Wetlands

, Volume 23, Issue 2, pp 436–448 | Cite as

Hyperspectral image data for mapping wetland vegetation

Article

Abstract

Data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) with 224 bands, each with 0.01-μm spectral resolution and 20-meter spatial resolution, were used to produce a vegetation map for a portion of Everglades National Park, Florida, USA. The vegetation map was tested for classification accuracy with a pre-existing detailed GIS wetland vegetation database compiled by manual interpretation of 1∶40,000-scale color infrared (CIR) aerial photographs. Although the accuracy varied greatly for different classes, ranging from 40 percent for scrub red mangroves (Rhizophora mangle) to 100 percent for spike rush (Eleocharis cellulosa) prairies, the Everglades communities generally were successfully identified, averaging 66 percent correct for all classes. In addition, the hyperspectral image data proved suitable for detecting the invasive exotic species lather leaf (Colubrina asiatica) that is sometimes difficult to differentiate on aerial photographs. The findings from this study have implications for operational uses of spaceborne hyperspectral image data that are now becoming available. Practical limitations of using such image data for wetland vegetation mapping include inadequate spatial resolution, complexity of image processing procedures, and lack of stereo viewing.

Key Words

hyperspectral image data AVIRIS Everglades exotic species wetland vegetation mapping vegetation communities/species automated classification geographic information system (GIS) 

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Literature Cited

  1. Anderson, R. R. and F. J. Wobber. 1973. Wetlands mapping in New Jersey. Photogrammetric Engineering 39:353–385.Google Scholar
  2. Boardman, J. W., F. A. Kruse, and R. O. Green 1995. Mapping target signatures via partial unmixing of AVIRIS data. p. 23–26. In R. O. Green (ed.) Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. JPL Publication, Pasadena CA, USA.Google Scholar
  3. CSES. 1999. A Tmospheric REMoval Program (ATREM) User’s Guide version 3.1. University of Colorado, Boulder, CO, USA.Google Scholar
  4. Craighead, F. C. 1971. The Trees of South Florida. University of Miami Press, Coral Gables, FL, USA.Google Scholar
  5. Gao, B. C. and A. F. H. Goetz. 1990. Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. Journal of Geophysical Research 45:3549–3564.CrossRefGoogle Scholar
  6. 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 26:65–74.CrossRefGoogle Scholar
  7. Gross, M. F. and V. Klemas. 1986. The use of airborne imaging spectrometry (AIS) data to differentiate marsh vegetation. Remote Sensing of Environment 19:97–103.CrossRefGoogle Scholar
  8. Jensen, J. R., M. E. Hodgson, E. J. Christensen, H. E. Mackey, L. R. Tinney, and R. R. Sharitz. 1986. Remote sensing inland wetlands: A multispectral approach. Photogrammetric Engineering and Remote Sensing 52:87–100.Google Scholar
  9. Kokaly, R. F., R. N. Clark, and K. E. Livo. 1998. Summaries of the 7th Annual JPL Airborne Earth Science Workshop. p. 245–254. In R. O. Green (ed.) AVIRIS Workshop. JPL Publication. Pasadena, CA, USA.Google Scholar
  10. Kruse, F. A., A. B. Lefkoff, J. B. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz. 1993. The spectral image processing system (SIPS)—Interactive visualization and analysis of image spectrometer data. Remote Sensing of Environment 44:145–163.CrossRefGoogle Scholar
  11. Lee, J. B., A. S. Woodyatt, and M. Berman. 1990. Enhancement of high spectral resolution remote sensing data by a noise-adjusted principal components transform. IEEE Transactions on Geoscience and Remote Sensing 28:295–304.CrossRefGoogle Scholar
  12. Lillesand, T. M. and R. W. Kiefer. 2000. Remote Sensing and Image Interpretation, fourth edition. John Wiley and Sons, New York, NY, USA.Google Scholar
  13. Madden, M., D. Jones, and L. Vilchek. 1999. Photointerpretation key for the Everglades Vegetation Classification System. Photogrammetric Engineering and Remote Sensing 65:171–177.Google Scholar
  14. Neuenschwander, A. L., M. M. Crawford, and M. J. Provancha. 1998. Mapping of coastal wetlands via hyperspectral AVIRIS data. p. 189–191. In T. I. Stein (ed.) IEEE International Geoscience and Remote Sensing Symposium Proceedings. Institute of Electrical and Electronics Engineers, Inc., Piscataway, NJ, USA.Google Scholar
  15. Pearlman, J. 1999. EO-1, A pathway to advanced spaceborne spectral imaging. Proceedings of the Thirteenth International Conference: Applied Geologic Remote Sensing 13:570–577.Google Scholar
  16. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. 1992. Numerical Recipes in FORTRAN: the Art of Scientific Computing, second edition. Cambridge, London, UK.Google Scholar
  17. Schneider, W. J. 1968. Color photographs for water resources studies. Photogrammetric Engineering 39:489–499.Google Scholar
  18. Spanglet, H. J., S. L. Ustin, and E. Rejmankova. 1998. Spectral reflectance characteristics of California subalpine marsh plant communities. Wetlands 18:307–319.CrossRefGoogle Scholar
  19. Vane, G. and A. F. H. Goetz. 1993. Terrestrial imaging spectrometry—current status, future trends. Remote Sensing of Environment 44:117–126.CrossRefGoogle Scholar
  20. Welch, R. and M. Madden. 1999. Vegetation Map and Digital Database of South Florida National Park Service Lands to Assess Long-term Effects of Hurricane, Andrew. Final Report to the U.S. Department of Interior, National Park Service, Cooperative Agreement 5280–9006. Center for Remote Sensing and Mapping Science, University of Georgia, Athens, GA, USA.Google Scholar
  21. Welch, R., M. Madden, and R. F. Doren. 1999. Mapping the Everglades. Photogrammetric Engineering and Remote Sensing 65: 163–170.Google Scholar
  22. Welch, R., M. Madden, and R. F. Doren. 2001. Maps and GIS databases for environmental studies of the Everglades. p. 259–279. In J. Porter and K. Porter (eds.) The Everglades, Florida, Bay and Corel Reefs of the Florida Keys: an Ecosystem Sourcebook. CRC Press LLC, Boca Raton, FL, USA.Google Scholar
  23. Welch, R., M. Remillard, and R. F. Doren. 1995. GIS database development for South Florida’s National Parks and Preserves. Photogrammetric Engineering and Remote Sensing 61:1371–1381.Google Scholar
  24. Welch, R., M. M. Remillard, and R. B. Slack. 1988. Remote sensing and geographic information system techniques for aquatic resource evaluation. Photogrammetric Engineering and Remote Sensing 54:177–185.Google Scholar

Copyright information

© Society of Wetland Scientists 2003

Authors and Affiliations

  1. 1.Center for Remote Sensing and Mapping Science (CRMS) Department of GeographyThe University of GeorgiaAthensUSA
  2. 2.Institute of History and AnthropologyUniversity of TsukubaTsukuba IbarakiJapan

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