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Comparison Of Spatial Association Approaches For Landscape Mapping Of Soil Organic Carbon Stocks, Bradley A. Miller, S. Koszinski, M. Wehrhan, M. Sommer
Comparison Of Spatial Association Approaches For Landscape Mapping Of Soil Organic Carbon Stocks, Bradley A. Miller, S. Koszinski, M. Wehrhan, M. Sommer
Bradley A Miller
The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to 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 …
Impact Of Multi-Scale Predictor Selection For Modeling Soil Properties, Bradley A. Miller, Sylvia Koszinski, Marc Wehrhan, Michael Sommer
Impact Of Multi-Scale Predictor Selection For Modeling Soil Properties, Bradley A. Miller, Sylvia Koszinski, Marc Wehrhan, Michael Sommer
Bradley A Miller
Applying a data mining tool used regularly in digital soil mapping, this research focuses on the optimal inclusion of predictors for soil–landscape modeling by utilizing as wide of a pool of variables as possible. Predictor variables for digital soil mapping are often chosen on the basis of data availability and the researcher's expert knowledge. Predictor variables commonly overlooked include alternative analysis scales for land-surface derivatives and additional remote sensing products. For this study, a pool of 412 potential predictors was assembled, which included qualitative location classes, elevation, land-surface derivatives (with a wide range of analysis scales), hydrologic indicators, as well …