Monitoring of Levee Deformation for Urban Flood Risk Management Using Airborne 3D Point Clouds
1. Introduction
2. Materials and Methods
2.1. Data Collection
The LiDAR dataset procured for this analysis was aligned with the CGCS2000 national coordinate system for planar positioning and referenced to the 1985 Chinese National Height Datum for altitudinal data. This dataset maintained a point cloud density of 16 points per square meter and was accompanied by ultrahighresolution imagery with a pixel size of 0.05 m. The spatial accuracy of the LiDAR point cloud was validated to a horizontal precision of 0.25 m and a vertical precision of 0.15 m. Data fidelity, particularly the altitudinal accuracy of the laser footprint, was meticulously verified through the use of ten strategically positioned ground control stations. This paper predominantly addresses the structural analysis of levees, aiming to assess the robustness of the proposed automated technique for point cloud extraction from levees exhibiting varied structural characteristics. To accommodate the disparity in spatial distribution between levee constructions and other landscape features, four distinct levee types along the Hengmen Waterway were selected as the focal regions in this case study.
2.2. Introduction to Levee Structure
2.3. Method
2.3.1. Main Process
The initial stage involves the application of a Statistical Outlier Removal (SOR) filter to the LiDAR dataset to expunge points that are not pertinent to the ground surface. Subsequent to this filtration, the Cloth Simulation Filter (CSF) algorithm is employed to discriminate between ground and nonground points effectively, culminating in the isolation of the nonground point subset.
The process commences with the exclusion of vegetationinfluenced points from the nonground subset, executed by analyzing the rate of change in the normal vectors of the point cloud. This is followed by the segmentation of the point cloud using a connected region labeling technique, which facilitates the derivation of initial levee seed points.
The investigation advances through the development and application of a regiongrowing algorithm, which is designed to augment the levee seed point subset. This strategic expansion yields a more comprehensive assemblage of levee point cloud data.
The detection of deformation within the levee point clouds is predominantly conducted through the analysis of variance in the angles between the normal vectors of the point cloud. This evaluation employs an adaptive, optimally selected neighborhood principal component analysis method to ascertain the presence and extent of deformed topographical features. Each of these steps is integral to the overarching objective of advancing the precision and efficacy of levee analysis through innovative point cloud data processing techniques.
2.3.2. Basic Principle of Levee Point Cloud Extraction
2.3.3. Levee Seed Point Selection
2.3.4. Evaluation
From the extraction results of the four types of levee point clouds in the aforementioned study regions, the method presented in this paper successfully extracted complete levees within the study range. To comprehensively evaluate these results, qualitative analysis alone is insufficient; a quantitative assessment of the algorithm’s effectiveness is also necessary for a thorough evaluation and analysis of the levee point cloud extraction results achieved in this study.
$$$$
where TP denotes the number of levee points correctly segmented; FN denotes the number of levee points that are not segmented; and FP denotes the number of levee points incorrectly segmented.
2.3.5. Adaptive Normal Vector Estimation
 (1)

Normal vector computation
 (2)

Determination of the optimal neighborhood based on PCA
$${L}_{\lambda}=\frac{\sqrt{{\lambda}_{1}}\sqrt{{\lambda}_{2}}}{\sqrt{{\lambda}_{1}}},{G}_{\lambda}=\frac{\sqrt{{\lambda}_{2}}\sqrt{{\lambda}_{3}}}{\sqrt{{\lambda}_{1}}},{S}_{\lambda}=\frac{\sqrt{{\lambda}_{3}}}{\sqrt{{\lambda}_{1}}}$$
$${E}_{f}={L}_{\lambda}\mathrm{ln}({L}_{\lambda}){G}_{\lambda}\mathrm{ln}({G}_{\lambda}){S}_{\lambda}\mathrm{ln}({S}_{\lambda})$$
$${K}_{\mathrm{optimized}}=\mathrm{arg}\mathrm{min}({E}_{f})$$
$${E}_{\eta}={\eta}_{1}\mathrm{ln}({\eta}_{1}){\eta}_{2}\mathrm{ln}({\eta}_{2}){\eta}_{3}\mathrm{ln}({\eta}_{3})$$
$${K}_{\mathrm{optimized}}=\mathrm{arg}\mathrm{min}({E}_{\eta})$$
where ${\eta}_{\mathrm{i}}=\frac{{\lambda}_{i}}{{\displaystyle \sum {\lambda}_{i}}}$, and i = 1, 2, and 3.
