Articles containing the keyword 'estimation'

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, E-mail: ak@mm.unknown

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, E-mail: kk@mm.unknown

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, E-mail: kk@mm.unknown

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, E-mail: tt@mm.unknown

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, E-mail: pk@mm.unknown
• Maltamo, E-mail: mm@mm.unknown
• Mykkänen, E-mail: rm@mm.unknown
• Päivinen, E-mail: rp@mm.unknown

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, E-mail: sp@mm.unknown

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, E-mail: kk@mm.unknown

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, E-mail: ju@mm.unknown

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 (RMSE_cv) 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 RMSE_cv 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 RMSE_cv 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, E-mail: tl@mm.unknown

• Liepiņš, Latvian State Forest Research Institute “Silava,” Rigas Street 111, LV-2169 Salaspils, Latvia https://orcid.org/0000-0003-3030-1122 E-mail: janis.liepins@silava.lv
• Jaunslaviete, Latvian State Forest Research Institute “Silava,” Rigas Street 111, LV-2169 Salaspils, Latvia https://orcid.org/0009-0000-7322-2729 E-mail: ieva.jaunslaviete@silava.lv
• Liepiņš, Latvian State Forest Research Institute “Silava,” Rigas Street 111, LV-2169 Salaspils, Latvia https://orcid.org/0000-0002-1179-8586 E-mail: kaspars.liepins@silava.lv
• Jansone, Latvian State Forest Research Institute “Silava,” Rigas Street 111, LV-2169 Salaspils, Latvia https://orcid.org/0000-0003-2748-3797 E-mail: liga.jansone@silava.lv
• Matisons, Latvian State Forest Research Institute “Silava,” Rigas Street 111, LV-2169 Salaspils, Latvia E-mail: roberts.matisons@silava.lv
• Lazdiņš, Latvian State Forest Research Institute “Silava,” Rigas Street 111, LV-2169 Salaspils, Latvia https://orcid.org/0000-0002-7169-2011 E-mail: andis.lazdins@silava.lv
• Jansons, Latvian State Forest Research Institute “Silava,” Rigas Street 111, LV-2169 Salaspils, Latvia https://orcid.org/0000-0001-7981-4346 E-mail: aris.jansons@silava.lv

In terms of assessing economic impact, one of the most important elements in the wood supply chain is the measurement of round wood. Besides the one-by-one measurement of logs, logs are often measured when stacked at the forest road. The gross stacked volume includes the volume of the wood, bark and airspace and is widely used for industrial wood assortments. The increasing international attention given to photo-optical measurement systems for portable devices is due to their simplicity of use and efficiency. The aim of this study was to compare the gross volumes of hardwood log stacks measured using one widespread photo-optical app with two manual section-wise volume estimations of log stacks based on the German framework agreement for timber trade (RVR). The manual volume estimations were done starting from the left (RVR_left) and right (RVR_right) sides of the log stacks. The results showed an average deviation of the photo-optical gross volume estimation in comparison to the manual estimation of –2.09% (RVR_left) and –3.66% (RVR_right) while the deviation between RVR_left and RVR_right was +2.54%. However, the log stack gross volume had a highly significant effect on the deviation and better accuracy with smaller deviation were reached for larger log stacks. Moreover, results indicated that the gross volume estimations of higher quality log stacks were closer for the three analyzed methods compared to estimations of poor-quality log stacks.

• Berendt, Department of Forest Utilization and Timber Markets, Eberswalde University for Sustainable Development, 16225 Eberswalde, Germany https://orcid.org/0000-0002-6285-7590 E-mail: ferreol.berendt@hnee.de
• Wolfgramm, Landesforst MV Anstalt des öffentlichen Rechts, Forstamt Billenhagen, 18182 Blankenhagen, Germany E-mail: felixwolfgramm@yahoo.de
• Cremer, Department of Forest Utilization and Timber Markets, Eberswalde University for Sustainable Development, 16225 Eberswalde, Germany E-mail: tobias.cremer@hnee.de

Since the 1990’s, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric k-nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 × 16 m² pixel size and units of 240 × 240 m², 1200 × 1200 m² and 12  000 × 12  000 m² aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12  000 × 12  000 m². The multiple testing corrections decreased the amount of significant p-values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.

