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During major floods, rivers erode their banks and thus recruit large wood pieces from the riparian zones. There is still a lack of knowledge about the transport of large wood, the volumes involved and the flux distribution, i.e. the large wood connectivity at catchment scale. During storm Alex (October 2020), the French Roya catchment (394 km2) experienced a paroxysmal morphogenic flood involving massive bank erosion. The riparian vegetation was largely recruited, with large wood contributing to logjams and bridge destruction. This paper presents a methodology for volumetric assessment of the large wood fluxes involved. Simple approaches are used to (i) quantify the inputs from stand density data from the national forest inventory and from source areas based on diachronic analysis of active channels highlighting the erosion of 87 ha of wooded areas; and (ii) quantify the volumes deposited via an exhaustive manual digitisation of 16,846 pieces of large wood deposited on 59 km of channels on the Roya and its tributaries. This catchment-scale, large wood connectivity analysis shows that the flood recruited and transported downstream a volume of around 14,000 m3 of large wood (uncertainty range: 7000–29,500 m3). Drone observations of the Roya River mouth in Italy and satellite images showing a raft of driftwood, several km long, drifting off the Roya River mouth in the aftermath of the flood corroborate our findings. • Storm Alex (Oct. 2020) triggered dramatic geomorphic changes in the Roya River (FR). • We quantified the volume of large wood (LW) eroded, conveyed and deposited. • Large wood production is deduced from eroded forest maps and forest stand density. • Large wood deposition is estimated by digitizing logs on aerial pictures. • Circa 14,000 m3 of LW were recruited, 900 m3 deposited, the rest went to the sea. [ABSTRACT FROM AUTHOR]

Urban areas are continuously expanding today, extending their influence on an increasingly large proportion of woods and trees located in or nearby urban and urbanizing areas, the so-called urban forests. Although these forests have the potential for significantly improving the quality the urban environment and the well-being of the urban population, data to quantify the extent and characteristics of urban forests are still lacking or fragmentary on a large scale. In this regard, an expansion of the domain of multipurpose forest inventories like National Forest Inventories (NFIs) towards urban forests would be required. To this end, it would be convenient to exploit the same sampling scheme applied in NFIs to assess the basic features of urban forests. This paper considers approximately unbiased estimators of abundance and coverage of urban forests, together with estimators of the corresponding variances, which can be achieved from the first phase of most large-scale forest inventories. A simulation study is carried out in order to check the performance of the considered estimators under various situations involving the spatial distribution of the urban forests over the study area. An application is worked out on the data from the Italian NFI. [ABSTRACT FROM AUTHOR]

This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates. [ABSTRACT FROM AUTHOR]

In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m3/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson's correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m3/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m3/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales. [ABSTRACT FROM AUTHOR]

The introduction of Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] in Europe has been one of the most important and extensive silvicultural experiments since the 1850s. This success was mainly supported by the species' wide genome and phenotypic plasticity even if the genetic origin of seeds used for plantations is nowadays often unknown. This is especially true for all the stands planted before the IUFRO experimentation in the 1960s. In this paper, a methodology to estimate the Douglas-fir provenances currently growing in Italy is proposed. The raw data from the last Italian National Forest Inventory were combined with literature information to obtain the current spatial distribution of the species in the country representing its successful introduction. Afterwards, a random forest classification model was run using downscaled climatic data as predictors and the classification scheme adopted in previous research studies in the Pacific North West of America. The analysis highlighted good matching between the native and the introduction range in Italy. Coastal provenances from British Columbia and the dry coast of Washington were detected as the most likely seed sources, covering 63.4% and 33.8% of the current distribution of the species in the country, respectively. Interior provenances and those from the dry coast of Oregon were also represented but limited to very few cases. The extension of the model on future scenarios predicted a gradual shift in suitable provenances with the dry coast of Oregon in the mid-term (2050s) and afterwards California (2080s) being highlighted as possible new seed sources. However, only further analysis with genetic markers and molecular methods will be able to confirm the proposed scenarios. A validation of the genotypes currently available in Italy will be mandatory as well as their regeneration processes (i.e., adaptation), which may also diverge from those occurring in the native range due to a different environmental pressure. This new information will also add important knowledge, allowing a refinement of the proposed modeling framework for a better support for forest managers. [ABSTRACT FROM AUTHOR]

