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The ICLUS tools use a statistical model of urban growth that is directly integrated with ArcGIS and incorporates scenarios of housing density and derived impervious surface cover based on the IPCC social, economic, and demographic storylines (A1, A2, 131, 132). The ICLUS outputs are derived from a pair of models: a demographic model that gener- ates population projections and a spatial allocation model (SERGoM, Theobald 2005). Each scenario is run for the conterminous United States, or for smaller regions as specified by the user, projected through 2100 by decade. Unlike some other model- ing approaches, ICLUS uses projected population growth to estimate future patterns of housing densi- ty and captures a wider gradient of urban land use (e.g. urban vs. rural) than is commonly captured in the categories utilized in land cover data sets such as the NLCD. These projections are based on derived relationships from historic data. An example of the output is shown in Figure 4 -5. This approach differs from that utilized by other models such as SLEUTH, which uses cellular automata to model emergent behavior from a set of initial conditions and behav- ioral rules. Cellular automata models are scale inde- pendent, allowing local, regional and continental Pboto: U.S. Global Change Research Program, 2009 scale processes to be described in a single framework. In the Southeast, the Biodiversity and Spatial Infor- mation Center (BaSIC) is currently using SLEUTH - R (Jantz et al. 2010) to model urban growth as part of the "Designing Sustainable Landscapes" project (DSL). The DSL Project uses vegetation and urban dynamics modeling to examine the potential impacts of landscape -level changes on the future capability of habitats to support wildlife populations (BaSIC, personal communication, www.basic.ncsu.edu /dsl). A third approach is being used by researchers from RENCI at UNC Charlotte (Renaissance Computing Institute, http: / /renci.uncc.edu /) who were initial- ly commissioned by the Open Space Protection Collaborative to develop urban growth models for more than 20 counties in the greater Charlotte region. RENCI's model uses satellite imagery to forecast future urban growth using logistic regression models that are integrated with population -based models of urbanization pressure. This work is being expanded to include two- thirds of the state by the end of 2011. The urban growth model developed by UNC Char- lotte has been used to look at potential conflicts