GEOSYSTEMS RESEARCH INSTITUTEMississippi State University

ANNUAL REPORT

We have faced many challenges over the past year, and we have also had some great successes.

The Northern Gulf Institute (NGI) received outstanding praise at its 5-year review in October 2009. When the Deepwater Horizon incident occurred in April, the NGI was able to gather researchers, scientists, and engineers from across the northern Gulf of Mexico to address the problems. Due principally to the diligent, team-building, and untiring work of Dr. Mike Carron, Interim NGI Director, the BP Company made a large award to NGI to fund several efforts to help understand and mitigate the impact. NSF awarded NGI/MSU Geosciences professor Deepak Mishra with a large Rapid Response Award to study the oil spill impact on Louisiana salt marshes.

In the Agricultural and Natural Resources area, Dr. Wes Burger has developed collaboration with the Mississippi Department of Marine Resources and Delta FARM to address the nutrient run-off that contributes to the hypoxia issues near the mouth of the Mississippi River. As this annual report goes to press, MSU has been notified by the USFWS of their intent to collaborate with GRI and other units at MSU to execute a large conservation cooperative. This 5-year opportunity was initiated and nursed to fruition by Dr. Burger. Dr. Scott Samson has continued to lead a state-wide effort to create a high-resolution digital map of Mississippi that included layers showing typography, roads, land use, etc. Dr. Samson has also worked with the MSU Extension Service and USDA-ARS to create a data-driven irrigation scheduling model for the production of row and small grain crops. Dr. John Madsen has continued to study and address tough invasive species issues, most recently building IPAMS – the Invasive Species Atlas of the MidSouth. Dr. Bill Cooke and his students are working with the USFS Fire Sciences Lab in Montana to improve the National Fire Danger Rating System and fire behavior models.

Our multi-year NASA small satellite project, led by Dr. Bob Ryan, is nearing completion. Many MSU faculty have learned how to specify and design small satellites, labs have been established in several engineering departments at MSU, and relationships have been established with several international corporations. The project has funded the refurbishment of a ground station at the Stennis Space Center, such that by the end of the year data from DMCii and possibly other small satellite signals should be able to be downlinked. A remote sensing mission operations center has been established in Starkville. Dr. Haibo Yao flew a hyperspectral sensor on the EPA ASPECT aircraft for detecting oil spills associated with the Deepwater Horizon incident. Ryan and Dr. Zhongping Lee supported Dr. Yao on processing and interpreting the data.

In addition to the BP-funded NGI-facilitated work, the NGI has funded other projects on which GRI scientists and engineers are addressing river flooding, watershed management, hurricanes and severe storm impacts on the Northern Gulf, and ecosystem assessment. All this work is led by Dr. Bill McAnally, NGI Co-Director and the MSU representative on the NGI Council of Fellows, the governing body of the NGI research program. Dr. McAnally has initiated several large projects that should begin to produce significant water-quality results as early as 2011.

In line with our strategic plan, we continue to advance the knowledge and technology of earth and its systems, to integrate geosystems science and engineering, to translate geospatial technologies into useful tools and skills, and to transition science and technology into practice to support our stakeholders and to improve policy and public awareness.

To my colleagues

Robert Moorhead, Director and Endowed Professor

Better Understand and Predict Earth’s Systems and

Develop Geospatial Technologies that Promote their Stewardship, Sustainability, and

Contributions to Prosperity Advance Discovery, Knowledge, and

Education using Multi-Disciplinary, Geospatially-driven Innovation to Improve Deci-

sion-Making and Contribute to Sustainable Resource Management

vision

mission

GRI researchers are using hyperspectral imaging technologies as an innovative non-invasive approach toward screening for toxigenic fungi and the presence of toxins in our country’s food and feed crops. Aflatoxins are naturally occurring mycotoxins that are produced by many species of a fungus Aspergillus.

Aflatoxins are among the most carcinogenic substances known, and for this rea-son, aflatoxin levels in food and feed are regulated by the Food and Drug Admin-istration in the US. The ability to detect aflatoxin in a rapid, non-invasive way is crucial to the grain industry, particularly to the corn industry.

Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Current aflatoxin detection and quantification methods are based on analytical tests. These analytical tests require the destruction of samples and can be costly and very time consuming.

GRI researchers Haibo Yao, Zuzana Hruska, Russell Kincaid, Ambrose Ononye, Wes Burger, and Bob Ryan are using hyperspectral imaging technologies to screen for toxigenic fungi and the presence of toxins in corn – a major feed crop for our country’s livestock industry. Scientists can detect the presence of the fungi and the presence of toxins associated with them by identifying a given specimen based on its spectral signature. Previous experiments have determined that additional spectral information about aflatoxin-producing fungus as well as the presence or absence of the toxin may be gained from combining hyperspectral imaging with UV excitation, where the resulting fluorescent image reveals more information than the reflected image. Our present study has led to the development of near real-time, non-invasive instruments that implement fluorescence hyperspectral imaging technology for aflatoxin detection in corn inoculated with an aflatoxin-producing strain of Aspergillus flavus.

Rapid and Non-Invasive Aflatoxin Detection in Corn

Disruptions to Rail-Impacts Analysis and Decision SupportGRI researchers are exploring the positive effects of combining Homeland Security issues with regional transportation infrastructure decision-making and economic development within the State of Mississippi and the southeast region. This provides a geographically specific, but highly transferable demonstration of a solution relevant to the Department of Homeland Security which integrates modeling systems with policy and decision-making.

This particular research identifies three specific rail disruption scenarios. The scenarios include an analysis of the Gulf Coast CSX corridor, which is vulnerable to natural disasters; the Vicksburg, MS, rail bridge, which is susceptible to a terrorist attack due to the bridge’s proximity to a nuclear plant; and the Memphis, TN, rail bridges, which support the nation’s 3rd largest rail hub and are a serious earthquake risk because they are directly on the New Madrid Fault. The three specific rail disruption scenarios were identified for test-bed analysis. After this was achieved, freight flow modeling data were forecasted by Oak Ridge National Laboratory with their Universal Rail Rerouting Model (URRM) and was collected. The utilization of the data provides alternative freight flow patterns when rail disruption is experienced in one of the specific regions.

The ongoing research efforts concentrate on integrating the forecasted freight flow disruptions patterns with specific economic modeling tools, such as REMI (Regional Economic Models, Inc.) to determine the economic impacts. In addition, GIS and Remote Sensing applications will be utilized to map and develop visualizations and imagery illustrating the impact a man-made and/or natural disaster would have on the region’s transportation network. These important research deliverables illustrate disruptions and economic impact patterns that will affect community and commerce flow. With this, planners in economic development, transportation, land-use and emergency evacuation can work with identical data to find solutions toward resiliency.

The investigators in this project are Bethany Stich, Jody Holland, Chuck O’Hara, Rodrigo Nobrega, Preeti Mali, Bijay Shrestha, Bob Eskridge, Kywaii Lawrence.

GeoVol is a custom application developed at GRI for visualizing atmospheric and oceanographic geospatial data. The application was developed to take advantage of many of the advancements in commodity hardware like GPUs (graphics processing units), multi- core CPUs (central processing units), and stereo hardware to provide interactive volume visualization of large, structured datasets.

GeoVol takes advantage of the GPU through its use of OpenGL Shaders. Volume rendering is a costly process that previously took a long time to generate images. However, with the increase in 3D texture lookup speed and the existence of graphics shaders, most of the volume rendering calculations can be offloaded to the graphics card where the massively parallel GPUs can process the rendering at interactive rates.

GeoVol also takes advantage of the computational power of multi-core CPUs. Many of the datasets being generated by atmospheric or oceanographic models are very large such that the entire run cannot fit in memory at once, either because they contain a large number of timesteps or the individual time steps themselves are large. Since the data cannot be loaded into memory at the application startup, the data must be streamed from the disk into memory as it is needed. In addition, in order to perform volume rendering, the data needs to be preprocessed so that it can be uploaded to the GPU’s memory. GeoVol uses a separate thread to handle the loading and processing of the data. This allows the rendering thread to maintain interactive rates since it can continue to handle user input and rendering until the data is available.

