Pre- and post-failure spatiotemporal evolution of loess landslides: a case study of the Jiangou landslide in Ledu, China

Zhu, Yaru; Qiu, Haijun; Yang, Dan; Liu, Zijing; Ma, Shuyue; Pei, Yanqian; He, Jianyin; Du, Chi; Sun, Huanhuan

doi:10.1007/s10346-021-01714-5

Cited by 36 publications

(7 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The role of UAV photogrammetry in landslide investigation has been widely recognized, and the high-precision digital elevation model (DEM) data and digital surface model (DSM) data obtained based on UAV photogrammetry technology can intuitively display the basic characteristics of landslides [54][55][56]. In the July 2021 survey, we mainly used the DJI Phantom4 Real-Time Kinematic (RTK) drone to obtain orthophotos of the landslide; meanwhile, we used the RTK global navigation satellite system to obtain the real-time coordinates of the control points as the mapping control.…”

Section: Unmanned Aerial Vehicle and Field Surveysmentioning

confidence: 99%

Efficient Identification and Monitoring of Landslides by Time-Series InSAR Combining Single- and Multi-Look Phases

et al. 2022

Self Cite

View full text Add to dashboard Cite

Identification and monitoring of unstable slopes across wide regions using Synthetic Aperture Radar Interferometry (InSAR) can further help to prevent and mitigate geological hazards. However, the low spatial density of measurement points (MPs) extracted using the traditional time-series InSAR method in topographically complex mountains and vegetation-covered slopes makes the final result unreliable. In this study, a method of time-series InSAR analysis using single- and multi-look phases were adopted to solve this problem, which exploited single- and multi-look phases to increase the number of MPs in the natural environment. Archived ascending and descending Sentinel-1 datasets covering Zhouqu County were processed. The results revealed that nine landslides could be quickly identified from the average phase rate maps using the Stacking method. Then, the time-series InSAR analysis with single- and multi-look phases could be used to effectively monitor the deformation of these landslides and to quantitatively analyze the magnitude and dynamic evolution of the deformation in various parts of the landslides. The reliability of the InSAR results was further verified by field investigations and Unmanned Aerial Vehicle (UAV) surveys. In addition, the precursory movements and causative factors of the recent Yahuokou landslide were analyzed in detail, and the application of the time-series InSAR method in landslide investigations was discussed and summarized. Therefore, this study has practical significance for early warning of landslides and risk mitigation.

show abstract

Section: Unmanned Aerial Vehicle and Field Surveysmentioning

confidence: 99%

Efficient Identification and Monitoring of Landslides by Time-Series InSAR Combining Single- and Multi-Look Phases

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to natural factors or human activity, many slopes are becoming unstable and showing a very high exposure to landslides (Zhou et al, 2022;Pei et al, 2023). Thus, it is necessary to monitor these unstable slopes and conduct early warning accordingly (Zhu et al, 2021;Yang et al, 2022). Recently, with the development of science and technology and the theory of multidisciplinary interaction, they provide new opportunities for the research on early detection, dynamic monitoring and risk reduction of unstable slopes (Liu et al, 2022;Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Editorial: Monitoring, early warning and mitigation of natural and engineered slopes—Volume II

Qiu

Nie

Asadi³

2023

Front. Earth Sci.

Self Cite

View full text Add to dashboard Cite

“…Generally, InSAR and UAV are employed for landslide early identification and detection over large areas, because of their high spatial resolutions. The authors of [5] investigated the spatial and temporal evolution characteristics of a landslide before and after damage with InSAR, UAV, and remote sensing, and found that InSAR and remote sensing could be used for the early identification of unstable slopes. In [6], GB-InSAR was used to monitor the deformation of talus slopes during excavation, and real-time dynamic landslide warning during excavation was achieved by calculating the change rate of the cross-section of the equidistant body of the slope.…”

Section: Introductionmentioning

confidence: 99%

Determination of Landslide Displacement Warning Thresholds by Applying DBA-LSTM and Numerical Simulation Algorithms

Dai

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Numerical simulation has emerged as a powerful technique for landslide failure mechanism analysis and accurate stability assessment. However, due to the bias of simplified numerical models and the uncertainty of geomechanical parameters, simulation results often differ greatly from the actual situation. Therefore, in order to ensure the accuracy and rationality of numerical simulation results, and to improve landslide hazard warning capability, techniques and methods such as displacement back-analysis, machine learning, and numerical simulation are combined to create a novel landslide warning method based on DBA-LSTM (displacement back-analysis based on long short-term memory networks), and a numerical simulation algorithm is proposed, i.e., the DBA-LSTM algorithm is used to invert the equivalent physical and mechanical parameters of the numerical model, and the modified numerical model is used for stability analysis and failure simulation. Taking the Shangtan landslide as an example, the deformation mechanism of the landslide was analyzed based on the field monitoring data, and subsequently, the superiority of the DBA-LSTM algorithm was verified by comparing it with DBA-BPNN (displacement back-analysis based on back-propagation neural network); finally, the stability of the landslide was analyzed and evaluated a posteriori using the warning threshold calculated by the proposed method. The analytical results show that the displacement back-analysis based on the machine learning (DBA-ML) algorithm can achieve more than 95% accuracy, and the deep learning algorithm exemplified by LSTM had higher accuracy compared to the classical BPNN algorithm, meaning that it can be used to further improve the existing intelligent inversion theory and method. The proposed method calculates the landslide’s factor of safety (FOS) before the accelerated deformation to be 1.38 and predicts that the landslide is in a metastable state after accelerated deformation rather than in failure. Compared to traditional empirical warning models, our method can avoid false warnings and can provide a new reference for research on landslide hazard warnings.

show abstract

Pre- and post-failure spatiotemporal evolution of loess landslides: a case study of the Jiangou landslide in Ledu, China

Cited by 36 publications

References 42 publications

Efficient Identification and Monitoring of Landslides by Time-Series InSAR Combining Single- and Multi-Look Phases

Efficient Identification and Monitoring of Landslides by Time-Series InSAR Combining Single- and Multi-Look Phases

Editorial: Monitoring, early warning and mitigation of natural and engineered slopes—Volume II

Determination of Landslide Displacement Warning Thresholds by Applying DBA-LSTM and Numerical Simulation Algorithms

Contact Info

Product

Resources

About