Approximation Theory Applied to DEM Vertical Accuracy Assessment

Authors


XiaoHang Liu, Department of Geography and Human Environmental Studies, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA. E-mail: xhliu@sfsu.edu

Abstract

Existing research on DEM vertical accuracy assessment uses mainly statistical methods, in particular variance and RMSE which are both based on the error propagation theory in statistics. This article demonstrates that error propagation theory is not applicable because the critical assumption behind it cannot be satisfied. In fact, the non-random, non-normal, and non-stationary nature of DEM error makes it very challenging to apply statistical methods. This article presents approximation theory as a new methodology and illustrates its application to DEMs created by linear interpolation using contour lines as the source data. Applying approximation theory, a DEM's accuracy is determined by the largest error of any point (not samples) in the entire study area. The error at a point is bounded by max(|δnode|+M2h2/8) where |δnode| is the error in the source data used to interpolate the point, M2 is the maximum norm of the second-order derivative which can be interpreted as curvature, and h is the length of the line on which linear interpolation is conducted. The article explains how to compute each term and illustrates how this new methodology based on approximation theory effectively facilitates DEM accuracy assessment and quality control.

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