Abstract
This study investigates the fall season similarity of reflectance spectra among salt marsh species to identify and map marsh vegetation types at species level using hyperspectral remote sensing. The medians of the reflectance spectra collected from canopies of dominant marsh vegetation (Phragmites australis, Spartina patens, Spartina alterniflora, and Distichlis spicata) in the New Jersey Meadowlands were compared using a set of statistical metrics. Results show that these marsh species are distinct and separable spectrally in the near infrared (NIR) region of the spectrum. The two Spartina species are similar in spectra and are the most difficult pairs to separate. However, the distribution of red-edge parameter (maximum inflection) suggests that red-edge may be useful for discriminating these two species. The results of this study can be applied to classify marsh vegetation at species level using remote sensing and to map ecotypes along salinity or oxygen gradients as a way to assess coastal wetlands health and condition.
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Andrea, S. L., A. Rango, K. M. Havstad, J. F. Paris, R. F. Beck, R. McNeely, and A. L. Gonzalez. 2004. Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment 93: 198–210.
Artigas, F. J. and J. Yang. 2004. Hyperspectral remote sensing of habitat heterogeneity between tide-restricted and tide-open areas in New Jersey Meadowlands. Urban Habitats 2: 1–18.
ASD. 1997. Analytical Spectral Devices. Technical Guide, Boulder, CO, USA.
Boyer, T. and S. Polasky. 2004. Valuing urban wetlands: a review of non-market valuation studies. Wetlands 24: 744–755.
Brown, A. J., M. Walter, and T. Cudahy. 2004. Remote mapping of earth’s earliest biosphere: hyperspectral imagery of the Pilbara Craton and applications for finding niches for life on Mars. International Journal of Astrobiology Supplement 1, Abstracts of AbSciCon, NASA Ames, Mountain View, CA, USA.
Cochrane, M. A. 2000. Using vegetation reflectance variability for species level classification of hyperspectral data. International Journal of Remote Sensing 21: 2075–2087.
Coops, N., M. Stanford, K. Old, M. Dudzinski, D. Culvenor, and C. Stone. 2003. Assessment of Dothistroma needle blight of Pinus radiata using airborne hyperspectral imagery. Phytopathology 93: 1524–1532.
Danson, F. M., M. D. Steven, T. J. Malthus, and J. A. Clark. 1992. High-spectral resolution data for determining leaf water content. International Journal of Remote Sensing 13: 461–470.
Davis, J. L., B. Nowicki, and C. Wigand. 2004. Denitrification in fringing salt marshes of Narragansett Bay, Rhode Island, USA. Wetlands 24: 870–878.
Gillespie, A. R., M. O. Smith, J. B. Adams, S. C. Willis, A. F. Fischer, and D. E. Sabol. 1990. Interpretation of residual images: spectral mixture analysis of AVIRIS images, Owens Valley, California. p. 243–270. In Proceedings of the Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop. Jet Propulsion Laboratory, Pasedena, CA, USA.
Hellings, S. E. and J. Gallagher. 1992. The effects of salinity and flooding on Phragmites australis. Journal of Applied Ecology 29: 41–49.
Knipling, E. B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment 1: 155–159.
NCSS. 1996. Statistical Analysis System. NCSS, Kaysville, UT, USA.
Okin, G. S., D. A. Roberts, B. Murray, and W. J. Okin. 2001. Practical limits on hyper spectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment 77: 212–225.
Price, J. C. 1992. Variability of high-resolution crop reflectance spectra. International Journal of Remote Sensing 13: 2593–2610.
Price, J. C. 1994. How unique are spectral signatures?. Remote Sensing Environment 49: 181–186.
Redfield, A. 1972. Development of a New England salt marsh. Ecological Monographs 42: 201–237.
Roberto, C., D. Bellingeri, D. Fasolini, and C. M. Marino. 2003. Retrieval of leaf area index in different vegetation types using high-resolution satellite data. Remote Sensing of Environment 86: 120–131.
Roberts, D. A., M. O. Smith, J. B. Adams, D. E. Sabol, A. R. Gillespie, and S. C. Willis. 1990. Isolating woody plant material and senescent vegetation from green vegetation. p. 42–57. In Proceedings of the Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop. Jet Propulsion Laboratory, Pasedena, CA, USA.
Schmidt, K. S. and A. K. Skidmore. 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment 85: 92–108.
Ustin, S. L., M. O. Smith, and J. B. Adams. 1993. Remote sensing of ecological processes: A strategy for developing ecological models using spectral mixture analysis. p. 339–357. In J. Ehleringer and C. Field (eds.) Scaling Physiological Processes: Leaf to Globe. Academic Press, San Diego, CA, USA.
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Artigas, F.J., Yang, J. Spectral discrimination of marsh vegetation types in the New Jersey Meadowlands, USA. Wetlands 26, 271–277 (2006). https://doi.org/10.1672/0277-5212(2006)26[271:SDOMVT]2.0.CO;2
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DOI: https://doi.org/10.1672/0277-5212(2006)26[271:SDOMVT]2.0.CO;2