This book presents recent developments in statistical methodologies with particular relevance to applications in forestry and environmental sciences. It discusses important methodologies like ranked set sampling, adaptive cluster sampling, small area estimation, calibration approach-based estimators, design of experiments, multivariate techniques, Internet of Things, and ridge regression methods. It also covers the history of the implementation of statistical techniques in Indian forestry and the National Forest Inventory of India. The book is a valuable resource for applied statisticians, students, researchers, and practitioners in the forestry and environment sector. It includes real-world examples and case studies to help readers apply the techniques discussed. It also motivates academicians and researchers to use new technologies in the areas of forestry and environmental sciences with the help of software like R, MATLAB, Statistica, and Mathematica.

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 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.

Part 1: Introduction Chapter 1: What is Natural Resources Research? Chapter 2: At Least Read This. Chapter 3: Sidetracks Part 2: Planning Chapter 4: Introduction to Research Planning Chapter 5: Concepts Underlying Experiments Chapter 6: Sampling Concepts Chapter 7: Surveys and Studies of Human Subjects Chapter 8: Surveying Land and Natural Populations Chapter 9: Planning Effective Experiments Part 3: Data Management Chapter 10: Data Management Issues and Problems Chapter 11: Use of Spreadsheet Packages Chapter 12: The Role of a Database Package Chapter 13: Developing a Data Management Strategy Chapter 14: Use of Statistical Software Part 4: Analysis Chapter 15: Analysis - Aims and Approaches Chapter 16: The DIY Toolbox - General Ideas 16.1 Opening the Toolbox 221 Chapter 17: Analysis of Survey Data Chapter 18: Analysis of Experimental Data Chapter 19: General Linear Models Chapter 20: The Craftsman's Toolbox Chapter 21: Informative Presentation of Tables, Graphs and Statistics Part 5: Where Next? Chapter 22: Current Trends and their Implications for Good Practice Chapter 23: Resources and Further Reading.

A Comprehensive Handbook of Statistical Concepts, Techniques and Software Tools.

The Swiss National Forest Inventory (NFI) is a forest survey on national level which started in 1982 and has already reached its 5th survey cycle (NFI5). It can be characterized as a multisource and multipurpose inventory where information is mainly collected from terrestrial field surveys using permanent sample plots. In addition, data from aerial photography, GIS and forest service questionnaires are also included. The NFI's main objective is to provide statistically reliable and sound figures to stakeholders such as politicians, researchers, ecologists, forest service, timber industry, national and international organizations as well as to international projects such as the Forest Resources Assessment of the United Nations. For Switzerland, NFI results are typically reported on national and regional level. State of the art methods are applied in all fields of data collection which have been proven to be of international interest and have even served as a basis for other European NFIs. The presented methods are applicable to any sample based forest inventory around the globe. In 2001 the Swiss NFI published its methods for the first time. Since then, many methodological changes and improvements have been introduced. This book describes the complete set of methods and revisions since NFI2. It covers various topics ranging from inventory design and statistics to remote sensing, field survey methods and modelling. It also describes data quality concepts and the software framework used for data storage, statistical analysis and result presentation.