Articles containing the keyword 'regression'

The prescribed burning of a 7.3 ha clear-cut and a 1.7 ha partially cut forest (volume 150 m³/ha) was carried out in Evo (61 °12'N, 25°07'E) on 1 June 1992. The forest was a mesic Myrtillus site type forest dominated by Norway spruce (Picea abies (L.) H. Karst.). Practically all the trees and the above-ground parts of the understorey vegetation died in the fire, while the mor layer was thinned by an average of 1.5 cm.

A study was made on the change of germinated seedling population in time and their dependence on environmental factors. Seedlings of Norway spruce, Scots pine (Pinus sylvestris L.), silver birch (Betula pendula Roth), pubescent birch (B. pubescens Ehrh.) and rowan (Sorbus aucuparia L.) were inventoried in 1993 and in 1994 on permanent plots, four times per growing season. Autoregression models were used to compare regeneration of tree species in the burned forest with regeneration in the burnt clear-cut area, and to study the effect of distance from nearest seed source to regeneration.

The average number of seedlings germinating in 1993 was higher than in 1994, probably because of differences between these consecutive years in regard to the amount of seed rain and weather conditions. The number of Norway spruce and rowan seedling was higher inside the forest area than in the clear-cut area. The distance to the bordering forest and to the closest seed tree did not explain the result. It is suggested that the more stable microclimatic conditions under the shade of dead tree promote germination and seedling establishment in the forest area. As rowan is a bird-dispersed species, it is likely that dead trees help the dispersal of rowan seed by providing birds place to sit and defecate. The shade provided by dead trees may influence the further succession of the tree stand and vegetation composition and diversity.

• Vanha-Majamaa, E-mail: iv@mm.unknown
• Suominen, E-mail: rs@mm.unknown
• Tonteri, E-mail: tt@mm.unknown
• Tuittila, E-mail: et@mm.unknown

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

Relationships between bulk density and organic matter (OM) content, textural properties and depth are described for forested mineral soils from Central and Northern Finland. Core samples were taken of 0–5, 30–35 and 60–65 cm layers at 75 plots. Three measures of bulk density were calculated: the bulk density of the < 20 mm fraction (BD₂₀), the bulk density of the < 2 mm fraction (BD₂), and laboratory bulk density (BD_l). BD_l was determined from the mass of a fixed volume of < 2 mm soil taken in the laboratory. All three measures of bulk densities were strongly correlated with organic matter content (r ≥ -0.63). Depth and gravel (2–20 mm) content (in the case of BD₂) were also important variables. BD_l was sensitive to clay contents > 7% but did significantly improve the prediction of both BD₂ and BD₂₀ in coarse soils (clay contents ≤ 7%). Predictive models were derived for coarse soils.

• Tamminen, E-mail: pt@mm.unknown
• Starr, E-mail: ms@mm.unknown

The paper demonstrates the possibility of using data from small relascope sample plots in the derivation of the regression models which predict the Weibull function parameters for the dbh-distribution. The Weibull parameters describing the basal area dbh-distribution were estimated for relascope sample plots from the Finnish National Forest Inventory. In the first stage of the estimation nonlinear regression analysis was employed to derive initial parameter estimates for the second stage, in which the maximum likelihood method was used. The parameter estimates were employed as dependent variables for the derivation of the regression models; the independent variables comprised of the compartment-wise stand variables generally estimated in ocular inventories.

The PDF includes an abstract in Finnish.

• Kilkki, E-mail: pk@mm.unknown
• Päivinen, E-mail: rp@mm.unknown

Dimensions (length, width and thickness) of needles in crowns of young Scots pine (Pinus sylvestris L.) were found to be related linearly to each other. Similarly, the needle area was linearly correlated with the needle biomass. In the lower crown, needle length was linearly correlated with the length of the shoot, but in the upper crown needle length did not vary according to any regular pattern. Needle density was negatively correlated with shoot length. In the lower crown the needle density varied 20–40 cm^-1 and in the upper crown 15– 20 cm^-1. The increasing angle of aging needles seemed to be characteristic for Scots pine shoots.

