Input data for spatial and environmental models may have been measured in the field or laboratory, derived from remotely sensed imagery or obtained from expert elicitation. Data are also often digitized, interpolated, classified or generalized prior to submission to a model. In all these cases errors are introduced. Although users may be aware that errors propagate through their models, they rarely pay attention to this problem. However, when the accuracy of the data is insufficient for the intended purpose then this may result in inaccurate model results, wrong conclusions and poor decisions. The purpose of this course is to familiarize participants with statistical methods to analyse uncertainty propagation in modelling, such that they can apply these methods to their own data and models. Both attribute and positional errors are considered. Attention is also given to the effects of spatial auto- and cross-correlations on the results of an uncertainty propagation analysis and on methods to determine the relative contribution of the individual sources of uncertainty to the accuracy of the final result. The methodology is illustrated with real-world examples.
Former occurrences of this course
7-11 Dec 2020 | 10-14 Dec 2018 | 5-12 Dec 2016 | 8-12 Dec 2014 | 17-21 Oct 2011 | 13-17 Dec 2010