1. Modeling land cover change in the Chesapeake Bay watershed An overview of the SLEUTH model Claire A. Jantz Shippensburg University Geography-Earth Science cajant@ship.edu Scott J. Goetz The Woods Hole Research Center sgoetz@whrc.org

2. Cellular Urban Models Cell-based models (grid or raster) Cells change state based on local neighborhood conditions Constraints to changes are introduced (e.g. slope suitability) –Local neighborhoods are not homogenous Rules reflect urban change processes or patterns

3. Patterns: Clustered vs. dispersed Edge growth vs. new centers of growth Proximity to transportation network

4. The SLEUTH model Developed by Keith Clarke (UCSB), sponsored by the USGS Urban Dynamics Program Widely used, well-established http://www.ncgia.ucsb.edu/projects/gig/

5. SLEUTH Applications in the United States

6. Global SLEUTH Applications

7. SLEUTH Urban Growth Model Urban / non-urban Growth rules –Spontaneous (dispersion coefficient) –New spreading center (breed coefficient) –Edge (spread coefficient) –Road-influenced (road gravity coefficient) Resistance to development –Slope (slope coefficient) –Excluded layer (user-defined)

8. SLEUTH Implementation Calibration –Train the model to simulate historic patterns of development Prediction –Forecast historic patterns of development into the future

9. Input data sets Transportation –GDT Slope –National Elevation Data Areas partially or wholly excluded from development (“excluded layer”) Urban time series –UMD/CBPO 1990 and 2000

10. Goetz, Jantz et al. (2004). Integrated analysis of ecosystem interactions with land use change: the Chesapeake Bay watershed, in Ecosystems and Land Use Change, 263-275. Urban Time Series

11. Urbanization in the Greater Baltimore – Washington D.C. area since 1986 8-August-2004 Washington Post 20 km

12. Year 2000

13. Urban/non-urban 1990 accuracy >79% 2000 accuracy >83% Jantz, P.A., S. J. Goetz and C.A. Jantz (2005). Urbanization and the loss of resource lands in the Chesapeake Bay watershed. Environmental Management 36(6): 808-825. Year 1990 (BLACK) Year 2000 (RED)

14. SLEUTH model calibration The greater Baltimore – Washington DC metropolitan area, circa 2000 Jantz C. A., Goetz S. J. & Shelley M. A. (2003) Using the SLEUTH urban growth model to simulate the land use impacts of policy scenarios in the Baltimore-Washington metropolitan region. Environment and Planning (B) 31(2): 251-271. mappedmodeled

15. Calibrating over a large region Challenges –Heterogeneity Patterns, rates of urban development Change –Computational challenge 167,000 km 2 ++ 27,976 x 20,129 cells  563,128,904 cells weeks of computing time..

16. % Developed (2000)

17. % Change (1990-2000)

18. Urban cluster density

19. Sub-regional modeling segments 13 cluster variables –Pattern metrics (developed area, edge density, patch density) –Change metrics –RUCA –Physiographic province K-means

20. Sub-region Area (km 2 ) New York west10,666 New York central20,454 New York east10,373 Pennsylvania northwest21,361 Pennsylvania northeast9,697 Pennsylvania north central15,572 Pennsylvania south central12,701 Pennsylvania southeast21,878 Virginia west16,758 Virginia central22,567 Virginia south7,180 Virginia south central22,901 Virginia Richmond-Norfolk21,640 Washington-Baltimore17,986 Delmarva15,152 Total area246,886

21. Forecasting Scenarios –“Current trends” –Best case vs. worst case User inputs –Excluded layer –Dynamic growth rates (“self-modification”) Output –Maps showing probabilities of change –Tabular summaries

22. Example from application in southeast Massachusetts

23. Maryland Urban Extent modeled with Sleuth “Current trends” “Best case”

24. Recent advances SLEUTH, version 3D –New calibration fit statistics –Calibration with two time steps of urban land cover—instead of four time steps (necessity) –Resolved scale issues related to “diffusion” parameter Diffuse development patterns can be captured using fine scale data

25. Key assumptions Process is not modeled explicitly –Pattern is linked to process Future development patterns are derived from historic trends, but… –Development rates can be modeled dynamically –Flexible scenarios set suitability for where exclusions (& attractions) occur Elements in the excluded layer do not change through time

26. Limitations Sensitivity to scale (grain) of input data Calibration parameters are not transferable –Scale sensitive –Site specific Pixel-scale forecasts are probabilistic Computational requirements

27. Advantages Capability to create broad-scale dynamic simulations at a high resolution (30 m cells) Limited range of data requirements Tightly linked to GIS and land cover observational data sets Strong visualization capability

28. Thank you! Jantz, P.A., S. J. Goetz and C.A. Jantz (2005). Urbanization and the loss of resource lands in the Chesapeake Bay watershed. Environmental Management 36(6): 808- 825. Jantz, C.A. and S.J. Goetz (2005). Analysis of scale dependencies in an urban land use change model. International Journal of Geographical Information Science 19 (2): 217-241. Goetz S. J., Jantz C. A., Prince S. D., Smith A. J., Wright R. and Varlyguin D. 2004. Integrated analysis of ecosystem interactions with land use change: the Chesapeake Bay watershed. In Ecosystems and Land Use Change, pp. 263-275. Eds. R. S. DeFries, G. P. Asner & R. A. Houghton. Geophysical Monograph Series, American Geophysical Union, Washington DC. Jantz, C.A., S.J. Goetz, and M. K. Shelley (2003). Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B 31 (2): 251- 271. WHRC website, Land Use Change in the Chesapeake Bay Watershed: http://whrc.org/midatlantic/ http://whrc.org/midatlantic/