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

George E Host, J. Schuldt, J. J.H. Ciborowski, Lucinda B Johnson, T. Hollenhorst, C. Richards

Research output: Contribution to journalArticle

23 Scopus citations

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 km 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.

Original languageEnglish (US)
Pages (from-to)5325-5342
Number of pages18
JournalInternational Journal of Remote Sensing
Volume26
Issue number23
DOIs
StatePublished - Dec 10 2005

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