2.3.6. Deformation Detection of Levee Depression
Depression deformations in levees most frequently manifest along the waterside slope, spanning from the jetty to the crest. These deformations originate at the waterside shoulder of the crest and extend downward, potentially culminating in breaches at the crest. Surface alterations within these depressed areas are markedly more conspicuous when contrasted with the regular levee surfaces. As the topology transitions from the normative to the depressed state, the point cloud normal vectors also exhibit significant alterations, with their directions becoming increasingly erratic. This results in an escalated variance of the angles between the normal vectors within the point clouds of the depressed levee sections. Consequently, this variance serves as a quantitative indicator for the identification of localized depression deformations in levee structures.
3. Results
3.1. Extraction of Levee Point Cloud
3.1.1. Extraction Results
3.1.2. Comparative Evaluation of Different Research Methods
 (1)

With traditional edge detection method
 (2)

With traditional regiongrowing algorithms
3.1.3. Evaluation of Accuracy
3.1.4. Results Analysis
 (1)

Qualitative analysis
 (2)

Quantitative analysis
3.2. Analysis of Deformation Detection Results
4. Discussion
This paper applies airborne LiDAR technology to levee point cloud extraction, achieving a method suitable for the automatic extraction of levee point clouds and the detection of local depression deformations in levees. This provides technical reserves and scientific support for the surveying, investigation, and management of levees. The main innovations of this paper are as follows:
In classifying and extracting levee point clouds based on nonground points, this paper improves the regiongrowing algorithm based on seed point sets. It introduces the degree of variation in the normal vectors of levee point clouds for preliminary classification. Subsequently, seed point sets are constructed using point cloud connected region labeling for region growing, enabling effective extraction of different types of levee point clouds and offering a practical solution for the automatic extraction of levee point clouds. The regiongrowing algorithm holds notable advantages in the realm of point cloud segmentation. Its inherent simplicity and intuitiveness make it accessible, allowing for a straightforward understanding of the segmentation process. The method excels in adapting to local characteristics, effectively capturing the structure and patterns within a given region. Its robustness against noise enhances its reliability, as it can mitigate the impact of isolated outliers. Furthermore, the resulting segmentation often exhibits local consistency, ensuring that points within a region share similar attributes and form coherent segments.
Change detection techniques applied to point cloud data can reveal subtle deformations that may not be apparent through traditional inspection methods through singlephase data. By using region growing for the levee detection, if there is no adaption considered, the sensitivity of the method to parameter choices, including seed point selection criteria and similarity thresholds. Inappropriate parameter settings can lead to suboptimal segmentation results, necessitating careful tuning. Additionally, the computational intensity of the regiongrowing algorithm, particularly for large point clouds, poses a challenge to efficiency. The method’s local nature may struggle with varying point densities and may not effectively capture global context information, potentially resulting in oversegmentation or undersegmentation. Moreover, the dependency on seed point selection adds another layer of complexity, as inaccurate seed choices can lead to incomplete or inaccurate segmentation outcomes. Nevertheless, point cloud data provide quantitative information about the elevation, shape, and spatial distribution of the levee. Through detailed analysis of the point cloud, engineers can quantify deformations, identify regions of concern, and assess the severity of the changes.
In the extraction of levee point clouds from nonground points, this study enhances the regiongrowing algorithm by refining seed point sets and incorporating variations in levee point cloud normal vectors for initial classification. It utilizes connected point cloud regions marked with seed points and employs the regiongrowing algorithm for the effective extraction of different levee point cloud types, offering a practical solution for automated extraction. The study also introduces a method for detecting levee depressions and deformations based solely on singlephase point cloud data. Leveraging the disorderly distribution of normal vectors, it uses an adaptive optimal neighborhood calculation to identify characteristic points, allowing rapid detection of regions experiencing levee depressions and deformations.
