Abstract
Submergent aquatic vegetation (SAV) is a powerful indicator of environmental conditions in both marine and fresh water ecosystems. Quickbird imagery was used to map SAV at three sites across the Great Lakes. Unsupervised classifications were performed at each site using summer Quickbird sensor data. At one site, a multi-temporal classification approach was added, combining visible red difference (May-August) with August red and green visible band data. Multi-temporal SAV classification was superior to single-date results at this site. Muck bottom was not seriously confused with SAV, which was unexpected. Multi-temporal classification results showed less confusion between deep water and SAV, although spectral variability due to sub-surface sandbar structure was a source of error in both single- and multi-date classifications. Nevertheless, some of the confounding effects of water column on SAV classification appear to have been mitigated using this multi-temporal approach. Future efforts would be well served by incorporating detailed, continuous, bathymetry data in the classification process. Quickbird sensor data are very useful for classifying SAV under US Great Lakes conditions. However, regional classification efforts using these data may be impractical at this time, as high cost, rigid tasking parameters and unpredictable water conditions limit availability of suitable imagery.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 5255-5274 |
| Number of pages | 20 |
| Journal | International Journal of Remote Sensing |
| Volume | 26 |
| Issue number | 23 |
| DOIs | |
| State | Published - Dec 10 2005 |
Bibliographical note
Funding Information:This research has been supported by a grant from the National Aeronautics and Space Administration (NAG5-11262-Sup 5) and through a co-operative agreement with US Environmental Protection Agency’s Science to Achieve Results (STAR) Estuarine and Great Lakes (EaGLe) program through funding to Great Lakes Environmental Indicators (GLEI), US EPA Agreement R828675-00. Although the research described in this article has been funded in part by the United States Environmental Protection Agency, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. The authors would like to thank Robert Howe, Victoria Harris and three anonymous reviewers for improving the quality of this manuscript. This is publication number 375 for the Center for Water and the Environment and number 51 for the NRRI NRGIS Laboratory.