Towards a set of national forest inventory indicators to be used for assessing the conservation status of the habitats directive forest habitat types

Modelling and testing forest ecosystems condition account

We developed an application model based on the System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) framework, endorsed by the United Nations Statistical Commission in 2021. This model enables mapping condition accounts for forest ecosystems using automated computation. We applied the model nationally in Spain between 2000 and 2015 to test its effectiveness. Our model follows five methodological steps to generate forest condition accounts: (i) definition and spatial delimitation of forest ecosystem types; (ii) selection of variables using the ecosystem condition typology encompassing physical, chemical, compositional, structural, functional, and landscape characteristics; (iii) establishment of reference levels, including lower (collapse) and upper (high ecosystem integrity) thresholds; (iv) aggregation of variables into condition index; and (v) calculation of a single condition index by rescaling the aggregated indicators between 0 and 1. The results obtained from the model provide valuable insights into the status and trends of individual condition indicators, as well as aggregated condition index values for forest ecosystems, in a spatially explicit manner. Overall, the condition of the forest ecosystems in Spain showed a slight increase, from 0.56 in 2000 to 0.58 in 2015. However, distinct trends were observed for each ecosystem type. For example, mixed Alpine and Macaronesia forests exhibited a significant improvement, while the continental Mediterranean coniferous forests did not show any change. This innovative approach to monitoring forest condition accounts has important potential applications in policy and decision-making processes. It can contribute to effective evidence-based nature conservation, ecosystem service management, and identifying restoration areas.

Toward a High Spatial Resolution Aerial Monitoring Network for Nature Conservation—How Can Remote Sensing Help Protect Natural Areas?

Aerial surveys have always significantly contributed to the accurate mapping of certain geographical phenomena. Remote sensing opened up new perspectives in nature monitoring with state-of-the-art technical solutions using modern onboard recording equipment. We developed the technical background and the methodology that supports detailed and cost-effective monitoring of a network of natural areas, thereby detecting temporal changes in the spatial pattern of land cover, species, biodiversity, and other natural features. In this article, we share our experiences of the technical background, geometric accuracy and results of comparisons with selected Copernicus Land Monitoring products and an Ecosystem Map based on the testing of our methodology at 25 sites in Hungary. We combined a high-spatial-resolution aerial remote sensing service with field studies to support an efficient nature conservation monitoring network at 25 permanent sites. By analyzing annually (or more frequently) orthophotos taken with a range of 0.5–5 cm spatial resolution and 3D surface models of aerial surveys, it is possible to map the upper canopy of vegetation species. Furthermore, it allows us to accurately follow the changes in the dynamics at the forest edge and upper canopy, or the changes in species’ dominance in meadows. Additionally, spatial data obtained from aerial surveys and field studies can expand the knowledge base of the High-Resolution Aerial Monitoring Network (HRAMN) and support conservation and restoration management. A well-conducted high-resolution survey can reveal the impacts of land interventions and habitat regeneration. By building the HRAMN network, nature conservation could have an up-to-date database that could prompt legal processes, establish protection designation procedures and make environmental habitat management more cost-effective. Landscape protection could also utilize the services of HRAMN in planning and risk reduction interventions through more reliable inputs to environmental models.

Deadwood stocks in south-western European forests: Ecological patterns and large scale assessments

Forest deadwood is a relevant factor in the provision of ecosystem services (forest biodiversity, carbon sequestration, recreational and aesthetic values), but it also influences the risk and impact of forest perturbations. Hence, reliable estimations are urgently need in the lack of detailed information in Mediterranean forests at large scales. In this study we provide, for the first time, national-level estimations for Spain based on the information from the Spanish National Forest Inventory (38,945 plots). In addition, we compare and validate two approaches for estimating deadwood stocks where data is lacking; the first of these being a modelling approach based on stand, climatic and physiographical variables, and the other considers the ratio between deadwood and living biomass. We also examine the different patterns stock across forest types in four biogeographical regions according to a broad-spectrum of species groups and forests with different degrees of anthropogenic influence. The degrees are based on levels of protection and naturalness categories. The modelling approach provides more robust deadwood estimates and better predictive capacity than the ratio approach. Alpine (6.09 Mg.ha^-1) and Atlantic (3.53 Mg.ha^-1) bioregion forests store significantly higher mean deadwood biomass stocks than Macaronesian and Mediterranean forests. However, the share of deadwood in relation to the total biomass stock is greater in Mediterranean biogeographical region. As regards species groups, the mean deadwood stock of mixed forests doubled the stocks found in conifer and broadleaved dominated forests. We also found significant differences in deadwood biomass stocks between forests with different levels of anthropogenic protection. However, forest types with intensive forest management had contrasting figures for deadwood stock. The mean values obtained at national level according to forest type, bioregion and degree of anthropogenic influence, provide baseline information for carbon accounting as well as for other forest policy planning and management strategies.