5. Conclusions
Airborne LiDAR technology, as a unique geospatial information acquisition technology, has been widely applied in fields such as mapping geographic information, urban planning, and environmental protection. The hardware system of airborne LiDAR technology has been extensively studied, and the focus of related research has shifted to point cloud postprocessing and analysis. Point cloud filtering and the extraction of different geometric features using airborne LiDAR remain research hotspots. Research on building extraction based on airborne LiDAR point cloud data has mainly focused on houses, roads, and power towers and relatively less on levee point cloud extraction in water conservancy engineering facilities. Additionally, multiphase point cloud data are commonly used for the deformation monitoring of buildings. Based on this, this paper conducts research on airborne LiDAR point cloud filtering, the extraction of different types of levee point clouds, and the detection of levee depression deformations using singlephase point cloud data. The main research contents and results are as follows:
A leveeadaptive regiongrowing algorithm based on seed point sets was developed for extracting levee point clouds. The algorithm first calculates the degree of variation in point cloud normal vectors as the criterion for selecting levee seed points, obtaining discrete seed points. Then, seed point sets are constructed through point cloud connected region labeling, and region growing is performed using the angle and height difference of point cloud normal vectors as growth criteria. The results show that the proposed algorithm effectively extracts point clouds of four different types of levees, retaining detailed information about the levees. In the cases we studied, the overall quality of levee point cloud extraction was 83%, 81%, 75%, and 72%, respectively, representing improvements of 11%, 10%, 8%, and 5% over traditional methods based on single seed points. This method has high accuracy in extracting both artificially constructed standard and nonstandard levee point clouds and can automatically extract levee point clouds with minimal human intervention, demonstrating significant practical value.
A method was developed to detect levee depression deformations using singlephase levee point cloud data by calculating the variance of normal vector angles. To accurately represent the local morphology of the levee point clouds, the method uses an adaptively optimal neighborhood to solve for normal vectors. Based on the experiment, the results showed that the method significantly improved the estimation accuracy of point cloud normal vectors. Applied to three levee depression detection experiments, the method effectively detected regions of levee depression deformations using singlephase point cloud data. In comparison with another commonly used method, our approach, on one hand, mitigates interference from other structures in the vicinity and, on the other hand, successfully achieves comprehensive extraction of the levee structure. Moreover, it effectively delineates the boundary between the levee and the ground features associated with it.
The primary focus of this paper is on the filtering of airborne LiDAR point cloud data, the extraction of levee point clouds, and the detection of levee deformations, yielding promising experimental results. However, there are regions for improvement, including the fusion of multisource data to enhance the accuracy of levee building extraction, the potential application of deep learning, particularly neural network algorithms, for point cloud classification in levee contexts, and the need to combine geometric structure information and multisource remote sensing information to accurately identify the deformation of the levees. The study suggests future research directions to address these aspects, aiming for a more comprehensive understanding and application of point cloud data in levee analysis and monitoring.
Author Contributions
Conceptualization, X.W. and Y.H.; methodology, X.W.; software, Y.W. (Yidan Wang) and X.L.; validation, Y.W. (Yidan Wang), Y.H., X.L., Y.W. (Yuli Wang), Y.L. and T.O.C.; formal analysis, X.W., X.L., Y.W. (Yuli Wang), Y.L. and T.O.C.; investigation, X.W., Y.H. and T.O.C.; resources, X.W., Y.W. (Yidan Wang), Y.H., X.L. and Y.L.; data curation, Y.W. (Yidan Wang), X.L., Y.W. (Yuli Wang) and Y.L.; writing—original draft preparation, X.W.; writing—review and editing, Y.H. and T.O.C.; visualization, Y.W. (Yidan Wang), X.L., Y.W. (Yuli Wang) and Y.L.; supervision, X.W., Y.H. and T.O.C.; project administration, Y.H. and T.O.C.; funding acquisition, X.W. and Y.H. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Natural Science Foundation of China (42001026) and the National Key R&D Program of China (2021YFC3001000).
