Comparison of data reduction algorithms for LiDAR-derived digital terrain model generalisation

A digital terrain model (DTM) is a three-dimensional representation of the terrain relief created from discrete points related to each other through their elevations. New technologies such as satellite remote sensing, airborne laser scanning and radar interferometry are efficient methods for constructing high-quality DTMs in a cost-effective manner. The accuracy of a DTM is influenced by a number of factors, including the accuracy, density and spatial distribution of elevation points, the terrain surface characteristics, etc. In this paper, direct comparisons of absolute and relative vertical accuracies are made between data reduction algorithms for the generalisation of DTM extracted from airborne Light Detection and Ranging (LiDAR) data. The absolute vertical accuracies are presented in terms of the mean error (ME), the mean absolute error (MAE) and the root mean square error (RMSE) and the relative vertical accuracies are characterised as per cent slope over Mount St Helens in southwest Washington State. The results show that LiDAR datasets can be reduced to 50 per cent density level by a uniform data reduction algorithm using triangulation with a linear interpolation method for the generalisation of DTM while still maintaining the quality of the original data.