scholarly journals MODEL DISTRIBUSI DIAMETER LIMA JENIS POHON PADA HUTAN TROPIKA BASAH DI KABUPATEN MAMUJU, SULAWESI BARAT

PERENNIAL ◽  
2010 ◽  
Vol 6 (1) ◽  
pp. 44
Author(s):  
Beta Putranto
Keyword(s):  
Tree Species ◽  
Polynomial Function ◽  
Natural Forest ◽  
Distribution Model ◽  
Diameter Distribution ◽  
Weibull Function ◽  
Suitable Model ◽  
Stem Number ◽  
Tree Diameter ◽  
Tree Diameter Distribution

The objective of this research is to establish the most suitable tree diameter distribution model for five tree species on tropical rain forest in Mamuju District. This research was conducted on two locations. The 50 square plots were sampled systematically for seedling, sapling, pole and tree measurements of bintangur, jambu-jambu, lada-lada, matoa and nyatoh species. The distance among plots was 50 m. Variables to be measured were stem number of seedlings to trees and tree diameter at breast height (1,3 m) of saplings to trees. About 70% of the data were used to establish the best model, the rest of 30% were used to validate the model. Models to be analyzed were fourth-order semi-logarithmic transformed polynomial function, Weibull density function and monotonic decreasing Weibull function. The results show that the five tree species have a similar diameter distribution pattern. The shape of this distribution is the typical of uneven-aged stand distribution on natural forest, namely reversed J shape. The most suitable model for five species is second-order semi-logarithmic transformed polynomial function. Key words: model, diameter distribution, natural forest, uneven-aged stand.

Performance of Weibull function as a diameter distribution model for Pinus thunbergii stands in the eastern coast of South Korea

2016 ◽  
Vol 13 (5) ◽  
pp. 822-830 ◽  
Author(s):  
Azyleah Cañizares Abino ◽  
Sung Yong Kim ◽  
Roscinto Ian Canicosa Lumbres ◽  
Mi Na Jang ◽  
Ho Joong Youn ◽  
...  
Keyword(s):  
South Korea ◽  
Pinus Thunbergii ◽  
Distribution Model ◽  
Diameter Distribution ◽  
Eastern Coast ◽  
Weibull Function

scholarly journals Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 859 ◽  
Author(s):  
Yuling Chen ◽  
Baoguo Wu ◽  
Zhiqiang Min
Keyword(s):  
Weibull Distribution ◽  
Maximum Entropy ◽  
Measurement Data ◽  
Similarity Index ◽  
Distribution Model ◽  
Diameter Distribution ◽  
Weibull Function ◽  
Prediction Ability ◽  
Actual Distribution ◽  
Modeling And Prediction

Research Highlights: Improving the prediction accuracy represents a popular forest simulation modeling issue, and exploring the optimal maximum entropy (MaxEnt) distribution is a new effective method for improving the diameter distribution model simulation precision to overcome the disadvantages of Weibull. Background and Objectives: The MaxEnt distribution is the closest to the actual distribution under the constraints, which are the main probability density distributions. However, relatively few studies have addressed the optimization of stand diameter distribution based on MaxEnt distribution. The objective of this study was to introduce application of the MaxEnt distribution on modeling and prediction of stand diameter distribution. Materials and Methods: The long-term repeated measurement data sets consisted of 260 diameter frequency distributions from China fir (Cunninghamia lanceolate (Lamb.) Hook) plantations in the southern China Guizhou. The Weibull distribution and the MaxEnt distribution were applied to the fitting of stand diameter distribution, and the modeling and prediction characteristics of Weibull distribution and MaxEnt distribution to stand diameter distribution were compared. Results: Three main conclusions were obtained: (1) MaxEnt distribution presented a more accurate simulation than three-parametric Weibull function; (2) the Chi-square test showed diameter distributions of unknown stands can be well estimated by applying MaxEnt distribution based on the plot similarity index method (PSIM) and Weibull distribution based on the parameter prediction method (PPM); (3) the MaxEnt model can deal with the complex nonlinear relationship and show strong prediction ability when predicting the stand distribution structure. Conclusions: With the increase of sample size, the PSIM has great application prospects in the dynamic prediction system of stand diameter distribution.

scholarly journals Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

Forests ◽  
2017 ◽  
Vol 8 (6) ◽  
pp. 184 ◽  
Author(s):  
Meinrad Abegg ◽  
Daniel Kükenbrink ◽  
Jürgen Zell ◽  
Michael Schaepman ◽  
Felix Morsdorf
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Diameter Distribution ◽  
Tree Diameter ◽  
Forest Inventories ◽  
Tree Diameter Distribution

QUANTIFYING TREE DIAMETER DISTRIBUTIONS WITH ONE-DIMENSIONAL DIFFUSION PROCESSES

2010 ◽  
Vol 18 (01) ◽  
pp. 205-221 ◽  
Author(s):  
PETRAS RUPŠYS ◽  
EDMUNDAS PETRAUSKAS
Keyword(s):  
Probability Density ◽  
Goodness Of Fit ◽  
Diffusion Processes ◽  
Growth Models ◽  
Transition Probability ◽  
Diameter Distribution ◽  
Discrete Observations ◽  
Data Set ◽  
Tree Diameter ◽  
Tree Diameter Distribution

This study presents diffusion processes methodology on tree diameter distribution problem. We use stochastic differential equation methodology to derive a univariate age-dependent probability density function of a tree diameter distribution. The purpose of this paper is to investigate the relationship between the stochastic linear and logistic shape diameter growth models and diameter distribution laws. We establish the probabilistic characteristics of stochastic growth models, such as the univariate transition probability density of tree diameter, the mean and variance of tree diameter. We carry out comparison of proposed continuous time stochastic models on the basis of Hong-Li, Gini, Shapiro-Wilk goodness-of-fit statistics and normal probability plot. Parameter estimations are based on discrete observations over age of trees. To model the tree diameter distribution, as an illustrative experience, a real data set from repeated measurements on a permanent sample plot of pine (Pinus sylvestris) stand in the Kazlu Ruda district at Lithuania is used. The results are implemented in the symbolic computational language MAPLE.

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