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Articles containing the keyword 'Johnson’s SB'

Category : Research article

article id 99, category Research article

Jouni Siipilehto. (2011). Local prediction of stand structure using linear prediction theory in Scots pine-dominated stands in Finland. Silva Fennica vol. 45 no. 4 article id 99. https://doi.org/10.14214/sf.99

Keywords: Pinus sylvestris; linear prediction; diameter distribution; Weibull; Johnson’s SB; height curve; stand characteristics

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This study produced a family of models for eight standard stand characteristics, frequency and basal area-based diameter distributions, and a height curve for stands in Finland dominated by Scots pine (Pinus sylvestris L.). The data consisted of 752 National Forest Inventory-based sample plots, measured three times between 1976 and 2001. Of the data, 75% were randomly selected for modelling and 25% left out for model evaluation. Base prediction models were constructed as functions of stand age, location and site providing strongly average expectations. These expectations were then calibrated with the known stand variables using linear prediction theory when estimating the best linear unbiased predictor (BLUP). Three stand variables, typically assessed in Finnish forest management planning fieldwork, were quite effective for calibrating the expectation for the unknown variable. In the case of optional distributions, it was essential to choose the weighting of the diameter distribution model such that the available input variables and the model applied were based on the same scale (e.g. arithmetic stand variables for frequency distribution). Additional input variables generally improved the accuracy of the validated characteristics, but the improvements in the predicted distributions were most noteworthy when the arithmetic mean and basal area-weighted median were simultaneously included in the BLUP estimation. The BLUP method provided a flexible approach for characterising relationships among stand variables, alternative size distributions and the height–diameter curve. Models are intended for practical use in the MOTTI simulator.

Siipilehto, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi

article id 300, category Research article

Jouni Siipilehto, Sakari Sarkkola, Lauri Mehtätalo. (2007). Comparing regression estimation techniques when predicting diameter distributions of Scots pine on drained peatlands. Silva Fennica vol. 41 no. 2 article id 300. https://doi.org/10.14214/sf.300

Keywords: Pinus sylvestris; drained peatland; dbh distribution; Johnson’s SB function; regression estimation methods

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We compared different statistical methods for fitting linear regression models to a longitudinal data of breast height diameter (dbh) distributions of Scots pine dominated stands on drained peatlands. The parameter prediction methods for two parameters of Johnson’s S_B distribution, fitted to basal-area dbh distributions, were: 1) a linear model estimated by ordinary least squares (OLS), 2) a multivariate linear model estimated using the seemingly unrelated regression approach (SUR), 3) a linear mixed-effects model with random intercept (MIX), and 4) a multivariate mixed-effects model (MSUR). The aim was to clarify the effect of taking into account the hierarchy of the data, as well as simultaneous estimation of the correlated dependent variables on the model fit and predictions. Instead of the reliability of the predicted parameters, we focused on the reliability of the models in predicting stand conditions. Predicted distributions were validated in terms of bias, RMSE, and error deviation in the generated quantities of the growing stock. The study material consisted of 112 successively measured stands from 12 experimental areas covering the whole of Finland (total of 608 observations). Two independent test data sets were used for model validation. All the advanced regression techniques were superior to OLS, when exactly the same independent stand variables were included. SUR and MSUR were ranked the overall best and second best, respectively. Their ranking was the same in the modeling data, whereas MSUR was superior in the peatland test data and SUR in the mineral soil test data. The ranking of the models was logical, but may not be widely generalized. The SUR and MSUR models were considered to be relevant tools for practical forest management planning purposes over a variety of site types and stand structures.

Siipilehto, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi
Sarkkola, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: ss@nn.fi
Mehtätalo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland E-mail: lm@nn.fi

article id 410, category Research article

Jouni Siipilehto, Juha Siitonen. (2004). Degree of previous cutting in explaining the differences in diameter distributions between mature managed and natural Norway spruce forests. Silva Fennica vol. 38 no. 4 article id 410. https://doi.org/10.14214/sf.410

Keywords: Picea abies; stand structure; naturalness; Johnson’s SB distribution

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The degree of naturalness was assessed in 37 mature (stand age 80 198 yrs) Norway spruce dominated stands located in southern Finland by measuring the number (0 610 ha–1) and basal area (0 33 m2 ha–1) of cut stumps. The Johnson’s SB distribution was fitted for living spruce trees to describe the dbh-frequency and basal area-dbh distributions. Regression models were constructed for predicting the parameters of the SB distribution using traditional stand parameters (median diameter, basal area, stem number) and the cut stump variables (number, basal area). Stump variables improved the models and enabled to explain the differences in diameter distributions between stands with varying intensity of past cutting. Model for basal area-dbh distribution was more accurate than dbh-frequency model in terms of regression statistics, but less accurate in terms of generated stand variables. The number and basal area of cut stumps seem to be useful and simple measures of stand naturalness which have potential uses in stand modelling and biodiversity-oriented forestry planning.

