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.
Highlights
A digital elevation model (DEM) represents basic data for spatial analysis of geographic information systems, which are widely applied in the fields of hydrology [1], soil [2], landform [3,4], geology [5], agriculture [6], and military applications [7,8]
Many scholars have studied the influence of sampling density and interpolation methods on DEM accuracy, the influence of DEM resolution on slope and aspect, and the influence of sampling interval and light detection and ranging (LIDAR) post spacing on slope and aspect
This paper focuses on the influence on a DEM as well as on the accuracy of the slope and aspect when the data source for DEM construction, the resolution of the DEM constructed, slope and aspect algorithms, and interpolation method for DEM construction are the same while the quantity of local sampling points selected for estimating point elevations is increased
Summary
A digital elevation model (DEM) represents basic data for spatial analysis of geographic information systems, which are widely applied in the fields of hydrology [1], soil [2], landform [3,4], geology [5], agriculture [6], and military applications [7,8]. Many scholars have studied the influence of sampling density and interpolation methods on DEM accuracy, the influence of DEM resolution on slope and aspect, and the influence of sampling interval and LIDAR post spacing on slope and aspect. This paper focuses on the influence on a DEM as well as on the accuracy of the slope and aspect when the data source for DEM construction, the resolution of the DEM constructed, slope and aspect algorithms, and interpolation method for. The sampling points and contour line data from three areas of different geomorphic types, including a loess gully area, loess hilly area, and high mountain area, are adopted, and the moving least squares (MLS) method is used to construct DEMs in order to study differences in DEMs constructed and the extracted slope and aspect for different numbers of search points in the neighbourhood
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