Several political initiatives aim to achieve net-zero emissions by the middle of the twenty-first century. In this context, forests are crucial as a carbon sink to store unavoidable emissions. Assessing the carbon sequestration potential of forest ecosystems is pivotal to the availability of accurate forest variable estimates for supporting international reporting and appropriate forest management strategies. Spatially explicit estimates are even more important for Mediterranean countries such as Italy, where the capacity of forests to act as sinks is decreasing due to climate change. This study aimed to develop a spatial approach to obtain high-resolution maps of Italian forest above-ground biomass (ITA-BIO) and current annual volume increment (ITA-CAI), based on remotely sensed and meteorological data. The ITA-BIO estimates were compared with those obtained with two available biomass maps developed in the framework of two international projects (i.e., the Joint Research Center and the European Space Agency biomass maps, namely, JRC-BIO and ESA-BIO). The estimates from ITA-BIO, JRC-BIO, ESA-BIO, and ITA-CAI were compared with the 2nd Italian NFI (INFC) official estimates at regional level (NUT2). The estimates from ITA-BIO are in good agreement with the INFC estimates (R² = 0.95, mean difference = 3.8 t ha⁻¹), while for JRC-BIO and ESA-BIO, the estimates show R² of 0.90 and 0.70, respectively, and mean differences of 13.5 and of 21.8 t ha⁻¹ with respect to the INFC estimates. ITA-CAI estimates are also in good agreement with the INFC estimates (R² = 0.93), even if they tend to be slightly biased. The produced maps are hosted on a web-based forest resources management Decision Support System developed under the project AGRIDIGIT (ForestView) and represent a key element in supporting the new Green Deal in Italy, the European Forest Strategy 2030 and the Italian Forest Strategy. © 2022 by the authors.

Vegetation structure is one of the key factors in forest ecosystems. Especially understory structure has major implications for wildlife habitat selection, reproduction, and survival. Structural indices traditionally used to characterize understory vegetation are based on field vegetation surveys describing general features such as leaf area index (LAI), canopy cover or vegetation height, hiding much of the three-dimensional vegetation structure complexity. The application of terrestrial laser scanning (TLS) in forest ecological and management applications is becoming more effective. In this study, we use TLS data to quantify spatial attributes of forest subcanopy in four different forest strata ranging from 0.5 m to 10 m from the ground. We collected data in 12 plots of mature European beech (Fagus sylvatica L.) forests and 12 plots of mature black pine (Pinus nigra subsp. laricio Maire) forests, located in the Sila National Park, Italy. We propose a TLS-based approach to estimate a fine-scale vegetation density using the Plant Density Index (PDI) and to test the PDI at different height classes. We found a significant relationship between the PDI and the number of trees belonging to the dominant layer, using the Spearman correlation coefficient (r = 0.83, p<inf>val</inf> = 0.001). Basing on PDI values, a cluster analysis of the four subcanopy strata was carried out for deriving clusters of structurally homogeneous forest plots. Results identified three clusters in terms of the vegetation features in the horizontal height classes: the first cluster primarily includes Beech forests characterized by plots with the highest tree densities; the second one includes both Beech and Pine forests characterized by dense ground vegetation and shrubs and an intermediate tree density; the third group is represented by Pine forests with massive presence of vegetation lower strata and moderate tree density. Then, PCA allowed identifying the relationship between the considered subcanopy layers and forest plots. © 2021 Elsevier B.V.

