Current issue: 56(2)

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Silva Fennica 1926-1997
Acta Forestalia Fennica

Articles containing the keyword 'logistic regression'

Category: Article

article id 5555, category Article
Mauno Pesonen, Arto Kettunen, Petri Räsänen. (1995). Modelling non-industrial private forest landowners’ strategic decision making by using logistic regression and neural networks: Case of predicting the choice of forest taxation basis. Silva Fennica vol. 29 no. 2 article id 5555.
Keywords: logistic regression; Finland; Neural Networks; forest owners; forest taxation; non-industrial private forest landowners,; timber management strategies
Abstract | View details | Full text in PDF | Author Info

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 (email)
  • Kettunen, E-mail: ak@mm.unknown
  • Räsänen, E-mail: pr@mm.unknown

Category: Article

article id 7514, category Article
Pekka Ripatti. (1996). Factors affecting partitioning of private forest holdings in Finland. Silva Fennica vol. 0 no. 252 article id 7514.
Keywords: forest policy; forest ownership; logistic regression; Finland; private forests; partitioning; forest holding size; forestry behaviour; ownership change
Abstract | View details | Full text in PDF | Author Info

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 (email)
article id 7505, category Article
Rauno Väisänen, Kari Heliövaara. (1994). Assessment of insect occurrence in boreal forests based on satellite imagery and field measurements. Silva Fennica vol. 0 no. 243 article id 7505.
Keywords: biodiversity; remote sensing; insect pests; geological maps; Scolytids; logistic regression models
Abstract | View details | Full text in PDF | Author Info

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 (email)
  • Heliövaara, E-mail: kh@mm.unknown
article id 7675, category Article
Erkki Tomppo. (1992). Satellite image aided forest site fertility estimation for forest income taxation. Silva Fennica vol. 0 no. 229 article id 7675.
Keywords: site quality; discriminant analysis; forest taxation; satellite images; segmentation; logistic regression analysis; Markov random field
Abstract | View details | Full text in PDF | Author Info

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 (email)
article id 7606, category Article
Kari Heliövaara, Rauno Väisänen, Auli Immonen. (1991). Quantitative biogeography of the bark beetles (Coleoptera, Scolytidae) in northern Europe. Silva Fennica vol. 0 no. 219 article id 7606.
Keywords: climate change; boreal forests; biodiversity; Nordic countries; multivariate methods; insect pests; biogeography; Scolytids; logistic regression models; faunal changes; Fennoscandia
Abstract | View details | Full text in PDF | Author Info

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 (email)
  • Väisänen, E-mail: rv@mm.unknown
  • Immonen, E-mail: ai@mm.unknown

