<![CDATA[Topographic mapping requires precise three-dimensional (3D) coordinates for geospatial applications. Conventional terrestrial surveys using Total Station (TS) and GNSS-RTK provide reliable accuracy but are time-consuming. Aerial LiDAR offers a faster alternative by generating high-density point clouds with wide coverage, although its accuracy must be evaluated. This study assesses the elevation accuracy of LiDAR compared to TS across six terrain categories: open ground surface, vegetation-covered surface, road section, sparsely populated settlement, densely populated settlement, and river. Data acquisition employed a 1 m grid to align horizontal coordinates and focused on elevation values. The evaluation included descriptive statistics, elevation difference histograms, Root Mean Square Error (RMSE), and linear correlation analysis. Results indicate that LiDAR achieved high accuracy in open ground and road sections, with low RMSE and correlation values equal to one. Accuracy decreased in settlements due to roof and wall reflections, and in dense vegetation where laser pulses were blocked by canopy. Despite these limitations, LiDAR effectively represented contour patterns after filtering, classification, and breakline addition. In rivers, LiDAR produced the largest deviations caused by water reflectivity, while TS remained precise for riverbed elevations. This study demonstrates that LiDAR is highly effective for mapping open areas and roads, applicable in settlements with further processing, and still useful in vegetated terrain through its multiple-return capability. However, water bodies require TS validation as a precision reference. Overall, LiDAR provides efficient wide-area data acquisition, while TS continues to serve as the precision standard in complex conditions under ISO/IEC 17025. The application of ISO/IEC 17025 is essential to ensure that measurement, calibration, and data validation comply with principles of accuracy, traceability, and uncertainty control, thereby making topographic mapping results accountable. ]]>
