Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach

Abstract

Ensuring that ground point density after raw point cloud processing meets the accuracy requirements for subsequent DEM construction represents a challenge for field operators during airborne LiDAR data acquisition. In this study, we propose a method to quantify DEM quality by combining the RMSE of elevation and terrain complexity, analyzing the DEM quality error curves constructed with different point cloud densities by a discrete difference peak-seeking method, to determine the optimal ground point density, and then constructing an ICP-NN algorithm for predicting the collected point cloud density. After analysis of DEM quality at eight point cloud dilution levels, the optimal ground point cloud densities were determined to be 2.43 pts/m2 (0.2 m resolution), 2.08 pts/m2 (1 m and 0.5 m resolution), and 1.84 pts/m2 (2 m resolution). Using the obtained optimal ground point densities, survey area slopes, canopy density, and elevation differences as eigenvalues, the ICP-NN model can be used to directly predict the collected point cloud density intervals in other regions, and the model has interval lengths ranging from 36 to 70.33 pts/m2 at 5 CLs. This method solves the problem of determining point cloud density in landslide surveys using airborne LiDAR and provides direct guidance for practical applications.

Keywords

• airborne LiDAR
• point cloud density
• terrain complexity
• discrete difference peak-seeking method
• inductive conformal prediction