Siipilehto, Finnish Forest Research Institute, Vantaa Research Centre, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi
Siitonen, Finnish Forest Research Institute, Vantaa Research Centre, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: juha.siitonen@metla.fi

article id 617, category Research article

Jouni Siipilehto. (2000). A comparison of two parameter prediction methods for stand structure in Finland. Silva Fennica vol. 34 no. 4 article id 617. https://doi.org/10.14214/sf.617

Keywords: Pinus sylvestris; Picea abies; parameter prediction; dbh and height distribution; Johnson’s SBB distribution; Näslund’s height curve

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The objective of this paper was to predict a model for describing stand structure of tree heights (h) and diameters at breast height (dbh). The research material consisted of data collected from 64 stands of Norway spruce (Picea abies Karst.) and 91 stands of Scots pine (Pinus sylvestris L.) located in southern Finland. Both stand types contained birch (Betula pendula Roth and B. pubescent Ehrh.) admixtures. The traditional univariate approach (Model I) of using the dbh distribution (Johnson’s SB) together with a height curve (Näslund’s function) was compared against the bivariate approaches, Johnson’s SBB distribution (Model II) and Model Ie. In Model Ie within-dbh-class h-variation was included by transforming a normally distributed homogenous error of linearized Näslund’s function to concern real heights. Basal-area-weighted distributions were estimated using the maximum likelihood (ML) method. Species-specific prediction models were derived using linear regression analysis. The models were compared with Kolmogorov-Smirnov tests for marginal distributions, accuracy of stand variables and the dbh-h relationship of individual trees. The differences in the stand characteristics between the models were marginal. Model I gave a slightly better fit for spruce, but Model II was better for pine stands. The univariate Model I resulted in clearly too narrow marginal h-distribution for pine. It is recommended applying of a constrained ML method for reasonable dbh-h relationship instead of using a pure ML method when fitting the SBB model.

Siipilehto, Finnish Forest Research Institute, Vantaa Research Centre, P.O. Box 18, FIN-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi

article id 650, category Research article

Jouni Siipilehto. (1999). Improving the accuracy of predicted basal-area diameter distribution in advanced stands by determining stem number. Silva Fennica vol. 33 no. 4 article id 650. https://doi.org/10.14214/sf.650

Keywords: dbh distribution; parameter prediction; Johnson’s SB distribution; Weibull distribution

Abstract | View details | Full text in PDF | Author Info

The objective of this paper was to study to what extent the accuracy of predicted basal-area diameter distributions (DDG) could be improved by means of stem number observations in advanced (H > 10 m) stands. In the Finnish forest management planning (FMP) inventory practice, stem number is determined only in young stands; in older stands stand basal area is used. The study material consisted of sixty stands of Norway spruce (Picea abies Karst.) and ninety-one stands of Scots pine (Pinus sylvestris L.) with birch (Betula pendula Roth and B. pubescens Ehrh.) admixtures in southern and eastern Finland. For test data, 167–292 independent, National Forest Inventory-based, permanent sample plots were used. DDGs were estimated with the maximum likelihood method. Species-specific models for predicting the distribution parameters were derived using regression analysis. The two-parameter Weibull distribution was compared to the three-parameter Johnson’s SB distributions in predicting DDGs. The models were based on either predictors that are consistent with current FMP (model G), or assuming an additional stem number observation (model G+N). The predicted distributions were compared in terms of the derived stand variables: stem number, total and timber volumes. The results were similar in modelling and test data sets. Methods, based on the SB distribution obtained with model (G+N), proved to give the most accurate description of the stand structure. Differences were marginal in stand total volumes. However, the error variation in stem number was 20% to 80% lower than when applying model (G). SB and Weibull distributions gave very much the same results if model (G) was applied.

Siipilehto, Finnish Forest Research Institute, Vantaa Research Centre, P.O. Box 18, FIN-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi

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