This book describes and provides many detailed examples of implementing Digital Soil Mapping (DSM) using R. The work adheres to Digital Soil Mapping theory, and presents a strong focus on how to apply it. DSM exercises are also included and cover procedures for handling and manipulating soil and spatial data in R. The book also introduces the basic concepts and practices for building spatial soil prediction functions, and then ultimately producing digital soil maps.
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
The book compiles the main ideas and methodologies that have been proposed and tested within these last fifteen years in the field of Digital Soil Mapping (DSM). Begining with current experiences of soil information system developments in various regions of the world, this volume presents states of the art of different topics covered by DSM: Conception and handling of soil databases, sampling methods, new soil spatial covariates, Quantitative spatial modelling, Quality assessment and representation of DSM outputs. This book provides a solid support to students, researchers and engineers interested in modernising soil survey approaches with numerical techniques. It is also of great interest for potential soil data users.* A new concept to meet the worldwide demand for spatial soil data * The first compilation of ideas and methodologies of Digital Soil Mapping * Offers a variety of specialities: soil surveying, geostatistics, data mining, fuzzy logic, remote sensing techniques, Geographical Information Science,...* Written by 82 researchers from 13 different countries
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.
Signi?cant technological advances have been few and far between in the past approximately one hundred years of soil survey activities. Perhaps one of the most innovative techniques in the history of soil survey was the introduction of aerial photographs as base maps for ?eld mapping, which replaced the conventional base map laboriously prepared by planetable and alidade. Such a relatively simple idea by today’s standards revolutionized soil surveys by vastly increasing the accuracy and ef?ciently. Yet, even this innovative approach did not gain universal acceptance immediately and was hampered by a lack of aerial coverage of the world, funds to cover the costs, and in some cases a reluctance by some soil mappers and cartog- phers to change. Digital Soil Mapping (DSM), which is already being used and tested by groups of dedicated and innovative pedologists, is perhaps the next great advancement in delivering soil survey information. However, like many new technologies, it too has yet to gain universal acceptance and is hampered by ignorance on the part of some pedologists and other scientists. DSM is a spatial soil information system created by numerical models that - count for the spatial and temporal variations of soil properties based on soil - formation and related environmental variables (Lagacheric and McBratney, 2007).
GlobalSoilMap: Basis of the global spatial soil information system contains contributions that were presented at the 1st GlobalSoilMap conference, held 7-9 October 2013 in Orléans, France. These contributions demonstrate the latest developments in the GlobalSoilMap project and digital soil mapping technology for which the ultimate aim is to produce a high resolution digital spatial soil information system of selected soil properties and their uncertainties for the entire world. GlobalSoilMap: Basis of the global spatial soil information system aims to stimulate capacity building and new incentives to develop full GlobalSoilMap products in all parts of the world.
This book will provide an exposure to recent developments in the field of geostatistical modeling, spatial variability of soil resources, and preparation of digital soil maps using R and GIS and potential application of it in agricultural resource management. Specifically following major areas are covered in the book.
The Soil Organic Carbon Mapping cookbook provides a step-by-step guidance for developing 1 km grids for soil carbon stocks. It includes the preparation of local soil data, the compilation and pre-processing of ancillary spatial data sets, upscaling methodologies, and uncertainty assessments. Guidance is mainly specific to soil carbon data, but also contains many generic sections on soil grid development, as it is relevant for other soil properties. This second edition of the cookbook provides generic methodologies and technical steps to produce SOC maps and has been updated with knowledge and practical experiences gained during the implementation process of GSOCmap V1.0 throughout 2017. Guidance is mainly specific to SOC data, but as this cookbook contains generic sections on soil grid development it can be applicable to map various soil properties.
This book presents the basic concepts of quantitative soil science and, within this framework, it seeks to construct a new body of knowledge. There is a growing need for quantitative approach in soil science, which arises from a general demand for improved economic production and environmental management. Pedometrics can be defined as the development and application of statistical and mathematical methods applicable to data analysis problems in soil science. This book shows how pedometrics can address key soil-related questions from a quantitative point of view. It addresses four main areas which are akin to the problems of conventional pedology: (i) Understanding the pattern of soil distribution in character space – soil classification, (ii) Understanding soil spatial and temporal variation, (iii) Evaluating the utility and quality of soil and ultimately, (iv) Understanding the genesis of soil. This is the first book that address these problems in a coherent quantitate approach.
This book provides a didactic overview of techniques for inferring information from soil spectroscopic data, and the codes in the R programming language for performing such analyses. It is intended for students, researchers and practitioners looking to infer soil information from spectroscopic data, focusing mainly on, but not restricted to, the infrared range of the electromagnetic spectrum. Little prior knowledge of the R programming language or digital soil spectra is required. We work through the steps to process spectroscopic data systematically.
