1. 1 Quantitative Assessment of the Accuracy of Spatial Estimation of Impervious Cover Anna Chabaeva Daniel Civco James Hurd Jason Parent Department of Natural Resources Management & Engineering The University of Connecticut U-4087, Room 308, 1376 Storrs Road Storrs, CT 06269-4087

2. 2Objectives Compare predicted amount of imperviousness to highly accurate and precise planimetric data Calculate percent imperviousness for 52 towns in Connecticut and New York on Tract Level with Subpixel Classification Impervious Surface Analysis Tool (ISAT) Population Density and Land Use-based Regression Model National Land Cover Data 2001 using

3. 3 Impervious Surface (IS) The imprint of land development on the landscape: Rooftops Buildings Pools Patios Transportation System Roads Sidewalks Driveways Parking lots

4. 4 Why Is Impervious Surface Important? Population density increases Urbanization Waterborne waste increases Water demand rises Drainage system modified Water resource problem Urban climate changes Flow velocity increases Receiving water quality deteriorates Pollution control problems Base flow reduces Peak runoff Rate increases Flood control problems Lag time and time base reduces Groundwater recharge reduces Runoff volume increases Stormwater quality deteriorates Impervious area increases Building density increases

5. 5 Influence of Impervious Surfaces on Water Quality 100 90 80 70 60 50 40 30 20 10 0 DEGRADED PROTECTED IMPACTED WATERSHED IMPERVIOUSNESS (%)

6. 6 Impervious Surface Measurement Methods Interpretive Approach Digitizing Point sampling (Cover Tool method) Spectral Approach Sub-pixel Classification Artificial Neural Networks Classification and Regression Tree (CART) Normalized Difference Vegetation Index (NDVI) Vegetation-Impervious surface-Soil (VIS) model Modeling Approach Population Density-based Impervious Surface Analysis Tool (ISAT) Regression Model

7. 7 Impervious Surface Measurement Methods Interpretive Approach Digitizing Point sampling (Cover Tool method) Spectral Approach Sub-pixel Classification Artificial Neural Networks Classification and Regression Tree (CART) Normalized Difference Vegetation Index (NDVI) Vegetation-Impervious surface-Soil (VIS) model Modeling Approach Population Density-based Impervious Surface Analysis Tool (ISAT) Regression Model

8. 8 Study Area NY CT

9. 9 Data Requirements All datasets are in State Plane feet, NAD83 coordinates Census tracts 2000 data Planimetric data National Land Cover Data (NLCD) 2001 Landsat ETM+ data

10. 10 Census Tract 2000 Data Original (green) and edited (red) tract data 82 tracts Obtained from: Cartographic Boundaries section of the U.S. Census Bureau Census tracts Town of Groton, CT

11. 11 Planimetric Data Planimetric data Town of Groton, CT Obtained from: Town municipalities Updated using 2004 CT DOQQs

12. 12 National Land Cover Data (NLCD) 2001 100 x 100 feet

13. 13 NLCD Impervious Cover http://www.mrlc.gov/pdfs/July_PERS.pdf Landsat ETM Imagery 0% 100% NLCD Imperviousness

14. 14 Impervious Surface Analysis Tool (ISAT) Land Use Land Cover Grid Set of Coefficients Polygon Shapefile http://www.csc.noaa.gov/crs/cwq/isat.html

15. 15 Regression Model where - b 1 is the constant term - b 2 is the coefficient for population density - b i are those for percentage of land cover category area within the tract - PopDen is the Population density - %A i are the percent of the land cover category area within the tract Calibration Data 221 NY tracts Validation Data 82 CT tracts

16. 16 Land Cover IS Coefficients

17. 17 Sub-pixel Classification Tract IS Estimation Actual ImperviousnessNLCD Imperviousness 82 tracts R 2 = 0.95 RMSE = 5.65 Town of Groton, CT

18. 18 ISAT Tract IS Estimation Actual Imperviousness ISAT Imperviousness 82 tracts R 2 = 0.93 RMSE = 5.48 Town of Groton, CT

19. 19 Regression Tract IS Estimation Actual Imperviousness Regression Imperviousness 82 tracts R 2 = 0.93 RMSE = 4.56 Town of Groton, CT

20. 20 ETIS Land Use Land Cover Grid Polygon Shapefile Population Density Set of Coefficients Custom Set of Coefficients Land Cover Change

21. 21 ETIS Predicted Percent Imperviousness for Connecticut Census Tracts

22. 22 Conclusions Population density and landcover-based method the highest accuracy ISAT and population density and landcover-based method homogeneous (lumped) measure Subpixel method spatially explicit easy to implement easy to modify and recalibrate

23. 23 Anna Chabaeva anna.chabaeva@uconn.edu Daniel Civco James Hurd Jason Parent anna.chabaeva@uconn.edu Department of Natural Resources Management & Engineering The University of Connecticut U-4087, Room 308, 1376 Storrs Road Storrs, CT 06269-4087 Quantitative Assessment of the Accuracy of Spatial Estimation of Impervious Cover