Forest Analytics with R

An Introduction

  • Andrew P. Robinson
  • Jeff D. Hamann

Part of the Use R book series (USE R)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Introduction and Data Management

    1. Front Matter
      Pages 1-1
    2. Andrew P. Robinson, Jeff D. Hamann
      Pages 3-17
    3. Andrew P. Robinson, Jeff D. Hamann
      Pages 19-72
  3. Sampling and Mapping

    1. Front Matter
      Pages 73-73
    2. Andrew P. Robinson, Jeff D. Hamann
      Pages 75-115
    3. Andrew P. Robinson, Jeff D. Hamann
      Pages 117-151
  4. Allometry and Fitting Models

    1. Front Matter
      Pages 153-153
    2. Andrew P. Robinson, Jeff D. Hamann
      Pages 155-173
    3. Andrew P. Robinson, Jeff D. Hamann
      Pages 175-218
    4. Andrew P. Robinson, Jeff D. Hamann
      Pages 219-273
  5. Simulation and Optimization

    1. Front Matter
      Pages 275-275
    2. Andrew P. Robinson, Jeff D. Hamann
      Pages 277-305
    3. Andrew P. Robinson, Jeff D. Hamann
      Pages 307-323
  6. Back Matter
    Pages 325-339

About this book

Introduction

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. 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.

Keywords

forest biometrics forest informatics forest management forest statistics natural resources management

Authors and affiliations

  • Andrew P. Robinson
    • 1
  • Jeff D. Hamann
    • 2
  1. 1.Dept. Mathematics and StatisticsUniversity of MelbourneParkvilleAustralia
  2. 2.Forest Informatics, Inc.Corvallis OregonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-7762-5
  • Copyright Information Springer Science+Business Media, LLC 2011
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-7761-8
  • Online ISBN 978-1-4419-7762-5
  • About this book