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Silva Fennica 1926-1997
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Articles containing the keyword 'estimation'.

Category: Research article

article id 1631, category Research article
Jonas Koala, Louis Sawadogo, Patrice Savadogo, Ermias Aynekulu, Janne Heiskanen, Mohammed Saïd. (2017). Allometric equations for below-ground biomass of four key woody species in West African savanna-woodlands. Silva Fennica vol. 51 no. 3 article id 1631. https://doi.org/10.14214/sf.1631
Highlights: Species-specific equations for belowground biomass (BGB) predicted biomass with less bias than generic equations; All the generic equations underestimated BGB; For accurate estimation of BGB in savanna-woodlands, species-specific equations are needed for more species.

Accurate estimates of both above-ground biomass (AGB) and below-ground biomass (BGB) are essential for estimating carbon (C) balances at various geographical scales and formulating effective climate change mitigation programs. However, estimating BGB is challenging, particularly for forest ecosystems, so robust allometric equations are needed. To obtain such equations for savanna-woodlands of the West African north sudanian zone, we selected four common native woody species (Anogeissus leiocarpa (DC.) Guill. & Perr., Detarium microcarpum Guill. & Perr., Piliostigma thonningii (Schumach.) Milne-Redh. and Vitellaria paradoxa C.F. Gaertn.). At two sites in Burkina Faso, we determined the BGB of 30 trees of each of these species by excavation, and measured various above-ground dimensional variables. The root:shoot ratio varied widely among the species, from 0.1 to 3.4. Depending on the species, allometric equations based on stem basal area at 20 cm height, basal area at breast height and tree height explained 50–95% of the variation in BGB. The best generic equation we obtained, based on basal area at 20 cm, explained 60% of the variation in BGB across the species. Three previously published generic allometric equations underestimated BGB by 8 to 63%. The presented equations should significantly improve the accuracy of BGB estimates in savanna-woodlands and help avoid costly needs to excavate root systems.

  • Koala, Centre National de Recherche Scientifique et Technologique (CNRST), Institut de l’Environnement et de Recherches Agricoles (INERA), Département Productions Forestières, 03 BP 7047, Ouagadougou 03, Burkina Faso ORCID ID:E-mail: ezeyamb@yahoo.fr (email)
  • Sawadogo, Centre National de Recherche Scientifique et Technologique (CNRST), Institut de l’Environnement et de Recherches Agricoles (INERA), Département Productions Forestières, 03 BP 7047, Ouagadougou 03, Burkina Faso ORCID ID:E-mail: sawadogo_ls@hotmail.com
  • Savadogo, World Agroforestry Centre & International Crop Research Institute for the Semi-Arid Tropics (ICRAF-ICRISAT), West and Central Africa Region-Sahel Node, BP 12404, Niamey, Niger ORCID ID:E-mail: savadogo.patrice@gmail.com
  • Aynekulu, World Agroforestry Centre (ICRAF), United Nations Avenue, P.O. Box 30677-00100, Nairobi, Kenya ORCID ID:E-mail: e.betemariam@cgiar.org
  • Heiskanen, University of Helsinki, Department of Geosciences and Geography, P.O. Box 68, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: janne.heiskanen@helsinki.fi
  • Saïd, International Livestock Research Institute (ILRI). P.O. Box 30709, Nairobi, Kenya ORCID ID:E-mail: m.said@cgiar.org
article id 2021, category Research article
Jonas Bohlin, Inka Bohlin, Jonas Jonzén, Mats Nilsson. (2017). Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory. Silva Fennica vol. 51 no. 2 article id 2021. https://doi.org/10.14214/sf.2021
Highlights: Image based forest attribute map generated using NFI plots show similar accuracy as currently used LiDAR based forest attribute map; Also similar accuracies were found for different forest types; Aerial images from leaf-off season is not recommended.

Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid- and south Sweden. Regression models were developed and applied to 12.5 m × 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image- and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.

  • Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden ORCID ID: http://orcid.org/0000-0002-3318-5967 E-mail: jonas.bohlin@slu.se (email)
  • Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden ORCID ID: http://orcid.org/0000-0003-1224-6684 E-mail: inka.bohlin@slu.se
  • Jonzén, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden ORCID ID:E-mail: jonas.jonzen@slu.se
  • Nilsson, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden ORCID ID: http://orcid.org/0000-0001-7394-6305 E-mail: mats.nilsson@slu.se
article id 1568, category Research article
Jouni Siipilehto, Harri Lindeman, Mikko Vastaranta, Xiaowei Yu, Jori Uusitalo. (2016). Reliability of the predicted stand structure for clear-cut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and pre-harvest measurement tool EMO. Silva Fennica vol. 50 no. 3 article id 1568. https://doi.org/10.14214/sf.1568
Highlights: An airborne laser scanning grid-based approach for determining stand structure enabled bi- or multimodal predicted distributions that fitted well to the ground-truth harvester data; EMO and Trestima applications needed stand-specific inventory for sample measurements or sample photos, respectively, and at their best, provided superior accuracy for predicting certain stand characteristics.

