An age-independent individual tree height prediction model for boreal spruce–aspen stands in Alberta

1994 ◽  
Vol 24 (7) ◽  
pp. 1295-1301 ◽  
Author(s):  
Shongming Huang ◽  
Stephen J. Titus
Keyword(s):  
Prediction Model ◽  
Nonlinear Regression ◽  
Prediction Method ◽  
Tree Height ◽  
White Spruce ◽  
Data Set ◽  
Individual Tree ◽  
Permanent Sample Plots ◽  
Height Prediction ◽  
Age Independent

This study presents an individual tree height prediction model for white spruce (Piceaglauca (Moench) Voss) and trembling aspen (Populustremuloides Michx.) grown in boreal mixed-species stands in Alberta. The model is based on a three-parameter Chapman–Richards function fitted to data from 164 permanent sample plots using the parameter prediction method. It is age independent and expresses tree height as a function of tree diameter, tree basal area, stand density, species composition, site productivity, and stand average diameter. This height-prediction model was fitted by weighted nonlinear regression for spruce and unweighted nonlinear regression for aspen. Almost all estimates of parameters were significant at α = 0.05 and model R2-values were high (0.9192 for white spruce and 0.9087 for aspen). No consistent underestimate or overestimate of tree heights was evident in plots of studentized residuals against predicted heights. The model was also tested on an independent data set representing the population on which the model was to be used. Results showed that the average prediction biases were not significant at α = 0.05 for either species, indicating that the model appropriately described the data and performed well when predictions were made.

An individual tree diameter increment model for white spruce in Alberta

1995 ◽  
Vol 25 (9) ◽  
pp. 1455-1465 ◽  
Author(s):  
Shongming Huang ◽  
Stephen J. Titus
Keyword(s):  
White Spruce ◽  
Model Parameters ◽  
Data Set ◽  
Individual Tree ◽  
Error Structure ◽  
Permanent Sample Plots ◽  
Tree Diameter ◽  
Diameter Increment ◽  
Diameter Increment Model ◽  
Age Independent

Based on a data set from 164 permanent sample plots, an age-independent individual tree diameter increment model is presented for white spruce (Piceaglauca (Moench) Voss) grown in the boreal mixed-species stands in Alberta. The model is age independent in that it does not explicitly require tree or stand age as input variables. Periodic diameter increment is modelled as a function of tree diameter at breast height, total tree height, relative competitiveness of the tree in the stand, species composition, stand density, and site productivity. Because data from permanent sample plots are considered time series and cross sectional, diagnostic techniques were applied to identify the model's error structure. Appropriate fit based on the identified error structure was accomplished using weighted nonlinear least squares with a first-order autoregressive process. Results show that (1) all model parameters are significant at α = 0.05 level, and (2) the plot of studentized residuals against predicted diameter increment shows no consistent underestimate or overestimate for diameter increment. The model was also tested on an independent data set representing the population on which it is to be used. Results show that the average prediction biases are not significant at α = 0.05 level, indicating that the model appropriately describes the data and performs well when predictions are made.

Meta-analysis of understory white spruce response to release from overstory aspen

2004 ◽  
Vol 80 (6) ◽  
pp. 694-704 ◽  
Author(s):  
Rongzhou Man ◽  
Ken J Greenway
Keyword(s):  
Growth Response ◽  
Mixed Model ◽  
Polynomial Regression ◽  
Meta Analysis ◽  
Basal Area ◽  
Tree Height ◽  
White Spruce ◽  
Response Ratio ◽  
Mixed Effect ◽  
Individual Tree

Meta-analysis was used to summarize the research results on the growth response of understory white spruce to release from overstory aspen from different studies available from published and unpublished sources. The data were screened for the suitability for meta-analysis. Treatment effect sizes were calculated using response ratio from mean cumulative increments of released and control trees since release in height, diameter, and volume and modeled using a polynomial mixed effect regression procedure. Predictor variables include linear, quadratic, and cubic components of three independent variables — initial tree height, number of years after release, and residual basal area at release — and their linear interactions. Models with a reasonable predictive power were developed for height, diameter, and volume response, but no significant model was identified for survival. The models developed in this study can be applied to predict the growth response of understory white spruce to release, based on the growth of unreleased control trees, initial tree height, residual basal area at release, and time since release. The individual tree prediction can be easily scaled up to stand level if residual tree density and distribution is known. Key words: meta-analysis, boreal mixedwood, mixed model, polynomial regression, response ratio, growth, survival

