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The framework for National Algal Biofuels Technology Roadmap was constructed at the Algal Biofuels Technology Roadmap Workshop, held December 9-10, 2008, at the University of Maryland-College Park. The Workshop was organized by the Biomass Program to discuss and identify the critical challenges currently hindering the development of a domestic, commercial-scale algal biofuels industry. This Roadmap presents information from a scientific, economic, and policy perspectives that can support and guide RD&D investment in algal biofuels.

Meeting the Energy Independence and Security Act (EISA) renewable fuels goals requires development
of a large sustainable domestic supply of diverse biomass feedstocks. Macroalgae, also known as
seaweed, could be a potential contributor toward this goal. This resource would be grown in marine
waters under U.S. jurisdiction and would not compete with existing land-based energy crops.
Very little analysis has been done on this resource to date. This report provides information needed for an

This model was developed at Idaho National Laboratory and focuses on crop production. This model is an agricultural cultivation and production model, but can be used to estimate biomass crop yields.

NOAA's National Centers for Coastal Ocean Science's (NCCOS's) PCMHAB program funds research to move promising technologies for preventing, controlling, or mitigating HABs and their impacts through development, to demonstration, and, finally application, culminating in wide spread use in the field by end-users. A more detailed description of the program and its projects are available at the link below.

National biomass feedstock assessments (Perlack et al., 2005; DOE, 2011) have focused on cellulosic biomass resources, and have not included potential algal feedstocks. Recent research (Wigmosta et al., 2011) provides spatially-­‐explicit information on potential algal biomass and oil yields, water use, and facility locations. Oak Ridge National Laboratory and Pacific Northwest National Lab are collaborating to integrate terrestrial and algal feedstock resource assessments. This poster describes preliminary results of this research.

Algae feedstocks for alternative fuels production are not economically competitive with fossil fuels at the present time. Furthermore, it has not yet been demonstrated that algae production systems offer improved sustainability characteristics.

Fast-growing, oil-producing species of microalgae have become the focus of attention for both biomass and biodiesel biofuels, but questions remain about scalability, economics, and the competition between large-scale microalgae cultivation and agriculture, with regard to water, fertilizer, and land use. By cultivating microalgae on domestic wastewater, the water and fertilizer problems can be overcome, and by using algae for improved wastewater treatment, economic and environmental benefits can be realized.

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level.

Many investigators need and use global land cover maps for a wide variety of purposes. Ironically, after many years of very limited availability, there are now multiple global land cover maps and it is not readily apparent (1) which is most useful for particular applications or (2) how to combine the different maps to provide an improved dataset. The existing global land cover maps at 1 km spatial resolution have arisen from different initiatives and are based on different remote sensing data and employed different methodologies. Perhaps more significantly, they have different legends.

Two of the most widely used land-cover data sets for the United States are the National Land-Cover Data (NLCD) at 30-m resolution and the Global Land- Cover Characteristics (GLCC) at 1-km nominal resolution. Both data sets were produced around 1992 and expected to provide similar land-cover information. This study investigated the spatial distribution of NLCD within major GLCC classes at 1-km unit over a total of 11 agricultural-related eco-regions across the continental United States.