Category: Research article

article id 1405, category Research article
Lauri Korhonen, Daniela Ali-Sisto, Timo Tokola. (2015). Tropical forest canopy cover estimation using satellite imagery and airborne lidar reference data. Silva Fennica vol. 49 no. 5 article id 1405.
Keywords: logistic regression; beta regression; forest area; international forest definition; ALOS AVNIR-2; vegetation index
Highlights: The fusion of airborne lidar data and satellite images enables accurate canopy cover mapping; The zero-and-one inflated beta regression is demonstrated in large area estimation; Forest/non-forest classification should be done directly, for example by using logistic regression.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 ORCID E-mail: (email)
  • Ali-Sisto, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail:
  • Tokola, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland. E-mail:
article id 952, category Research article
Lauri Korhonen, Inka Pippuri, Petteri Packalén, Ville Heikkinen, Matti Maltamo, Juho Heikkilä. (2013). Detection of the need for seedling stand tending using high-resolution remote sensing data. Silva Fennica vol. 47 no. 2 article id 952.
Keywords: forest management; airborne laser scanning; logistic regression; seedling stand; tending; support vector machine
Abstract | Full text in HTML | Full text in PDF | Author Info
Seedling stands are problematic in airborne laser scanning (ALS) based stand level forest management inventories, as the stem density and species proportions are difficult to estimate accurately using only remotely sensed data. Thus the seedling stands must still be checked in the field, which results in an increase in costs. In this study we tested an approach where ALS data and aerial images are used to directly classify the seedling stands into two categories: those that involve tending within the next five years and those which involve no tending. Standard ALS-based height and density features, together with texture and spectral features calculated from aerial images, were used as inputs to two classifiers: logistic regression and the support vector machine (SVM). The classifiers were trained using 208 seedling plots whose tending need was estimated by a local forestry expert. The classification was validated on 68 separate seedling stands. In the training data, the logistic model’s kappa coefficient was 0.55 and overall accuracy (OA) 77%. The SVM did slightly better with a kappa = 0.71 and an OA = 86%. In the stand level validation data, the performance decreased for both the logistic model (kappa = 0.38, OA = 71%) and the SVM (kappa = 0.37, OA = 72%). Thus our approach cannot totally replace the field checks. However, in considering the stands where the logistic model predictions had high reliability, the number of misclassifications reduced drastically. The SVM however, was not as good at recognizing reliable cases.
  • Korhonen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: (email)
  • Pippuri, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail:
  • Packalén, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail:
  • Heikkinen, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail:
  • Maltamo, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail:
  • Heikkilä, Finnish Forest Centre, Public Services, Maistraatinportti 4 A, FI-00240 Helsinki, Finland E-mail:
article id 100, category Research article
Annika Kangas, Lauri Mehtätalo, Antti Mäkinen, Kalle Vanhatalo. (2011). Sensitivity of harvest decisions to errors in stand characteristics. Silva Fennica vol. 45 no. 4 article id 100.
Keywords: forest planning; inventory; measurement errors; decision making; logistic regression; regression tree
Abstract | View details | Full text in PDF | Author Info
In forest planning, the decision maker chooses for each stand a treatment schedule for a predefined planning period. The choice is based either on optimization calculations or on silvicultural guidelines. Schedules for individual stands are obtained using a growth simulator, where measured stand characteristics such as the basal area, mean diameter, site class and mean height are used as input variables. These characteristics include errors, however, which may lead to incorrect decisions. In this study, the aim is to study the sensitivity of harvest decisions to errors in a dataset of 157 stands. Correct schedules according to silvicultural guidelines were first determined using error-free data. Different amounts of errors were then generated to the stand-specific characteristics, and the treatment schedule was selected again using the erroneous data. The decision was defined as correct, if the type of harvest in these two schedules were similar, and if the timings deviated at maximum ±2 for thinning and ±3 years for clear-cut. The dependency of probability of correct decisions on stand characteristics and the degree of errors was then modelled. The proposed model can be used to determine the required level of measurement accuracy for each characteristics in different kinds of stands, with a given accuracy requirement for the timing of treatments. This information can further be utilized in selecting the most appropriate inventory method.
  • Kangas, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: (email)
  • Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail:
  • Mäkinen, Simosol Oy, Riihimäki, Finland E-mail:
  • Vanhatalo, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail:
article id 473, category Research article
Ulla Mattila, Tuula Nuutinen. (2007). Assessing the incidence of butt rot in Norway spruce in southern Finland. Silva Fennica vol. 41 no. 1 article id 473.
Keywords: Norway spruce; logistic regression; Heterobasidion; Butt rot; quality loss
Abstract | View details | Full text in PDF | Author Info
The aim of this study was to analyze the occurrence of butt rot damage to Norway spruce in different parts of southern Finland and to quantify the associated loss of quality. The data used in the study are from the 9th National Forest Inventory and consist of 5998 sample plots and 8007 spruce sample trees of saw-timber size. To predict the probability of damage to stands and trees, logistic regression models were constructed. Separate models were made for the whole study area, for the area where the general risk of Heterobasidion root and butt rot damage is high and for the area where the damage frequency is relatively low. In the high-risk area, the probability of damage decreased with increasing elevation and increased with increasing temperature sum. In addition, damage was more common on fertile sites and less common on peatlands; and thick peat layer decreased the risk of damage. The probability of damage was also higher in stands where special or selective cuttings had been carried out. In the sample tree data, the probability of damage increased slightly with increasing diameter and age of the tree. In the low-risk areas, elevation was the only variable that explained the probability of damage to a spruce tree. Site fertility and previous cuttings (more than ten years ago) explained the probability of damage to stands only weakly. For spruce damaged by butt rot, the saw-timber volume was reduced, on average, by 60% both in the high-risk area and in the low-risk area.
  • Mattila, The Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: (email)
  • Nuutinen, The Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail:
article id 316, category Research article
Sonja Vospernik. (2006). Probability of bark stripping damage by red deer (Cervus elaphus) in Austria. Silva Fennica vol. 40 no. 4 article id 316.
Keywords: logistic regression; bark stripping; red deer
Abstract | View details | Full text in PDF | Author Info
Bark stripping by red deer (Cervus elaphus) causes considerable damage to Austrian forests, however, the incidence of bark stripping was never examined from large scale survey data. In this manuscript we present a logistic regression model for bark stripping damage (static model) and a model for recent (5-year period) bark stripping damage to previously undamaged trees (dynamic model) developed from Austrian National Forest Inventory data. Both models showed bark stripping damage to be most frequent in core red deer habitat areas and less frequent in less suitable habitat. Damage was concentrated at elevations of 400–1200 m and in alluvial forests (only static model). Norway spruce (Picea abies), European ash (Fraxinus excelsior), Sweet chestnut (Castanea sativa) and Sorbus spp. had 11–12 times more injuries than all the other species. Red deer preferred the smallest trees with a breast height diameter of 5 cm for bark stripping and damage probability decreased rapidly for trees with a breast height diameter greater than 25 cm. Our static model showed a maximum of bark stripping damage in stands with a mean height of 20 m. In the dynamic model the probability for bark stripping damage decreased with decreasing mean height. Also, in the static model the probability for bark stripping damage increased with increasing spruce proportion and with increasing stand density whereas in the dynamic model the proportion of previous bark stripping damage was a good predictor. Goodness of fit and discrimination of both models were good. In combination with forest growth models, the bark stripping models can be used to predict the risk of damage associated with different forest and habitat management options.
  • 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: (email)
article id 519, category Research article
Magnus Lindén, Gudmund Vollbrecht. (2002). Sensitivity of Picea abies to butt rot in pure stands and in mixed stands with Pinus sylvestris in southern Sweden. Silva Fennica vol. 36 no. 4 article id 519.
Keywords: Pinus sylvestris; Norway spruce; Picea abies; logistic regression; Heterobasidion annosum; Butt rot; mixed forest
Abstract | View details | Full text in PDF | Author Info
Repeatedly sampled data from permanent experimental plots in southern Sweden were used to model butt rot development in Norway spruce growing in pure stands and in mixed stands with Scots pine. The data come from 29 sites with pure spruce, altogether 100 plots, and from 15 sites of mixed spruce and pine, altogether 22 plots. A logistic model provided the best fit to the data. The study material revealed that in mixed stands the proportion of spruce trees with butt rot is lower than in pure Norway spruce stands. The difference in the incidence of butt rot cannot be explained by silviculture or windthrow since both factors are accounted for in the study. The most significant effect on butt rot development in Norway spruce by an admixture of Scots pine, was found when the Scots pine admixture was 50%. In order to reduce the incidence of butt rot in Norway spruce, the study material indicate that there is little to be gained by increasing the Scots pine admixture to much more than 50%.
  • Lindén, Southern Swedish Forest Research Centre c/o Asa Experimental Forest, SLU, S-360 30 Lammhult, Sweden E-mail: (email)
  • Vollbrecht, Southern Swedish Forest Research Centre c/o Asa Experimental Forest, SLU, S-360 30 Lammhult, Sweden E-mail:
article id 593, category Research article
Anneli Jalkanen. (2001). The probability of moose damage at the stand level in southern Finland. Silva Fennica vol. 35 no. 2 article id 593.
Keywords: National Forest Inventory; Alces alces; multilevel logistic regression; risk management
Abstract | View details | Full text in PDF | Author Info
The probability of moose damage was studied in sapling stands and young thinning stands in southern Finland. Data from the eighth National Forest Inventory in 1986–92 were used for modelling. The frequency of damage was highest at the height of two to five meters and at the age of ten to twenty years (at the time of measurement). Moose preferred aspen stands the most and least preferred Norway spruce stands. Scots pine and silver birch were also susceptible to damage. Logistic regression models were developed for predicting the probability that moose damage is the most important damaging agent in a forest stand. The best predictive variables were the age and dominant species of the stand. Variables describing the site were significant as cluster averages, possibly characterizing the area as a food source (fertility and organic soil), as well as the lack of shelter (wall stand). When sample plot, cluster and municipality levels were compared, it was found that most of the unexplained variance was at the cluster level. To improve the model, more information should be obtained from that level. The regression coefficients for aspen as supplementary species, and for pine as dominant species, had significant variance from cluster to cluster (area to area). It was also shown that the occurrence of aspen is closely connected to the occurrence of moose damage in pine sapling stands.
  • Jalkanen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: (email)

