scholarly journals Using linear mixed model and dummy variable  model approaches to construct compatible single-tree biomass equations at different scales – A case study for Masson pine in Southern China

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 101-115 ◽  
Author(s):  
L.Y. Fu ◽  
W.S. Zeng ◽  
S.Z. Tang ◽  
R.P. Sharma ◽  
H.K. Li
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Southern China ◽  
Forest Biomass ◽  
Tree Biomass ◽  
Variable Model ◽  
Masson Pine ◽  
Dummy Variable ◽  
Single Tree ◽  
Biomass Models

The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models. The aboveground biomass data on Masson pine (Pinus massoniana) from nine provinces in southern China were used to develop generalized single-tree biomass models using both linear mixed model and dummy variable model methods. An allometric function requiring only diameter at breast height was used as a base model for this purpose. The results showed that the aboveground biomass estimates of individual trees with identical diameters were different among the forest origins (natural and planted) and geographic regions (provinces). The linear mixed model with random effect parameters and dummy model with site-specific (local) parameters showed better fit and prediction performance than the population average model. The linear mixed model appears more flexible than the dummy variable model for the construction of generalized single-tree biomass models or compatible biomass models at different scales. The linear mixed model method can also be applied to develop other types of generalized single-tree models such as basal area growth and volume models.  

Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales — a case study for Masson pine (Pinus massoniana) in southern China

2011 ◽  
Vol 41 (7) ◽  
pp. 1547-1554 ◽  
Author(s):  
Wei Sheng Zeng ◽  
Hui Ru Zhang ◽  
Shou Zheng Tang
Keyword(s):  
Southern China ◽  
Forest Biomass ◽  
Pinus Massoniana ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Variable Model ◽  
Masson Pine ◽  
Dummy Variable ◽  
Single Tree ◽  
Biomass Equations

It is fundamental for monitoring and assessment of national forest biomass to develop generalized single-tree biomass models suitable for large-scale forest biomass estimation. However, the compatibility of forest biomass estimates among different scales is a real problem. Based on the aboveground biomass data of Masson pine (Pinus massoniana Lamb.) in southern China, generalized single-tree biomass equations applying to national and regional forest biomass estimation were constructed using the dummy variable model method, which provided an effective approach to solving the compatibility of forest biomass estimates among different scales. The results show that aboveground biomass estimates of individual trees with the same diameter are different to some extent among the forest origins and geographic regions and that a dummy model with specific (local) parameters is better than a population-average model. The dummy variable model can be applied to develop other generalized compatible models such as tree volume equations.

scholarly journals 2PL Model: Compare Generalized Linear Mixed Model with Latent Variable Model based IRT framework

2021 ◽  
Author(s):  
Jihong Zhang ◽  
Terry Ackerman ◽  
Yurou Wang
Keyword(s):  
Latent Variable ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
R Package ◽  
Estimation Accuracy ◽  
Variable Model ◽  
Large Scale Assessment ◽  
Measurement Models ◽  
Lower Accuracy

Fitting item response theory (IRT) models using the generalized mixed logistic regression model (GLMM) has become more popular in large-scale assessment because GLMM allows combining complicated multilevel structures (i.e., students are nested in classrooms which are nested in schools) with IRT measurement models. However, the estimation accuracy of item parameters between these two models is not well examined. This study aimed to compare the estimation results of the GLMM based 2PL model (using the PLmixed R package) with the traditional IRT model (using flexMIRT software) under different sample sizes (N= 500, 1000, 5000) and test length (J = 15, 21) conditions. The simulation results showed that for both the GLMM-based method and the traditional method, item threshold estimates had lower bias than item discrimination parameters. We also found that according to the simulation study, GLMM estimates via PLmixed had lower accuracy than traditional IRT modeling via flexMIRT for items with high discrimination.

Generic linear mixed-effects individual-tree biomass models forPinus massonianain southern China

2014 ◽  
Vol 76 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Liyong Fu ◽  
Weisheng Zeng ◽  
Huiru Zhang ◽  
Guangxing Wang ◽  
Yuancai Lei ◽  
...  
Keyword(s):  
Southern China ◽  
Mixed Effects ◽  
Tree Biomass ◽  
Individual Tree ◽  
Linear Mixed Effects ◽  
Biomass Models

scholarly journals The Effects of Combining the Variables in Allometric Biomass Models on Biomass Estimates over Large Forest Areas: A European Beech Case Study

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1428
Author(s):  
Erick O. Osewe ◽  
Ioan Dutcă
Keyword(s):  
Nonlinear Models ◽  
Forest Biomass ◽  
Tree Height ◽  
European Beech ◽  
Biomass Estimation ◽  
Parameter Estimates ◽  
Variable Model ◽  
Separate Variable ◽  
Biomass Models ◽  
Fagus Sylvatica L

Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D2H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D2H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q > 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q < 2.0 (i.e., by –3.9% for Q = 1.50), whereas for Q = 2.03, the differences were minimum (0.1%). The standard errors of mean AGB per hectare were similar for Q = 2.03 (differences up to 0.2%), and the differences increased with the Q-ratio, by up 10.2% for Q = 5.05. Therefore, we demonstrated for European beech that combining the variables in allometric biomass models when Q ≠ 2.0 resulted in biased estimates of mean AGB per hectare and of uncertainty.

scholarly journals The effects of seasonal climate variability on dengue annual incidence in Hong Kong: A modelling study

2020 ◽  
Author(s):  
Hsiang-Yu Yuan ◽  
Jingbo Liang ◽  
Pei-Sheng Lin ◽  
Kathleen Sucipto ◽  
Mesfin Mengesha Tsegaye ◽  
...  
Keyword(s):  
Hong Kong ◽  
Climate Variability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Southern China ◽  
Annual Incidence ◽  
Control Measures ◽  
Average Intensity ◽  
Seasonal Climate ◽  
First Case

ABSTRACTIn recent years, dengue has been rapidly spreading and growing in the tropics and subtropics. Located in southern China, Hong Kong’s subtropical monsoon climate may favour dengue vector populations and increase the chance of disease transmissions during the rainy summer season. An increase in local dengue incidence has been observed in Hong Kong ever since the first case in 2002, with an outbreak reaching historically high case numbers in 2018. However, the effects of seasonal climate variability on recent outbreaks are unknown. As the local cases were found to be spatially clustered, we developed a Poisson generalized linear mixed model using pre-summer monthly total rainfall and mean temperature to predict annual dengue incidence (the majority of local cases occur during or after the summer months), over the period 2002-2018 in three pre-defined areas of Hong Kong. Using leave-one-out cross-validation, 5 out of 6 observations of area-specific outbreaks during the major outbreak years 2002 and 2018 were able to be predicted. 42 out of a total of 51 observations (82.4%) were within the 95% confidence interval of the annual incidence predicted by our model. Our study found that the rainfall before and during the East Asian monsoon (pre-summer) rainy season is negatively correlated with the annual incidence in Hong Kong while the temperature is positively correlated. Hence, as mosquito control measures in Hong Kong are intensified mainly when heavy rainfalls occur during or close to summer, our study suggests that a lower-than-average intensity of pre-summer rainfall should also be taken into account as an indicator of increased dengue risk.

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