Current issue: 58(5)

Scopus CiteScore 2023: 3.5
Scopus ranking of open access forestry journals: 17^th
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Articles by Lauri Mehtätalo

Category : Editorial

article id 10257, category Editorial

Lauri Mehtätalo. (2019). Reporting modern statistical analyses: reproducible and transparent. Silva Fennica vol. 53 no. 3 article id 10257. https://doi.org/10.14214/sf.10257

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Mehtätalo, University of Eastern Finland, Faculty of Science and Forestry, School of Computing, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.mehtatalo@uef.fi

Category : Research article

article id 22026, category Research article

Annika Kangas, Mari Myllymäki, Lauri Mehtätalo. (2023). Understanding uncertainty in forest resources maps. Silva Fennica vol. 57 no. 2 article id 22026. https://doi.org/10.14214/sf.22026

Keywords: autocorrelation; ensemble modelling; kriging; quantile; random forest; sequential Gaussian simulation

Highlights: Forest resources maps without uncertainty assessment may lead to false impression of precision; Suitable tools for visualization of map products are lacking; Kriging method provided accurate uncertainty assessment for pixel-level predictions; Quantile random forest algorithm slightly underestimated the pixel-level uncertainties; With simulation it is possible to assess the uncertainty also for landscape-level characteristics.

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Maps of forest resources and other ecosystem services are needed for decision making at different levels. However, such maps are typically presented without addressing the uncertainties. Thus, the users of the maps have vague or no understanding of the uncertainties and can easily make wrong conclusions. Attempts to visualize the uncertainties are also rare, even though the visualization would be highly likely to improve understanding. One complication is that it has been difficult to address the predictions and their uncertainties simultaneously. In this article, the methods for addressing the map uncertainty and visualize them are first reviewed. Then, the methods are tested using laser scanning data with simulated response variable values to illustrate their possibilities. Analytical kriging approach captured the uncertainty of predictions at pixel level in our test case, where the estimated models had similar log-linear shape than the true model. Ensemble modelling with random forest led to slight underestimation of the uncertainties. Simulation is needed when uncertainty estimates are required for landscape level features more complicated than small areas.

Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Myllymäki, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0002-2713-7088 E-mail: mari.myllymaki@luke.fi
Mehtätalo, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80101 Joensuu, Finland https://orcid.org/0000-0002-8128-0598 E-mail: lauri.mehtatalo@luke.fi

article id 9980, category Research article

Eino Levkoev, Lauri Mehtätalo, Katri Luostarinen, Pertti Pulkkinen, Anatoly Zhigunov, Heli Peltola. (2018). Development of height growth and frost hardiness for one-year-old Norway spruce seedlings in greenhouse conditions in response to elevated temperature and atmospheric CO₂ concentration. Silva Fennica vol. 52 no. 3 article id 9980. https://doi.org/10.14214/sf.9980

Keywords: climate change; Picea abies; needles; tree growth; seedlings; cold acclimation

Highlights: Elevated temperature resulted in increased height growth, delayed onset and shortened duration of autumn frost hardiness development in Norway spruce seedlings; Elevated temperature increased variation between genotypes in height growth and frost hardiness development; Elevated atmospheric CO₂ concentration had no effect on the development of height or autumn frost hardiness in Norway spruce seedlings.

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The mean temperature during the potential growing season (April–September) may increase by 1 °C by 2030, and by 4 °C, or even more, by 2100, accompanied by an increase in atmospheric CO₂ concentrations of 536–807 ppm, compared to the current climate of 1981–2010, in which atmospheric CO₂ is at about 350 ppm. This may affect both the growth and frost hardiness of boreal trees. In this work, we studied the responses of height and autumn frost hardiness development in 22 half-sib genotypes of one-year-old Norway spruce (Picea abies (L.) Karst.) seedlings to elevated temperatures and atmospheric CO₂ concentration under greenhouse conditions. The three climate treatments used were: T+1 °C above ambient and ambient CO₂; T+4 °C above ambient and ambient CO₂; and T+4 °C above ambient and elevated CO₂ (700 ppm). The height growth rate and final height were both higher under T+4 °C compared to T+1 °C. Temperature increase also delayed the onset, and shortened the duration, of autumn frost hardiness development. Elevated CO₂ did not affect the development of height or frost hardiness, when compared to the results without CO₂ elevation under the same temperature treatment. Higher temperatures resulted in greater variation in height and frost hardiness development among genotypes. Three genotypes with different genetic backgrounds showed superior height growth, regardless of climate treatment; however, none showed a superior development of autumn frost hardiness. In future studies, clonal or full-sib genetic material should be used to study the details of autumn frost hardiness development among different genotypes.

