Wednesday, 9 November 2005
280-3

This presentation is part of: Soil-Landscape Modeling and Geospatial Analysis

Spatial Data Mining and Soil-Landscape Modeling Applied to Soil Survey.

Abdelhamid A. Elnaggar and Jay S. Noller.

Data mining techniques are studied to recover knowledge from geodatabases in order to improve updates of existing soil maps and to help in developing a preliminary soil map for neighboring unmapped areas. Classification tree, one of the most widely used inductive learning methods is used here to retrieve the expert knowledge embedded in the soil-landscape model used by the Harney County, Oregon soil survey (ca. 1980-2003). Spatial environmental data of geology, vegetation, precipitation, terrain attributes (elevation, slope, and aspect) and landsat ETM+ data at a resolution of 30 m were used to predict soil map units. The classification tree proved to be a powerful tool in retrieving the spatial relations between soil map units in the reference area of Harney County. By extrapolating the soil prediction model, a preliminary soil map of an adjacent unmapped area of Malheur County, Oregon is developed with reasonable accuracy.

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