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Surface biomass characterization plays a key role in wildfire management. It allows classifying vegetation fuels flammability for fire risk analysis, to define silvicultural prescriptions for fire hazard reduction, to plan prescribed burning, or to model fire behavior and its effects, such as greenhouse gas and pollutant emissions. To facilitate fuel classification and analysis of potential fire behavior and effects in Italy, we harmonized 634 measurements of surface wildland fuels from Alpine, temperate and Mediterranean environments. The dataset provides quantitative data for duff, fine dead fuels and downed woody material, live grasses and shrubs fuel components. Surface fuel data were harmonized by subdividing loads (Mg ha-1) to standard size classes for dead (0-6, 6-25 and 25-75 mm) and live (0-6, 6-25 mm) fuels, collecting percent cover and depth/height (cm) of the various fuel components, and classifying observations into 19 fuelbed categories. To ensure comparability with existing vegetation classification systems, we classified each observation according to the European Fuel Map, the Corine Land Cover classes (level IV), the European Forest Types, and the forest categories of the Italian National Forest Inventory. The dataset and a photo description of each fuelbed category are available as Supplementary material. This dataset is the first step to develop several products at the national scale such as: (i) fuel type classification and mapping; (ii) carbon stock and wildfire emission estimates; (iii) calibration of fuel models for the simulation of fire behavior and effects. [ABSTRACT FROM AUTHOR]

Forest biomass is a renewable energy source, more climate-friendly than fossil fuels and widely available in Europe. The wood energy chain has been suggested as a means to re-activate forest management and improve the value of forest stands in marginalized rural areas. However, wall-to-wall estimates of forest biomass, needed to design the location and size of power and heat biomass plants in any given territory, are notoriously difficult to obtain. This paper tests an algorithm to predict forest biomass using publicly available Landsat satellite imagery in the Liguria region, northern Italy. We used regional forest inventory data to train and validate an artificial neural network (ANN) classifier that uses remotely-sensed information such as three principal components of Landsat-5 TM spectral bands, the Enhanced Vegetation Index (EVI), and topography, to retrieve aboveground live tree volume. Percent root mean square error was -9% and -23% for conifers and broadleaves respectively in the calibration dataset, and -27% and -24% in the validation dataset. The reconstructed volume map was updated to present day using current volume increment rates reported by the Italian National Forest Inventory. A wall-to-wall map of forest biomass from harvest residues was finally produced based on species-specific wood density, biomass expansion factors, volume logged for timber assortments, forest accessibility, and topography. Predicted aboveground forest volume ranged from 81 to 391 m³ ha-1. In forests available for wood supply (70% of the total), planned volume removals averaged 25.4 m³ ha-1, or 18.7% of the average standing stock across. Biomass available for bioenergy supply was 1.295.921 million Mg dry matter or 8.95 Mg ha-1. This analysis workflow can be replicated in all mountain regions with a predominant broadleaved coppice component. [ABSTRACT FROM AUTHOR]

Human activities have the potential to enhance carbon sequestration by the world's forests and contribute to climate change mitigation. Voluntary carbon trading is currently the only option to pursue and reward carbon sequestration by forestry activities. Carbon credits for enhanced sequestration can be sold to partners wishing to offset their own emissions. Here we illustrate the steps taken to design guidelines for the generation of voluntary carbon credits by improved forest management in Piemonte, Italy. The guidelines have been developed in a joint effort by academia, regional administrations, forest owners and professional consultants. In particular, we show how to compute the baseline and the additionality of credit-generating forest management activities, and how to reconcile the generation of forest carbon credits with law requirements, technical limitations, and the provision of other ecosystem services. To illustrate the profitability of carbon credit generation, we simulated the application of carbon credit guidelines to two forest-rich mountain watersheds in the southern part of the Piemonte region. The two dominating tree species are beech (Fagus sylvatica L.) and chestnut (Castanea sativa Mill.). We computed current forest carbon stock and carbon credits generated in 20 years under business as usual and an alternative biomass retention scenario. The IFM resulted in an avoided harvest of 39,362 m³ for a net total of 64,014 MgCO2e after subtracting harvest emissions, or 38 Mg ha-1 throughout the permanence period of 20 years. These steps can be replicated in other mountain regions where there is interest in promoting this ecosystem service as an alternative or an addition to production-oriented forest management. [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]