Effects of precommercial thinning on the forest value chain in northwestern New Brunswick: Part 1 – Roundwood production and stumpage value

2013 ◽  
Vol 89 (04) ◽  
pp. 446-457 ◽  
Author(s):  
Doug G. Pitt ◽  
Len Lanteigne ◽  
Michael K. Hoepting ◽  
Jean Plamondon
Keyword(s):  
Value Chain ◽  
Abies Balsamea ◽  
Stand Age ◽  
Annual Increment ◽  
Data Set ◽  
Individual Tree ◽  
Green River ◽  
Precommercial Thinning ◽  
Permanent Sample Plots ◽  
Sequential Evaluation

The Green River precommercial thinning trials were established between 1959 and 1961 in naturally regenerating balsam fir (Abies balsamea [L.] Mill.)-dominated stands, an average of eight years after overstory removal. Three nominal spacings of 4 ft (1.2 m), 6 ft (1.8 m) and 8 ft (2.4 m) were compared to an unthinned control in six replicate blocks. In the fall of 2008, following completion of the ninth sequential evaluation of the study’s 48 permanent sample plots, three of the six replicates were clearcut harvested and data were collected on roundwood product recovery and value. These data were used to construct treatment-invariant (p ≥ 0.18) functions predicting product volume from tree diameter, allowing the volume of studwood, sawlogs and pulpwood to be predicted for the full Green River data set (all 6 replicates) through time. Mean annual increment of gross merchantable volume culminated in all treatments around stand age 45. Thinning to a nominal spacing of 6 ft, resulting in 1600 merchantable stems per ha by stand age 30, offered the best balance of individual tree and stand growth, producing 20% more gross merchantable volume and 26% more sawlog volume than unthinned stands, potentially increasing landowner stumpage revenues by 22% (p < 0.01). The sawlog volume produced in unthinned stands could be realized up to 15 years sooner in thinned stands, suggesting that PCT may offer substantive flexibility in balancing forest-level wood supply objectives.

scholarly journals Congestion Prediction in FPGA Using Regression Based Learning Methods

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1995
Author(s):  
Pingakshya Goswami ◽  
Dinesh Bhatia
Keyword(s):  
Prediction Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Physical Design ◽  
Design Flow ◽  
Training Data ◽  
Regression Problem ◽  
Data Set ◽  
Congestion Prediction ◽  
Routing Congestion

Design closure in general VLSI physical design flows and FPGA physical design flows is an important and time-consuming problem. Routing itself can consume as much as 70% of the total design time. Accurate congestion estimation during the early stages of the design flow can help alleviate last-minute routing-related surprises. This paper has described a methodology for a post-placement, machine learning-based routing congestion prediction model for FPGAs. Routing congestion is modeled as a regression problem. We have described the methods for generating training data, feature extractions, training, regression models, validation, and deployment approaches. We have tested our prediction model by using ISPD 2016 FPGA benchmarks. Our prediction method reports a very accurate localized congestion value in each channel around a configurable logic block (CLB). The localized congestion is predicted in both vertical and horizontal directions. We demonstrate the effectiveness of our model on completely unseen designs that are not initially part of the training data set. The generated results show significant improvement in terms of accuracy measured as mean absolute error and prediction time when compared against the latest state-of-the-art works.

scholarly journals Developing and Validating Nonlinear Height–Diameter Models for Major Tree Species of Ontario's Boreal Forests

2001 ◽  
Vol 18 (3) ◽  
pp. 87-94 ◽  
Author(s):  
Changhui Peng ◽  
Lianjun Zhang ◽  
Jinxun Liu
Keyword(s):  
Tree Species ◽  
Boreal Forests ◽  
Model Performance ◽  
Tree Height ◽  
Volume Estimation ◽  
Absolute Difference ◽  
Model Parameters ◽  
Individual Tree ◽  
Permanent Sample Plots ◽  
Nonlinear Growth

Abstract Six commonly used nonlinear growth functions were fitted to individual tree height-diameter data of nine major tree species in Ontario's boreal forests. A total of 22,571 trees was collected from new permanent sample plots across the northeast and northwest of Ontario.The available data for each species were split into two sets: the majority (90%) was used to estimate model parameters, and the remaining data (10%) were reserved to validate the models. The performance of the models was compared and evaluated by model, R2, mean difference, and mean absolute difference. The results showed that these six sigmoidal models were able to capture the height–diameter relationships and fit the data equally well, but produced different asymptote estimates. Sigmoidal models such as Chapman–Richards, Weibull, and Schnute functions provided the most satisfactory height predictions. The effect of model performance on tree volume estimation was also investigated. Tree volumes of different species were computed by Honer's volume equations using a range of diameters and the predicted tree total height from the six models. For trees with diameter less than 55 cm, the six height-diameter models produced very similar results for all species, while more differentiation among the models was observed for large-sized trees (e.g., diameters > 80 cm). North. J. Appl. For. 18:87–94.

