Articles containing the keyword 'logistic'

In this study, logistic regression and neural networks were used to predict non-industrial private forests (NIPF) landowners’ choice of forest taxation basis. The main frame of reference of the study was the Finnish capital taxation reform of 1993. As a consequence of the reform, landowners were required to choose whether to be taxed according to site-productivity or realized-income during the coming transition period of thirteen years.

The most important factor affecting the landowners’ choice of taxation basis was the harvest rate during the transition period, i.e. the chosen timber management strategy. Furthermore, the estimated personal marginal tax rate and the intention to cut timber during next three years affected the choice. The descriptive landowner variables did not have any marked effect on the choice of forest taxation basis.

On average, logistic regression predicted 71% of the choices correctly; the corresponding figure for neural networks was 63%. In both methods, the choice of site-productivity taxation was predicted more accurately than the choice of realized-income taxation. An increase in the number of model variable did not significantly improve the results of neural networks and logistic regression.

• Pesonen, E-mail: mp@mm.unknown
• Kettunen, E-mail: ak@mm.unknown
• Räsänen, E-mail: pr@mm.unknown

Questions of the small size of non-industrial private forest (NIPF) holdings in Finland are considered and factors affecting their partitioning are analysed. This work arises out of Finnish forest policy statements in which the small average size of holdings has been seen to have negative influence on the economics of forestry. A literature survey indicates that the size of holdings is an important factor determining the costs of logging and silvicultural operations, while its influence on the timber supply is slight.

The empirical data are based on a sample of 314 holdings collected by interviewing forest owners in 1980–86. In 1990–91 the same holdings were resurveyed by means of a postal inquiry and partly by interviewing the forest owners. The principal objective was to collect data to assist in quantifying ownership factors that influence partitioning among different kinds of NIPF holdings. Thus, the mechanism of partitioning was described and a maximum likelihood logistic regression model was constructed using seven independent holding and ownership variables.

One out of four holdings had undergone partitioning in conjunction with a change in ownership, one fifth among family owned holdings and nearly a half among jointly owned holdings. The results of the logistic regression model indicate, for instance, that the odds on partitioning is about three times greater for jointly owned holdings than for family owned ones. Also, the probabilities of partitioning were estimated and the impact of independent dichotomous variables on the probability of partitioning ranged between 0.02 and 0.01. The low value of Hosmer-Lemeshow test statistics indicates a good fit of the model, and the rate of correct classification was estimated to be 88% with a cut-off point of 0.5.

The average size of holdings undergoing ownership changes decreased from 29.9 ha to 28.7 ha over the approximate interval 1983–90. In addition, the transition probability matrix showed that the trends towards smaller size categories mostly concerned the small size categories. The results can be used in considering the effects of the small size holdings for forestry and if the purpose is to influence partitioning through forest or rural policy.

• Ripatti, E-mail: pr@mm.unknown

The presence/absence data of 27 forest insect taxa (Retinia resinella, Formica spp., Pissodes spp., several scolytids) and recorded environmental variation were used to investigate the applicability of modelling insect occurrence based on satellite imagery. The sampling was based on 1,800 sample plots (25 m by 25 m) placed along the sides of 30 equilateral triangles (side 1 km) in a fragmented forest area (approximately 100 km²) in Evo, Southern Finland. The triangles were overlaid on land use maps interpreted from satellite images (Landsat TM 30 m multispectral scanner imagery 1991) and digitized geological maps. Insect occurrence was explained using either environmental variables measured in the field or those interpreted from the land use and geological maps. The fit of logistic regression models carried between species, possibly because some species may be associated with characteristics of single trees while other species with stand characteristics. The occurrence of certain insect species at least, especially those associated with Scots pine, could be relatively accurately assessed indirectly on the basis of satellite imagery and geological maps. Models based on both remotely sensed and geological data better predicted the distribution of forest insects except in the case of Xylechinus pilosus, Dryocetes sp. and Trypodendron lineatum, where the differences were relatively small in favour of the models based on field measurements. The number of species was related to habitat compartment size and distance from the habitat edge calculated from the land use maps, but logistic regressions suggested that other environmental variables in general masked the effect of these variables in species occurrence at the present scale.

