An integrated framework for wide-area active landslide detection with InSAR observations and SAR pixel offsets

Zhang, Chenglong; Li, Zhenhong; Yu, Chen; Chen, Bin; Ding, Mingtao; Zhu, Wu; Yang, Jing; Liu, Zhenjiang; Peng, Jianbing

doi:10.1007/s10346-022-01954-z

Cited by 30 publications

(16 citation statements)

References 56 publications

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“…The proposed framework complements the landslide prevention frameworks presented in previous studies [67], [23] and the landslide early warning system demonstrated in [72]. SAR data processing in the framework is nearly automated while accounting for both efficiency and accuracy.…”

Section: Significance Of Our Proposed Technical Frameworkmentioning

confidence: 86%

“…Moreover, Multi-Temporal InSAR (MTI) approaches (e.g., Persistent Scatterer InSAR (PSI) [19], Small BAselines Subset (SBAS) [20] and SqueeSAR [21]) are able to address the decorrelation problem and estimate high-precision surface displacement. With the extensive accumulation of SAR images, MTI approaches have been successfully adopted to produce landslide inventories in the Jinsha River Basin [22], [23], the Three Gorges Reservoir region [24], the Dadu River Basin [25], the Xiaojiang River Basin [26], the Bailong River Basin [27], the Yalong River Basin [28], and the Niulanjiang River Basin [29].…”

Section: Introductionmentioning

confidence: 99%

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InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River

Song

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Landslides have attracted extensive attention and research worldwide because of their tremendous destructiveness, and many catastrophic landslides have occurred along river corridors and reservoir bank slopes. The detection and detailed monitoring of potential landslides are prerequisites for landslide disaster prevention, with recent advance in spaceborne Interferometric Synthetic Aperture Radar (InSAR) having effectively addressed this challenge. In this study, a wide-area landslide detection and monitoring framework, combining multiple InSAR techniques, is proposed to investigate active landslides along the upper reaches of the Yellow River from Longyang Gorge to Liujia Gorge (UYRLL), north-west China. A total of 597 active landslides have been mapped in this region for the first time. Our analyses suggest that a large percentage of the detected landslides are distributed at an elevation of 2,000-3,000 m with slope angles of 5-30° and their spatial distribution density is correlated with regional tectonic activity. Multi-Temporal InSAR (MTI) techniques have also been adopted to analyze the surface motion characteristics of two typical landslides (i.e., the Lijia landslide and the Xijitan landslide), showing that these two landslides are dominated by linear motion. The landslide movements also contain slight non-linear oscillations, inferred

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Section: Significance Of Our Proposed Technical Frameworkmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River

Song

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A large number of potential landslides have not been discovered (Xu et al, 2019), and the early discovery of potential landslides has always been a great challenge worldwide. At present, the characteristics of surface deformation [extracted by Interferometric Synthetic Aperture Radar (InSAR) technique], geomorphology, and/ or topography are employed to identify potential (active) landslides across extensive regions (e.g., Wang Y. et al, 2022;Zhang et al, 2022). These studies have significantly contributed to and facilitated the progress of potential landslide identification.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Wang

et al. 2023

Front. Earth Sci.