Accurate timber assortment information is required before cuttings to optimize wood allocation and logging activities. Timber assortments can be derived from diameter-height distribution that is most often predicted from the stand characteristics provided by forest inventory. The aim of this study was to assess and compare the accuracy of three different pre-harvest inventory methods in predicting the structure of mainly Scots pine-dominated, clear-cut stands. The investigated methods were an area-based approach (ABA) based on airborne laser scanning data, the smartphone-based forest inventory Trestima app and the more conventional pre-harvest inventory method called EMO. The estimates of diameter-height distributions based on each method were compared to accurate tree taper data measured and registered by the harvester’s measurement systems during the final cut. According to our results, grid-level ABA and Trestima were generally the most accurate methods for predicting diameter-height distribution. ABA provides predictions for systematic 16 m × 16 m grids from which stand-wise characteristics are aggregated. In order to enable multimodal stand-wise distributions, distributions must be predicted for each grid cell and then aggregated for the stand level, instead of predicting a distribution from the aggregated stand-level characteristics. Trestima required a sufficient sample for reliable results. EMO provided accurate results for the dominating Scots pine but, it could not capture minor admixtures. ABA seemed rather trustworthy in predicting stand characteristics and diameter distribution of standing trees prior to harvesting. Therefore, if up-to-date ABA information is available, only limited benefits can be obtained from stand-specific inventory using Trestima or EMO in mature pine or spruce-dominated forests.