Levkoev, University of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eino.levkoev@uef.fi
Mehtätalo, University of Eastern Finland, Faculty of Science and Forestry, School of Computing, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.mehtatalo@uef.fi
Luostarinen, University of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: katri.luostarinen@uef.fi
Pulkkinen, Natural Resources Institute Finland (Luke), Production systems, Haapastensyrjä Breeding Station, FI-16200 Läyliäinen, Finland E-mail: pertti.pulkkinen@luke.fi
Zhigunov, Saint-Petersburg State Forest Technical University, Forestry Faculty, RU-194021, Institutskiy per. 5, Saint-Petersburg, Russia E-mail: a.zhigunov@bk.ru
Peltola, University of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: heli.peltola@uef.fi

article id 1718, category Research article

Mihails Čugunovs, Eeva-Stiina Tuittila, Lauri Mehtätalo, Laura Pekkola, Ida Sara-Aho, Jari Kouki. (2017). Variability and patterns in forest soil and vegetation characteristics after prescribed burning in clear-cuts and restoration burnings. Silva Fennica vol. 51 no. 1 article id 1718. https://doi.org/10.14214/sf.1718

Keywords: disturbance; restoration; sampling; spatial variability; prescribed fire; SOM stocks

Highlights: Soil parameter variability is similar across sites of different disturbance type; Variability of understory vegetation biomass and cover is higher and more different between sites than soil variability; Sites studied here reflect well the assumed disturbance-type gradient based on PCA; Sampling six forest sites per treatment should provide good statistical power to capture the differences in soil organic matter stocks.

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Forest ecological restoration by burning is widely applied to promote natural, early-successional sites and increase landscape biodiversity. Burning is also used as a forest management practice to facilitate forest regeneration after clearcutting. Besides the desired goals, restoration burnings also affect soil biogeochemistry, particularly soil organic matter (SOM) and related soil carbon stocks but the long-term effects are poorly understood. However, in order to study these effects, a reliable estimate of spatial variability is first needed for effective sampling. Here we investigate spatial variability of SOM and vegetation features 13 years after burnings and in combination with variable harvest levels. We sampled four experimental sites representing distinct management and restoration treatments with an undisturbed control. While variability of vegetation cover and biomass was generally higher in disturbed sites, soil parameter variability was not different between the four sites. The joint ecological patterns of soil and vegetation parameters across the whole sample continuum support well the prior assumptions on the characteristic disturbance conditions within each of the study sites. We designed and employed statistical simulations as a means to plan prospective sampling. Sampling six forest sites for each treatment type with 30 independent soil cores per site would provide enough statistical power to adequately capture the impacts of burning on SOM based on the data we obtained here and statistical simulations. In conclusion, we argue that an informed design-based approach to documenting the ecosystem effects of forest burnings is worth applying both through obtaining new data and meta-analysing the existing.

