ENVIRONMENTAL ASSESSMENT Fuzzy Decision Analysis for Integrated Environmental Vulnerability Assessment of the Mid-Atlantic Region

A fuzzy decision analysis method for integrating ecological indicators was developed. This was a combination of a fuzzy ranking method and the analytic hierarchy process (AHP). The method was capable of 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 were able to point out areas that were in relatively poor condition and/or vulnerable to future deterioration. The method offered 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. Regional analysis of environmental condition and vulnerability (Boughton and others 1999) represents a significant assessment challenge (Jones and others 1997). New sources of information from satellite imagery (O’Neill and others 1997) and new principles developed in landscape ecology (O’Neill and others 1999) provide exciting opportunities. To take advantage of these opportunities, however, a number of technical problems need to be addressed (e.g., Riitters and others 1997, Wickham and others 1997). One of the most important problems of regional vulnerability assessment involves integrating information from many different sources into an overall ranking of relative risk (Wickham and others 1999). At the smaller spatial scale of a watershed, focusing on specific end points (EPA 1998) or devising an index of overall environmental integrity (Ott 1978, Karr and others 1986, Karr 1991) may represent feasible approaches to integration, although these simple approaches have faced serious criticism (DeAngelis and others 1990, Suter 1993). The problem becomes even more complex at the regional scale where information may be available on terrestrial and aquatic ecosystems, land-use changes, and a variety of simultaneous stressors. One of the critical problems of integrated assessment is dealing with uncertainty that arises from different sources, such as error in measurement and/or modeling, imprecision in knowledge of relationships between stressors and receptors, and even ambiguity in the meaning of risk. The set of measured and calculated values being integrated are complexly interrelated and cannot be considered as statistically independent. A careful approach to integrated assessment, founded on state space theory (Johnson 1988, Kersting 1988) or multivariate analysis (Boyle and others 1984, Smith and others 1989), seems to be required.