There are two versions of GeoVol that are designed for different levels of hardware. One version is built on the VRJuggler libraries and is designed to be used within a CAVE-like system. Within the environment, users can interactively fly through the volumetric data and fully immerse themselves in the simulation.

However, CAVE systems carry a large price tag and are not widely available or accessible. Therefore, a second version of GeoVol is built on Qt that can be run on most modern desktops and laptops. If present, it is able to use OpenGL stereo hardware such as NVIDIA’s 3D Vision to provide a low-cost virtual environment experience. While it is not a requirement for GeoVol, stereo rendering can give the user visual clues for depth and spatial relationships. Since 3D Vision only requires a stereo capable monitor, it also provides a portable solution which we’ve used several times for demos and conferences.

The principal researchers for this project are Phil Amburn, John van der Zwaag and Derek Irby.

GeoVol ImagingThe Mississippi Digital Earth Model (MDEM), led by GRI Extension Professor Scott Samson and Associate Director Wes Burger, is a dynamic and growing endeavor to create, update and maintain a geospatial computer model of the State of Mississippi. MDEM is coordinated with the USGS National Map and compatible with Geographic Information Systems (GIS) usage by the international geospatial community. Its seven framework layers – orthoimagery, geodetic control, elevation and bathymetry, hydrography, transportation, government boundaries and cadastral data – are the standard components of digital maps as defined by the Federal Geographic Data Committee’s National Spatial Data Infrastructure (FGDC NSDI).

GRI’s nationally recognized Geospatial Education and Outreach Project (GEO-Project) is an important component of MDEM. The GEO-Project is a unique statewide program envisioned and created by Samson to aid local and state agencies in fully utilizing MDEM data. The GEO-Project mission is to teach Mississippi’s state, county, municipal and civic personnel to understand and use GIS software so that they, in turn, may better serve the people of Mississippi by converting from paper to digital record-keeping and digitally-assisted emergency response.

Samson, GRI Research Associate Gunnar Olson, and Geosciences Department Instructor Nel Ruffin comprise the GEO-Project team. All three have earned teaching au-thorizations from ESRI, creators of ArcGIS software, along with portfolio- and performance-based certifications of experience and proficiency in GIS theory and teaching. Workshops offered by the GEO-Project include ten courses created by ESRI and two original courses designed by Olson to meet the specific needs of Mississippi’s tax assessors.

The GEO-Project team travels the state with “GIS laptop labs” consisting of 12 computers and GIS software, a projector, a printer and assorted peripheral equipment. Taking workshops to the participants has proven to be a successful strategy in making the training fully available. From June 2006 through June 2010 the GEO-Project team has led a total of 183 workshops in 62 Mississippi counties, reaching 1,874 students at an estimated savings to the state of approximately $1.9 million dollars. As a further outreach effort, team members also provide follow-up counseling and instruction to users at their agency offices, if requested.

Even in these difficult financial times, the GEO-Project continues to receive full grantor support. The Project’s geographic reach is growing, and the number of students requesting GIS courses taught by Project professors and instructors continues to increase as well. According to Burger, “…it is very evident, as shown by continued growth, that the GEO-Project outreach is clearly meeting a true need and is delivered in an exemplary fashion.”

MDEM Digital Earth

Hydrometeorology research at the Geosystems

Research Institute focuses on the impacts and evaluation of severe weather, especially hurricanes. Visualization tools are created by GRI researchers to elucidate important

information and data. Some examples of the ongoing research at GRI within hydrometeorology are:

Coastal Resiliency from Hurricane ImpactsGRI researchers have developed geospatial technologies to improve our understanding of coastal resiliency from hurricane

impacts in regards to wetland areas - areas which provide a line of defense for coastal communities. This research utilizes integrated numerical modeling, in-situ observations, and remote sensing techniques. Pat Fitzpatrick,

author of two books and hundreds of scientific papers on hurricanes, is spearheading this effort.