  • Siipilehto, Natural Research Institute Finland (Luke), Management and Production of Renewable Resources, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: jouni.siipilehto@luke.fi (email)
  • Lindeman,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano ORCID ID:E-mail: harri.lindeman@luke.fi
  • Vastaranta, University of Helsinki, Department of Forest Sciences, P.O. Box 62 (Viikinkaari 11), FI-00014 University of Helsinki ORCID ID:E-mail: mikko.vastaranta@helsinki.fi
  • Yu, Finnish Geospatial Research Institute (FGI), Department of Remote Sensing and Photogrammetry, National Land Survey of Finland, P.O. Box 15 (Geodeetinrinne 2), FI-02431, Masala, Finland ORCID ID:E-mail: xiaowei.yu@maanmittauslaitos.fi
  • Uusitalo,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano ORCID ID:E-mail: jori.uusitalo@luke.fi
article id 1218, category Research article
Mikko Niemi, Mikko Vastaranta, Jussi Peuhkurinen, Markus Holopainen. (2015). Forest inventory attribute prediction using airborne laser scanning in low-productive forestry-drained boreal peatlands. Silva Fennica vol. 49 no. 2 article id 1218. https://doi.org/10.14214/sf.1218
Highlights: Following current forest inventory practises, stem volume was predicted in low-productive drained peatlands (LPDPs) with a root mean square error (RMSE) of 13.7 m3 ha–1; When 30 reference plots measured from LPDPs were added to the prediction, RMSE was decreased to 10.0 m3 ha–1; Additional reference plots from LPDPs did not affect the forest inventory attribute predictions in productive forests.
Nearly 30% of Finland’s land area is covered by peatlands. In Northern parts of the country there is a significant amount of low-productive drained peatlands (LPDPs) where the average annual stem volume growth is less than 1 m3 ha–1. The re-use of LPDPs has been considered thoroughly since Finnish forest legislation was updated and the forest regeneration prerequisite was removed from LPDPs in January 2014. Currently, forestry is one of the re-use alternatives, thus detailed forest resource information is required for allocating activities. However, current forest inventory practices have not been evaluated for sparse growing stocks (e.g., LPDPs). The purpose of our study was to evaluate the suitability of airborne laser scanning (ALS) for mapping forest inventory attributes in LPDPs. We used ALS data with a density of 0.8 pulses per m2, 558 field-measured reference plots (500 from productive forests and 58 from LPDPs) and k nearest neighbour (k-NN) estimation. Our main aim was to study the sensitivity of predictions to the number of LPDP reference plots used in the k-NN estimation. When the reference data consisted of 500 plots from productive forest stands, the root mean square errors (RMSEs) for the prediction accuracy of Lorey’s height, basal area and stem volume were 1.4 m, 2.7 m2 ha–1 and 13.7 m3 ha–1 in LPDPs, respectively. When 30 additional reference plots were allocated to LPDPs, the respective RMSEs were 1.1 m, 1.7 m2 ha–1 and 10.0 m3 ha–1. Additional reference plot allocation did not affect the predictions in productive forest stands.
  • Niemi, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland ORCID ID:E-mail: mikko.t.niemi@helsinki.fi (email)
  • Vastaranta, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland ORCID ID:E-mail: mikko.vastaranta@helsinki.fi
  • Peuhkurinen, Arbonaut Oy Ltd., Latokartanontie 7 A, FI-00700, Finland ORCID ID:E-mail: jussi.peuhkurinen@arbonaut.com
  • Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland ORCID ID:E-mail: markus.holopainen@helsinki.fi
article id 164, category Research article
Aki Suvanto, Matti Maltamo. (2010). Using mixed estimation for combining airborne laser scanning data in two different forest areas. Silva Fennica vol. 44 no. 1 article id 164. https://doi.org/10.14214/sf.164
Airborne laser scanning (ALS) data have become the most accurate remote sensing technology for forest inventories. When planning new inventories the costs of fieldwork could be reduced if datasets of old inventory areas are effectively reused in the new area. The aim of this study was to apply mixed estimation using a combination of existing and new field datasets in area-based approach. Additionally, combining datasets with mixed estimation was compared with constructing new local models with smaller datasets. The two forest study areas were in Juuka and Matalansalo, which are located about 120 km apart in eastern Finland. ALS-based regression models were constructed using datasets of Matalansalo (472 reference plots) and Juuka (10–212 reference plots). Models were developed for the basal area median tree diameter and height, mean tree height, stem number, basal area and volume. The work was based on a simulation approach which involved five methods for approximating the regression coefficients. The first method merged the datasets using ordinary least squares (OLS) regression models, whereas the second and third methods combined datasets using mixed estimation on different weighting principles, and the final two estimated local models with predetermined and new independent variables. The results indicate that mixed estimation can improve the accuracy of derived stand variables compared with basic OLS models. Additionally, a sample of 40–50 plots was enough to build local models for basal area and volume and produce at least the equal accuracy of results than any other methods in this study.
  • Suvanto, Blom Kartta Oy, Teollisuuskatu 18, FI-80100 Joensuu, Finland ORCID ID:E-mail: aki.suvanto@blomasa.com (email)
  • Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box, FI-80101, Joensuu, Finland ORCID ID:E-mail:
article id 237, category Research article
Jussi Peuhkurinen, Matti Maltamo, Jukka Malinen. (2008). Estimating species-specific diameter distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs: a distribution-based approach. Silva Fennica vol. 42 no. 4 article id 237. https://doi.org/10.14214/sf.237
The low-density airborne laser scanning (ALS) data based estimation methods have been shown to produce accurate estimates of mean forest characteristics and diameter distributions, according to several studies. The used estimation methods have been based on the laser canopy height distribution approach, where various laser pulse height distribution -derived predictors are related to the stand characteristics of interest. This approach requires very delicate selection methods for selecting the suitable predictor variables. In this study, we introduce a new nearest neighbor search method that requires no complicated selection algorithm for choosing the predictor variables and can be utilized in multipurpose situations. The proposed search method is based on Minkowski distances between the distributions extracted from low density ALS data and aerial photographs. Apart from the introduction of a new search method, the aims of this study were: 1) to produce accurate species-specific diameter distributions and 2) to estimate factual saw log recovery, using the estimated height-diameter distributions and a stem data bank. The results indicate that the proposed method is suitable for producing species-specific diameter distributions and volumes at the stand level. However, it is proposed, that the utilization of more extensive and locally emphasized reference data and auxiliary variables could yield more accurate saw log recoveries.
  • Peuhkurinen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Maltamo, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Malinen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail:
