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Digital Elevation Model (DEM) is a numerical representation of the Earth's topography using an array of cells or pixels. DEMs are important for various purposes such as hydrological studies and environmental monitoring in situations where there is a need to assess land use changes and deforestation and disaster response. In this study, various spatial interpolation methods, such as Inverse distance weighting (IDW) technique, Natural Neighbor, and Triangulated Irregular Network (TIN) methods, were compared to evaluate the performance in generating accurate and reliable DEMs. Factors such as computational efficiency and ease of implementation were considered to determine the most suitable method. The precision of the generated DEMs was assessed through metrics such as root mean square error (RMSE) and mean absolute bias (MAB). The predicted elevation models were compared with elevation values obtained through ground surveys. When comparing the elevation values recorded by the two data sources (USGS 3D Elevation Program (USGS 3DEP) and Unmanned Aerial Vehicle (UAV)), a raster agreement analysis was employed and the result shows that TIN recorded the highest agreement of 92%, while the binning technique was inconsistent in its prediction of the elevation values based on the data source. In regions with high forest cover, the results were less accurate due to the limited number of last returns available for developing the DEM. This study also found that TIN recorded the fastest processing time per square acre. The presence of vegetation significantly influences the quantity of Light Detection and Ranging (LiDAR) pulses capable of reaching the forest floor, subsequently impacting the quantity of points available for the creation of a DEM.
Adedapo et al. (Thu,) studied this question.