Čugunovs, University of Eastern Finland, School of Forest Sciences, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mihails.cugunovs@uef.fi
Tuittila, University of Eastern Finland, School of Forest Sciences, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, Finland http://orcid.org/0000-0001-8861-3167 E-mail: eeva-stiina.tuittila@uef.fi
Mehtätalo, University of Eastern Finland, School of Computing, Science Park, Länsikatu 15, P.O. Box 111, 80101 Joensuu, Finland http://orcid.org/0000-0002-8128-0598 E-mail: lauri.mehtatalo@uef.fi
Pekkola, University of Eastern Finland, School of Forest Sciences, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: laura.pekkola@gmail.com
Sara-Aho, University of Eastern Finland, School of Forest Sciences, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: ida.sara-aho@mhy.fi
Kouki, University of Eastern Finland, School of Forest Sciences, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, Finland http://orcid.org/0000-0003-2624-8592 E-mail: jari.kouki@uef.fi

article id 1087, category Research article

Ilkka Korpela, Lauri Mehtätalo, Lauri Markelin, Anne Seppänen, Annika Kangas. (2014). Tree species identification in aerial image data using directional reflectance signatures. Silva Fennica vol. 48 no. 3 article id 1087. https://doi.org/10.14214/sf.1087

Keywords: forestry; reflectance calibration; BRDF; mixed-effects modeling; Monte-Carlo simulation

Highlights: Multispectral reflectance data showed a strong and spectrally correlated tree effect; There was no gain in species classification from using species-specific differences of directional reflectance in real data and only a marginal improvement in simulated data; The directional signatures extracted in multiple images are obscured by the intrinsic within-species variation, correlated observations and inherent reflectance calibration errors.

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Tree species identification using optical remote sensing is challenging. Modern digital photogrammetric cameras enable radiometrically quantitative remote sensing and the estimation of reflectance images, in which the observations depend largely on the reflectance properties of targets. Previous research has shown that there are species-specific differences in how the brightness observed changes when the viewing direction in an aerial image is altered. We investigated if accounting for such directional signatures enhances species classification, using atmospherically corrected, real and simulated multispectral Leica ADS40 line-camera data. Canopy in direct and diffuse illumination were differentiated and species-specific variance-covariance structures were analyzed in real reflectance data, using mixed-effects modeling. Species classification simulations aimed at elucidating the level of accuracy that can be achieved by using images of different quality, number and view-illumination geometry. In real data, a substantial variance component was explained by tree effect, which demonstrates that observations from a tree correlate between observation geometries as well as spectrally. Near-infrared band showed the strongest tree effect, while the directionality was weak in that band. The gain from directional signatures was insignificant in real data, while simulations showed a potential gain of 1–3 percentage points in species classification accuracy. The quality of reflectance calibration was found to be important as well as the image acquisition geometry. We conclude that increasing the number of image observations cancels out random observation noise and reflectance calibration errors, but fails to eliminate the tree effect and systematic calibration inaccuracy. Directional reflectance constitutes a marginal improvement in tree species classification.

Korpela, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland E-mail: ilkka.korpela@helsinki.fi
Mehtätalo, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.mehtatalo@uef.fi
Markelin, Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O. Box 15, FI-02431 Masala, Finland E-mail: lauri.markelin@fgi.fi
Seppänen, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: anne.seppanen@arbonaut.com
Kangas, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland E-mail: annika.kangas@helsinki.fi

article id 1057, category Research article

Jouni Siipilehto, Lauri Mehtätalo. (2013). Parameter recovery vs. parameter prediction for the Weibull distribution validated for Scots pine stands in Finland. Silva Fennica vol. 47 no. 4 article id 1057. https://doi.org/10.14214/sf.1057

Keywords: linear prediction; diameter distribution; Weibull; stand characteristics; parameter recovery

Highlights: A parameter recovery method (PRM) was developed for forest stand inventories and compared with previously developed parameter prediction methods (PPM) in Finland; PRM for the 2-parameter Weibull function provided compatibility for the main stand characteristics: stem number, basal area and one of the four optional mean characteristics; PRM provided comparable and at its best, superior accuracy in volume characteristics compared with PPM.

