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A 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, deforestation, and disaster response. In this study, various spatial interpolation methods, such as the 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. We also considered factors such as computational efficiency and ease of implementation 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. Furthermore, a raster agreement analysis was used to compare the elevation values recorded by the two data sources, the USGS 3D Elevation Program (USGS 3DEP) and Unmanned Aerial Vehicle (UAV). The analysis revealed that TIN method demonstrated the highest level of agreement at 92%, while the binning technique showed inconsistency in predicting 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. • Different spatial interpolation techniques were compared to generate DEMs in highly dense forest landscapes. • The TIN method outperformed other techniques in terms of accuracy and computational efficiency. • DEM accuracy is lower in areas with highly dense forest cover due to limited laser beam penetration. • The TIN method is best for creating DEMs in highly dense forest cover due to its high accuracy and faster processing time. [ABSTRACT FROM AUTHOR]

A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs. [ABSTRACT FROM AUTHOR]