Data Availability Statement
The airborne Lidar data used in this study are unavailable due to policy restrictions.
Acknowledgments
The anonymous reviewers are acknowledged for their valuable comments.
Conflicts of Interest
Author Xionghui Liao was employed by the company Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., in China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that can be construed as a potential conflict of interest.
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(a) Geographical extent of the aerial LiDAR survey over the Hengmen Waterway; (b) configuration of the airborne LiDAR scanning apparatus; (c) flowchart of the LiDAR point cloud processing protocol [40].
Figure 2.
Point clouds and image data of study regions: las data (a–d) for Levees 1–4; (e–h) images for Levees 1–4.
Figure 2.
Point clouds and image data of study regions: las data (a–d) for Levees 1–4; (e–h) images for Levees 1–4.
Figure 3.
Schematic representation of the levee’s constituent elements.
Figure 3.
Schematic representation of the levee’s constituent elements.
Figure 4.
Key elements of the levee 3D point cloud.
Figure 4.
Key elements of the levee 3D point cloud.
Figure 5.
Flowchart of monitoring of levee deformation for urban flood risk management using airborne 3D point clouds.
Figure 5.
Flowchart of monitoring of levee deformation for urban flood risk management using airborne 3D point clouds.
Figure 6.
Flowchart of levee point cloud extraction.
Figure 6.
Flowchart of levee point cloud extraction.
Figure 7.
Number of points of (a) levee and (b) vegetation point clouds with the change rate of normal vector in depression region.
Figure 7.
Number of points of (a) levee and (b) vegetation point clouds with the change rate of normal vector in depression region.
Figure 8.
Seed point selection processes: (a) las data of Levee 1; (b) CSFfiltered nonground points; (c) a set of seed points constructed by connected region markers; (d) levee’s seed point set with the vegetation points within the red circle being incorrectly classified.
Figure 8.
Seed point selection processes: (a) las data of Levee 1; (b) CSFfiltered nonground points; (c) a set of seed points constructed by connected region markers; (d) levee’s seed point set with the vegetation points within the red circle being incorrectly classified.
Figure 9.
Normal vector (a) distribution and (b) orientation of levee plane.
Figure 9.
Normal vector (a) distribution and (b) orientation of levee plane.
Figure 10.
Impact of different neighborhood sizes on the estimation results of normal vectors about (a) large rolling region and (b) flat region. Large neighborhoods are denoted with dashed lines and small ones with solid lines; circles show the range and arrows point to the normal vector direction.
Figure 10.
Impact of different neighborhood sizes on the estimation results of normal vectors about (a) large rolling region and (b) flat region. Large neighborhoods are denoted with dashed lines and small ones with solid lines; circles show the range and arrows point to the normal vector direction.
Figure 11.
Flowchart of levee point cloud extraction process.
Figure 11.
Flowchart of levee point cloud extraction process.
Figure 12.
A damaged levee surface.
Figure 12.
A damaged levee surface.
Figure 13.
Flowchart of depressed levee region detection from point clouds.
Figure 13.
Flowchart of depressed levee region detection from point clouds.
Figure 14.
Extracted point cloud based on the proposed workflow for (a) band part and (b) straight part of (c) Levee 1.
Figure 14.
Extracted point cloud based on the proposed workflow for (a) band part and (b) straight part of (c) Levee 1.
Figure 15.
Extracted point cloud based on the proposed workflow for (a) band part and (b) straight part of (c) Levee 2.
Figure 15.
Extracted point cloud based on the proposed workflow for (a) band part and (b) straight part of (c) Levee 2.
Figure 16.
Extracted point cloud based on the proposed workflow for (a) straight part and (b) band part of (c) Levee 3.
Figure 16.
Extracted point cloud based on the proposed workflow for (a) straight part and (b) band part of (c) Levee 3.
Figure 17.
Extracted point cloud based on the proposed workflow for (a) band part and (b) straight part of (c) Levee 4.
Figure 17.
