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Soil sand particles play a crucial role in soil erosion because they are more susceptible to being detached and transported by erosive forces than silt and clay particles. Therefore, in soil erosion assessment and mitigation, it is crucial to model and predict soil sand particles at unsampled locations using appropriate methods. The study was aimed to evaluate the ability of a multivariate approach based on non-stationary geostatistics to merge LiDAR and visible-near infrared (Vis-NIR) diffuse reflectance data with laboratory analyses to produce high-resolution maps of soil sand content. Remotely sensed, high-resolution LiDAR-derived topographic attributes can be used as auxiliary variables to estimate soil textural particle-size fractions. The proposed approach was compared with the commonly used univariate approach of ordinary kriging to evaluate the contribution of auxiliary variables. Soil samples (0–0.20 m depth) were collected at 135 locations within a 139 ha forest catchment with granitic parent material and subordinately alluvial deposits, where soils classified as Typic Xerumbrepts and Ultic Haploxeralf crop out. A number of linear trend models coupled with different auxiliary variables were compared. The best model for predicting sand content was the one with elevation derived from LIDAR data as the only auxiliary variable. Although the improvement in estimation over the univariate model was rather marginal, the proposed approach proved very flexible and scalable to include any type of auxiliary variable. The application of LiDAR data is expected to expand as it allows the high-resolution prediction of soil properties, generally insufficiently sampled, at different spatial scales. [ABSTRACT FROM AUTHOR]

Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha-1 and from 34.2 to 22.1 Mg ha-1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model. [ABSTRACT FROM AUTHOR]

The study of afforestation is crucial to monitor land transformations and represents a central topic in sustainable development procedures, in terms of climate change, ecosystem services monitoring, and planning policies activities. Although surveying afforestation is important, the assessment of the growing forests is difficult, since land cover has different durations depending on the species. In this context, remote sensing can be a valid instrument to evaluate the afforestation process. Nevertheless, while a vast literature on forest disturbance exists, only a few studies focus on afforestation and almost none directly exploits remote sensing data. This study aims to automatically classify non-forest, afforestation, and forest areas using remote sensing data. To this purpose, we constructed a reference dataset of 61 polygons that suffered a change from non-forest to forest in the period 1988-2020. The reference data were constructed with the Land Use Inventory of Italy and through photointerpretation of orthophotos (1988-2012, spatial resolution 50 × 50 cm) and very high-resolution images (2012-2020, spatial resolution 30 × 30 cm). Using Landsat Best Available Pixel composites time-series (1984-2020) we calculated 52 temporal predictors: four temporal metrics (median, standard deviation, Pearson's correlation coefficient R, and slope) calculated for 13 different bands (the six Landsat spectral bands, three Spectral Vegetation Indices, and four Tasseled Cap Indices). To verify the possibility of distinguishing afforestation from non-forest and forest, given the differences between them can be minimal, we tested four different models aiming at classifying the following categories: (i) non-forest/afforestation, (ii) afforestation/forest, (iii) non-forest/forest and (iv) non-forest/afforestation/forest. Temporal predictors were used with random forest which was calibrated using random search, validated using k-fold Cross-Validation Overall Accuracy (OAcv), and further using out-ofbag independent data (OAoob). Results illustrate that the distinction of afforestation/forest reaches the largest OAcv (87%), followed by non-forest/forest (83%), non-forest/afforestation (75%) and non-forest/afforestation/forest (72%). The different OA values confirm that the difference in photosynthetic activity between forest and afforestation can be analysed through remote sensing to distinguish them. Although remote sensing data are currently not exploited to monitor afforestation areas our results suggest it may be a valid support for country-level monitoring and reporting. [ABSTRACT FROM AUTHOR]

