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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: Combining the economics and the environmental and social aspects of bioenergy chain implementation is a difficult task, particularly for wood fuel production. Multiple aspects, such as financial analysis, technology development, natural resource exploitation and avoided environmental pollution, must be considered simultaneously. Thus, decision support models seem to be the proper tools for the overall definition of strategies in the wood energy sector. The objective of this study has been to develop a partial equilibrium model able to quantify the socio-economic and environmental effects of policy, technology and best biomass allocation scenarios on the forest residue chain. The model, based on multi-objective linear programming and spatial analysis, considers the financial benefits/losses and the potential trends of three compartments: sawmills, forest enterprises and energy plants. In addition, the model computes avoided emissions for bioheat and bioelectricity production and introduces an impact indicator for the road transport of biomass. Model outputs are defined using a multi criteria approach. The main results stress the importance of both environmental parameters and the implementation of organic Rankine cycle technology for the optimization of the entire bioenergy chain. The model was tested in an Italian Alpine region (province of Trento). [Copyright &y& Elsevier]