Extracted point cloud based on the proposed workflow for (a) band part and (b) straight part of (c) Levee 4.
Figure 18.
Traditional Canny edge detection algorithm for Levees 1 and 2 (a,c); leveeadaptive regiongrowing algorithm for Levees 1 and 2 (b,d).
Figure 18.
Traditional Canny edge detection algorithm for Levees 1 and 2 (a,c); leveeadaptive regiongrowing algorithm for Levees 1 and 2 (b,d).
Figure 19.
Seed selection by traditional regiongrowing algorithm for Levees 1 and 2 (a,c) with the vegetation points within the red circles being incorrectly classified and leveeadaptive regiongrowing algorithm for Levees 1 and 2 (b,d).
Figure 19.
Seed selection by traditional regiongrowing algorithm for Levees 1 and 2 (a,c) with the vegetation points within the red circles being incorrectly classified and leveeadaptive regiongrowing algorithm for Levees 1 and 2 (b,d).
Figure 21.
Identified levee deformation of Region 1: (a) original point cloud; (b) angle variance of the normal vectors; (c) elevation (TIN); (d) change in levee crown; (e) reconstruction of levee depression region; (f) levee depression region.
Figure 21.
Identified levee deformation of Region 1: (a) original point cloud; (b) angle variance of the normal vectors; (c) elevation (TIN); (d) change in levee crown; (e) reconstruction of levee depression region; (f) levee depression region.
Figure 22.
Identified levee deformation of Region 2: (a) original point cloud; (b) angle variance of the normal vectors; (c) elevation (TIN); (d) change in levee crown; (e) reconstruction of levee depression region; (f) levee depression region.
Figure 22.
Identified levee deformation of Region 2: (a) original point cloud; (b) angle variance of the normal vectors; (c) elevation (TIN); (d) change in levee crown; (e) reconstruction of levee depression region; (f) levee depression region.
Figure 23.
Identified levee deformation Region 3: (a) original point cloud; (b) angle variance of the normal vectors; (c) elevation (TIN); (d) change in levee crown; (e) reconstruction of levee depression region; (f) levee depression region.
Figure 23.
Identified levee deformation Region 3: (a) original point cloud; (b) angle variance of the normal vectors; (c) elevation (TIN); (d) change in levee crown; (e) reconstruction of levee depression region; (f) levee depression region.
Figure 20.
Statistical comparison between traditional regiongrowing and leveeadaptive regiongrowing algorithms: (a) detection rate; (b) accuracy rate; (c) quality rate.
Figure 20.
Statistical comparison between traditional regiongrowing and leveeadaptive regiongrowing algorithms: (a) detection rate; (b) accuracy rate; (c) quality rate.
Table 1.
Aerial survey technical parameters.
Table 1.
Aerial survey technical parameters.
Parameters  Value 

Device type  Harrier68I 
Scanning angle (°)  60 
Impulse frequency (Khz)  400 
Camera heading scan angle (°)  43.99 
Camera side scan angle (°)  56.653 
Relative flying height (m)  400 
Aircraft ground speed (m/s)  34 
Image ground resolution (m)  0.048 
Number of images  1445 
Point cloud density (points/m^{2})  16.98 
Course overlap (%)  60 
Lateral overlap (%)  30 
Laser scan overlap (%)  35 
Flight line spacing (m)  302 
Number of flight lines  9 
Total course length (km)  186 
Estimated operating time (h)  2.8 
Speed per hour (km/h)  122 
Table 2.
Statistics of extraction evaluation indices of four types of levees in the study area.
Table 2.
Statistics of extraction evaluation indices of four types of levees in the study area.
Extraction Algorithm 
Evaluation Index 
Experimental Data  

Levee 1  Levee 2  Levee 3  Levee 4  
Leveeadaptive regiongrowing algorithm  Detection rate (%)  95  93  81  85 
Accuracy (%)  87  86  84  83  
Quality (%)  83  81  75  72  
Traditional regiongrowing algorithm  Detection rate (%)  83  82  79  77 
Accuracy (%)  84  85  82  86  
Quality (%)  72  71  68  69 
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