Canadian Forest Service Publications
GIS-fuzzy logic based approach in modeling soil texture: Using part of the clay belt and Hornepayne region in Ontario Canada as a case study. 2016. Akumu, C.E.; Johnson, J.A.; Etheridge, D.; Uhlig, P.; Woods, M.; Pitt, D.G.; McMurray, S. Geoderma (240)13-24.
Available from: Great Lakes Forestry Centre
Catalog ID: 35795
CFS Availability: PDF (request by e-mail)
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There is a growing global need to generate high resolution digital soil maps for numerous ecological applications. We aim to address this issue by modeling and mapping soil texture using Geographic Information Systems (GIS) and fuzzy logic techniques over parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study. This was performed based on the soil-environment model (case-based reasoning approach) using a 10-m LiDAR Digital Elevation Model (DEM) and derivatives such as slope, surface curvature, smooth multi-path wetness index, slope position classification in combination with landcover, mode of deposition and spatial cases of soil texture information. A map of six soil textural classes (organic, coarse loamy, silt, clay, fine sand, and coarse sand) was produced at 10-m resolution across 430,076 ha that proved accurate in validation of 79% of the time. The application of these techniques and approach could enable soil scientists to easily generate and improve current digital soil maps.
Plain Language Summary
We tested a method to predict and map soil texture on the landscape to address needs for digital soil maps of high spatial resolution. We used terrain attributes derived from a 10-metre LiDAR Digital Elevation Model and information about the soil formative environment, such as elevation, curvature, slope position classification, landforms, slope, wetness index and landcover. The resulting map contained six soil textural classes (organic, coarse loamy, silt, clay, fine sand, and coarse sand) at 10-m resolution across 430,076 ha of boreal forest in northeastern Ontario. In validation, this map had an overall accuracy of about 79%. We expect that accuracy might be improved by increasing the number of environmental variables used. The application of these techniques and approach could enable soil scientists to easily generate and improve current digital soil maps, which are useful in numerous applications such as resource modeling, ecological land classification, environmental planning and forestry.
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