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This exploratory study presents an objective and consistent approach for assessing forest structure across multiple European countries, focusing on the distributions of tree species and tree diameter at breast height (DBH) as assessed by European National Forest Inventories (NFIs) and one landscape inventory. We distinguish six structural classes, ranging from mono-specific plots with a narrow (regular) DBH distribution to multi-species plots with a wide (irregular) DBH distribution. We used tree level data on basal area, species, and diameter, from 18 countries, and harmonised the data as much as possible by adopting a common diameter measurement threshold and by scaling the different plot radii to one ha. Data from 255,418 inventory plots indicate that roughly half of the forests are dominated by a single-species, while the other half consists of multiple-species. According to our approach, the predominant structural type in the surveyed countries is characterized by single-species dominance (56%) and a narrow DBH distribution. The relatively small plot radii across inventories and the diameter threshold of 10 cm also contribute to dominance of this structural type. The single-species regular class was the most prevalent ranging from 35.8% in Switzerland to 79.7% in Spain. The second most important was the multiple-species regular class, present on 37.7% of the forest area. Although the plots are not weighed to the full forest area, these results indicate a regular forest structure on 94% of Europe's forests. The distribution of forest area per country over the categories varied only moderately. A shortcoming of a groundbased study is the challenge of harmonisation due to the different plot design of NFIs, showing a range in the plot radii from 9 to 25 meters hampering the comparison between countries. The results as presented at 0.2 degrees resolution (approximately 20 x 20 km) provide insight into forest structure in a consistent manner and can be updated in the future based on new releases of forest inventories. Although we did not study the effect of forest management on the current structure, these results are a basis to report temporal and spatial effects of management changes at this semi-high resolution, highly relevant to the EU Nature Restoration Law. We see this spatially explicit result as very promising, with advantages compared to the alternative of highly aggregated international statistics..
Background Forests are vital carbon sinks, and accurately assessing their maximum capacity to store carbon in absence of disturbances, further called as Potential Carbon Storage Capacity (PCSC), is essential for national greenhouse gas inventory (NGHGI) and effective forest management. Estimating PCSC at large scales is challenging due to ecological variability, which can be addressed through a site-quality-dependent approach that accounts for local environmental and management conditions. Characterization of PCSC through a site quality gradient as a reference for comparing the current value of aboveground carbon (AGC), in both absolute and relative terms, could therefore enable comparable estimates across regions. This study presents a methodology for Pinus sylvestris L. forests across Europe, linking site quality to AGC stocks. Results Site quality was quantified using the site form (SF) index, which corresponds to the dominant height at a reference dominant diameter. PCSC was estimated by fitting \(\:AGC-SF\) curves at the FAO Global Ecological Zone level. Results show that SF effectively reflects productivity, with different PCSC reference levels for the different ecological zones. The PCSC fitted models were used as a baseline for case studies in Latvia and Spain, confirming their value as reference conditions for characterizing disturbance intensity in both old-growth and managed forests. Conclusions This study presents a methodology for estimating the PCSC of forests dominated by Pinus sylvestris L. across European ecological zones, with potential applicability to other forest types. The framework provides a baseline for PCSC estimation, supporting NGHGIs and policies such as the Paris Agreement and EU Forest Strategy. In addition, the study findings also highlighted that using the PCSC for forest ecosystems is crucial to ensure consistent information regarding (i) evaluation of past carbon stock losses due to human and natural disturbances, (ii) prediction of potential carbon stock gains through changes in forest management, and (iii) assessment of mitigation benefits lost when forests are managed with carbon reserves below their maximum capacity.
Climate change narrows viable management alternatives in the Landes de Gascogne Forest. By testing a wide range of management regimes under contrasting climate and risk scenarios, we show that the capacity of timber damage varies across the landscape. Identifying where flexible, low-risk regimes can still sustain carbon sequestration helps decision-makers select robust strategies that reconcile local limits with regional planning needs. Regional forest management integrates diverse forest types and objectives, yet the impacts of climate change at sub-regional scales and identifying suitable management regimes for mitigation require further investigation. We align regional strategies with site-specific constraints by assessing how maritime pine (Pinus pinaster Ait.) management in the Landes de Gascogne Forest can reconcile local climatic limitations with regional production goals. We aim to identify local management flexibility and quantify the resulting trade-offs with regional carbon sequestration and carbon export under contrasted scenarios. Our cross-scale framework integrated 8 × 8 km climate projections with process-based forest simulations. Using multi-objective optimization, we explored how the set of feasible management alternatives changes across space under different climate scenarios and disturbance risks. Climate change consistently reduced service provision supply across scenarios. Under the most worst-case scenario, regional outcomes depended on the ability of local management regimes to mitigate risk, up to thresholds beyond which no current regimes remained viable. Climate effects amplified spatial disparities, reinforcing productivity in the southwest while increasing water limitations in eastern areas. Sub-regional flexibility emerged as a key lever to buffer losses in carbon-related services. This framework links local decision-making capacity with regional constraints, supporting adaptive, climate-informed forest planning.
While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides reducing the training and rendering complexity, applying inverse graphics in the latent space enables a valuable interoperability with other latent-based 2D methods. The major challenge is that inverse graphics cannot be directly applied to such image latent spaces because they lack an underlying 3D geometry. In this paper, we propose an Inverse Graphics Autoencoder (IG-AE) that specifically addresses this issue. To this end, we regularize an image autoencoder with 3D-geometry by aligning its latent space with jointly trained latent 3D scenes. We utilize the trained IG-AE to bring NeRFs to the latent space with a latent NeRF training pipeline, which we implement in an open-source extension of the Nerfstudio framework, thereby unlocking latent scene learning for its supported methods. We experimentally confirm that Latent NeRFs trained with IG-AE present an improved quality compared to a standard autoencoder, all while exhibiting training and rendering accelerations with respect to NeRFs trained in the image space. Our project page can be found at https://ig-ae.github.io .
