The Effect of LiDAR Sampling Density on DTM Accuracy for Areas with Heavy Forest Cover

Laser scanning via LiDAR is a powerful technique for collecting data necessary Digital Terrain Model (DTM) generation, even in densely forested areas. observations located at the ground level can be separated from initial point cloud and used as input generation of interpolation. This paper proposes quantitative analysis accuracy DTMs (and derived slope maps) obtained focused on conditions common to most forestry activities (rough, steep terrain with forest cover). Three interpolation algorithms were tested: Inverse Distance Weighted (IDW), Natural Neighbour (NN) Thin-Plate Spline (TPS). Research was mainly issue density. To analyze its impact quality surface modelling, density filtered set artificially lowered (from 0.89 0.09 points/m2) by randomly removing 10% increments. provides comprehensive method evaluating DTM accuracy. While reduction leads less accurate all cases (as expected), exact pattern varies algorithm. The LiDAR-derived relatively good when sampling reduced 0.40–0.50 points/m2 (50–60 % density), long suitable algorithm IDW proved resilient reductions below approximately 0.60 points/m2). In case estimation, similar, except difference between other two more pronounced than Based this research, we conclude that an adequate morphological modelling surface, significantly reduced.