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
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 SB 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 ORCID ID:E-mail: jouni.siipilehto@metla.fi (email)
  • Sarkkola, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail:
  • Mehtätalo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland ORCID ID:E-mail:
article id 334, category Research article
Jouni Siipilehto. (2006). Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands. Silva Fennica vol. 40 no. 3 article id 334. https://doi.org/10.14214/sf.334
The aim was to produce models for a large number of stand characteristics of Norway spruce dominated stands. A total of 227 national forest inventory based permanent stand plots, dominated by Norway spruce (Picea abies), were used in modelling eight stand variables as a function of the stand mean biological age and site characteristics. The basic models were able to characterize the average development of the modelled stand variables, but resulted in a relatively high RMSE. Basal area (G) and stem number (N) were the most inaccurate, having a RMSE of 34–41%, while that of mean diameter and height characteristics varied between 16–20%. The expectations and error variances of the basic models were calibrated with known stand variables using linear prediction theory. The best linear unbiased predictor (BLUP) with a single stand variable used for calibration proved to be ineffective for unknown G and N, but relatively effective for the unknown mean characteristics. However, calibration with one sum and one mean characteristic proved to be effective, and additional calibration variables enhanced the precision only marginally. The BLUP method provided a flexible approach when characterizing the relationships between a large number of stand variables, thus enabling multiple use of these models because they were not fixed to a specific inventory system.
  • Siipilehto, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: jouni.siipilehto@metla.fi (email)
article id 332, category Research article
Marc Palahí, Timo Pukkala, Antoni Trasobares. (2006). Calibrating predicted tree diameter distributions in Catalonia, Spain. Silva Fennica vol. 40 no. 3 article id 332. https://doi.org/10.14214/sf.332
Several probability density functions have been used in describing the diameter distributions of forest stands. In a case where both the stand basal area and number of stems per hectare are assessed, the fitted or predicted distribution is scaled using only one of these variables, with the result that the distribution often gives incorrect values for the other variable. Using a distribution that provides incorrect values for known characteristics means wasting information. Calibrating the distribution so that it is compatible with the additional information on stand characteristics is a way to avoid such wasting. This study examined the effect of calibration on the accuracy of the predicted diameter distributions of the main tree species of Catalonia. The distributions were calibrated with and without considering the prediction errors of the frequencies of diameter classes. When prediction errors were assumed, the calibration was done with and without making allowance for estimation errors in the stand level calibration variables. Calibrated distributions were more accurate than non-calibrated in terms of sums of different powers of diameters. The set of calibration variables that gave the most accurate results included six stand variables: number of trees per hectare, stand basal area, basal-area-weighted mean diameter, non-weighted mean diameter, median diameter, and basal area median diameter. Of the tested three-variable combinations the best was: number of trees per hectare, stand basal area, and basal-area-weighted mean diameter. Means were more useful calibration variables than medians.
  • Palahí, Centre Tecnológic Forestal de Catalunya. Passeig Lluis Companys, 23, 08010, Barcelona, Spain ORCID ID:E-mail: marc.palahi@ctfc.es (email)
  • Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland ORCID ID:E-mail:
  • Trasobares, Foreco Technologies, Av. Diagonal 416, Estudio 2, Barcelona 08037, Spain ORCID ID:E-mail:
article id 368, category Research article
Eero Muinonen. (2005). Generating a raster map presentation of a forest resource by solving a transportation problem. Silva Fennica vol. 39 no. 4 article id 368. https://doi.org/10.14214/sf.368
Necessary tools for raster map generation, for the approach based on the calibration estimator, were developed and implemented. The allocation of the area weight of each pixel to sample plots was formulated as a transportation problem, using a spectral distance measure as a transportation cost, and solved using the transportation simplex algorithm. Pixel level accuracy was calculated for the methods based on the calibration estimator so that the results could be compared with the results of the nearest neighbour estimation, the reference sample plot method (RSP) at pixel level. Local averaging in a 3 x 3 window was performed for each generated raster map as a postprocessing phase to smooth the map. Test plot results were calculated both for the unfiltered raster map and the filtered raster map. RSP produced the smallest RMSE in the pooled test data. Local averaging with a 3 x 3 filter decreased the pixel level error – and the bias – and the differences between the methods are smaller. Without local averaging, the pixel level errors of the methods based on solving the transportation problem were high. Raster map generation using the methods of this study forms an optional part – followed possibly by the classification of the pixel level results – of the whole computation task, when the area weight computation is based on the calibration estimation. For larger areas than in the present study, such as municipalities, the efficiency of the method based on the transportation model must be improved before it is a usable tool, in practice, for raster map generation. For nearest neighbour methods, the area size is not such a problem, because the inventory area is processed pixel by pixel.
  • Muinonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: eero.muinonen@metla.fi (email)
article id 486, category Research article
Arto Haara. (2003). Comparing simulation methods for modelling the errors of stand inventory data. Silva Fennica vol. 37 no. 4 article id 486. https://doi.org/10.14214/sf.486
Forest management planning requires information about the uncertainty inherent in the available data. Inventory data, including simulated errors, are infrequently utilised in forest planning studies for analysing the effects of uncertainty on planning. Usually the errors in the source material are ignored or not taken into account properly. The aim of this study was to compare different methods for generating errors into the stand-level inventory data and to study the effect of erroneous data on the calculation of specieswise and standwise inventory results. The material of the study consisted of 1842 stands located in northern Finland and 41 stands located in eastern Finland. Stand-level ocular inventory and checking inventory were carried out in all study stands by professional surveyors. In simulation experiments the methods considered for error generation were the 1nn-method, the empirical errors method and the Monte Carlo method with log-normal and multivariate log-normal error distributions. The Monte Carlo method with multivariate error distributions was found to be the most flexible simulation method. This method produced the required variation and relations between the errors of the median basal area tree characteristics. However, if the reference data are extensive the 1nn-method, and in certain conditions also the empirical errors method, offer a useful tool for producing error structures which reflect reality.
