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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Digital Soil Mapping Using Landscape Stratification For Arid Rangelands In The Eastern Great Basin, Central Utah, Brook B. Fonnesbeck
Digital Soil Mapping Using Landscape Stratification For Arid Rangelands In The Eastern Great Basin, Central Utah, Brook B. Fonnesbeck
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
In some parts of the western US there is limited publicly available soil information that can be used to make land management decisions on both public and private land. A goal of the USDI Bureau of Land Management (BLM) in Utah was to map an area in central Utah where such soil maps and value-added information was not available for management and restoration decisions following a wildfire. In 2007, the Milford Flat Fire had burned more than 363,000 acres, removing vegetation that was holding erosion-sensitive soils in place. Following inconsistent results from stabilization and restoration efforts, this study was funded …
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 …
Machine Learning For Predicting Soil Classes In Three Semi-Arid Landscapes, Colby W. Brungard, Janis L. Boettinger, Michael C. Duniway, Skye A. Wills, Thomas C. Edwards Jr.
Machine Learning For Predicting Soil Classes In Three Semi-Arid Landscapes, Colby W. Brungard, Janis L. Boettinger, Michael C. Duniway, Skye A. Wills, Thomas C. Edwards Jr.
Plants, Soils, and Climate Faculty Publications
Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set …