Guidelines For The Statistical Analysis Of Forest Vegetation Management Data
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Author | : Amanda Frances Linnell Nemec |
Publisher | : Province of British Columbia, Forest Science Research Branch |
Total Pages | : 92 |
Release | : 1992 |
Genre | : Forest management |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : 1292 |
Release | : 1994 |
Genre | : Municipal government publications |
ISBN | : |
An indexing, abstracting and document delivery service that covers current Canadian report literature of reference value from government and institutional sources.
Author | : Timothy R. H. Pearson |
Publisher | : |
Total Pages | : 48 |
Release | : 2007 |
Genre | : Carbon sequestration |
ISBN | : |
Measurement guidelines for forest carbon sequestration were developed to support reporting by public and private entities to greenhouse gas registries. These guidelines are intended to be a reference for designing a forest carbon inventory and monitoring system by professionals with a knowledge of sampling, statistical estimation, and forest measurements. This report provides guidance on defining boundaries; measuring, monitoring, and estimating changes in carbon stocks; implementing plans to measure and monitor carbon; and developing quality assurance and quality control plans to ensure credible and reproducible estimates of the carbon credits.
Author | : Caryl L. Elzinga |
Publisher | : DIANE Publishing |
Total Pages | : 190 |
Release | : 1998-05 |
Genre | : Science |
ISBN | : 9780788148378 |
This annotated bibliography documents literature addressing the design and implementation of vegetation monitoring. It provides resources managers, ecologists, and scientists access to the great volume of literature addressing many aspects of vegetation monitoring: planning and objective setting, choosing vegetation attributes to measure, sampling design, sampling methods, statistical and graphical analysis, and communication of results. Over half of the 1400 references have been annotated. Keywords pertaining to the type of monitoring or method are included with each bibliographic entry. Keyword index.
Author | : J. W. Van Roessel |
Publisher | : Food & Agriculture Org. |
Total Pages | : 156 |
Release | : 1986 |
Genre | : Nature |
ISBN | : 9789251024515 |
Author | : |
Publisher | : |
Total Pages | : 364 |
Release | : 1993 |
Genre | : Forests and forestry |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : 556 |
Release | : 1985 |
Genre | : Forests and forestry |
ISBN | : |
Author | : United States. Office of Personnel Management |
Publisher | : |
Total Pages | : 446 |
Release | : 1994 |
Genre | : Civil service positions |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : 500 |
Release | : 1990 |
Genre | : |
ISBN | : |
Author | : Andrew P. Robinson |
Publisher | : Springer Science & Business Media |
Total Pages | : 342 |
Release | : 2010-11-05 |
Genre | : Medical |
ISBN | : 1441977627 |
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.