Since the enactment of the EU Habitats Directive, the conservation status of forest habitat types, habitats of species, and species has become the central concept of the Directive's nature conservation legislation. Despite its role, it has drawn relatively little attention. Within a rather short research period, a few research papers have addressed the existing definitions, indicators for the conservation status assessment, and assessment techniques. This paper attempted to complete the set of measurable indicators available in national forest inventories and connect them with the forest habitat types' conservation status components (area, function, structure, and prospects). A set of 40 indicators was defined, labelled with one or more of the four conservation status components and assessed with the quality dimensions. The analysis uncovered that five indicators could be used to assess the component of range and area, 20 that of structure, 22 that of function and 27 that of prospects. It also showed that conventional forestry indicators such as tree species, diameter at breast height, and regeneration are less sensitive regarding the data quality. Conversely, some typical biodiversity indicators lacked completeness, timeliness, and precision. In addition to this analysis, the data distributions (data for them were provided by the national forest inventories of Italy, Slovenia, and Spain) of some indicators were analysed. Based on all the results, it was also possible to conclude that there is a shortage of national forest inventory indicators for the assessments of the area and function conservation status components. While the area component should be described with the indicators of forest habitat type fragmentation, mingling and perforation with non-forest and other forest vegetation communities, the functional component is bereft of indicators describing processes such as biomass growth and carbon cycling. Future research should thus search for more indicators to represent all conservation status components in a more balanced way. More efforts should also be expended into the harmonisation of indicators. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the role of the oomycete Phytophthora× cambivora in the decline affecting European beech (Fagus sylvatica) in the Nebrodi Regional Park (Sicily, southern Italy). In a survey of a beech forest stand in the heart of the park, Phytophthora× cambivora was the sole Phytophthora species recovered from the rhizosphere soil and fine roots of trees. Both A1 and A2 mating type isolates were found. Direct isolation from the stem bark of trees showing severe decline symptoms and bleeding stem cankers yielded exclusively P. gonapodyides, usually considered as an opportunistic pathogen. The mean inoculum density of P.× cambivora in the rhizosphere soil, as determined using the soil dilution plating method and expressed in terms of colony forming units (cfus) per gm of soil, the isolation frequency using leaf baiting, and the percentage of infected fibrous roots from 20 randomly selected beech trees with severe decline symptoms (50 to 100 foliage transparency classes) were 31.7 cfus, 80%, and 48.6%, respectively. These were significantly higher than the corresponding mean values of 20 asymptomatic or slightly declining trees, suggesting P.× cambivora is a major factor responsible for the decline in the surveyed stand. [ABSTRACT FROM AUTHOR]

The forestry sector has been significantly affected by the recent advances in geomatics. Photogrammetric aerial acquisitions provide full coverage of forests, making possible a wall-to-wall mapping of the main forestry key variables, such as tree height and tree density, needed for forest planning and management purposes. This work summarizes the role that digital aerial photogrammetry (DAP) is expected to have in the forest planning context and analyzes the main products from DAP that are significantly required. According to their technical features, some strategies are proposed to program acquisitions in order to consciously set operational parameters needed for obtaining accuracy of measures compliant with forest planning requirements. Admitting that, in most cases, forest planners externalize DAP acquisitions to flying companies, the authors propose simple strategies for making an ex-post evaluation of flight conditions through a conscious processing of the external orientation parameters of images resulting after the bundle adjustment. Theoretical aspects are exemplified with reference to a practical case study relying on an aerial acquisition that, in 2019, the Canavese Forestry Consortium (NW Italy) made to support the ongoing redaction of its new forest plans. Finally, some economic concerns are presented to demonstrate the capability of this technique to absorb the most of costs associated with ground surveys, especially when large areas have to be investigated. In particular, about 66% of cost savings were found when comparing DAP-based forest mapping to traditional forest inventory strategies. [ABSTRACT FROM AUTHOR]

This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kinds of tree out of forest (TOF) structures: forest patches (FP), isolated trees (IT), tree hedgerows (THR), distinguished from forest (F). Ground truth validation allows estimating the accuracy of classification. Four optical bands and six spectral indices are compared detecting images' T areas: B2, B3, B4 and B8 bands, Negative Luminance (NL), Normalized Difference Vegetation index (NDVI), Green NDVI (GNDVI), Blue NDVI (BNDVI), Panchromatic NDVI (PNDVI) and Enhanced Vegetation Index (EVI). NL shows a relatively better capability for TOF detection and classification, with overall accuracy (OA) exceeding 92% and p-value = 10−5. Experiments were conducted on optical data acquired by Sentinel-2 in 2016 over the Alfina highland, central Italy. The tree characteristics were extracted exploiting GNU Octave Image Package. Our results show that this new approach could be extended to the detection and mapping of TOF within large areas of agroforestry landscape. [ABSTRACT FROM AUTHOR]

Fungi are among the most important organisms on earth, and they are essential components of terrestrial ecosystems. Their reproductive structures are strictly dependent and affected by environmental conditions, and community dynamics over time and space may be indirect indicators of the health status of forests. We combined macrofungal biodiversity indices in eight Mediterranean forests in Italy and surveyed 160 plots by standardized surveys, to evaluate the role of macrofungi as early predictors of change in the forest structure. The results show that indices of fungal diversity are influenced by geographic and floristic conditions, and inter- and intra-annual temperature and rainfall fluctuations affect the formation of fungal fruiting bodies. These findings suggest that environmental changes could be reflected by macrofungi, and conservation initiatives should consider the pivotal role that fungi play in biodiversity monitoring. [ABSTRACT FROM AUTHOR]

