Wetlands

, 29:931 | Cite as

Satellite remote sensing of isolated wetlands using object-oriented classification of Landsat-7 data

  • Robert C. Frohn
  • Molly Reif
  • Charles Lane
  • Brad Autrey
Article

Abstract

There has been an increasing interest in characterizing and mapping isolated depressional wetlands due to a 2001 U.S. Supreme Court decision that effectively removed their protected status. Our objective was to determine the utility of satellite remote sensing to accurately detect isolated wetlands. Image segmentation and object-oriented analysis were applied to Landsat-7 imagery from January and October 2000 to map isolated wetlands in the St. Johns River Water Management District of Alachua County, Florida. Accuracy for individual isolated wetlands was determined based on the intersection of reference and remotely sensed polygons. The January data yielded producer and user accuracies of 88% and 89%, respectively, for isolated wetlands larger than 0.5 acres (0.20 ha). Producer and user accuracies increased to 97% and 95%, respectively, for isolated wetlands larger than 2 acres (0.81 ha). Recently, the Federal Geographic Data Committee recommended that all U.S. wetlands 0.5 acres (0.20 ha) or larger should be mapped using 1-m aerial photography with an accuracy of 98%. That accuracy was nearly achieved in this study using a spatial resolution that is 900 times coarser. Satellite remote sensing provides an accurate, relatively inexpensive, and timely means for classifying isolated depressional wetlands on a regional or national basis.

