Growth and yield models for Dahurian larch plantations

1999 ◽  
Vol 10 (4) ◽  
pp. 233-235
Author(s):  
Yuan Jinlan
Keyword(s):  
Growth And Yield ◽  
Dahurian Larch ◽  
Dahurian Larch Plantations ◽  
Growth And Yield Models

Deriving Tree Diameter Growth and Probability of Survival Equations from Successive Diameter Distributions

2008 ◽  
Vol 54 (1) ◽  
pp. 31-35
Author(s):  
Thomas G. Matney ◽  
Emily B. Schultz
Keyword(s):  
General Procedure ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Diameter Growth ◽  
Individual Tree ◽  
Statistical Probability ◽  
Growth And Survival ◽  
Tree Diameter ◽  
Growth And Yield Models ◽  
Diameter Distributions

Abstract Many growth and yield models have used statistical probability distributions to estimate the diameter distribution of a stand at any age. Equations for approximating individual tree diameter growth and survival probabilities from dbh can be derived from these models. A general procedure for determining the functions is discussed and illustrated using a loblolly pine spacing study. The results from the spacing study show that it is possible to define tree diameter growth and survival probability functions from diameter distributions with an accuracy sufficient to obtain a link between the individual tree and diameter growth and yield models.

Growth variations of Dahurian larch plantations across northeast China: Understanding the effects of temperature and precipitation

2021 ◽  
Vol 292 ◽  
pp. 112739
Author(s):  
Bingrui Jia ◽  
Hongru Sun ◽  
Herman Henry Shugart ◽  
Zhenzhu Xu ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Northeast China ◽  
Dahurian Larch ◽  
Temperature And Precipitation ◽  
Effects Of Temperature ◽  
Dahurian Larch Plantations

scholarly journals Spatial Tree Mapping Using Photography

2010 ◽  
Vol 27 (2) ◽  
pp. 68-74 ◽  
Author(s):  
Adam R. Dick ◽  
John A. Kershaw ◽  
David A. MacLean
Keyword(s):  
Spatial Location ◽  
Growth And Yield ◽  
Absolute Distance ◽  
Widespread Application ◽  
Visualization Software ◽  
Dependent Growth ◽  
Spatial Locations ◽  
Growth And Yield Models ◽  
Field Plots ◽  
Forest Conditions

Abstract Stem maps describing the spatial location of trees sampled in a forest inventory are used increasingly to model relationships between neighboring trees in distance-dependent growth and yield models, as well as in stand visualization software. Current techniques and equipment available to acquire tree spatial locations prohibit widespread application because they are time-consuming, costly, and prone to measurement error. In this report, we present a technique to derive stem maps from a series of digital photographs processed to form a seamless 360° panorama plot image. Processes are described to derive distance from plot center and azimuth to each plot tree. The technique was tested on 46 field plots (1,398 sample trees) under a range of forest conditions and compared with traditional methods. Average absolute distance error was 0.38 ± 0.44 m, and average absolute azimuth error was 2.3 ± 2.5°. Computed average horizontal accuracy was 0.40 ± 0.42 m, with 85% of measured trees being within 0.5 m of the field-measured tree location.

scholarly journals Loblolly and Slash Pine Growth and Yield Prediction for Regional Timber Supply Projection Algorithms

1999 ◽  
Vol 23 (4) ◽  
pp. 230-237
Author(s):  
Bruce E. Borders ◽  
Jeffrey B. Jordan
Keyword(s):  
Loblolly Pine ◽  
Analysis Data ◽  
Growth And Yield ◽  
Yield Prediction ◽  
Projection Algorithms ◽  
Timber Supply ◽  
Usda Forest Service ◽  
Forest Inventory And Analysis ◽  
Pine Stands ◽  
Growth And Yield Models

Abstract Regional and national timber supply models require standing inventory update procedures. To date, most inventory update procedures used in regional timber supply algorithms have not made use of growth and yield methodology. We present growth and yield models to update standing inventories for natural and planted slash and loblolly pine stands in Georgia. These models were fitted to USDA Forest Service Forest Inventory and Analysis data obtained from the sixth survey of Georgia and should prove useful in regional timber supply projection algorithms. South. J. Appl. For. 23(4):230-237.