  • Haara, Finnish Forest Research Institute, Joensuu Research Centre, P.O.Box 68, FIN-80101 Joensuu, Finland ORCID ID:E-mail: arto.haara@metla.fi (email)
article id 514, category Research article
Jukka Malinen. (2003). Locally adaptable non-parametric methods for estimating stand characteristics for wood procurement planning. Silva Fennica vol. 37 no. 1 article id 514. https://doi.org/10.14214/sf.514
The purpose of this study was to examine the use of the local adaptation of the non-parametric Most Similar Neighbour (MSN) method in estimating stand characteristics for wood procurement planning purposes. Local adaptation was performed in two different ways: 1) by selecting local data from a database with the MSN method and using that data as a database in the basic k-nearest neighbour (k-nn) MSN method, 2) by selecting a combination of neighbours from the neighbourhood where the average of the predictor variables was closest to the target stand predictor variables (Locally Adaptable Neighbourhood (LAN) MSN method). The study data used comprised 209 spruce dominated stands located in central Finland and was collected with harvesters. The accuracy of the methods was analysed by estimating the tree stock characteristics and the log length/diameter distribution produced by a bucking simulation. The local k-nn MSN method was not notably better than the k-nn MSN method, although it produced less biased estimates on the edges of the input space. The LAN MSN method was found to be a more accurate method than the k-nn methods.
  • Malinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland ORCID ID:E-mail: jukka.malinen@joensuu.fi (email)
article id 522, category Research article
Annika Kangas, Matti Maltamo. (2002). Anticipating the variance of predicted stand volume and timber assortments with respect to stand characteristics and field measurements. Silva Fennica vol. 36 no. 4 article id 522. https://doi.org/10.14214/sf.522
Several models and/or several variable combinations could be used to predict the diameter distribution of a stand. Typically, a fixed model and a fixed variable combination is used in all conditions. The calibration procedure, however, makes it possible to choose the measurement combination from among many possibilities, although the model used is fixed. In this study, the usefulness of utilizing additional stand characteristics for calibrating the predicted diameter distribution is examined. Nine measurement strategies were tested in predicting the total stand volume, sawlog volume and pulpwood volume. The observed errors of these variables under each strategy were modeled as a function of basal area, basal area median diameter and number of stems. The models were estimated in three steps. First, an Ordinary Least Squares (OLS) model was fitted to the observed errors. Then, a variance function was estimated using the OLS residuals. Finally, a weighted Seemingly Unrelated Regression (SUR) analysis was used to model the observed errors, using the estimated variance functions as weights. The estimated models can be used to anticipate the precision and accuracy of predicted volume characteristics for each stand with different variable combinations and, consequently, to choose the best measurement combination in different stands.
  • Kangas, University of Helsinki, Dept. of Forest Resources Management, P.O. Box 27, 00014 University of Helsinki, Finland ORCID ID:E-mail: annika.kangas@helsinki.fi (email)
  • Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland ORCID ID:E-mail:
article id 520, category Research article
Rüdiger Grote. (2002). Foliage and branch biomass estimation of coniferous and deciduous tree species. Silva Fennica vol. 36 no. 4 article id 520. https://doi.org/10.14214/sf.520
Under changing environmental conditions, biomass development on the tree and the stand level may differ from today, regardless if the induced change is due to a shift in the general climate properties or to forest management. Under these conditions, tree biomass can not be derived from tables based on former investigations but has to be defined from particular biomass investigations, which generally calculate tree and stand biomass from sample branches using allometric relationships. Therefore, sample measurements on harvested trees are needed. In this paper, foliage and branch biomass estimation for 6 Norway spruces (Picea abies) and 6 beeches (Fagus sylvatica) harvested in a 56-year-old mixed stand in southern Germany is presented. Different allometric models are investigated to derive branch biomass from branch dimension for both species. The equations that are based on branch length, foliated branch fraction, and branch diameter are used for tree and stand level estimates. However, the variation within the 6 trees of each species was too large for a reliable calculation of stand biomass, especially in case of beech branch wood. Furthermore, the necessity of allometric relations and their applicability in individual-tree models is discussed, and the importance of suitable branch- and tree selection is underlined.
  • Grote, TU München, Chair of Forest Yield Science, Am Hochanger 13, D-85354 Freising, Germany ORCID ID:E-mail: ruediger.grote@lrz.tu-muenchen.de (email)
article id 620, category Research article
Annika Kangas, Matti Maltamo. (2000). Performance of percentile based diameter distribution prediction and Weibull method in independent data sets. Silva Fennica vol. 34 no. 4 article id 620. https://doi.org/10.14214/sf.620
Diameter distribution is used in most forest management planning packages for predicting stand volume, timber volume and stand growth. The prediction of diameter distribution can be based on parametric distribution functions, distribution-free parametric prediction methods or purely non-parametric methods. In the first case, the distribution is obtained by predicting the parameters of some probability density function. In a distribution-free percentile method, the diameters at certain percentiles of the distribution are predicted with models. In non-parametric methods, the predicted distribution is a linear combination of similar measured stands. In this study, the percentile based diameter distribution is compared to the results obtained with the Weibull method in four independent data sets. In the case of Scots pine, the other methods are also compared to k-nearest neighbour method. The comparison was made with respect to the accuracy of predicted stand volume, saw timber volume and number of stems. The predicted percentile and Weibull distributions were calibrated using number of stems measured from the stand. The information of minimum and maximum diameters were also used, for re-scaling the percentile based distribution or for parameter recovery of Weibull parameters. The accuracy of the predicted stand characteristics were also compared for calibrated distributions. The most reliable results were obtained using the percentile method with the model set including number of stems as a predictor. Calibration improved the results in most cases. However, using the minimum and maximum diameters for parameter recovery proved to be inefficient.
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail: annika.kangas@metla.fi (email)
  • Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland ORCID ID:E-mail:

