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

Changes to forest production drivers (light, water, temperature, and site nutrient) over the last 55 years have been documented in peer-reviewed literature. The main objective of this paper is to review documented evidence of the impacts of climate change trends on forest productivity since the middle of the 20th century. We first present a concise overview of the climate controls of forest production, provide evidence of how the main controls have changed in the last 55 years, followed by a core section outlining our findings of observed and documented impacts on forest productivity and a brief discussion of the complications of interpreting trends in net primary production (NPP). At finer spatial scales, a trend is difficult to decipher, but globally, based on both satellite and ground-based data, climatic changes seemed to have a generally positive impact on forest productivity when water was not limiting. Of the 49 papers reporting forest production levels we reviewed, 37 showed a positive growth trend, five a negative trend, three reported both a positive and a negative trend for different time periods, one reported a positive and no trend for different geographic areas, and two reported no trend. Forests occupy 52% of the Earth’s land surface and tend to occupy more temperature and radiation-limited environments. Less than 7% of forests are in strongly water-limited systems. The combined and interacting effects of temperature, radiation, and precipitation changes with the positive effect of CO2, the negative effects of O3 and other pollutants, and the presently positive effects of N will not be elucidated with experimental manipulation of one or a few factors at a time. Assessments of the greening of the biosphere depend on both accurate measurements of rates (net ecosystem exchange, NPP), how much is stored at the ecosystem level (net ecosystem production) and quantification of disturbances rates on final net biome production.

/pdf/impacts-of-climate-change-on-natural-forest-productivity-49rtnvdj6k.pdf

Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century

The main objective of this study was to develop reliable processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating tree crown diameter by measuring individual trees identifiable on the three-dimensional lidar surface. In addition, the study explored the importance of the lidar-derived crown diameter for estimating tree volume and biomass. The lidar dataset was acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern United States. For identifying individual trees, lidar processing techniques used data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to es...

/pdf/measuring-individual-tree-crown-diameter-with-lidar-and-51spkpayi6.pdf

Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass

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

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

GROWTH AND YIELD PREDICTION. Estimating the Volumes and Weights of Individual Trees. Evaluating Site Quality. Growing Stock and Stand Density. Predicting Growth and Yield. FINANCIAL ASPECTS OF TIMBER MANAGEMENT. Forest Finance. Taxes and Risk in the Evaluation of Forest Investments. TIMBER MANAGEMENT PLANNING. Timber Management - Some Introductory Comments. Stand-Level Management Planning. Forest-Level Management Planning: Basic Concepts. Forest-Level Management Planning: Current Techniques. Appendices. Index.

J. L. Clutter

Papers

Timber Management-A Quantitative Approach.

Timber management: a quantitative approach.