Evaluating the impact of different point cloud sampling techniques on digital elevation model accuracy – a case study of Kituro, Kenya

Abstract

Accurate digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) data are crucial for terrain analysis applications. As established in the literature, higher point density improves terrain representation but requires greater data storage and processing capacities. Therefore, point cloud sampling is necessary to reduce densities while preserving DEM accuracy as much as possible. However, there has been a limited examination directly comparing the effects of various sampling algorithms on DEM accuracy. This study aimed to help fill this gap by evaluating and comparing the performance of three common point cloud sampling methods octree, spatial, and random sampling methods in high terrain. DEMs were then generated from the sampled point clouds using three different interpolation algorithms: inverse distance weighting (IDW), natural neighbor (NN), and ordinary kriging (OK). The results showed that octree sampling consistently produced the most accurate DEMs across all metrics and terrain slopes compared to other methods. Spatial sampling also produced more accurate DEMs than random sampling but was less accurate than octree sampling. The results can be attributed to differences in how the sampling methods represent terrain geometry and retain microtopographic detail. Octree sampling recursively subdivides the point cloud based on density distributions, closely conforming to complex microtopography. In contrast, random sampling disregards underlying densities, reducing accuracy in rough terrain. The findings guide optimal sampling and interpolation methods of airborne lidar point clouds for generating DEMs for similar complex mountainous terrains.