ISSN 1003-8035 CN 11-2852/P
    程强,周兴泉,张肖. 基于多期无人机影像对比的滑坡变形特征和形成机理分析−以四川新-金公路唐家湾滑坡为例[J]. 中国地质灾害与防治学报,2023,34(0): 1-11. DOI: 10.16031/j.cnki.issn.1003-8035.202303027
    引用本文: 程强,周兴泉,张肖. 基于多期无人机影像对比的滑坡变形特征和形成机理分析−以四川新-金公路唐家湾滑坡为例[J]. 中国地质灾害与防治学报,2023,34(0): 1-11. DOI: 10.16031/j.cnki.issn.1003-8035.202303027
    CHENG Qiang,ZHOU Xingquan, . Kinematics and mechanism analysis of the Tangjiawan landslide based on muti-phase UAV photogrammetry—A case study of the Tangjiwan landslide of the Xin-to-Jinyang county highway in Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(0): 1-11. DOI: 10.16031/j.cnki.issn.1003-8035.202303027
    Citation: CHENG Qiang,ZHOU Xingquan, . Kinematics and mechanism analysis of the Tangjiawan landslide based on muti-phase UAV photogrammetry—A case study of the Tangjiwan landslide of the Xin-to-Jinyang county highway in Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(0): 1-11. DOI: 10.16031/j.cnki.issn.1003-8035.202303027

    基于多期无人机影像对比的滑坡变形特征和形成机理分析以四川新-金公路唐家湾滑坡为例

    Kinematics and mechanism analysis of the Tangjiawan landslide based on muti-phase UAV photogrammetry—A case study of the Tangjiwan landslide of the Xin-to-Jinyang county highway in Sichuan Province

    • 摘要: 研究滑坡变形特征对于分析滑坡形成机理和制定防治措施至关重要。本文以工程诱发的唐家湾滑坡为研究对象,通过工程施工前后的6期无人机影像得到高分辨率DOM,基于相邻两期DOM中识别的特征点作为监测点,根据其位置的变化得出地表位移矢量数据,进而结合地质勘探和深部位移监测分析滑坡变形特征和形成机理。研究表明工程建设前滑坡区无明显变形(第1个观测周期),工程施工后的第2观测周期(20210315—20210606)、第3 观测周期(20210606—20210908)和第4观测周期(20210908—20211103)滑坡主滑区平均变形速率分别为53.0,103.2和62.5 mm·d−1,至第5观测周期(20211103—20220103)变形速率趋于0。第2观测周期的滑坡后缘的弃渣堆载是滑坡的直接触发因素,降雨促进了滑坡变形的发展,而随着雨季的结束和前缘的堆载反压滑坡变形速率逐渐降低。本文研究表明利用多期无人机高清影像可获取大范围、长时序地表变形信息,可作为一种有效的滑坡变形监测手段。

       

      Abstract: Studying the kinematics of landslides is crucial for analyzing failure mechanism and designing remedial measures. This paper focuses on the Tandjiawan landslide that occurred during a highway construction. Five periods of high-resolution digital orthophoto maps (DOM) were generated using unmanned aerial vehicle (UAV)- based photogrammetry, spanning both pre- and post- landslide conditions. Two successive UAV orthophotos were treated as observation periods, and corresponding features were identified in both images to establish monitoring points. Furthermore, two-dimensional displacement vectors were then computed by comparing orthographic images from each observation period based on these corresponding features. The analysis of kinematics and failure mechanism were conducted in conjunction with geological surveys and inclinometer measurements. The findings reveal that there was no significant deformation in the landslide area before the engineering construction of the highway (1st observation period). After construction, during the second observation period (March 15, 2021, to June 6, 2021), the third observation period (June 6, 2021, to September 8, 2021), and the fourth observation period (September 8, 2021, to November 3, 2021), the average deformation rates of the main sliding area of the landslide were 53.0 mm/day, 103.2 mm/day and 62.5 mm/day, respectively. By the fifth observation period (November 3, 2021, to January 3, 2022), the deformation rates had trended towards zero. The deposition of spoil at the rear of the landslide during the second observation period was the direct triggering factor, and rainfall facilitated the development of landslide deformation. As the rainy season ended and the front-end loading increased, the landslide deformation rate gradually decreased. This paper demonstrates that multi-period UAV photogrammetry can provide spatiotemporal surface deformation information for landslide areas, serving as an effective tools for landslide deformation monitoring.

       

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