• 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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  • 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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  • 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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