Scientists have the challenge of educating policymakers about the distribution and magnitude of environmental processes and their implications. The increase in remote sensing systems, availability of digital data, and geographic information systems can help to provide the kinds of visual information that can be useful in this arena. We now have the ability to assemble datasets that can describe, in broad ways, changes in Earth systems, and provide a baseline understanding of some processes. We can map the spatial distribution of forests, prime agricultural lands, and threats to the quality of resources using Geographic Information Systems (GIS).

Way discussed two prototypes for using such systems. The first involved applications of a database for the conterminous United States, accomplished by 17 graduate students in a 10-week period. They gathered digital databases from a variety of sources including USGS and EROS Data Center's Vegetation Characterization Dataset, and used these data to produce a set of maps that work to define issues on the macro scale, and demonstrate the ability to generate useful secondary datasets from primary data sources. Way showed a set of maps that characterized land elements including: commercially valuable timberland, financially valuable agricultural land, heterogeneity of vegetation classes, water resources including subsurface aquifers and surface water sources, and a future urbanization scenario.

This project tested different types of land use projections. Perhaps most significantly, it helped to identify "red flags" -- areas particularly at risk or of interest -- and generated some unexpected results. For example, the mapping project that sought to identify commercially valuable forest areas most at risk flagged the southern U. S. pine forests as the area most at risk, rather than the old growth forests of the Pacific Northwest which, while they have strong wildlife value, are often on slopes too steep to make them very important commercially. In the mapping of agricultural land most at risk, California's San Joaquin Valley showed up as an area at risk from urbanization.

The researchers also experimented with future scenario planning using the maps they generated. They attempted to protect sensitive and valuable areas of forest, agriculture, wildlife habitat, and other key resources by reallocating human population from these areas to other parts of the U.S. Way showed examples of some of these reallocations.

The second prototype Way demonstrated was based on the effects of differential air pollution on forests, agriculture, and people in Eastern Europe. The researchers sought to determine the drag on gross domestic product related to air pollution on the economies of these countries. Using AVHRR data and government maps, they first found out where the forests, agricultural land and people are located and then mapped the effects of ozone. Such maps can be powerful tools for informing policymakers.

In this study, emitters of sulfur dioxide (SO₂) were mapped including high smoke stacks and residential areas. A dispersion model was developed and incorporated in the GIS. Pollution risk of regional sulfur deposition was plotted in tons of SO₂ per 100 square kilometers and verified with published statistical data.

Landsat images were used to show in more detail the magnitude of long term SO₂ damage in forested areas. As soils acidify, forests die and are immediately cut. Replanting is not successful; even removing the topsoil has not been effective. The study also examined the association of pollution and human disease rates in order to estimate the social costs of air pollution in dollars per ton. As a fraction of gross domestic product, these effects were shown to be significant in terms of drag on the national economy on -- the magnitude of 7-15%, in this study.

This presentation helped to demonstrate what GIS can do well and what it can not currently be expected to do. It is difficult to incorporate time-dependent phenomenon in a GIS. Still, it is a good tool for getting a first cut on a problem. GIS is excellent for static mapping or for visualizing change from multiple points in time. It is useful for hypothesis formulation and can help to identify "red flags" to prioritize more detailed studies.