The PDF includes an abstract in Finnish.

• Ross, E-mail: jr@mm.unknown
• Kellomäki, E-mail: sk@mm.unknown
• Oker-Blom, E-mail: po@mm.unknown
• Ross, E-mail: vr@mm.unknown
• Vilikainen, E-mail: lv@mm.unknown

The objective of this study has been to discover some of the basic principles on which an increment for a large forest area might be forecast. Because the stands in a large forest area vary considerably in density and are subject to different kinds of treatment, the main interest falls on the stand characteristics which determine the increment percentage in such forest conditions as these. The material used in the study has been published earlier, it consisted of sample plots of Scots pine (Pinus sylvestris L.) stands (Nyyssönen 1954).

Increment functions are of great importance in the increment forecast for cutting budget. Because 60-80% of the variation in the increment percentage can be explained by stand characteristics in circumstances where the age of the stand is 40-130 years and the volume vary with a coefficient of variation 0.6-0.7, regression equations for increment percentage may be based on a number of sample plots smaller than in a growing stock inventory in the same conditions. It is possible to get accurate results with relatively small number of sample plots. Furthermore, the smaller amount of increment sample plots makes it possible to develop measurement techniques.

The increment functions enable study of increment as a biological process. However, conclusions about biological process on the basis of regression equations should be made with caution. Still, regression analysis is a powerful tool in yield studies.

The PDF includes a summary in Finnish.

• Kuusela, E-mail: kk@mm.unknown
• Kilkki, E-mail: pk@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

Models that predict forest development are essential for sustainable forest management. Constructing growth models via regression analysis or fitting a family of sigmoid equations to construct compatible growth and yield models are two ways these models can be developed. In this study, four species-specific models were developed and compared. A compatible growth and yield stand basal area model and a five-year stand basal area growth model were developed for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). The models were developed using data from permanent inventory plots from the Swedish national forest inventory and long-term experiments. The species-specific models were compared, using independent data from long-term experiments, with a stand basal area growth model currently used in the Swedish forest planning system Heureka (Elfving model). All new models had a good, relatively unbiased fit. There were no apparent differences between the models in their ability to predict basal area development, except for the slightly worse predictions for the Norway spruce growth model. The lack of difference in the model comparison showed that despite the simplicity of the compatible growth and yield models, these models could be recommended, especially when data availability is limited. Also, despite using more and newer data for model development in this study, the currently used Elfving model was equally good at predicting basal area. The lack of model difference indicate that future studies should instead focus on model development for heterogeneous forests which are common but lack in growth and yield modelling research.

• Goude, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, SE-230 53 Alnarp, Sweden https://orcid.org/0000-0002-2179-292X E-mail: martin.goude@slu.se
• Nilsson, Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, SE-230 53 Alnarp, Sweden E-mail: urban.nilsson@slu.se
• Mason, School of Forestry, University of Canterbury, Private Bag 4800, Christchurch, New Zealand E-mail: euan.mason@canterbury.ac.nz
• Vico, Department of Crop Production Ecology, Swedish University of Agricultural Sciences, SE-750 07 Uppsala, Sweden E-mail: giulia.vico@slu.se

Management of Scots pine (Pinus sylvestris L.) in Norway requires a forest growth and yield model suitable for describing stand dynamics of even-aged forests under contemporary climatic conditions with and without the effects of silvicultural thinning. A system of equations forming such a stand-level growth and yield model fitted to long-term experimental data is presented here. The growth and yield model consists of component equations for (i) dominant height, (ii) stem density (number of stems per hectare), (iii) total basal area, (iv) and total stem volume fitted simultaneously using seemingly unrelated regression. The component equations for stem density, basal area, and volume include a thinning modifier to forecast stand dynamics in thinned stands. It was shown that thinning significantly increased basal area and volume growth while reducing competition related mortality. No significant effect of thinning was found on dominant height. Model examination by means of various fit statistics indicated no obvious bias and improvement in prediction accuracy in comparison to existing models in general. An application of the developed stand-level model comparing different management scenarios exhibited plausible long-term behavior and we propose this is therefore suitable for national deployment.