Forecasting Episodic Changes in Hurricane Intensity and Structure GRI researchers are providing greater insight into forecasting time-sensitive trends of rapid formation, changing intensity,

and changing wind field area (or size) of hurricanes over the Gulf Mexico in the interest of reducing the uncertainty in the risk posed to Gulf Coast residents and infrastructure. Scientists are identifying key features or processes present in the

ambient atmosphere and in the Gulf of Mexico that led to critical episodic changes in the intensity and structure of recent hurricanes: Humberto, Gustav, and Ike. Christopher Hill is the lead investigator for this research.

Satellite Rainfall Applications for Surface HydrologyResearchers at GRI are evaluating how soil moisture states simulated by land surface models are impacted when

forced with various precipitation datasets. These datasets are from a collection of Global

Precipitation Mission satellite constellation configurations gathered over the continental United States. The researchers are examining the impact of omitting certain types of satellites and sensors upon the performance of the NRL-Blend high resolution precipitation product. The omission experiments are designed to examine satellite constellation configurations that may exist during the global precipitation mission. Valentine Anantharaj leads the research for this project and has published in numerous journals and co-authored a book entitled The NRL-Blend High Resolution Precipitation Product and its Application to Land Surface Hydrology.

Spatial Technology and High Performance Computing for Improving Prediction of Surface Water QualityWilliam McAnally is leading the effort to improve coastal management decisions by demonstrating the best use of new data and modeling technologies for ecosystem management. The use of advanced spatial data analysis and high performance computing capabilities for the development of input for surface water quality models, enhancing model performance and demonstrating and displaying model results are investigated. McAnally and co-investigators, Vladimir Alarcon and John Cartwright, are specifically contributing to the improved management of Mobile Bay, with benefits to the Alabama-Mississippi coastal zone and Mississippi Sound.

Weather Research and Forecasting Modeling SystemThe research of Haldun Karan and Yongzuo Li includes assimilation of NEXRAD radial winds in a regional mesoscale model and the use of Lagrangian models to estimate the transport and dispersion of gasses/particles over the Southeastern United States. They are providing daily atmospheric wind and other conditional fore-casting over the Northern Gulf of Mexico. Most recently plume (smoke) forecast information has been developed to assess how the smoke due to burning oil over the Gulf of Mexico propagates in time.

Hy • dro • me • te • o • rol • o • gy (hahy-druh-mee-tee-uh-rol-uh-jee) nounThe study of the transfer of water and energy between the land surface and the lower atmosphere

82 Students involved in GRI Research

Evaluating Water Quality through Spatial TechnologiesGRI researchers are monitoring the Northern Gulf of Mexico (NGOM) ecosystem through remote sensing technologies. They are creating a better understanding of the dynamics of harmful algae blooms in the region. These blooms cause negative impacts to other organisms by producing natural toxins, by mechanical damage to other organisms, or by other means. It is the desire of the researchers to create indices for evaluating the health of an ecosystem through ground truth observations and standardized algorithms which produce and evaluate the spatial and temporal variations of water quality parameters.

The study of concentrations of suspended organic matter (SOM), suspended inorganic matter (SIM), and water clarity in the NGOM will address not only the spatial variations, but also the temporal changes of water quality of the NGOM waters in the past decade. The research will also identify any significant changes in the water quality of this region after the significant drop in population in New Orleans after Hurricane Katrina. The observing parameters for water quality will be water clarity and concentrations of SIM and SOM. Water clarity is a direct and first order measurement of the status of the health of an ecosystem, while SIM and SOM provide more in-depth evaluation of water constituents. Such information is critical to evaluate the health and stress of an ecosystem, which are required to help federal and state agencies determine nutrient discharge regulations.