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Disastrous landslides have become a focus of the world’s attention. Landslide susceptibility evaluation (LSE) can predict where landslides may occur and has caught the attention of scientists all over the world. This work establishes integrated criteria of potential landslide recognition and combines the historical landslides and newly-identified potential landslides to improve the accuracy, rationality, and practicability of a LSE map. Moreover, slope units can well reflect the topographic constraint to landslide occurrence and development, and Graph Convolutional Network (GCN) can well portray the topological and feature relation among various slope units. The combination of slope units and GCN is for the first time employed in LSE. This work focuses on Wanzhou District, a famous landslide-serious region in the Three Gorges reservoir area, and employs multisource data to conduct potential landslide recognition and LSE and to reveal the distribution characteristics of high landslide susceptibility. Some new viewpoints are suggested as follows. 1) The established criteria of potential landslide recognition consist of the characteristics of active deformation, stratum and lithology, tectonics, topography, micro-geomorphology, environment, meteorology, earthquakes, and human engineering activity. These criteria can well eliminate 4 types of false alarm regions and is successfully validated by field survey. 2) 34 potential landslides are newly discovered, and the movement of these potential landslides were controlled or induced by the combined action of soft-hard interbedding rock mass, steep topography, frequent tectonic movement, strong fluvial erosion, abundant precipitation, and intensive road and building construction. 3) The GCN algorithm reaches a relatively high accuracy (AUC: 0.941) and outperforms the other representative machine learning algorithms of Convolutional Neural Network (AUC: 0.926), Support Vector Machine (AUC: 0.835), and CART Tree (AUC: 0.762). 4) High landslide susceptibility is caused by the coupled action of weathered rock cavities, soft rock and swelling soil, strong river erosion, abundant rainfall, and intensive human engineering activity.

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“…Moreover, Landsat, ALOS, Quick Bird, and other satellites with high-resolution images have been widely used for landslide identification; however, they only identify landslide hazards with clear deformation (McDonald and Grubbs, 1975;Sato and Harp, 2009;Youssef et al, 2009;García-Davalillo et al, 2014). InSAR with centimeter-high precision and all-weather surface observation technology can perform large-scale landslide identification and monitoring to build a landslide hazard inventory (Woods et al, 2020;Urgilez Vinueza et al, 2022;Zhang et al, 2022). Because of the satellite lateral imaging mode, the terrain may cause geometric distortion of SAR images, including perspective shortening, overlap, and shadow (Jie et al, 2018).…”

mentioning

confidence: 99%

“…Based on the discrete element method An et al (2021) established an adapted Hertz-Mindlin contact model between particles and the ground surface to accurately simulate the landslide dynamic process. Zhang et al (2012) simulated the entire process of failure and instability of the Jiweishan high-speed remote landslide in Chongqing via PFC 3D software. Smoothed particle hydrodynamics (SPH) based on Lagrangian particle-based meshless methods have been widely employed in applications in geotechnical engineering (Peng et al, 2019;Zhu et al, 2020).…”

mentioning

confidence: 99%

Critical area identification and dynamic process simulation for landslide hazard chain formation in the upstream Jinsha River

et al. 2023

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The upper reaches of the Jinsha River, with their complex terrain and active tectonic movements, are vulnerable to landslide-induced hazard chain events, which endanger the safety of residents and infrastructure construction. Based on the analysis of the development background of the hazard chain in the upstream area of the Jinsha River, five factors, including the lithology, distance to faults, distance to rivers, peak ground acceleration, and slope degree, were selected to identify the critical landslide-prone areas. Principal component and grey correlation analyses were then conducted to determine the contributions of these different factors. Based on ArcGIS, the study zone was categorized into five classes of landslide susceptibility: very high, high, moderate, low, and very low. The identification of the critical target areas for landslide hazard chain formation showed satisfactory accuracy. The very high- and high-susceptibility areas are concentrated along the Jinsha River. The dynamic process of a typical landslide in a very high-susceptibility area was numerically simulated using OpenLISEM. The high-precision Baige landslide data of the study area were used to calibrate the practicality of the input mass parameters, including cohesion, internal friction angle, D50, and D90. The movement and accumulation processes of a typical landslide were then numerically simulated with the verified data. The entire landslide accumulation covers an area of 0.45 km2, with a length of 1,600 m and a width of 270 m. Thus, the OpenLISEM model, which combines mass, topography, and landcover parameters, is feasible for the numerical simulation of landslide dynamic processes. The prediction of the dynamic processes and accumulation morphology of landslides can provide a reference for the formation processes and mechanisms of the landslide-induced hazard chain in the upper Jinsha River.

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An integrated framework for wide-area active landslide detection with InSAR observations and SAR pixel offsets

Cited by 30 publications

References 56 publications

InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River

InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Critical area identification and dynamic process simulation for landslide hazard chain formation in the upstream Jinsha River

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