Introduction To Forestry Science Iml

A senior high textbook focusing on the North American forest regions; classification, anatomy, and diseases of trees; forest management and products; and urban forestry.

This an applied science book that integrates science principles with forestry practices. Topics include cell structure and function, cell growth and reproduction, principles of ecology, plant anatomy and physiology, entomology, water and air quality, and preservation of endangered species. This textbook serves as an introductory forestry book that covers the principles and practices of forest management commonly utilized within the United States. Attention is given to regional differences in forests and forest management. Forest management planning for both private and public forests is discussed, along with laws and regulations that govern the use of forests. The latest technologies and their applications -- as they pertain to forest management -- are included, such as global positioning systems, uses of satellites to map forests, integrated pest management, and fire management and suppression. Each chapter addresses career options related to forests and forest management.

The Laboratory Manual is a valuable tool designed to enhance your understanding of the concepts discussed in the core textbook. Each chapter begins wth a list of objectives, followed by activities designed to achieve the objectives, and concludes with a list of references for further study.

Forest biology. Forest management. Forest products.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.