High-Precision Terrain Modeling of Tailing Ponds Using UAV LiDAR Point Cloud Data

This study proposes a high-precision methodology for dry beach extraction and safety parameter calculation in tailing ponds using UAV LiDAR data. Leveraging the Difference Consistency Iterative Closest Point (DICP) algorithm, this approach achieves precise point cloud registration, while an improved progressive morphological filtering method effectively separates ground and non-ground points. Bilinear interpolation is further applied to address data gaps, enhancing data completeness and terrain continuity. The resulting Digital Elevation Model (DEM) exhibits high spatial resolution and minimal elevation error, meeting the precision requirements of engineering applications. This method demonstrates the viability and efficiency of UAV LiDAR technology for detailed terrain monitoring and safety assessment in tailing pond environments. The study concludes with a discussion on optimizing these techniques for complex terrains and exploring their potential applications in broader environmental monitoring contexts.