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Use of GIS and remotely sensed data for a priori identification of reference areas for Great Lakes coastal ecosystems

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Abstract:

Identification of reference conditions for ecological assessments of coastal ecosystems poses a challenging problem in highly modified landscapes. A method is described for characterizing disturbance in coastal ecosystems using remotely sensed land classification and other publicly available GIS data. Within ecoregions bordering the US Great Lakes coast, aquatic habitats bordering the shoreline were classified into five ecological types: high‐energy shoreline, embayments, open‐coast, river‐influenced and protected wetlands. Degree of anthropogenic disturbance in contributing areas to these ecosystems was assessed using a watershed approach for wetland types or a moving window approach for high‐energy shorelines. Anthropogenic stress variables included proportions of agricultural or residential land use, information on population and road density, and distance from the nearest point source. Polygons (wetlands) or pixels (high‐energy shoreline) were categorized as ‘reference' if the magnitude of the most severe stressor, based on its cumulative frequency distribution within that ecoregion, placed it within the lowest 20th percentile. For shorelines, adjacent ‘reference' pixels were agglomerated into polygons and a final ranking of polygons containing at least 2┬ákm of shoreline was used to identify candidate reference areas. A subset of these sites is currently being sampled for fish, macroinvertebrates and physical habitat attributes. This a priori approach to reference area identification will allow managers to identify biological correlates of reference conditions, providing a benchmark for bioassessment and restoration efforts in coastal regions.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160500219364

Affiliations: 1: Natural Resources Research Institute, University of Minnesota Duluth, MN 55811‐1442, USA 2: University of Wisconsin‐Superior, Superior, WI 54880‐4500, USA 3: University of Windsor, Windsor, Ontario, Canada N9B 3P4 4: Minnesota Sea Grant, University of Minnesota, Duluth, MN 55812, USA

Publication date: 2005-12-10

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