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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.
Digital Soil Mapping is the creation and the population of a geographically referenced soil database. It is generated at a given resolution by using field and laboratory observation methods coupled with environmental data through quantitative relationships. Digital soil mapping is advancing on different fronts at different rates all across the world. This book presents the state-of-the art and explores strategies for bridging research, production, and environmental application of digital soil mapping.It includes examples from North America, South America, Europe, Asia, and Australia. The chapters address the following topics: - evaluating and using legacy soil data - exploring new environmental covariates and sampling schemes - using integrated sensors to infer soil properties or status - innovative inference systems predicting soil classes, properties, and estimating their uncertainties - using digital soil mapping and techniques for soil assessment and environmental application - protocol and capacity building for making digital soil mapping operational around the globe.
Predictive Soil Mapping (PSM) is based on applying statistical and/or machine learning techniques to fit models for the purpose of producing spatial and/or spatiotemporal predictions of soil variables i.e. maps of soil properties and classes at different resolutions. It is a multidisciplinary field combining statistics, data science, soil science, physical geography, remote sensing, geoinformation science and a number of other sciences. Predictive Soil Mapping with R is about understanding the main concepts behind soil mapping, mastering R packages that can be used to produce high quality soil maps, and about optimizing all processes involved so that also the production costs can be reduced. The online version of the book is available at: https: //envirometrix.github.io/PredictiveSoilMapping/ Pull requests and general comments are welcome. These materials are based on technical tutorials initially developed by the ISRIC's Global Soil Information Facilities (GSIF) development team over the period 2014?2017