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The moment-based parameter recovery method (PRM) has not been applied in Finland since the 1930s, even after a continuation of forest stand structure modelling in the 1980s. This paper presents a general overview of PRM and some useful applications. Applied PRM provided compatibility for the included stand characteristics of stem number (N) and basal area (G) with either mean (D), basal area-weighted mean (DG), median (DM) or basal area-median (DGM) diameter at breast height (dbh). A two-parameter Weibull function was used to describe the dbh-frequency distribution of Scots pine stands in Finland. In the validation, PRM was compared with existing parameter prediction models (PPMs). In addition, existing models for stand characteristics were used for the prediction of unknown characteristics. Validation consisted of examining the performance of the predicted distributions with respect to variation in stand density and accuracy of the localised distributions, as well as accuracy in terms of bias and the RMSE in stand characteristics in the independent test data set. The validation data consisted of 467 randomly selected stands from the National Forest Inventory based plots. PRM demonstrated excellent accuracy if G and N were both known. At its best, PRM provided accuracy that was superior to any existing model in Finland – especially in young stands (mean height < 9 m), where the RMSE in total and pulp wood volumes, 3.6 and 5.7%, respectively, was reduced by one-half of the values obtained using the best performing existing PPM (8.7–11.3%). The unweighted Weibull distribution solved by PRM was found to be competitive with weighted existing PPMs for advanced stands. Therefore, using PRM, the need for a basal area weighted distribution proved unnecessary, contrary to common belief. Models for G and N were shown to be unreliable and need to be improved to obtain more reliable distributions using PRM.

Siipilehto, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi
Mehtätalo, University of Eastern Finland, School of Computing, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.mehtatalo@uef.fi

article id 69, category Research article

Tarja Wallenius, Risto Laamanen, Jussi Peuhkurinen, Lauri Mehtätalo, Annika Kangas. (2012). Analysing the agreement between an Airborne Laser Scanning based forest inventory and a control inventory – a case study in the state owned forests in Finland. Silva Fennica vol. 46 no. 1 article id 69. https://doi.org/10.14214/sf.69

Keywords: forest inventory; quality assessment; airborne laser scanning

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Airborne laser scanning based forest inventories have recently shown to produce accurate results. However, the accuracy varies according to the test area and used methodology and therefore, an unambiguous and practical quality assessment will be needed as a part of each inventory project. In this study, the accuracy of an ALS inventory was evaluated with a field sampling based control inventory. The agreement between the ALS inventory and the control inventory was analysed with four methods: 1) root mean square error (RMSE) and bias, 2) scatter plots with 95% confidence intervals, 3) Bland-Altman plots and 4) tolerance limits within Bland-Altman plots. Each method has its own special features which have to be taken into account when the agreement is analysed. The pre-defined requirements of the ALS inventory were achieved. A simplified control inventory approach with a slightly narrower focus is proposed to be used in the future. The Bland-Altman plots with the tolerance limits are proposed to be used in quality assessments of operational ALS inventories. Further studies to improve the efficiency of quality assessment are needed.

Wallenius, Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland E-mail: tarja.wallenius@metsa.fi
Laamanen, Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland E-mail: rl@nn.fi
Peuhkurinen, Oy Arbonaut Ltd, Helsinki, Finland E-mail: jp@nn.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: lm@nn.fi
Kangas, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: ak@nn.fi

article id 100, category Research article

Annika Kangas, Lauri Mehtätalo, Antti Mäkinen, Kalle Vanhatalo. (2011). Sensitivity of harvest decisions to errors in stand characteristics. Silva Fennica vol. 45 no. 4 article id 100. https://doi.org/10.14214/sf.100

Keywords: forest planning; inventory; measurement errors; decision making; logistic regression; regression tree

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In forest planning, the decision maker chooses for each stand a treatment schedule for a predefined planning period. The choice is based either on optimization calculations or on silvicultural guidelines. Schedules for individual stands are obtained using a growth simulator, where measured stand characteristics such as the basal area, mean diameter, site class and mean height are used as input variables. These characteristics include errors, however, which may lead to incorrect decisions. In this study, the aim is to study the sensitivity of harvest decisions to errors in a dataset of 157 stands. Correct schedules according to silvicultural guidelines were first determined using error-free data. Different amounts of errors were then generated to the stand-specific characteristics, and the treatment schedule was selected again using the erroneous data. The decision was defined as correct, if the type of harvest in these two schedules were similar, and if the timings deviated at maximum ±2 for thinning and ±3 years for clear-cut. The dependency of probability of correct decisions on stand characteristics and the degree of errors was then modelled. The proposed model can be used to determine the required level of measurement accuracy for each characteristics in different kinds of stands, with a given accuracy requirement for the timing of treatments. This information can further be utilized in selecting the most appropriate inventory method.

