• Towards mapping soil carbon landscapes: Issues of sampling scale and transferability

    This study examines the spatial patterns and accuracies of predictions made by different spatial modelling methods on sample sets taken at two different scales. These spatial models are then tested on independent validation sets taken at three different scales. Each spatial modelling method produced similar, but unique, maps of soil organic carbon content (SOC%). Kriging approaches excelled at internal spatial prediction with more densely spaced sample points.

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  • Soil mapping, classification, and pedologic modeling: History and future directions

    Soil mapping, classification, and pedologic modelling have been important drivers in the advancement of our understanding of soil. Advancement in one of these highly interrelated areas tend to lead to corresponding advances in the others. Traditionally, soil maps have been desirable for purposes of land valuation, agronomic planning, and even in military operations. The expansion of the use of soil knowledge to address issues beyond agronomic production, such as land use planning, environmental concerns, energy security, water security, and human health, to name a few, requires new ways to communicate what we know about the soils we map as well as bringing forth research questions that were not widely considered in earlier soils studies.

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  • The Real Benefits of Digital Soil Mapping

    Traditional soil mapping methods are not as bad as they are sometimes made out to be, which can cause us to misunderstand the advancements we’ve made with digital soil mapping. In many cases, the most significant difference between traditional and digital soil maps is the quality and amount of data available to make the map. This is where digital soil mapping truly shines, because it would be practically impossible to utilize all of the data we have now for mapping soil using traditional methods.

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  • Spatial modeling of organic carbon in degraded peatland soils of northeast Germany

    The objective of this study was to evaluate the ability of high-resolution, minimally invasive sensor data to predict spatial variation of soil organic carbon stocks within highly degraded peatland soils in northeast Germany. Soil organic carbon density was related to elevation, electrical conductivity, and peat thickness. Modeling peat thickness based on sensor data needs additional research, but seems to be a valuable set of covariates in digital soil mapping.

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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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  • Digital classification of hillslope position

    Classification of elevation rasters with this digital model of hillslope position represent 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 soil map unit complexes, and (3) identify map unit inclusions that occur due to subtle topographic variation.

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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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  • Impact of multi-scale predictor selection for modeling soil properties

    Results suggest that models with limited predictor pools can substitute other predictors to compensate for unavailable variables. However, a better performing model was always found by considering predictor variables at multiple scales. Although the scale effect of the modifiable area unit problem is generally well known, this study suggests digital soil mapping efforts would be enhanced by the greater consideration of predictor variables at multiple analysis scales.

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  • Multi-scale Parameter Selection for Predicting Soil Organic Carbon (2014 Digital Soil Mapping Workshop)

    This poster presents work analyzing the impact on model performance from utilizing a wide range of DTA analysis scales together with RS data, some of which have a resolution that could be considered too coarse for fine resolution DSM. Data mining techniques were used to select parameters from large pools of base maps for modelling the variables needed to estimate soil organic carbon (SOC) stocks. The best performing models were then used to produce maps of the SOC stock and estimated prediction error.

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