Category: Research note

article id 310, category Research note
Timo Tahvanainen, Kalle Kaartinen, Timo Pukkala, Matti Maltamo. (2007). Comparison of approaches to integrate energy wood estimation into the Finnish compartment inventory system. Silva Fennica vol. 41 no. 1 article id 310. https://doi.org/10.14214/sf.310
The harvesting of energy wood from young stands is increasing as the demand for renewable wood fuel is growing. Energy wood consists of stems, tree tops, branches and needles, depending on the size of the trees and the logging method used. The current forest inventory and planning systems used in private forests in Finland do not produce estimates of energy wood components. In stands typical for energy wood harvesting, a large share of energy wood consists of trees smaller than the minimum size for pulpwood. In this study, energy wood was included into the calculation system of compartment inventory, and a procedure for simulating the thinning treatments in young stands was developed. The results for six inventory alternatives and prediction of energy wood were compared with the use of inventory material from 37 young stands that have plenty of energy wood. The measurement of additional stand characteristics and the use of a calibration estimation method was tested, as well as the use of plot-level inventory data instead of stand level data. The results showed that the measurement of the number of trees per hectare, in addition to stand basal area and mean diameter, improved the energy wood estimates. The additional minimum and maximum diameters improved the precision of the estimates, but did not affect bias. The removal estimates were more precise when plot-level data was used, rather than stand-level data. The removal estimates were higher with plot-level data. The results suggest that, in heterogeneous young stands, plot by plot prediction would give more accurate removal estimates than the calculation of a corresponding prediction at the stand-level.
  • Tahvanainen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: timo.tahvanainen@metla.fi (email)
  • Kaartinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuun yliopisto, Finland ORCID ID:E-mail:
  • Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuun yliopisto, Finland ORCID ID:E-mail:
  • Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuun yliopisto, Finland ORCID ID:E-mail:
article id 324, category Research note
Matti Katila. (2006). Empirical errors of small area estimates from the multisource National Forest Inventory in Eastern Finland. Silva Fennica vol. 40 no. 4 article id 324. https://doi.org/10.14214/sf.324
The precision of multisource national forest inventory (MS-NFI) estimators and simple synthetic estimators based on NFI field data only was assessed employing an independent inventory data set of several small areas in Eastern Finland. There were seven test units of size 100 km2 and three test units of size 1 km2 for which a systematic field sampling was carried out. The ‘improved’ MS-NFI method yielded the most precise estimates for mean volume and mean volume of pine and spruce: relative root mean square errors (RMSE*) were 5%, 12% and 15% for 100 km2 test units and 13%, 27% and 40% for 1 km2 test units respectively. The stratified MS-NFI method was best for broad-leaved volume estimation. Synthetic estimation based on the NFI9 field plots post-stratified with coarse scale forest variable maps from NFI8 resulted in RMSE*s comparable to those of the ordinary MS-NFI in areas of 100 km2 for mean volume and mean volume of pine and spruce. The amount of variation between the field inventory estimates for the test units explained by the MS-NFI estimators remained the same or increased when the size of the area increased from of 1 km2 to 100 km2 and up to 2000 km2. The validation of the largest areas was made against the NFI9 field inventory estimates for groups of municipalities in the study area.
  • Katila, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland ORCID ID:E-mail:
article id 324, category Research note
Matti Katila. (2006). Empirical errors of small area estimates from the multisource National Forest Inventory in Eastern Finland. Silva Fennica vol. 40 no. 4 article id 324. https://doi.org/10.14214/sf.324
The precision of multisource national forest inventory (MS-NFI) estimators and simple synthetic estimators based on NFI field data only was assessed employing an independent inventory data set of several small areas in Eastern Finland. There were seven test units of size 100 km2 and three test units of size 1 km2 for which a systematic field sampling was carried out. The ‘improved’ MS-NFI method yielded the most precise estimates for mean volume and mean volume of pine and spruce: relative root mean square errors (RMSE*) were 5%, 12% and 15% for 100 km2 test units and 13%, 27% and 40% for 1 km2 test units respectively. The stratified MS-NFI method was best for broad-leaved volume estimation. Synthetic estimation based on the NFI9 field plots post-stratified with coarse scale forest variable maps from NFI8 resulted in RMSE*s comparable to those of the ordinary MS-NFI in areas of 100 km2 for mean volume and mean volume of pine and spruce. The amount of variation between the field inventory estimates for the test units explained by the MS-NFI estimators remained the same or increased when the size of the area increased from of 1 km2 to 100 km2 and up to 2000 km2. The validation of the largest areas was made against the NFI9 field inventory estimates for groups of municipalities in the study area.
  • Katila, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland ORCID ID:E-mail:
article id 653, category Research note
Desta Fekedulegn, Mairitin P. Mac Siurtain, Jim J. Colbert. (1999). Parameter estimation of nonlinear growth models in forestry. Silva Fennica vol. 33 no. 4 article id 653. https://doi.org/10.14214/sf.653
Partial derivatives of the negative exponential, monomolecular, Mitcherlich, Gompertz, logistic, Chapman-Richards, von Bertalanffy, Weibull and the Richard’s nonlinear growth models are presented. The application of these partial derivatives in estimating the model parameters is illustrated. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating top height to age of Norway spruce (Picea abies L.) from the Bowmont Norway Spruce Thinning Experiment. Formulas that provide good initial values of the parameters are specified. Clear definitions of the parameters of the nonlinear models in the context of the system being modelled are found to be critically important in the process of parameter estimation.
  • Fekedulegn, Department of Statistics, West Virginia University, Eberly College of Arts and Sciences, P.O. Box 6330, Morgantown, WV26506, USA ORCID ID:E-mail: fdesta@stat.wvu.edu (email)
  • Mac Siurtain, University College Dublin, Ireland ORCID ID:E-mail:
  • Colbert, USDA Forest Service, Northeastern Research Station, Morgantown, West Virginia ORCID ID:E-mail:

Category: Article

article id 7116, category Article
Kustaa Kallio. (1960). Metsikön taksatorinen tiheys keskipituuteen ja tiheyteen perustuvassa kuutiomäärän arvioinnissa. Acta Forestalia Fennica vol. 71 no. 7 article id 7116. https://doi.org/10.14214/aff.7116
English title: The mensurational density of a stand in estimating the volume on the basis of the mean height and the density class.

