基于SBAS-InSAR技术的澜沧江流域云龙段滑坡早期识别

Early identification of landslides in Yunlong segment of Lancang River Basin by SBAS-InSAR technology

  • 摘要: 澜沧江流域云龙段地处滇西高山峡谷区,区内地势险峻,地质环境复杂,为滑坡灾害的形成和发育孕育了良好条件. 该区域滑坡灾害频发给人民生命财产带来了重大损失,利用传统方式对滑坡进行调查费时费力,难以满足新形势下快速对滑坡进行识别的需求. 本文利用Sentinel-1A升降轨数据,基于SBAS-InSAR技术,在获取沿雷达视线方向形变的基础上,进一步得到沿斜坡方向的形变,并结合Google Earth光学影像开展滑坡识别研究. 联合升降轨共识别出滑坡47处,其中升轨识别滑坡30处,降轨识别滑坡22处,升降轨共同识别5处,并针对性对识别的滑坡进行验证和典型滑坡形变监测与分析. 结果表明:联合升降轨监测能够更加全面地对滑坡进行识别,且结合斜坡方向形变开展识别能够提高滑坡识别准确率. 研究成果为在滇西高山峡谷地区开展滑坡识别提供了思路与借鉴.

     

    Abstract: The Yunlong segment of the Lancang River Basin is situated in the alpine and gorge region of western Yunnan. The terrain in this area is precipitous and the geological environment is intricate, offering good circumstances for the formation and development of landslide disasters. The frequent occurrence of landslide disasters in this area has caused substantial losses to people's lives and properties. Utilizing traditional methods to investigate landslides is time-consuming and labor-intensive, and it is challenging to meet the demand for the rapid identification of landslides under the new circumstances. In this paper, by using Sentinel-1A ascending and descending orbit data and based on the SBAS-InSAR technology, the deformation vector in the radar line-of-sight direction is decomposed into the deformation in the direction of the steepest slope, and in combination with Google Earth optical images, the landslide identification research is conducted. A total of 47 landslides are jointly identified by ascending and descending orbits, among which 30 landslides are identified by the ascending orbit, 22 landslides are identified by the descending orbit, and 5 landslides are identified jointly by both orbits. The identified landslides are verified and the deformation monitoring and analysis of typical landslides are carried out. The results indicate that the combined lifting rail monitoring is capable of identifying the landslide more comprehensively, and the recognition accuracy of the landslide can be enhanced by integrating the slope direction deformation. The research results offer ideas and references for landslide identification in the alpine and gorge areas of western Yunnan.

     

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