Mixed forests are particularly interesting for forest structure and diversity analyses, as higher complexity and diversity can be expected in these forests compared to pure ones. Integrating different approaches in the analyses of structure and diversity in these forests can provide complementary information on non-spatial, spatial and functional diversity patterns. The study aimed at evaluating the spatio-temporal dynamics in forest structure and diversity in a semi-natural mixed oak-hornbeam floodplain forest. All standing trees were mapped and inventoried in 1995, 2005 and 2016 in three 1-ha mixed forest stands, with different soil moisture regime (xeric, mesic, moist conditions). Traditional, non-spatial structure and diversity measures were coupled with spatially-explicit and functional diversity measures. Results indicated that the three stands showed limited variation in stand structure and similar non-spatial diversity attributes, despite the different species composition. Only the extension to spatial and functional analyses was able to reveal more pronounced differences of diversity patterns, as higher complexity, species mingling, and functional tree complementarity was observed in the moister stand. These findings support use of spatially-explicit measurements in traditional inventory measurement protocols to allow more refined analysis of diversity patterns. On the other hand, functional diversity can be easily implemented in diversity analyses, as it requires species abundance information (which is traditionally collected in forest inventory) and species-specific tree traits which can be inferred from literature. © 2019 Elsevier Ltd

Spatially-explicit information on forest composition provides valuable information to fulfil scientific, ecological and management objectives and to monitor multiple changes in forest ecosystems. The recently developed Sentinel-2 (S2) satellite imagery holds great potential for improving the classification of forest types at medium-large scales due to the concurrent availability of multispectral bands with high spatial resolution and quick revisit time. In this study, we tested the ability of S2 for forest type mapping in a Mediterranean environment. Three operational S2 images covering different phenological periods (winter, spring, summer) were processed and analyzed. Ten 10 m and 20 m bands available from S2 and four vegetation indices (VIs) were used to evaluate the ability of S2 to discriminate forest categories (conifer, broadleaved and mixed forests) and four forest types (beech forests; mixed spruce-fir forests; chestnut forests; mixed oak forests). We found that a single S2 image acquired in summer cannot discriminate neither the considered forest categories nor the forest types and therefore multitemporal images collected at different phenological periods are required. The best configuration yielded an accuracy > 83% in all considered forest types. We conclude that S2 can represent an effective option for repeated forest monitoring and mapping. © 2018 Centro di Ricerca per la Selvicoltura Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.

Traditionally, taper equations are developed from measurements collected through a destructive sampling of trees. Terrestrial laser scanning (TLS) enables high levels of accuracy of individual tree parameters measurement avoiding tree felling. With this study, we wanted to assess the performance of two approaches to calibrate a taper function: using stem diameters extracted from TLS point clouds and measured at different tree heights with the traditional and usual forest instruments. We compared the performance of four taper equations built with data collected by TLS and traditional survey in a European beech (Fagus sylvatica L.) forests of mount Amiata (Tuscany Region, Italy). We computed the volume of stem sections 1.00 m long by integrating the most performing TLS-based taper equation and by the Huber, Smalian and cone formulas applied on the diameter and height values measured with the traditional field surveys. We conducted the analysis of error distribution in volume estimates computed integrating the most performing TLS-based taper function along the stem. We tested if the differences in the volume estimate of the two methods were significant. Schumacher and Hall (1933) equation was the most performing taper function both in case of using TLS and traditional surveyed data, being the TLS-based function more performant (rRMSE = 6.90% vs 9.17%). Its performance did not increase when diameter values were extracted from TLS point clouds with a higher frequency (i.e. 25.0 cm vs 1.00 m). By integrating the TLS-based Schumacher and Hall (1933) function, the sections with the highest error resulted from 5.00 to 7.00 m of stem height (i.e. RMSE from 14.72 to 19.14 dm³ and rRMSE from 13.00 to 17.76%). This study case represents the first attempts to develop a taper equation for European beech of mount Amiata using values of stem diameter and height extracted from the TLS point cloud. The results demonstrated that TLS produces the same stem volume estimates as traditional method avoiding falling trees. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.

