• Oracles and Science: The Trouble with Predictions

    We all want to know the future, but what is the best way to make predictions? Assuming we don’t have a crystal ball, we have to find patterns in the available information and make our best, informed guess. This is what scientists do.

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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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  • 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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  • 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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  • Error Propagation Toolbox

    error propagation

    New estimated errors are calculated for each raster cell based on the combination of the two input rasters.

    Quantifying uncertainty can be a very useful and often important aspect of evaluating results of calculations, particularly in modelling. The same applies for spatial layer mashups where the grids provide the input variables for equations that are calculated spatially (i.e. raster calculator). This toolbox for ArcGIS uses standard error propagation equations to simultaneously calculate the result of basic math expressions along with the estimated error of that result. The measured or estimated errors for the input variables are required. Error covariances can also be included in the calculation of error propagation, but are not required.

    Download the Error Propagation Toolbox (178.6 KB)



  • 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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  • 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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  • Potential benefits of wetland filters for tile drainage systems

    Nitrate concentration and stream discharge data from USGS National Stream Quality Accounting Network monitoring stations in the upper Mississippi River (UMR) and Ohio River basins were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations. The model accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Modeling results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas.

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