This research promises to establish not only a baseline of the water-quality status of the broad NGOM waters, but also continuous monitoring the spatial and temporary variability of NGOM waters. This will ultimately help establish interrelationships between water quality, land-use, human impacts, and environmental changes in the NGOM. All are much needed information for ecosystem modeling and for providing resource management agencies with decision making tools.

Water quality studies such as these will particularly assist coastal managers as they evaluate and prioritize the coastal marsh restoration effort which will take place due to the much publicized Deepwater Horizon oil spill in the Gulf of Mexico.

Investigators involved in water quality through spatial technologies research at GRI are Zhongping Lee, Deepak Mishra, Haibo Yao, and Bob Ryan.

(Financials include NGI)

GRI Proposal Submissions

FY10

FY09

FY08

FY07

FY06

FY05 $11,317,965

$43,805,384

$28,135,873

$68,847,805

$27,566,620

$52,223,222

GRI Expenditures

FY10

FY09

FY08

FY07

FY06

FY05 $8,023,595

$12,253,767

Undergraduates 36

$17,762,903

PhD 25

$7,172,915

$19,454,973

Masters 16

$18,448,345

Postdocs 5

PublicationsBook or Book Chapter

Alarcon, V. J., McAnally, W. H., Wasson, L. L., Martin, J., & Cartwright, J. H. (2009). Using NEXRAD Precipitation Data for Enriching Hydrological and Hydrodynamic Models in the Northern Gulf of Mexico. Computational Methods in Science and Engineering: Advances in Computational Science (Maroulis, G. and Simos, T. E., Eds.). Melville, New York: American Institute of Physics. Volume 1148, 646-650.

Alarcon, V. J., McAnally, W. H., Diaz-Ramirez, J., Martin, J., & Cartwright, J. H. (2009). A Hydrological Model of the Mobile River Watershed. (G. Maroulis, T.E. Simos, Eds.), Computational Methods in Science and Engineering: Advances in Computational Science. Melville, New York: American Institute of Physics. Volume 1148, 641-645.

Alarcon, V. J. (2009). Computational Water Resources in Coastal Areas. Computational Methods in Science and Engineering: Advances in Computational Science. (Maroulis, G. and Simos, T. E., Eds.). Melville, New York: American Institute of Physics. Volume 1148, 640-641.

Aziz, W., Alarcon, V. J., McAnally, W. H., Martin, J., & Cartwright, J. H. (2009). An Application of the Mesh Generation and Refinement Tool to Mobile Bay, Alabama, USA. Computational Methods in Science and Engineering: Advances in Computational Science (Maroulis, G. and Simos, T. E., Eds.). Melville, New York: American Institute of Physics. Volume 1148, 651-656.

Madsen, J. D. (2009). Chapter 1: Impacts of Invasive Aquatic Plants on Aquatic Biology, pp. 1-8. In: Biology and Control of Aquatic Plants: A Best Management Practices Handbook. (Gettys, L.A., W.T. Haller, and M. Bellaud, Eds.). Marietta, GA: Aquatic Ecosystem Restoration Foundation. 210.

Madsen, J. D. (2009). Chapter 13.2: Eurasian watermilfoil, pp. 95-98. In: Biology and Control of Aquatic Plants: A Best Management Practices Handbook. (Gettys, L.A., W.T. Haller, and M. Bellaud, Eds.). Marietta, GA: Aquatic Ecosystem Restoration Foundation. 210.

Madsen, J. D. (2009). Appendix D: Developing a Lake Management Plan, pp. 167-172. In: Biology and Control of Aquatic Plants: A Best Management Practices Handbook. (Gettys, L.A., W.T. Haller, and M. Bellaud, Eds.). Marietta, GA: Aquatic Ecosystem Restoration Foundation. 210.

Turk, F. J., Mostovoy, G. V., & Anantharaj, V. G. (2010). The NRL-Blend High Resolution Precipitation Product and its Application to Land Surface Hydrology. (Gebremichael, Mekonnen and Hossain, Faisal, Eds.), Satellite Rainfall Applications for Surface Hydrology. Springer Science+Business Media.

