Progress with the design of a soil uncertainty database, and associated tools for simulating spatial realisations of soil properties
Linda Lilburne, Allan Hewitt and Steve Ferriss
Private Bag 69, Lincoln, Canterbury 8152, New Zealand
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Uncertainty assessment in soil information has been well served by the geostatistical community. Mathematical techniques based on kriging theory allow for spatial autocorrelation of soil measurements to be characterised in a semi-variogram, and then conditionally simulated, providing spatial realisations of soil properties of interest. However, in some countries with a particularly diverse landscape and a poor network of soil samples, data measurements are simply too sparse for this approach to be used, especially in a national- scale database. This is the case for New Zealand where soil surveys are based on soil– landform relationships, producing polygonal rather than raster data. An alternative, cruder approach is needed for characterising uncertainty that relies upon a cohort of very experienced pedologists. This paper describes how uncertainty information has been incorporated in the design of S-map, New Zealand’s new national spatial soils database. These uncertainty data are primarily derived from expert knowledge due to the lack of other sources. Some tools have been written in Python to access this uncertainty information and create randomly simulated spatial realisations for a variety of soil properties.
Keywords: expert knowledge, simulation, soil data model, soil survey
In: Caetano, M. and Painho, M. (eds). Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 5 – 7 July 2006, Lisboa, Instituto Geográfico Português