The slip-surface geometry of a landslide is crucial for stability analysis and early warning, making its accurate and detailed characterization essential for hazard assessment and mitigation. However, traditional methods for obtaining slip-surface geometry are resource-intensive, requiring significant manpower and materials. Some studies have used the three-dimensional (3D) deformation from remote sensing data to infer detailed slip-surface geometry of landslides using the mass conservation method. However, this method usually requires prior data to calibrate the model parameters, and its underlying assumptions may not be valid for all landslide scenarios. In this study, we designed an Extended Vector Inclination Method (EVIM) to determine detailed landslide slip-surface geometry and established a framework for deriving landslide thickness based on 3D deformation derived from SAR and optical remote sensing data. The experiments based on actual events (Hooskanaden, Shangxintian, and Daopo landslide) and simulations demonstrate that EVIM can reliably estimate landslide thickness in a manner consistent with in-situ measurements. Furthermore, simulations indicate that the magnitude of deformation and the error in the constrained 3D deformation affect the accuracy of our method. We conclude that in cases where the mass conservation method is difficult to apply (e.g., landslides lacking prior information), the EVIM may serve as an alternative method. The proposed framework enables rapid slip-surface inversion, making it well-suited for regional-scale landslide stability assessments.

The kinematic characteristics of landslides are crucial for understanding their underlying mechanisms. However, many landslides are influenced by multiple factors, posing significant challenges in interpreting their kinematic processes. This study presents a strategy to address these challenges by integrating Interferometric Synthetic Aperture Radar (InSAR), Independent Component Analysis (ICA), and geophysical models. The Huangtupo landslide, a typical giant landslide influenced by multiple factors, was selected as the study area due to its significant threat to the safety of residents in the Three Gorges Reservoir area. Using InSAR, the deformation characteristics of the landslide were identified. ICA applied to the InSAR measurements revealed two primary deformation patterns, which were further linked to geomechanical processes using in-situ data. The first independent component indicated that one of the major motions of the landslide was periodic sliding driven by precipitation. The second component suggested that soil swelling and shrinking, induced by changes in precipitation and reservoir water levels, resulted in opposing motion patterns in the front and rear parts of the landslide during annual cycles. The interpretations were subsequently validated using kinematic models. This study concludes that the proposed strategy can be extended to similar applications and offers new insights into the Huangtupo landslide.

InSAR and optical pixel offset tracking (POT) are two efficient tools for monitoring landslide displacements, but limitations in resolving 3D displacements constrain the full exploration of kinematic behaviors, especially for complex landslides exhibiting diverse movement types. In this study, we propose a technical route that combines SAR and optical images to reveal the spatiotemporal evolution of the Lanuza landslide (Spain). In the temporal domain, ascending and descending Sentinel-1 SAR images were acquired to retrieve the line-of-sight (LOS) displacements. STL and cross wavelet transform were integrated to calculate the time lag between displacements and environmental factors. In the spatial domain, a two-stage method combining feature point matching and DeepFlow (FPM-DF) was proposed to retrieve the non-rigid horizontal displacements from optical images. A strain model and Bayesian inversion framework (SM-BIF) were integrated to invert 3D displacement fields. The mass conservation method was subsequently applied to estimate the landslide thickness. The results indicate that (1) the periodic terms of displacement are in phase with the freeze-thaw cycle of solifluction, which can intensify earthflow movement. (2) FPM-DF method is more efficient than the traditional POT method, especially for smallscale displacement fields, achieving reductions of standard deviations by 38 % and 51 % in the EW and NS directions, respectively. (3) the SM-BIF method reduces the maximum standard deviations of the 3D displacement field compared to the SM-VCE method, and the maximum thickness of the earthflow is approximately 22 m. This study can provide valuable insights into comprehensive monitoring of complex landslides with multiplatform remote sensing datasets.