Category: Research note

article id 269, category Research note
Christian Kiffner, Elisabeth Rössiger, Oliver Trisl, Rainer Schulz, Ferdinand Rühe. (2008). Probability of recent bark stripping damage by red deer (Cervus elaphus) on Norway spruce (Picea abies) in a low mountain range in Germany – a preliminary analysis. Silva Fennica vol. 42 no. 1 article id 269.
Keywords: forest management; logistic regression; wildlife management
Abstract | View details | Full text in PDF | Author Info
Red deer (Cervus elaphus) can cause considerable damage to forest stands by bark stripping. Here, we examined the probability of bark stripping of susceptible Norway spruce (Picea abies) during winter in relation to local environmental characteristics in the western Harz Mountains, Lower Saxony, Germany. We present the results of a multiple logistic regression model for recent bark stripping damage by red deer which we developed from two systematic cluster sampling inventories after two winter periods along with associated meteorological data and records of bagged deer. Our model suggests that the risk of bark stripping increased significantly (P  0.05) with rising slope angle, cumulating snow depth and increasing index values of red deer population density. Spruces growing in closed forest stands were debarked at a higher probability than spruces located close to forest edges. Further on, spruce stands on eastern slopes had a lower probability of bark damage than spruce stands on northern slopes. Other tested variables (altitude, length of daily solar irradiation, duration of snow cover, age of spruce stand within the age range of 16–50 years) had no significant effect on the probability of new bark stripping. We conclude that red deer in the western Harz Mountains seem to use bark as food resource at preferred locations and in times of low food availability. To improve fit and predictive power of bark stripping models we recommend including stand characteristics. We propose to reduce the population size of red deer in order to diminish bark stripping damages to an economically acceptable level.
  • 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: (email)
  • 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:
  • Trisl, Planungsbüro Trisl, In der Schleene 7, 36037 Waake, Germany E-mail:
  • Schulz, University Göttingen, Büsgen-Institute, Department of Ecological Informatics, Biometry and Forest Growth, Büsgenweg 4, 37077 Göttingen, Germany E-mail:
  • 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:

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