Kangas, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: lm@nn.fi
Mäkinen, Simosol Oy, Riihimäki, Finland E-mail: am@nn.fi
Vanhatalo, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: kv@nn.fi

article id 111, category Research article

Ilona Pietilä, Annika Kangas, Antti Mäkinen, Lauri Mehtätalo. (2010). Influence of growth prediction errors on the expected losses from forest decisions. Silva Fennica vol. 44 no. 5 article id 111. https://doi.org/10.14214/sf.111

Keywords: growth prediction; uncertainty; forest information; updating; inoptimality loss

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In forest planning, forest inventory information is used for predicting future development of forests under different treatments. Model predictions always include some errors, which can lead to sub-optimal decisions and economic loss. The influence of growth prediction errors on the reliability of projected forest variables and on the treatment propositions have previously been examined in a few studies, but economic losses due to growth prediction errors is an almost unexplored subject. The aim of this study was to examine how the growth prediction errors affected the expected losses caused by incorrect harvest decisions, when the inventory interval increased. The growth models applied in the analysis were stand-level growth models for basal area and dominant height. The focus was entirely on the effects of growth prediction errors, other sources of uncertainty being ignored. The results show that inoptimality losses increased with the inventory interval. Average relative inoptimality loss was 3.3% when the inventory interval was 5 years and 11.6% when it was 60 years. Average absolute inoptimality loss was 230 euro ha^–1 when the inventory interval was 5 years and 860 euro ha^–1 when it was 60 years. The average inoptimality losses varied between development classes, site classes and main tree species.

Pietilä, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ip@nn.fi
Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mäkinen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: am@nn.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi

article id 218, category Research article

Md. Nurul Islam, Mikko Kurttila, Lauri Mehtätalo, Arto Haara. (2009). Analyzing the effects of inventory errors on holding-level forest plans: the case of measurement error in the basal area of the dominated tree species. Silva Fennica vol. 43 no. 1 article id 218. https://doi.org/10.14214/sf.218

Keywords: inoptimality loss; dominated tree species; erroneous inventory data; forest plan

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

Accurate inventory data are required for ensuring optimal net return on investment from the forest. Erroneous data can lead to the formulation of a non-optimal plan that can cause inoptimality losses. Little is known of the effect of using erroneous stand inventory data in preparing holding-level forest plans. This study reports on an approach for analyzing such inoptimality losses. Furthermore, inoptimality losses caused by measurement errors in the basal area of the dominated tree species were investigated in a case study. Based on the inventory data including routine measurements by 67 measurers, four measurer groups were created with different measurement error profiles for the basal area of the dominated tree species. This was followed by measurement error simulations for each group and by adding these to the accurate control inventory data to create erroneous data of different error profiles. Three different forest plans were then constructed by using erroneous data of each group. The plans were then analyzed and compared with plans based on correct data. The effect of measurement errors on the net present value from the whole planning period, and on the amount of remaining growing stock at the end of planning period, were analyzed and utilized in calculating the inoptimality losses. It was concluded that even errors involving dominated tree species can cause significant changes in the holding-level forest plans.

Islam, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: nurul.islam@joensuu.fi
Kurttila, Finnish Forest Research Institute, Joensuu Research Unit, FI-80101 Joensuu, Finland E-mail: mk@nn.fi
Mehtätalo, University of Helsinki, Dept. of Forest Resource Management, FI-00014 University of Helsinki, Finland E-mail: lm@nn.fi
Haara, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: ah@nn.fi

article id 282, category Research article

Annika Kangas, Lauri Mehtätalo, Matti Maltamo. (2007). Modelling percentile based basal area weighted diameter distribution. Silva Fennica vol. 41 no. 3 article id 282. https://doi.org/10.14214/sf.282