Key Words

detection imagery mapping segmentation 

Literature Cited

  1. Association of State Wetland Managers (ASWM). 2001. Consistent methods for identifying waters that may no longer be regulated under the Clean Water Act following the SWANCC decision. Association of State Wetland Managers, Berne, NY, USA.Google Scholar
  2. Atunes, A. F. B., C. Lingnau, and J. C. Da Silva. 2003. Objectoriented analysis and semantic network for high resolution image classification. Anais XI SBSR, Belo Horizonte, Brasil. 05–10 Abril 2003, INPE, p. 273–79.Google Scholar
  3. Baatz, M. and A. Schäpe. 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. p. 12–23,In J. Strobl, T. Blaschke, and G. Griesebner (eds.) Angewandte Geographische Informations-Verbeitung XII. Wichmann Verlag, Karlsruhe, Germany.Google Scholar
  4. Benz, U. C., P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58: 239–58.CrossRefGoogle Scholar
  5. Comer, P., K. Goodin, A. Tomaino, G. Hammerson, G. Kittel, S. Menard, C. Nordman, M. Pyne, M. Reid, L. Sneddon, and K. Snow. 2005. Biodiversity values of geographically isolated wetlands in the United States. NatureServe, Arlington, VA, USA.Google Scholar
  6. Costa, M. P. F., O. Niemann, E. Novo, and F. Ahern. 2002. Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat. International Journal of Remote Sensing 23: 1401–26.CrossRefGoogle Scholar
  7. Downing, D. M., C. Winer, and L. D. Wood. 2003. Navigating through the Clean Water Act jurisdiction: a legal review. Wetlands 23: 475–93.CrossRefGoogle Scholar
  8. Edwards, A. L. and R. R. Sharitz. 2000. Population genetics of two rare perennials in isolated wetlands:Sagittaria isoetiformis andS. teres (Alismataceae). American Journal of Botany 87: 1147–1158.CrossRefPubMedGoogle Scholar
  9. Gibbons, J. W. 2003. Terrestrial habitat: a vital component for herpetofauna of isolated wetlands. Wetlands 23: 630–35.CrossRefGoogle Scholar
  10. Green, A. A., M. Berman, P. Switzer, and M. D. Craig. 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing 26: 65–14.CrossRefGoogle Scholar
  11. Haralick, R. M. 1986. Statistical image texture analysis. p. 247–80,In T. Y. Young and K. S. Fu (eds.) Handbook of Pattern Recognition and Image Processing. Academic Press, New York, NY, USA.Google Scholar
  12. Heber, M. 2008. FGDCdraft wetland mapping standard. FGDC Wetland Subcommittee and Wetland Mapping Standard Workgroup. Environmental Protection Agency, Office of Water, Washington, DC, USA.Google Scholar
  13. Hess, L. L., J. M. Melack, E. M. L. M. Novo, C. C. F. Barbosa, and M. Gastil. 2003. Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sensing of Environment 87: 404–28.CrossRefGoogle Scholar
  14. Hodgson, M. E., J. R. Jensen, H. E. Mackey Jr, and M. C. Coulter. 1987. Remote sensing of wetland habitat: a wood stork example. Photogrammetric Engineering and Remote Sensing 53: 1075–80.Google Scholar
  15. Leibowitz, S. G. 2003. Isolated wetlands and their functions: an ecological perspective. Wetlands 23: 517–31.CrossRefGoogle Scholar
  16. Leibowitz, S. G. and T.-L. Nadeau. 2003. Isolated wetlands: state-of-the-science and future directions. Wetlands 23: 663–84.CrossRefGoogle Scholar
  17. McCauley, L. A. and D. G. Jenkins. 2005. GIS-based estimates of former and current depressional wetlands in an agricultural landscape. Ecological Applications 15: 1199–1208.CrossRefGoogle Scholar
  18. National Research Council (NRC). 1995. Wetlands: Characteristics and Boundaries. National Academy Press, Washington, DC, USA.Google Scholar
  19. Ozesmi, S. L. and M. E. Bauer. 2002. Satellite remote sensing of wetlands. Wetlands Ecology and Management 10: 381–402.CrossRefGoogle Scholar
  20. Pearson, S. M. 1994. Landscape-level processes and wetland conservation in the southern Appalachian mountains. Water, Air, and Soil Pollution 77: 321–32.CrossRefGoogle Scholar
  21. Pekkarinen, A. 2002. A method for the segmentation of very high spatial resolution images of forested landscapes. International Journal of Remote Sensing 23: 2817–36.CrossRefGoogle Scholar
  22. Sader, S. A., D. Ahl, and W.-S. Liou. 1995. Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine. Remote Sensing of Environment 53: 133–44.CrossRefGoogle Scholar
  23. Schiewe, J. 2003. Integration of multi-sensor data for landscape modeling using a region-based approach. ISPRS Journal of Photogrammetry and Remote Sensing 57: 371–79.CrossRefGoogle Scholar
  24. Semlitsch, R. D. and J. R. Bodie. 1998. Are small, isolated wetlands expendable? Conservation Biology 12: 1129–33.CrossRefGoogle Scholar
  25. Story, M. and R. Congalton. 1986. Accuracy assessment: a user’s perspective. Photogrammetric Engineering and Remote Sensing 52: 397–99.Google Scholar
  26. Tiner, R. W. 2003a. Estimated extent of geographically isolated wetlands in selected areas of the United States. Wetlands 23: 636–52.CrossRefGoogle Scholar
  27. Tiner, R. W. 2003b. Geographically isolated wetlands of the United States. Wetlands 23: 494–516.CrossRefGoogle Scholar
  28. Tiner, R. W., H. C. Bergquist, G. P. DeAlessio, and M. J. Starr. 2002. Geographically isolated wetlands: a preliminary assessment of their characteristics and status in selected areas of the United States. U. S. Department of the Interior, Fish and Wildlife Service, Northeast Region, Hadley, MA, USA.Google Scholar
  29. USGS. 2003. Landsat: a global land-observing program. U.S. Geological Survey, Reston, VA, USA. Fact Sheet 023-03.Google Scholar
  30. Winter, T. C. and J. W. Labaugh. 2003. Hydrologic considerations in defining isolated wetlands. Wetlands 23: 532–40.CrossRefGoogle Scholar

Copyright information

© Society of Wetland Scientists 2009

Authors and Affiliations

  • Robert C. Frohn
    • 1
    • 2
  • Molly Reif
    • 1
  • Charles Lane
    • 3
  • Brad Autrey
    • 3
  1. 1.Dynamac Corporationc/o U.S. Environmental Protection Agency (U.S. EPA)CincinnatiUSA
  2. 2.Department of GeographyUniversity of CincinnatiCincinnatiUSA
  3. 3.U.S. Environmental Protection Agency (U.S. EPA)CincinnatiUSA

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