• Väisänen, E-mail: rv@mm.unknown
• Heliövaara, E-mail: kh@mm.unknown

Two operative forest site class estimation methods utilizing satellite images have been developed for forest income taxation purposes. For this, two pixelwise classification methods and two post-processing methods for estimating forest site fertility are compared using different input data. The pixelwise methods are discriminant analysis, based on generalized squared distances, and logistic regression analysis. The results of pixelwise classifications are improved either with mode filtering within forest stands or assuming a Markov random field type dependence between pixels. The stand delineation is obtained by using ordinary segmentation techniques. Optionally, known stand boundaries given by the interpreter can be applied. The spectral values of images are corrected using a digital elevation model of the terrain. Some textural features are preliminary tested in classification. All methods are justified by using independent test data.

A test of the practical methods was carried out and a cost-benefit analysis computed. The estimated cost saving in site quality classification varies from 14% to 35% depending on the distribution of the site classes of the area. This means a saving of about 2.0–4.5 million FMK per year in site fertility classification for income taxation purposes. The cost savings would rise even to 60% if that version of the method were chosen where field checking is totally omitted. The classification accuracy at the forest holding level would still be similar to that of traditional method.

The PDF includes a summary in Finnish.

• Tomppo, E-mail: et@mm.unknown

Biogeographical patterns of the Scolytidae in Fennoscandia and Denmark, based on species incidence data from the approximately 70 km x 70 km quadrats (n = 221) used by Lekander et al. (1977), were classified to environmental variables using multivariate methods (two-way indicator species analysis, detrended correspondence analysis, canonical correspondence analysis).

The distributional patterns of scolytid species composition showed similar features to earlier presented zonations based on vegetation composition. One major difference, however, was that the region was more clearly divided in an east-west direction. Temperature variables associated with the location of the quadrat had the highest canonical coefficient values on the first axis of the CCA. Although these variables were the most important determinants of the biogeographical variation in the beetle species assemblages, annual precipitation and the distribution of Picea abies also improved the fit of the species data.

Samples with the most deviant rarity and typicality indices for the scolytid species assempblages in each quadrat were concentrated in several southern Scandinavian quadrats, in some quadrats in northern Sweden, and especially on the Swedish islands (Öland, Gotland, Gotska Sandön) in the Baltic Sea. The use of rarity indices which do not take the number of species per quadrat, also resulted high values for areas near Stockholm and Helsinki with well-known faunas. Methodological tests in which the real changes in the distribution of Ips acuminatus and I. amitinus were used as indicators showed that the currently available multivariate methods are sensitive to small faunal shifts even, and thus permit analysis of the fauna in relation to environmental changes. However, this requires more detailed monitoring of the species’ distributions over longer time spans.

Distribution of seven species (Scolytus intricatus, S. laevis, Hylurgops glabratus, Crypturgus cinereus, Pityogenes salasi, Ips typographus, and Cyleborus dispar) were predicted by logistic regression models using climatic variables. In spite of the deficiencies in the data and the environmental variables selected, the models were relatively good for several but not for all species. The potential effects of climate change on bark beetles are discussed.

The PDF includes a summary in Finnish.

• Heliövaara, E-mail: kh@mm.unknown
• Väisänen, E-mail: rv@mm.unknown
• Immonen, E-mail: ai@mm.unknown

Road transport produces 90% of greenhouse gas emissions in timber transport in Finland. It is therefore necessary to understand the factors that affect driving speed, fuel consumption, and ultimately, emissions. The objective of this study was to assess the effect of road characteristics on timber truck driving speed and fuel consumption. Data from the fleet management and transport management systems of two timber trucks were collected over a year. A sample of 104 trips was drawn, and the tracking points were overlaid on the road data in a geographical information system. Thereafter, work phases were determined for the points, and they were visually classified into road and pavement classes. Subsequently, the data of 80 trips were utilised in regression analysis to further study the effects of the visually interpreted variables on driving speed and fuel consumption. Fuel consumption was explained by the proportion of forest roads and distance travelled with a loader, and the number of crossings and season when driving without a load. When driving with a load, both asphalt and gravel pavements decreased consumption, in contrast to an unpaved road. Crossings increased fuel consumption, as did the winter and spring months, and the total laden mass of the truck. In conclusion, the study showed that the functional Finnish road and pavement classes can be used to predict driving speed and fuel consumption.

