Journal Publications

  1. 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.
  2. 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.
  3. Dixon, B. 2004. Prediction of Ground Water Vulnerability using an integrated GIS-based neuro-fuzzy techniques. Journal of Spatial Hydrology. 4(2):1 38.
  4. Dixon, B. 2004. Ground water vulnerability mapping: a GIS and fuzzy rule based integrated tool. Journal of Applied Geography. 25: 327 347.
  5. Dixon, B. 2005. Applicability of Neuro-fuzzy techniques in predicting ground water vulnerability: A sensitivity analysis. Journal of Hydrology. 309: 17 - 38
  6. B. Dixon 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.
  7. Dixon, B. Li D., Earls, J and Xinhua Liu. 2007. The Study on Groundwater Vulnerability Assessment Method. Environmental Protection Science. 33 (5):50 - 55.
  8. 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.
  9. J. Earls 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):570580
  10. 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.
  11. 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.
  12. Dixon, B. and Earls, J1. 2009. Resample or not?! Effects of Resolution of DEMs In Watershed Modeling. Hydrological Processes.  23(12): 1714 1724. 
  13. Casper A.F, B. Dixon, J. Earls, and J.A. Gore. 2011. 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. 27(3):269-282 (DOI:10.1002/rra.1355, 2010, Feb, 1st)
  14. Williams, N1., 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.
  15. 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.
  16. Samui, P3. and Dixon B. 2011. Application of Support Vector Machine and Relevance Vector Machine to Determine Evaporative losses in reservoir. Hydrological Processes.  (DOI:10.1002/hyp.8278, Sep8, 2011)
  17. Dixon, B and Earls, J. 2011. Effects of Urbanization on Streamflow Using SWAT with Real and Simulated Meteorological Data.  [In press: Journal of Applied Geography]
  18. Baumstark, R.,  Dixon B., Carlson P., Palandro, D., and K. Kolasa. 2012. Alternative spatially enhanced integrative techniques for mapping seagrass in Florida’s marine ecosystem. [In press: International Journal of Remote Sensing]
  19. Dixon, B. 2011. Sensitivity Analysis of Application of SVM and ANN Algorithms to Landuse Classification” A Spatial Uncertainty Perspective.  [In review: Journal of Applied Geography]
  20. Buck, K., R. Johns, B. Dixon, and Guo, D. 2012.  Assessment of Influence on Modifiable Cancer Risk among Teens. [In review: Cancer]
  21. Dixon, B.  and Lothe1, A. 2012. JAVA Program for Calculation of Attenuation Factor of Pesticides. [In review: Journal of Environmental Modeling and Software]
  22. Samui, P3. and Dixon B. 2012. Determination of Contaminated Wells: A Relevance Vector Machine Approach. [In review: Environmental Modeling and Software]
  23. Nekesha B. Williams and Dixon, B. 2012. Sediment supply, transport and delivery: Towards a spatially-integrated conceptual framework for linking watershed source to aquatic sinks of sediment into coastal ecosystems. [In review: International Journal of Geographic Information Systems]
  24. Dixon, B. 2012. Revisiting Applicability of GIS-based Neuro-Fuzzy Techniques in Predicting ground-water vulnerability: An Assessment of Transferability and Sensitivity. [In review: Hydrology Journal]
  25. Duffy R. and Dixon B. 2012. Developing a Habitat Suitability Model for Seagrass in Charlotte Harbor [In review: Applied Geography Journal]
  26. Bradley F, Dixon B, A. Hoare and P. Coble. 2012. Linking Watershed, Soil and Landuse Characteristics to the Spatial Variability of In-stream Water Quality in Selected Florida Watersheds. [In review: Applied Geography Journal]
  27. Batita, W. Dixon B. and I. Manakos. 2012. Prediction and Estimation of Soil Erosion using RUSLE and MUSLE Models: A Comparative Study [In review: Applied Geography Journal]
  28. Johns, R, Dixon, B., Z. Westmark, C. McHan and T. Stanley. 2012. Access to Essential Services in St. Petersburg Florida. [In preparation]
  29. Berg, K, Dixon, B, S.Wang and R. Ferner. 2012. Space-Time Dynamics of Crime Analysis. [In preparation]
  30. Dixon, B, Earls, J and C. K. King. 2012. Regional Scale Vulnerability Assessment to Pathogen: An Integrative Approach [In preparation]
  31. K. Couchiano, Dixon, B. and Earls J. 2012. GIS based analysis of Manatee Protection Efforts and Environmental Perspectives. [In preparation]

Text Book

  1. Dixon, B. Uddameri, V. and C. Ray.  2013. GIS and Geocomputation for Water Resources, Science and Engineering. Wiley and Sons. [In preparation]

Book Chapters/Invited Paper

  1. Williams, N. B1., 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
  2. Li, D. Dixon, B., Earls, J. F. Bradley and Xinghua, Liu. 2007The Study on Vulnerability Assessment in Groundwater Recharge Area of Jinan. Environmental Protection, 378(8B):59 61. Environmental Protection of China Press.
  3. 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.
  4. 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.
  5. 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.
  6. 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.


