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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (6) : 263-274     DOI: 10.6046/zrzyyg.2024246
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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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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.

Keywords deformation 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)     
ZTFLH:  TP79  
Issue Date: 31 December 2025
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Feng GAO
Honghuai ZHANG
Wei ZHOU
Xingxing WANG
Liying SUN
Wenxin XU
Di WU
Cite this article:   
Feng GAO,Honghuai ZHANG,Wei ZHOU, et al. Monitoring and identification of potential geological hazard sites using comprehensive remote sensing in Ningbo, Zhejiang Province[J]. Remote Sensing for Natural Resources, 2025, 37(6): 263-274.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024246     OR     https://www.gtzyyg.com/EN/Y2025/V37/I6/263
Fig.1  Topography map of Ningbo City
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 data information
Fig.2  Flowchart for integrating InSAR, high-resolution optical imagery, and UAV LiDAR for comprehensive remote sensing identification of landslide hazard points
Fig.3  CMT-InSAR deformation parameter inversion process combining PS and DS targets
Fig.4  Common slope morphology diagram of geological hazards
Fig.5  Ningbo InSAR surface deformation rate map (2020—2022)
Fig.6  Histogram of average deformation rate inversion error statistics
Fig.7  Local schematic diagram of extracting settlements, roads, and other carriers
Fig.8  Distribution map of suspected landslide geological hazard points (1 092 points)
Fig.9  Slope units and slope map derived from DEM processing
Fig.10  0.5 m high-resolution imagery of vegetation coverage
Fig.11  Distribution map of suspected geological hazard points after overlay analysis (142 points)
Fig.12  Schematic diagram of deformation points for field verification on satellite image and DEM
Fig.13  Temporal sequence of relative deformation magnitude of the point marked by red circles in Figure 12
Fig.14  Field survey photographs
Fig.15  Combination diagram of field verification point slopes and slope units
Fig.16  Temporal sequence of relative deformation magnitude of the point marked by red circles in figure(a) in Chenjiakeng Village, Ninghai County
Fig.17  Image of suspected landslide points in Chenjiakeng Village, Ninghai County
Fig.18  Point cloud rendered DEM image with slope and slope gradient maps generated from DEM in Chenjiakeng Village, Ninghai County
Fig.19  Temporal sequence of relative deformation magnitude of the point marked by red circles in figure(a) in Wangao Village, Xiangshan County
Fig.20  Field survey photographs in Wangao Village, Xiangshan County
Fig.21  Point cloud rendered DEM image with slope and slope gradient maps generated from DEM
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