Keywords: stand structure; diameter distribution; prediction; interpolation

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

In percentile method, percentiles of the diameter distribution are predicted with a system of models. The continuous empirical diameter distribution function is then obtained by interpolating between the predicted values of percentiles. In Finland, the distribution is typically modelled as a basal-area weighted distribution, which is transformed to a traditional density function for applications. In earlier studies it has been noted that when calculated from the basal-area weighted diameter distribution, the density function is decreasing in most stands, especially for Norway spruce. This behaviour is not supported by the data. In this paper, we investigate the reasons for the unsatisfactory performance and present possible solutions for the problem. Besides the predicted percentiles, the problems are due to implicit assumptions of diameter distribution in the system. The effect of these assumptions can be somewhat lessened with simple ad-hoc methods, like increasing new percentiles to the system. This approach does not, however, utilize all the available information in the estimation, namely the analytical relationships between basal area, stem number and diameter. Accounting for these, gives further possibilities for improving the results. The results show, however, that in order to achieve further improvements, it would be recommendable to make the implicit assumptions more realistic. Furthermore, height variation within stands seems to have an important contribution to the uncertainty of some forest characteristics, especially in the case of sawnwood volume.

Kangas, Department of Forest Resources Management, P.O.Box 27, FI-00014 University of Helsinki, Finland E-mail: ak@nn.fi
Mehtätalo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi
Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.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

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

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 333, category Research article

Lauri Mehtätalo, Matti Maltamo, Annika Kangas. (2006). The use of quantile trees in the prediction of the diameter distribution of a stand. Silva Fennica vol. 40 no. 3 article id 333. https://doi.org/10.14214/sf.333

Keywords: stand structure; inventory; percentile; order statistics

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

This study deals with the prediction of the basal area diameter distribution of a stand without using a complete sample of diameters from the target stand. Traditionally, this problem has been solved by either the parameter recovery method or the parameter prediction method. This study uses the parameter prediction method and the percentile based diameter distribution with a recent development that makes it possible to improve these predictions by using sample order statistics. A sample order statistic is a tree whose diameter and rank at the plot are known, and is referred to in this paper as a quantile tree. This study tested 13 different strategies for selection of the quantile trees from among the trees of horizontal point sample plots, and compared them with respect to RMSE and the bias of four criterion variables in a dataset of 512 stands. The sample minimum was found to be the most promising alternative with respect to RMSE, even though it introduced a rather large amount of bias in the criterion variables. Other good and less biased alternatives are the second and third smallest trees and the tree closest to the plot centre. The use of minimum is recommended for practical inventories because its rank is probably easiest to determine correctly in the field.

Mehtätalo, Yale School of Forestry and Environmental Studies, 205 Prospect Street, New Haven, CT 06511, USA E-mail: lauri.mehtatalo@metla.fi
Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
Kangas, University of Helsinki, Department of Forest Resources Management, P.O.Box 27, FI-00014 University of Helsinki, Finland E-mail: ak@nn.fi

article id 395, category Research article

Lauri Mehtätalo. (2005). Height-diameter models for Scots pine and birch in Finland. Silva Fennica vol. 39 no. 1 article id 395. https://doi.org/10.14214/sf.395

Keywords: mixed model; longitudinal analysis; random parameter; stand development

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

Height-Diameter (H-D) models for two shade-intolerant tree species were estimated from longitudinal data. The longitudinal character of the data was taken into account by estimating the models as random effects models using two nested levels: stand and measurement occasion level. The results show that the parameters of the H-D equation develop over time but the development rate varies between stands. Therefore the development of the parameters is not linked to the stand age but to the median diameter of the basal-area weighted diameter distribution (DGM). Models were estimated with different predictor combinations in order to produce appropriate models for different situations. The estimated models can be localized for a new stand using measured heights and diameters, presumably from different points in time, and the H-D curves can be projected into the future.

Mehtätalo, Finnish Forest Research Institute, Joensuu Research Centre, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: lauri.mehtatalo@metla.fi

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