• CLORPT: Spatial Association in Soil Geography

    From as early as 500 BCE, humans have recognized that some things vary together in space. This is essentially correlation, but the spatial aspect sometimes adds a special twist. The first scientific application of spatial association to soil mapping that we know about was by E.W. Hilgard. He observed that knowledge of the geology and type of vegetation were useful indicators for predicting soil type. Today in digital soil mapping, we still utilize these concepts, but because we use much more quantitative variables, we typically describe this method as spatial regression, or a little more specifically, environmental correlation.

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  • Issues of Sampling Scale and Transferability for Digital Soil Mapping (2015 EGU General Assembly)

    Poster examining the effects of sampling scale and spatial modelling methods on predicted patterns of SOC% and the associated errors. (presented in Vienna, Austria)

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  • Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

    A comparison of direct and indirect approaches for mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m‾²), covering an area of 122 km², with accompanying maps of estimated error. Although the indirect approach fit the spatial variation better and had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. The optimal approach would depend upon the intended use of the map.

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  • Fundamentals of Spatial Prediction

    In the process of creating a map, geographers often have to engage in the activity of spatial prediction. Although there are many tools we use to accomplish this task, they generally boil down to the use of one or two fundamental concepts: spatial association and spatial autocorrelation.

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  • Digital Classification of Hillslope Position for Defining Soil Map Units (2014 SSSA Conference)

    This poster provides examples from different landscapes demonstrating the hillslope position model’s ability to better place soil delineations where defendable landscape breaks exist. The results of this model are base maps that can be used to (1) improve research on toposequences by providing explicit definitions of each hillslope element’s location, (2) facilitate the disaggregation of soils currently mapped as complexes due to topographic variation, and (3) identify map unit inclusions in areas of subtle topographic variation. The base maps developed by the model can also help identify areas of possible mismapping, especially where soil boundaries cross topographic breaks.

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  • The historical role of base maps in soil geography

    This paper reviews the historical development of base maps used for soil mapping, and evaluates the dependence of soil mapping on base maps. Formerly, as a reference for spatial position, paper base maps controlled the cartographic scale of soil maps. However, this relationship is no longer true in geographic information systems. Today, as parameters for digital soil maps, base maps constitute the library of predictive variables and constrain the supported resolution of the soil map.

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