Landslides frequently disrupt highway networks in mountainous regions globally, presenting a grave threat to the safety of vehicles and pedestrians. A quantitative assessment of landslide risk within the highway network is crucial for the implementation of targeted monitoring, early warning, and engineering interventions. In this study, a non-contact quantitative risk assessment framework for translational landslides is proposed, which integrates Interferometric Synthetic Aperture Radar (InSAR), geophysical inversion, and numerical simulation techniques. The framework can reliably estimate the surface velocity, subsurface geometry, volume, and potential spatial consequences of landslides, and assess the potential damages and losses resulting from the landslide failure. Taking Lashagou L6 landslide in Linxia City, China as a case study, the results demonstrate a robust agreement between the InSAR-derived two-dimensional (2D) displacements and Global Navigation Satellite System (GNSS) observations, which are suitable for the inversion of active landslide thickness. The minimum mean error between the inverted landslide thickness and the observed borehole thickness is 0.8 m. The maximum thickness of the inverted basal sliding surface is 4.1 m, corresponding to a landslide volume of 1.25 x 104 m3. Fully considering the uncertainties within the technical framework, an estimated 1.2-1.7 vehicles are projected to be impacted, resulting in 5.3-7.7 casualties. The proposed non-contact framework demonstrates high reliability and considerable application versatility, particularly beneficial in inaccessible mountainous regions.

Regarding the deficiency of traditional deformation monitoring in effectively detailing deformation of local unique monitoring objects due to the overall deformation model, this paper proposes a three-layer mixed deformation model, i. e., block, region, and overall deformation, based on terrestrial 3D laser scanning technology. A block-based deformation calculation method is also designed. This method mainly includes object segmentation, deformation estimation, and deformation fusion, and can automatically extract deformation information of different scales without prior monitoring information. Simulation results show that under this method, the mean angle change estimation error of RANSAC algorithm plane fitting regression is 1. 21 '', and the estimation reliability increases with an increase in block size within a certain range. The results of the landslide experiment show that the minimum value method has less displacement estimation noise, and a 0. 2 m block size segmentation can provide further deformation estimation details. The proposed method is particularly suitable for monitoring fields with nonuniform deformation characteristics, and has certain theoretical and practical significance for promoting the transformation of disaster monitoring from "point monitoring" to "surface monitoring" for landslides and other disasters that are difficult for personnel to reach.

Catastrophic landslides occur frequently in Guizhou Province, China, and the landslides in this area have special geomorphological, geological, and anthropogenic features. In order to detect and explore the distribution pattern and control factors of active landslides in Guizhou, firstly, a total of 693 active landslides throughout Guizhou Province were mapped based on the deformation rate, which was obtained by spatiotemporal filtering and Intermittent Small Baseline Subset (ISBAS) Interferometric Synthetic Aperture Radar (InSAR) techniques. Then, the relationships between the detected landslides and elevation, aspect, slope gradient, and stratigraphic lithology were analysed. Moreover, it was found that the landslides were mainly concentrated in three stratigraphic combinations, that is T1f~P2l−d, T1f~T1yn, and T2g~T1yn. Thereafter, the correlation coefficients between the landslide density and elevation and distance to the stratigraphic boundary were 0.54 and −0.19, indicating that the distribution of landslides was significantly controlled by the elevation and the boundary of specific stratigraphic combinations. Finally, we chose a typical landslide to explore how landslide development was controlled by the combined effects of elevation and stratigraphy by using ascending and descending InSAR results. We revealed that landslides occurred primarily in areas with a steep slope and a stratigraphy characterized by mudstone and sandstone.