• Katila, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland; https://orcid.org/0000-0001-6946-5736 E-mail: matti.katila@luke.fi
• Rajala, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: tuomas.rajala@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi

Diameter at breast height (DBH) and height (H) of trees are two important variables used in forest management plans. However, collecting the measurements of H is time-consuming and costly. Instead, the H-DBH relationship is modeled and used to estimate H. But, ignoring the effects of slope, aspect and tree competition on the H-DBH relationship often impedes the improvement of H predictions. In this study, to improve predictions of Cyclobalanopsis glauca (Thunb.) Oerst. tree H in mixed forests, we compared eleven H-DBH models and examined the influence of slope and aspect on the H-DBH relationship using 426 trees. We then improved Hegyi competition index and explored its effect on the H predictions by including it in the selected models. Results showed 1) There were statistically significant effects of slope and aspect on the H-DBH relationship; 2) The log transformation and exponential model performed best for sunny- and shady-steep, respectively, and the Gompertz’s model was optimal for both sunny- and shady-gentle; 3) Compared with the whole dataset, the division of the data into the slope and aspect sub-datasets significantly reduced the RMSE of H predictions; 4) Compared with the selected models without competition index, adding the original Hegyi and improved Hegyi_I into the models improved the H predictions but only the models containing the improved Hegyi_I significantly increased the prediction accuracy at the significant level of 0.1. This study implied that modeling the H-DBH relationship under different slopes and aspects and including the improved Hegyi_I provided the great potential to improve the H predictions.

• Long, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: shisheng3604@21cn.com
• Zeng, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: zengsiqi@21cn.com
• Liu, Faculty of Forestry, Central South University of Forestry and Technology, Changsha, Hunan 410004, China E-mail: liufl680@126.com
• Wang, Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China; Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA E-mail: gxwang@siu.edu

Accurate estimates of aboveground biomass (AGB) strongly depend on the suitability and precision of allometric models. Diospyros mespiliformis Hochst. ex A. DC. is a key component of most sub-Sahara agroforestry systems and, one of the most economically important trees in Africa. Despite its importance, very few scientific information exists regarding its biomass and carbon storage potential. In this study direct method was used to develop site-specific biomass models for D. mespiliformis tree components in Burkina Faso. Allometric models were developed for stem, branch and leaf biomass using data from 39 tree harvested in Sudanian savannas of Burkina Faso. Diameter at breast height (DBH), tree height, crown diameter (CD) and basal diameter (D₂₀) were regressed on biomass component using non-linear models with DBH alone, and DBH in combination with height and/or CD as predictor variables. Carbon content was estimated for each tree component using the ash method. Allometric models differed between the experimental sites, except for branch biomass models. Site-specific models developed in this study exhibited good model fit and performance, with explained variance of 81–98%. Using models developed from other areas would have underestimated or overestimated biomass by between –72% and +98%. Carbon content in aboveground components of D. mespiliformis in Tiogo, Boulon and Tapoa-Boopo was 55.40% ± 1.50, 55.52% ± 1.06 and 55.63% ± 1.00, respectively, and did not vary significantly (P-value = 0.909). Site-specific models developed in this study are useful tool for estimating carbon stocks and can be used to accurately estimate tree components biomass in vegetation growing under similar conditions.

