Assessing spatial uncertainty in predictive geomorphological mapping: A multi-modelling approach

Maps of earth surface processes and the potential distribution of landforms make an important contribution to theoretical and applied geomorphology. Because decision making often depends on information based on spatial models, there is a great need to develop methodology to evaluate the spatial unce...

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Bibliographic Details
Published in:Computers & geosciences 2010-03, Vol.36 (3), p.355-361
Main Authors: Luoto, Miska, Marmion, Mathieu, Hjort, Jan
Format: Article
Language:English
Subjects:
Accuracy
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geomorphology
Landforms
Mapping
Mathematical models
Modelling
Patterned ground
Periglacial
Uncertainty
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Summary:Maps of earth surface processes and the potential distribution of landforms make an important contribution to theoretical and applied geomorphology. Because decision making often depends on information based on spatial models, there is a great need to develop methodology to evaluate the spatial uncertainty resulting from those models. In this study we developed a new method to produce maps of the uncertainty of predictions provided by ten state-of-the-art modelling techniques for sorted (SP) and non-sorted (NSP) patterned ground in subarctic Finland at a 1.0-ha resolution. Six uncertainty classes represent the modelling agreement between the different modelling techniques. The resulting uncertainty maps reflect the reliability of the estimates for the studied periglacial landforms in the modelled area. Our results showed a significant negative correlation between the degree of uncertainty and the accuracy of the modelling techniques. On average, when all ten models agreed, the mean area under the curve (AUC) values were 0.904 (NSP) and 0.896 (SP), these values decreased to 0.416 (NSP) and 0.518 (SP), respectively, when only five models agreed. Mapping of the uncertainty of predictions in geomorphology can help scientists to improve the reliability of their data and modelling results. The predictive maps can be interpreted simultaneously with the uncertainty information, improving understanding of the potential pitfalls of the modelling.
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2009.07.008