A geographic information systems based landscape classification model to enhance soil survey: A southern Illinois case study

ABSTRACT:

This paper presents an innovative geographic information systems (GIS)-based model that can assist soil scientists in soil survey by providing quantitative data on soils and landscape characteristics. Using GIS data exploration techniques, nine data layers (percent slope, slope length, profile curvature, tangential curvature, mean curvature, flow accumulation, flow line density, distance to troughs, and distance to summits) were extracted from U.S. Geological Survey 30-m (98.43 ft) digital elevation models. A GIS clustering algorithm identified 50 landscape signatures from the nine data layers. A maximum likelihood discriminate analysis classifier was employed using the nine data layers and the 50 landscape signatures to create landscape classification information. This model provides an objective understanding of the soil-landscape relationship. The model was tested at a case study site in a quarter section of Massac County, Illinois, an area of homogeneous loess. Preliminary analysis indicates that the statistical relevance of the model is high, as a regression equation obtained a coefficient of determination (R²) of .88. Thus the potential predictive capabilities of the model are great, and should be extended to heterogeneous landscapes through further testing. This GIS landscape model could be used by soil scientists to advance their knowledge of the soil-landscape relationship, and ultimately enhance soil survey in the future.