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Digital soil mapping
R literacy for digital soil mapping
Getting spatial in R-. Preparatory and exploratory data analysis for digital soil mapping
Continuous soil attribute modeling and mapping
Categorical soil attribute modeling and mapping
Some methods for the quantification of prediction uncertainties for digital soil mapping
Using digital soil mapping to update, harmonize and disaggregate legacy soil maps
Combining continuous and categorical modeling: Digital soil mapping of soil horizons and their depths
Digital soil assessment: A simple enterprise suitability example.
Foreword; Preface; Endorsements; Acknowledgements; Contents; 1 Digital Soil Mapping; 1.1 The Fundamentals of Digital Soil Mapping; 1.2 What Is Going to Be Covered in this Book?; References; 2 R Literacy for Digital Soil Mapping; 2.1 Objective; 2.2 Introduction to R; 2.2.1 R Overview and History; 2.2.2 Finding and Installing R; 2.2.3 Running R: GUI and Scripts; 2.2.4 RStudio; 2.2.5 R Basics: Commands, Expressions, Assignments, Operators, Objects; 2.2.6 R Data Types; 2.2.7 R Data Structures; 2.2.8 Missing, Indefinite, and Infinite Values; 2.2.9 Functions, Arguments, and Packages
2.2.10 Getting Help2.2.11 Exercises; 2.3 Vectors, Matrices, and Arrays; 2.3.1 Creating and Working with Vectors; 2.3.2 Vector Arithmetic, Some Common Functions, and Vectorised Operations; 2.3.3 Matrices and Arrays; 2.3.4 Exercises; 2.4 Data Frames, Data Import, and Data Export; 2.4.1 Reading Data from Files; 2.4.2 Creating Data Frames Manually; 2.4.3 Working with Data Frames; 2.4.4 Writing Data to Files; 2.4.5 Exercises; 2.5 Graphics: The Basics; 2.5.1 Introduction to the Plot Function; 2.5.2 Exercises; 2.6 Manipulating Data; 2.6.1 Modes, Classes, Attributes, Length, and Coercion
2.6.2 Indexing, Sub-setting, Sorting, and Locating Data2.6.3 Factors; 2.6.4 Combining Data; 2.6.5 Exercises; 2.7 Exploratory Data Analysis; 2.7.1 Summary Statistics; 2.7.2 Histograms and Box Plots; 2.7.3 Normal Quantile and Cumulative Probability Plots; 2.7.4 Exercises; 2.8 Linear Models: The Basics; 2.8.1 The lm Function, Model Formulas, and Statistical Output; 2.8.2 Linear Regression; 2.8.3 Exercises; 2.9 Advanced Work: Developing Algorithms with R; Reference; 3 Getting Spatial in R; 3.1 Basic GIS Operations Using R; 3.1.1 Points; 3.1.2 Rasters
3.2 Advanced Work: Creating Interactive Maps in R3.3 Some R Packages That Are Useful for Digital Soil Mapping; Reference; 4 Preparatory and Exploratory Data Analysis for Digital Soil Mapping; 4.1 Soil Depth Functions; 4.1.1 Fit Mass Preserving Splines with R; 4.2 Intersecting Soil Point Observations with Environmental Covariates; 4.2.1 Using Rasters from File; 4.3 Some Exploratory Data Analysis; References; 5 Continuous Soil Attribute Modeling and Mapping; 5.1 Model Validation; 5.1.1 Model Goodness of Fit; 5.1.2 Model Validation; 5.2 Multiple Linear Regression
5.2.1 Applying the Model Spatially5.2.1.1 Covariate Table; 5.2.1.2 Raster Predictions; 5.2.1.3 Directly to Rasters Using Parallel Processing; 5.3 Decision Trees; 5.4 Cubist Models; 5.5 Random Forests; 5.6 Advanced Work: Model Fitting with Caret Package; 5.7 Regression Kriging; 5.7.1 Universal Kriging; 5.7.2 Regression Kriging with Cubist Models; References; 6 Categorical Soil Attribute Modeling and Mapping; 6.1 Model Validation of Categorical Prediction Models; 6.2 Multinomial Logistic Regression; 6.3 C5 Decision Trees; 6.4 Random Forests; References
R literacy for digital soil mapping
Getting spatial in R-. Preparatory and exploratory data analysis for digital soil mapping
Continuous soil attribute modeling and mapping
Categorical soil attribute modeling and mapping
Some methods for the quantification of prediction uncertainties for digital soil mapping
Using digital soil mapping to update, harmonize and disaggregate legacy soil maps
Combining continuous and categorical modeling: Digital soil mapping of soil horizons and their depths
Digital soil assessment: A simple enterprise suitability example.
