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Conventional soil maps represent a valuable source of information about soil characteristics, however they are subjective, very expensive, and time-consuming to prepare. Also, they do not include explicit information about the conceptual mental model used in developing them nor information about their accuracy, in addition to the error associated with them. Decision tree analysis (DTA) was successfully used in retrieving the expert knowledge embedded in old soil survey data. This knowledge was efficiently used in developing predictive soil maps for the study areas in Benton and Malheur Counties, Oregon and accessing their consistency. A retrieved soil-landscape model from a reference area in Harney County was extrapolated to develop a preliminary soil map for the neighboring unmapped part of Malheur County. The developed map had a low prediction accuracy and only a few soil map units (SMUs) were predicted with significant accuracy, mostly those shallow SMUs that have either a lithic contact with the bedrock or developed on a duripan. On the other hand, the developed soil map based on field data was predicted with very high accuracy (overall was about 97%). Salt-affected areas of the Malheur County study area are indicated by their high spectral reflectance and they are easily discriminated from the remote sensing data. However, remote sensing data fails to distinguish between the different classes of soil salinity. Using the DTA method, five classes of soil salinity were successfully predicted with an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data compared to that predicted by using DTA. Hence, DTA could be a very helpful approach in developing soil survey and soil salinity maps in more objective, effective, less-expensive and quicker ways based on field data.
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).
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.
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
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.
Soil Mapping and Process Modeling for Sustainable Land Use Management is the first reference to address the use of soil mapping and modeling for sustainability from both a theoretical and practical perspective. The use of more powerful statistical techniques are increasing the accuracy of maps and reducing error estimation, and this text provides the information necessary to utilize the latest techniques, as well as their importance for land use planning. Providing practical examples to help illustrate the application of soil process modeling and maps, this reference is an essential tool for professionals and students in soil science and land management who want to bridge the gap between soil modeling and sustainable land use planning. Offers both a theoretical and practical approach to soil mapping and its uses in land use management for sustainability Synthesizes the most up-to-date research on soil mapping techniques and applications Provides an interdisciplinary approach from experts worldwide working in soil mapping and land management
This book contains papers presented at the 6th Global Workshop on Digital Soil Mapping, held 11-14 November 2014 at the Institute of Soil Science, Chinese Academy of Sciences of Nanjing, China. Digital soil mapping is advancing on different fronts at different paces throughout the world. The researches and applications on DSM are moving from method development to realizations in different scales and regions, serving the generation of national and continental to global soil grids. Meanwhile, new ideas and insights on mapping complex soil-landscapes such as flat plains,anthropogenically altered agriculture and urban spaces are emerging, with the help of new paradigms and models.The goal of the sixth workshop was to review and discuss the state of the art in digital soil mapping, and to explore strategies for bridging research, production, and environmental applications. This book provides a very useful and comprehensive overview of the status of digital soil mapping, in which graduate students, scientists and specialists working within the field of geography can find the spatial prediction approaches and related theory.
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.
A treatise on soil cartography, it deals with methods and techniques, use of computers, and application of statistics for mapping soil cover and covers things required for the interpretation of results obtained, and for determining the most economical itinerary to attain that purpose.