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Journal of Environmental Informatics

Online ISSN 1684-8799 / Print ISSN 1726-2135

 

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   Volume 11   Number 2   June  2008 = non-subscribed

doi:10.3808/jei.200800114 About DOIs

JEI 11(2) 2008, Pages 90-102  

© 2008 ISEIS. All rights reserved.

A Comparison of Nebraska Reservoir Classes Estimated from Watershed-Based Classification Models and Ecoregions

H. N. N. Bulley1*, D. B. Marx2, J. W. Merchant3, J. C. Holz4 and A. A. Holz4

  1. Department of Geography and Geology, DSC 260, University of Nebraska-Omaha, Omaha, NE 68182, USA
  2. Department of Statistics, 342 Hardin Hall, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
  3. Center for Advanced Land Management Information Technologies, School of Natural Resources, 306 Hardin Hall, University of Nebraska-Lincoln, Lincoln, NE 68583-0973, USA
  4. School of Natural Resources, 507 Hardin Hall, University of Nebraska - Lincoln, Lincoln, NE 68583-0995, USA

*Corresponding author. Tel: +1 402 5543107 Fax: +1 402 5543518 Email: hbulley@mail.unomaha.edu

 

Abstract

Regulatory agencies have been investigating a number of alternatives for classifying lakes into hydogeologically and ecologically similar assemblages that will facilitate establishment of attainable water quality standards. Concerns over the ability of traditional statistical classifiers to effectively classify environmental data have led to increasing interest in machine (predictive) learning classification tools such as decision trees. This paper compares the performance (classification strength) of a classification tree-based watershed classification model of Nebraska reservoirs to a discriminant analysis (DA)-based watershed classification system and reservoir classes derived from Omernik’s Level IV Ecoregions. The performance of classification tree and DA-based watershed classification methods were also compared with respect to their cross-validation prediction errors. The results suggest that both watershed-based classification approaches (classification tree and DA) were more effective than Omernik’s Level IV ecoregions in accounting for observed variations in water quality characteristics of Nebraska reservoirs. Moreover, this study demonstrates the utility of a classification tree algorithm, either as a supplement or alternative to DA, in handling the complexities of watershed variables and classifying Nebraska reservoirs for the purpose of water quality management. The classification tree also provides water resource managers with a useful interpretive classification interface.


Keywords: classification tree, discriminant analysis, ecoregions, reservoirs, water quality, watershed

 

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Cite this paper as: H. N. N. Bulley, D. B. Marx, J. W. Merchant, J. C. Holz and A. A. Holz, 2008. A Comparison of Nebraska Reservoir Classes Estimated from Watershed-Based Classification Models and Ecoregions. Journal of Environmental Informatics, 11(2), 90-102. doi:10.3808/jei.200800114


