Effect of DEM Interpolation Neighbourhood on Terrain Factors

Abstract

Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, mean aspect, and RMSEs of slope and aspect from the interpolated DEM. As the moving least squares (MLS) method can maintain the inherent properties and other characteristics of a surface, this method is chosen for DEM interpolation. Three areas containing different types of topographic features are selected for study. Simulated data from a Gauss surface is also used for comparison. First, the impact of the number of points on the DEM root mean square error (RMSE) is analysed. The DEM RMSE in the three study areas decreases gradually with the number of points in the neighbourhood. In addition, the effect of the number of points in the neighbourhood on mean slope and mean aspect was studied across varying topographies through regression analysis. The two variables respond differently to changes in terrain. However, the RMSEs of the slope and aspect in all study areas are logarithmically related to the number of points in the neighbourhood and the values decrease uniformly as the number of points in the neighbourhood increases. With more points in the neighbourhood, the RMSEs of the slope and aspect are not sensitive to topography differences and the same trends are observed for the three studied quantities. Results for the Gauss surface are similar. Finally, this study analyses the spatial distribution of slope and aspect errors. The slope error is concentrated in ridges, valleys, steep-slope areas, and ditch edges while the aspect error is concentrated in ridges, valleys, and flat regions. With more points in the neighbourhood, the number of grid cells in which the slope error is greater than 15° is gradually reduced. With similar terrain types and data sources, if the calculation efficiency is not a concern, sufficient points in the spatial autocorrelation range should be analysed in the neighbourhood to maximize the accuracy of the slope and aspect. However, selecting between 10 and 12 points in the neighbourhood is economical.

A regional digital bathymetric model fusion method based on topographic slope: A case study of the south China sea and surrounding waters

“…For example, the prediction accuracy of submarine plain is higher than that of seamount. For areas with complex terrain, the model obtained by fusion was more robust, with the support of topographic factors [ 20 , 21 ]. Considering the topography and slope grading criteria of the study area, this study distinguished the data in the study area into 5 sub-regions of 0°–1°, 1°–5°, 5°–10°, 10°–20°, and 20°–80°, and the results of slope grading are shown in Fig.…”

Exploring modern bathymetry: A comprehensive review of data acquisition devices, model accuracy, and interpolation techniques for enhanced underwater mapping

“…When the selection of interpolation points contains many adjacent points, the root mean square error (RMSE) may not be sensitive enough to detect topographical differences (Zhu et al, 2019b). As a result, interpolating large-scale DEM data has become another research direction in recent years.…”

“…Increasing the sampling density improves the accuracy of the generated DEM and increases the raster resolution (Zhu et al, 2019b). However, surface reconstruction of scattered data is an illposed problem, and as the number of sample points increases, most calculation algorithms become too time-consuming .…”

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

“…We intended to conduct LAS data processing in open-source environment and to use the most widespread algorithms. Although there are several research studies presenting details and suggestions concerning the best practice to generate DTMs [13,48,49], there has been no comprehensive analysis of how the multiple factors of noise filtering, ground point classification, interpolation techniques, DTM resolution, and fluvial geomorphology together influence the accuracy of the models. Finding the DTM the most precisely reflects the terrain characteristics is crucial in such a flat environment where even a centimeter-scale error can change the topography and therefore change the determination of the waterflow direction (flood risk management), sediment accumulation (floodplain land use management) or the identification of fluvial forms.…”