Effects of Sample Plot Size and Prediction Models on Diameter Distribution Recovery

2021 ◽  
Author(s):  
Josh B Bankston ◽  
Charles O Sabatia ◽  
Krishna P Poudel
Keyword(s):  
Loblolly Pine ◽  
Method Of Moments ◽  
Prediction Models ◽  
Unit Area ◽  
Hybrid Approach ◽  
Diameter Distribution ◽  
Sample Plot ◽  
Plot Size ◽  
Model Form ◽  
Recovery System

Abstract Distribution of tree diameters in a stand is characterized using models that predict diameter moments and/or percentiles in conjunction with a mathematical system to recover the parameters of an assumed statistical distribution. Studies have compared Weibull diameter distribution recovery systems but arrived at different conclusions regarding the best approach for recovering a stand’s diameter distribution from predicted stand-level statistics. We assessed the effects of sample plot size and diameter moments/percentiles prediction models on the accuracy of three approaches used in recovering Weibull distribution parameters—method of moments, percentile method, and moments-percentile hybrid method. Data from five plot sizes, four of which were virtually created from existing larger plots, from unthinned loblolly pine (Pinus taeda) plantations, were used to fit moments/percentile prediction models and to evaluate the accuracy of the diameter distribution recovered using three approaches. Both plot size and prediction model form affected the accuracy of the recovery approaches as indicated by the changes in their ranking from one plot size to another for the same model form. The method of moments approach ranked best when the evaluation error index did not account for tree stumpage value, but the moments-percentile hybrid approach ranked best when stumpage value was considered. Study Implications Diameter distribution recovery techniques make it possible to disaggregate trees per unit area, predicted by the whole stand growth and yield models, into diameter and utilization product classes. Thus, the techniques provide insights into stand structure, which can guide management decisions such as thinning and selection harvesting. The techniques are also used to generate yield tables by product class, which are important inputs into harvest scheduling optimization programs. An accurate diameter recovery technique is therefore critical to forest management and planning. Based on the findings of this study, the best approach of developing a diameter distribution recovery system for unthinned loblolly pine plantations would be to use the hybrid approach, with tree diameter data collected from plots of at least one-tenth hectare. The well-known (and, most likely, widely used) method of moments approach may not be the best choice. For predicting stand diameter moments and order statistics used in a diameter distribution recovery system, it would be best to use a linear additive model that incorporates a measure of stand density, such as relative spacing and/or number of trees per unit area, and a measure of the stand’s stage of development, such as dominant height and/or age.

Combining a predicted diameter distribution with an estimate based on a small sample of diameters

2011 ◽  
Vol 41 (4) ◽  
pp. 750-762 ◽  
Author(s):  
Lauri Mehtätalo ◽  
Carlos Comas ◽  
Timo Pukkala ◽  
Marc Palahí
Keyword(s):  
Prediction Models ◽  
Mixed Effects ◽  
Small Sample ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Sample Plot ◽  
Data Set ◽  
Linear Predictor ◽  
Forest Management Planning ◽  
Measured Sample

The diameter distribution of a forest stand is of great interest in many situations, including forest management planning and the related prediction of growth and yield. The estimation of the diameter distribution may be based on, for example, a measured sample of diameters or the application of previously estimated parameter prediction models (PPMs), which relate the parameters of an assumed distribution function to some stand characteristics. We propose combining these two information sources. The approach is adopted from the mixed-effects modelling theory. The PPMs are treated as mixed-effects models, the residuals being stand effects. These stand effects are predicted using a small sample of tree diameters with the best linear predictor. A study conducted with a Spanish pine data set showed that in a situation where the predictors of the PPM include errrors, the prediction can be improved even by using a sample plot of as few as five sample trees. Vice versa, a distribution based on a sample plot of 3–15 sample trees can be significantly improved by utilizing existing PPMs. An additional simulation study was conducted to further investigate how the violation of different underlying assumptions of the method affects the performance.

scholarly journals Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

2019 ◽  
Vol 11 (7) ◽  
pp. 797 ◽  
Author(s):  
Atte Saukkola ◽  
Timo Melkas ◽  
Kirsi Riekki ◽  
Sanna Sirparanta ◽  
Jussi Peuhkurinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Prediction Models ◽  
Basal Area ◽  
Sample Plot ◽  
Weighted Mean ◽  
Individual Tree ◽  
Plot Size ◽  
Head Positions ◽  
Mean Square Errors

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m2). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (Dg) and basal-area weighted mean height (Hg) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m2. Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.