The interest in tree phenology monitoring is increasing because this trait is a robust indicator of the impacts of climate change on natural and managed ecosystems. Different approaches to monitor phenology at different spatial scales, from in situ monitoring to remote sensing, are used to investigate spring and/or autumn phenological changes. In Mediterranean area, most of phenological changes occur during cloudy periods (spring and autumn), leading to a loss of information also for very high temporal resolution satellites. Instead, cloud-uninfluenced sensors, such as radar sensors, can allow to bypass this problem and produce a temporally continuous coverage. In this paper, we analyzed the spring phenology of two European beech (Fagus sylvatica L.) populations, located at different latitudes in Mediterranean area. Weekly in situ monitoring of leaf-out has been correlated with data collected by Synthetic Aperture Radar. Spring phenological phases were monitored in situ following a modified BBCH-code with a 5-scores scale (from 1 - buds closed and covered by scales, to 5 - leaf completely unfolded). The score 3 (young leaves starting to emerge from the bud) was considered the bud break. Different site conditions based on aspect (northern and southern) and altitudinal gradient (high and low altitude) have been considered. The aim was to test and implement a new methodology able to decrease the frequency of the field sampling, using remote data, to extend more detailed information on geographical scale, and to reconstruct past phenology. Results showed a statistically significant different length of the vegetative spring period, spanning from dormant buds, up to leaves completely unfolded, between sites. Through Synthetic Aperture Radar estimation, this study demonstrates that leaf-out can be monitored with an extreme accuracy. The phenophase score 4 and 5 estimation showed the best performance (RMSE < of 4 days), phenophases score 2 and 3 showed promising performances (4 days < RMSE <5 days), while phenophases score 1 seems to be not easily detectable, although it can be extrapolated with an RMSE <6 days. This radar approach fixes the cloud problem typical of multispectral approach and very frequent in phenophase change periods in Mediterranean climate. This study promotes the proposed remote sensing approach as a very useful tool to monitor growing season starting in remote areas, helping to reduce in situ observations and allowing past phenology reconstruction. © 2020 Elsevier Inc.

Biodiversity will probably be threatened by climate change effects and the Mediterranean area is a well know hotspot of genetic diversity. Climatic data are a very important source of information for those studies and the aim of this work was to study and compare eight methods for spatial interpolation of climatic data and indices including parametric and non-parametric methods, deterministic, regressive and geostatistical. The Root Mean Square Error (RMSE), relative RMSE (rRMSE) and relative BIAS (rBIAS) were calculated to assess algorithm’s performances in a Mediterranean region. None of the eight methods performed much better than others with a very complex physiographic environment. The range of errors was very high and rRMSE varied from 3.8% to 295%. Anyway, even in case of low differences among methods and despite the necessity of the assumption of normality of data, the interpolation at local scale with parametric and geostatistical methods (e.g. kriging or cokriging) should be preferred to globally-interpolated climatic data due to the possibility to obtain the distribution of prediction’s error. © 2017, Patron Editore S.r.l. All rights reserved.

Regions of provenance of reproductive materials are a mandatory tool for the management of forest genetic resources in Europe. Italian regulation (D. Lgs. 386/2003), in contrast with other European countries, gives this role to regional administrations. Here we present a map of the Regions of Provenance for Abruzzi in central Italy. Three environmental zones were identified according to climatic variability using a spatial clustering procedure and the three main forest species for the region were mapped: European beech (Fagus sylvatica L.), European black pine (Pinus nigra J.F. Arnold) and Turkey oak (Quercus cerris L.). The results show that the main driving factors for clustering are temperature regimes reflecting the contrast between the continental climate of the interior of the region versus the Mediterranean climate on the coast. This effect was also evident in the delineation of the Regions of Provenance for the three main species where elevation plays a key role in the selection of seed stands. The Regions of Provenance of the three species will be used for the correct management of forest reproductive materials collected from the six seed stands of the Region. © 2016 Ugo Chiavetta.