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Recent Additions
December 11, 2006
March 21, 2005
- Ground-Water Vulnerability to Nitrate Contamination at
Multiple Thresholds in the Mid-Atlantic Region Using Spatial Probability Models
- The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency's Regional Vulnerability Assessment Program, has developed a set of statistical tools to support regional-scale, ground-water quality and vulnerability assessments. This abstract describes the tools and links to the full scientific investigation report.
January 4, 2005
- ReVA Environmental Decision Toolkit - The objective is to assist decision makers in making more informed decisions and in estimating the large-scale changes that might result from their actions. This website includes a basic introduction to the environmental vulnerability assessment approach, and an overview of the ReVA tools and their applications.
November 8, 2004
- Report on the Evaluation of Integration Methods - The Regional Vulnerability Assessment (ReVA) Program has focused initially on the synthesis of existing data. We have used the same set of spatial data and synthesized these data using a total of 11 existing and newly developed integration methods. These methods were evaluated in terms of 1) how well each individual method performs given different data issues that are encountered with existing data, and 2) how effectively each method addresses different types of assessment questions.
Headline: ReVA / MAIA Conference in 2003
The first ReVA conference was held May 13-15, 2003 in Valley Forge, PA. This conference focused on work that has been done in our pilot study in the mid-Atlantic region as part of MAIA (Mid-Atlantic Integrated Assessment) and also looked ahead to additional research that is planned as we expand to include additional endpoints (e.g. estuarine health) in that region and gearing up for a second region.
2003 ReVA-MAIA Conference
Presentations
Headline: A fuzzy decision analysis method for integrating ecological indicators is developed using a combination of a fuzzy distance measure method, principal component analysis, and the Analytic Hierarchy Process (AHP).
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 of providing an integrated ecological index 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. Some spatial patterns can be revealed from results of this work. For example, watersheds located near urban centers (e.g., Philadelphia, Washington D.C.) have relative high impact index scores. A buffer zone between areas of good and bad conditions is not seen very clearly, suggesting that any future environmental policy applied to the region should be developed in a very careful manner to avoid further environmental degradation. 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.
Headline: A means for measuring risks to natural resources, in terms of human welfare, by integrating both socio-economic and ecological endpoints is being developed.
This research will measure risks to natural resources, in terms of human welfare, by integrating both socio-economic and ecological endpoints. The research will incorporate ecological indicators being developed through the Regional Vulnerability Assessment (ReVA) and link them spatially with available data on socio-economic condition of regions. This integration will offer an exceptional opportunity to examine sustainability issues by linking changes in economic accounts with availability and condition of natural infrastructure. Our aim will be to develop information that allows more than just a description of current condition, but instead provides "leading indicators" of future conditions under various management scenarios. We will use two main approaches:
- Develop a suite of spatial risk indicators that will show where projected changes in environmental resources are likely to produce costs or hardships due to dominant economic activities or other socio-economic conditions;
- Employ regional economic models to evaluate the economic effects of investment in (e.g. restoration) and use of ecological resources under projected land use change.
The suite of indicators taken as a whole is designed to reflect the risk of socio-economic disruption. Yet, the indicators can be broken down into various categories that reflect aspects of socio-economic condition that can be directly associated with resource use decisions. These categories are:
- Quality of Life (for individuals and households)
- Resource Pressure (efficiency and rate of resource use in business and community)
- Economic Risk Management (governmental efforts for maintaining resources)
Regional economic impact models will be used to assess potential outcomes of various mixes of environmental investments. We will create a new economic sector that characterizes restoration activities and use it to show the impacts of restoration on job creation, incomes, taxes and other measures of economic activity. The regional approach will allow us to examine issues such as scarcity at appropriate scales, for example the amount of recreational birding opportunities within a particular ecoregion and how that will change with expanded development. Regional models will be used to provide information on:
- Economic and Environmental Outcomes of Projected Future Land Use Change
- Economic and Environmental Outcomes of Restoration Investment Scenarios
We will also consider issues at the local scale of cities, counties or watersheds. Case studies will demonstrate how the indicators can be used to show economic effects of land use change on individuals and communities. For example, projections of costs for roads, schools, and other infrastructure will be evaluated. Also, monetary effects from resource changes (e.g., cost of lowering wells due to change in ground water levels) and other effects (e.g. change in average commute times) will be estimated.
Headline: A new research paper examines the risk that landscapes will experience future forest fragmentation.