• Anttila, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0002-6131-392X E-mail: perttu.anttila@luke.fi
• Ojala, UPM Metsä, Sirkkalantie 13 b, FI-80100 Joensuu, Finland E-mail: johannes.ojala@upm.com
• Palander, School of Forest Sciences, University of Eastern Finland (UEF), Yliopistokatu 7, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-9284-5443 E-mail: teijo.s.palander@uef.fi
• Väätäinen, Natural Resources Institute Finland (Luke), Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-6886-0432 E-mail: kari.vaatainen@luke.fi

Because today’s tree planting machines do a good job silviculturally, the Nordic forest sector is interested in finding ways to increase the planting machines’ productivity. Faster seedling reloading increases machine productivity, but that solution might require investments in specially designed seedling packaging. The objective of our study was to compare the cost-efficiency of cardboard box concepts that increase the productivity of tree planting machines with that of today’s two most common seedling packaging systems in southern Sweden. We modelled the total cost of these five different seedling packaging systems using data from numerous sources including manufacturers, nurseries, contractors, and forest companies. Under these southern Swedish conditions, the total cost of cardboard box concepts that increase the productivity of intermittently advancing tree planting machines was higher than the cost of the cultivation tray system (5–49% in the basic scenario). However, the conceptual packaging system named ManBox_fast did show promise, especially with increasing primary transport distances and increased planting machine productivities and hourly costs. Thus, our results show that high seedling packing density is of fundamental importance for cost-efficiency of cardboard box systems designed for mechanized tree planting. Our results also illustrate how different factors in the seedling supply chain affect the cost-efficiency of tree planting machines. Consequently, our results underscore that the key development factor for mechanized tree planting in the Nordic countries is the development of cost-efficient seedling handling systems between nurseries and planting machines.

• Ersson, SLU, School of Forest Management, SE-739 21 Skinnskatteberg, Sweden https://orcid.org/0000-0003-2442-7482 E-mail: back.tomas.ersson@slu.se
• Sundblad, Skogforsk, Uppsala Science Park, SE-751 83 Uppsala, Sweden E-mail: lars-goran.sundblad@skogforsk.se
• Manner, Skogforsk, Uppsala Science Park, SE-751 83 Uppsala, Sweden https://orcid.org/0000-0002-4982-3855 E-mail: jussi.manner@skogforsk.se

Studies of intra-annual growth are particularly useful for understanding tree growth because of their high temporal resolution. This study was performed in Austria and included hourly band dendrometer data of 244 annual tree recordings from six tree species (Picea abies (L.) Karst., Pinus sylvestris L., Larix decidua Mill., Abies alba Mill., Fagus sylvatica L., Quercus spp. (Quercus petraea (Matt.) Liebl., Quercus robur L.) sampled on five sites with contrasting site conditions in pure and mixed stands and on trees of different social position. Measurements encompassed 1–7 years. Cumulative diameter increment was modelled by logistic mixed-effects models with random effects at the tree and year level. The results showed large differences in seasonal growth patterns between sites, with a clearly shorter growing season at the drier sites. Species specific response on dry sites could be linked to drought characteristics, whereas response on more humid sites was related to light requirements or successional status. The deciduous trees showed earlier growth culmination and shorter growing periods than the evergreen species. Individual tree growth of Quercus spp., P. abies, and F. sylvatica was positively affected by mixture whereas L. decidua, P. sylvestris and A. alba showed no or adverse mixture effects. Mixture effects differed between years and social position. Furthermore, increment culmination was earlier in mixed stands, but shifts were minor. Tree growth differed by social position with dominant trees showing the largest increment and the longest growth duration, with shifts in tree growth patterns due to social position being as large as those between different sites.

• Strieder, Boku, University of Natural Resources and Life Sciences Vienna, Department of Forest- and Soil Sciences, Institute of Forest Growth, Peter-Jordan-Str. 82, A-1190 Vienna, Austria https://orcid.org/0000-0001-6398-8536 E-mail: emanuel.strieder@students.boku.ac.at
• Vospernik, Boku, University of Natural Resources and Life Sciences Vienna, Department of Forest- and Soil Sciences, Institute of Forest Growth, Peter-Jordan-Str. 82, A-1190 Vienna, Austria http://orcid.org/0000-0002-4201-6444 E-mail: sonja.vospernik@boku.ac.at

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

New mortality models were developed for the purpose of improving long-term growth and yield simulations in Finland, Norway, and Sweden and were based on permanent national forest inventory plots from Sweden and Norway. Mortality was modelled in two steps. The first model predicts the probability of survival, while the second model predicts the proportion of basal area in surviving trees for plots where mortality has occurred. In both models, the logistic function was used. The models incorporate the variation in prediction period length and in plot size. Validation of both models indicated unbiased mortality rates with respect to various stand characteristics such as stand density, average tree diameter, stand age, and the proportion of different tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and broadleaves. When testing against an independent dataset of unmanaged spruce-dominated stands in Finland, the models provided unbiased prediction with respect to stand age.