The lack of multi-dimensional data is one of the major gaps which limit the knowledge and the assessment possibilities of European forests. Nowadays, the most extensive and complete data on the European forest statuses are given by National Forest Inventories (NFIs) which provide information about the extent of forest’s resources and their composition and structure. Traditionally, NFIs collect data related to trees, with a limited consideration of other habitat components, such as ground vegetation. This information which goes beyond the mere arboreal component is instead essential for a more complete forest biodiversity assessment. This paper is aimed at introducing the ICP Forests LI-BioDiv database which resulted from BioSoil Forest Biodiversity, a large collaborative European project. This database is organized as a multi-dimensional forest geodatabase that contains forest structure and vegetation records collected in 19 European countries in the period of 2005–2008. The data were acquired from 3311 geocoded plots where several different types of data were gathered: stand-level general information, tree-level data, deadwood, canopy closure and floristic composition. This paper is structured in order to: (1) give a clear overview of the raw data available in the database and to (2) present an elaboration of raw data to calculate simple plot-level forest variables (biomass, deadwood volume, alpha diversity). On the basis of the results we achieved, the LI-BioDiv database appears useful mainly for research purposes aimed at studying cross-relationships between multiple forest variables and not for an operative use for monitoring and assessing European forest. In particular, we hope that this contribution can stimulate scientists to carry out cross-analysis of the database for defining future forest biodiversity indicators that could be introduced into the field protocols of the NFIs in Europe. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Our research group has recently proposed a strategy to simulate net forest carbon fluxes based on the coupling of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC. The outputs of the two models are combined through the use of a proxy of ecosystem distance from equilibrium condition which accounts for the occurred disturbances. This modeling strategy is currently applied to all Italian forest areas using an available set of NDVI images and ancillary data descriptive of an 8-year period (1999–2006). The obtained estimates of forest net primary production (NPP) are first analyzed in order to assess the importance of the main model drivers on relevant spatial variability. This analysis indicates that growing stock is the most influential model driver, followed by forest type and meteorological variables. In particular, the positive influence of growing stock on NPP can be constrained by thermal and water limitations, which are most evident in the upper mountain and most southern zones, respectively. Next, the NPP estimates, aggregated over seven main forest types and twenty administrative regions in Italy, are converted into current annual increment of standing volume (CAI) by specific coefficients. The accuracy of these CAI estimates is finally assessed by comparison with the ground data collected during a recent national forest inventory. The results obtained indicate that the modeling approach tends to overestimate the ground CAI for most forest types. In particular, the overestimation is notable for forest types which are mostly managed as coppice, while it is negligible for high forests. The possible origins of these phenomena are investigated by examining the main model drivers together with the results of previous studies and of older forest inventories. The implications of using different NPP estimation methods are finally discussed in view of assessing the forest carbon budget on a national basis. © 2015, Springer-Verlag Berlin Heidelberg.

A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the interyear GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO<inf>2</inf> concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO<inf>2</inf> concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes. © 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.

Urban soundscapes are increasingly recognized as fundamental for both ecological integrity and human well-being, yet the complex interplay between the vegetation structure, seasonal dynamics, and microclimatic factors in shaping these soundscapes remains poorly understood. This study tests the hypothesis that vegetation structure and seasonally driven biological activity mediate the balance and the quality of the urban acoustic environment. We investigated seasonal and spatial variations in five acoustic indices (NDSI, ACI, AEI, ADI, and BI) within a historical urban garden in Castelfranco Veneto, Italy. Using linear mixed-effects models, we analyzed the effects of season, microclimatic variables, and vegetation characteristics on soundscape composition. Non-parametric tests were used to assess spatial differences in vegetation metrics. Results revealed strong seasonal patterns, with spring showing increased NDSI (+0.17), ADI (+0.22), and BI (+1.15) values relative to winter, likely reflecting bird breeding phenology and enhanced biological productivity. Among microclimatic predictors, temperature (p < 0.001), humidity (p = 0.014), and solar radiation (p = 0.002) showed significant relationships with acoustic indices, confirming their influence on both animal behaviour and sound propagation. Spatial analyses showed significant differences in acoustic patterns across points (Kruskal–Wallis p < 0.01), with vegetation metrics such as tree density and evergreen proportion correlating with elevated biophonic activity. Although the canopy height model did not emerge as a significant predictor in the models, the observed spatial heterogeneity supports the role of vegetation in shaping urban sound environments. By integrating ecoacoustic indices, LiDAR-derived vegetation data, and microclimatic parameters, this study offers novel insights into how vegetational components should be considered to manage urban green areas to support biodiversity and foster acoustically restorative environments, advancing the evidence base for sound-informed urban planning. © 2025 by the authors.