• Ouédraogo, University Joseph Ki-Zerbo, UFR/SVT, Laboratory of Plant Biology and Ecology, 03 B.P. 7021 Ouagadougou 03, Burkina Faso E-mail: okorotimi@yahoo.fr
• Dimobe, University Joseph Ki-Zerbo, UFR/SVT, Laboratory of Plant Biology and Ecology, 03 B.P. 7021 Ouagadougou 03, Burkina Faso; University of Dédougou, Institut des Sciences de l’Environnement et du Développement Rural (ISEDR), BP 139 Dédougou, Burkina Faso; West African Science Service Center on Climate Change and Adapted Land Use, Competence Center, Avenue Muamar Ghadhafi, Ouagadougou, BP 9507, Burkina Faso https://orcid.org/0000-0001-5536-9700 E-mail: kangbenidimobe@gmail.com
• Thiombiano, University Joseph Ki-Zerbo, UFR/SVT, Laboratory of Plant Biology and Ecology, 03 B.P. 7021 Ouagadougou 03, Burkina Faso E-mail: adjima_thiombiano@yahoo.fr

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 E-mail: ezeyamb@yahoo.fr
• 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 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 E-mail: savadogo.patrice@gmail.com
• Aynekulu, World Agroforestry Centre (ICRAF), United Nations Avenue, P.O. Box 30677-00100, Nairobi, Kenya 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 E-mail: janne.heiskanen@helsinki.fi
• Saïd, International Livestock Research Institute (ILRI). P.O. Box 30709, Nairobi, Kenya E-mail: m.said@cgiar.org

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 http://orcid.org/0000-0002-3318-5967 E-mail: jonas.bohlin@slu.se
• Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden 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 E-mail: jonas.jonzen@slu.se
• Nilsson, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0001-7394-6305 E-mail: mats.nilsson@slu.se

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 E-mail: jouni.siipilehto@luke.fi
• Lindeman,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano 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 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 E-mail: xiaowei.yu@maanmittauslaitos.fi
• Uusitalo,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano E-mail: jori.uusitalo@luke.fi

• 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 E-mail: mikko.t.niemi@helsinki.fi
• 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 E-mail: mikko.vastaranta@helsinki.fi
• Peuhkurinen, Arbonaut Oy Ltd., Latokartanontie 7 A, FI-00700, Finland 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 E-mail: markus.holopainen@helsinki.fi

• Suvanto, Blom Kartta Oy, Teollisuuskatu 18, FI-80100 Joensuu, Finland E-mail: aki.suvanto@blomasa.com
• Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box, FI-80101, Joensuu, Finland E-mail: mm@nn.fi

• Peuhkurinen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: jp@nn.fi
• Maltamo, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
• Malinen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: jm@nn.fi

• 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

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

• Palahí, Centre Tecnológic Forestal de Catalunya. Passeig Lluis Companys, 23, 08010, Barcelona, Spain E-mail: marc.palahi@ctfc.es
• Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland E-mail: tp@nn.fi
• Trasobares, Foreco Technologies, Av. Diagonal 416, Estudio 2, Barcelona 08037, Spain E-mail: at@nn.es

• Muinonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: eero.muinonen@metla.fi

• Haara, Finnish Forest Research Institute, Joensuu Research Centre, P.O.Box 68, FIN-80101 Joensuu, Finland E-mail: arto.haara@metla.fi

• Malinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: jukka.malinen@joensuu.fi

• Kangas, University of Helsinki, Dept. of Forest Resources Management, P.O. Box 27, 00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
• Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland E-mail: mm@nn.fi

• Grote, TU München, Chair of Forest Yield Science, Am Hochanger 13, D-85354 Freising, Germany E-mail: ruediger.grote@lrz.tu-muenchen.de

• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: annika.kangas@metla.fi
• Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: mm@nn.fi

• Tahvanainen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: timo.tahvanainen@metla.fi
• Kaartinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuun yliopisto, Finland E-mail: kk@nn.fi
• Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuun yliopisto, Finland E-mail: tp@nn.fi
• Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuun yliopisto, Finland E-mail: mm@nn.fi

• Katila, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: mk@nn.fi

• Fekedulegn, Department of Statistics, West Virginia University, Eberly College of Arts and Sciences, P.O. Box 6330, Morgantown, WV26506, USA E-mail: fdesta@stat.wvu.edu
• Mac Siurtain, University College Dublin, Ireland E-mail: mpms@nn.ie
• Colbert, USDA Forest Service, Northeastern Research Station, Morgantown, West Virginia E-mail: jjc@nn.us