| 典型黄土丘陵区地质灾害隐患识别与时序监测 |
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| 引用本文： | 尹玉玲,徐素宁,王军,胡克.典型黄土丘陵区地质灾害隐患识别与时序监测[J].水文地质工程地质,2023,50(2):141-149. |
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| 作者姓名： | 尹玉玲 徐素宁 王军 胡克 |
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| 作者单位： | 1.中国地质大学（北京）海洋学院，北京 100083 |
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| 基金项目： | 自然灾害防治体系建设补助资金遥感调查项目（NXYSDZC-2021-025）；基于国产卫星的地质灾害调查监测系统研制与示范应用（发改办高技[2012]2083号） |
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| 摘 要： | 宁厦南部地区以黄土丘陵地貌为主，区内沟壑纵横，小型滑坡较为发育，地表形变监测难度大。为探索黄土丘陵区的地质灾害隐患识别方法，以宁夏回族自治区固原市泾源县为研究区，应用SBAS-InSAR技术对采集到的2016年7月—2021年5月的11期升轨L波段ALOS-2数据进行处理，得到形变速率结果。联合高分光学影像，根据形变速率、形变规模、坡度、形变区到承灾体的距离等因素进行综合分析，在泾源县共识别疑似隐患27处。经实地验证，其中22处形变迹象较明显、而且有明确的承灾体，确定为地质灾害隐患。对其中典型隐患点进行时序形变分析，发现这些区域在监测时间段内有持续显著的地表形变，最大沉降速率达到91.53 mm/a。结果表明：在黄土丘陵区，应用L波段SAR数据，采用SBASInSAR技术的地质灾害形变监测效果显著，联合高分辨率的光学影像数据、应用综合遥感识别的方法，在该地区地质灾害隐患识别的正确率较高，具有很好的适用性。未来可编程采集升、降轨结合的L波段数据、结合无人机LiDAR数据做更深入的研究，以进一步提高地质灾害隐患识别的准确率，为地质灾害精准防治做好技术支撑。
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| 关 键 词： | 地质灾害隐患识别 InSAR技术 光学遥感技术 黄土丘陵区 时序监测 |
| 收稿时间： | 2022-11-01 |
Identification and time series monitoring of hidden dangers of geological hazards in the typical loess hilly regions |
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| Affiliation: | 1.School of Ocean Sciences, China University of Geosciences （Beijing）, Beijing 100083, China2.Land Satellite Application Center, Ministry of Natural Resources, Beijing 100048, China3.Mineral Geological Survey Institute of Ningxia Huizu Zizhiqu, Yinchuan, Ningxia 750021, China |
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| Abstract: | The geomorphology of southern Ningxia is dominated by loess hills, with gullies and well-developed small landslides in the area, making surface deformation monitoring difficult. To explore the identification method of geological hazards in the loess hilly area, Jingyuan district in the city of Guyuan in Ningxia Huizu Zizhiqu is taken as the study area, and the SBAS-InSAR technology is applied to process a total of 11 periods of ascending L-band ALOS-2 data collected from July 2016 to May 2021 to obtain the deformation rate of the Jingyuan district. Combined with high-resolution optical images, a comprehensive analysis is carried out according to factors such as deformation rate, deformation scale, slope, and disaster-bearing body. A total of 27 suspected hidden dangers are identified. After field verification, 22 of them show obvious signs of deformation and have clear hazard-bearing bodies. The time-series deformation analysis of the typical hidden danger points shows that these areas have continuous and significant surface deformation during the monitoring period, and the maximum subsidence rate reaches 91.53 mm/a. The results show that the combined L-band SAR and high-definition optical image data and the application of the integrated remote sensing identification method are highly accurate and are of high applicability in the area. The next step is to collect L-band data on a combination of ascending and descending orbits and to conduct in-depth research on the basis of LiDAR data from drones in order to further improve the accuracy of geological hazard identification and to provide technical support for the precise prevention and control of geological hazards. |
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