茂密植被山区岩溶漏斗遥感识别方法

符明俊; 何阳; 董秀军; 邓博

doi:10.11932/karst20250106

茂密植被山区岩溶漏斗遥感识别方法

doi: 10.11932/karst20250106

符明俊^{1, 2,},
何阳^3, ,,
董秀军³,
邓博³

1.
中国地质大学（武汉）环境学院, 湖北武汉 430074
2.
海南省地质环境监测总站, 海南海口 570106
3.
成都理工大学地质灾害防治与地质环境保护国家重点实验室, 四川成都 610059

基金项目: 国家自然科学基金(42072306)

详细信息

作者简介:
符明俊（1983－），男，硕士，主要研究方向为地下水污染与防治。E-mail：1061305829@qq.com

通讯作者:
何阳（1999－），男，硕士，主要研究方向为地质灾害早期识别。E-mail：heyn9@qq.com。

中图分类号: TP75；P642.25
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- 文章访问数: 13
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- 被引次数: 0
出版历程
- 收稿日期: 2023-11-23
- 刊出日期: 2025-02-25

Research on remote sensing identification of dolines with dense vegetation cover based on point cloud principal component analysis point cloud principal component analysis

FU Mingjun^{1, 2
,},
HE Yang^{3
, ,},
DONG Xiujun³,
DENG Bo³

1.
School of Environmental Studies, China University of Geosciences, Wuhan, Hubei 430074, China
2.
Hainan Geological Environment Monitoring Station, Haikou, Hainan 570106, China
3.
State Key Laboratory of Geological Hazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan 610059, China

摘要

摘要: 岩溶渗漏是碳酸盐岩发育地区建设抽水蓄能电站面临最主要的工程地质问题之一，岩溶漏斗主要受构造影响，一般发育在断层附近，水沿着岩溶漏斗的孔道与地下暗河相通就可能导致水库渗漏，受限于岩溶漏斗隐蔽性强的特点，传统调查手段难以高效实现岩溶漏斗准确排查，为解决传统岩溶漏斗调查手段效率低下的问题，文章以某抽水蓄能电站库区作为研究对象，提出一种基于点云主成分分析的岩溶漏斗自动识别方法，以快速识别并提取库区的岩溶漏斗。其方法首先利用机载LiDAR技术获取研究区滤除植被后的地面三维点云数据，再针对岩溶漏斗下凹形态方向性强的特点，通过K-D树最邻近算法及主成分分析，提出一种指标特征值比值完成岩溶漏斗的初步提取，最后采用基于密度聚类算法的漏斗频数、长度、方向3种滤波算法对初提取结果进行背景滤噪。该方法采用受试者工作特征曲线检验AUC值可达0.854，F-score为0.859，其能适用于高植被覆盖下岩溶地貌发育地区的岩溶漏斗的识别与调查。
- 机载LiDAR /
- 岩溶漏斗 /
- 三维点云 /
- 自动识别 /
- 主成分分析
Abstract: China's karst areas are widely distributed, with a total area of over 3.4 million square kilometers, accounting for one-third of the country's total land area. Karst engineering geological problems are widespread and difficult issues in the development of water conservancy and hydropower in Southwest China. In order to achieve the goals of "carbon peak" and "carbon neutrality", China is currently accelerating the construction of pumped storage power stations. However, karst leakage is one of the most important engineering geological problems faced by the construction of pumped storage power stations in areas with carbonate rock development. Therefore, in order to avoid reservoir leakage, it is of great significance to comprehensively, quickly, and accurately identify locations of dolines in reservoir and dam areas for water conservancy and hydropower engineering construction.The traditional investigation of dolines in reservoirs mainly uses the method of manual ground investigation and drilling. However, in the mountainous areas with dense vegetation cover in Southwest China, manual investigation is often very inefficient and limited by the strong concealment of dolines, making it difficult to achieve accurate and efficient investigation of large-scale dolines. To solve the problem of low efficiency in the investigation of dolines in dense vegetation areas, this article takes a reservoir area of pumped storage power station as a study area and proposes an automatic identification method for dolines based on point cloud principal component analysis to quickly identify and extract dolines in the reservoir area.The study area is located in the northeast of Sichuan Province, with an area of 10 km². It is situated at the junction of the Qianlongmen mountains and the northwest of the Sichuan basin, in the tilted core of the Yangtianwo area. The terrain slope is generally above 30°, and the maximum relative height difference in the area is 1,070 m. The main vegetation in the area is forest, consisting of evergreen broad-leaved secondary forests and shrubs. In the study area, carbonate rock formations, such as the Upper Devonian Maoba Formation (D₃m), Shawozi Formation (D₃s), and Middle Devonian Guanwushan Formation (D₂g), are mainly exposed with strong karst development.Firstly, airborne LiDAR technology was used to obtain ground 3D point cloud data in the study area after vegetation was filtered out. Then, in response to the strong directional characteristics of the concave shape of dolines, a preliminary extraction of dolines was achieved by the K-D tree nearest neighbor algorithm and principal component analysis, and an indicator eigenvalue ratio p was proposed. Finally, three filtering algorithms based on density clustering algorithm, namely, funnel frequency, length, and direction, were used to filter the background noise of the initial extraction results. The method used Receiver Operating Curve (ROC) to test the AUC value which was 0.854, and F-score is 0.859. This method is suitable for identifying and investigating dolines in karst areas with dense vegetation cover.
- airborne LiDAR /
- doline /
- point cloud /
- automatic identification /
- principal component analysis

