Active landslide identification with a combined method of D-InSAR and random forest model
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摘要:
灾害的早期识别是防灾减灾领域的关键技术。文中以甘肃省舟曲县为例，利用2018年1月－2019年1月Sentinel-1A雷达卫星降轨数据和2021年5月Sentinel-2光学遥感影像数据，通过D-InSAR技术获取研究区地表形变信息，利用随机森林模型识别潜在的滑坡体。结果表明：使用已有的滑坡数据集，采用随机森林模型能够较好地识别出潜在滑坡体。潜在滑坡点分布位置均位于地表形变量大的区域。舟曲县整体形变沿东西向发生，主要分布于舟曲县东北和西南方向，与潜在滑坡点高度重合。识别出的潜在滑坡点（立节乡北山滑坡），年形变量达到0.12 m，于2021年1月18日发生滑坡，该滑坡典型案例也印证了文中方法的有效性。
Abstract:Early identification of disaster is a key technical problem in disaster prevention and mitigation. In this study, Zhouqu County, Gansu Province was taken as an example. Based on Sentinel-1A radar satellite orbit landing data from January 2018 to January 2019 and Sentinel-2 optical remote sensing image data from May 2021, D-InSAR technology was used to obtain surface deformation information in the study area, and Random Forest model was used to identify potential landslides. The results show that using the existing landslide data set, the random forest model can identify the potential landslide well. The distribution locations of potential landslide are all located in areas with large surface shape variables. The overall deformation occurred along the east-west direction, mainly distributed in the northeast and southwest directions of Zhouqu County, and overlapped with the potential landslide. The identified potential landslide point (Beishan landslide in Lijie Township) has an annual variable of 0.12 m, and the landslide occurred on January 18, 2021. This typical landslide case also confirms the effectiveness of the proposed method.
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Key words:
- landslide /
- disaster identification /
- InSAR /
- random forest /
- Zhouqu
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表 1 卫星数据参数
Table 1. Satellite data parameters
卫星数据源 轨道方向 波段 中心入射角/（°） 分辨率/m 日期 Sentinel-1A 降轨 C 29～36 5×20 2018-01-21 Sentinel-1A 降轨 C 29～36 5×20 2019-01-04 Sentinel-2 红、绿、蓝 10 2021-05-09 
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