<![CDATA[Accurate representation of the Earth's surface is fundamental for geospatial analysis and environmental studies. This research examines and compares the Digital Terrain Model (DTM) and Digital Surface Model (DSM), both generated from LiDAR data acquired over the ITERA campus, a region characterized by heterogeneous land cover, including built-up areas, vegetation, and open ground. Data were collected using a DJI M350 drone equipped with an APUS sensor at an altitude of 100 meters. The methodology involved preprocessing point cloud data, classifying ground and non-ground points, and generating elevation models using Triangulated Irregular Network (TIN) interpolation with a 0.5-meter grid. Vertical accuracy was evaluated through 12 reference checkpoints measured with Geodetic GPS technology. Results demonstrate that the DSM achieved higher vertical accuracy, with a root-mean-square error (RMSE) of 0.060 meters, compared to the DTM's RMSE of 0.342 meters. Analysis of the normalized DSM (nDSM) revealed elevation differences of up to ±38 meters between DSM and DTM, effectively identifying vertical structures such as dense vegetation and campus buildings. These findings confirm the DSM is more reliable for analyzing the absolute heights of surface objects, while the DTM remains essential for modeling natural landforms, even in areas with complex land cover and greater uncertainty.]]>
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