A Thinned/Unthinned Loblolly Pine Growth and Yield Simulator for Planted Cutover Site-Prepared Land in the Mid-Gulf South

1992 ◽  
Vol 16 (2) ◽  
pp. 70-75 ◽  
Author(s):  
Thomas G. Matney ◽  
Robert M. Farrar
Keyword(s):  
Pinus Taeda ◽  
Loblolly Pine ◽  
Senior Author ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Quadratic Mean ◽  
Allocation Procedure ◽  
Recovery System ◽  
Least Squares Adjustment ◽  
Pinus Taeda L

Abstract The components and structure of a thinned/unthinned growth and yield simulator, developed by the MSU Growth and Yield Cooperative, for planted loblolly pine (Pinus taeda L.) on cutover site-prepared land are generally described. Prior to the first thinning, a three-parameter Weibull diameter distribution recovery system supported on the arithmetic-mean, quadratic-mean, and minimum-stand diameters is employed. At the first thinning, or if an initial tree list is supplied as input, a parameter-free least squares adjustment, diameter moment recovery system is used to allocate mortality and diameter growth to the elements of the tree list. Because thinnings from below essentially are equivalent to heavy competition mortality, the parameter-free mortality allocation procedure is adapted for allocating thinned trees to the before-thinning tree list. Copies of a PC-compatible computer program implementing the model can be obtained by writing to the senior author. South. J. Appl. For. 16(2):70-75.

scholarly journals Automating Uniformity Trials to Optimize Precision of Agronomic Field Trials

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1254
Author(s):  
Marcus Jones ◽  
Marin Harbur ◽  
Ken J. Moore
Keyword(s):  
Web Application ◽  
Unit Area ◽  
Field Trials ◽  
State University ◽  
Iowa State University ◽  
Plot Size ◽  
Maize Field ◽  
Process Uniformity ◽  
Field Plots ◽  
Uniformity Trials

Plot size has an important impact on variation among plots in agronomic field trials, but is rarely considered during the design process. Uniformity trials can inform a researcher about underlying variance, but are seldom used due to their laborious nature. The objective of this research was to describe variation in maize field trials among field plots of varying size and develop a tool to optimize field-trial design using uniformity-trial statistics. Six uniformity trials were conducted in 2015–2016 in conjunction with Iowa State University and WinField United. All six uniformity trials exhibited a negative asymptotic relationship between variance and plot size. Variance per unit area was reduced over 50% with plots 41.8 m2 in size and over 75% when using a plot size >111.5 m2 compared to a 13.9 m2 plot. Plot shape within a fixed plot size did not influence variance. The data illustrated fewer replicates were needed as plot size increased, since larger plots reduced variability. Use of a Shiny web application is demonstrated that allows a researcher to upload a yield map and consider uniformity-trial statistics to inform plot size and replicate decisions.

scholarly journals Prediction of Whole-Stem α-Cellulose Yield, Lignin Content, and Wood Density in Juvenile and Mature Loblolly Pine

2010 ◽  
Vol 34 (2) ◽  
pp. 84-90 ◽  
Author(s):  
Michael J. Aspinwall ◽  
Bailian Li ◽  
Steven E. McKeand ◽  
Fikret Isik ◽  
Marcia L. Gumpertz
Keyword(s):  
Wood Density ◽  
Loblolly Pine ◽  
Prediction Models ◽  
Lignin Content ◽  
Property Values ◽  
Wood Property ◽  
Stem Wood ◽  
Cellulose Yield ◽  
Weighted Averages ◽  
Breast Height

Abstract Models were developed for predicting whole-stem α-cellulose yield, lignin content, and wood density in 14- and 20-year-old loblolly pine across three different sites. Also, the relationships between juvenile-, transition-, and mature-wood α-cellulose yield, lignin content, and wood density at breast-height and overall whole-stem wood property values were examined. Whole-stem wood property weighted averages were calculated by taking 12-mm core samples at breast height and at 2.4-m incremental heights up each tree, and breast-height wood property values were then used to predict whole-stem weighted averages. Despite large differences in growth across sites and both ages, whole-stem models based on whole cores taken at breast height were not significantly different among sites, and coefficients of determination (R2) were 0.87, 0.74, and 0.78 for α-cellulose, lignin, and wood density, respectively. Generally, whole-stem prediction models based on sections of wood at breast height were not significantly different among sites and were less effective than cores as predictors, explaining between 39 and 82% of the variation in whole-stem wood traits. The results of this study indicate that the relationship between breast height and whole-stem wood chemical properties (and density) is predictable and consistent across sites in both juvenile and mature loblolly pine.

scholarly journals A New Diameter Distribution Model for Unmanaged Loblolly Pine Plantations in East Texas

2006 ◽  
Vol 30 (1) ◽  
pp. 13-20 ◽  
Author(s):  
Young-Jin Lee ◽  
Dean W. Coble
Keyword(s):  
Loblolly Pine ◽  
Pine Plantations ◽  
Growth And Yield ◽  
Distribution Model ◽  
Diameter Distribution ◽  
Yield Prediction ◽  
East Texas ◽  
Distribution Models ◽  
Parameter Recovery ◽  
Diameter Classes

