FY2010 - Oak Ridge National Laboratory

Director’s R&D Fund—
Emerging Science and Technology for Sustainable Bioenergy
mileage. The Biofuel Infrastructure Logistics and Transportation model (BILT) minimizes the total
annualized cost of supplying the demanded ethanol from the available biomass. The model selects the
source and quantity for all of the biomass, preprocessing, and refinery locations and sizes, volumes sent
along each link, and the county-level distribution. A web-based interface displays national biomass data
and model results including transportation demand by route. Initial testing focused on four states
(Pennsylvania, Maryland, Virginia, and West Virginia). A serial implementation solved the problem close
to optimality within a few minutes but required 24 hours to reach proven optimality. A parallel solver
implementation on the Jaguar supercomputer using only 128 nodes solved to optimality in about 3 min.
Tests have also been performed on a quad processor version of the Gurobi MIP solver. The model is now
being integrated into a national long-range economic model.
05238
Spatiotemporal Data Mining Framework for Monitoring Biomass
at Regional and Global Scales
Ranga Raju Vatsavai, Auroop R. Ganguly, Forrest M. Hoffman, Thomas Paul Karnowski,
Christopher T. Symons, and Varun Chandola
Project Description
This project is addressing two key research challenges that are essential to realizing U.S. energy security,
a task that has figured prominently in the recent Office of Biomass Program report. These two challenges
are (1) a cost-effective solution to continuously monitor biomass and (2) scalable solutions for specieslevel
information extraction from high-resolution images. Conventional techniques are not adequate for
continuous biomass monitoring over large geographic regions. Change-detection techniques, such as
differencing, significance testing, and probabilistic approaches, are not sufficient for identifying changes
in croplands. We are addressing this problem by developing new spatiotemporal data mining (STDM)
approaches with specific focus on (1) efficiently monitoring croplands based on spectral, phenological,
biogeophysical characteristics by reducing false positives (false changes); (2) drastically reducing the
ground-truth data required to build models; (3) easily adapting models to diverse geographic settings with
minimal retraining; and (4) automatically recognizing sub-classes such as crop types or species (e.g.,
switchgrass, Chinese tallow, rapeseed, corn, wheat, soybean) from aggregate classes, such as agriculture,
with minimal additional ground-truth. We are addressing scalability issues using modern computing
infrastructure, especially distributed and cloud computing.
Mission Relevance
With recent government emphasis on biofuel development for reducing dependency on foreign oil and
reducing carbon emissions from energy production and consumption (e.g., DOE Office of Energy
Efficiency and Renewable Energy's Office of Biomass Programs, Biomass Multi-Year Program Plan), the
landscape of the United States and many other countries is going to change dramatically in coming years.
However, biomass monitoring (changes over time) over large geographic regions using remote sensing
images poses several challenges. The project will develop automated techniques that exploit the subtle
multidimensional signals inherent in biomass monitoring through the joint use of coarse-spatial resolution
(MODIS) data and moderate- and fine-spatial resolution satellite images to enable the extraction of
multitemporal biomass information, including crop types and their conditions. We expect that the
research will be of great interest to the DOE Office of Biomass program and other government agencies
such as the Department of Agriculture and the National Aeronautics and Space Administration, who are
working on similar programs (e.g., Global Agricultural Monitoring).
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