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Digital Soil Mapping Using Landscape Stratification for Arid Rangelands in the Eastern Great Basin, Central Utah

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Digital Soil Mapping Using Landscape Stratification for Arid Rangelands in the Eastern Great Basin, Central Utah Utah State University DigitalCommons@USU All Graduate Theses and Dissertations Graduate Studies 5-2015 Digital Soil Mapping Using Landscape Stratification for Arid Rangelands in the Eastern Great Basin, Central Utah Brook B. Fonnesbeck Utah State University Follow this and additional works at: https://digitalcommons.usu.edu/etd Part of the Soil Science Commons Recommended Citation Fonnesbeck, Brook B., "Digital Soil Mapping Using Landscape Stratification for Arid Rangelands in the Eastern Great Basin, Central Utah" (2015). All Graduate Theses and Dissertations. 4525. https://digitalcommons.usu.edu/etd/4525 This Thesis is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Theses and Dissertations by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. DIGITAL SOIL MAPPING USING LANDSCAPE STRATIFICATION FOR ARID RANGELANDS IN THE EASTERN GREAT BASIN, CENTRAL UTAH by Brook B. Fonnesbeck A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in Soil Science Approved: _____________________________ _____________________________ Dr. Janis L. Boettinger Dr. Joel L. Pederson Major Professor Committee Member _____________________________ _____________________________ Dr. R Douglas Ramsey Dr. Mark R. McLellan Committee Member Dean of the School of Graduate Studies UTAH STATE UNIVERSITY Logan, Utah 2015 ii Copyright © Brook B. Fonnesbeck 2015 iii ABSTRACT Digital Soil Mapping Using Landscape Stratification for Arid Rangelands in the Eastern Great Basin, Central Utah by Brook B. Fonnesbeck, Master of Science Utah State University, 2015 Major Professor: Dr. Janis L. Boettinger Department: Plants, Soils and Climate Digital soil mapping typically involves inputs of digital elevation models, remotely sensed imagery, and other spatially explicit digital data as environmental covariates to predict soil classes and attributes over a landscape using statistical models. Digital imagery from Landsat 5, a digital elevation model, and a digital geology map were used as environmental covariates in a 67,000-ha study area of the Great Basin west of Fillmore, UT. A “pre-map” was created for selecting sampling locations. Several indices were derived from the Landsat imagery, including a normalized difference vegetation index, normalized difference ratios from bands 5/2, bands 5/7, bands 4/7, and bands 5/4. Slope, topographic curvature, inverse wetness index, and area solar radiation were calculated from the digital elevation model. The greatest variation across the study area was found by calculating the Optimum Index Factor of covariates, choosing band 7, normalized difference ratio bands 5/2, normalized difference vegetation index, slope, iv profile curvature, and area solar radiation. A 20-class ISODATA unsupervised classification of these six data layers was reduced to 12. Comparing the 12-class map to a geologic map, 166 sites were chosen weighted by areal extent; 158 sites were visited. Twelve points were added using case-based reasoning to total 170 points for model training. A validation set of 50 sites was selected using conditioned Latin Hypercube Sampling. Density plots of sample sets compared to raw data produced comparable results. Geology was used to stratify the study area into areas above and below the Lake Bonneville highstand shoreline. Raster data were subset to these areas, and predictions were made on each area. Spatial modeling was performed with three different models: random forests, support vector machines, and bagged classification trees. A set of covariates selected by random forests variable importance and the set of Optimum Index Factor covariates were used in the models. The Optimum Index Factor covariates produced the best classification using random forests. Classification accuracy was 45.7%. The predictive rasters may not be useful for soil map unit delineation, but using a hybrid method to guide further sampling using the pre-map and standard sampling techniques can produce a reasonable soil map. (113 pages) v PUBLIC ABSTRACT Digital Soil Mapping Using Landscape Stratification for Arid Rangelands in the Eastern Great Basin, Central Utah Brook B. Fonnesbeck 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 to create soil maps for a part of the burned area west of Fillmore, UT. Soil maps were created over an area of more than 146,000 acres using predictive statistical models that incorporated geographic information systems and statistical software. Over two field seasons soil data were collected by excavating and describing the soil more than 150 sites over the project area. The coordinates of physical locations were recorded, and soils were sampled, described, characterized, and classified to a soil series that could be used to make interpretations for management and restoration decisions. Two sets of sampling sites were collected: one to create models and maps of soils in the project area, and another set to validate the accuracy of those maps. The vi project area was split into two areas: one above the Lake Bonneville highstand shoreline, and one below. Points were separated out between those above and those below the shoreline. Modeling results were less accurate than desired below the shoreline, but could be useful to guide further mapping and refining of subsequent soil maps. The dominant soil order predicted was Aridisols; some had high calcium carbonate content, and some had high clay content with high sodium. The soil distribution above the shoreline was estimated since there were not enough points to model any soils with accuracy. vii ACKNOWLEDGMENTS I would like to thank the United States Department of the Interior Bureau of Land Management for funding this research project, as well as Jeremy Jarnecke and Lisa Bryant for taking an interest in my work and project. I would also like to thank the staff of the BLM Field Office in Fillmore, UT, including Bill Thompson, Mike Gates, and Dave Whitaker, for assistance in the field and maps of the project area. I would especially like to thank Dr. Janis L. Boettinger for providing every opportunity to conduct this research project, providing expert knowledge of digital soil mapping, and encouragement to push through to the end. I would like to thank the Utah Agricultural Experiment Station at Utah State University for providing additional funding for this project. I would also like to thank my committee members, Dr. R. Douglas Ramsey and Dr. Joel L. Pederson. This project consisted of a good portion of their fields of expertise, and I could not have done this project without their practical advice and encouragement. I would also like to thank my wife, Lacy Fonnesbeck, for putting up with me the last three years while I worked through this project, supporting me in my graduate career, and loving me through it all. I could not ask for a better companion by my side. I would like to thank Suzann Kienast-Brown for her assistance and advice in the wide world of digital soil mapping, and helping to break up the monotony in the lab. I would like to thank John R. Lawley for his advice, friendship, encouragement, and unselfish assistance in the field and in the lab, without which this project could not have been completed. I would like to thank Dr. Colby Brungard for his advice, assistance, knowledge, friendship, viii and encouragement through the long hours of field work and research in the lab. I would also like to thank the student staff in the Soil Genesis Lab who put in a lot of work to analyze samples and data: Dan Horne, Ingrid Merrill, Jon Jones, Vance Almquist, Leanna Hayes, Angie Swainston, and Jeremiah Armentrout. Lastly, I would like to thank my family for their love and support through all the years I’ve put into my education. Brook Fonnesbeck ix CONTENTS Page ABSTRACT ....................................................................................................................... iii PUBLIC ABSTRACT ........................................................................................................ v ACKNOWLEDGMENTS ................................................................................................ vii LIST OF TABLES ............................................................................................................. xi LIST OF FIGURES .......................................................................................................... xii INTRODUCTION .............................................................................................................. 1 MATERIALS AND METHODS ........................................................................................ 7 Study Area Description ................................................................................................. 7 Data Layers ................................................................................................................
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