Foreword; Preface; Endorsements; Acknowledgements; Contents; 1 Digital Soil Mapping; 1.1 The Fundamentals of Digital Soil Mapping; 1.2 What Is Going to Be Covered in this Book?; References; 2 R Literacy for Digital Soil Mapping; 2.1 Objective; 2.2 Introduction to R; 2.2.1 R Overview and History; 2.2.2 Finding and Installing R; 2.2.3 Running R: GUI and Scripts; 2.2.4 RStudio; 2.2.5 R Basics: Commands, Expressions, Assignments, Operators, Objects; 2.2.6 R Data Types; 2.2.7 R Data Structures; 2.2.8 Missing, Indefinite, and Infinite Values; 2.2.9 Functions, Arguments, and Packages
2.2.10 Getting Help2.2.11 Exercises; 2.3 Vectors, Matrices, and Arrays; 2.3.1 Creating and Working with Vectors; 2.3.2 Vector Arithmetic, Some Common Functions, and Vectorised Operations; 2.3.3 Matrices and Arrays; 2.3.4 Exercises; 2.4 Data Frames, Data Import, and Data Export; 2.4.1 Reading Data from Files; 2.4.2 Creating Data Frames Manually; 2.4.3 Working with Data Frames; 2.4.4 Writing Data to Files; 2.4.5 Exercises; 2.5 Graphics: The Basics; 2.5.1 Introduction to the Plot Function; 2.5.2 Exercises; 2.6 Manipulating Data; 2.6.1 Modes, Classes, Attributes, Length, and Coercion
2.6.2 Indexing, Sub-setting, Sorting, and Locating Data2.6.3 Factors; 2.6.4 Combining Data; 2.6.5 Exercises; 2.7 Exploratory Data Analysis; 2.7.1 Summary Statistics; 2.7.2 Histograms and Box Plots; 2.7.3 Normal Quantile and Cumulative Probability Plots; 2.7.4 Exercises; 2.8 Linear Models: The Basics; 2.8.1 The lm Function, Model Formulas, and Statistical Output; 2.8.2 Linear Regression; 2.8.3 Exercises; 2.9 Advanced Work: Developing Algorithms with R; Reference; 3 Getting Spatial in R; 3.1 Basic GIS Operations Using R; 3.1.1 Points; 3.1.2 Rasters
3.2 Advanced Work: Creating Interactive Maps in R3.3 Some R Packages That Are Useful for Digital Soil Mapping; Reference; 4 Preparatory and Exploratory Data Analysis for Digital Soil Mapping; 4.1 Soil Depth Functions; 4.1.1 Fit Mass Preserving Splines with R; 4.2 Intersecting Soil Point Observations with Environmental Covariates; 4.2.1 Using Rasters from File; 4.3 Some Exploratory Data Analysis; References; 5 Continuous Soil Attribute Modeling and Mapping; 5.1 Model Validation; 5.1.1 Model Goodness of Fit; 5.1.2 Model Validation; 5.2 Multiple Linear Regression
5.2.1 Applying the Model Spatially5.2.1.1 Covariate Table; 5.2.1.2 Raster Predictions; 5.2.1.3 Directly to Rasters Using Parallel Processing; 5.3 Decision Trees; 5.4 Cubist Models; 5.5 Random Forests; 5.6 Advanced Work: Model Fitting with Caret Package; 5.7 Regression Kriging; 5.7.1 Universal Kriging; 5.7.2 Regression Kriging with Cubist Models; References; 6 Categorical Soil Attribute Modeling and Mapping; 6.1 Model Validation of Categorical Prediction Models; 6.2 Multinomial Logistic Regression; 6.3 C5 Decision Trees; 6.4 Random Forests; References