Comparative of Digital Terrain Model Quality Based on Cloth Simulation Filter and Simple Outlier Elimination in LiDAR Data - Saputra, Randi Adrian; Aldiansyah, Septianto (Journal of Geographical Sciences and Education, 2026)
The quality of Digital Terrain Models (DTM) from LiDAR data is highly dependent on the effectiveness of the ground filtering process, especially in areas with complex mountainous topography and dense vegetation cover. This study aims to systematically analyze and compare the quality of DTMs generated from three different processing schemes, namely Raw DTM, Simple Outlier Elimination DTM (ELM), and Cloth Simulation Filter DTM (CSF), using LiDAR point cloud data on Mount Aso, Japan. The methods used involved ground filtering using CSF and ELM, followed by IDW interpolation, as well as descriptive statistical evaluation and Hillshade visualization with enhanced Z Factor. The ELM filter proved ineffective in producing statistics identical to the raw DTM, while CSF successfully reduced the average elevation from 130.73 m to 124.06 m and confirmed that vegetation noise was eliminated. In complex terrains, future LiDAR research should prioritize adaptive algorithms like CSF over simple statistical filters to ensure higher accuracy in digital terrain representation.