References: 68

  1. Baker, C., Lawrence, R., Montagne, C. and Patten, D. (2006). Mapping wetlands and riparian areas using Landsat ETM+ Imagery and Decision-Tree-Based models, Wetlands, 26(2), 465-474. doi:10.1672/0277-5212(2006)26[465:MWARAU]2.0.CO;2
  2. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984). Classification and Regression Trees, Wadsworth, Belmont, California.
  3. Bulley, H.N.N. (2004). A Watershed-Based Classification System for Lakes in Agriculturally Dominated Ecosystems: A Case Study of Nebraska Reservoirs, Ph.D. Dissertation, University of Nebraska.
  4. Bulley, H.N.N., Merchant, J.W., Marx, D.B., Holz, J.C. and Holz, A.A. (2007). A GIS-Based Approach to Watershed Classification for Nebraska Reservoirs, J. Am. Water Resour. Assoc., 43(3). doi:10.1111/j.1752-1688.2007.00048.x
  5. CALMIT (Center for Advanced Land Management Information Technologies), University of Nebraska-Lincoln; http://www.calmit.unl.edu/gap/landcover.shtml.
  6. Conquest, L.L., Ralph, S.C. and Naiman, R.J. (1994). Implementation of large-scale stream monitoring efforts: Sampling design and data analysis issues, in L. Loeb and A. Spacie (Eds.), Biological Monitoring of Aquatic Systems, Lewis Publishers, Boca Raton, Florida, pp. 69-90.
  7. De' ath, G. and Fabricius, K.E. (2000). Classification and regression trees: a simple yet powerful technique for ecological data analysis, Ecol., 8(11), 3178-3192. doi:10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2
  8. Detenbeck, N.E., Elonen, C.M., Taylor, D.L., Anderson, L.E., Jicha, T.M. and Batterman, S.L. (2004). Region, landscape, and scale effects on Lake Superior tributary water quality, J. Am. Water Resour. Assoc., 40(3), 705-720. doi:10.1111/j.1752-1688.2004.tb04454.x
  9. Detenbeck, N.E., Elonen, C.M., Taylor, D.L., Anderson, L.E., Jicha, T.M. and Batterman, S.L. (2003). Effects of hydro geomorphic region, catchment storage and mature forest base flow and snowmelt stream water quality in second-order Lake Superior Basin tributaries, Freshwater Biol., 48(5), 912-927. doi:10.1046/j.1365-2427.2003.01056.x
  10. Dunteman, G.H. (1984). Introduction to Multivariate Analysis, Sage Publications, Beverly Hills, California.
  11. Efron, B. (2003). Second thoughts on the bootstrap, Stat. Sci., 18(2), 135-140. doi:10.1214/ss/1063994968
  12. Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap, Chapman-Hall, New York.
  13. Emmons, E.E., Jennings, M.J. and Edwards, C. (1999). An alternative classification method for northern Wisconsin lakes, Can. J. Fisheries Aquatic Sci., 56(4), 661-669. doi:10.1139/cjfas-56-4-661
  14. EPA (US Environmental Protection Agency) (2003a). Withdrawal of revisions to the water quality planning and management regulation and revisions to the national pollutant discharge elimination system program in support of revisions to the water quality planning and management regulation, final rule, Federal Register, 68(53), 13607-13614.
  15. EPA (US Environmental Protection Agency) (2003b). Total Maximum Daily Loads. http://www.epa.gov/owow/tmdl/intro.html.
  16. EPA (US Environmental Protection Agency) (2002). Levels III and IV Ecoregions of the Continental United States (revision of Omernik, 1987), EPA National Health and Environmental Effects Laboratory, Western Ecology Division, Corvallis, Oregon.
  17. EPA (US Environmental Protection Agency) (2000). Nutrient Criteria Technical Guidance Manual for Lakes and Reservoirs, Report No. EPA-822-B00-001, Washington DC.
  18. Feldesman, M.R. (2002). Classification trees as an alternative to linear discriminant analysis, Am. J. Phys. Anthropol., 119, 257-275. doi:10.1002/ajpa.10102
  19. Friedman, J.H. (2006). Recent advances in predictive machine learning, J. Classification, 23(2), 175-197. doi:10.1007/s00357-006-0012-4
  20. Gahegan, M. and West, G. (1998). The Classification of Complex Geographic Datasets: An Operational Comparison of Artificial Neural Network and Decision Tree Classifiers. http://www.geocomputation.org/1998/61/gc_61.htm.
