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The occurrence of moose damage was studied using data from three National Forest Inventories (NFIs) accomplished between 1986 and 2008 in Finland. The combined data included a total of 97â390 young stands. The proportion of moose damage increased from 3.6% to 8.6% between the 8th NFI (1986â1994) and the 10th NFI (2004â2008). The majority (75%) of the damage occurred in Scots pine-dominated stands. The proportion of damage was higher in aspen-dominated stands than in stands dominated by any other tree species. The tree species mixture also had a clear effect on the occurrence of damage. Pure Scots pine stands had less damage than mixed Scots pine stands, and moose damage decreased linearly with the increasing proportion of Scots pine. Stands on mineral soil had more frequent moose damage than stands on peatlands. The fertility class of the site had no straightforward effect on the damage frequency. Artificially regenerated stands had more damage than naturally regenerated stands. Accomplished soil preparation measures and the need for thinning or clearing operations increased moose damage. High proportions of moose damage in young stands were found around the country. In the 10th NFI, the largest concentration of damage was found in southwestern Finland. Our study shows the temporal and spatial changes in the occurrence of moose damage and pinpoints some important silvicultural factors affecting the relative risk of young stands over a large geographical area.

In Finland, there are currently two, parallel sample-plot-based forest inventory systems, which differ in their methodologies, sampling designs, and objectives. One is the National Forest Inventory (NFI), aimed at unbiased inventory results at national and regional level. The other is the Forest Centreâs management-oriented forest inventory based on interpretation of airborne laser scanning and aerial images, with the aim of locally accurate stand-level forest estimates. The National Forest Inventory utilises relascope sample plots with systematic cluster sampling. This inventory method is optimised for accuracy of regional volume estimates. In contrast, the management-oriented forest inventory utilises circular sample plots with an allocation system covering certain pre-defined forest classes in the inventory area. This method is optimised to produce reference data for interpretation of the remote-sensing materials in use. In this study, we tested the feasibility of the National Forest Inventory sample plots in provision of additional reference data for the management-oriented inventory. Various combinations of NFI plots and management inventory plots were tested in the interpretation of the laser and aerial-image data. Adding NFI plots in the reference data generally improved the accuracy of the volume estimates by tree species but not the estimates of total volume or stand mean height and diameter. The difference between the plot types in the NFI and management inventories causes difficulties in combination of the two datasets.

Accurate field measurement of the forest canopy cover is too laborious to be used in extensive forest inventories. A possible alternative to the separate canopy cover measurements is to utilize the correlations between the percent canopy cover and easier-to-measure forest variables, especially the basal area. A fairly new analysis technique, the beta regression, is specially designed for modelling percentages. As an extension to the generalized linear models, the beta regression takes into account the distribution of the model residuals, and uses a logistic link function to ensure logical predictions. In this study, the beta regression method was found to perform well in conifer dominated study area located in central Finland. The same model shape, with basal area, tree height and an additional predictor (Scots pine: site fertility, Norway spruce: percentage of hardwoods) as independent variables, produced good results for both pine and spruce dominated sites. The models had reasonably high pseudo R-squared values (pine: 0.91, spruce: 0.87) and low standard errors (pine: 6.3%, spruce: 5.9%) for the fitting data, and also performed well in a cross validation test. The models were also tested on separate test plots located in a different geographical area, where the prediction errors were slightly larger (pine: 8.8%, spruce: 7.4%). In pine plots, the model fit was further improved by introducing additional predictors such as stand age and density. This improved also the performance of the models in the cross validation test, but weakened the results for the external data set. Our results indicated that the beta regression method offers a noteworthy alternative to separate canopy cover measurements, especially if time is limited and the models can be applied in the same region where the modelling data were collected.

Estimation of forest canopy cover has recently been included in many forest inventory programmes. In this study, after discussing how canopy cover is defined, different ground-based canopy cover estimation techniques are compared to determine which would be the most feasible for a large scale forest inventory. Canopy cover was estimated in 19 Scots pine or Norway spruce dominated plots using the Cajanus tube, line intersect sampling, modified spherical densiometer, digital photographs, and ocular estimation. The comparisons were based on the differences in values acquired with selected techniques and control values acquired with the Cajanus tube. The statistical significance of the differences between the techniques was tested with the nonparametric Kruskall-Wallis analysis of variance and multiple comparisons. The results indicate that different techniques yield considerably different canopy cover estimates. In general, labour intensive techniques (the Cajanus tube, line intersect sampling) provide unbiased and more precise estimates, whereas the estimates provided by fast techniques (digital photographs, ocular estimation) have larger variances and may also be seriously biased.

A multi-source forest inventory method is applied to the estimation of forest resources in the area of the Hebei Forest Bureau in Heilongjiang province in North-East China. A stratified systematic cluster sampling design was utilised in field measurements. The design was constructed on the basis of information from earlier stand-level inventories, aerial orthophotographs, experiences from other sampling inventories and the available budget. Sample tree volumes were estimated by means of existing models. New models were constructed and their parameters estimated for tallied tree volumes and volume increments. The estimates for the area of the Bureau were computed from field measurements, and for the areas of the forest farms estimated from field measurements and satellite images. A k-nearest neighbour method was utilised. This method employing satellite image data makes it possible to estimate all variables, particularly for smaller areas than that possible using field measurements only. The methods presented, or their modifications, could also be applied to the planning and realisation of forest inventories elsewhere in Temperate or Boreal zones. The inventory in question gave an estimate of 114 m3/ha (the multi-source inventory 119 m3/ha) instead of 72 m3/ha as previously estimated from available information. Totally nineteen tree species, genera of species or tree species groups were identified (Appendix 1). The forests were relatively young, 60% of them younger than 40 years and 85% younger than 60 years.

