Forest Analytics with R: An Introduction (Use R)
By Andrew P. Robinson, Jeff D. Hamann
* Publisher: Springer
* Number Of Pages: 354
* Publication Date: 2010-12-14
* ISBN-10 / ASIN: 1441977619
* ISBN-13 / EAN: 9781441977618
Product Description:
Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The
authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications.
The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and
using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming.
The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics,
and very basic applied mathematics.
Table of Contents
Preface
*
Acknowledgments
Contents
Part I Introduction and Data Management
Chapter 1 Introduction
*
1.1 This Book
o
1.1.1 Topics Covered in This Book
o
1.1.2 Conventions Used in This Book
o
1.1.3 The Production of the Book
*
1.2 Software
o
1.2.1 Communicating with R
o
1.2.2 Getting Help
+
1.2.2.1 Getting Help Locally
+
1.2.2.2 Getting Help Remotely
o
1.2.3 Using Scripts
o
1.2.4 Extending R
o
1.2.5 Programming Suggestions
o
1.2.6 Programming Conventions
o
1.2.7 Speaking Other Languages
*
1.3 Notes about Data Analysis
Chapter 2 Forest Data Management
*
2.1 Basic Concepts
*
2.2 File Functions
o
2.2.1 Text Files
o
2.2.2 Spreadsheets
o
2.2.3 Using SQL in R
o
2.2.4 The foreign Package
o
2.2.5 Geographic Data
o
2.2.6 Other Data Formats
*
2.3 Data Management Functions
o
2.3.1 Herbicide Trial Data
o
2.3.2 Simple Error Checking
o
2.3.3 Graphical error checking
o
2.3.4 Data Structure Functions
*
2.4 Examples
o
2.4.1 Upper Flat Creek in the UIEF
+
2.4.1.1 Tree Data
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2.4.1.2 Plot-Level Data
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2.4.1.3 Spatial Data
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2.4.2 Sweetgum Stem Profiles
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2.4.3 FIA Data
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2.4.4 Norway Spruce Profiles
o
2.4.5 Grand Fir Profiles
o
2.4.6 McDonald–Dunn Research Forest
+
2.4.6.1 Stand Data
+
2.4.6.2 Plot Data
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2.4.6.3 Tree Data
o
2.4.7 Priest River Experimental Forest
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2.4.7.1 Ground Data
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2.4.7.2 Remotely Sensed Data
o
2.4.8 Leuschner
*
2.5 Summary
Part II Sampling and Mapping
Chapter 3 Data Analysis for Common Inventory Methods
*
3.1 Introduction
o
3.1.1 Infrastructure
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3.1.2 Example Datasets
*
3.2 Estimate Computation
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3.2.1 Sampling Distribution
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3.2.2 Intervals from Large-Sample Theory
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3.2.3 Intervals from Linearization
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3.2.4 Intervals from the Jackknife
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3.2.4.1 A Brief History of the Jackknife
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3.2.5 Intervals from the Bootstrap
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3.2.5.1 Implementation
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3.2.5.2 Innovations
o
3.2.6 A Simulation Study
*
3.3 Single-Level Sampling
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3.3.1 Simple Random Sampling
+
3.3.1.1 Analysis for Simple Random Sampling
o
3.3.2 Systematic Sampling
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3.3.2.1 Analysis for Systematic Sampling
*
3.4 Hierarchical Sampling
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3.4.1 Cluster Sampling
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3.4.1.1 Analysis for Cluster Sampling
o
3.4.2 Two-Stage Sampling
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3.4.2.1 Analysis for Two-Stage Sampling
*
3.5 Using Auxiliary Information
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3.5.1 Stratified Sampling
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3.5.1.1 Analysis for Stratified Sampling
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3.5.1.2 Combinations of Designs
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3.5.2 Ratio Estimation
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3.5.2.1 Analysis for Ratio Estimation
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3.5.2.2 Combinations of Designs
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3.5.3 Regression Estimation
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3.5.3.1 Analysis for Regression Estimation
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3.5.3.2 Combinations of Designs
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3.5.4 3P Sampling
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3.5.4.1 Analysis for 3P Sampling
o
3.5.5 VBAR
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3.5.5.1 Analysis for VBAR
*
3.6 Summary
Chapter 4 Imputation and Interpolation
*
4.1 Introduction
*
4.2 Imputation
o
4.2.1 Examining Missingness Patterns
o
4.2.2 Methods for Imputing Missing Data
+
4.2.2.1 Weighting Procedures
+
4.2.2.2 Imputation-Based Procedures
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4.2.2.3 Model-Based Procedures
o
4.2.3 Nearest-Neighbor Imputation
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4.2.4 Expectation-Maximization Imputation
o
4.2.5 Comparing Results
*
4.3 Interpolation
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4.3.1 Methods of Interpolation
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4.3.2 Ordinary Kriging
+
4.3.2.1 Theory
+
4.3.2.2 Descriptions of Spatial Correlation
o
4.3.3 Semi-variogram Estimation
o
4.3.4 Prediction
*
4.4 Summary