The slip-surface geometry and volume of landslides are fundamental for landslide modeling and mechanism interpretation. The movement of a landslide, which is generally controlled by the slip-surface geometry, can be obtained from interferometric synthetic aperture radar (InSAR) measurements. However, there is a lack of a general approach for inferring the slip-surface geometry and volume of landslides through the InSAR-derived deformation field. Here we developed a geometry-based method to determine the landslide slip-surface geometry and volume using InSAR measurements and applied it to the Jinsha River Basin, a landslide-prone area that poses a significant threat to residents and infrastructure. Through the InSAR-derived displacement, topography, and Google Earth images, 50 creeping landslides were identified in the study area. Based on the displacement field, the landslide slip-surface slope was inverted under the assumption that the landslide displacement was parallel to the slip-surface. Then the two-dimensional slip-surface depths and volumes of selected landslides were inferred using the slip-surface slope. Comparisons with the in-situ data suggest that the results obtained in this study are reliable. The mapped landslides in the study area have depths of approximately 16-160 m along the central axis and volumes ranging from 483,412 to 135,789,944 m(3). The derived volume-area relationship and 2D slip-surface depth suggest that deep-seated landslide is a major landslide type in the study area. We conclude that our method can infer the slip-surface geometry of creeping landslides based on InSAR observation and our results have improved the understanding of the landslide mechanisms in the Jinsha River Basin, China.

This work employs synthetic aperture radar interferometry technology to investigate infrastructure deformation in which discontinuous and irregular interferometric fringes make phase unwrapping (PhU) challenging. This study aimed to improve the reliability and practicability of PhU through the number of redundant observations to optimize PhU networks. The proposed PhU networks optimization strategy can improve the efficiency of PhU and accuracy. In addition, we evaluated the reliability of selected networks based on two popular methods. Finally, we used the Edgelist PhU method to demonstrate the reliability of optimized networks. Experiments were carried out on the Nanjing Dashengguan Yangtze River high-speed railway bridge, China, and on buildings deformation in Xi'an, China, the results of which indicate that the proposed method can effectively balance the accuracy and efficiency of PhU.

Landslide extraction is one of the most popular topics in remote sensing. Numerous techniques have been proposed to manage the landslide identification problem. However, most aim to extract landslides that have already occurred or delineate the potential landslide manually. It is greatly important to identify and delineate potential landslides automatically, which has not been investigated. In this paper, we propose an automatic identification and delineation method, i.e., object-based image analysis (OBIA) of potential landslides by integrating optical imagery with a deformation map. We applied a deformation map generated by the interferometric synthetic aperture radar (InSAR) technique, rather than the digital elevation model (DEM) for landslide segmentation. Then, we used a classification and regression tree (CART) model with the spectral, spatial, contextual and deformation characteristics for landslide classification. For accuracy assessment, we implemented the evaluation indicators of recall and precision. The proposed method is verified in both specific landslide-prone regions (Jinpingzi and Shuanglongtan landslides) and a large catchment of the Jinsha River, China. By comparing our results with the ones using purely optical imagery, the precision of the Jinpingzi landslide is improved by 14.12%, and the recall and precision of the Shuanglongtan landslide are improved by 3.1% and 3.6%, respectively, and the recall for the large catchment is improved by 9.95%. Our method can improve delineation of potential landslides significantly, which is crucial for landslide early warning and prevention.

Monitoring surface deformation associated with geohazards is a prerequisite for geological disaster prevention. Interferometric synthetic aperture radar (InSAR) has the ability to capture ground deformation of landslides with high precision over a large area. However, in mountainous regions this capability is often limited by decorrelation noise and atmospheric phase artifacts. Over Eldorado National Forest, California, where many landslides need to be monitored and investigated, InSAR images are severely affected by atmospheric noise and the coherence is highly variable throughout the year, challenging InSAR techniques to effectively detect movement of active landslides. In order to obtain reliable measurements, we have designed an interferogram selection method and an InSAR segment processing (SP) technique to improve the deformation measurement. Compared with the traditional non-segment processing (NSP), the SP technique has demonstrated advantages in reducing the impact of atmospheric noise. Our results from both the ascending and descending InSAR datasets based on SP indicate that many landslides along the Highway 50 corridor were creeping at a rate of less than 10 cm/year during the investigation period. We have found that landslide movements in the study region present obvious seasonal patterns. The precipitation and pore-water measurements and our hydrogeological diffusion models suggest that the seasonal movements of these landslides are primarily driven by the pore-water pressures, and the peak deformation of the landslides may occur in the dry season (May to October) due to the time lag of precipitation infiltration. In addition, we have observed subtle upward movement of the landslides after the precipitation begins, which is likely caused by the swelling of clay-rich landslide body due to an increase in the pore pressure. Furthermore, several other localized unstable regions which may contain potential landslide hazards were also detected and mapped in the study area, and their dynamics need further investigation. We conclude that InSAR is capable of detecting slow landslide motions over difficult terrains if associated artifacts in the interferograms are suppressed. InSAR time-series measurements along with hydrogeological models enable us to characterize the time delay between peaks of landslide motions and precipitation.

