成都理工大学地质灾害防治与地质环境保护全国重点实验室,四川 成都,610059
许强,博士,教授,主要从事地质灾害防治的理论与方法研究。xq@cdut.edu.cn
收稿:2025-03-21,
纸质出版:2025-11-05
移动端阅览
许强,董秀军,赵建军,等.基于机载LiDAR的2·8宜宾筠连山体滑坡几何特征分析[J].武汉大学学报(信息科学版),2025,50(11):2145-2153.
XU Qiang,DONG Xiujun,ZHAO Jianjun,et al.Geometric Characteristics Analysis of the 2·8 Junlian Landslide, Yibin
许强,董秀军,赵建军,等.基于机载LiDAR的2·8宜宾筠连山体滑坡几何特征分析[J].武汉大学学报(信息科学版),2025,50(11):2145-2153. DOI:10.13203/j.whugis20250076
XU Qiang,DONG Xiujun,ZHAO Jianjun,et al.Geometric Characteristics Analysis of the 2·8 Junlian Landslide, Yibin DOI: 10.13203/j.whugis20250076.
2025-02-08,中国四川省宜宾市筠连县金坪村发生高位山体滑坡灾害。滑坡体在运动过程中转化为碎屑流,最终形成大面积碎屑堆积体,造成人员伤亡和道路损毁,引起国内社会广泛关注。针对灾后应急响应需求,采用无人机搭载机载激光雷达及倾斜光学相机,获取滑坡后高质量点云和光学影像数据,构建数字高程模型、数字正射影像及三维实景模型,并结合滑前卫星和航空遥感数据,快速准确地提取滑坡体面积、周长和方量等几何形态及分布特征。结果显示,滑坡体面积约为1.8×10
5
m²,周长达2 562.8 m,物源体积为3.51×10
5
m
3
,堆积体积为5.75×10
5
m
3
。分析结果为灾害链生效应评估、抢险路径优化及风险防控区划提供了高精度数据支撑。
Objectives
2
On February 8
2025
a high-positioned landslide occurred in Jinping Village,Junlian County
Yibin City
Sichuan Province,China. The landslide body transformed into debris flow during the movement
ultimately forming an extensive debris accumulation body that caused casualties
road damage and significant public concern across China. To address post-disaster emergency response demands and enable precise hazard assessment
airborne light detection and ranging (LiDAR) technology was applied to rapidly characterize key geometric parameters of the landslide
including areal extent
perimeter
and volumetric changes.
Methods
2
Post-disaster data acquisition occurred on February 10-11
2025
using a unmanned aerial vehicle equipped with a LiDAR system and oblique optical cameras. The acquisition yielded 30 points/m² density point cloud data and 0.05 m resolution optical imagery
from which an 0.1 m resolution digital elevation model (DEM)
0.05 m digital orthophoto map (DOM)
and 3D reality model were generated. Meanwhile
pre-event data
including 1 m resolution satellite and 5 m resolution DEM were also acquired to facilitate comprehensive analysis. Leveraging these datasets
multi-perspective systematic identification and precise extraction of landslide geometric parameters were conducted. For landslide volume
resampling and registering the two-phase DEMs followed by applying the DEM differencing algorithm were used.
Results
2
The landslide covere
d an area of about 1.8×10
5
m² with a perimeter of 2 562.8 m
measuring 908 m in length
with maximum/minimum widths of 296 m and 65 m respectively. The source volume was calculated at 3.51×10
5
m³
and the accumulation volume is 5.75×10
5
m³.
Conclusions
2
The analysis provide high-precision data support for assessing disaster chain effects
optimizing emergency rescue route
and delineating risk prevention and control zones.
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