佘金星, 许强, 杨武年, 等. 2023. 九寨沟地震地质灾害隐患早期识别与分析研究[J]. 工程地质学报, 31(1): 207-216. doi: 10.13544/j.cnki.jeg.2020-515.
    引用本文: 佘金星, 许强, 杨武年, 等. 2023. 九寨沟地震地质灾害隐患早期识别与分析研究[J]. 工程地质学报, 31(1): 207-216. doi: 10.13544/j.cnki.jeg.2020-515.
    She Jinxing, Xu Qiang, Yang Wunian, et al. 2023. Early identification and analysis of earthquake and geological hazards in Jiuzhaigou[J]. Journal of Engineering Geology, 31(1): 207-216. doi: 10.13544/j.cnki.jeg.2020-515.
    Citation: She Jinxing, Xu Qiang, Yang Wunian, et al. 2023. Early identification and analysis of earthquake and geological hazards in Jiuzhaigou[J]. Journal of Engineering Geology, 31(1): 207-216. doi: 10.13544/j.cnki.jeg.2020-515.

    九寨沟地震地质灾害隐患早期识别与分析研究

    EARLY IDENTIFICATION AND ANALYSIS OF EARTHQUAKE AND GEOLOGICAL HAZARDS IN JIUZHAIGOU

    • 摘要: 针对传统地质灾害调查手段难以有效识别高位远程、高植被覆盖下地质灾害隐患问题,本文研究采用InSAR、机载LiDAR、无人机光学遥感等技术,系统开展了九寨沟地震区域地质灾害隐患早期识别工作。通过SAR数据处理、激光点云数据处理、无人机影像处理等过程,构建了一套集成纹理特征、形变特征、形态特征的地质灾害隐患识别遥感解译图谱。通过综合应用多源遥感技术,完成了九寨沟核心景区230 km2范围内的地质灾害隐患早期识别任务,突破了以往地质灾害灾害调查灾害隐患看不见、看不清、看不准的难题,提高了该区域地质灾害隐患识别的成功率。研究表明,综合应用InSAR、机载LiDAR、无人机遥感等探测技术可以有效提高艰险复杂山地环境地质灾害隐患的识别率,可以为地质灾害隐患早期识别提供技术支撑。

       

      Abstract: Due to the complex topographic conditions and dense vegetation development in Jiuzhaigou region, it is difficult for the traditional manual geological disaster investigation methods to achieve a detailed investigation in a short time. Airborne LiDAR technology has the technical advantage of being able to penetrate through plant gaps and detect microscopic damage to geological objects. InSAR technology has the technical advantage of detecting deformation and damage of geological bodies from macroscopic perspective. Therefore, the two technologies are combined to assist in field verification. By building a macro-to micro-scale geohazard investigation model, it can reduce the workload of manual investigation, improve the identification of potential hazards, and improve the efficiency of geohazard investigation.Based on the above, an internationally advanced high-performance LiDAR scanner was used in this study to achieve the objective of acquiring high-density laser point clouds within 230km2 of the study area. High-accuracy digital elevation model(DEM)data of the study area was obtained through a combination of data pre-processing, point cloud denoising, automatic classification and manual vegetation classification operations. The DEM data did not do microtopography removal, but instead retained the microtopography for disaster hazard identification. Based on the data, a three-dimensional interpretation environment was set up. The identification of major geological hazards under the vegetation was carried out by combining the DEM-derived results, optical remote sensing data and the results of previous geological hazard investigations. Meanwhile, the surface deformation monitoring of 230km2 in the study area was carried out based on ALOS-2 L-band data and Sentinel-1 data and data processing operations. The integration and interpretation of the airborne LiDAR survey results, the InSAR results and the optical remote sensing image results, enhanced the field verification work, and the field investigation of the difficult hidden hazards, which helped further enrich and optimize the practical effect of the technology system. Ultimately, the identification of geological hazards in the study area was completed. The study demonstrated that the multi-source remote sensing technologies such as airborne LiDAR and InSAR can significantly increase the accuracy and efficiency of geohazard investigation, as well as effectively solve the problem of identifying geohazard hazards in complex terrain and high vegetation cover areas.

       

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