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Barnali M. Dixon
GIS and Remote Sensing
Professor, Ph.D., University of Arkansas, 2001
Email: Dr. Barnali Dixon
Phone: (727) 873-4025 (O), or (727) 873-4863 (Lab)

I have extensive experience in the application and teaching of Geographic Information Systems (GIS), remote sensing, Global positioning Systems (GPS), geostatistics, fuzzy logic and neural networks for environmental modeling. My areas of research interests include advancement of environmental modeling through enhancement of remotely sensed data (image processing) and GIS using fuzzy logic, neural networks and neuro-fuzzy techniques. I have applied environmental models including soil erosion, surface and ground water quality, ground-water vulnerability, watershed risk assessment and management (soils, land-use and water quality relationship), contaminant transport processes, land-use and ground-water recharge, rainfall- runoff simulation, and land use planning (urbanization, soils and water quality relationship).


Selected Publications:

  1. Douglas S. B. Dixon and D. Griffin. 2017. Assessing Intrinsic and Specific Vulnerability Models Ability to Predict Groundwater Vulnerability to Groups of Similar Pesticides: a Comparative Study. Physical Geography. 10.1080/02723646.2017.1406300
  2. Pruden, J, R. Mbatu, R. Johns and B. Dixon. 2017. Measuring conservation success beyond the traditional biological criteria: Case of conservation projects in Costa Rica, Mekong Valley, and Cameroon. Natural Resources Forum. 10.1111/1477-8947.12132
  3. Naghibi, S. A.,  H. R. Pourghasemi and B. Dixon. 2016. A comparative assessment between classification and regression tree, random forest and boosted regression tree data mining models for groundwater spring potential mapping in the Koohrang Watershed, Iran. Environmental Monitoring and Assessment 188(1):1 – 27.
  4. MouLeong T, Ficklin D. L, Dixon, B. Ab Latif Ibrahim and Zulkifli Yusop. 2015. Impacts of DEM resolution, source and resampling technique on SWAT-simulated streamflow. Applied Geography Journal. 63: 357-368 (http://www.sciencedirect.com/science/article/pii/S0143622815001794)
  5. Üstüner M., F. Balık Şanlı, and B. Dixon. 2015. Application of Support Vector Machines for Landuse Classification Using High-Resolution RapidEye Images: A sensitivity Analysis. European Journal of Remote Sensing.  48:403-422 (http://www.tandfonline.com/doi/abs/10.5721/EuJRS20154823)
  6. Baumstark, R.,  Dixon B., Carlson P., Palandro, D., and K. Kolasa. 2013. Alternative spatially enhanced integrative techniques for mapping seagrass in Florida’s marine ecosystem. International Journal of Remote Sensing. 34(4), 1248–1264.
  7. Fijani E., Moghaddam A. A. Tsai, Frank T.-C.  Nadiri, A., and B. Dixon. 2013. Optimization of DRASTIC Model by Supervised Committee Machine Artificial Intelligence for Groundwater Vulnerability Assessment in Maragheh-Bonab Plain Aquifer, Iran. Journal of Hydrology. 503:89-100
  8. Johns R, Dixon, B., and C. McHan  2013. Evaluating Food Desert in Saint Pertesrburg. The Florida Geographer. Volume 44: 15 – 37 (http://journals.fcla.edu/flgeog/article/view/82367)
  9. Casper F., B. Dixon, Steimle, E.T, Hall, M.L, and R.N. Conmy. 2012. High Resolution Mapping of the Spatial Variability of Water Quality in a River: Improvements from Integration of Geospatial and Sensor Technologies with Unmanned Surface Vehicles. Applied Geography. 32(2): 455 – 464.
