A stand table projection system for interior Douglas-fir in British Columbia, Canada

Jang, Woongsoon; Eskelson, Bianca N.I.; Marshall, Peter; Moss, Ian D.

doi:10.1016/j.foreco.2017.11.048

Cited by 4 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mathematicians [50,51] obtained several famous typical distributions in statistics from some common constraints, such as Gaussian distribution (Normal distribution), Gamma distribution and Exponential distribution, and proved that these common distributions in nature are actually special cases of maximum entropy principle. Jaynes proved mathematically that the probability of the maximum entropy prediction is the predominance in all the predictions of random events [51][52][53]. In the physical sense, the maximum entropy principle reveals the internal rules of the information system.…”

Section: Discussionmentioning

confidence: 99%

“…In practice, the future values of the probability distribution parameters for the diameter distribution is determined or estimated from stand characteristics (e.g., density, age, and mean tree size). In the study of dynamic prediction of stand diameter structure based on probability distribution, two main methods of PPM and PRM have been applied for predicting these parameters in different tree species and forest types over the last 45 years [18,52]. However, when the correlation between parameter estimation and the whole stand characteristics was weak, the accuracy of dynamic prediction of stand diameter distribution by these two methods was not high.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

Chen

Min

2019

Forests

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

Chen

Min

2019

Forests

View full text Add to dashboard Cite

show abstract

“…[12,13]. While stand table projection methods exhibit potential for short-term predictions (less than 10 years), their effectiveness diminishes for the long temporal dynamics of forests, particularly in uneven-aged forests [13,24,25]. On the other hand, matrix models serve as a logical and formalized extension of the stand table projection method, enabling long-term predictions for complex forest structures.…”

Section: Introductionmentioning

confidence: 99%

A Climate-Sensitive Transition Matrix Growth Model for Masson Pine (Pinus massoniana Lamb.) Natural Forests in Hunan Province, South-Central China

Wang

Meng

2023

Forests

View full text Add to dashboard Cite

Masson pine natural forests are ecologically and economically valuable forest ecosystems extensively distributed across China. However, they have been subject to deforestation due to human disturbance. Moreover, climate change affects the growth, mortality, and recruitment of forests, yet available forest growth models do not effectively analyze the impacts of climate. A climate-sensitive transition matrix model (CM) was developed using data from 330 sample plots collected during the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventories in Hunan Province. To assess model robustness, two additional models were created using the same data: a non-climate-sensitive transition matrix model (NCM) and a fixed probability transition matrix model (FM). The models were compared using tenfold cross-validation and long-term predictive performance analysis. The cross-validation results did not show any significant differences among the three models, with the FM performing slightly better than the NCM. However, the application of the CM for long-term prediction (over a span of 100 years) under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) revealed distinct dynamics that demonstrated enhanced reliability. This is attributed to the consideration of climate variables that impact forest dynamics during long-term prediction periods. The CM model offers valuable guidance for the management of Masson pine natural forests within the context of changing climatic conditions.

show abstract

Carbon pools in forest systems and new estimation based on an investigation of carbon sequestration

Wang,

Xu,

et al. 2024

Journal of Environmental Management

View full text Add to dashboard Cite

A stand table projection system for interior Douglas-fir in British Columbia, Canada

Cited by 4 publications

References 47 publications

Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

Stand Diameter Distribution Modeling and Prediction Based on Maximum Entropy Principle

A Climate-Sensitive Transition Matrix Growth Model for Masson Pine (Pinus massoniana Lamb.) Natural Forests in Hunan Province, South-Central China

Carbon pools in forest systems and new estimation based on an investigation of carbon sequestration

Contact Info

Product

Resources

About