In Finland ocular estimation of the growing stock has been made by means of volume tables based on the mean height and density class, or on the dominant height and density class of the stand. The author has observed that if the volume of a stand is estimated by employment of both tables, the results vary markedly from one another. Furthermore, volume of fully stocked stands in the dominant height tables show an approximate correspondence with the volumes of managed normal stands in Southern Finland.

The purpose of this study is therefore to develop volume tables for coniferous trees, based on the density class and the mean height; these tables should give the same volume for a stand as the dominant height tables.

Volume per hectare of 187 Scots pine (Pinus sylvestris L.) stands and 120 Norway spruce (Picea abies (L.) Karst.) stands on different forest types were estimated using the relascope method in Southern Finland. With the volume and the measured mean and dominant heights as a basis, the density classes were extracted from both mean height tables and the dominant height tables. The investigation indicates that the author estimated the dense stands too thinly, and the thin ones too densely, and that the erroneous estimation of the density can be corrected by comparison of the ocular estimations and the corresponding measurements. The density can be measured by means of crown closure, stem number per hectare or the basal area per hectare.

The PDF includes a summary in English.

  • Kallio, ORCID ID:E-mail:
article id 5524, category Article
Annika Kangas. (1994). Classical and model based estimators for forest inventory. Silva Fennica vol. 28 no. 1 article id 5524. https://doi.org/10.14214/sf.a9158

In this study, model-based and design-based inference methods are used for estimating mean volume and its standard error for systematic cluster sampling. Results obtained with models are compared to results obtained with classical methods. The data are from the Finnish National Forest Inventory. The variation of volume in ten forestry board districts in Southern Finland is studied. The variation is divided into two components: trend and correlated random errors. The effect of the trend and the covariance structure on the obtained mean volume and standard error estimates is discussed. The larger the coefficient of determination of the trend model, the smaller the model-based estimates of standard error, when compared to classical estimates. On the other hand, the wider the range and level of autocorrelation between the sample plots, the larger the model-based estimates of standard error.

  • Kangas, ORCID ID:E-mail:
article id 5520, category Article
Kari T. Korhonen. (1993). Mixed estimation in calibration of volume functions of Scots pine. Silva Fennica vol. 27 no. 4 article id 5520. https://doi.org/10.14214/sf.a15681

Regression models for estimating stem volume of Scots pine (Pinus sylvestris L.) were constructed using sample tree data measured in the 7th and 8th National Forest Inventory of Finland. Stem volume were regressed on diameter, basal area of growing stock, and geographic location. The results of the study show that using second order trend surface to describe the geographic variation of the residuals gives satisfactory results. Using mixed estimation for combining old and new sample tree data improves the efficiency of an inventory. The weight of the prior information must be low, because remarkable differences in stem form was found in the two inventories.

The PDF includes an abstract in Finnish.

  • Korhonen, ORCID ID:E-mail:
article id 5491, category Article
Kari T. Korhonen. (1992). Calibration of upper diameter models in large scale forest inventory. Silva Fennica vol. 26 no. 4 article id 5491. https://doi.org/10.14214/sf.a15652

Models for estimating the upper diameter of trees were constructed using sample tree data measured in the 7th National Forest Inventory in Finland. Calibration of the models was tested with data from the 8th National Forest Inventory. The results showed that using mixed estimation for combining the two data sets improves the reliability of the models. Models and methods used in this study can be recommended for use in forest inventories.

The PDF includes an abstract in Finnish.

  • Korhonen, ORCID ID:E-mail:
article id 5421, category Article
Tiina Tonteri. (1990). Inter-observer variation in forest vegetation cover assessments. Silva Fennica vol. 24 no. 2 article id 5421. https://doi.org/10.14214/sf.a15575

Differences in vegetation cover estimation by field biologists of the 8th National Forest Inventory in Finland were tested. Eleven observers estimated the canopy coverages of six forests taxa in 25 sample plots, located in one stand. The experiment was arranged after the field work. The coverage of Vaccinium vitis-idaea and the ground layer appeared to be the most difficult to estimate. The mean of the highest estimator was about double that of the lowest one. The least abundant species and the sample plots with the smallest coverages had the largest estimation errors. The most important compositional gradient of the data was natural, even though the test was made in a homogenous area. However, the effect of the observer could be recognized. The differences between observers could be caused by the differences both in visual estimation level and in placing the sampling frame. The results suggest that tests should always be made when several observers are used in vegetation surveys. If calibration is used, it should be made separately for each species.

The PDF includes an abstract in Finnish.

