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Accurate measurement of forest growing stock is a prerequisite for implementing climate-smart forestry strategies. This study deals with the use of airborne laser scanning data to assess carbon stock at the tree level. It aims to demonstrate that the combined use of two unsupervised techniques will improve the accuracy of estimation supporting sustainable forest management. Based on the heterogeneity of tree height and point cloud density, we classified 31 forest stands into four complexity categories. The point cloud of each stand was further divided into three horizontal layers, improving the accuracy of tree detection at tree level for which we calculated volume and carbon stock. The average accuracy of tree detection was 0.48. The accuracy was higher for forest stands with lower tree density and higher frequency of large trees, as well as a dense point cloud (0.65). The prediction of carbon stock was higher with a bias ranging from -0.3% to 1.5% and a root mean square error ranging from 0.14% to 1.48%. Key words: tree detection, airborne laser scanning (ALS), forest structure, carbon stock, climate-smart forestry. La mesure precise du capital forestier en croissance est un prerequis a l'implantation de strategies forestieres intelligentes face au climat. Cette etude porte sur l'utilisation de donnees de balayage laser aeroporte pour evaluer le stock de carbone a l'echelle de l'arbre. Elle vise a demontrer que l'utilisation combinee de deux techniques non supervisees va ameliorer la precision de l'estimation sur laquelle s'appuie un amenagement forestier durable. Sur la base de l'heterogeneite de la hauteur des arbres et de la densite du nuage de points, nous avons classe 31 peuplements forestiers dans quatre categories de complexite. Le nuage de points de chaque peuplement a par la suite ete divise en trois couches horizontales, ce qui ameliore la precision de la detection de chacun des arbres pour lesquels nous avons calcule le volume et le stock de carbone. La precision moyenne de la detection des arbres etait de 0,48. La precision etait plus elevee pour les peuplements forestiers qui avaient une plus faible densite et une frequence plus elevee de gros arbres, ainsi qu'un nuage de points dense (0,65). La prediction du stock de carbone etait plus elevee avec un biais allant de -0,3 a 1,5 % et un ecart moyen quadratique (EMQ) entre 0,14 et 1,48 %. [Traduit par la Redaction] Mots-cles: detection des arbres, balayage laser aeroporte (BLA), structure de la foret, stock de carbone, foresterie intelligente face au climat., 1. Introduction In Europe, forests cover about 35% of the total land area (SoEF 2020) and play a significant role in climate change mitigation thanks to their capacity to remove [...]

The transition towards more resilient and sustainable agricultural systems must start from smallholder farms (SHs), that are responsible for one third of total crop production, are crucial to preserve ecosystems services, but are restive to adopt precision viticulture (PV) tools because benefits are considered insufficient to justify the costs. PV could help SHs to face with climate variability, maintaining high quality standards in the vineyard and to increase grapevine resilience adopting strategic cultural practices. This paper focus on evaluating some canopy management techniques (leaf removal at different phenological stages) on Italian grapevine landraces through field survey and UAV remote sensing, to obtain an automated estimation of the vine status in terms of canopy architecture, vine vigour, and berry traits. Findings showed as the adoption of canopy management practices, like the leaf removal, can increase the productive performance of the vines by regulating canopy growth, improving berry quality, and at the same time can increase the environmental sustainability of viticulture. Remote sensing restores a real-time vegetational indices (VIs) at vine scale that SHs could use to maximize quality and sustainability through a more efficient and site-specific management of the vineyard.

Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber–leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level. [ABSTRACT FROM AUTHOR]

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]

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]

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]

BACKGROUND Bushfires are becoming an increasing issue for the wine sector due to grape and vine losses and smoke taint in wine. Smoke affects vine physiology and the smoke's volatile phenols are absorbed by plants and berries, contaminating the wine. Our hypothesis was that, for the first time, unmanned aerial vehicle (UAV)‐based visible images can be used to study the physiology of smoke‐affected vines and to assess compromised vines. RESULTS: Procanico vines were exposed to two smoke treatments, a week apart. Gas exchanges and leaf biochemical traits were measured in the short term (30 min after smoke exposure) and in the long term (24 h after smoke exposure). Canopy damage was assessed with conventional vegetation indices (VIs) and by an innovative index derived by UAV‐based visible images, the Canopy Area Health Index (CAHI). Gas exchange showed a reduction after the first smoke exposure, but the vines recovered within 24 h. The second smoke exposure led to an irreversible reduction in functional parameters. The VIs exhibited significant differences and CAHI presented a damage gradient related to bushfire nearby. CONCLUSION: The vineyard damage assessment by UAV‐based visible images may represent a tool to study the physiological activity of smoke‐affected vines and to quantify the loss of destroyed or damaged vines. © 2020 Society of Chemical Industry [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]

At present, the climate changes issue is perhaps the greatest threat that is affecting people and the environment. Forest ecosystems have a key role in the mitigation of climate change. In this context, the prediction of the evolution and growth dynamics of the forests including carbon and water fluxes, and in relation to forest management has become a primary objective. The present study aims at defining a protocol for data collection and the workflow for using the 3D-CMCC-CNR-FEM model in a small mountain watershed in the Calabria region. Within this work we synergistically integrate data coming from different methods (e.g., LiDAR, eddy covariance and sample area) to predict forest dynamics (growth, carbon and water fluxes). Carbon and water fluxes will be simulated considering also the effects of forest management. [ABSTRACT FROM AUTHOR]

