摘要
机载激光雷达(LiDAR,light detection and ranging)数据能有效去除植被,获取真实的地表形态,从而为植被覆盖区的地质灾害风险评价提供新的方法和手段。汕尾火山嶂山体陡峻、植被茂密,是滑坡、崩塌和泥石流的易发地,本文首先采用高分辨率LiDAR数据生成高精度DEM数据以及坡度、坡向、曲率、起伏度、粗糙度和山体阴影等地形因子,综合高分一号遥感影像进行滑坡/崩塌解译共获得滑坡/崩塌44处;然后基于变维分形模型确定各解译因子对滑坡/崩塌形成的权重后计算获得每个解译滑坡/崩塌的确认概率,剔除概率较低的滑坡/崩塌3处;最后根据沟谷特征将火山嶂划分为6个子区,基于各个子区的地形特征、滑坡/崩塌密度和体量以及人类活动分布进行地质灾害风险评价。结果表明基于LiDAR数据生成的高精度地形因子可以有效地去除植被影响,是植被覆盖区地质灾害解译的有效手段。
Airborne LiDAR(light detection and ranging)data are effective for geological hazard risk assessment in vegetation-covered areas because vegetation information can be removed and thus provide true surface morphology.Huoshanzhang in Shanwei,Guangdong Province is a steep and densely vegetated area that is prone to landslides,collapses,and mudslides.This study adopted high-resolution LiDAR data to generate high-precision DEM data and extract terrain factors such as slope,aspect,curvature,undulation,roughness,and mountain shadows,combined with remote sensing images of GF-1 satellite,identified a total of 44 landslides/collapses.Among them,three low-probability landslides/collapses were removed based on the variable dimensional and fractal model,the determined weight of each terrain factor,and the confirmed probability of each interpreted landslide/collapse.The area was divided into 6 sub-regions according to the characteristics of the valleys and the geological hazard risk assessment of each sub-region was conducted based on the terrain characteristics,landslide/collapse density and volume,and human activities.The results indicate that high-precision terrain factors generated from LiDAR data of vegetation impacts eliminated are an effective source for geological hazard interpretation in vegetation-covered areas.
作者
甄俊伟
黄智炜
章桂芳
曾探
王同皓
ZHEN Junwei;HUANG Zhiwei;ZHANG Guifang;ZENG Tan;WANG Tonghao(The Seventh Geological Brigade of Guangdong Provincial Geological Bureau,Huizhou 516000,China;School of Earth Science and Engineering,Sun Yat-sen University,Zhuhai 519082,China;Guangdong Provincial Key Lab of Geological Processes and Mineral Resources,Zhuhai 519082,China;Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519082,China)
出处
《中山大学学报(自然科学版)(中英文)》
CAS
CSCD
北大核心
2024年第1期10-23,共14页
Acta Scientiarum Naturalium Universitatis Sunyatseni
基金
2022年度中央财政地质灾害防治专项(441501-2022-00826)
广东国家公园建设专项(2021GJGY026)
南方海洋科学与工程广东省实验室(珠海)创新团队建设项目(311022004)
广州市科技计划项目(201707010209)
广东省基础与应用基础研究基金(2019A1515010733)。