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

中文翻译：

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LiDAR采样密度对森林覆盖率高的地区DTM精度的影响

通过LiDAR进行的激光扫描是一种强大的技术，即使在茂密的森林地区，该技术也可以收集生成数字地形模型（DTM）所需的数据。可以将位于地面的LiDAR观测值与初始点云分离，并用作通过插值生成数字地形模型（DTM）的输入。本文提出了对从LiDAR数据获得的DTM（及其衍生的坡度图）准确性的定量分析，并着重于大多数林业活动（崎terrain，陡峭，有森林覆盖的地形）常见的条件。测试了三种插值算法：逆距离加权（IDW），自然邻域（NN）和薄板样条线（TPS）。研究主要集中在点数据密度问题上。要分析其对地表建模质量的影响，²）通过以10％的增量随机删除点观测值。这提供了评估LiDAR接地点密度对DTM精度影响的综合方法。尽管在所有情况下（如预期的那样）点密度的降低都会导致DTM的准确性降低，但确切的模式因算法而异。只要使用合适的插值算法（如IDW），即使LiDAR采样密度降低到0.40–0.50点/ m ²（初始点密度的50–60％），源自LiDAR的DTM的精度也相对较高。事实证明，对于密度降低至约0.60点/ m ²以下时，弹性较小）。在斜率估计的情况下，模式相对相似，只是IDW与其他两种算法之间的精度差异比DTM精度的情况更为明显。基于这项研究，我们得出结论，即使采样密度显着降低，LiDAR还是一种收集地表建模所需的形态数据的适当方法。