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 journalArticlepeer-review

27 Scopus citations


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
Issue number23
StatePublished - Dec 10 2005

Bibliographical note

Funding Information:
This research was supported by a grant from the US Environmental Protection Agency’s Science to Achieve Results (STAR) Estuarine and Great Lakes (EaGLe) program through funding to the Reference Condition US EPA Agreement EPA/R-82877701-0. This project also receives support from the related Great Lakes environmental indicators (GLEI) project, US EPA Agreement EPA/R-8286750 ( This document 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. This is contribution number 369 of the Centre for Water and the Environment of the Natural Resources Research Institute.


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