Wersal, R. M., & Getsinger, K. D. (2009). Chapter 3: Impact of Invasive Aquatic Plants on Waterfowl pp. 19-23. In: Biology and Control of Aquatic Plants: A Best Management Practices Handbook. (Gettys, L.A., W.T. Haller, and M. Bellaud, Eds.), Aquatic Ecosystem Restoration Foundation, Marietta, GA. 210.

Yao, H., & Lewis, D. (2010). Chapter 2: Spectral Pre-Processing and Calibration Techniques. (Da-Wen Sun, Eds.), Hyperspectral Imaging for Food Quality Analysis and Control. Elsevier.

Peer-Reviewed Journals

Burger, L. W., Conover, R. R., & Linder, E. T. (2009). Bird Response to Field Border Presence and Width. Wilson Journal of Ornithology. 121, 548-555.

Du, Q., Zhu, W., & Fowler, J. E. (2009). Segmented Principal Component Analysis for Parallel Compression of Hyperspectral Imagery. IEEE Geoscience and Remote Sensing Letters. 6(4), 713-717.

Du, Q., & Reza, N. (2009). Fast Real-Time Onboard Processing of Hyperspectral Imagery for Detection and Classification. Journal of Real-Time Image Processing. 4(3), 273-286.

Durbha, S. S., King, R., & Younan, N. H. (2010). Wrapper-Based Feature Subset Selection for Rapid Image Information Mining. IEEE Geoscience and Remote Sensing Letters. 7(1), 43 - 47.

Evans, K. O., Burger, L. W., Faircloth, B. C., Palmer, W. E., & Carroll, J. P. (2009). Effects of Tissue Sampling Methods on Growth and Survival of Neonatal Northern Bobwhite. Journal of Wildlife Management. 73, 1241-1244.

Fan, X. (2009). Impacts of Soil Heating Condition on Precipitation Simulations in the Weather Research and Forecasting Model. Monthly Weather Review. 137(7), 2263-2285.

Fowler, J. E. (2009). Compressive-Projection Principal Component Analysis. IEEE Transactions on Image Processing. 18(10), 2230-2242.

Grala, K., & Cooke, W. H. (2010). Spatial and Temporal Characteristics of Wildfires in Mississippi, USA. International Journal of Wildland Fire. 19(1), 14-28.

Holly, D. C., Ervin, G. N., Jackson, C. R., Diehl, S. V., & Kirker, G. T. (2009). Effect of an Invasive Grass on Ambient Rates of Decomposition and Microbial Community Structure: A search for Causality. Biological Invasions. DOI: 10.1007/s10530-008-9364-5. 11(1855), 1868.

Holt, R., Burger, L. W., Dinsmore, S. J., Smith, M. D., Szukaitis, S. J., & Godwin, K. D. (2009). Effects of Radio-marking on Early Post-release Survival of Northern Bobwhite. Journal of Wildlife Management. 73, 989-995.

Hu, C., Lee, Z. P., Ma, R., Yu, K., Li, D., & Shang, S. (2010). MODIS Observations of Cyanobacteria Blooms in Taihu Lake, China. Journal of Geophysical Research - Oceans. 115, C04002, DOI:10.

Jin, J., Tang, L., Hruska, Z., & Yao, H. (2009). Classification of Toxigenic and Atoxigenic Strains of Aspergillus Flavus with Hyperspectral Imaging. Computers and Electronics in Agriculture. 69, 158–164.

Karan, H., Fitzpatrick, P. J., Hill, C. M., Xiao, Q., & Lim, E. (2010). The Formation of Two Prefrontal Squall Lines, and the Impact of WSR-88D Radial Winds in a WRF Simulation. Weather and Forecasting. 25, 242-262.

Lee, Z. P., Arnone, R., Hu, C., Werdell, P. J., & Lubac, B. (2010). Uncertainties of Optical Parameters and their Propagations in an Analytical Ocean Color Inversion Algorithm. Applied Optics. 49(3), 369-381.