  21. German, G.W.H., West, G.A.W. and Gahegan, M.G. (1999). Statistical and AI techniques in GIS classification: A comparison, in Proc. of the 11th Annual Colloquium of the Spatial Information Research Centre, University of Otago, Dunedin, New Zealand.
  22. Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M. and Tyler, D. (2002). The national elevation dataset, Photogramm. Eng. Remote Sensing, 68(1), 5-11.
  23. Ghaffari, A., Priestnall, G. and Clarke, M.L. (2006). Artificial neural networks and decision tree classifier performance on medium resolution ASTER data to detect gully networks in southern Italy, in Proc. SPIE Vol. 6064, 60641Q.
  24. Goel, P.K., Prasher, S.O., Patel, R.M., Landry, J.A., Bonnell, R.B. and Viaub, A.A. (2003). Classification of hyper spectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn, Comput. Electron. Agric., 39(2), 67-93. doi:10.1016/S0168-1699(03)00020-6
  25. Hastie, T., Tibshirani, R. and Freiedman, J.H. (2001). The Elements of Statistical Learning, Springer-Verlag, New York.
  26. Hawkins, C.P., Norris, R.H., Gerritsen, J., Hughes, R.M., Jackson, S.K., Johnson, R.K. and Stevenson, R.J. (2000). Evaluation of landscape classifications for the prediction of freshwater biota: synthesis and recommendations, J. North Am. Benthol. Soc., 19(3), 541-556. doi:10.2307/1468113
  27. Holz, J.C. (2002). Lake and reservoir classification in agriculturally dominated ecosystems, in EPA 2002 Aquatic Ecosystem Classification Workshop, Denver, CO.
  28. Huberty, C.J. and Olejnik, S. (2006). Applied Manova and Discriminant Analysis, 2nd Edition, John Wiley & Sons, New York.
  29. Hunt, E.B., Marin, J. and Stone, P.J. (1966). Experiments in Induction, Academic Press, New York.
  30. James, F.C. and McCulloch, C.E. (1990). Multivariate Analysis in Ecology and Systematics: Panacea or Pandora's Box? Ann. Rev. Ecol. Syst., 21, 129-166. doi:10.1146/annurev.es.21.110190.001021
  31. Jenerette, G.D., Lee, J., Waller, D. and Carlson, R.E. (2002). Multivariate analysis of the Ecoregion delineation for aquatic ecosystems, Environ. Manage., 29(1), 67-75. doi:10.1007/s00267-0010041-z
  32. Johnsgard, P.E. (2001). The Nature of Nebraska: Ecology and Biodiversity, University of Nebraska Press, Lincoln, Nebraska.
  33. Joy, M.K. and Death, R.G. (2005). Modeling of freshwater fish and macro-crustacean assemblages for biological assessment in New Zealand, in S. Lek, M. Scardi, P.F.M. Verdonshot, J.P Descy, and Y.S. Park (Eds), Modeling Community Structure in Freshwater Ecosystems, Springer, Berlin, pp. 76-89.
  34. Karels, T.J., Bryant, A.A. and Hik, D.S. (2004). Comparison of discriminant function and classification tree analyses for age classification of marmots, OIKOS, 105(3), 575-587. doi:10.1111/j.0030-1299.2004.12732.x
  35. Kuzelka, R.D., Flowerdale, C.A., Manley, R.N. and Rundquist, B.C. (1993). Flat Water: A History of Nebraska and its Water, Conservation and Survey Division, IANR, University of Nebraska-Lincoln, Resource Report No. 12.
  36. Lauritzen, S.L. and Spiegelhalter, D.J. (1988). Local computations with probabilities on graphical structures and their application to expert systems, J. Royal Stat. Soc., 50(2), 157-224.
  37. Legendre, P. and Legendre, L. (1998). Numerical Ecology, 2nd Edition, Elsevier Science, BV, Amsterdam.
  38. Lomnicky, G.A. (1995). Lake classification in the glacially influenced landscape of the North Cascade Mountains, Washington, USA. PhD Dissertation. Oregon State University, Orego.
  39. Loveland, T.R. and Merchant, J.W. (2004). Ecoregions and ecoregionalization: Geographical and ecological perspectives, Environ. Manage., 34, S1-S13. doi:10.1007/s00267-003-5181-x
  40. Luoto, M. and Hjort, J. (2005). Evaluation of current statistical approaches for predictive geomorphological mapping, Geomorphology, 67(3-4), 299-315. doi:10.1016/j.geomorph.2004.10.006
  41. McGarigal, K., Cushman, S. and Stafford, S. (2000). Multivariate Statistics for Wildlife and Ecology Research, Springer-Verlag, New York.
  42. Minka, T.P. and Picard, R.W. (1997). Interactive learning using aĦ°society of modelsĦħ, Pattern Recog., 30(4), 565-581.