  1. J. M. McKimmey, B. Dixon, H.D. Scott and C. M. Scarlat. 2002. Soils of Mississippi County, Arkansas. Special report series. Arkansas Agricultural Experiment Station. Pub # 970. University of Arkansas, Fayetteville.
  2. Dixon, B., T. H. Udouj, H. D. Scott, R. L. Johnson and J.M. McKimmey. 2001. Soils of  Randolph County, Arkansas. Special report series. Arkansas Agricultural Experiment Station. Pub. # 199. University of Arkansas, Fayetteville.
  3. Dixon, B., T. H. Udouj, H. D. Scott, and J.M. McKimmey. 2001. Soils of Clay County, Arkansas. Special report series. Arkansas Agricultural Experiment Station. Pub # 202. University of Arkansas, Fayetteville.
  4. Johnson, R.L., B. Dixon, H. D. Scott, J.M. McKimmey and T.H. Udouj. 1999. Soils of Jackson County, Arkansas. Special report series. Arkansas Agricultural Experiment Station. Pub. # 192. University of Arkansas, Fayetteville.
  5. Scott, H.D., B. Dixon, J.M. McKimmey, T. H. Udouj and R. L. Johnson. 1998. Soil of Desha County, Arkansas. Special report series. Arkansas Agricultural Experiment Station. Pub. # 187. University of Arkansas, Fayetteville.

Edited Volume(s)/ Peer Reviewed Conference Proceedings Papers

  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.
  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.  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

Technical Reports and Other publications

  1. Dixon, B. 2009.  Existing methods of Nitrate Monitoring. Report completed for Harmonic Nitrate Monitoring. 64 p.
  2. Dixon, B. 2008.  Applicability of the SWAT model to quantify the effects of urbanization on the water budget for the Charlie Creek watershed. USGS Final report. 32 p.
  3. Dixon, B. 2008. Identifying Potential Watershed Nutrient Links to Karenia Red Tides: Integrated GIS Watershed Characterization of a southwest Florida coastal counties. FWRI Final report. 25 p.
  4. Earls J. and Dixon, B. 2007. Methodology for Sensitivity Analysis of the SWAT Model to the Resolution of Input, Calibration and Validation of Data. USFSRG Completion Report. 15 p.
  5. Dixon, B. 2006. Ground Water Vulnerability Delineation Using Integrated GIS and Neuro-Fuzzy Methods. FWRRC Completion Report. 30 p. Subcontract UF-EIES-0404012-USF (3/1/04 - 2/28/05).
  6. Dixon, B, H. D. Scott and A. M. Mauromoustakos. 2005. Ground Water Vulnerability Delineation Using Neural Networks, Fuzzy Logic, and Neuro-Fuzzy Techniques: Arkansas. USDA- CSREES Completion report 115 p.
  7. Dixon B. 2004. Application of Neural Networks and Neuro-Fuzzy Methods to Ground Water Vulnerability Mapping: A GIS-based Integrated Approach in Hillsborough County. Funded by FL. Dept. of Environmental protection, FL. Completion report 75 p.
  8. Leung, C. and Dixon, B. 2003. Pre-schoolers’ vocabulary acquisition and understanding of scientific concepts from participation in repeated read aloud events involving informational picture books. Juvenile Welfare Board of Pinellas County and USF. 62 p. Collaborative for Children Families and Communities: Completion Report.  62 p.
  9. Dixon, B and H. D. Scott. 2001. Application of fuzzy logic to predict ground water vulnerability in Northwest Arkansas. AWRC-USGS Completion Report, MSC # 240
  10. Dixon, B. and H. D. Scott. 1998. Use of fuzzy logic with modified DRASTIC parameters to predict ground water contamination.  In (H. D. Scott, ed.) Vulnerability and use of ground and surface waters in the southern Mississippi valley region.  AWRC Completion Report No. 269, 16 51.
  11. Dixon, B. 2001. Application of Neuro-fuzzy techniques to predict ground water  vulnerability in Northwest Arkansas. Ph.D. Dissertation. University of Arkansas, Fayetteville, Arkansas.
  12. Mitra, B. 1995. Application of fuzzy logic to identify soil erosion, M.A. Thesis, University of Arkansas. Fayetteville. Arkansas.
  13. Mitra, B. 1991. Suri and Its Environs: A case study in environmental geomorphology, M.A.Thesis, Visva Bharati University. Santiniketan, West Bengal, India.