Development of Digital Terrain Model Under High Dense Forest Cover Using USGS and Drone Lidar Data

Digital Terrain Models (DTMs) are used for various purposes, including land use planning, forest resource management, and soil science. However, the accuracy of DTMs can be limited by the uncertainties of using different interpolation techniques to create them. Thus, this study looked at how other estimation techniques, such as inverse distance weighting (IDW), natural neighbor, and triangulated irregular network (TIN), affect the accuracy and reliability of a DTM for practical use. The results showed that DTMs created by averaging the values of all points in a cell had the highest accuracy (RMSE = 4.97), followed by TIN (RMSE = 5.29), with other techniques like IDW and nearest neighbor recording similar accuracy. A spatial agreement between data sources showed that TIN had the highest agreement at 92%, while DTMs created by averaging all points in a cell had the lowest agreement at 25%.