• Siipilehto, Natural Resources Institute Finland (Luke), Natural resources, P.O. Box 2, FI-00790 Helsinki, Finland E-mail: jouni.siipilehto@luke.fi
• Allen, Norwegian Institute of Bioeconomy Research (NIBIO), Division of Forest and Forest Products, NO-1431 Ås, Norway; Larson and McGowin Inc., Mobile, AL 36607, USA https://orcid.org/0000-0002-7824-2849 E-mail: micky.allen@nibio.no
• Nilsson, Swedish University of Agricultural Sciences (SLU), Southern Swedish Forest Research Centre, SE-23053 Alnarp, Sweden https://orcid.org/0000-0002-7624-4031 E-mail: urban.nilsson@slu.se
• Brunner, Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management, NO-1432 Ås, Norway https://orcid.org/0000-0003-1668-9714 E-mail: andreas.brunner@nmbu.no
• Huuskonen, Natural Resources Institute Finland (Luke), Natural resources, P.O. Box 2, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-8630-3982 E-mail: saija.huuskonen@luke.fi
• Haikarainen, Natural Resources Institute Finland (Luke), Natural resources, P.O. Box 2, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-8703-3689 E-mail: soili.haikarainen@luke.fi
• Subramanian, Swedish University of Agricultural Sciences (SLU), Southern Swedish Forest Research Centre, SE-23053 Alnarp, Sweden https://orcid.org/0000-0003-2777-3241 E-mail: narayanan.subramanian@slu.se
• Antón-Fernández, Norwegian Institute of Bioeconomy Research (NIBIO), Division of Forest and Forest Products, NO-1431 Ås, Norway https://orcid.org/0000-0001-5545-3320 E-mail: clara.anton.fernandez@nibio.no
• Holmström, Swedish University of Agricultural Sciences (SLU), Southern Swedish Forest Research Centre, SE-23053 Alnarp, Sweden https://orcid.org/0000-0003-2025-1942 E-mail: emma.holmstrom@slu.se
• Andreassen, Norwegian Institute of Bioeconomy Research (NIBIO), Division of Forest and Forest Products, NO-1431 Ås, Norway https://orcid.org/0000-0003-4272-3744 E-mail: kjellandreassen@gmail.com
• Hynynen, Natural Resources Institute Finland (Luke), Natural resources, P.O. Box 2, FI-00790 Helsinki, Finland E-mail: jari.hynynen@luke.fi

In wintertime, the payload capacity of a timber truck is reduced by snow that accumulates on the structures of the truck. The aim of this study was to quantify the potential payload loss due to snow and winter accessories and to predict the loss with weather variables. Tare weights of eight timber trucks were collected at mill receptions in Finland over a one-year period. Monthly and annual loss of potential payload was estimated using the tare measurements in summer months as a reference. Each load was also connected with weather data at the location and time of delivery and payload loss explained by the weather data with the aid of regression models. The maximum loss of payload varied between 1560 kg and 3100 kg. On a monthly basis, the highest losses occurred in January, when the median values varied between 760 kg and 2180 kg. Over the year, the payload loss ranged between the trucks from 0.5% to 1.5% (from 1.9% and 5.1% in January) of the total number of loads in the study. Payload loss was found to increase with decreasing temperature, increasing relative humidity and increasing precipitation. Although the average payload loss was not very high, the biggest losses occur just during the season of highest capacity utilization. Big differences were also found in the tare weights between the trucks. The results of the study give incentive to develop truck and trailer structures that reduce the adherence of snow.

• Anttila, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0002-6131-392X E-mail: perttu.anttila@luke.fi
• Nummelin, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: tuomas.nummelin@luke.fi
• Väätäinen, Natural Resources Institute Finland (Luke), Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: kari.vaatainen@luke.fi
• Laitila, Natural Resources Institute Finland (Luke), Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0003-4431-3319 E-mail: juha.laitila@luke.fi