  10. Dixon B and Earls, J. 2012. Effects of Urbanization on Streamflow Using SWAT with Real and Simulated Meteorological Data. Applied Geography Journal. 35(1): 174-190.
  11. Samui P. and Dixon B. 2012. Application of Support Vector Machine and Relevance Vector Machine to Determine Evaporative losses in reservoir. Hydrological Processes.  Volume 26, Issue 9, pages 1361–1369. (DOI: 10.1002/hyp.8278, Sep8, 2011)
  12. Casper A.F, B. Dixon, J. Earls, and J.A. Gore. 2011. Linking a spatially explicit watershed model (SWAT) with an in-stream fish habitat model (PHABSIM): A case study of setting minimum flows and levels in a low gradient, sub-tropical river. Rivers Research and Applications. 27(3):269-282 (DOI: 10.1002/rra.1355, 2010, Feb, 1st)
  13. Williams N., B. Dixon and A. J. Pyrtle. 2011. Estimating Soil Loss from Two Coastal Watersheds in Puerto Rico with RUSLE. Interdisciplinary Environmental Review (IER) 1(4) 108 – 127.
  14. Dixon B. 2009. A Case Study Using SVM, NN and Logistic Regression in a GIS to Predict Wells Contaminated with Nitrate-N. Hydrogeology Journal. 17:1507 – 1520.
  15. Dixon B. and Earls, J1. 2009. Resample or not?! Effects of Resolution of DEMs In Watershed Modeling. Hydrological Processes.  23(12): 1714 – 1724.
  16. Dixon B. and Candade N. 2008. Multispectral landuse classification using neural networks and support vector machines: one or the other or both? International Journal of Remote Sensing. 29(4) 1185 – 1206.
  17. Earls J. and Dixon B. 2008. A Comparison of SWAT Model-Predicted Potential Evapotranspiration: Using Real and Modeled Meteorological Data. Vadose Zone Journal: Special issue paper. Multiscale Mapping: Physical Concepts and Mathematical Techniques. Soil Science Society of America. 7(2):570–580
  18. Earls J. and Dixon, B. 2008. Using the Fractal Dimension to Differentiate Between Natural & Artificial Wetlands. Interdisciplinary Environmental Review (IER), Vol. X, (no. 1): 33-44.
  19. Dixon B. and Earls, J. 2007. Examining Spatio-Temporal Relationships of landuse change, population growth and water quality in the SWFWMD. Interdisciplinary Environmental Review (IER). Vol. IX (no.11) : 71 – 93.
  20. Dixon B., Li D., Earls, J and Xinhua Liu. 2007. The Study on Groundwater Vulnerability Assessment Method. Environmental Protection Science. 33 (5): 50 – 55.
  21. Dixon B. 2005. Applicability of Neuro-fuzzy techniques in predicting ground water vulnerability: A sensitivity analysis. Journal of Hydrology. 309: 17 – 38
  22. Dixon B. 2004. Prediction of Ground Water Vulnerability using an integrated GIS-based neuro-fuzzy techniques. Journal of Spatial Hydrology. 4(2):1 – 38.
  23. Dixon B. 2004. Ground water vulnerability mapping: a GIS and fuzzy rule based integrated tool. Journal of Applied Geography. 25: 327 – 347.
  24. Dixon B., H.D. Scott, J.C. Dixon, and K.F. Steele. 2002. Prediction of Aquifer Vulnerability to Pesticides Using Fuzzy Rule-Based Models at the Regional Scale. Physical Geography 23:130 – 152.
  25. Mitra B., H. D. Scott, J.C. Dixon and J.M. McKimmey. 1998. Application of fuzzy logic to the prediction of soils erosion in a large watershed. Geoderma. 86:183 – 209.

