Forest Analytics with R

1. Front Matter

   Pages i-xv

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2. Introduction and Data Management

   1. Front Matter

      Pages 1-1

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   2. Introduction

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 3-17

   3. Forest Data Management

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 19-72

3. Sampling and Mapping

   1. Front Matter

      Pages 73-73

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   2. Data Analysis for Common Inventory Methods

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 75-115

   3. Imputation and Interpolation

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 117-151

4. Allometry and Fitting Models

   1. Front Matter

      Pages 153-153

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   2. Fitting Dimensional Distributions

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 155-173

   3. Linear and Non-linear Modeling

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 175-218

   4. Fitting Linear Hierarchical Models

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 219-273

5. Simulation and Optimization

   1. Front Matter

      Pages 275-275

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   2. Simulations

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 277-305

   3. Forest Estate Planning and Optimization

      • Andrew P. Robinson, Jeff D. Hamann

      Pages 307-323

6. Back Matter

   Pages 325-339

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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.

• forest biometrics
• forest informatics
• forest management
• forest statistics
• natural resources management

From the reviews:

“The material presented in this text is more than sufficient for a dedicated module of an applied statistics course … . The authors develop, and demonstrate, solutions to common forestry data handling and analysis challenges … . Whilst much of the text may be regarded as standard for the topic, the last chapter addresses an area harvest strategy which is well worth reading on its own … . The text is well written, easy to read and I recommend it to anyone interested in biometrics.” (Carl M. O’Brien, International Statistical Review, Vol. 80 (1), 2012)

• Dept. Mathematics and Statistics, University of Melbourne, Parkville, Australia

  Andrew P. Robinson

• Forest Informatics, Inc., Corvallis Oregon, USA

  Jeff D. Hamann

Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document.  Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University.  Both authors have presented numerous R workshops to forestry professionals and scientists, and others.

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