Wuhan, the largest city in central China, has experienced rapid urban development leading to land subsidence as well as environmental concerns in recent years. Although a few studies have analyzed the land subsidence of Wuhan based on ALOS-1, Envisat, and Sentinel-1 datasets, the research on long-term land subsidence is still lacking. In this study, we employed multi-temporal InSAR to investigate and reveal the spatiotemporal evolution of land subsidence over Wuhan with ALOS-1, Envisat, and Sentinel-1 images from 2007–2010, 2008–2010, 2015–2019, respectively. The results detected by InSAR were cross-validated by two independent SAR datasets, and leveling observations were applied to the calibration of InSAR-derived measurements. The correlation coefficient between the leveling and InSAR has reached 0.89. The study detected six main land subsidence zones during the monitoring period, with the maximum land subsidence velocity of −46 mm/a during the 2015–2019 analysis. Both the magnitude and the extent of the land subsidence have reduced since 2017. The causes of land subsidence are discussed in terms of urban construction, Yangtze river water level changes, and subsurface water level changes. Our results provide insight for understanding the causes of land subsidence in Wuhan and serve as reference for city management for reducing the land subsidence in Wuhan and mitigating the potential hazards.

The Pusa landslide, in Guizhou, China, occurred on 28 August 2017, caused 26 deaths with 9 missing. However, few studies about the pre-event surface deformation are provided because of the complex landslide formation and failure mechanism. To retrieve the precursory signal of this landslide, we recovered pre-event deformation with multi-sensor synthetic aperture radar (SAR) imagery. First, we delineated the boundary and source area of the Pusa landslide based on the coherence and SAR intensity maps. Second, we detected the line-of-sight (LOS) deformation rate and time series before the Pusa landslide with ALOS/PALSAR-2 and Sentinel-1A/B SAR imagery data, where we found that the onset of the deformation is four months before landslide event. Finally, we conceptualized the failure mechanism of the Pusa landslide as the joint effects of rainfall and mining activity. This research provides new insights into the failure mechanism and early warning of rock avalanches.

Wuhan, the largest city in central China, has experienced rapid urban development leading to land subsidence as well as environmental concerns in recent years. Although a few studies have analyzed the land subsidence of Wuhan based on ALOS-1, Envisat, and Sentinel-1 datasets, the research on long-term land subsidence is still lacking. In this study, we employed multi-temporal InSAR to investigate and reveal the spatiotemporal evolution of land subsidence over Wuhan with ALOS-1, Envisat, and Sentinel-1 images from 2007–2010, 2008–2010, 2015–2019, respectively. The results detected by InSAR were cross-validated by two independent SAR datasets, and leveling observations were applied to the calibration of InSAR-derived measurements. The correlation coefficient between the leveling and InSAR has reached 0.89. The study detected six main land subsidence zones during the monitoring period, with the maximum land subsidence velocity of −46 mm/a during the 2015–2019 analysis. Both the magnitude and the extent of the land subsidence have reduced since 2017. The causes of land subsidence are discussed in terms of urban construction, Yangtze river water level changes, and subsurface water level changes. Our results provide insight for understanding the causes of land subsidence in Wuhan and serve as reference for city management for reducing the land subsidence in Wuhan and mitigating the potential hazards.

