Mapping submergent aquatic vegetation in the US Great Lakes using Quickbird satellite data

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.