  • Tonteri, ORCID ID:E-mail:
article id 5392, category Article
Pekka Kilkki, Matti Maltamo, Reijo Mykkänen, Risto Päivinen. (1989). Use of the Weibull function in estimating the basal area dbh-distribution. Silva Fennica vol. 23 no. 4 article id 5392. https://doi.org/10.14214/sf.a15550

The paper continues an earlier study by Kilkki and Päivinen concerning the use of the Weibull function in modelling the diameter distribution. The data consists of spruces (Picea abies (L.) H. Karst.) measured on angle count sample points of the National Forest Inventory of Finland. First, maximum likelihood estimation method was used to derive the Weibull parameters. Then, regression models to predict the values of these parameters with stand characteristics were calculated. Several methods to describe the Weibull function by a tree sample were tested. It is more efficient to sample the trees at equal frequency intervals than at equal diameter intervals. It also pays to take separate samples for pulpwood and saw timber.

The PDF includes an abstract in Finnish.

  • Kilkki, ORCID ID:E-mail:
  • Maltamo, ORCID ID:E-mail:
  • Mykkänen, ORCID ID:E-mail:
  • Päivinen, ORCID ID:E-mail:
article id 5278, category Article
Shikui Peng. (1986). A comparison of replacement strategies in continuous forest inventory. Silva Fennica vol. 20 no. 3 article id 5278. https://doi.org/10.14214/sf.a15457

Three replacement strategies in continuous forest inventory of the Enso-Gutzeit Company have been presented and discussed. The first strategy adopts data from only the last two inventory occasions; the second strategy employs data from all four occasions, in which there are two groups of permanent plots measured on the first three occasions and independently on the last two occasions; the third strategy also utilizes data from all four occasions, but includes only one group permanent plots measured on all four occasions. Results indicate that the last strategy is best for efficiency. The difference between the first two strategies is small. 

The PDF includes an abstract in Finnish.

  • Peng, ORCID ID:E-mail:
article id 7519, category Article
Jori Uusitalo. (1997). Pre-harvest measurement of pine stands for sawing production planning. Acta Forestalia Fennica no. 259 article id 7519. https://doi.org/10.14214/aff.7519

To enhance the utilization of the wood, the sawmills are forced to place more emphasis on planning to master the whole production chain from the forest to the end product. One significant obstacle to integrating the forest-sawmill-market production chain is the lack of appropriate information about forest stands. Since the wood procurement point of view in forest planning systems has been almost totally disregarded there has been a great need to develop an easy and efficient pre-harvest measurement method, allowing separate measurement of stands prior to harvesting. The main purpose of this study was to develop a measurement method for Scots pine (Pinus sylvestris L.) stands which forest managers could use in describing the properties of the standing trees for sawing production planning.

Study materials were collected from ten Scots pine stands located in North Häme and South Pohjanmaa, in Southern Finland. The data comprise test sawing data on 314 pine stems, diameter at breast height (dbh) and height measures of all trees and measures of the quality parameters of pine sawlog stems in all ten study stands as well as the locations of all trees in six stands. The study was divided into four sub-studies which deal with pine quality prediction, construction of diameter and dead branch height distributions, sampling designs and applying height and crown height models. The final proposal for the pre-harvest measurement method is a synthesis of the individual sub-studies.

Quality analysis resulted in choosing dbh, distance from stump height to the first dead branch, crown height and tree height as the most appropriate quality characteristics of Scots pine. Dbh and dead branch height are measured from each pine sample tree while height and crown height are derived from dbh measures by aid of mixed height and crown height models. Pine and spruce diameter distribution as well as dead branch height distribution are most effectively predicted by the kernel function. Roughly 25 sample trees seem to be appropriate in pure pine stands. In mixed stands the number of sample trees needs to be increased in proportion to the intensity of pines in order to attain the same level of accuracy.

  • Uusitalo, ORCID ID:E-mail:
article id 7508, category Article
Tapani Lahti. (1995). Understorey vegetation as an indicator of forest site potential in Southern Finland. Acta Forestalia Fennica no. 246 article id 7508. https://doi.org/10.14214/aff.7508

The relationship between site characteristics and understorey vegetation composition was analysed with quantitative methods, especially from the viewpoint of site quality estimation. Theoretical models were applied to an empirical data set collected from the upland forests of Southern Finland comprising 104 sites dominated by Scots pine (Pinus sylvestris. L.) and 165 sites dominated by Norway spruce (Picea abies (L.) H. Karst.). Site index H100 was used as an independent measure of site quality.

A new model for the estimation of site quality at sites with a known understorey vegetation composition was introduced. It is based on the application of Bayes’ theorem to the density function of site quality within the study area combined with the species-specific presence-absence response curves. The resulting probability density function may be used for calculating an estimate for the site variable

Using this method, a jackknife estimate of site index H100 was calculated separately for pine- and spruce-dominated sites. The results indicated that the cross-validation root mean squared error (RMSEcv) of the estimates improved from 2.98 m down to 2.34 m relative to the ”null” model (standard deviation of the sample distribution) in pine-dominated forests. In spruce-dominated forests RMSEcv decreased from 3.94 m down to 3.19 m.

In order to assess these results, four other estimation methods based on understorey vegetation composition were applied to the same data set. The results showed that none of the methods was clearly superior to the others. In pine-dominated forests RMSEcv varied between 2.34 and 2.47 m, and the corresponding range for spruce-dominated forest was from 3.13 to 3.57 m.

  • Lahti, ORCID ID:E-mail:

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