为了获取2017年6月24日四川省茂县特大滑坡滑前的形变信息并分析其诱发因素,该文利用覆盖滑坡区域滑前40景Sentinel-1数据,采用合成孔径雷达干涉点目标分析(IPTA)技术,解算出该滑坡滑前2014年10月9日至2017年6月19日期间形变的年速率及形变的时间序列,并与降雨数据进行了对比分析.对于滑坡区植被覆盖严重的问题,该文利用永久散射体目标(PSC)对影像配准窗口大小及过采样因子不敏感的特性来识别PS点.分析结果表明,茂县滑坡在滑动前两年多时间内具有明显的形变,最大形变速率达到4(cm·a-1),而且在滑坡发生前半个月内形变有突然增大的现象,推测该滑坡滑前持续的强降雨是导致滑坡失稳的主要诱因.

The Xinmo landslide occurred on 24 June 2017 and caused huge casualties and property losses. As characteristics of spatiotemporal pre-collapse deformation are a prerequisite for further understanding the collapse mechanism, in this study we applied the interferometric synthetic aperture radar (InSAR) technique to recover the pre-collapse deformation, which was further modeled to reveal the mechanism of the Xinmo landslide. Archived SAR data, including 44 Sentinel-1 A/B data and 20 Envisat/ASAR data, were used to acquire the pre-collapse deformation of the Xinmo landslide. Our results indicated that the deformation of the source area occurred as early as 10 years before the landslide collapsed. The deformation rate of source area accelerated about a month before the collapse, and the deformation rate in the week before the collapse reached 40 times the average before the acceleration. Furthermore, the pre-collapse deformation was modeled with a distributed set of rectangular dislocation sources. The characteristics of the pre-collapse movement of the slip surface were acquired, which further confirmed that a locked section formed at the bottom of the slope. In addition, the spatial-temporal characteristics of the deformation was found to have changed significantly with the development of the landslide. We suggested that this phenomenon indicated the expansion of the slip surface and cracks of the landslide. Due to the expansion of the slip surface, the locked section became a key area that held the stability of the slope. The locked section sheared at the last stage of the development, which triggered the final run-out. Our study has provided new insights into the mechanism of the Xinmo landslide.

针对甘肃永靖县的黑方台地区滑坡不断对当地居民人身及财产安全构成重大威胁的现状,该文选取高分辨率的升降轨TerraSAR数据、3 m分辨率的DEM数据和30 m分辨率的SRTM DEM数据,利用InSAR技术对该地区的地表形变进行监测,主要结果如下:用Stacking技术获取了黑方台的形变速率图,识别出14处不稳定滑坡体;用SBAS-InSAR技术对典型滑坡体进行时间序列监测,将InSAR结果投影到滑坡方向与已有的GPS结果进行比较,最大较差为6 mm,最大中误差为3 mm.结果 表明,InSAR技术用来识别与监测黄土滑坡方便可靠,并且精度较高.

Unwrapping error is a common error in the InSAR processing, which will seriously degrade the accuracy of the monitoring results. Based on a gross error correction method, Quasi-accurate detection (QUAD), the method for unwrapping errors automatic correction is established in this paper. This method identifies and corrects the unwrapping errors by establishing a functional model between the true errors and interferograms. The basic principle and processing steps are presented. Then this method is compared with the L1-norm method with simulated data. Results show that both methods can effectively suppress the unwrapping error when the ratio of the unwrapping errors is low, and the two methods can complement each other when the ratio of the unwrapping errors is relatively high. At last the real SAR data is tested for the phase unwrapping error correction. Results show that this new method can correct the phase unwrapping errors successfully in the practical application.

采用3类InSAR产品和DEM数据开展金沙江流域乌东德水电站段的潜在滑坡探测,成功识别出多处已知和未知的滑坡点,并探测出滑坡体的形态及稳定性,提供了一种高效的大范围滑坡探测技术.同时采用小基线集InSAR技术对金坪子滑坡进行监测,不仅获得该滑坡的空间分区特征,也获取重点滑坡区的时间序列结果,并且与地面监测结果比较,精度达1.8cm.展示了不同InSAR技术在不同尺度滑坡调查与监测中的应用特点.

Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.