Abstract A parameter recovery procedure for the Weibull distribution function based on four percentile equations was used to develop a diameter distribution yield prediction model for unmanaged loblolly pine (Pinus taeda L.) plantations in East Texas. This model was compared with the diameter distribution models of Lenhart and Knowe, which have been used in East Texas. All three models were evaluated with independent observed data. The model developed in this study performed better than the other two models in prediction of trees per acre and cubic-foot volume per acre (wood and bark, excluding stump) across diameter classes. Lenhart’s model consistently underestimated the larger-diameter classes because it was developed originally with data mostly collected in young plantations. Knowe’s model overestimated volume in sawtimber-sized trees, which could lead to overestimations of volume in older loblolly pine plantations found in East Texas. An example also is provided to show users how to use this new yield prediction system. These results support the recommendation that forest managers should use growth and yield models designed and/or calibrated for the region in which they are implemented.South. J. Appl.For. 30(1):13–20.

A hybrid approach to couple the discrete velocity method and Method of Moments for rarefied gas flows

2020 ◽  
Vol 410 ◽  
pp. 109397 ◽  
Author(s):  
Weiqi Yang ◽  
Xiao-Jun Gu ◽  
Lei Wu ◽  
David R. Emerson ◽  
Yonghao Zhang ◽  
...  
Keyword(s):  
Method Of Moments ◽  
Rarefied Gas ◽  
Hybrid Approach ◽  
Gas Flows ◽  
Discrete Velocity ◽  
Rarefied Gas Flows ◽  
Discrete Velocity Method

Postthinning growth and yield of row-thinned and selectively thinned loblolly and slash pine plantations

1989 ◽  
Vol 19 (2) ◽  
pp. 247-256 ◽  
Author(s):  
V. C. Baldwin Jr. ◽  
D. P. Feduccia ◽  
J. D. Haywood
Keyword(s):  
Loblolly Pine ◽  
Unit Area ◽  
Basal Area ◽  
Site Index ◽  
Slash Pine ◽  
Pine Plantations ◽  
Growth And Yield ◽  
Growth Responses ◽  
Diameter Increment ◽  
Study Results

This study compared growth responses in planted loblolly pine (Pinustaeda L.) and slash pine (P. elliottii Engelm.) stands thinned by using three row-felling methods and at the same density levels, three selective felling methods. The study plots were in six plantations, aged 15–22 years, located in central Louisiana. Growth was measured 5 and 10 years after plot installation. Site index varied from 19.5 to 31.7 m (base age 50) and initial planting densities ranged from 1993 to 2989 trees/ha. Study results show there will likely be less diameter increment and less net basal area and cubic-metre volume per unit area growth and yield, and the growth will be in smaller-sized trees, if row thinning is used rather than selective thinning from below. These differences will probably be greater in slash pine plantations than in loblolly pine plantations.

scholarly journals Prediction across healthcare settings: a case study in predicting emergency department disposition

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yuval Barak-Corren ◽  
Pradip Chaudhari ◽  
Jessica Perniciaro ◽  
Mark Waltzman ◽  
Andrew M. Fine ◽  
...  
Keyword(s):  
Prediction Models ◽  
Hybrid Approach ◽  
Model Performance ◽  
Admission Rate ◽  
Severity Index ◽  
Site Specific ◽  
Healthcare Settings ◽  
Severity Index Score ◽  
High Acuity

AbstractSeveral approaches exist today for developing predictive models across multiple clinical sites, yet there is a lack of comparative data on their performance, especially within the context of EHR-based prediction models. We set out to provide a framework for prediction across healthcare settings. As a case study, we examined an ED disposition prediction model across three geographically and demographically diverse sites. We conducted a 1-year retrospective study, including all visits in which the outcome was either discharge-to-home or hospitalization. Four modeling approaches were compared: a ready-made model trained at one site and validated at other sites, a centralized uniform model incorporating data from all sites, multiple site-specific models, and a hybrid approach of a ready-made model re-calibrated using site-specific data. Predictions were performed using XGBoost. The study included 288,962 visits with an overall admission rate of 16.8% (7.9–26.9%). Some risk factors for admission were prominent across all sites (e.g., high-acuity triage emergency severity index score, high prior admissions rate), while others were prominent at only some sites (multiple lab tests ordered at the pediatric sites, early use of ECG at the adult site). The XGBoost model achieved its best performance using the uniform and site-specific approaches (AUC = 0.9–0.93), followed by the calibrated-model approach (AUC = 0.87–0.92), and the ready-made approach (AUC = 0.62–0.85). Our results show that site-specific customization is a key driver of predictive model performance.

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