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FUZZY DECISION ANALYSIS FOR INTEGRATED ENVIRONMENTAL VULNERABILITY ASSESSMENT OF THE MID-ATLANTIC REGION
Citation:
Tran, L. T., C. G. Knight, R. V. O'Neill, E R. Smith, K. H. Riitters, AND J D. Wickham. FUZZY DECISION ANALYSIS FOR INTEGRATED ENVIRONMENTAL VULNERABILITY ASSESSMENT OF THE MID-ATLANTIC REGION. JOURNAL OF ENVIRONMENTAL MANAGEMENT 29(6):845-859, (2003).
Impact/Purpose:
Provide regional-scale, spatially explicit information on the extent and distribution of both stressors and sensitive resources.
Develop and evaluate techniques to integrate information on exposure and effects so that relative risk can be assessed and management actions can be prioritized.
Predict consequences of potential environmental changes under alternative future scenarios.
Effectively communicate economic and quality of life trade-offs associated with alternative environmental policies.
Develop techniques to prioritize areas for ecological restoration.
Identify information gaps and recommend actions to improve monitoring and focus research.
There are two task objectives that reflect the work done by LCB in support of the ReVA Program objectives:
Provide information management, spatial analysis support, and data and information accessibility for the ReVA Program
Provide program management support, technology transfer, and outreach.
Description:
A fuzzy decision analysis method for integrating ecological indicators is developed. This is a combination of a fuzzy ranking method and the Analytic Hierarchy Process (AHP). The method is capable ranking ecosystems in terms of environmental conditions and suggesting cumulative impacts across a large region. Using data on land-cover, population, roads, streams, air pollution, and topography of the Mid-Atlantic region, we are able to point out areas which are in relatively poor condition and/or vulnerable to future deterioration. The method offers an easy and comprehensive way to combine the strengths of fuzzy set theory and the AHP for ecological assessment. Furthermore, the suggested method can serve as a building block for the evaluation of environmental policies.