Non-stationary Modelling and Simulation of LIDAR DEM Uncertainty

Juha Oksanen* and Tapani Sarjakoski
Department of Geoinformatics and Cartography Finnish Geodetic Institute Masala, Finland

Abstract: Appropriate modelling and simulation of digital elevation model (DEM) uncertainty is among the most long- lasting of topics in geographical information science, because DEMs and terrain analysis are widely used in tasks with high societal impact. Decisions based on the analysis are expected to be of better quality if the uncertainty of the analysis results is taken into account. Despite the long research history of the topic, a few big challenges have decelerated the final break- through of the uncertainty-aware terrain analysis. Firstly, the utilisation of Monte Carlo simulation, which is the most flexible method for investigating the propagation of uncertainty in terrain analysis, is time-consuming. Moreover, the use of massive high-resolution DEMs based on airborne light detection and ranging (LIDAR) has made the performance issue even worse. Secondly, mainstream uncertainty-aware terrain analysis is done by applying stationary models of DEM uncertainty, even though the number of experiments has proven that the uncertainty would be modelled more realistically as a non-stationary stochastic process. The paper demonstrates how the process convolution method can be applied in a realistic and efficient non-stationary simulation of LIDAR DEM uncertainty.

Keywords: process convolution, unconditional simulation

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