The fusion of optical satellite imagery, strips of lidar data and field plots is a promising approach for the inventory of tropical forests. Airborne lidars also enable an accurate direct estimation of the forest canopy cover (CC), and thus a sample of lidar strips can be used as reference data for creating CC maps which are based on satellite images. In this study, our objective was to validate CC maps obtained from an ALOS AVNIR-2 satellite image wall-to-wall, against a lidar-based CC map of a tropical forest area located in Laos. The reference CC values which were needed for model training were obtained from a sample of four lidar strips. Zero-and-one inflated beta regression (ZOINBR) models were applied to link the spectral vegetation indices derived from the ALOS image with the lidar-based CC estimates. In addition, we compared ZOINBR and logistic regression models in the forest area estimation by using >20% CC as a forest definition. Using a total of 409 217 30 × 30 m population units as validation, our model showed a strong correlation between lidar-based CC and spectral satellite features (root mean square error = 12.8%, R² = 0.82). In the forest area estimation, a direct classification using logistic regression provided better accuracy than the estimation of CC values as an intermediate step (kappa = 0.61 vs. 0.53). It is important to obtain sufficient training data from both ends of the CC range. The forest area estimation should be done before the CC estimation, rather than vice versa.

• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland; (current) University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland http://orcid.org/0000-0002-9352-0114 E-mail: lauri.z.korhonen@helsinki.fi
• Ali-Sisto, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: dheikkil@student.uef.fi
• Tokola, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland. E-mail: timo.tokola@uef.fi

• Korhonen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
• Pippuri, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: inka.pippuri@uef.fi
• Packalén, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
• Heikkinen, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: ville.heikkinen@uef.fi
• Maltamo, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
• Heikkilä, Finnish Forest Centre, Public Services, Maistraatinportti 4 A, FI-00240 Helsinki, Finland E-mail: juho.heikkila@metsakeskus.fi

• Holzleitner, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Peter Jordanstrasse 82/3, 1190 Vienna, Austria E-mail: franz.holzleitner@boku.ac.at
• Kanzian, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Peter Jordanstrasse 82/3, 1190 Vienna, Austria E-mail: christian.kanzian@boku.ac.at
• Höller, Institute of Forest Engineering, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Peter Jordanstrasse 82/3, 1190 Vienna, Austria E-mail: norbert.hoeller@boku.ac.at

• Karttunen, Lappeenranta University of Technology, LUT Savo Sustainable Technologies, Mikkeli, Finland E-mail: kalle.karttunen@lut.fi
• Väätäinen, The Finnish Forest Research Institute, Joensuu, Finland E-mail: kari.vaatainen@metla.fi
• Asikainen, The Finnish Forest Research Institute, Joensuu, Finland E-mail: antti.asikainen@metla.fi
• Ranta, Lappeenranta University of Technology, LUT Savo Sustainable Technologies, Mikkeli, Finland E-mail: tapio.ranta@lut.fi

• 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

• Ersson, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-90183 Umeå, Sweden E-mail: back.tomas.ersson@slu.se
• Bergsten, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-90183 Umeå, Sweden E-mail: ub@nn.se
• Lindroos, Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-90183 Umeå, Sweden E-mail: ol@nn.se

• Windisch, Finnish Forest Research Institute, Joensuu Research Unit, Yliopistokatu 6, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: johannes.windisch@metla.fi
• Sikanen, University of Eastern Finland, School of Forest Science, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: ls@nn.fi
• Röser, Finnish Forest Research Institute, Joensuu Research Unit, Yliopistokatu 6, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: dr@nn.fi
• Gritten, University of Eastern Finland, School of Forest Science, Yliopistokatu 7, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: dg@nn.fi

• Nurminen, Metsätoimisto Tuomo Nurminen, Joensuuntie 5 B 8, FI-41800 Korpilahti, Finland E-mail: tn@nn.fi
• Korpunen, Finnish Forest Research Institute, Parkano Research Unit, Kaironiementie 54, FI-39700 Parkano, Finland E-mail: hk@nn.fi
• Uusitalo, Finnish Forest Research Institute, Parkano Research Unit, Kaironiementie 54, FI-39700 Parkano, Finland E-mail: ju@nn.fi

• Kanzian, University of Applied Life Sciences Vienna, Institute of Forest Engineering, Peter Jordan Strasse 82, A-1190 Vienna, Austria E-mail: christian.kanzian@boku.ac.at
• Holzleitner, University of Applied Life Sciences Vienna, Institute of Forest Engineering, Peter Jordan Strasse 82, A-1190 Vienna, Austria E-mail: fh@nn.at
• Stampfer, University of Applied Life Sciences Vienna, Institute of Forest Engineering, Peter Jordan Strasse 82, A-1190 Vienna, Austria E-mail: ks@nn.at
• Ashton, Southern Regional Extension Forestry, Forestry Bldg. 4-420, University of Georgia, Athens, GA 30602, USA E-mail: sa@nn.us