scholarly journals Growth and yield models for eucalyptus stands obtained by differential equations

2017 ◽  
Vol 74 (5) ◽  
pp. 364-370
Author(s):  
Adriano Ribeiro de Mendonça ◽  
Natalino Calegario ◽  
Gilson Fernandes da Silva ◽  
Samuel de Pádua Chaves e Carvalho
Keyword(s):  
Differential Equations ◽  
Growth And Yield ◽  
Growth And Yield Models

A simple growth and yield model for assessing changes in standing volume across Canada’s forests

2009 ◽  
Vol 85 (1) ◽  
pp. 57-64 ◽  
Author(s):  
C -H. Ung ◽  
P Y Bernier ◽  
X J Guo ◽  
M -C. Lambert
Keyword(s):  
Predictive Power ◽  
Robust Regression ◽  
Forest Growth ◽  
Growth Potential ◽  
Growth And Yield ◽  
Stand Age ◽  
Yield Model ◽  
Wood Volume ◽  
National Model ◽  
Growth And Yield Models

We have adjusted two growth and yield models to temporary sample plots from across Canada, and used climate variables in lieu of phytometric indices such as site index to represent, in part, the site-level variability in growth potential. Comparison of predicted increments in plot-level height, basal area and merchantable wood volume to increments of these variables measured in permanent sample plots shows a moderate to poor predictive ability. Comparison with the performance of four operational growth and yield models from different provinces across Canada shows comparable predictive power of this new model versus that of the provincial models. Based on these results, we suggest that the simplification of regional growth and yield models may be achieved without further loss of predictive power, and that the large error in the prediction of growth increment is mostly associated with the use of temporary sample plots which, by definition, contain little information on stand dynamics. We also suggest that, because of the empirical nature of these growth and yield models, the scale of application should determine the appropriate scale of the model. National estimates of forest growth are therefore less likely to be biased if obtained from a national model only than if obtained from a combination of regional models, where those exist, gap-filled with estimates from a national model. Key words: yield model, merchantable wood volume, stand age, climatic variables, simultaneous regression, robust regression

A Graphical Comparison of Loblolly Pine Growth-and-Yield Models

2013 ◽  
Vol 37 (3) ◽  
pp. 169-176 ◽  
Author(s):  
James E. Henderson ◽  
Scott D. Roberts ◽  
Donald L. Grebner ◽  
Ian A. Munn
Keyword(s):  
Loblolly Pine ◽  
Growth And Yield ◽  
Graphical Comparison ◽  
Growth And Yield Models

Comparison of a forest process model (3-PG) with growth and yield models to predict productivity at Bago State Forest, NSW

2001 ◽  
Vol 64 (2) ◽  
pp. 111-122 ◽  
Author(s):  
P. K. Tickle ◽  
N. C. Coops ◽  
S. D. Hafner
Keyword(s):  
Process Model ◽  
Growth And Yield ◽  
Growth And Yield Models

scholarly journals Forest Stand Growth Models: What For?

1985 ◽  
Vol 61 (1) ◽  
pp. 19-22 ◽  
Author(s):  
Stephen J. Titus ◽  
Robert T. Morton
Keyword(s):  
Growth Models ◽  
Forest Stand ◽  
Growth And Yield ◽  
Effective Management ◽  
Management Planning ◽  
Computing Power ◽  
Stand Growth ◽  
The Future ◽  
Decision Making Processes ◽  
Growth And Yield Models

Until very recently foresters have relied on infrequent inventories to provide static descriptions of large forest areas for management planning. With the quantum increases in computing power, the massing of forestry data, and the increasing pressure for effective management planning, it is becoming necessary to view the forest as dynamic, and subject to manipulation for management purposes. Prediction of changes to forest structure and yield must be made to update old data and project stands into the future. This paper reviews the current sources of literature on growth and yield, discusses basic types and components of growth models, and gives some examples of important uses for growth and yield models. The future will see increased use of computers for analysis of forestry data including even more sophisticated growth and yield models linked to both inventory and decision making processes.

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