• Kuehne, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: christian.kuehne@nibio.no
• McLean, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: paul.mclean@nibio.no
• Maleki, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: kobra.maleki@nibio.no
• Antón-Fernández, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: clara.anton.fernandez@nibio.no
• Astrup, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: rasmus.astrup@nibio.no

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m² collected over 350 km² of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.

• Ørka, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no
• Hansen, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway; Norwegian Forest Extension Institute, Honnevegen 60, NO-2836 Biri, Norway https://orcid.org/0000-0001-5174-4497 E-mail: eh@skogkurs.no
• Dalponte, Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige, TN, Italy https://orcid.org/0000-0001-9850-8985 E-mail: michele.dalponte@fmach.it
• Gobakken, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no
• Næsset, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no

Tree height-diameter allometry reflects the response of specific species to above and belowground resource allocation patterns. However, traditional methods (e.g. stepwise regression (SR)) may ignore model uncertainty during the variable selection process. In this study, 450 trees of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) grown at five spacings were used. We explored the height-diameter allometry in relation to stand and climate variables through Bayesian model averaging (BMA) and identifying the contributions of these variables to the allometry, as well as comparing with the SR method. Results showed the SR model was equal to the model with the third highest posterior probability of the BMA models. Although parameter estimates from the SR method were similar to BMA, BMA produced estimates with slightly narrower 95% intervals. Heights increased with increasing planting density, dominant height, and mean annual temperature, but decreased with increasing stand basal area and summer mean maximum temperature. The results indicated that temperature was the dominant climate variable shaping the height-diameter allometry for Chinese fir plantations. While the SR model included the mean coldest month temperature and winter mean minimum temperature, these variables were excluded in BMA, which indicated that redundant variables can be removed through BMA.

• Lu, Key Laboratory of Tree Breeding and Cultivation of the National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, P. R. China; Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, P. R. China E-mail: 18556439861@163.com
• Chhin, Division of Forestry and Natural Resources, West Virginia University, 322 Percival Hall, 1145 Evansdale Dr, Morgantown, West Virginia, 26506, USA E-mail: steve.chhin@mail.wvu.edu
• Zhang, Key Laboratory of Tree Breeding and Cultivation of the National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, P. R. China E-mail: xqzhang85@caf.ac.cn
• Zhang, Key Laboratory of Tree Breeding and Cultivation of the National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, P. R. China; Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, P. R. China E-mail: xqzhang85@yahoo.com

The tree stem volume models of Norway spruce, Scots pine and silver and downy birch currently used in Finland are based on data collected during 1968–1972. These models include four different formulations of a volume model, with three different combinations of independent variables: 1) diameter at height of 1.3 m above ground (dbh), 2) dbh and tree height (h) and 3) dbh, h and upper diameter at height of 6 m (d6). In recent National Forest Inventories of Finland, a difference in the mean volume prediction between the models with and without the upper diameter as predictor has been observed. To analyze the causes of this difference, terrestrial laser scanning (TLS) was used to acquire a large dataset in Finland during 2017–2018. Field-measured predictors and volumes predicted using spline functions fitted to the TLS data were used to re-calibrate the current volume models. The trunk form is different in these two datasets. The form height is larger in the new data for all diameter classes, which indicates that the tree trunks are more slender than they used to be. One probable reason for this change is the increase in stand densities, which is at least partly due to changed forest management. In models with both dbh and h as predictors, the volume is smaller a given h class in the data new data than in the old data, and vice versa for the diameter classes. The differences between the old and new models were largest with pine and smallest with birch.

