The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field.

Background: The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results: While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions: The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research.

Economically-oriented forestry aims to sustain timber harvest revenues, while ecologically-oriented management supplies suitable habitat for species using deadwood as primary habitat. As these objectives are conflicting, planning for economic and ecological sustainability involves compromise and trade-offs. We analyze the spatial trade-offs between the economic value from timber harvesting and the volume of deadwood in the boreal forest. We assess these trade-offs from three perspectives: (1) landscape characteristics, affected by conservation strategies; (2) forest management promoting either economic or ecological values; (3) uncertainty in inventory errors undermining the estimate of the two sustainability objectives. To reveal the tradeoffs between the forest economic and ecological values we simulated and optimized a production landscape in Finland 30 years into the future accounting for uncertainty in biomass and deadwood inventories. We found that, with a limited reduction in timber harvesting (7%), (i) the amount of deadwood increased more in non-aggregated (45%) than in aggregated (16%) stands, (ii) constraining stands in adjacent areas further increased deadwood (21%) respect to the matrix and (iii) 7% of connected stand area harbored ≥20 m³/ha deadwood supporting survival of near-threatened species. Our results demonstrate that the structure of the landscape for biodiversity can be improved with limited economic losses. However, improving habitat configuration requires larger economic losses than only increasing habitat amount, but its ecological benefits are larger both for common and red-listed species. We found that management oriented towards stand aggregation not only creates connected areas with high deadwood of high value biodiversity but also improves the value of the whole matrix by decreasing intensive timber harvesting and energy wood collection. Finally, we found that uncertainties alter the estimate of the potential of the forest landscape to supply deadwood, and this can affect the choice of management actions to allocate over the landscape. To conclude, our results demonstrate the trade-offs between economic forest use and conservation are affected differently by landscape characteristics, forest management and uncertainty in inventory errors. As such these drivers should be considered when optimizing the forest for multiple uses.

We explore the factors affecting the optimal plot design (size and type as well as the subsample tree selection strategies within a plot) and their relative importance in defining the optimal plot design in amultipurpose forest inventory. The factors include time used to lay out the plot and to make the tree measurements within the plot, the between-plot variation of each of the variables of interest in the area, and the measurement and model errors for the different variables. We simulate different plot types and sizes and subsample tree selection strategies on measuredtest areas from North Lapland. The plot types used are fixed-radius, concentric and relascope plots. Weselect the optimal type and size first at plot level using a cost-plus-loss approach and then at cluster level byminimizing the weighted standard error with fixed budget. As relascope plots are very efficient at the plot level for volume and basal area, and fixed-radius plots for stems per ha, the optimal plot type strongly depends on the relative importance of these variables. The concentric plot seems to be a good compromise between these two in many cases. The subsample tree selection strategy was more important in selecting optimal plot than many other factors. In cluster level, the most important factor is the transfer time between plots. While the optimal radius of plots and other parameters were sensitive to the measurement times and other cost factors, the concentric plot type was optimal in almost all studied cases. Subsample tree measurement strategies need further studies, as they were an important cost factor. However, their importance to the precision was not as clear.