1 quantitative assessment of the accuracy of spatial estimation of impervious cover anna chabaeva...

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1 Quantitative Assessment of Quantitative Assessment of the Accuracy of Spatial the Accuracy of Spatial Estimation of Impervious Estimation of Impervious Cover 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

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Quantitative Assessment of Quantitative Assessment of the Accuracy of Spatial the Accuracy of Spatial

Estimation of Impervious Estimation of Impervious CoverCover

Anna ChabaevaDaniel CivcoJames HurdJason Parent

Department of Natural Resources Management & Engineering

The University of ConnecticutU-4087, Room 308, 1376 Storrs Road

Storrs, CT 06269-4087

2

ObjectivesObjectives

Compare predicted amount of imperviousness to highly accurate and precise planimetric data

Calculate percent imperviousness for52 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

Impervious Surface Impervious Surface (IS)(IS)

The imprint of land development on the landscape:

Rooftops

• Buildings• Pools• Patios

Transportation System

• Roads• Sidewalks• Driveways• Parking lots

4

Why Is Impervious Surface Why Is Impervious Surface Important?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 controlproblems

Base flow reduces

Peak runoff Rate increases

Flood controlproblems

Lag time and time base reduces

Groundwater recharge reduces

Runoff volume increases

Stormwater quality deteriorates

Impervious area increases

Building density increases

5

Influence of Impervious Surfaces Influence of Impervious Surfaces on Water Qualityon Water Quality

100

90

80

70

60

50

40

30

20

10

0

DEGRADED

PROTECTED

IMPACTED

WA

TE

RS

HE

D IM

PE

RV

IOU

SN

ES

S (

%)

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Impervious Surface Measurement Impervious Surface Measurement MethodsMethods

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

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Impervious Surface Measurement Impervious Surface Measurement MethodsMethods

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

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Study AreaStudy Area

NY

CT

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Data Data RequirementsRequirements

All datasets are in State Plane feet, NAD83 coordinates

• Census tracts 2000 data• Planimetric data • National Land Cover Data (NLCD) 2001• Landsat ETM+ data

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Census Tract 2000 Census Tract 2000 DataData

Original (green) and edited (red) tract data

82 tracts

Obtained from:Cartographic Boundaries section of the U.S. Census Bureau

Census tractsTown of Groton, CT

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Planimetric DataPlanimetric Data

Planimetric dataTown of Groton, CT

Obtained from:Town municipalities

Updated using 2004 CT DOQQs

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National Land Cover Data (NLCD) National Land Cover Data (NLCD) 20012001

100 x 100 feet

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NLCD Impervious CoverNLCD Impervious Cover

http://www.mrlc.gov/pdfs/July_PERS.pdfLandsat ETM Imagery

0%

100%

NLCD Imperviousness

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Impervious Surface Analysis Tool Impervious Surface Analysis Tool (ISAT)(ISAT)

Land Use Land Use Land Cover Land Cover

GridGrid

Set of Set of CoefficientsCoefficients

Polygon Polygon ShapefileShapefile

http://www.csc.noaa.gov/crs/cwq/isat.html

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Regression Regression ModelModel

ii AbPopDenbb %IS 21

where - b1 is the constant term

- b2 is the coefficient for population density

- bi are 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

Calibration Data221 NY tracts

Validation Data82 CT tracts

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Land Cover IS Coefficients Land Cover IS Coefficients

ISAT Regression11 Water 0.3% -0.34823521 Open Space Developed 11.9% -0.08264822 Low Intensity Developed 28.3% 0.17671323 Medium Intensity Developed 45.6% 0.20268624 High Intensity Developed 71.7% 0.60676831 Barren Land 19.7% 0.46526641 Deciduous Forest 2.8% -0.21388942 Evergreen Forest 8.2% -0.14466143 Mixed Forest 9.1% -0.40597151 Shrub/Scrub 4.7% -0.11363071 Grassland 5.0% -0.56479481 Pasture/Hay 7.3% -0.07136282 Cultivated Crops 23.5% -1.00558790 Woody Wetlands 0.7% 095 Emergent Herbaceous Wetlands 3.5% -0.781098N/A Population Density N/A 0.000098

CoefficientsClass NameClass #

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Sub-pixel Classification Tract IS Sub-pixel Classification Tract IS EstimationEstimation

Actual Imperviousness NLCD Imperviousness

82 tractsR2 = 0.95 RMSE = 5.65

Town of Groton, CT

0

20

40

60

80

100

0 20 40 60 80 100

Actual % IS

NL

CD

Su

b-p

ixel

%IS

18

0

20

40

60

80

100

0 20 40 60 80 100

Actual % IS

NL

CD

ISA

T %

IS

ISAT Tract IS EstimationISAT Tract IS EstimationActual Imperviousness ISAT Imperviousness

82 tractsR2 = 0.93 RMSE = 5.48

Town of Groton, CT

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0

20

40

60

80

100

0 20 40 60 80 100

Actual % IS

NL

CD

Reg

ress

ion

%IS

Regression Tract IS EstimationRegression Tract IS EstimationActual Imperviousness Regression Imperviousness

82 tractsR2 = 0.93 RMSE = 4.56

Town of Groton, CT

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ETISETISLand Use Land Use

Land Cover Land Cover GridGrid

Polygon Polygon ShapefileShapefile

Population Population DensityDensity

Set of Set of CoefficientsCoefficients

Custom Set Custom Set of of

CoefficientsCoefficients

Land Cover Land Cover ChangeChange

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ETIS Predicted Percent ETIS Predicted Percent Imperviousness for ConnecticutImperviousness for Connecticut

Census Tracts

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ConclusionConclusionss

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

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Anna [email protected]

Daniel CivcoJames Hurd

Jason Parent

Department of Natural Resources Management & Engineering

The University of ConnecticutU-4087, Room 308, 1376 Storrs Road

Storrs, CT 06269-4087

Quantitative Assessment of Quantitative Assessment of the Accuracy of Spatial the Accuracy of Spatial

Estimation of Impervious Estimation of Impervious CoverCover