Forest Biometrics presents the methods of mathematical statistics and biometrics that are significant to forestry. This book explores other fields related to forestry, which are explained with the help of a large number of practical examples. Organized into 25 chapters, this book starts with an overview of the variety of data that play a significant role in forest management, including the age of trees, the damage caused by storms, the fluctuation of timber prices, bark beetle infestation, and timber volume. This text then examines the factors that are responsible for a random distribution of the values in biological experimentation. Other chapters consider the important advantages of sample surveys compared to complete enumerations, include cheaper samples, wider applicability, quick results, and greater accuracy. The final chapter deals with the factors to be considered in determining the best time for harvesting of timber. This book is a valuable resource for students, research project leaders, and practical workers.

Biometry for Forestry and Environmental Data with Examples in R focuses on statistical methods that are widely applicable in forestry and environmental sciences, but it also includes material that is of wider interest. Features: · Describes the theory and applications of selected statistical methods and illustrates their use and basic concepts through examples with forestry and environmental data in R. · Rigorous but easily accessible presentation of the linear, nonlinear, generalized linear and multivariate models, and their mixed-effects counterparts. Chapters on tree size, tree taper, measurement errors, and forest experiments are also included. · Necessary statistical theory about random variables, estimation and prediction is included. The wide applicability of the linear prediction theory is emphasized. · The hands-on examples with implementations using R make it easier for non-statisticians to understand the concepts and apply the methods with their own data. Lot of additional material is available at www.biombook.org. The book is aimed at students and researchers in forestry and environmental studies, but it will also be of interest to statisticians and researchers in other fields as well.

Biometry for Forestry and Environmental Data with Examples in R focuses on statistical methods that are widely applicable in forestry and environmental sciences, but it also includes material that is of wider interest. Features: · Describes the theory and applications of selected statistical methods and illustrates their use and basic concepts through examples with forestry and environmental data in R. · Rigorous but easily accessible presentation of the linear, nonlinear, generalized linear and multivariate models, and their mixed-effects counterparts. Chapters on tree size, tree taper, measurement errors, and forest experiments are also included. · Necessary statistical theory about random variables, estimation and prediction is included. The wide applicability of the linear prediction theory is emphasized. · The hands-on examples with implementations using R make it easier for non-statisticians to understand the concepts and apply the methods with their own data. Lot of additional material is available at www.biombook.org. The book is aimed at students and researchers in forestry and environmental studies, but it will also be of interest to statisticians and researchers in other fields as well.

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.

Harold Burkhart and Bronson Bullock have updated the quintessential introduction to forest measurements, providing a new generation of forestry students at all levels with the concepts and methods they need for career success. With attention to detail and clear, precise language, the authors present timber measurement techniques applicable to any tree inventory regardless of management objectives. Assuming no more mathematical background than algebra and plane trigonometry, the authors begin with basic statistical concepts to ensure that even introductory students benefit from the book’s concise explanations. Comprehensive coverage of sampling designs, land measurements, tree measurements, forest inventory field methods, and growth projections assures utility for foresters throughout their education and beyond. The new edition includes expanded discussions of information technology and geospatial information systems commonly employed in assessing forest resources. Recognizing the needs of contemporary forest inventories and models, a new chapter on assessing forest carbon builds on the foundations of traditional forest measurements, sampling, and modeling. Abundant photographs and illustrations highlight and clarify important concepts, while many numerical examples allow readers to become comfortable with the quantitative tools employed by foresters.

Forest Growth and Yield Modeling synthesizes current scientific literature and provides insights in how models are constructed. Giving suggestions for future developments, and outlining keys for successful implementation of models the book provides a thorough and up-to-date, single source reference for students, researchers and practitioners requiring a current digest of research and methods in the field. The book describes current modelling approaches for predicting forest growth and yield and explores the components that comprise the various modelling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model. Single source reference providing an evaluation and synthesis of current scientific literature Detailed descriptions of example models Covers statistical techniques used in forest model construction Accessible, reader-friendly style

Forest mensuration – the science of measurement applied to forest vegetation and forest products – holds value for basic ecology as well as sustainable forest management. As demands on the world’s forests have grown, scientists and professionals are increasingly called on to quantify forest composition, structure, and the goods and services forests provide. Grounded in geometry, sampling theory, and ecology as well as practical field experience, forest mensuration offers opportunities for creative problem solving and critical thinking. This fifth edition of the classic volume, Forest Mensuration, includes coverage of traditional and emerging topics, with attention to SI and Imperial units throughout. The book has been reorganised from the fourth edition to better integrate non-timber and ecological aspects of forest mensuration at the tree, stand, forest, and landscape scales throughout. The new edition includes new chapters that specifically address the integration of remotely sensed data in the forest inventory process, and inventory methods for dead and downed wood. One unifying theme, not only for traditional forestry but for the non-timber inventory and for remote sensing, is the use of covariates to make sampling more efficient and spatially explicit. This is introduced in the introductory chapter on statistics and the chapter on sampling designs has been restructured to highlight this approach and lay the foundation for further learning. New examples will be developed throughout the textbook with an emphasis on current issues and international practice. Students in applied forestry programs will find ample coverage of forest products and timber inventory, while expanded material on biodiversity, biomass and carbon inventory, downed dead wood, and the growing role of remote sensing in forest assessment will be valuable to a broader audience in applied ecology.