Municipal stormwater: Impervious cover maps

Impervious cover is defined as land surfaces with a low capacity for soil infiltration, such as any hard surface material including roof tops, asphalt, or concrete. Impervious cover limits water infiltration and induces high runoff rates.

Maps on this page are static, showing impervious cover change between the years 1990 and 2000 for select cities and communities in Minnesota. Accuracy information is summarized after the list of available maps.

For interactive GIS or GIS data downloads of statewide impervious cover maps for Minnesota, visit the University of Minnesota's Remote Sensing and Geospatial Analysis Laboratory Web site:

HTML icon Remote Sensing and Geospatial Analysis Lab (University of Minnesota)


For more information about the content on this page, contact David Fairbairn, Minnesota Pollution Control Agency (MPCA), 651-757-2659.

Select MS4s

Counties and communities

Minnesota River Communities

Crow River Communities

Benton County

Chisago County

Isanti County

Mille Lacs County

Sherburne County

Stearns County

Wright County

Map Information

What to expect from the map

When looking at the land cover and impervious maps on this site, it is important to remember that no map is a perfect representation of reality. There are always errors in maps and we need to keep in mind how accurate they are, and whether that level of accuracy is sufficient for the ways we want to use the map information. Based on the 30-meter resolution of the Landsat data used to create these maps, it's important to keep in mind that these maps will be most accurate for viewing geographic patterns over larger areas (e.g., county or city, rather than neighborhood).

How accurate are these maps?

In general, accuracy is assessed by comparing the finished map to a second set of reference data -- not the data that was initially used for classifier training or modeling the relationship. By using a different set of known ground locations, we can check how accurately the model was able to extrapolate the relationship (between the initial set of ground data and its associated pixels) across the entire image.

The result of an accuracy assessment provides us with an overall accuracy of the map based on an average of the accuracies for each class in the map. For example in a land cover map the water class could be very accurate but some of the vegetation classes might be less accurate. Or, in the case of Urban/Developed areas, the heavily developed areas are usually more accurately identified then the lightly developed. Thus, categories of imperviousness (80-100%) are more consistently identified as Urban/Developed than the lightly developed (0-10%). For example, bare soils resulting from dry cycles have caused us varying degrees of classification error in urban settings. In general, our analyses are 85% to 96% accurate based on statewide and regional regressions (details available below). If you locate particular areas of concern, please contact David Fairbairn of the MPCA (651-757-2659) or Dr. Marvin Bauer at the University of Minnesota Remote Sensing and Geospatial Laboratory.