Sampling Techniques for Forest Inventories

Containing many recent developments available for the first time in book form, this concise and up-to-date work presents the statistical concepts and tools needed to conduct a modern forest inventory. It develops the Monte Carlo approach for both simple and complex sampling schemes and explores design-based, model-assisted, and model-dependent inference, including geostatistics and Kriging procedures. The book also explains the design of optimal sampling schemes based on anticipated variance, introduces the g-weight technique for variance estimation, and presentsthe stereological approach to transect sampling. In addition, it includes numerous case studies, simulations, and instructive problems with solutions.

Presents the statistical concepts and tools needed to conduct a modern forest inventory. This book develops the Monte Carlo approach for both simple and complex sampling schemes and explores design-based, model-assisted, and model-dependent inference, including geostatistics and Kriging procedures.

the text is an excellent addition to the forest and natural resource inventory literature, and nicely compliments classic and contemporary texts focused broadly on applied concepts and tools but lacking in rigorous statistical treatment. this text is an important contribution to the literature, as it provides a unique, mathematically rigorous tour of classical and modern topics on forest sampling theory. Of particular importance is the text's emphasis on model-based methods, which are becoming increasingly important within forest and natural resources inventory.The American Statistician, Vol. 63, No. 3, August 2009, and JASA, Winter 2008 In this field, the author is a leading expert who presents the modern state of the art, in particular his own work of the last two decades. a valuable, up-to-date reference book for the theoretical aspects of forest inventories and sampling .Dietrich Stoyan, Biometrical Journal, Vol. 51, 2009 This compact little volume is packed with important and useful ideas.Donald E. Myers, University of Arizona, Technometrics, May 2009 This is an important reference for those wanting to understand the theory of sampling in forest inventory, and also for those with graduate- or postgraduate-level skills in statistics who apply these techniques in the forestry industry. Despite its length, the book provides reasonably thorough coverage of the theory of statistics applied to forest inventories. a very useful, up-to-date reference book on the theory of statistics as it should be applied to forest inventory.International Statistical Review, 2008

Autorentext

Mandallaz, Daniel

Klappentext

Emphasizing sound theoretical foundations, this book presents the essential concepts and tools required in finite populations, and systematically develops the Monte Carlo approach in infinite populations to analyze or design complex forest inventories. It thoroughly discusses design-based, model-assisted, and model-dependent inference as well as the design of optimal sampling schemes based on the anticipated variance, the g-weight technique, small area estimation, contingency tables, and transect sampling. The book contains roughly 30 problems with complete solutions as well as case studies and simulations that illustrate the various techniques.

Zusammenfassung
Presents the concepts and tools required in finite populations, and develops the Monte Carlo approach in infinite populations to analyze or design complex forest inventories. This book discusses design-based, model-assisted, and model-dependent inference as well as the design of optimal sampling schemes based on the anticipated variance.

Inhalt
Preface. Introduction and Terminology. Sampling Finite Populations: The Essentials.Sampling Finite Populations: Advanced Topics.Forest Inventory: One-Phase Sampling Schemes. Forest Inventory: Two-Phase Sampling Schemes.Forest Inventory: Advanced Topics.Geostatistics. Case Study.Optimal Sampling Schemes for Forest Inventory.The Swiss National Forest Inventory. Estimating Change and Growth. Transect-Sampling. Appendices. Bibliography. Index.