• Kangas, Natural Resources Institute Finland (Luke), Yliopistokatu 6 B, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
• Henttonen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: helena.henttonen@luke.fi
• Pitkänen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-5389-8713 E-mail: timo.p.pitkanen@luke.fi
• Sarkkola, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: sakari.sarkkola@luke.fi
• Heikkinen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0003-3527-774X E-mail: juha.heikkinen@luke.fi

Growing demand for wood products, combined with efforts to conserve natural forests, have supported a steady increase in the global extent of planted forests. Here, a two-phase sampling strategy for large-scale assessment of the total area and the total wood volume of fast-growing forest tree crops within agricultural land is presented. The first phase is performed using tessellation stratified sampling on high-resolution remotely sensed imagery and is sufficient for estimating the total area of plantations by means of a Monte Carlo integration estimator. The second phase is performed using stratified sampling of the plantations selected in the first phase and is aimed at estimating total wood volume by means of an approximation of the first-phase Horvitz-Thompson estimator. Vegetation indices from Sentinel-2 are exploited as freely available auxiliary information in a linear regression estimator to improve the design-based precision of the estimator based on the sole sample data. Estimators of the totals and of the design-based variances of total estimators are presented. A simulation study is developed in order to check the design-based performance of the two alternative estimators under several artificial distributions supposed for poplar plantations (random, clustered, spatially trended). An application in Northern Italy is also reported. The regression estimator turns out to be invariably better than that based on the sole sample information. Possible integrations of the proposed sampling scheme with conventional national forest inventories adopting tessellation stratified sampling in the first phase are discussed.

• Marcelli, University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems, Viterbo, Italy; Fondazione Edmund Mach, Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, San Michele all’Adige, Italy E-mail: agnese.marcelli@student.unisi.it
• Mattioli, University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems, Viterbo, Italy; CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: walter.mattioli@crea.gov.it
• Puletti, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: nicola.puletti@crea.gov.it
• Chianucci, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: fchianucci@gmail.com
• Gianelle, Fondazione Edmund Mach, Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, San Michele all’Adige, Italy E-mail: damiano.gianelle@fmach.it
• Grotti, CREA, Research Centre for Forestry and Wood, Arezzo, Italy; University of Roma La Sapienza, Department of Architecture and Design, Rome, Italy E-mail: mirkogrotti@gmail.com
• Chirici, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: gherardo.chirici@unifi.it
• D' Amico, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: giovanni.damico@unifi.it
• Francini, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy; University of Molise, Department of Agricultural, Environmental and Food Sciences, Campobasso, Italy E-mail: saverio.francini@gmail.com
• Travaglini, University of Firenze, Department of Agriculture, Food, Environment and Forestry, Florence, Italy E-mail: davide.travaglini@unifi.it
• Fattorini, University of Siena, Department of Economics and Statistics, Siena, Italy E-mail: lorenzo.fattorini@unisi.it
• Corona, CREA, Research Centre for Forestry and Wood, Arezzo, Italy E-mail: piermaria.corona@crea.gov.it

Korean pine (Pinus koraiensis Siebold & Zucc.) is economically the most important tree species in northeast China. Korean pine plantations are established and managed for the production of timber and seeds. Despite the importance of the species, few models have been developed for the comparison of alternative management schedules. Model development is affected by the fact that permanent sample plots and thinning experiments have not been designed and managed for modeling purposes. The permanent sample plots include few non-thinned plots, and weak trees are removed in thinning treatments, leading to low mortality rate. Moreover, the measurement interval is irregular. This study used optimization-based modeling approach in tree-level diameter increment and survival modeling to deal with the above problems. Models for self-thinning limit were developed to alleviate the problem of underestimated mortality arising from the features of the data. In addition, improved site index and individual-tree height models were developed. The model of Lundqvist and Korf was used as the site index model and the model proposed by Schumacher as the height model. Quantile regression was used to model the maximum stand basal area and maximum number of trees as a function of mean tree diameter and site index. Tree diameter, stand basal area, basal area in larger trees and site index were used as the predictors of diameter increment and tree survival. The models developed in this study constitute a model set that is suitable for simulation and optimization studies. The models produced simulation results that correspond to measured stand development.

