A Comparative Study of Curvature and Grid Data Reduction Algorithms for Lidar-derived Digital Terrain Models

Abstract A digital terrain model (DTM) is defined as the digital cartographic representation of the elevation of the earth's surface created from discrete elevation points. DTMs have been applied to a diverse field of tasks, such as forest management, urban planning, ice sheet mapping, flood control, road design, hydraulic simulation, visibility analysis of the terrain, and topographic change quantification. In parallel with the developments in data processing technologies; satellite remote sensing, airborne laser scanning, and radar interferometry become efficient sources for constructing high quality DTMs in a cost-effective manner. The accuracy of DTM is influenced by several factors such as, the accuracy, the density, and the spatial distribution of elevation points, the terrain surface characteristics, and the interpolation methods. In this study, direct comparisons are made between curvature and grid data reduction algorithms for airborne Light Detection and Ranging (LiDAR) derived DTMs. DTMs with %25, 50 and 75 sampling densities interpolated by the triangulation with the linear interpolation method are compared with the DTM constituted with %100 airborne LiDAR point cloud over the Mount St. Helens in southwest Washington State as the test area. The results show that LiDAR datasets can be reduced to 50% density level by a grid data reduction algorithm while still maintaining the quality of the derived DTM.