Biometry for Forestry and Environmental Data

Mehtätalo, Lauri; Lappi, Juha

doi:10.1201/9780429173462

Cited by 85 publications

(65 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, the similar accuracy of both solutions is not a surprise. Nonetheless, the use of mixed-effects models as a solution enabling the description of group-specific relationships for all groups of the dataset [19] is definitely worth considering, especially when there is a possibility of non-invasively obtaining additional data for random effects prediction [50].…”

Section: Discussionmentioning

confidence: 99%

“…In the case of the fixed-effects model (first strategy), we used all fitted model parameters, whereas for the mixed-effects model, we applied fixed parameters only. For both fitting strategies, the predicted stem volume was computed by numerical integration [11,19]. The volumes for both strategies in combination with the volumes calculated using the empirical volume equation currently used in Poland [31]:…”

Section: Volume Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Fixed- and Mixed-Effects Approaches to Taper Modeling for Scots Pine in West Poland

Bronisz

Zasada

2019

Forests

View full text Add to dashboard Cite

Diameter measurements along the stem, which are the basis for taper models, usually have a hierarchical structure. Mixed-effects models, where fixed and random effects are distinguished, are a possible solution for this type of data. However, in order to fully absorb the potential of this method, random effects prediction, which requires additional measurements (diameter along stem), is recommended. This article presents a comparison of various fitting methods (mixed- and fixed-effects model approaches) of the variable-exponent taper model created by Kozak for determining the outside bark diameter along the stem and predicting the tree volume of Scots pine trees in west Poland. During the analysis, it was assumed that no additional measured data were available for practical use; therefore, for the mixed-effects model approach, fixed effects prediction without random effects was applied. Both fitting strategies were compared based on modeling and an independent validation data set. The comparison of mixed- and fixed-effects fitting strategies for the diameter along the stem indicated that the taper model fitted using the mixed-effects model approach better fit the data. Moreover, the error rate for the total tree volume prediction for the independent data set was lower for the mixed-effects model solution than for the fixed-effects one.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Volume Predictionmentioning

confidence: 99%

Comparison of Fixed- and Mixed-Effects Approaches to Taper Modeling for Scots Pine in West Poland

Bronisz

Zasada

2019

Forests

View full text Add to dashboard Cite

show abstract

“…Two predictor variables are of special interest: the time interval over which the ingrowth trees are appearing and the plot size. In the literature, these variables are called exposure variables (Baetschmann and Winkelmann 2012;Mehtätalo and Lappi 2020). A natural assumption is that the expected number of ingrowth counts is proportional to both exposure time and the plot size.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, the ingrowth is modeled in the framework of generalized linear mixed models (Stroup 2013;Zuur et al 2012;Mehtätalo and Lappi 2020). More specifically, the ingrowth is modeled using a zero-inflated negative binomial mixed model.…”

Section: Introductionmentioning

confidence: 99%

Analyzing ingrowth using zero-inflated negative binomial models

Lappi¹,

Pukkala²

2020

Silva Fenn.

Self Cite

View full text Add to dashboard Cite

Ingrowth is an important element of stand dynamics in several silvicultural systems, especially in continuous cover forestry. Earlier predictive models for ingrowth in Finnish forests are few and not based on up-to-date statistical methods. Ingrowth is here defined as the number of trees over 1.3 m entering a plot. This study developed new ingrowth models for Scots pine ( L.), (Picea abies (L.) H. Karst.) and birch ( Roth and Ehrh.) using data from the permanent sample plots of the Finnish national forest inventory. The data were over-dispersed compared to a Poisson process and had many zeros. Therefore, a zero-inflated negative binomial model was used. The total and species-specific stand basal areas, temperature sum and fertility class were used as predictors in the ingrowth models. Both fixed-effects and mixed-effects models were fitted. The mixed-effects model versions included random plot effects. The mixed-effects models had larger likelihoods but provided biased predictions. Also censored prediction was considered where only a certain maximum number of ingrowth trees were accepted for a plot. The models predicted most pine ingrowth in pine-dominated stands on sub-xeric and xeric sites where stand basal area was low. The predicted amount of spruce ingrowth was maximized when the basal area of spruce was 13 m ha. Increasing temperature sum increased spruce ingrowth. Predicted birch ingrowth decreased with increasing stand basal area and towards low fertility classes. An admixture of pine increased the predicted amount of spruce ingrowth.Pinus sylvestrisNorway spruceBetula pendulaB. pubescens2â1

show abstract

“…For example, due to properties of the site, two Scots pine trees from the same stand are generally more alike than two Scots pine trees from different stands. The variance-covariance structure between observations affects the standard errors of the estimated regression coefficients, so ignoring within group correlations in the model construction phase may lead to severe problems in parameter estimates and model inference (Mehtätalo and Lappi 2020). Therefore, instead of regular linear models that are fitted with the ordinary least squares method and the assumption that the residuals are uncorrelated, linear-mixed effects (LME) models should be used to take the correlation structure into account.…”

Section: Linear Mixed-effects Modelsmentioning

confidence: 99%

Predicting commercial tree quality by means of airborne laser scanning

Karjalainen¹

2020

Diss. For.

View full text Add to dashboard Cite

Airborne laser scanning (ALS) is widely used to predict the total volume of trees in a forest stand. However, in operational forestry, it is usually not sufficient to consider the total volume only, because the various tree species and timber assortments are priced differently. As tree quality strongly affects how harvested logs are assigned to different timber assortments, tree quality information prior to harvesting, for example, would improve the planning of harvesting operations. The main aim of this thesis was to test different methods to predict tree quality, especially sawlog volume, by means of ALS. The three sub-studies of this thesis were implemented using datasets from eastern Finland (3 datasets) and southeastern Norway (1 dataset). All the study forests were dominated by Scots pine (Pinus sylvestris L.) or Norway spruce (Picea abies (L.) Karst.). The first study focused on the effects of transferring tree-level models between inventory areas. In the second study, various methods to predict plot-level (30 m × 30 m) sawlog volume were tested. The third study focused on the field-calibrations of stand-level merchantable and sawlog volumes by using basal area measurements. All the ALS-based predictions were made with either linear mixed-effects models or k-nearest neighbor imputations at the tree or plot-levels (15 m × 15 m). The results showed that there is only weak correlation between the ALS metrics and tree quality. Nevertheless, sawlog volume predictions with relative root mean squared error values between 20-30 % were obtained after aggregations to the 30 m × 30 m and standlevels. Moreover, the study-specific results showed that a notable decrease in accuracy can be expected when tree-level models are transferred between inventory areas, and that basal area information is not generally useful to increase the accuracy of sawlog volume predictions in Norway spruce dominated stands.

show abstract

Biometry for Forestry and Environmental Data

Cited by 85 publications

References 0 publications

Comparison of Fixed- and Mixed-Effects Approaches to Taper Modeling for Scots Pine in West Poland

Comparison of Fixed- and Mixed-Effects Approaches to Taper Modeling for Scots Pine in West Poland

Analyzing ingrowth using zero-inflated negative binomial models

Predicting commercial tree quality by means of airborne laser scanning

Contact Info

Product

Resources

About