Deformation process and kinematic evolution of the large Daxiaowan earthflow in the NE Qinghai-Tibet Plateau

Li, Yuanxi; Zhang, Yi; Meng, Xingmin; Su, Xiaojun; Liu, Wangcai; Wang, Aijie; Guo, Fuyun; Liang, Yiwen

doi:10.1016/j.enggeo.2023.107062

Cited by 6 publications

(1 citation statement)

References 62 publications

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“…In terms of the input features, InSAR deformation results can be input into the prediction model in different forms. The widely used InSAR deformation forms include time series InSAR deformation and differential InSAR deformation [95]. However, directly using InSAR deformation may ignore the effect of scattered high-deformation areas induced by data-processing errors, which are excluded during visual interpretation.…”

Section: Advantages Of the Modelmentioning

confidence: 99%

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Liang,

Zhang,

et al. 2023

Remote Sensing

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Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic datasets to build the optimal model. The result indicates that the hot spot map, aspect, and Google Earth image as input features based on the U-Net model can achieve the best performance, with the precision, recall, F1 score, and intersection over union (IoU) being 0.822, 0.835, 0.823, and 0.705, respectively. Our method promotes the development of identifying active landslides using InSAR technology automatically and rapidly at a regional scale. Moreover, applying a new method for automatically and rapidly identifying potential landslides in susceptible areas is necessary for landslide hazard mitigation and risk management.

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Section: Advantages Of the Modelmentioning

confidence: 99%

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Liang,

Zhang,

et al. 2023

Remote Sensing

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Whole Life Cycle Monitoring of Landslide Deep Deformation via Triboelectric Nanogenerator

Liu,

Bi,

Zhang

et al. 2024

Adv Funct Materials

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The displacement monitoring of the slow landslide deep deformation is an important part of the landslide early warning system. However, the traditional energy supply mode of depth sensors limits the large‐scale landslide monitoring of the whole life cycle. Therefore, it is necessary to realize self‐powered monitoring for deep deformation at extremely low speed. Herein, an extremely low‐speed triboelectric tilt sensor (ELS‐TTS) is proposed for whole life cycle monitoring of landslide deep deformation without an energy supply. The triboelectric self‐sensing unit based on the triboelectric nanogenerator (TENG) and the instantaneous structure of gravity energy storage is designed. A prototype of ELS‐TTS with a resolution of 0.075° is fabricated and experimented. ELS‐TTS can stably monitor extremely low speeds of below 0.0257° s−1 in different environments and motion states, and the monitoring error is less than 0.13°. Furthermore, a real‐time monitoring system for deep landslides based on a multi‐level forewarning method is proposed to realize the whole life cycle monitoring of landslides and the multi‐level forewarning of dangerous states. This work further promotes the development of TENG in the fields of natural disaster prevention and structural health monitoring.

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Forecast volume of potential landslides in alpine-canyon terrain using time-series InSAR technology: a case study in the Bailong River basin, China

Liu,

Zhang,

Meng

et al. 2023

Landslides

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Deformation process and kinematic evolution of the large Daxiaowan earthflow in the NE Qinghai-Tibet Plateau

Cited by 6 publications

References 62 publications

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model

Whole Life Cycle Monitoring of Landslide Deep Deformation via Triboelectric Nanogenerator

Forecast volume of potential landslides in alpine-canyon terrain using time-series InSAR technology: a case study in the Bailong River basin, China

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