Impervious Surface Estimation: A Comparison of Techniques

Impervious Surface Estimation: A Comparison of Techniques Anna ChabaevaDaniel CivcoJames Hurd Department of Natural Resources Management & Engineering The University of Connecticut U-4087, Room 308, 1376 Storrs Road Storrs, CT 06269-4087

Objectives Calculate percent imperviousness for10 towns in Connecticut on Tract Level & Watershed Level with Subpixel Classification Impervious Surface Analysis Tool (ISAT)Population Density and Land Use-based Regression Model using National Land Cover Data 2001Connecticut Land Cover Data 2002 Comparepredicted amount of imperviousness to highly accurate and precise planimetric data

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

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

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

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

Study Area

Data Requirements All datasets are in Connecticut State Plane feet, NAD83 coordinates • Town boundary data • Census tracts 2000 data • CT watershed boundary data • Planimetric data • Connecticut Land Cover data (CCL) 2002 • National Land Cover Data (NLCD) 2001 • Landsat ETM+ data

Including large waterbodies Excluding large waterbodies Town of Milford, CT Town Boundary Data 10 towns Obtained from: Map and Geographic Information Center (MAGIC)

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

Watersheds included Watersheds omitted Watersheds, Town of Groton, CT Watershed Data 248 drainage basins Obtained from: CT DEP

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

High Density Development Medium Density Development Low Density Development Turf & Grass Other Grasses & Agriculture Deciduous Forest Coniferous Forest Water Non-forested Wetland Forested Wetland Tidal Wetland Barren Land Utility Right-of-Ways Connecticut Land Cover Data (CCL) 2002 September 8, 2002

National Land Cover Data (NLCD) 2001

CCL Imperviousness Landsat ETM Imagery NLCD Imperviousness NLCD 2001 0% 100% NLCD and CCL Impervious Cover

http://clear.uconn.edu/projects/imperviouslis/project.htm Long Island Sound Regional Impervious Surface Study

http://clear.uconn.edu/projects/landscape/impervious_surfaces.htmhttp://clear.uconn.edu/projects/landscape/impervious_surfaces.htm CLEAR Impervious Surface Project

Land Use Land Cover Grid Polygon Shapefile Set of Coefficients Impervious Surface Analysis Tool (ISAT)

Validation Data 20% - 16 tracts Calibration Data 80% - 66 tracts Regression Model where - b1 is the constant term - b2 is the coefficient for population density - biare those for percentage of land cover category area within the tract - PopDen is the Population density - %Ai are the percent of the land cover category area within the tract

Land Cover IS Coefficients

Sub-pixel Classification Tract IS Estimation Actual Imperviousness CCL Imperviousness Town of Groton, CT 82 tracts R2 = 0.95 RMSE = 7.47

Sub-pixel Classification Tract IS Estimation Actual Imperviousness NLCD Imperviousness Town of Groton, CT 82 tracts R2 = 0.93 RMSE = 5.76

ISAT Tract IS Estimation Actual Imperviousness CCL Imperviousness Town of Groton, CT 82 tracts R2 = 0.93 RMSE = 4.81

ISAT Tract IS Estimation Actual Imperviousness NLCD Imperviousness Town of Groton, CT 82 tracts R2 = 0.92 RMSE = 5.36

Regression Tract IS Estimation Actual Imperviousness NLCD Imperviousness Town of Groton, CT 82 tracts R2 = 0.96 RMSE = 3.22

Sub-pixel Classification Basin IS Estimation Actual Imperviousness CCL Imperviousness Town of Groton, CT 248 basins R2 = 0.94 RMSE = 3.94

Sub-pixel Classification Basin IS Estimation Actual Imperviousness NLCD Imperviousness Town of Groton, CT 248 basins R2 = 0.86 RMSE = 4.66

ISAT Basin IS Estimation Actual Imperviousness CCL Imperviousness Town of Groton, CT 248 basins R2 = 0.91 RMSE = 5.17

ISAT Basin IS Estimation Actual Imperviousness NLCD Imperviousness Town of Groton, CT 248 basins R2 = 0.81 RMSE = 4.98

Regression Basin IS Estimation Actual Imperviousness NLCD Imperviousness Town of Groton, CT 248 basins R2 = 0.87 RMSE = 2.95

Population Density & Landcover-based Model

Regression Model-based Predicted Percent Imperviousness for Connecticut Census Tracts

Regression Model-based Predicted Percent Imperviousness for Connecticut Watersheds

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

Impervious Surface Estimation: A Comparison of Techniques Anna Chabaevaanna.chabaeva@uconn.eduDaniel CivcoJames Hurd Thank you! Department of Natural Resources Management & Engineering The University of Connecticut U-4087, Room 308, 1376 Storrs Road Storrs, CT 06269-4087

Impervious Surface Estimation: A Comparison of Techniques