Statistics for Spatial Data.

This book attempts to make growth models more accessible to foresters and others interested in mixed forests, whether planted or natural. There is an increasing interest in,
 and controversy surrounding the use of mixed plantations and natural forests, and rational discussion and resolution of management options require reliable growth models linked to other
 information systems. It is my hope that this book will help researchers to build better models, and will help users to understand how the models work and thus to appreciate their strengths
 and weaknesses. During recent years, vast areas of natural forest, especially in the tropics, have been logged or converted to other uses. Well-meaning forest managers have often been
 over-optimistic in estimating forest growth and yields, and this has contributed to over-cutting in some forests. Growth models can provide objective forecasts, offering forest managers the
 information needed to maintain harvests within the sustainable capacity of the forest, and providing quantitative data for land use planners to make informed decisions on land use
 alternatives. In this way, I hope that this book will contribute to the conservation and sustainable management of natural forests in the tropics and elsewhere. This is not a "How to do it"
 manual with step-by-step instructions to build a growth model for mixed forests. Unfortunately, modelling these forests isn't that easy. There is no single "best" way to build a model for
 these forests. Rather, many approaches can be used, and the best one depends on the data available, the time and expertise available to build the model, the computing resources, and the
 inferences that are to be drawn from the model. So instead of writing a "cookbook" with one or two recipes, I review and illustrate some of the many approaches available, indicate the
 requirements of and output from each, and highlight their strengths and limitations. The book emphasizes empirical-statistical models rather than physiological-process type models, not
 because they are superior, but because they have proven utility and offer immediate benefits for forest management. A more comprehensive treatment of all the options is beyond the scope of
 this book, which is intended to serve as a ready reference manual for those building growth models for forest management. Because of my limited linguistic ability, the material covered is
 more-or-less restricted to English-language material. I have not attempted to review all the published work on growth modelling (it would be a huge task), but have tried to highlight
 examples that may be applicable to mixed forests in tropical areas. I hope that the language and terminology used in this book will be accessible to all readers, especially those for whom
 English is a second language. The glossary may help to clarify some terms, and those that have a specific technical meaning are printed in italics the first time they are used. Readers
 should consult the glossary to clarify the meaning of these words unless they are sure of the meaning. Exercises are given at the end of each chapter to reinforce points made in the
 chapter. These are simple exercises, deliberately chosen so that they can be completed quickly with pen and paper or PC and spreadsheet, but within these constraints, I have tried to keep
 them realistic. Some exercises (e.g. 9.1 and 10.3) require more specialized statistical analyses, but many commercial statistical packages (e.g. GLIM) are suitable. Where possible, these
 exercises draw on real data, but some data were simulated to create interesting exercises with few data. Whilst my approach places more responsibility on the reader to choose and develop a
 suitable modelling methodology, I hope it will help readers gain a better understanding of modelling, which should in turn lead to better models and more reliable predictions. And I hope
 that better models will provide better information, greater understanding, and better management of mixed forests.

https://espace.library.uq.edu.au/view/UQ:8211/ModellingForestG.pdf

Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests

There is considerable interest today in estimating the biomass of trees and forests for both practica1 forestry issues and scientific purposes. New techniques and procedures are brought together along with the more traditional approaches to estimating woody biomass. General model forms and weighted analysis are reviewed, along with statistics for evaluating and comparing biomass models. Additivity and harmonization are addressed, and weight-ratio and density-integral approaches are discussed. Subsampling methods on trees to derive unbiased weight estimates are examined., and ratio and difference sampling estimators are considered in detail. Errorcomponents forstand biomass estimates are examined. This paper reviews quantitative principles and gives specific examples for prediction of tree biomass. The examples should prove useful for understanding the principles involved

/pdf/assessing-tree-and-stand-biomass-a-review-with-examples-and-gavz6ln7so.pdf

Assessing tree and stand biomass: a review with examples and critical comparisons

Unbiased estimation of population density is a major and unsolved problem in animal trapping studies. This paper describes a new and general method for estimating density from closed-population capture-recapture data. Many estimators exist for the size (N) and mean capture probability (p) of a closed population. These statistics suffer from an unknown bias due to edge effect that varies with trap layout and home range size. The mean distance between successive captures of an individual (d) provides information on the scale of individual movements, but is itself a function of trap spacing and grid size. Our aim is to define and estimate parameters that do not depend on the trap layout. In the new method, simulation and inverse prediction are used to estimate jointly the population density (D) and two parameters of individual capture probability, magnitude (go) and spatial scale (σ), from the information in N, p and d. The method uses any configuration of traps (e.g. grid, web or line) and any choice of closed-population estimator. It is assumed that home ranges have a stationary distribution in two dimensions, and that capture events may be simulated as the outcome of competing Poisson processes in time. The method is applied to simulated and field data. The estimator appears unusually robust and free from bias.

/pdf/density-estimation-in-live-trapping-studies-4jutjmebkk.pdf

Density estimation in live‐trapping studies

http://www7.nau.edu/mpcer/direnet/publications/publications_f/files/Fettig_CJ_Klepzig_KD_Billings_RF_etal_The_Effectiveness.pdf

The effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United States

1. Introduction.- 2. Tree Form and Stem Taper.- 3. Tree-stem Volume Equations.- 4. Tree Weight and Biomass Estimation.- 5. Quantifying Tree Crowns.- 6. Growth Functions.- 7. Evaluating Site Quality.- 8. Quantifying Stand Density.- 9. Indices of Individual-tree Competition.- 10. Modeling Forest Stand Development.- 11. Whole-stand Models for Even-aged Stands.- 12. Diameter-distribution Models for Even-aged Stands.- 13. Size-class Models for Even-aged Stands.- 14. Individual-tree Models for Even-aged Stands.- 15. Growth and Yield Models for Uneven-aged Stands.- 16. Modeling Response to Silvicultural Treatments.- 17. Modeling Wood Characteristics.- 18. Model Implementation and Evaluation.-

Modeling Forest Trees and Stands

Segmented Polynomial Regression Applied to Taper Equations

Five families of competition indices were evaluated and compared on the basis of simple correlation with loblolly pine individual tree growth and multiple correlation with growth in the presence of...

A comparison of competition measures for predicting growth of loblolly pine trees

Des indices de competition dependants de la distance ont ete modifies pour modeliser la croissance d'arbres individuels de plantations et de peuplements homogenes. Les indices modifies permettent d'ajuster la croissance en fonction de la taille relative des arbres voisins. Les arbres morts ou recoltes sont consideres comme favorisant la croissance. Les donnees utilisees proviennent d'une etude d'espacement dans une plantation d'eucalyptus au Portugal

Harold E. Burkhart

Papers

Modeling Forest Trees and Stands

Segmented Polynomial Regression Applied to Taper Equations

A comparison of competition measures for predicting growth of loblolly pine trees

Distance-Dependent Competition Measures for Predicting Growth of Individual Trees

Evaluation of Two Methods for Cubic-Volume Prediction of Loblolly Pine to Any Merchantable Limit