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自然资源遥感  2025, Vol. 37 Issue (6): 263-274    DOI: 10.6046/zrzyyg.2024246
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
浙江省宁波地区地质灾害隐患点遥感综合监测识别
高峰1(), 张弘怀1, 周伟2(), 王星星3, 孙丽影1, 许文新1, 吴迪3
1.宁波市自然资源生态修复和海洋管理服务中心,宁波 315000
2.中国科学院数字地球重点实验室,北京 100094
3.浙江省测绘科学技术研究院,杭州 311100
Monitoring and identification of potential geological hazard sites using comprehensive remote sensing in Ningbo, Zhejiang Province
GAO Feng1(), ZHANG Honghuai1, ZHOU Wei2(), WANG Xingxing3, SUN Liying1, XU Wenxin1, WU Di3
1. Ningbo Natural Resources Ecological Restoration and Marine Management Service Center, Ningbo 315000, China
2. Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing 100094, China
3. Zhejiang Academy of Surveying and Mapping, Hangzhou 311100, China
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摘要 

浙江省宁波地区地处华东沿海,境内地貌类型多样、地质环境复杂,受汛期影响易引发滑坡、崩塌、泥石流等地质灾害,开展宁波地区地表形变监测研究对地质灾害防治具有重要意义。该研究通过将干涉雷达(interferometric synthetic aperture radar, InSAR)、高分辨率光学影像、无人机激光雷达(light detection and ranging, LiDAR)等多种遥感手段相结合,对宁波地区开展综合遥感滑坡地质灾害监测,获得潜在地质灾害隐患点分布信息,并提取典型高风险坡体的灾害隐患点详细位置与坡体形态细节信息。该研究采用的永久散射体与分布式散射体联合构网的时序雷达干涉(combined-multi-temporal InSAR, CMT-InSAR)方法,有效增加了植被覆盖丘陵地貌条件下的高相干点密度,提高了形变监测的覆盖度和精准度。实验结果表明,宁波地区整体地表较为平稳,局部沿海区域因填海建设等活动导致地表形变较大,沉降速率超过-20 mm/a。在山区,高风险点位主要集中在奉化区、宁海县、余姚市和象山县,部分区域地表年均形变速率达到-20~-7 mm/a,且形变反演结果与野外实地考察情况一致。该研究为山区丘陵地貌的地质灾害早期识别与监测提供了一套高精度、多层次、长周期的监测手段。

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高峰
张弘怀
周伟
王星星
孙丽影
许文新
吴迪
关键词 地表形变监测InSAR地质灾害宁波地质灾害防治高分光学影像无人机LiDAR    
Abstract

Ningbo, located in Zhejiang Province along the eastern coast of China, features diverse landforms and a complex geological environment. It is prone to geological hazards such as landslides, rockfalls, and debris flows, particularly during the flood season. Therefore, it is of great importance to conduct surface deformation monitoring in Ningbo for geological hazard prevention and control. This study integrated multiple remote sensing methods, including interferometric synthetic aperture radar (InSAR), high-resolution optical imagery, and unmanned aerial vehicle-based light detection and ranging (LiDAR). On this basis, landslide hazard monitoring was conducted in Ningbo using comprehensive remote sensing, obtaining the distribution of potential geological hazard sites, from which detailed locations and morphological information of typical high-risk slopes were extracted. Moreover, this study employed a combined-multi-temporal InSAR (CMT-InSAR) method, which integrated permanent and distributed scatterers to form a network. This method effectively increased the density of high-coherence points under vegetated hilly conditions, enhancing the coverage and accuracy of deformation monitoring. As indicated by the experimental results, Ningbo exhibited an overall stable land surface. However, local coastal areas showed significant surface deformation due to activities such as land reclamation, with a subsidence rate exceeding -20 mm/a. In mountainous areas, high-risk sites were primarily concentrated in the Fenghua District, Ninghai County, Yuyao City, and Xiangshan County, with some areas featuring annual average surface deformation rates ranging from -20 to -7 mm/a. The deformation inversion results aligned with field survey observations. This study proposes a high-precision, multi-level, and long-term approach for the early identification and monitoring of geological hazards in mountainous and hilly areas.

