Predictive soil mapping in the Mojave Desert of California

Predictive soil mapping (PSM) can be defined as the development of a numerical or statistical model of the relationships among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. PSM is made possible by geocomputational technologies developed over the past few decades. For example, advances in geographic information science, digital terrain modeling, and remote sensing have created a tremendous potential to improve the quality and efficiency of soil mapping. The primary focus of this dissertation is to develop and test PSM methods using existing soil survey data at two study sites located in the Mojave Desert of California, where there are nearly 6 million hectares of land to be mapped and only limited financial resources. Soil maps for the Mojave were considered low priority until concerns regarding management of the delicate desert ecosystems and their biodiversity became important. Knowledge of the soil resource is critical for land management decisions in the Mojave Desert. The specific goal of this dissertation was to produce models of spatial soil information (soil map unit, soil taxa, and soil properties) that can be used to produce more robust soil maps for surveyed areas and preliminary maps of non-mapped areas. Results from Chapter 4 suggest that classification tree analysis can be used to predict soil taxonomic class with reasonable accuracy from environmental variables. The technique could be used in soil survey to extrapolate obvious soil landscape relationships from one site to another, allowing soil experts to concentrate their field mapping effort in unique areas. Chapter 5 compared several PSM techniques with a sparse soil survey and a field data set collected to model soil texture attributes from remotely sensed imagery and digital elevations models. The results demonstrated that non-spatial statistical methods outperformed geostatistical approaches. The results also suggest that soil survey field data can be used as input to predictive soil mapping techniques. In the future, the methods describe above could be used after a traditional soil survey is complete to create spatially distributed soil property maps from the soil profile characterization data collected in the field.