HTML

图 1 点云特征值比值计算原理

Figure 1. Calculation principle of the eigenvalue ratio of point clouds

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图 2 滤波方法原理示意图

Figure 2. Schematic diagram of the principle of the filtering method

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图 3 调查区工程布置图

Figure 3. Engineering layout of the survey area

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图 4 航线规划图

Figure 4. Route plan

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图 5 调查区数据成果

Figure 5. Data results of the survey area

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图 6 岩溶漏斗

Figure 6. Doline

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图 7 点云特征值比值识别效果（a.特征值比值栅格图；b.三维点云显示；d.岩溶漏斗；c、e、f.岩溶漏斗剖面图）

Figure 7. Recognition effect of point cloud eigenvalue ratio (a.eigenvalue ratio grid map; b.3D point cloud display; d.doline; c, e ,f.sectional views of doline)

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图 8 漏斗滤波过程

Figure 8. Doline filtering process

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图 9 各采样半径模型ROC曲线检验结果

Figure 9. ROC curve test results for each sampling radius model

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图 10 自动提取与人工解译结果对比

Figure 10. Comparison between automatic extraction and manual interpretation

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表 1 机载LiDAR系统与光学相机各项参数

Table 1. Parameters of airborne LiDAR system and optical camera

雷达型号	RIEGL VUX-1 LR激光雷达系统	激光发射频率	750000点/秒
尺寸	227×180×125 mm	扫描速度	200次扫描/秒
重量	3.5 kg	相机型号	SONY A7RII
测量精度	15 mm	相机焦距	24 mm
最大测量范围	1350 m	相机像素	4240MP

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表 2 各采样半径提取漏斗模型评价结果

Table 2. Evaluation results of the funnel model extracted by each sampling radius

类型	真正阳率	假正阳率	准确率	F-score
3 m采样半径	81.03%	14.88%	82.14%	0.816
4 m采样半径	81.97%	15.31%	83.47%	0.827
5 m采样半径	82.55%	14.85%	84.53%	0.835
6 m采样半径	83.99%	11.33%	87.96%	0.859
7 m采样半径	83.35%	13.49%	85.38%	0.844

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表 3 自动提取岩溶漏斗结果

Table 3. Results of automatic extraction of dolines

	总数量	重叠数量	未发现数量	新增数量	重叠率
自动提取/个	305	243	16	46	93.8%

下载: 导出CSV

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