• Jin, Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, People’s Republic of China http://orcid.org/0000-0003-2971-2709 E-mail: xingji_jin@163.com
• Pukkala, Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, People’s Republic of China; University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.pukkala@uef.fi
• Li, Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, People’s Republic of China http://orcid.org/0000-0002-4058-769X E-mail: fengrili@126.com
• Dong, Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, People’s Republic of China http://orcid.org/0000-0002-3985-9475 E-mail: ldonglihu2006@163.com

Despite the numerous studies on year-to-year variation of tree growth, the physiological mechanisms controlling annual variation in growth are still not understood in detail. We studied the applicability of data-driven approach i.e. different regression models in analysing high-dimensional data set including continuous and comprehensive measurements over meteorology, ecosystem-scale water and carbon fluxes and the annual variation in the growth of app. 50-year-old Scots pine stand in southern Finland. Even though our dataset covered only 16 years, it is the most extensive collection of interactions between a Scots pine ecosystem and atmosphere. The analysis revealed that height growth was favoured by high water potential of the tree and carbon gain during the bud forming period and high water potential during the elongation period. Diameter growth seemed to be favoured by a winter with high precipitation and deep snow cover and a spring with high carbon gain. The obtained models had low generalization performance and they would require more evaluation and iterative validation to achieve credibility perhaps as a mixture of data-driven and first principle modeling approaches.

• Kulmala, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: liisa.kulmala@helsinki.fi
• Žliobaitė, Aalto University, Department of Computer Science and Helsinki Institute for Information Technology, P.O. Box 11000, FI-00076 Aalto, Finland; University of Helsinki, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Finland E-mail: zliobaite@gmail.com
• Nikinmaa, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: eero.nikinmaa@helsinki.fi
• Nöjd, Natural Resources Institute Finland (Luke), Bio-based business and industry, Tietotie 2, FI-02150 Espoo, Finland E-mail: pekka.nojd@luke.fi
• Kolari, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland; University of Helsinki, Department of Physics, P.O. Box 64, FI-00014 University of Helsinki, Finland E-mail: pasi.kolari@helsinki.fi
• Kabiri Koupaei, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: kourosh.kabiri@helsinki.fi
• Hollmén, Aalto University, Department of Computer Science and Helsinki Institute for Information Technology, P.O. Box 11000, FI-00076 Aalto, Finland; University of Helsinki, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Finland E-mail: jaakko.hollmen@aalto.fi
• Mäkinen, Natural Resources Institute Finland (Luke), Bio-based business and industry, Tietotie 2, FI-02150 Espoo, Finland E-mail: harri.makinen@luke.fi

The aim of this study was to examine how well stem volume, above-ground biomass and dominant height can be predicted using nationwide airborne laser scanning (ALS) based regression models. The study material consisted of nine practical ALS inventory projects taken from different parts of Finland. We used field sample plots and airborne laser scanning data to create nationwide and regional models for each response variable. The final models had one or two ALS predictors, which were chosen based on the root mean square error (RMSE), and cross-validated. Finally, we tested how much predictions would improve if the nationwide models were calibrated with a small number of regional sample plots. Although forest structures differ among different parts of Finland, the nationwide volume and biomass models performed quite well (leave-inventory-area-out RMSE 22.3% to 33.8%, mean difference [MD] –13.8% to 18.7%) compared with regional models (leave-plot-out RMSE 20.2% to 26.8%). However, the nationwide dominant height model (RMSE 5.4% to 7.7%, MD –2.0% to 2.8%, with the exception of the Tornio region – RMSE 11.4%, MD –9.1%) performed nearly as well as the regional models (RMSE 5.2% to 6.7%). The results show that the nationwide volume and biomass models provided different means than real means at regional level, because forest structure and ALS device have a considerable effect on the predictions. Large MDs appeared especially in northern Finland. Local calibration decreased the MD and RMSE of volume and biomass models. However, the nationwide dominant height model did not benefit much from calibration.

• Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi
• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
• Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi

We determined empirical models for estimating total aboveground as well as stem, branches, and foliage dry biomass of young (age up to 16 years) silver birch (Betula pendula Roth.) growing on the post-agricultural lands. Two sets of allometric models for trees with a height below or above 1.3 m (small and large trees respectively) were developed. Simplified models were elaborated based exclusively on appropriate tree diameter (diameter at ground level for small trees, diameter at breast height for large trees), while expanded models also included tree height. Total aboveground biomass was estimated as the sum of biomass of all tree components. To assure additivity of the developed equations, the seemingly unrelated regression approach for the final model fitting was used. Expanded models in both tree groups were characterized by a better fit to the data (R² for total aboveground biomass for small and large trees equaled 0.8768 and 0.9752, respectively). Diameter at breast height appeared to be a better predictor than diameter at ground level – simplified models had better fit for large trees (R² for total aboveground biomass equals 0.9611) than for small ones (R² = 0.7516). The developed equations provide biomass predictions consistent with available Latvian, Estonian, Finnish, Swedish, and Norwegian models for silver birch.

• Bronisz, Laboratory of Dendrometry and Forest Productivity, Faculty of Forestry, Warsaw University of Life Sciences – SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland E-mail: karol.bronisz@wl.sggw.pl
• Strub, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, 30605, USA E-mail: strub@mcfns.com
• Cieszewski, Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, 30605, USA E-mail: thebiomat@gmail.com
• Bijak, Laboratory of Dendrometry and Forest Productivity, Faculty of Forestry, Warsaw University of Life Sciences – SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland E-mail: szymon.bijak@wl.sggw.pl
• Bronisz, Laboratory of Dendrometry and Forest Productivity, Faculty of Forestry, Warsaw University of Life Sciences – SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland E-mail: agnieszka.bronisz@wl.sggw.pl
• Tomusiak, Laboratory of Dendrometry and Forest Productivity, Faculty of Forestry, Warsaw University of Life Sciences – SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland E-mail: robert.tomusiak@wl.sggw.pl
• Wojtan, Laboratory of Dendrometry and Forest Productivity, Faculty of Forestry, Warsaw University of Life Sciences – SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland E-mail: rafal.wojtan@wl.sggw.pl
• Zasada, Laboratory of Dendrometry and Forest Productivity, Faculty of Forestry, Warsaw University of Life Sciences – SGGW, Nowoursynowska 159, 02-787 Warsaw, Poland E-mail: michal.zasada@wl.sggw.pl

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

The density of Picea abies [L.] Karst. regeneration on different microsites, the quantity and quality of woody microsites, and seedling occurrence probability on stumps and fallen deadwood were studied in a subalpine forest that has been under protection for approximately 30–40 years (Gorce Mountains in the western Carpathians). Thirty percent of seedlings and 29% of saplings grew on stumps and fallen deadwood, while the remaining regeneration occurred on soil surface and mounds created by uprooted trees. The occurrence probability of Picea seedlings on fallen deadwood increased with deadwood diameter and decay stage and with the volume of living trees, and decreased with increased density of living trees, sapling density, and land slope. Furthermore, seedlings were more likely to grow on stumps with a greater diameter and in plots with higher sapling density, but less likely to grow on higher stumps. Stumps and fallen deadwood covered about 4% of the forest floor, but the material that is most important for promoting regeneration (strongly decomposed logs and those of a diameter exceeding 30 cm) took up only about 22 m² ha^-1. We have concluded that in a subalpine forest that has been protected for 30–40 years regeneration processes take place mostly on soil surface and stumps. The role of fallen deadwood increases over time as a greater number of suitable logs (in terms of size and decay stage) become available.

