurements, the spatio-temporal evolution patterns of dune migration in the study area were analyzed. From the spatial perspective, the sand dunes move along a northwest-southeast axis with four detected transport pathways. Our research also shows that around 1087.7 km2 of dune fields present an active status. The active sand dunes are currently encroaching on around 155.5 km2 and 4.4 km2 of land each year outside and inside the oasis, respectively, representing a problem of rapid desertification. Temporally, the displacement time series along the dominant migration direction appears as seasonal variations that are seemly consistent with the changes in local atmospheric conditions. The proposed algorithm provides a new perspective to investigate the spatio-temporal evolution of dune migration with medium-resolution L8 and S2 optical datasets.

Synthetic Aperture Radar Interferometry (InSAR) provides an effective tool to study slow-moving landslides. However, InSAR observations are often contaminated by tropospheric artefacts due to spatial and temporal variations of atmospheric refractivity. Particularly, the topography-dependent stratified delays may introduce seasonal oscillation biases into InSAR-measured deformation time series under steep terrains, which cannot be removed by conventional spatial and temporal filtering. In this study we proposed two complementary approaches to correct the stratified tropospheric delays for time series InSAR analysis when studying single landslides. One is the Iterative Linear Model (ILM) as an improved version of the traditional Linear Model (LM). The other is to fuse tropospheric delays predicted by several global weather models (FDWM) with different temporal intervals and spatial resolutions. Both methods are integrated into the standard Small BAseline Subset (SBAS) time series analysis procedure. We evaluated the proposed methods in three landslide-prone areas in southwest China using Sentinel-1 datasets. The experimental results demonstrated that the ILM method removed the seasonal stratified delays mixed in deformation time series, unaffected by the deforming points. The FDWM method achieved an optimal combination of tropospheric delay predictions by four weather models, i.e. ERA-Interim, ERA5, HRES ECMWF, and MERRA-2. Validations using in-situ GPS measurements suggested that the original Root Mean Squared (RMS) values of interferometric phases declined by more than 35% after both ILM and FDWM corrections. The ILM had better performances than the FDWM to correct stratified delay for single landslides, whereas the FDWM can be an effective alternative when the ILM is inapplicable in case of limited coherent points.