Quantifying and Describing Urbanizing Landscapes in the Northeast United States

Abstract

This paper describes research at the University of Connecticut using remote sensing technology to detect and describe land-cover features. Land-use decision makers are the intended audience of this research. Impervious surfaces, forest fragmentation and urban growth are three dynamic elements of landscape that have been of particular interest to the researchers and land use officials. Techniques have been developed to extract impervious surface data directly from Landsat imagery to estimate non-point source pollution impacts on watershed. A model has been created to quantify and describe forest fragmentation over various geographic areas. Researchers have also developed an urban growth model that uses land-cover information to quantify and categorize urban change. The urban growth model combines spatially detailed and widely available multi-temporal satellite imagery with a model that creates a map of urban growth. Combining these urban growth maps over several time periods allows decision makers to see the changes that have occurred in an area as a result of past policies, and incorporate these lessons into future land use decisions.

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