Introduction to Forestry Data Analysis with R
Preface
The role of the forester and natural resource professional has expanded. Data, analysis, and interpretation are now more central to our work than ever before, and today’s practitioners are expected to move confidently between field measurements and analytical workflows. With unprecedented volumes of information on forested systems produced through inventories, sensor networks, remote sensing platforms, and long-term monitoring programs, practitioners are increasingly called upon to act as data analysts—wrangling, visualizing, and defending quantitative results in support of sound management decisions.
This book is for readers on two paths: students beginning their analytical training and practitioners who want to modernize and streamline their workflows. Our goal is to help you build proficiency in R and the tidyverse ecosystem, and to connect familiar estimators and inventory principles to clear, repeatable, and reproducible data analysis pipelines. Whether you’re new to these tools or looking for more efficient ways to implement classical methods, the emphasis is on transparent workflows you can adapt to common estimation settings.
Chapters 1 through 9 form an introduction to practical computing with R. We start from base R—objects, vectors, data frames, indexing, functions, and input/output—and build toward pipe-based workflows. Along the way, we develop data wrangling with dplyr (verbs and joins), reshaping with tidyr, iteration, small reusable functions, visualization with ggplot2, and reproducible project habits that scale from a single stand to large inventories. The aim is pragmatic: readable code, consistent structure, and habits that make forest data easier to organize, verify, summarize, and explain.
Chapters 10 through 13 turn to forest inventory and estimation. Here we build directly on the computing habits established earlier and introduce and apply well-established statistical tools—simple random sampling, systematic designs, stratification, cluster and multistage sampling, and ratio and regression estimators. The mathematical foundations of these methods were developed by generations of forest biometricians and applied statisticians whose work continues to guide the profession. Our contribution is to bring these classical ideas into a modern workflow: clear illustrations, transparent calculation paths, and reproducible implementations that make it easier to move from field measurements to defensible estimates and uncertainty statements.
Our hope is that this book helps you develop an adaptable codebase and a reliable, efficient way of working—a workflow you can reuse, audit, and share. By the end, you should be able to take a raw inventory dataset, turn it into analysis-ready tables and figures, compute estimates and standard errors correctly, and communicate results in a way that supports defensible decisions as a forester, analyst, and steward of forested landscapes.
Acknowledgments
During the long process of writing this book, we have benefited greatly from the generous review, technical comments, contributed material, and encouragement of Anthony D’Amato, Ken Desmarais, Mike Eckley, Lutz Fehrmann, Anton Grafström, Ed Green, Juha Heikkinen, Kim Iles, Malcolm Itter, Annika Kangas, Braeden Klaty, Rayleigh Lei, David MacFarlane, John Paul McTague, Vincent Melfi, David Orwig, Jarred Saralecos, Elliot Shannon, Göran Ståhl, Tim Vredenburg, and Aaron Weiskittel. We are also grateful to the Cow Creek Band of Umpqua Tribe of Indians for sharing forest inventory data that form the basis of several exercises and examples in this book.