Part III Allometry and Fitting Models
Chapter 5 Fitting Dimensional Distributions
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5.1 Diameter Distribution
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5.2 Non-parametric Representation
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5.3 Parametric Representation
o
5.3.1 Parameter Estimation
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5.3.1.1 Plug-in Principle
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5.3.1.2 Method of Moments
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5.3.1.3 Maximum Likelihood
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5.3.2 Some Models of Choice
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5.3.3 Profiling
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5.3.4 Sampling Weights
Chapter 6 Linear and Non-linear Modeling
*
6.1 Linear Regression
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6.1.1 Example
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6.1.2 Thinking about the Problem
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6.1.3 Fitting the Model
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6.1.4 Assumptions and Diagnostics
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6.1.5 Examining the Model
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6.1.6 Using the Model
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6.1.7 Testing Effects
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6.1.8 Transformations
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6.1.9 Weights
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6.1.10 Generalized Least-Squares Models
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6.2 Non-linear Regression
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6.2.1 Example
o
6.2.2 Thinking about the Problem
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6.2.3 Fitting the Model
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6.2.4 Assumptions and Diagnostics
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6.2.5 Examining the Model
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6.2.6 Using the Model
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6.2.7 Testing Effects
o
6.2.8 Generalized Non-linear Least-Squares Models
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6.2.9 Self-starting Functions
*
6.3 Back to Maximum Likelihood
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6.3.1 Linear Regression
o
6.3.2 Non-linear Regression
o
6.3.3 Heavy-Tailed Residuals
Chapter 7 Fitting Linear Hierarchical Models
*
7.1 Introduction
o
7.1.1 Effects
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7.1.1.1 Fixed Effects
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7.1.1.2 Random Effects
+
7.1.1.3 Mixed-up Effects
o
7.1.2 Model Construction
o
7.1.3 Solving a Dilemma
o
7.1.4 Decomposition
*
7.2 Linear Mixed-Effects Models
o
7.2.1 A Simple Example
+
7.2.1.1 Linear Regression
+
7.2.1.2 Mixed Effects
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7.2.1.3 Mixed Effects, Unique Variances
+
7.2.1.4 Mixed Effects, Unique Variances, Autocorrelation
*
7.3 Case Study: Height and Diameter Model
o
7.3.1 Height vs. Diameter
o
7.3.2 Use More Data
o
7.3.3 Adding Fixed Effects
o
7.3.4 The Model
+
7.3.4.1 More on Z
+
7.3.4.2 More on b
+
7.3.4.3 More on D
*
7.4 Model Wrangling
o
7.4.1 Monitor
o
7.4.2 Meddle
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7.4.3 Modify
o
7.4.4 Compromise
*
7.5 The Deep End
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7.5.1 Maximum Likelihood
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7.5.2 Restricted Maximum Likelihood
*
7.6 Non-linear Mixed-Effects Models
o
7.6.1 Hierarchical Approach
*
7.7 Further Reading
Part IV Simulation and Optimization
Chapter 8 Simulations
*
8.1 Generating Simulations
o
8.1.1 Simulating Young Stands
+
8.1.1.1 Loading the rconifers Package
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8.1.1.2 Creating and Using a sample.data Object
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8.1.1.3 Generating Young-Stand Simulations
o
8.1.2 Simulating Established Stands
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8.1.2.1 Source Code
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8.1.2.2 Compile, Attach, Call, and Wrap External Code
+
8.1.2.3 Generating Established-Stand Simulations
*
8.2 Generating Volumes
o
8.2.1 The Taper Function
o
8.2.2 Computing Merchantable Height
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8.2.3 Summarizing Log Volumes by Grade
o
8.2.4 Young-Stand Volumes
o
8.2.5 Established-Stand Volumes
+
8.2.5.1 From Summary Statistics
+
8.2.5.2 From Tree Lists
*
8.3 Merging Yield Streams
*
8.4 Examining Results
o
8.4.1 Volume Distribution
o
8.4.2 Mean Annual Increment
*
8.5 Exporting Yields
*
8.6 Summary
Chapter 9 Forest Estate Planning and Optimization
*
9.1 Introduction
*
9.2 Problem Formulation
*
9.3 Strict Area Harvest Schedule
o
9.3.1 Objective Function
o
9.3.2 Adding Columns
o
9.3.3 Naming Columns
o
9.3.4 Bounding Columns
o
9.3.5 Setting Objective Coefficients
o
9.3.6 Adding Constraints
+
9.3.6.1 Stand Area Constraints
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9.3.6.2 Strict Area Control
o
9.3.7 Solving
o
9.3.8 Results
+
9.3.8.1 Objective Function
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9.3.8.2 Decision Variables
+
9.3.8.3 Harvested Area
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9.3.8.4 Woodflow
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9.3.8.5 Reduced Costs
+
9.3.8.6 Slack/Surplus
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9.3.8.7 Shadow Prices
o
9.3.9 Archiving Problems
o
9.3.10 Cleanup
*
9.4 Summary
References
Index