Li, J., Du, Q., & Sun, C. (2009). A Modified Box-Counting Method for Image Fractal Dimension Estimation. Pattern Recognition. 42(11), 2460-2469.

Licciardi, G., Pacifici, F., Tuia, D., Prasad, S., West, T. R., Giacco, F., Thiel, C., Inglada, J., Christophe, E., Chanussot, J., & Gamba, P. (2009). Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest. IEEE Transactions on Geoscience and Remote Sensing. 47(11), 3857-3865.

Mercer, A., Shafer, C. M., Doswell, C. A., Richman, M. B., & Leslie, L. M. (2009). Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks. Monthly Weather Review. American Meteorological Society. 137, 4355-4368.

Mishra, S., Mishra, D. R., & Schluchter, W. (2009). A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach. Remote Sensing. 1, 758-775.

Nobrega, R. A. A., O’Hara C., Sadasivuni, R., & Dumas, J. (2009). Bridging Decision-making Process and Environmental Needs in Corridor planning (DOI: 10.1108/14777830910990744). Management of Environmental Quality International Journal (ISSN: 1477-7835). Hamburg, Germany: Emerald Group Publishing Limited. 20(6), 16 pages.

Prasad, S., & Bruce, L. (2009). Information Fusion in Kernel-Induced Spaces for Robust Subpixel Hyperspectral ATR. IEEE Geoscience and Remote Sensing Letters. 6(3), 572-576.

Sanyal, J., Zhang, S., Bhattacharya, G., Amburn, P., & Moorhead, R. J., II (2009). A User Study to Compare Four Uncertainty Visualization Methods for 1D and 2D Datasets. IEEE Transactions on Visualization and Computer Graphics. Proceedings of Visualization/Information Visualization 2009. 15(5).

Simpson, A., Jarnevich, C., Madsen, J. D., Westbrooks, R., Fournier, C., Mehrhoff, L., Browne, M., Graham, J., & Sellers, E. (2009). Invasive Species Information Networks: Collaboration at Multiple Levels for Prevention, Early Detection, and Rapid Response to Invasive Alien Species. Biodiversity. 10(2), 5-13.

Steed, C. A., Fitzpatrick, P. J., Jankun-Kelly, T. J., Yancey, A. N., & Swan, J. E. (2009). An Interactive Parallel Coordinates Technique Applied to a Tropical Cyclone Climate Analysis. Computers and Geosciences. Elsevier. 35, 1529-1539.

Wersal, R. M., Madsen, J. D., Woolf, T. E., & Eckberg, N. (2010). Assessment of Herbicide Efficacy on Eurasian Watermilfoil and Impacts to the Native Submersed Plant Community in Hayden Lake, Idaho, USA. Journal of Aquatic Plant Management. 48, 5-11.

Wersal, R. M., Madsen, J. D., Massey, J. H., Robles, W., & Cheshier, J. (2010). Comparison of Daytime and Night-time Applications of Diquat and Carfentrazone-ethyl for Control of Parrotfeather and Eurasian Watermilfoil. Journal of Aquatic Plant Management. 48, 56-58.

Wersal, R. M., & Madsen, J. D. (2010). Combinations of Penoxsulam and Diquat as Foliar Applications for Control of Waterhyacinth and Common Salvinia: Evidence of Herbicide Antagonism. Journal of Aquatic Plant Management. 48, 21-25.

Wu, Y., Nair, U. S., Pielke, R. A., Sr, McNider, R. T., Christopher, S. A., & Anantharaj, V. G. (2009). Impact of Land Surface Heterogeneity on Mesoscale Atmospheric Dispersion. Boundary Layer Meteorology. Springer. 133(3), 367-389.

Xiao, Q., Zhang, X., Davis, C., Tuttle, J., Holland, G., & Fitzpatrick, P. J. (2009). Experiments of Hurricane Initialization with Airborne Doppler Radar Data for the Advanced Research Hurricane WRF (AHW) Model. Monthly Weather Review. American Meteorological Society. 137, 2758-2777.

