Abstract
This paper makes a general evaluation of the spatial interpolation accuracy of elevation data. Six common interpolators were examined, including Kriging, inverse distance to a power, minimum curvature, modified Shepard’s method, radial basis functions, and triangulation with linear interpolation. The main properties and mathematical procedures of the interpolation algorithms were reviewed. In order to obtain full evaluation of the interpolations, both statistical (including root-mean-square-error, standard deviation, and mean) and spatial accuracy measures (including accuracy surface, and spatial autocorrelation) were employed. It is found that the accuracy of spatial interpolation of elevations was primarily subject to input data point density and distribution, grid size (resolution), terrain complexity, and interpolation algorithm used. The variations in interpolation parameters may significantly improve or worsen the accuracy. Further researches are needed to examine the impacts of terrain complexity in details and various data sampling strategies. The combined use of variogram models, accuracy surfaces, and spatial autocorrelation represents a promising direction in mapping spatial data accuracy.
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Weng, Q. (2006). An Evaluation of Spatial Interpolation Accuracy of Elevation Data. In: Riedl, A., Kainz, W., Elmes, G.A. (eds) Progress in Spatial Data Handling. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-35589-8_50
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DOI: https://doi.org/10.1007/3-540-35589-8_50
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