Abstract
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.
Highlights
A Digital Elevation Model (DEM) is defined by the U.S Geological Survey as a “digital cartographic representation of the elevation of the land at regularly spaced intervals in x and y directions, with z-values referenced to a common vertical datum” [1] (p. 805)
The accuracy of the LiDAR-derived Digital Terrain Model (DTM) 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
LiDAR data was collected over a test area located in a mountainous area. 5% of the initial ground points were separated from the data set for validation purposes (n = 52,358)
Summary
A Digital Elevation Model (DEM) is defined by the U.S Geological Survey as a “digital cartographic representation of the elevation of the land at regularly spaced intervals in x and y directions, with z-values referenced to a common vertical datum” [1] (p. 805). A DEM models the elevation of the ground surface devoid of any vegetation or man-made structures ( referred to as the bare-earth surface). LiDAR (Light Detection and Ranging) is a mapping technique developed in the 1980s, which has since become the preferred method for collecting morphological data necessary for the creation of large-scale DTMs [2]. It is widely used in research, education and management of both private and public resources. For land covered with forest vegetation, LiDAR is among the few data sources that allow the collection of accurate ground data for DTM generation
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