Key wordsdeformation monitoring    interferometric synthetic aperture radar (InSAR)    geological hazard    geological hazard prevention and control in Ningbo    high-resolution optical image    unmanned aerial vehicle-based light detection and ranging (UAV-LiDAR)
收稿日期: 2024-07-15      出版日期: 2025-12-31
ZTFLH:  TP79  
通讯作者: 周伟(1984-),女,博士,主要从事SAR遥感影像处理研究。Email: zhouwei@aircas.ac.cn
作者简介: 高峰(1971-),男,高级工程师,主要从事城市工程测量、城市地质安全、地质灾害、地面沉降、地下水、地下三维模型以及相关工作的信息化研究。Email: gf_ep398@126.com
引用本文:   
高峰, 张弘怀, 周伟, 王星星, 孙丽影, 许文新, 吴迪. 浙江省宁波地区地质灾害隐患点遥感综合监测识别[J]. 自然资源遥感, 2025, 37(6): 263-274.
GAO Feng, ZHANG Honghuai, ZHOU Wei, WANG Xingxing, SUN Liying, XU Wenxin, WU Di. Monitoring and identification of potential geological hazard sites using comprehensive remote sensing in Ningbo, Zhejiang Province. Remote Sensing for Natural Resources, 2025, 37(6): 263-274.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2024246      或      https://www.gtzyyg.com/CN/Y2025/V37/I6/263
Fig.1  宁波市地形图
成像时间
(UTC)
2020-07-21; 2020-08-14; 2020-09-07; 2020-10-01; 2020-10-25; 2020-11-18; 2020-12-12; 2021-01-05; 2021-01-29; 2021-02-22; 2021-03-18; 2021-04-11; 2021-05-05; 2021-05-29; 2021-06-22; 2021-08-09; 2021-09-26; 2021-10-20; 2021-12-07; 2021-12-31; 2022-01-24; 2022-02-17; 2022-03-13; 2022-04-30; 2022-05-24; 2022-06-17; 2022-08-04; 2022-08-28; 2022-09-21; 2022-10-15; 2022-12-02;
影像覆盖范围
升降轨 降轨 分辨率/m 5
模式 Extra Fine超宽精细模式 极化方式 VV
幅宽/(km×km) 125×125 处理级别 SLC(单视
复数图像)
Tab.1  Radarsat-2数据信息表
Fig.2  集成InSAR、高分光学影像、无人机LiDAR的综合遥感滑坡灾害隐患点识别流程图
Fig.3  联合PS与DS目标点的CMT-InSAR形变参数反演流程
Fig.4  地质灾害常见坡体形态图
Fig.5  宁波InSAR地表形变速率图(2020—2022年)
Fig.6  平均形变速率反演误差统计直方图
Fig.7  居民地、道路等承灾体提取局部示意图
Fig.8  疑似滑坡地质灾害隐患点位分布图(1 092个)
Fig.9  基于DEM处理得到的坡体单元与坡度图
Fig.10  0.5 m高分影像的植被覆盖
Fig.11  经叠加分析后疑似地质灾害隐患点位分布图(142个)
Fig.12  野外核查形变点在卫星影像与三维数字地面模型上的位置示意图
Fig.13  图12中红圈所标示形变点的时间序列相对形变量
Fig.14  野外现场踏勘照片
Fig.15  野外核查点坡度与坡体单元结合图
Fig.16  宁海县陈家坑村典型形变点的时间序列相对形变量示意图
Fig.17  宁海县陈家坑村疑似滑坡点位影像图
Fig.18  宁海县陈家坑村点云渲染DEM图与根据DEM生成的坡体、坡度图
Fig.19  象山县王岙村典型形变点的时间序列相对形变量示意图
Fig.20  象山县王岙村野外现场踏勘照片
Fig.21  点云渲染DEM图与根据DEM生成的坡体、坡度图
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