  43. Mitchell, T.M. (1997). Machine Learning, McGraw-Hill, New York.
  44. Momen, B. and Zehr, J.P. (1998). Watershed classification using discriminant analyses of lake water-chemistry and terrestrial characteristics, Ecol. Appl., 8(2), 497-07. doi:10.2307/2641089
  45. Neapolitan, R.E. (1990). Probabilistic Reasoning in Expert systems: Theory and Algorithms, John Wiley & Sons, New York.
  46. Olden, J.D. and Jackson, D.A. (2002). A comparison of statistical approaches for modelling fish species distributions, Freshwater Biol., 47(10), 1976-1995. doi:10.1046/j.1365-2427.2002.00945.x
  47. Omernik, J.M. (2003). The misuse of hydrologic unit maps for extrapolation, reporting and ecosystem management, J. Am.Water Resour. Assoc., 39(3), 563-573.
  48. Omernik, J.M. and Bailey, R.G. (1997). Distinguishing between watersheds and ecoregions, J. Am. Water Resour. Assoc., 33(5), 935-949. doi:10.1111/j.1752-1688.1997.tb04115.x
  49. Omernik, J.M. (1987). Ecoregions of the Conterminous United States, Ann. Assoc. Am. Geogr., 77, 118-125.
  50. Park, Y.S., Verdonschot, P.F.M., Chon, T.S. and Lek, S. (2003). Patterning and predicting aquatic macro invertebrate diversities using artificial neural network, Water Res., 37(8), 17749-1758. doi:10.1016/S0043-1354(02)00557-2
  51. Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, CA.
  52. Quinlan, J.R. (1986). Induction of decision trees, Mach. Learn., 1(1), 81-106.
  53. Ripley, B.D. (1996). Pattern Recognition and Neural Networks, Cambridge University Press.
  54. Rokach, L. and Maimon, O. (2005). Top-down induction of decision trees classifiers - a survey, IEEE Trans. Syst., Man, Cybern., 35(4), 476-487. doi:10.1109/TSMCC.2004.843247
  55. Robertson, D.M. and Saad, D.A. (2003). Environmental water-quality zones for streams: A regional classification scheme, Environ. Manage., 31(5), 581-602. doi:10.1007/s00267-002-2955-5
  56. Rohm, C.M., Omernik, J.M., Woods, A.J. and Stoddard, J.L. (2002). Regional characteristics of nutrient concentrations in streams and their application to nutrient criteria development, J. Am. Water Resour. Assoc., 38(1), 213-239. doi:10.1111/j.1752-1688.2002.tb01547.x
  57. Ronchetti, E., Field, C. and Blanchard, W. (1997). Robust linear model selection by cross-validation, J. Am. Stat. Assoc., 92(439), 1017-1023. doi:10.2307/2965566
  58. RuleQuest Research. See5?: An Informal Tutorial. http://rulequest.com/see5-win.html.
  59. SAS Institute Inc. (2000). SAS? Version 8 Users Manual, SAS Institute Inc., Cary, NC.
  60. Severn, A.A., Holz, J.C., Barrow, T.A., Hoagland, K.D., Bulley, H. and Merchant, J.W. (2001). Lake classification in the Sand Hills Region of Nebraska, Poster presentation, North America Lake Management Society 21st International Symposium, Madison, WI.
  61. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions, J. Royal Stat. Soc., Series B, 36, 111-147.
  62. Van Sickle, J. and Hughes, R.M. (2000). Classification strengths of ecoregions, catchments and geographic clusters for aquatic vertebrates in Oregon, J. North Am. Benthol. Soc., 19(3), 370-384. doi:10.2307/1468101
  63. USGS (US Geological Survey) (2003). Elevation Derivatives for National Applications (EDNA). http://edna.usgs.gov.
  64. Vayssieres, M.P., Plant, R.E. and Allen-Diaz, B.H. (2000). Classification trees: An alternative non-parametric approach for predicting species distributions, J. Vegetation Sci., 11(5), 679-694. doi:10.2307/3236575
  65. Verbyla, D.L. (1987). Classification trees: A new discrimination tool, Can. J. For. Res., 17, 1150-1152.
  66. Verdin, K.L. and Verdin, J.P. (1999). A topological system for delineation and codification of the Earth's river basins, J. Hydrol., 218(1-2), 1-12. doi:10.1016/S0022-1694(99)00011-6
  67. Wetzel, R.G. and Likens, G.E. (2000). Limnological Analysis, 3rd Edition, Springler Verlag, New York.
  68. Winter, T.C. (1999). The relation of streams, lakes, and wetlands to groundwater flow systems, Hydrogeol. J., 7(1), 28-45. doi:10.1007/s100400050178

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