Yao, H., Hruska, Z., Kincaid, R., Brown, R. L., Cleveland, T. E., & Bhatnagar, D. (2010). Correlation and Classification of Single Kernel Fluorescence Hyperspectral Data with Aflatoxin Concentration in Corn Kernels Inoculated With Aspergillus Flavus Spores. Food Additives and Contaminants. Tuln, Austria: Food Additives and Contaminants. 27(5), 701-709.

Peer-Reviewed Conference Papers

Aanstoos, J.V., O’Hara C., Prasad, S., Dabbiru, L., Nobrega, R. A. A., & Lee, M. (2009). Screening of Earthen Levees Using Synthetic Aperture Radar. 2009 Fall Meeting. San Francisco, CA, USA: American Geophysical Union.

Alarcon, V. J., McAnally, W. H., Ervin, G. N., & Brooks, C. P. (2010). Using MODIS Land-Use/Land-Cover Data and Hydrological Modeling for Estimating Nutrient Concentrations. (Taniar, D.; Gervasi, O.; Murgante, B.; Pardede, E.; Apduhan, B.O., Eds.), Computational Science and Its Applications. Berlin: Springer-Verlag Lecture Notes in Computer Science. 1(6016), 501-514.

Alarcon, V. J., & O’Hara C. (2010). Scale-Dependency and Sensitivity of Hydrological Estimations to Land Use and Topography for a Coastal Watershed in Mississippi. (Taniar, D.; Gervasi, O.; Murgante, B.; Pardede, E.; Apduhan, B.O., Eds.), Computational Science and Its Applications. Berlin: Springer-Verlag Lecture Notes in Computer Science. 1(6016), 491-500.

Alarcon, V. J., McAnally, W. H., Aziz,, Wali, & Cartwright, J. H. (2009). A Hydrodynamic Model of Mobile Bay, Alabama. Proceedings WMSCI-09, (Callaos, N., Chu, H., Eshraghian, K., Lesso, W., and Zinn, C. D. Eds.). Orlando, Florida.: IIIS. IV, 30-35.

Amburn, P., Berberich, M., Moorhead II, R. J., Dyer, J., & Brill, M. (2009). Geospatial Visualization Using Hardware Accelerated Real-Time Volume Rendering. Proceedings of IEEE Oceans 2009. Biloxi, MS.

Anantharaj, V. G., & Nair, U. S. (2009). Quantifying Systematic Biases of Toa Shortwave Fluxes in Climate Model Simulations. iLEAPS ECSW Proceedings. Melbourne, Australia: iLEAPS Project Office.

Anantharaj, V. G., Nair, U. S., Lawrence, P., Chase, T. N., Christopher, S., & Jones, T. (2009). Comparison of CERES TOA Shortwave Fluxes to CCSM3 Simulations with MODIS-derived Land Surface Parameters. Water in a Changing Climate: 6th International Scientific Conference on the Global Energy and Water Cycle and 2nd Integrated Land Ecosystems-Atmosphere Processes Study Conference. Melbourne, Australia: GEWEX and iLEAPS Project Offices.

Baca, J. A., Swillie, S., Monceaux, W., & Rappold, K. (2009). Designing for Usability in a High Performance Computing Application: A Case Study. International Multi-Conference on Engineering and Technological Innovation. Orlando, FL.

Baca, J. A. (2009). Incorporating Usability in the Software Process. USACE Research and Development Conference. Memphis, TN.

Balakrishna, G., Durbha, S. S., King, R., & Younan, N. H. (2009). Sensor Web and Data Mining Approaches for Harmful Algal Bloom Detection and Monitoring in the Gulf of Mexico Region. Proceedings of IEEE International Geosciences and Remote Sensing Symposium. Cape Town, South Africa.

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Editor, Debbie McBrideCo-editor, Brandon Cobianchi

Designer, Dianna JanusPhotography, Mississippi State University

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The information contained in this publication is for the annual reporting period beginningJuly 1, 2009 through June 30, 2010.

The Geosystems Research Institute is a member center of the High Performance Computing Collaboratory at Mississippi State University.

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