Title

Mapping and monitoring the extent of submerged aquatic vegetation in the Laurentian Great Lakes with multi-scale satellite remote sensing

Document Type

Article

Publication Date

8-15-2013

Abstract

A satellite-based algorithm intended to map submerged aquatic vegetation (SAV), which was mostly the nuisance algae Cladophora, for the Laurentian Great Lakes has been developed and successfully demonstrated in test areas in Lakes Michigan and Ontario. The new Submerged Aquatic Vegetation Mapping Algorithm (SAVMA) first utilizes deep water (opaque) radiance values to correct shallow water values (transparent) so that depth invariant reflectance values for all three visible Landsat bands of the lake bottom can be classified. Combinations of two bands are then used to generate a depth invariant bottom type index. The algorithm then maps the lake bottom into three types: sand, dense SAV, less dense SAV by thresh-holding the depth invariant reflectance values. The SAVMA also generates a biomass estimate by assigning an average dry weight obtained by field sampling to both the dense and less dense SAV areas identified by the algorithm.

The algorithm performance was successfully evaluated on Lake Michigan at the Sleeping Bear Dunes National Lakeshore (SBDNL) using Cladophora locations provided by diver survey as well as from an independent National Park Service monitoring. The SAVMA correctly mapped Cladophora to an approximate accuracy of 85%, where the misclassification was a result of mixed pixels due to the resolution of the Landsat data and imprecise atmospheric corrections. The algorithm was also successfully evaluated in Lake Ontario near Pickering, ON. The SAVMA was then used to generate both short and long term time-series analyses of Cladophora extent at SBDNL.

Publisher's Statement

© 2013 Published by Elsevier B.V. on behalf of International Association for Great Lakes Research. Publisher's version of record: http://dx.doi.org/10.1016/j.jglr.2013.05.006

Publication Title

Journal of Great Lakes Research

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