LiDAR Point-Cloud Density Optimization for Geohazard Surveys in Densely Vegetated Mountainous Areas

WU Fu; LIAO Zeyuan; HE Na; LIU Chang; WU Qiuju; MO Yingfei; PAN Xingyu; JIANG Yaojing; LI Chunling; HUANG Xin; WANG Yuxiang; DONG Xiujun

doi:10.13203/j.whugis20230386

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LiDAR Point-Cloud Density Optimization for Geohazard Surveys in Densely Vegetated Mountainous Areas

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- LiDAR Point-Cloud Density Optimization for Geohazard Surveys in Densely Vegetated Mountainous Areas
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 4, Pages: 751-761(2026)
- 作者机构：
  
  1.广西壮族自治区地质环境监测站，广西南宁，530000
  2.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
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20230386
  CLC： P237
- Received：10 October 2024，
  
  Published：05 April 2026
- 稿件说明：
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吴福,廖泽源,何娜，等.面向茂密植被山区地质灾害调查的LiDAR点云密度优化[J].武汉大学学报（信息科学版）,2026,51(4):751-761.

WU Fu,LIAO Zeyuan,HE Na,et al.LiDAR Point-Cloud Density Optimization for Geohazard Surveys in Densely Vegetated Mountainous Areas[J].Geomatics and Information Science of Wuhan University,2026,51(4):751-761.
吴福,廖泽源,何娜，等.面向茂密植被山区地质灾害调查的LiDAR点云密度优化[J].武汉大学学报（信息科学版）,2026,51(4):751-761. DOI：10.13203/j.whugis20230386

WU Fu,LIAO Zeyuan,HE Na,et al.LiDAR Point-Cloud Density Optimization for Geohazard Surveys in Densely Vegetated Mountainous Areas[J].Geomatics and Information Science of Wuhan University,2026,51(4):751-761. DOI：10.13203/j.whugis20230386

摘要

无人机机载激光雷达（laser detecting and ranging，LiDAR）技术因受环境影响较小、调查视场角独特、能“穿透”植被等优点，已成为植被茂密山区地质灾害调查的新型有效技术手段。但现有规范中针对地质灾害识别的点云密度采集标准尚未完善，现行作业多参照测绘规范确定点云密度，难以满足地质灾害早期识别对陡坎、冲沟等微地貌的精细化表征需求。针对中国广西地区地质灾害规模较小、植被覆盖茂密的特点，选取 4 种较大比例尺，探究不同点云密度对数字高程模型（digital elevation model

DEM）质量的影响规律，引入局部地形复杂度作为定量评价指标，推导最佳地面点云密度，并结合不同植被郁闭度下的激光穿透率，反算得到适配的采集点云密度推荐值。结果表明，局部地形复杂度可有效表征 DEM 的微地貌保留完整度，通过多组点云密度对比实验确定了最优地面与采集点云密度，建立了适用于广西茂密植被山区机载 LiDAR 地质灾害调查的点云密度参考标准。

Abstract

Objectives

Unmanned aerial vehicle

(

UAV)-borne laser detecting and ranging (LiDAR)

which is relatively insensitive to environmental conditions and offers a distinctive viewing geometry with the capability to penetrate vegetation

has become an effective tool for geohazard investigations in densely vegetated mountainous regions. However

point-density requirements tailors to geohazard identification remain underdeveloped. Current practice largely follows general surveying specifications

which often fail to support the fine-scale depiction of micro-topographic features (e.g.

scarps and gullies) needed for early-stage detection.

Methods

Focusing on Guangxi

China

where geohazards are typically small in scale and vegetation cover is dense

we examined the effects of varying point-cloud densities on DEM (digital elevation model) quality using four relatively large map scales. Local terrain complexity was introduced as a quantitative indicator to derive the optimal ground-point density. LiDAR penetration rates under different canopy-closure conditions were further incorporated to back-calculate recommended acquisition point densities.

Results

Local terrain complexity effectively reflected the completeness of micro-topographic preservation in DEM. Multi-group comparisons across point-cloud densities determined the optimal ground-point density and the corresponding acquisition point density

and produced canopy-closure–adaptive recommended acquisition densities.

Conclusions

We establishe a point-density reference standard for unmanned aerial vehicle-borne LiDAR–based geohazard surveys in densely vegetated mountainous areas of Guangxi

enabling more reliable DEM representation of micro-topography to support early geohazard identification.

关键词

Keywords

references

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