In the last decades, the ecological, economic and social analysis of benefits provided by ecosystem to society has coved a great importance in the scientific international literature. The main studies focuses on three aspects: ecosystem services potential supply, demand and use. The potential supply is defined as the ecosystem biophysical capacity to supply services, while the demand represents the amount of service desired by the society. The third aspect considers the current use of a ecosystem service in order to satisfy the social demand. This study focused on the evaluation of social demand on ecosystem services provided by forests at local level (Calabria region) under the project "ALForLab". The main objective of the present study is to analyze the stakeholders' opinions and preferences of forest sector in Calabria Region about relevance of forest ecosystem services and categories of ecosystem services. The study, conducted in four areas (Pollino, Sila, Catena Costiera and Serre Calabre), was structured in three steps: (i) identification and classification of stakeholders (stakeholder analysis); (ii) administration of a questionnaire to the previously identified stakeholders; (iii) statistical analysis of the data collected by questionnaire. Stakeholders' opinions and preferences were analyzed in order to highlight the differences between study areas and groups of stakeholders. At the end of the stakeholder analysis, 320 stakeholders were identified and contacted, 71 of whom filled in the questionnaire (response rate of 22%). The results show that the two main categories of ecosystem services are the supporting services and regulating services. According to the stakeholders' opinions the three most important forest ecosystem services are: biodiversity, landscape and hydrogeological protection. Landscape is particularly important because it is stric - tly linked to the cultural value and social identity of a territory. This survey show that the characteristics and peculiarities of each study area are the main factors that influence the stakeholders' preferences on single elements of the landscape. In the study areas characterized by a high natural value (Pollino, Sila and Serre Calabre) the stakeholders consider the natural elements of the landscape (forests and water bodies) as the most important, while in the most urbanized study area (Catena Costiera) the anthropic and cultural elements have a key importance. Finally, the stakeholders consider the forest fires, the illegal cuttings and the forest management abandonment the three main important threat factors to forests functionality in Calabria. [ABSTRACT FROM AUTHOR]

In Italy, the value of bioeconomy is 251 billion € and more than 1.7 million of workforce is employed in this sector. The forest-wood chain cover about 15% of total value of bioeconomy. The consultation draft of Italian Bioeconomy Strategy was presented on November 22, 2016. This document identifies three macro-sectors (agrifood, marine bioeconomy, forestry and biobased industry) with the respective objectives and priorities. In addition, some Italian regions are organizing to devise a regional strategy detailing the key points of the EU Bioeconomy Strategy and Italian Bioeconomy Strategy in accordance with local peculiarities. In order to develop a regional strategy for the forest sector it is important to investigate the social demands and needs involving the local stakeholders. The aim of the study is to investigate the stakeholders' opinions of Calabria Region (Italy) about the priorities of forest-wood chain to include in the future regional bioeconomy strategy. The stakeholders' opinions were collected through the face-to-face administration of a structured questionnaire to 99 local stakeholders. The respondents assessed the importance of a set of objectives aimed to enhance the forest-wood chain at local level (economic exploitation of wood products; diffusion of forest certification; orientation of production to market demand; coordination of forest-wood-energy chain's actors; implementation of environmental and cultural forest values; improvement of the level of mechanization in wood processing; improvement of the efficiency of forestry workers). In addition, the respondents assessed the capacity of some specific activities to achieve the above mentioned objectives. The collected data were used to define two strategies aimed to enhance the forest-wood chain in Calabria Region. The results show that for the interviewed stakeholders the main three objectives are: the implementation of environmental and cultural forest values, the improvement of the efficiency of forestry workers, and the diffusion of forest certi - fication. In addition, the stakeholders highlighted the importance of the activities of environmental education for the citizens and the professional training courses for the forest operators. Finally, the diffusion of forest certification is a key issue for the interviewed stakeholders. This issue can be achieved facilitating the access of the certified products to the market and creating a chain-of-custody system for the wood products. [ABSTRACT FROM AUTHOR]

At present, the climate changes issue is perhaps the greatest threat that is affecting people and the environment. Forest ecosystems have a key role in the mitigation of climate change. In this context, the prediction of the evolution and growth dynamics of the forests including carbon and water fluxes, and in relation to forest management has become a primary objective. The present study aims at defining a protocol for data collection and the workflow for using the 3D-CMCC-CNR-FEM model in a small mountain watershed in the Calabria region. Within this work we synergistically integrate data coming from different methods (e.g., LiDAR, eddy covariance and sample area) to predict forest dynamics (growth, carbon and water fluxes). Carbon and water fluxes will be simulated considering also the effects of forest management. [ABSTRACT FROM AUTHOR]

Interest in the use of biomass for energy has increased significantly in the last few years. The latest report by the Intergovernmental Panel on Climate Change highlights the influence mankind has had on the climate: an unprecedented increase in GHG levels in the last 800,000 years and a rise of 40% in CO2 concentrations since pre-industrial times. The challenge now is to find energy alternatives, and in this context, one important option is bioenergy, one of the most important energy sources of the future. In light of this, the goal of this paper was to assess the sustainable potential of woodfuel resources in Italy using WISDOM methodology. WISDOM, developed by the FAO, has been applied in many countries around the world. From this study, at national level, household consumption was at 19.3 Mt in 2003 (average value), while the potential supply of woody biomass (productivity) was 24.9 Mt (average value), with a surplus of almost 6 million tons between household consumption and productivity. This study represents an advance in knowledge of the biomass potential for energy use in Italy, and, as such, is subject to possible future improvement. Forest bioenergy development creates good opportunities to mobilize the production potential of European forests, and to contribute to a more climate- -friendly, bio-based economy. [ABSTRACT FROM AUTHOR]