Forest planning, forest management, and forest policy require updated, reliable, and harmonized spatial datasets. In Italy a national geographic Forest Information System (FIS) designed to store and facilitate the access and analysis of spatial datasets is still missing. Among the different information layers which are useful to start populating a FIS, two are essential for their multiple use in the assessment of forest resources: (i) forest mapping, and (ii) data from Airborne Laser Scanning (ALS). Both layers are not available wall-to-wall for Italy, though different local sources of information potentially useful for their implementation already exist. The objectives of this work were to: (i) review forest maps and ALS data availability in Italy; (ii) develop for the first time a high resolution forest mask of Italy which was validated against the official statistics of the Italian National Forest Inventory; (iii) develop the first mosaic of all the main ALS data available in Italy producing a consistent Canopy Height Model (CHM). An on-line geographic FIS with free access to both layers from (ii) and (iii) was developed for demonstration purposes. The total area of forest and other wooded lands computed from the forest mask was 102,608.82 km2 (34% of the Italian territory), i.e., 1.9% less than the NFI benchmark estimate. This map is currently the best wall-to-wall forest mask available for Italy. We showed that only the 63% of the Italian territory (the 60% of the forest area) is covered by ALS data. These results highlight the urgent need for a national strategy to complete the availability of forest data in Italy. [ABSTRACT FROM AUTHOR]

Forests in southern Italy are mainly located in mountainous areas, where ground-based extraction is still the most common harvesting technique. In particular, 60% of southern Italy's forests are on slopes with an angle of inclination between 20-60%. The low level of mechanization in forest operations is due to the difficult site conditions, as well as the small-scale characteristics of both the forest owners and the harvesting contractors. The most common work method uses chainsaws to fell the trees, and animals or farm tractors equipped with winches for bunching and extraction. This study assesses the productivity and cost effectiveness of extraction with a purpose-built John Deere 548H skidder, including a comparison of winch and grapple configurations. The results show that the productivity of skidding depends on distance as well as the condition of the skid trail. The number of trees per cycle and volume of each load also had a clear effect. While large purpose-built skidders represent a significant investment, this study demonstrates that the productivity is very high compared to traditional extraction methods and the resulting extraction costs are very competitive. As such, this study indicates that, over time, southern Italian harvesting operations should invest in purpose-built harvesting systems. [ABSTRACT FROM AUTHOR]

This review is a follow-up to the first meeting of the Forest modeling working group (FMWG) of the Italian Society of Silviculture and Forest Ecology (SISEF), held in December 2009. 18 talks were delivered to an audience of 40 researchers. We review the state of the art of forest ecosystem modeling in Italy, highlight findings from Italian research groups, and summarize relevant issues. Developing on the discussion session of the meeting, we indicate current research gaps and future challenges for modelers, forest ecologists and foresters alike, with a special emphasis on model validation, data availability, and communication between researchers and managers. [ABSTRACT FROM AUTHOR]

Information about forest cover and its characteristics are essential in national and international forest inventories, monitoring programs, and reporting activities. Two of the most common forest variables needed to support sustainable forest management practices are forest cover area and growing stock volume (GSV m3 ha−1). Nowadays, national forest inventories (NFI) are complemented by wall-to-wall maps of forest variables which rely on models and auxiliary data. The spatially explicit prediction of GSV is useful for small-scale estimation by aggregating individual pixel predictions in a model-assisted framework. Spatial knowledge of the area of forest land is an essential prerequisite. This information is contained in a forest mask (FM). The number of FMs is increasing exponentially thanks to the wide availability of free auxiliary data, creating doubts about which is best-suited for specific purposes such as forest area and GSV estimation. We compared five FMs available for the entire area of Italy to examine their effects on the estimation of GSV and to clarify which product is best-suited for this purpose. The FMs considered were a mosaic of local forest maps produced by the Italian regional forest authorities; the FM produced from the Copernicus Land Monitoring System; the JAXA global FM; the hybrid global FM produced by Schepaschencko et al., and the FM estimated from the Corine Land Cover 2006. We used the five FMs to mask out non-forest pixels from a national wall-to-wall GSV map constructed using inventory and remotely sensed data. The accuracies of the FMs were first evaluated against an independent dataset of 1,202,818 NFI plots using four accuracy metrics. For each of the five masked GSV maps, the pixel-level predictions for the masked GSV map were used to calculate national and regional-level model-assisted estimates. The masked GSV maps were compared with respect to the coefficient of correlation (ρ) between the estimates of GSV they produced (both in terms of mean and total of GSV predictions within the national and regional boundaries) and the official NFI estimates. At the national and regional levels, the model-assisted GSV estimates based on the GSV map masked by the FM constructed as a mosaic of local forest maps were closest to the official NFI estimates with ρ = 0.986 and ρ = 0.972, for total and mean GSV, respectively. We found a negative correlation between the accuracies of the FMs and the differences between the model-assisted GSV estimates and the NFI estimate, demonstrating that the choice of the FM plays an important role in GSV estimation when using the model-assisted estimator. [ABSTRACT FROM AUTHOR]