基于机载LiDAR技术植被茂密区小型滑坡识别与评价

doi:10.6046/zrzyyg.2023101

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山体滑坡会导致生命和财产损失,获取完整的滑坡空间分布图及对易发区域的准确判定有利于指导生产、生活和生态空间优化。在滑坡调查过程中,茂密的植被覆盖使滑坡调查难度加大,机载激光雷达(light detection and ranging,LiDAR)技术的穿透能力使真实地形特征得以呈现,从而实现植被茂密区滑坡识别。该文通过仿地飞行获取研究区LiDAR点云数据,基于点云数据得到数字高程模型(digital elevation model,DEM),在山体阴影分析、彩色增强显示及三维场景模拟基础上,识别出区域内已有滑坡的位置与规模,经野外核实,滑坡解译精度为86.4%。针对滑坡易发区评价问题,以现有滑坡为样本,首次采用遥感分类思维开展滑坡易发区划定,采用小区域内与滑坡发育有关的高程、坡度和地表起伏度组合成影像,以支持向量机为分类方法,判定出滑坡易发区域,经滑坡检验样本分析,滑坡识别精度为81.91%。研究表明: 基于高精度的LiDAR数据及其视觉增强后的图像能识别小型滑坡,采用支持向量机分类法可以准确确定滑坡易发区,为下一步三生空间规划与优化提供依据。

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	陈刚
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	喻永祥
	车增光
	刘汉湖
	杨容浩

关键词 ： LiDAR, 滑坡, 山体阴影, 支持向量机, 滑坡易发性评价

Abstract：

Landslides may cause the loss of lives and property, and an accurate and complete map showing the spatial distribution of landslides and the determination of landslide susceptibility areas assist in guiding the optimization of the production, living, and ecological spaces. However, landslide investigations are complicated by dense vegetation. LiDAR technology enables the presentation of actual terrain features, thereby achieving landslide identification in densely vegetated areas. This study obtained the LiDAR point cloud data of the study area through ground-imitating flight and then built a digital elevation model (DEM) through data processing. Then, based on mountain shadow analysis, color-enhanced presentation, and 3D scene simulation, the locations and scales of existing landslides in the study area were identified. The field verification revealed an interpretation accuracy of landslides of up to 86.4%. For the assessment of landslide susceptibility areas, this study, with existing landslides as samples, delineated landslide susceptibility areas through remote sensing classification for the first time. Specifically, images were synthesized using the landslide-related elevations, slopes, and surface undulations, and then landslide susceptibility areas were determined using the support vector machine (SVM) classification method. The analysis of the inspection samples reveals a landslide identification accuracy of 81.91%. The results show that the image identification based on high-accuracy LiDAR data and visually enhanced images allows for the delineation of small landslides and that the SVM classification method enables the accurate location of landslide susceptibility areas. This study provides a basis for the future planning and optimization of the production, living, and ecological spaces.

Key words： LiDAR landslide mountain shadow SVM landslide susceptibility area

收稿日期: 2023-04-18 出版日期: 2024-09-03

ZTFLH:

TP79

基金资助:江苏省地质灾害风险普查省级技术支持项目(苏自然资函〔2021〕1420号)

通讯作者: 刘汉湖(1978-),男,博士,教授,主要从事遥感地质方面的教学和科研工作。Email: liuhanhu@cdut.edu.cn。

作者简介: 陈刚(1987-),男,硕士,高级工程师,主要从事于地质灾害调查与评价、水文地质与工程地质的研究。Email: 504739487@qq.com。

引用本文:

陈刚, 郝社锋, 蒋波, 喻永祥, 车增光, 刘汉湖, 杨容浩. 基于机载LiDAR技术植被茂密区小型滑坡识别与评价[J]. 自然资源遥感, 2024, 36(3): 196-205.
CHEN Gang, HAO Shefeng, JIANG Bo, YU Yongxiang, CHE Zengguang, LIU Hanhu, YANG Ronghao. Identification and assessment of small landslides in densely vegetated areas based on airborne LiDAR technique. Remote Sensing for Natural Resources, 2024, 36(3): 196-205.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023101 或 https://www.gtzyyg.com/CN/Y2024/V36/I3/196

Fig.1 研究区地理位置示意

Fig.2 LiDAR数据采集与处理流程

Fig.3 研究区原始点云及地面点提取效果

Fig.4 双向山体阴影图和多向山体阴影图中光线模拟方向

Fig.5 SVM的基本思想示意图

Fig.6 不同制作方式下的山体阴影图

Fig.7-1 彩色增强显示效果图

Fig.7-2 彩色增强显示效果图

Fig.8 研究区野外踏勘滑坡照片

Fig.9 小型滑坡遥感解译特征

Fig.10 滑坡易发区分布

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