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

Liu, Wangcai; Zhang, Yi; Meng, Xingmin; Wang, Aijie; Li, Yuanxi; Su, Xiaojun; Ma, Kaiqi; Li, Hengyuan; Chen, Guan

doi:10.1007/s10346-023-02135-2

Cited by 4 publications

(2 citation statements)

References 60 publications

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“…Zhouqu County, situated in the middle section of the Bailong River Basin, stands out as one of China's areas most significantly affected by landslide disasters [3]. Due to historical tectonic movements, the region is characterized by numerous large-scale ancient landslides [4].…”

Section: Introductionmentioning

confidence: 99%

The Displacement Analysis and Prediction of a Creeping Ancient Landslide at Suoertou, Zhouqu County, China

Fang,

Zhang,

et al. 2024

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

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The ancient Suoertou landslide seriously threatens the surrounding population's lives and property. Monitoring and predicting this landslide is crucial to ensure the affected areas' safety. The previous research on the landslide's displacement characteristics and mechanisms has lacked detailed analyses. Additionally, its future development trends must be understood. Therefore, we conducted a detailed analysis of the ancient Suoertou landslide's displacement characteristics and mechanisms using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) monitoring results from 2018 to 2023. Furthermore, by applying a gated recurrent unit (GRU) prediction mode that incorporates the refined displacement characteristics and mechanisms, we forecasted the landslide's displacement trends. The results show that this landslide is currently undergoing overall slow displacement with violent fluctuations in localized areas. Tectonic movement, precipitation, human activities, river erosion, and other factors interact, forming a vicious development displacement mechanism. According to our prediction, the displacement of this landslide will be in a trend of fluctuating increase from June 2023 to June 2024. In particular, the local area will undergo abnormal displacement acceleration. The results of the current research provide a scientific basis upon which to monitor landslides, promote their management, and reduce the risk of losses due to landslide disasters.

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Section: Introductionmentioning

confidence: 99%

The Displacement Analysis and Prediction of a Creeping Ancient Landslide at Suoertou, Zhouqu County, China

Fang,

Zhang,

et al. 2024

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

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“…Some scholars have also detected and studied typical geological hazards in the Qinling-Daba Mountains based on InSAR technology and analyzed the principles of their movement and the factors that induce movement. Study areas have included the Bailong River Basin [20,21], the Jiangdingya landslide [22], and typical landslides in Lueyang County [23], with studies reaching a shared, clear understanding of the typical landslides in the region. Some scholars have also used a variety of data types to jointly identify areas with high concentrations of potential geohazards or single geohazards in complex and dangerous mountainous areas and analyzed the recognition effect [24][25][26][27][28], providing a foundation for subsequent scholars to use in choosing appropriate data for monitoring and research analysis.…”

Section: Introductionmentioning

confidence: 99%

Adaptability Analysis of Sentinel−1A and ALOS/PALSAR−2 in Landslide Detection in the Qinling-Daba Mountains

Yang,

Zhang,

et al. 2023

Applied Sciences

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Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a mature deformation-monitoring technology, InSAR has been widely used in landslide detection, but the steep terrain and dense vegetation in the Qinling-Daba Mountains make detection challenging. Hence, it is important to choose suitable data sources and methods for landslide detection via InSAR in this area. This study was the first to collect ALOS/PALSAR−2 and Sentinel−1A images to detect landslides in the Qinling-Daba Mountains, applying a method combining IPTA and SBAS. In total, 88 landslides were detected and validated. The results show that the deformation-detection error rate of Sentinel−1A is 2% higher than that of ALOS/PALSAR−2 and that its landslide-recognition rate is 47.7% lower than that of ALOS/PALSAR−2. Upon comparing and analyzing the visibility, coherence, closed−loop residuals, and typical time series of landslide deformation from the two kinds of data, it was found that the extremely low quality of available Sentinel−1 A summer data is a major factor influencing that system’s performance. ALOS/PALSAR−2 is more likely to detect landslides in areas with high vegetation coverage, meeting more than 90% of the monitoring needs. It is thus highly suitable for landslide detection in the Qinling–Daba Mountains, where seasonality is significant. In this paper, for the first time, multiple data sources are compared in detail with regard to their utility in landslide detection in the Qinling–Daba Mountains. A large number of accuracy metrics are applied, and the results are analyzed. The study provides important scientific support for the selection of data sources for future landslide monitoring in the Qinling–Daba Mountain area and similar areas and for the selection of methods to evaluate the accuracy of InSAR monitoring.

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Early warning of landslides based on statistical analysis of landslide motion characteristics and AI Earth Cloud InSAR processing system: a case study of the Zhenxiong landslide in Yunnan Province, China

Li,

Niu

et al. 2024

Landslides

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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

Cited by 4 publications

References 60 publications

The Displacement Analysis and Prediction of a Creeping Ancient Landslide at Suoertou, Zhouqu County, China

The Displacement Analysis and Prediction of a Creeping Ancient Landslide at Suoertou, Zhouqu County, China

Adaptability Analysis of Sentinel−1A and ALOS/PALSAR−2 in Landslide Detection in the Qinling-Daba Mountains

Early warning of landslides based on statistical analysis of landslide motion characteristics and AI Earth Cloud InSAR processing system: a case study of the Zhenxiong landslide in Yunnan Province, China

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