National forest inventories typically estimate individual tree volumes using models that rely on measurements of predictor variables such as tree height and dia

• Key message A dataset of forest resource projections in 23 European countries to 2040 has been prepared for forest-related policy analysis and decision-making. Due to applying harmonised definitions, while maintaining country-specific forestry practices, the projections should be usable from national to international levels. The dataset can be accessed at https://doi.org/10.5061/dryad.4t880qh . The associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/8f93e0d6-b524-43bd-bdb8-621ad5ae6fa9 .

In this paper a new concept for modeling uneven-aged forests (UEAF) is presented. The term UEAF in this article encloses all forests that deviate from the even-aged structure. The matrix model is area-based, in that the forest under study is described by a distribution of areas over fixed state-spaces spanned by stem number and volume per hectare classes. Dynamics is introduced as transitions of areas inside the state-space during the simulation. Harvesting activities and the occurrence of calamities are explicitly handled. The model is designed to be suitable for large-scale analyses. The concept was tested in an application to Austrian National Forest Inventory (NFI) data. Results shown, including a comparison to older inventory data, indicate that it is worth further elaborating on the concept and the model. The work will be continued and in the next step the model concept will be applied in several other countries.

The accurate large-scale classification of tree species is crucial for the monitoring, protection, and management of the Earth’s invaluable forest ecosystems. Numerous previous studies have recognized the suitability of satellite imagery, particularly Sentinel-2 imagery, for this task. In this study, we utilized a dense phenology Sentinel-2 time series, which offered consistent data across multiple granules, to map tree species across the entire forested area in Austria. Aiming for the classification scheme to more accurately represent actual forest conditions, we included mixed tree species and sparsely populated classes (classes with sparse canopy cover) alongside pure tree species classes. To enhance the training data for the mixed and sparse classes, synthetic data creation was employed. Autocorrelation has significant implications for the validation of thematic maps. To investigate the impact of spatial dependency on validation data, two methods were employed at numerous split and buffer distances: spatial split validation and a validation method based on a buffered ground reference probability samples provided by the National Forest inventory (NFI). While a random training data holdout set yielded 99% accuracy, the spatial split validation resulted in 74% accuracy, emphasizing the importance of accounting for spatial autocorrelation when validating with holdout sets derived from polygon-based training data. The validation based on NFI data resulted in 55% overall accuracy, 91% post-hoc pure class accuracy, and 79% accuracy when confusions in phenological proximity were disregarded (e.g., spruce–larch confused with spruce). The significant differences in accuracy observed between spatial split and NFI validation underscore the challenge for polygon-based training data to capture ground reference forest complexity, particularly in areas with diverse forests. This hardship is further accentuated by the pure class accuracy of 91%, revealing the substantial impact of mixed stands on the accuracy of tree species maps.

Key message: Truncating the angle count sampling plots leads to a higher stock estimate because fewer trees are missed. It also causes a higher uncertainty of the estimates. Context: The Austrian National Forest Inventory changed its plot design to angle count sampling plots that are limited to circles of 300 m². A fixed plot size is beneficial for multistage inventory systems. Aims: This study aims to quantify the ensuing effects on the national forest inventory on the plot and the national level regarding stock estimates and their uncertainty along with effects on as well as of upper diameter models. Methods: Fully surveyed large-scale plots with simulated angle count samples were used to gauge the increase of variability. The full NFI 2007/09 data set was employed to simulate truncations at different distances as well as to parameterise eight alternatives for upper diameter models and to analyse the effects on the stock estimates. Results: There is an increase in the stock estimate because the probability of missing sample trees is reduced. The change in upper diameter models had little effect. Conclusion: The truncation removes a source of bias, facilitates field work, and is a favourable way forward.

The dataset includes Pan-European maps of timber volume (Vol), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10×10 m for the reference year 2020. In addition, a measure of prediction uncertainty is provided for each pixel. The maps have been created using a combination of a Sentinel-2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.