Recent Presentation:

  1. Kyle Flanagan1 and B. Dixon 2017. Integration of Terrestrial Source, Landuse, and Watershed Hydrogeology in MPA. Management using SWAT. American Association of Geographers. Boston, MA.April 5th – 9th..
  2. Rivenbark1T., B. Dixon, and C. Stallings. 2017. Cost-effectiveness of integrated GIS and remote sensing seagrass landscape characterization methods. American Association of Geographers. Boston, MA. April 5th – 9th..
  3. Rivenbark1 T., B. Dixon, and D. Griffin. 2017. Analysis of contamination from septic systems in coastal water of Florida using GIS data. American Association of Geographers. Boston, MA. April 5th – 9th.
  4. L. Meirose1, B. Dixon and C. Brown. 2017. Examining the Effects of DEM Resolution and Fractal Dimension on Slope Characterization and Soil Erosion in the Rio Fajardo Watershed, Puerto Rico. American Association of Geographers. Boston, MA. April 5th – 9th. Poster
  5. McGrane, C and B. Dixon. 2017. Pinellas County Environmental Justice Analysis. American Association of Geographers. Boston, MA. April 5th – 9th. Poster
  6. Lyons K.1 and B. Dixon. 2016. Evaluating the Effects of Precipitation Extremes on Watershed Hydrology Under Current and Projected Future Climate Conditions Using SWAT. American Association of Geographers. San Francisco, CA. March 29 – April 2.
  7. Flanagan, K. and B. Dixon. 2016. Rethiking MPA: Integration of Watershed Urbanization. American Association of Geographers. San Francisco, CA. March 29 – April 2.
  8. Cope, S and B. Dixon. 2016. Integration of GIS and logistic regression to develop a habitat suitability model for predicting seagrass distribution. American Association of Geographers. San Francisco, CA. March 29 – April 2.
  9. Rivenbark T, B. Dixon and C. Stallings. 2016. Integrated GIS and Remotely Sensed Method: A comparison of cost and accuracy for sea grass mapping. American Association of Geographers. San Francisco, CA. March 29 – April 2. Poster
  10. Douglas S1. and B. Dixon. 2015. Mapping of Groundwater Vulnerability Using Spatially Integrated Pesticide Attenuation Factor. American Association of Geographers. Chicago, IL. April 21 – 25.
  11. Merton E1 and B. Dixon. 2015. Analyzing the edge-effect: applying fractal analysis to mitigated wetlands in Tampa Bay, Florida. American Association of Geographers. Chicago, IL. April 21 – 25.
  12. Lyons K.1 and B. Dixon. 2015. Modifying the Revised Universal Soil Loss Equation (RUSLE) R and LS factors to identify soil erosion ‘hot spots’ in the Rio Fajardo watershed. American Association of Geographers. Chicago, IL. April 21 – 25.
  13. Terrano1J. D. Stewart, B Dixon. 2015. Determining Power Plant and Population Vulnerability to Storm Surges in Pinellas and Pasco County: A GIS Based Approach. 51st Annual FSG Meeting, Jacksonville, FL, Feb 6-8 Poster
  14. Merton E1 and B. Dixon. 2014. Location of mitigated and natural wetlands: an environmental variable analysis. SouthEastern Division of American Association of Geographers (SEEDAG). Athens, GA. Nov 23 – 25.
  15. Lyons K1 and B. Dixon. 2014. It’s all downhill from here! The effect of DEM resolution on modeling soil erosion risk potential. SouthEastern Division of American Association of Geographers (SEEDAG). Athens, GA. Nov 23 – 25.
  16. Lyons, K1 and B. Dixon. 2014. Evaluating Soil Erosion Potential in Response to Landuse Changes within the Fajardo River Basin, Puerto Rico. American Association of Geographers. Tampa, Fl. April 8 – 12. Poster
  17. Merton, E.1 and B. Dixon. 2014. Where the mitigated wetlands are: a spatially integrated environmental analysis. American Association of Geographers. Tampa, Fl. April 8 – 12. Poster
  18. Douglas, S.1 and B. Dixon. 2014. Analysis of well contamination with respect to landuses and karst features: An integrated geospatial approach. American Association of Geographers. Tampa, Fl. April 8 – 12. Poster
  19. Earls J.1, B. Dixon, and Ruiliang Pu. 2014. Development of A Risk Assessment Index Tool (RAIT) for Pollutants On Organic Farms: Using An Integrated Geospatial Method. American Association of Geographers. Tampa, Fl. April 8 – 12.
  20. Johns, R., Dixon B. 2013. Evaluating Food Deserts in St. Petersburg, Florida. 48th Annual FSG Meeting, Gainesville. Talahasee, FL, Feb 8-10.
  21. Williams, N. B1. and B, Dixon. 2011. Predicting Sediment Yield in a Tropical Watershed: A GIS based Conceptual Model. GSA Annual Meeting. Minneapolis, MN. Oct  9 – 12. Poster
  22. King. C1. and B. Dixon, 2011. Integrating Virulo model and virus parameters in mapping ground water contamination risk to pathogens. 34th Applied Geography Conferences. CA. Oct 19 – 21. Refereed

Recent Invited Speaker:

  1. Dixon, B and V. Uddameri. 2016. GIS-based Integrated Modeling Approach:  DRASTIC, Fuzzy Logic and Logistic Regression. International Conference on Water and Environment. Shanghai, China,July 23-26
  2. Dixon 2015. Integration of AI with Geospatial Technologies. Center for disaster mitigation and management, VIT, India, Feb 24-27
  3. Williams, N1£.  Dixon and A. Johnson. 2012. Linking Soil Loss to Sediment Delivery in the Jobos Bay Estuary, Puerto Rico. National Estuarine Research Reserve System (NERRS). NOAA’s Estuarine Reserves Division and the National Estuarine Research Reserve Association (NERRA). Shepherdstown, WV. Nov 27 – 30.
  4. Dixon, B. 2012. Statistical Regional modeling of nitrate in groundwater. Impacts of Excess Nitrogen in the Environment on Human Health: RCN Human Health Conference. National Institutes of Health (NIH), North Bethesda. MD. Nov 14 – 15.
  5. Dixon, B. 2012. Mapping the Tribes and the Terrain: Geospatial Analysis/Human Geography Consideration of Yemeni Tribes. Tribal Dynamics III Yemen Workshop: USSOCOM’s Interagency Task force. University of South Florida’s Citizenship Initiative and the Center for the Study of International Languages and Cultures (CSILC). Tampa, FL, April 23-24.

Invited papers:

  1. Samui P. and Dixon B. 2014. Determination of Contaminated Wells: A Relevance Vector Machine Approach. [Accepted: Journal of Urban and Environmental Engineering (JUEE)] http://www.journal-uee.org/
  2. Williams N. B., B. Dixon and A. Johnson. 2010. Developing a conceptual framework for linking soil erosion to sediment deposition: Patterns in coastal ecosystems in the Caribbean.  IMPACT 20 (4):15-16
  3. Li D. Dixon, B., Earls, J. F. Bradley and Xinghua, Liu. 2007.  The Study on Vulnerability Assessment in Groundwater Recharge Area of Jinan. Environmental Protection, 378(8B):59 – 61. Environmental Protection of China Press.
  4. Earls J. and Dixon, B. 2005. A comparative study of the effects of input resolution on the SWAT model. Pages 213 – 222. In (C. A. Brebbia, and J. S. Antunes do Carmo eds.) River Basin Management III. WIT Press, Southampton, UK.
  5. Dixon B. 2004. Can an integrated ground water vulnerability mapping tool facilitate sensitivity analysis in a spatial domain?? In (J. F. Martin-Daque; C. A. Brebbia; A. e. Godfrey and J.R. Diaz de Teran eds.) Geo Environment. WIT Press, Southampton, UK.
  6. Dixon B. 2002. Application of Neuro-Fuzzy techniques to predict ground water vulnerability. Pages 485 – 495. In (C. A. Brebbia, ed.) Risk Analysis III. WIT Press, Southampton, UK.
  7. Mitra B., J. M. McKimmey and H. D. Scott. 1997. Development and use of digital databases in agricultural research. Trends in Agronomy, 1:1-17.

Conference Proceeding:

  1. King, C, and B. Dixon. 2011. Integrating Virulo model and virus parameters in mapping ground water contamination risk to pathogens. Vol. 34, pages 267 – 275. In (Jay. Lee, Editor). Papers of The Applied Geography Conferences. Redlands, CA.
  2. Williams, N.B, B. Dixon and A.  Johnson. 2010. Linking watersheds’ hydrologic response to sediment delivery: A conceptual framework. In (Garcia, Pedro M. Editor). International Specialty Conference and 8th Caribbean Islands Water Resources Congress on Tropical Hydrology and Sustainable Water Resources in a Changing Climate (Proceedings). American Water Resources Association Technical Publication, Middleburg, Virginia, TPS-10-2, CD-ROM. ISBN 1-882132-83-1
  3. Dixon, B, Earls, J. A. F. Casper, J. A Gore. 2009. Integrating Spatially Explicit Watershed Models With In-Stream Habitat Models: A Discussion on Constraints With Regard to the Resolution of Data. AWRA Spring Specialty Conference: Managing Water Resources and Development is a Changing Climate.  Paper in AWRA conference CD. May 4 – 6th Anchorage, Alaska. http://www.awra.org/tools/members/Proceedings/0905conference/oral.html
  4. Dixon, B and Earls J. 2008. An estimation of Regional Soils Erosion Vulnerability using RUSLE-V. Papers of IASTED International Conference on Applied Simulation and Modeling. Corfu, Greece, June 23rd – 25th.
  5. Earls, J. and B. Dixon.2008. The Influence of Resolution on the SWAT Model: Examining Neighboring Basins. Spring Specialty Conference GIS and Water Resources V.  San Mateo, CA, Mar 17-19, 2008. Paper on Conference CD AWRA.
  6. Earls, J and B. Dixon. 2007. Application of the Soil and Water Assessment Tool (SWAT) in modeling the effects of landuse change on watershed hydrology. Vol. 30, pages 541-522. In (L. Harrington & J. Harrington, Jr, eds.). Papers of The Applied Geography Conferences. Indianapolis, IN.
  7. Earls, J and B. Dixon. 2007. Spatial Interpolation of Rainfall Data Using ArcGIS: A Comparative Study. 27th Annual ESRI International User Conference. http://www10.giscafe.com/link/display_detail.php?link_id=22230.  San Diego, June 18-22, 2007.
  8. A.F. Casper, M.L. Hall, B. Dixon and E.T. Steimle. 2007. Combining Data Collection from Unmanned Surface Vehicles with Geospatial Analysis: Tools for Improving Surface Water Sampling, Monitoring, and Assessment. Proceedings of OCEANS 2007 MTS/IEEE Vancouver. 2007ISBN CD-ROM: 0-933957-35-1,Vancouver, British Columbia. September 29 – October 4
  9. Earls J., N. Candade1 and B. Dixon. 2006. A Comparative Study of Landsat 5 TM Landuse Classification Methods including Unsupervised Classification, Neural Network and Support Vector Machine for Use in a Simple Hydrologic Budget Model. ASPRS  Annual Conference – Prospecting for Geospatial Information Integration – Reno, NV – May 1-5.
  10. Earls J and Dixon, B. 2006 The Influence of Resolution on the SWAT Model: Examining Neighboring Basins.  In (Maidment, David R. and John S. Grounds III, eds). GIS and Water Resources IV. Proceedings of the American Water Resources Association’s 2006 Spring Specialty Conference. American Water Resources Association, Middleburg, Virginia, TPS-06-1, CD-ROM. ISBN 1-882132-70-X
  11. Earls, J and Dixon, B. 2006. Comparison of annual calibration of SWAT model at differing resolutions. In (Mark Colosimo & Donald F. Potts, eds). Adaptive Management of Water Resources.  AWRA Summer Specialty Conference MT, June 26-28. ISBN:  1-882132-71-8.
  12. Earls, J1. and Dixon, B. 2005. Calculation of Evapotranspiration and Hydrologic budget from Landsat TM derived landuse maps for two unique drainage basins. Vol. 28, pages 413-422. In (G. A. Tobin and B. E. Montz, eds.). Papers of the Applied Geography Conferences. Washington D.C.
  13. Dixon, B. and Candade, N1. 2004. Comparison of Neural Network and Neuro-fuzzy Techniques in Ground Water Vulnerability Mapping: A Case Study. Pages 1 – 10. In (Kenneth J. Lanfear and David R. Maidment, eds.) AW RA’s 2004 Spring Specialty Conference “Geographic Information Systems (GIS) and Water Resources III.” American Water Resources Association, Middleburg, Virginia, TPS-04-1, CD-ROM.
  14. Candade, N and Dixon, B. 2004. Multispectral classification of Landsat images: Comparison of Support Vector Machine and Neural Network classifiers. Presentation.  ASPRS Annual Meeting. Denver, May 2004. Mira Digital Publishing. Bethesda, Maryland. ISBN 1-57083-072-X.
  15. Dixon, B. 2003. Can contamination potential of ground water to pesticides be identified from hydrogeological parameters?  Vol. 26, pages 237 – 247. In (B. E. Montz and G. A. Tobin, eds.) Papers and Proceedings of The Applied Geography Conferences. University of Colorado at Colorado Springs, Colorado Springs, Co.
  16. Dixon, B. 2002. Can ground water sampling strategy be improved by incorporating fuzzy logic in a GIS? Vol. 25, Pages 254 – 264. In  (B. E. Montz and G. A. Tobin, eds.) Papers and Proceedings of The Applied Geography Conferences. Binghamton University, Binghamton, NY


  • GIS 3006: Computer Cartography
  • GIS 4035C: Remote Sensing of The Environment
  • GEO 4141C: Geographic Methods and Techniques
  • GIS 4043C: Geographic Information Systems
  • GIS 6038C: Advanced Remote Sensing
  • GIS 4300: Environmental modeling with GIS
  • GIS 5049: GIS for Non Majors
  • EVR 6936: Seminar in Environmental Science
  • EVR 6934: Soil, Water and Landuse
  • GIS 6306: Environmental Applications of GIS.

Interested in learning more on Geography?

Contact Dr. Dixon