For a given percentage reduction of forest cover, the sensitivity of landscapes to future forest fragmentation depends on the current forest pattern. Landscape vulnerability is additionally related to current forest amount, land ownership, and projections of land-cover change. For example, landscapes that contain forests in linear patterns and private ownerships are at more risk than those that contain public forests in large blocks. This research applies models of landscape sensitivity with scenarios of future land-cover change to evaluate relative risk of forest fragmentation in the mid-Atlantic region.
Headline: The initial phase of ReVA was to develop profiles based on existing information for select environmental stressors across the mid-Atlantic.
The initial phase of ReVA was to develop profiles based on existing information for select environmental stressors across the mid-Atlantic. The stressors Atlas documents the initial attempt and contains geographics profiles for nine stressors.
In considering which stressor categories would be featured in this atlas, ReVA staff developed the following criteria, all of which had to be met by a candidate stressor.
- The candidate stressor appears to represent a significant hazard to regional ecosystem processes, as indicated by preliminary modeling and/or empirical (qualitative/quantitative) data.
- Its exposure pathways are known to the extent that mediating processes are sufficiently documented to support a quantitative assessment of ecological risk.
- Data, interpolation techniques, and/or models required for the development of a particular stressor profile currently exist or are technically feasible in the foreseeable future.
- A principal investigator within ORD is available with the appropriate expertise to champion the development of the stressor profile.
Through the application of the stressor selection criteria, ReVA staff in consultation with EPA Region 3, selected nine stressor categories for stressor profile development:
| Acid Deposition Coal Mining Human Population |
Landscape Pattern Agricultural Nitrogen Ground-Level Ozone |
Agricultural Use of Pesticides Soil Erosion/Sedimentation Solar UV-B Radiation |
Stressor profiles were obtained by a variety of methods. In some cases, spatial data was immediately accessible, for example, census data and remote imagery for human land uses. In other cases, the information was available as point-space monitoring data. The areas between monitoring points were re-interpolated by several methods. In still other cases, mathematical models were used to simulate the patterns based on the distribution of their known or inferred sources. The model can be as simple as making assumptions about fertilization and runoff rates from fields, or as complicated as multimedia fate and transport models to simulate fate and transport of pollutants from monitored factory smokestacks. Specific methods are presented for each profile in the atlas.
Each stressor typically has multiple exposure pathways and often multiple receptors. Therefore a key component of each stressor profile is the development of conceptual exposure models. From these models, researchers can begin developing the tools necessary to model and estimate the risks of exposure.
Headline: Remotely-sensed satellite data is now being used to identify recent surface mining activity in the Appalachians.
Mountain Top Removal/Valley Fill is a mining process where the vegetation, soil and rock overlaying a coal seam are removed to gain access to the coal. The rock debris from this process are then deposited in surrounding valleys, creating a valley-fill. Remotely-sensed satellite data is now being used to identify recent surface mining activity of this type in the Appalachians. It is estimated that more than 900 miles of intermittent and perennial streams have been covered by valley fills in Appalachia In 1994 less than 1,000 acres were permitted for mountaintop mining. By 1997 more than 12,000 acres were permitted.
The purpose of this effort was to create an updated spatial dataset showing the current extent of surface mining in the Appalachians. A landcover dataset produced by a consortium of federal agencies exists, but reflects landcover from the early 1990's. As mentioned, much has changed in the intervening time period. In this study we used satellite imagery (Landsat 7 ETM+) from 1999. Formal classification of satellite imagery can be expensive and time-consuming. In this study we used a different approach - rather than going through the classification process, we instead looked at changes in a vegetation index derived from the same satellite imagery. Normalized Difference Vegetation Index or NDVI, can be thought of as a measure of vegetation 'greeness' and it fluctuates according to the type of vegetation present and the season of the year. A key advantage of NDVI is that is easy and quick to calculate and it doesn't require a great deal of data preprocessing. Conversion of forest or vegetated landcover to a surface mine is marked by a dramatic negative shift in NDVI values, from a vegetated surface to in essence, bare rock.
In this study NDVI values were calculated for the early 1990's data and again for the 1999 data. The measurements from the two time periods were subtracted. Areas that experienced dramatic declines in NDVI from the early 1990's to 1999 were identified as potential mining sites. Areas that experienced this dramatic NDVI loss and which also fell within mining permit boundaries were classified as new mines. However, since the mining permit boundaries are not complete, the remaining areas were looked at visually in conjunction with other ancillary datasets and classified appropriately.