Introduction to Forestry Data Analysis with R equips students and practitioners with the skills needed to move confidently between field measurements and modern analytical workflows. As forestry, ecology, and natural resource management become increasingly data-driven, professionals are expected not only to collect information but also to organize, analyze, visualize, and defend quantitative results. This book responds to that shift by integrating foundational forest inventory concepts with practical computing in R.
Distinct from both generic programming texts and traditional mensuration references, this volume teaches R through real forestry datasets and operational examples. The first half develops core programming skills-data wrangling, visualization, and reproducible workflows-while the second half applies these tools to forest inventory, monitoring, and estimation. Classical methods developed by forest biometricians are presented alongside transparent, step-by-step computational implementations, enabling readers to connect statistical theory with modern, repeatable analysis.
Key Features:
- Introduces R and the tidyverse using forestry-specific datasets and management questions
- Develops reproducible workflows for data import, cleaning, transformation, visualization, and reporting
- Presents forest inventory concepts including simple random, systematic, stratified, cluster, and multistage sampling
- Implements classical estimators using script-based computing
- Integrates spatial data handling and mapping for areal sampling frames and field-based applications
- Emphasizes practical problem-solving, code organization, and analytical habits that scale from single stands to large inventories
Introduction to Forestry Data Analysis with R is intended for undergraduate and graduate students in forestry, natural resources, and environmental science, as well as practitioners seeking to modernize and streamline their analytical workflows. Whether readers are learning R for the first time or adapting established inventory methods to contemporary datasets, it provides a clear, practical, and reproducible foundation for data-driven forest analysis.