• Bujoczek, University of Agriculture in Krakow, E-mail: lbujoczek@gmail.com
• Bujoczek, University of Agriculture in Krakow, E-mail: bujoczek.m@gmail.com
• Banaś, University of Agriculture in Krakow, E-mail: rlbanas@cyf-kr.edu.pl
• Zięba, University of Agriculture in Krakow, E-mail: rlzieba@cyf-kr.edu.pl

• Uotila, Finnish Forest Research Institute, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: karri.uotila@metla.fi
• Saksa, Finnish Forest Research Institute, Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: timo.saksa@metla.fi
• Rantala, Metsä Group, Lielahdenkatu 10, FI-33400 Tampere, Finland E-mail: juho.rantala@metsagroup.com
• Kiljunen, Metsähallitus, Asemakatu 7, FI-70107 Kuopio, Finland E-mail: nuutti.kiljunen@metsa.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

• 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

• 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

• Kindermann, International Institute for Applied Systems Analysis, Laxenburg, Austria E-mail: kinder@iiasa.ac.at
• McCallum, International Institute for Applied Systems Analysis, Laxenburg, Austria E-mail: im@nn.at
• Fritz, International Institute for Applied Systems Analysis, Laxenburg, Austria E-mail: sf@nn.at
• Obersteiner, International Institute for Applied Systems Analysis, Laxenburg, Austria E-mail: mo@nn.at

• Fox, School of Forest and Ecosystem Science, University of Melbourne, Burnley Campus, 500 Yarra Blvd, Richmond, Victoria 3121 Australia E-mail: jcfox@unimelb.edu.au
• Bi, Forest Resources Research, New South Wales Department of Primary Industries, PO Box 100, Beecroft, NSW 2119 Australia E-mail: hb@nn.au
• Ades, School of Forest and Ecosystem Science, University of Melbourne, Burnley Campus, 500 Yarra Blvd, Richmond, Victoria 3121 Australia E-mail: pka@nn.au

• Korhonen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@joensuu.fi
• Korhonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ktk@nn.fi
• Stenberg, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail: ps@nn.fi
• Maltamo, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
• Rautiainen, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail: mr@nn.fi

• Raiskila, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sr@nn.fi
• Pulkkinen, Department of Forest Ecology, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: mp@nn.fi
• Laakso, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: tl@nn.fi
• Fagerstedt, Department of Biological and Environmental Sciences, Plant Biology, P.O. Box 65, FI-00014 University of Helsinki, Finland E-mail: kf@nn.fi
• Löija, VTT Building and Transport, P.O. Box 1806, FI-02044 VTT, Finland E-mail: ml@nn.fi
• Mahlberg, VTT Building and Transport, P.O. Box 1806, FI-02044 VTT, Finland E-mail: rm@nn.fi
• Paajanen, VTT Building and Transport, P.O. Box 1806, FI-02044 VTT, Finland E-mail: lp@nn.fi
• Ritschkoff, VTT Building and Transport, P.O. Box 1806, FI-02044 VTT, Finland E-mail: acr@nn.fi
• Saranpää, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: pekka.saranpaa@metla.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

• 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

• Magnussen, Canadian Forest Service, Victoria, BC, Canada. V8Z 1M5 E-mail: smagnussen@pfc.forestry.ca
• Alfaro, Canadian Forest Service, Victoria, BC, Canada. V8Z 1M5 E-mail: ria@nn.ca
• Boudewyn, Canadian Forest Service, Victoria, BC, Canada. V8Z 1M5 E-mail: pb@nn.ca

• 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

• Maltamo, Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: matti.maltamo@forest.joensuu.fi
• Eerikäinen, Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: ke@nn.fi

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