Deriving tree diameter distributions using Bayesian model averaging

Bullock, Bronson P.; Boone, Edward L.

doi:10.1016/j.foreco.2007.01.024

Cited by 19 publications

(10 citation statements)

References 15 publications

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“…Tree diameter distribution approaches provide foresters with methods of characterising a forest stand and predicting stand yield and size class structure on the basis of a specific probability function (Bailey and Dell 1973;Cao and Burkhart 1984;Rennolls et al 1985;Brooks et al 1992;Bullock and Boone 2007). Historically, foresters tried to describe stand diameter distributions using theoretical models, with forest growth and yield modelers using probability density functions to model diameter distributions (Lindsay et al 1996).…”

Section: Introductionmentioning

confidence: 99%

Characterising the diameter distribution of Sal plantations by comparing normal, lognormal and Weibull distributions at Tilagarh Eco-park, Bangladesh

Deb¹,

Salman²,

Halim³

et al. 2014

Southern Forests: a Journal of Forest Science

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For many years foresters have been using statistical probability density functions to describe and characterise stand structure. Predicting the current and future yields of a stand is essential for successful stand and timber management. Implicit prediction of current yield is accomplished by using diameter distribution methods. All diameter distribution yield systems predict the number of trees per unit area by diameter class. In this study, the normal, lognormal and the three-parameter Weibull probability density function were compared to characterise the diameter distributions of Sal (Shorea robusta) plantations grown at Tilagarh Eco-park, Bangladesh. Data from 70 plots, established in three plantations, were used for this study. The Weibull parameters were estimated by the maximum likelihood and moments estimator methods. A one-sample Kolmogorov-Smirnov test was used for the goodness of fit for all models. The Kolmogorov-Smirnov test results showed that both lognormal and Weibull distributions were suitable to characterise the diameter distributions of Sal plantations in the study area and may be applicable for other Sal forests in Bangladesh.

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Section: Introductionmentioning

confidence: 99%

Characterising the diameter distribution of Sal plantations by comparing normal, lognormal and Weibull distributions at Tilagarh Eco-park, Bangladesh

Deb¹,

Salman²,

Halim³

et al. 2014

Southern Forests: a Journal of Forest Science

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“…Two commonly used distributions in other natural resource Bayesian applications are the Uniform and beta. [5][6][7] When using the Uniform distribution to describe prior information, a range of values is assumed to be equally likely. Thus, the forester only has an idea of the range of likely tons.…”

Section: Incorporating Prior Informationmentioning

confidence: 99%

“…Bayesian methods have been used in many natural resource applications. [6][7][8] However, there is limited published literature that addresses the use of Bayesian methods when conducting a forest inventory to estimate average tons within a given stand of timber. Therefore, the objectives of this paper are to (i) explain some of the differences between frequentist and Bayesian inference when estimating average tons, (ii) describe the process behind using Bayesian methodology to estimate average tons, and (iii) provide an example from an actual forest inventory to estimate average tons.…”

Section: Incorporating Prior Informationmentioning

confidence: 99%

Using Bayesian methodology to incorporate personal knowledge when estimating average tons per acre of loblolly pine plantations

2018

FREIJ

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Foresters often don't fully utilize available information when estimating average stand tons ac-1. Previous experience, and if available historic inventory data, of the same tract or similar tracts can be used as prior information in a Bayesian context to reduce uncertainty associated with average ton estimates. Bayesian methods produce a posterior distribution dependent both on the current forest inventory sample and prior information about the probabilities associated with any one average tons ac-1 actually being the true average tons. A more complete description and background of using Bayesian methods to incorporate personal knowledge, when estimating average tons, is provided. Additionally, a practical example using data obtained from an actual forest inventory conducted in a loblolly pine (Pinus taeda L.) plantation is presented. Using conventional variable plot sampling techniques, average tons was estimated at 52.9 tons ac-1. However, when determining a posterior distribution using a Beta distribution to quantify prior personal knowledge of similar sites, the estimate of average tons changed to 51.6 tons ac-1. When using a Uniform distribution to quantify prior knowledge the estimated average tons did not change. For this example, ton estimates were not substantially changed when using a Bayesian approach; however, the inferential statements that can be made about the true average tons are different than when using a frequentist inference approach.

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“…The Weibull function has been widely used due to its flexibility in modeling reverse-J, skewed, and unimodal shapes. [4] Furthermore, the Weibull function is not required to estimate frequencies because of integration [5] ; in this respect, the Weibull function was chosen to model size and mass of the almond in this research.…”

Section: Introductionmentioning

confidence: 99%

“…Sharan et al modeled weight and length of the Himachal tomato using the two-parameter Weibull function and it was found that weight and longitudinal axis length of the Himachal tomato are both described satisfactorily by the Weibull distribution function. [12] Bullock and Boone derived tree diameter distributions using the Weibull function [5] and Smith and Bullock compared parameter estimation techniques to calculate the Weibull parameter for diameter distributions of loblolly pine in a genotype and environment (GxE) study. The Weibull probability density function was used for this modeling and the analyses indicated that the Weibull provides a more accurate canopy description for environmental and physiological modeling.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Geometric Size Distribution of Almond

Mahmoodi

Khazaei

Mohamadi

2011

International Journal of Food Properties

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The purpose of this study was modeling the mass and size distribution of three varieties of almond and its kernel (seed) using the Weibull distribution function. Furthermore, some physical properties of seeds were measured using an image processing technique. A two-parameter Weibull distribution function was chosen for modeling the size and mass distributions. The Weibull distribution of width was better modeled than the other sizes and the distribution of sizes mainly were better modeled than the mass. The mass distribution of almonds was approached to the normal probability density function, whereas the dimension distributions of seeds had negatively skewed.

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Deriving tree diameter distributions using Bayesian model averaging

Cited by 19 publications

References 15 publications

Characterising the diameter distribution of Sal plantations by comparing normal, lognormal and Weibull distributions at Tilagarh Eco-park, Bangladesh

Characterising the diameter distribution of Sal plantations by comparing normal, lognormal and Weibull distributions at Tilagarh Eco-park, Bangladesh

Using Bayesian methodology to incorporate personal knowledge when estimating average tons per acre of loblolly pine plantations

Modeling of Geometric Size Distribution of Almond

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