We describe the methodology applied in the 12th national forest inventory of Finland (NFI12) and describe the state of Finland’s forests as well as the development of some key parameters since 1920s. According to the NFI12, the area of forestry land (consisting of productive and poorly productive forest, unproductive land, and other forestry land) is 26.2 M ha. The area of forestry land has decreased from 1920s to 1960s due to expansion of agriculture and built-up land. 20% of the forestry land is not available for wood supply and 13% is only partly available for wood supply. The area of peatlands is 8.8 M ha, which is one third of the forestry land. 53% of the current area of peatlands is drained. The volume of growing stock, 2500 M m, is 1.7 times the volume estimated in NFI1 in the 1920s for the current territory of Finland. The estimated annual volume increment is 107.8 M m. The increment estimate has doubled since the estimate of NFI2 implemented in late 1930s. The annual mortality is estimated to 7 M m, which is 0.5 M m more than according to the previous inventory. Serious or complete damage was observed on 2% of the productive forest available for wood supply. The amount of dead wood is on average 5.8 m ha in productive forests. Since the NFI9 (1996–2003) the amount of dead wood has increased in South Finland and decreased in North Finland both in protected forests and forests available for wood supply (FAWS). The area of natural or almost natural forests on productive forest is 380 000 ha, out of this, 42 000 ha are in FAWS and 340 000 ha in protected forests.3 3 3 3 3 –1

Airborne scanning LiDAR is a promising technique for efficient and accuratebiomass mapping due to its capacity for direct measurement of the three-dimensionalstructure of vegetation. A combination of individual tree detection (ITD) and an area-basedapproach (ABA) introduced in Vastaranta et al. [1] to map forest aboveground biomass(AGB) and stem volume (VOL) was investigated. The main objective of this study was totest the usability and accuracy of LiDAR in biomass mapping. The nearest neighbourmethod was used in the ABA imputations and the accuracy of the biomass estimation wasevaluated in the Finland, where single tree-level biomass models are available. The relativeroot-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9%and 26.4% when field measurements were used in training the ABA. When ITDmeasurements were used in training, the respective accuracies ranged between 28.5%–34.9%and 29.2%–34.0%. Overall, the results show that accurate plot-level AGB estimates can beachieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL wasencouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that itis not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide acost-efficient application for acquiring training data for ABA in forest biomass mapping.

Research Highlights: We show the difference in the long-term effects on economic and ecological forest values between four forest management scenarios of a large representative forest landscape. The scenarios were largely formulated by stakeholders representing the main views on how to manage north-European forests. Background and Objectives: Views on how to balance forest management between wood production and biodiversity differ widely between different stakeholder groups. We aim to show the long-term consequences of stakeholder-defined management scenarios, in terms of ecological and economic forest values. Materials and Methods: We simulated management scenarios for a forest landscape in Sweden, based on the management objectives and strategies of key stakeholders. We specifically investigated the difference in economic forest values coupled to wood supply and ecological indicators coupled to structural biodiversity between the scenarios over a 100-year period. The indicators were net present value, harvest, growing stock and increment, along with deadwood volume, the density of large trees, area of old forests and mature broadleaf-rich forests. Results: We show that the scenarios have widely different outcomes in terms of the studied indicators, and that differences in indicator outcome were largely due to different distributions in management regimes, i.e., the proportion of forest left unmanaged or under even-aged management or continuous cover forest, as well as specific retention practices. Retention and continuous cover forestry mitigate the negative effects that clear-cut forestry has upon biodiversity. Conclusions: We found that an increase in the forest area under the continuous cover forestry regime could be a cost-efficient way to increase structural diversity in managed boreal forests. On the other hand, no single management regime performed best with respect to all indicators, which means that a mixture of several management regimes is needed to balance conflicting objectives. We also show that the trade-off between economic and ecological indicators was not directly proportional, meaning that an increase in structural biodiversity may be obtained at a proportionally low cost with appropriate management planning.

Aim: Climate change is expected to have major impacts on terrestrial biodiversity at all ecosystem levels, including reductions in species‐level distribution and abundance. We aim to test the extent to which land use management, such as setting‐aside forest from production, could reduce climate‐induced biodiversity impacts for specialist species over large geographical gradients.Location: Sweden.Methods: We applied ensembles of species distribution models based on citizen science data for six species of red‐listed old‐forest indicator fungi confined to spruce dead wood. We tested the effect on species habitat suitabilities of alternative climate change scenarios and varying amounts of forest set‐aside from production over the coming century.Results: With 3.6% of forest area set‐aside from production and assuming no climate change, overall habitat suitabilities for all six species were projected to increase in response to maturing spruce in set‐aside forest. However, overall habitat suitabilities for all six species were projected to decline under climate change scenario RCP4.5 (intermediate–low emissions), with even greater declines projected under RCP 8.5 (high emissions). Increasing the amount of forest set‐aside to 16% resulted in significant increases in overall habitat suitability, with one species showing an increase. A further increase to 32% forest set‐aside resulted in considerably more positive trends, with three of six species increasing.Main conclusions: There is interspecific variation in the importance of future macroclimate and resource availability on species occurrence. However, large‐scale conservation measures, such as increasing resource availability through setting aside forest from production, could reduce future negative effects from climate change, and early investment in conservation is likely to reduce the future negative impacts of climate change on specialist species.

The objective of this study was to make preliminary investigations between accurately measured field biomasses and terrestrial laser scanning (TLS) measurements including tree crown and stem diameters. Stem and crown biomass were determined based on detailed field measurements of the individual tree stem, bark, branch and needles. At the tree level, field measurements were intensive and thus material consisted of only 20 trees located at 11 stands. Stem and crown diameters were extracted manually from TLS point clouds and used as predictors for total biomass. Correlations from 0.96 to 0.99 between predicted and field measured biomass estimates were obtained. Examination of stem form predictions showed that various diameters measured by TLS could enhance the tree level stem curve predictions. Results are rather promising, but more field data is needed for developing practical modelling means. Our further studies will concentrate on automation of TLS data processing and use the of TLS features in the biomass estimation.