Abstract
Adequate biomass feedstock supply is an important factor in evaluating the financial feasibility of alternative site locations for bioenergy facilities and for maintaining profitability once a facility is built. We used newly developed spatial analysis and logistics software to model the variables influencing feedstock supply and to estimate and map two components of the supply chain for a bioenergy facility: (1) the total biomass stocks available within an economically efficient transportation distance; (2) the cost of logistics to move the required stocks from the forest to the facility. Both biomass stocks and flows have important spatiotemporal dynamics that affect procurement costs and project viability. Though seemingly straightforward, these two components can be difficult to quantify and map accurately in a useful and spatially explicit manner. For an 8 million hectare study area, we used raster-based methods and tools to quantify and visualize these supply metrics at 10 m2 spatial resolution. The methodology and software leverage a novel raster-based least-cost path modeling algorithm that quantifies off-road and on-road transportation and other logistics costs. The results of the case study highlight the efficiency, flexibility, fine resolution, and spatial complexity of model outputs developed for facility siting and procurement planning.
Highlights
Forest management for timber production, ecological restoration, and wildfire risk mitigation produces large amounts of woody biomass that can be used for bioenergy and bioproducts
To illustrate some of the benefits of a raster-based approach to quantifying woody biomass supply chain logistics, we present a case study for a potential facility location in Helena, Montana, USA
We separated the supply chain into two independent spatial analyses that quantified the available biomass stocks and the delivered costs from every location on the landscape (Section 3.2), and combined those outputs to generate cost curves for a specific facility location (Section 3.3). Doing this in a raster environment proved to be efficient in terms of processing time and storage space, and conforms to real-world logistics constraints, such as hydrologic features
Summary
Forest management for timber production, ecological restoration, and wildfire risk mitigation produces large amounts of woody biomass that can be used for bioenergy and bioproducts. Though traditional inventory methods remain important in forestry today, there is a wealth of remotely sensed data that can be used to characterize landscapes, such as satellite imagery and digital elevation models (DEM) While these types of data have typically been used to stratify landscapes, they can be used to draw relationships that further identify forest characteristics at the spatial resolution of a plot [17] and to provide high-resolution information about potential biomass supply and costs across stand and forest boundaries. The stand boundaries can be defined post hoc in ways that suit the analysis at hand and used to statistically estimate parameters that characterize the area within those stands Storing data in this manner, as raster surfaces, provides a great deal of flexibility with regard to defining geometric relationships among plot, stand, and landscape attributes, with the primary limitation being digital storage space, because raster surfaces typically require more disk space than vector formats. Mass is referred to on a dry weight basis
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