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Barnali M. Dixon
Geography
Associate Professor, Ph.D., University of Arkansas, 2001
Email: Dr. Barnali Dixon
Phone: (727) 873-4066 (O), or (727) 873-4025 (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
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.

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.

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.

Dixon, B. 2004. Prediction of Ground Water Vulnerability using an integrated GIS-based neuro-fuzzy techniques. Journal of Spatial Hydrology. 4(2): 1 – 38.

Dixon, B. 2005. Ground water vulnerability mapping: a GIS and fuzzy rule based integrated tool. Journal of Applied Geography. 25: 327 – 347.

Dixon, B. 2005. Applicability of Neuro-fuzzy techniques in predicting ground water vulnerability: A sensitivity analysis. Journal of Hydrology. 309: 17 - 38.

Dixon, B. and Candade, N. 2005. Use of Support Vector Machine in classification of Multispectral Landsat images [Accepted: International Journal of Remote Sensing].

Earls, J and B. Dixon. 2007. Examining Spatio-Temporal Relatonships of landuse change, population growth and water quality in the SWFWMD [Accepted: Interdisciplinary Environmental Review (IER).

Dixon, B. 2006. A Case Study Using SVM, NN and Logistic Regression in a GIS to Predict Contaminated Wells. [in review: Journal of Environmental quality]

Dixon, B. and Alothe, Abhijit. 2005. JAVA Program for Calculation of Attenuation Factor of Pesticides. [in review: Journal of Spatial Hydrology ]

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.

Dixon, B. and Earls, J. 2009. Resample or not?! Effects of Resolution of DEMs In Watershed Modeling. Journal of hydrological Processes.  23(12): 1714 – 1724.

Casper A.F., B. Dixon, J. Earls, and J.A. Gore. 2010. Ecohydrology in ungauged river basins: Constraints in the integration watershed hydrology
models with instream habitat models when setting minimum flows and levels. Rivers Research and Applications 26:1-14

Book Chapters
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.

Dixon, B. 2004. Can an integrated ground water vulnerability mapping tool facilitate sensitivity analysis in a spatial domain?? In (C. A. Brebbia, ed.) Geo Environment. WIT Press, Southampton, UK.

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.

Peer Reviewed Conference Proceeding
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.

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.

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.

Dixon, B. and Candade, N. 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 Ed.) 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.

Dixon, B. 2004. Can an integrated ground water vulnerability mapping tool facilitate sensitivity analysis in a spatial domain?? In (C. A. Brebbia, ed.) Geo Environment. WIT Press, Southampton, UK.

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

Earls, J . 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.

Earls , J., N. Candade 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. (in press)

Earls , J and Dixon, B. 2006 The Influence of Resolution on the SWAT Model: Examining Neighboring Basins. Spring Specialty Conference GIS and Water Resources IV. Houston, TX, May 8-10. ( in press).

Courses
GEO 3180C: Digital Thematic Mapping
GEO 4131C: Remote Sensing of the Environment
GEO 4141C: Geographic Methods and Techniques
GEO 4151C: Introduction to GIS
GEO 5134: Advanced Remote Sensing

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