Quantifying the Effect of LiDAR Data Density on DEM Quality

Abstract

LiDAR sensors capture three-dimensional point clouds with high accuracy and density; since they are regularly obtained, interpolation methods are required to generate a regular grid. Given the large size of its files, processing becomes a challenge for researchers with not very powerful computer stations. This work aims to balance the sampling density and the volume of data, preserving the sensitivity of representation of complex topographic shapes as a function of three surface descriptors: slope, curvature, and roughness. This study explores the effect of the density of LiDAR data on the accuracy of the Digital Elevation Model (DEM), using a ground point cloud of 32 million measurements obtained from a LiDAR flight over a complex topographic area of 156 ha. Digital elevation models with different relative densities to the total point dataset were produced (100, 75, 50, 25, 10, and 1 % and at different grid sizes 23, 27, 33, 46, 73, and 230cm). Accuracy was evaluated using the Inverse Distance Weighted and Kriging interpolation algorithms, obtaining 72 surfaces from which their error statistics were calculated: root mean square error, mean absolute error, mean square error, and prediction effectiveness index; these were used to evaluate the quality of the results in contrast with validation data corresponding to 10 % of the original sample. The results indicated that Kriging was the most efficient algorithm, reducing data to 1 % without statistically significant differences with the original dataset, and curvature was the morphometric parameter with the most significant negative impact on interpolation accuracy.