• 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 .

Global change—particularly climate change, forest management, and atmospheric deposition—has significantly altered forest growing conditions in Europe. The influences of these changes on beech growth (Fagus sylvatica L.) were investigated for the past 80 years in Belgium, using non-linear mixed effects models on ring-width chronologies of 149 mature and dominant beech trees (87–186 years old). The effects of the developmental stage (i.e., increasing tree size) were filtered out in order to focus on time-dependent growth changes. Beech radial growth was divided into a low-frequency signal (=growth rate), mainly influenced by forest management and atmospheric deposition, and into a high-frequency variability (≈mean sensitivity), mainly influenced by climate change. Between 1930 and 2008, major long-term and time-dependent changes were highlighted. The beech growth rate has decreased by about 38% since the 1950–1960s, and growth variability has increased by about 45% since the 1970–1980s. Our results indicate that (1) before the 1980s, beech growth rate was not predominantly impacted by climate change but rather by soil alteration (i.e., soil compaction and/or nitrogen deposition); and (2) since the 1980s, climate change induced more frequent and intense yearly growth reductions that amplified the growth rate decrease. The highlighted changes were similar in the two ecoregions of Belgium, although more pronounced in the lowlands than in the uplands.

The original article was erroneously published without applying all the provided proof corrections in Section 5 and Table 1.

Most remote sensing assessments of forest change focus on canopy cover, whereas the dynamics of vertical growth remain less investigated despite their sensitivity to external forcings. Yet, the increasing availability of canopy height products from multiple platforms facilitates spatially explicit characterization of vertical growth dynamics, provided that systematic measurement biases between sources are properly addressed. Here, we present a transferable workflow to derive plot-level indicators of vertical growth dynamics from mixed-source canopy height model (CHM) time series, harmonized against forest inventory plots at ~1000 m² resolution. We estimated plot-level vertical growth to model reference trajectories as a function of initial canopy height. Plot-level deviations from these trajectories were quantified, providing contextualized growth information. This approach was illustrated in the Belgian Ardenne using multi-source CHMs derived from recent aerial imagery and lidar data (2006-2021). Across all acquisitions, harmonization reduced top-of-canopy height bias from 2.53 m to 0.01 m (RMSE = 1.79 m, R² = 0.92), with canopy structure rather than acquisition parameters as the primary driver of bias. Plot-level growth estimates decreased as initial heights increased, with steeper declines in broadleaf than coniferous stands. Spatially clustered growth deviations (Moran's I = 0.30–0.44, p < 0.001) suggested systematic spatial variations in performances among otherwise similar forests. By leveraging heterogeneous sources of canopy height, this workflow enables spatially exhaustive characterization of forest growth, adding structural understanding into multi-scale forest monitoring and resilience assessments.

Canopy height growth is a key determinant of forest ecosystems’ state and functioning. Traditional field inventories lack the spatiotemporal coverage needed to capture spatial height growth heterogeneity, but the increasing availability of canopy height time series from multiple platforms facilitates spatially explicit characterization of growth dynamics, provided that systematic measurement biases between sources are properly addressed. Here, we present a workflow to map patterns of vertical growth based on recent aerial imagery and lidar data, regularly acquired across Belgian temperate forests over 2006-2021, and harmonized against ground-based inventories at ~1000 m² spatial resolution. We estimated plot-level vertical growth, to then model species-specific reference trajectories from which were quantified plot-level deviations, providing both absolute and contextualized growth assessments. Across all acquisitions, harmonization reduced the mean top-of-canopy height bias from 2.53 m (RMSE = 3.26 m, R² = 0.73) to 0.01 m (RMSE = 1.79 m, R² = 0.92). Canopy structure (e.g. heterogeneity and openness), rather than acquisition parameters (e.g. aerial surveying period), was the main source of bias when estimating forest height from aerial imagery. Resulting plot-level estimates of growth typically decreased as initial heights increased, with steeper declines in broadleaf stands compared to coniferous. Importantly, deviations from reference vertical growth were spatially clustered (Moran's I = 0.30-0.44, p < 0.001), suggesting systematic variations among otherwise similar forests and potential hotspots of under- or over-performance. Beyond its spatially exhaustive characterization of forest growth, this transferable workflow leverages heterogeneous height sources to add structural understanding into multi-scale adaptive management and resilience assessments.