scholarly journals Stem Taper and Volume of Managed Red Alder

2007 ◽  
Vol 22 (1) ◽  
pp. 61-66 ◽  
Author(s):  
David Hibbs ◽  
Andrew Bluhm ◽  
Sean Garber
Keyword(s):  
Pacific Northwest ◽  
Tree Height ◽  
Data Set ◽  
Individual Tree ◽  
Red Alder ◽  
Volume Table ◽  
Taper Function ◽  
Independent Evaluation ◽  
The Pacific ◽  
Taper Equations

Abstract Ataper equation and a volume table are presented for red alder (Alnus rubra Bong.) trees grown in plantations. Fourteen diameter measurements from each of 234 trees were collected from nine plantations throughout the Pacific Northwest. Diameter inside bark (dib) along the stemwas fitted to a variable exponent model form. Individual tree merchantable volume was then estimated as volume inside bark by integrating the taper function from 6 in. (stump height) to the height at a 5-in. (diameter outside bark) top. Incorporating two easily measured tree variables—dbhand total tree height—provided an accurate fit. Model results and the use of an independent evaluation data set of plantation-grown trees indicated that the model presented here was a better predictor of dib in managed stands than previously published red alder taper equations. Thisequation provides reliable dib and merchantable volume predictions and is an improvement over previous red alder volume and taper equations.

Estimating a system of nonlinear simultaneous individual tree models for white spruce in boreal mixed-species stands

1999 ◽  
Vol 29 (11) ◽  
pp. 1805-1811 ◽  
Author(s):  
Shongming Huang ◽  
Stephen J Titus
Keyword(s):  
Picea Glauca ◽  
Tree Height ◽  
White Spruce ◽  
Ordinary Least Squares ◽  
Tree Level ◽  
Parameter Estimates ◽  
Mixed Species ◽  
Individual Tree ◽  
Left Hand ◽  
Diameter Increment

A system of three interdependent, tree-level nonlinear equations was fitted. The system was used in an individual tree simulator to predict total tree height, periodic tree diameter increment, and height increment for white spruce (Picea glauca (Moench) Voss) grown in boreal mixed-species stands in Alberta. Because the variables appeared on the left-hand side of the equations also appeared on the right-hand side of the equations in the system, the system was estimated using nonlinear simultaneous techniques. Testing of cross-equation correlations using the Breusch and Pagan statistic indicated that the error terms of the related equations in the system are significantly correlated, suggesting that the parameter estimates obtained from simultaneous techniques are consistent and asymptotically more efficient than those obtained from ordinary least squares procedures applied to individual equations of the system.

Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests

2008 ◽  
Vol 38 (3) ◽  
pp. 553-565 ◽  
Author(s):  
H. Temesgen ◽  
V. J. Monleon ◽  
D. W. Hann
Keyword(s):  
Relative Position ◽  
Fixed Effects ◽  
Stand Density ◽  
Tree Height ◽  
Predictive Performance ◽  
Douglas Fir ◽  
Fixed Effects Model ◽  
Nonlinear Mixed Effects Models ◽  
Data Set ◽  
Height Prediction

Using an extensive Douglas-fir data set from southwest Oregon, we examined the (1) performance and suitability of selected prediction strategies, (2) contribution of relative position and stand-density measures in improving tree height (h) prediction values, and (3) effect of different subsampling designs to fill in missing h values in a new stand using a regional nonlinear model. Nonlinear mixed-effects models (NMEM) substantially improved the accuracy and precision of height prediction over the conventional nonlinear fixed-effects model (NFEM) that assumes the observations are independent, particularly when a few trees are subsampled for height. The predictive performance of a correction factor on a NFEM with relative position and stand-density measures was comparable to that of a NMEM when four or more trees were subsampled for height. When two or more heights were randomly subsampled, the NMEM efficiently explained the differences in the height–diameter relationship because of the variations in relative position of trees and stand density without having to incorporate them into the model. When only one height was subsampled, selecting the largest diameter tree in the stand would result in a lower predicted root mean square error (RMSE) than randomly selecting the height, regardless of the model form or fitting strategy used.

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