Environmental Monitoring and Assessment

, Volume 81, Issue 1–3, pp 383–392 | Cite as

Preliminary Investigation of Submerged Aquatic Vegetation Mapping using Hyperspectral Remote Sensing

  • David J. Williams
  • Nancy B. Rybicki
  • Alfonso V. Lombana
  • Tim M. O'Brien
  • Richard B. Gomez


The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.

submerged aquatic vegetation remote sensing hyperspectral species mapping estuarine ecosystems epiphyte reflectance spectroscopy computational techniques 


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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • David J. Williams
    • 1
  • Nancy B. Rybicki
    • 2
  • Alfonso V. Lombana
    • 2
  • Tim M. O'Brien
    • 3
  • Richard B. Gomez
    • 4
  1. 1.Landscape Ecology Branch, Environmental Sciences DivisionU.S. Environmental Protection AgencyReston
  2. 2.National Research Program, Water Resources DivisionU.S. Geological SurveyReston
  3. 3.Environmental Concern Inc.Michaels
  4. 4.Center for Earth Observing and Space Research, School of Computational SciencesGeorge Mason UniversityFairfax

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