Monitoring of Levee Deformation for Urban Flood Risk Management Using Airborne 3D Point Clouds

Author:

Wang Xianwei12ORCID,Wang Yidan12,Liao Xionghui13,Huang Ying14ORCID,Wang Yuli1,Ling Yibo1,Chan Ting On12ORCID

Affiliation:

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China

2. Guangdong Provincial Engineering Research Center for Public Security and Disasters, Guangzhou 510006, China

3. Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510006, China

4. Guangxi Academy of Sciences, Nanning 530007, China

Abstract

In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China.

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Publisher

MDPI AG

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