• Nurminen, University of Joensuu, Faculty of Forest Sciences; Forest Agency Tuomo Nurminen, Joensuuntie 5 B 8, FI-41800 Korpilahti, Finland E-mail: tn@nn.fi
• Heinonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: jh@nn.fi

• Mattila, The Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: um@nn.fi
• Nuutinen, The Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: tn@nn.fi

• Vospernik, University of Natural Resources and Applied Life Sciences, Institute of Forest Growth Research, Department of Forest and Soil Sciences, Peter-Jordan-Stra§e 82, A-1190 Vienna, Austria E-mail: sonja.vospernik@boku.ac.at

• Spinelli, CNR/IVALSA, Via Madonna del Piano - Palazzo F, I-50019 Sesto Fiorentino (FI), Italy E-mail: spinelli@ivalsa.cnr.it
• Nati, CNR/IVALSA, Via Madonna del Piano - Palazzo F, I-50019 Sesto Fiorentino (FI), Italy E-mail: cn@nn.it
• Magagnotti, CNR/IVALSA, Via Madonna del Piano - Palazzo F, I-50019 Sesto Fiorentino (FI), Italy E-mail: nm@nn.it

• Lindén, Southern Swedish Forest Research Centre c/o Asa Experimental Forest, SLU, S-360 30 Lammhult, Sweden E-mail: magnus.linden@ess.slu.se
• Vollbrecht, Southern Swedish Forest Research Centre c/o Asa Experimental Forest, SLU, S-360 30 Lammhult, Sweden E-mail: gv@nn.se

• Jalkanen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: anneli.jalkanen@metla.fi

This review systematically analyses and classifies research and review papers focusing on discrete event simulation applied to wood transport, and therefore illustrates the development of the research area from 1997 until 2017. Discrete event simulation allows complex supply chain models to be mapped in a straightforward manner to study supply chain dynamics, test alternative strategies, communicate findings and facilitate understanding of various stakeholders. The presented analyses confirm that discrete event simulation is well-suited for analyzing interconnected wood supply chain transportation issues on an operational and tactical level. Transport is the connective link between interrelated system components of the forest products industry. Therefore, a survey on transport logistics allows to analyze the significance of entire supply chain management considerations to improve the overall performance and not only one part in isolation. Thus far, research focuses mainly on biomass, unimodal truck transport and terminal operations. Common shortcomings identified include rough explanations of simulation models and sparse details provided about the verification and validation processes. Research gaps exist concerning simulations of entire, resilient and multimodal wood supply chains as well as supply and demand risks. Further studies should expand upon the few initial attempts to combine various simulation methods with optimization.

• Kogler, Institute of Production and Logistics, Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, A-1180 Vienna, Austria https://orcid.org/0000-0001-8811-152X E-mail: christoph.kogler@boku.ac.at
• Rauch, Institute of Production and Logistics, Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, A-1180 Vienna, Austria http://orcid.org/0000-0002-5812-4415 E-mail: peter.rauch@boku.ac.at

• Röser, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: dominik.roser@metla.fi
• Mola-Yudego, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: bmy@nn.fi
• Prinz, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: rp@nn.fi
• Emer, University of Padova, Department of Land, Agriculture and Forest Systems, Legnaro (PD), Italy E-mail: be@nn.it
• Sikanen, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: ls@nn.fi

• Kiffner, University Göttingen, Büsgen-Institute, Department of Forest Zoology and Forest Protection incl. Wildlife Biology and Game Management, Büsgenweg 3, 37077 Göttingen, Germany E-mail: ckiffne@gwdg.de
• Rössiger, University Göttingen, Büsgen-Institute, Department of Forest Zoology and Forest Protection incl. Wildlife Biology and Game Management, Büsgenweg 3, 37077 Göttingen, Germany E-mail: er@nn.de
• Trisl, Planungsbüro Trisl, In der Schleene 7, 36037 Waake, Germany E-mail: ot@nn.de
• Schulz, University Göttingen, Büsgen-Institute, Department of Ecological Informatics, Biometry and Forest Growth, Büsgenweg 4, 37077 Göttingen, Germany E-mail: rs@nn.de
• Rühe, University Göttingen, Büsgen-Institute, Department of Forest Zoology and Forest Protection incl. Wildlife Biology and Game Management, Büsgenweg 3, 37077 Göttingen, Germany E-mail: fr@nn.de