Wood use in bio-building should be considered as one of the main pillars of sustainability. According to international standards, beech wood (Fagus sylvatica L.) is a rather less durable species as it is subject to degradation due to weathering, though it is abundantly available to a more intense use. Service life of beech products and wood products in general can be enhanced by different methods, such as heat treatments, but new chances are offered by coating technologies. However, to ensure protection from wood-destroying organisms, most commercial coatings include components that could harm human health, other organisms and the environment. Therefore, coating industry has to develop more eco-friendly solutions in order to decrease its impact on human health and environment. The objective of this article was to modify commercial acrylic varnish by adding cellulose nanocrystals (CNC) and lignin (LN) extracted from beech wood and investigate their effect on water sorption, additive dispersion in the coating matrix and coating resistance to bacterial attack. Contact angle, weight gain and colour difference were analysed and FT-IR mapping was applied. The performance of CNC was promising, as it enhanced water sorption of the modified coating. However, protection against bacterial attack was not satisfying. On the other hand, chemically unmodified lignin did not show positive effects as component in the coating formulation. Nevertheless, the currently limited usage of these two renewable and abundant resources urgently calls for their more efficient utilization, in order to create additional value with industry side-streams producing novel bio-based materials. Further experiments are needed in order to obtain better dispersion of the particles and higher resistance to bacterial attacks. [ABSTRACT FROM AUTHOR]

The motivations for the National Research Project on the Short Wood Supply Chain stem from the recognition of the great importance of the wood-based bio-economy for the future carbon-free society. In addition to the essential ecological benefits provided by forests, natural wood resources are expected to play a significant role due to the increasing wood demand for structural and non-structural uses. However, in Italy most industrial wood products are made from imported timber, which results in poor attention to local forestry, loss of traditional silvicultural know-how and reduced forest employment in most mountain areas. The project specifically aims: (i) to assess the feasability and sustainability of wood short supply chains; (ii) to promote new models of environmental certification and energy efficiency analysis; (iii) to create innovative products for bio-building (CrossLam, laminated timber, wood panels) using wood assortments from sustainable forest management in Italy; (iv) to characterise the functionality of wood products (with attention paid to architectural issues), predict wood degradation rate and assess conservation methods. Attention is specifically devoted to most common forest species in Italy, such as beech, pines, chestnut, deciduous oaks and poplar and to test thermal and surface treatments with natural substances and nano-materials. [ABSTRACT FROM AUTHOR]

Ervedo Giordano passed away in Rome on April 24, 2021. This note describes some of his major scientific contributions to research in silviculture and forest ecology, and sketches out the remarkable personality and character traits that he displayed throughout his life. [ABSTRACT FROM AUTHOR]

Forests cover nearly 40% of European land, with different country percentage and patchy distribution. The European forestry sector highlights that forest areas have different ownership: private (by firms, individual, or organizations) and public (State, communities or municipalities). The number of forestry holdings, size of landholding, and ownership types influence and drive forest management, governance and various other socio-economic linked issues. Moreover, forest owners determine management objectives and policies which influence the application of Sustainable Forest Management (SFM) practices. Several tools were developed to promote SFM, including forest certification. Numerous forest certification schemes are present across the world but the Forest Stewardship Council (FSC) and the Programme for the Endorsement of Forest Certification (PEFC) are those prevalent. However, a map of certified forests is lacking, although mapping would be essential to locate the percentage of forest that are certified to be sustainably managed. The study mapped forest certification across 43 European states, according to 499 FSC and 284 PEFC reports and assessed the proportion of certified forest area on public and private land and the rate of increase. This research was carried out collecting information on European certified forest companies/owners and locating geographically their forests at sub-national level (regions, NUTS 2). The database of the Joint COST Action FACESMAP/UNECE/FAO was an important data source. At European level, about six percent of the forest is certified under FSC scheme, while about seven percent under PEFC scheme. As forest certification is a useful tool to manage forests aiming at the integration of economic, ecological and social sustainability, the knowledge of the location and area of certified forest in Europe could be important in motivating decision makers to increase these sustainably managed areas. [ABSTRACT FROM AUTHOR]