Policy Implications of Remote Sensing in Understanding Urban Environments: Developing a Wetlands Inventory for Community Decision-Making in Lucas County, Ohio

  • Patrick L. Lawrence
  • Kevin Czajowski
  • Nathan Torbick


Remote Sensing Geographic Information System Coastal Wetland Knowledge Engineer Hydric Soil 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson, J.E. and J. E. Perry. 1996. Characterization of Wetland Plant Stress Using Leaf Spectral Reflectance: Implications for Wetland Remote Sensing. Wetlands 16:477–487.Google Scholar
  2. Barrett, E.C. and L.F. Curtis. 1992. Introduction to Environmental Remote Sensing, 3rd ed. London: Chapman & Hall.Google Scholar
  3. Carletta, J. 1996. Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics. 22:249–254.Google Scholar
  4. Ehrenfeld, J.G. 2000. Evaluating wetlands within an urban context. Ecological Engineering 15: 253–265.CrossRefGoogle Scholar
  5. Friedl, M., Brodley, C. 1997. Decision Tree Classification of Land Cover from Remotely Sensed Data. Remote Sensing of Environment. 61:399–409.CrossRefGoogle Scholar
  6. Goward, S., Masek, J., Williams, D., Irons, J., Thompson, R. 2001. the Landsat 7 mission: Terrestrial research and applications for the 21st century. Remote Sensing of Environment. 78:3–12.CrossRefGoogle Scholar
  7. Hardisky, M.A., M.F. Gross, and V. Klemas. 1986. Remote Sensing of Coastal Wetlands. Bioscience 36:453–460.Google Scholar
  8. Jensen, J. 1996. Introductory Digital Image Processing: a Remote Sensing Perspective. New Jersey: Prentice Hall.Google Scholar
  9. Johnston R.M., Barson M.M. 1993. Remote Sensing of Australian Wetlands: An Evaluation of Landsat TM Data for Inventory and Classification. Australian Journal of Maine and Freshwater Resources. 44:235–252.Google Scholar
  10. Kindscher, K., Fraser, A., Jakubauskas, M.E., and Debinski, D.M. 1998. Identifying wetland meadows in Grand Teton National Park using remote sensing and average wetland values. Wetlands Ecology and Management. 5:265–273.CrossRefGoogle Scholar
  11. Lawrence, P.L., Horvat, M., Grigore, M., Czajkowski, K. and Torbick, N. (2003). Challenges and Limitations Using Remote Sensing to Delineate Wetlands in Northwest Ohio. Poster at Ohio Geospatial Technology Conference for Agriculture and Natural Resource Applications. Columbus, Ohio.Google Scholar
  12. Levine, N.S. and Roten, H.L. 2001. Wetlands Assessment and Identification using Remote Sensing and GIS Data within a Knowledge-Based Classifier. Geological Association of America Annual Meeting 2001. Paper 123-0.Google Scholar
  13. Lillesand, T.M. and R.W. Kiefer. 1994. Remote Sensing and Photo Interpretation, 3rd. ed. New York: John Wiley & Sons.Google Scholar
  14. Lunetta R.S., Barlogh M.E. 1999. Application of mult-temporal Landsat 5 TM imagery for wetland identification. Photogrammetric Engineering and Remote Sensing. 65:303–1310.Google Scholar
  15. Lyon J.G. 2001. Wetland Landscape Characterization: GIS, Remote Sensing, and Image Analysis. Sleeping Bear Press.Google Scholar
  16. MacDonald T.A. 1999. Wetland rehabilitation and remote sensing, in Streever E. (ed.), An International Perspective on Wetlands Rehabilitation. Boston: Kluwer Academic Publishers:251–264.Google Scholar
  17. Maumee RAP 1997. Maumee River Remedial Action Plan: Strategic Plan. Ohio EPA Northwest District Office, Bowling Green, OH. www.maumeerap.orgGoogle Scholar
  18. Mitsch, J. and Gosselink, J.G. 2000. The value of wetlands: importance of scale and landscape setting. Ecological Economics 35: 25–33.CrossRefGoogle Scholar
  19. Munyati, C. 1999. Wetland change detection on the Kafue Flats, Zambia by classification of a multitemporal remote sensing image dataset. International Journal of Remote Sensing. 21:1787–1806.CrossRefGoogle Scholar
  20. Ohio Department of Natural Resources. 2003. A History of Ohio Wetlands. Scholar
  21. Ozesmi, S.L. and Bauer, M.E. 2002. Satellite remote sensing of wetlands. Wetlands Ecology and Management. 10:381–402.CrossRefGoogle Scholar
  22. Sader, S.A., Ahl., D., and Wen-Shu, L. 1995. Accuracy of Landsat-TM and GIS Rule-Based Methods for Forest Wetland Classification in Maine. Remote Sensing of the Environment. 53:133–144.CrossRefGoogle Scholar
  23. Schaal, G. 1995. Methods used in the Ohio Wetland Inventory. Columbus, Ohio: Ohio Department of Natural Resources.Google Scholar
  24. Schmidt, K.S. and Skidmore, A.K. 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment. 85:92–108.CrossRefGoogle Scholar
  25. Shuman, C.S. and Ambrose, R.F. 2003. A Comparison of Remote Sensing and Ground-Based Methods for Monitoring Wetland Restoration Success. Restoration Ecology. 11:325–333.CrossRefGoogle Scholar
  26. Tilton, D. L. 1995. Integrating wetlands into planned landscapes. Landscape and Urban Planning 35: 205–209.CrossRefGoogle Scholar
  27. Tiner, R. 1999. Wetland Indicators. A Guide to Wetland Identification, Delineation, Classification, and Mapping. New York: Lewis Publishers.Google Scholar
  28. Townsend, P. A., and Walsh, S. J. 2001. Remote sensing of forested wetlands: Application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA. Plant Ecology 157:129–149.CrossRefGoogle Scholar
  29. Yi, G.D., Risley, M., Koneff, M., and Davis, C. 1994. Development of Ohio’s GIS-based wetlands inventory. Journal of Soil and Water Conservation 49:23–28.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Patrick L. Lawrence
    • 1
  • Kevin Czajowski
    • 1
  • Nathan Torbick
    • 1
  1. 1.Department of Geography and PlanningUniversity of ToledoToledoUSA

Personalised recommendations