Using the Finnish MELA model, a set of scenarios were produced and used to map the possibilities and risks surrounding the utilisation of peatlands in wood production in Finland. One of the scenarios was an estimate of allowable-cut calculated by maximising the net present value of the future revenues using a four per cent interest rate subject to non-decreasing flow of wood, saw logs and net income over a 50-year period, and net present value after the 50 year period greater or equal than in the beginning. The estimate for maximum regionally sustained removal in 1996â2005 was 68 million m3 per year â approaching 74 million m3 during the next decades. In this scenario, 14 per cent of all cuttings during the period 1996â2005 would be made on peatlands, which comprise ca. 31 per cent of the total area of forestry land. By the year 2025, the proportion of peatland cuttings would increase to over 20 per cent. The increase in future cutting possibilities on peatlands compensated for a temporary decrease in cuttings and growing stock on mineral soils. The allowable-cut effect was especially pronounced in northern Finland, where peatlands play an important role in wood production. In addition, the sensitivity of cutting possibilities for assumptions related to growth and price were analysed. The estimate of maximum sustainable yield as defined here seems to be fairly robust on the whole, except in northern Finland where the cutting scenarios were sensitive to the changes in the price of birch pulpwood. The proportion of peatland stands that are profitable for timber production depends on the interest rate: the higher the rate of interest the less peatland stands are thinned. The effect of cutting profile on future logging conditions and resulting costs were analysed in two forestry centres. If clear cuttings on mineral soils are to be cut first, an increase in future logging costs is inevitable.

Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model. Examination of spatial distribution of residuals showed that spatial correlation of residuals is lower for semiparametric and mixed models than for parametric models with fixed regressors. Mixed models and semiparametric models can both be used for describing the effect of geographic location on stem form.

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

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

Correlation functions of the mean volume, land use class and soil class were estimated using the data of the Finnish National Forest Inventory. Estimated functions were used for approximating the standard error of e.g. the mean volume of a cluster of plots. Standard error estimates can be used for comparing different inventory designs. The PDF includes an abstract in Finnish.

Persistence of standing dead trees (snags) is an important determinant for their role for biodiversity and dead wood associated carbon fluxes. How fast snags fall varies widely among species and regions and is further influenced by a variety of stand‐ and tree‐level factors. However, our understanding of this variation is fragmentary at best, partly due to lack of empirical data.Here, we took advantage of the accruing time series of snag observations in the Finnish, Norwegian and Swedish National Forest Inventories that have been followed in these programs since the mid‐1990s. We first harmonized observations from slightly different inventory protocols and then, using this harmonized dataset of ca. 43,000 observations that had a consistent 5‐year census interval, we modelled the probability of snags of the main boreal tree species Pinus sylvestris, Picea abies and Betula spp. falling, as a function of tree‐ and stand‐level variables, using Bayesian logistic regression modelling.The models were moderately good at predicting snags remaining standing or falling, with a correct classification rate ranging from 68% to 75% among species.In general, snag persistence increased with tree size and climatic wetness, and decreased with temperature sum, advancing stage of decay, site productivity and disturbance intensity (mainly harvesting).Synthesis and applications: The effect of harvesting demonstrates that an efficient avenue to increase the amount of snags in managed forests is protecting them during silvicultural operations. In the warmer future, negative relationship between snag persistence and temperature suggests decreasing the time snags remain standing and hence decreasing habitat availability for associated species. As decomposition rates generally increase after fall, decreasing snag persistence also implies substantially faster release of carbon from dead wood. [ABSTRACT FROM AUTHOR]

This study's objectives were to suggest harmonised criteria for the definition of mixed forests for two Nordic countries, describe their principal mixture types, and provide an overview of their current extent. We used national forest inventory data compiled in Finland and Sweden, considering the forest available for wood supply (FAWS), excluding seedling and sapling plots before canopy closure. The definition of the mixed forest was based on the threshold criteria, which indicate the basal area proportion of the dominant tree species of the total in a stand. The proportion of mixed forests increased with higher threshold criteria: 21% to 42% in Finland and 24% to 49% in Sweden, as the threshold criterion was changed from 65% to 85%. With a threshold criterion of 75%, the area of mixed FAWS was 5.6 million ha (31% of FAWS) in Finland and 6.5 million ha (36%) in Sweden. The dominant mixture type was the pine-spruce-birches mixture (31%) in Finland and the pine-spruce mixture (29%) in Sweden. The proportion of peatland forest of mixed forests was similar in the countries: 9-10%. The mixed forests proportion increased from north boreal to hemiboreal, increasing with more mature development classes. [ABSTRACT FROM AUTHOR]

According to protection targets of EU Biodiversity Strategy 2030, 30% of EU's land and sea area should be legally protected by 2030. While the actual targets and even terms are still to be defined, the strategy's possible impacts can be simulated for policy support purposes. The strategy aims to increase protection by setting aside forests and extending closer-to-nature forest management practices. We simulated Finland's forest resources with the European Forestry Dynamics Model (EFDM) considering the protection targets. According to our simulations, the total growing stock and net present value will be lower with the additional protection than by carrying on the business-as-usual management. The additional protection resulted in intensified harvesting in the remaining forest area available for wood supply with the current harvest level. This may weaken the positive ecological impacts of protection. Reducing the harvest level from the current level is not a likely alternative either, because the demand for biomass is predicted to increase in the future, while the supply is insufficient, and EU policies aim to avoid shifting the negative impacts of resource extraction outside the EU. The feasibility of the additional protection depends on the actual implementation and definitions. [ABSTRACT FROM AUTHOR]