Temporal Characterization of Impervious Surfaces for the State of Connecticut James D. Hurd & Daniel...

Temporal Characterization of Impervious Surfaces for the State of Connecticut


James D. Hurd & Daniel L. Civco

Center for Land use Education And Research (CLEAR)

Department of Natural Resources Management & EngineeringThe University of Connecticut

U-4087, Room 308, 1376 Storrs RoadStorrs, CT 06269-4087

May 26, 2004

Center for Land use Education And Research

(CLEAR)

Center for Land use Education And Research

(CLEAR)

A collaboration of the Land, Sea and Space Grant programs at the University of Connecticut

A collaboration of the Land, Sea and Space Grant programs at the University of Connecticut

NEMOLERIS

GVIForestr

y

National NEMO

Network

GTP

To provide information, education and assistance to land use decision makers, in support of balancing growth and natural resource protection.

MISSION:

CLEARCLEAR

Publications

Tech toolsTech tools

Web

Presentations

Presentations

Demonstration ProjectsDemonstration Projects

CLEARCLEAR

http://CLEAR.uconn.edu

CLEARCLEAR

Maps and Statistics on:- Land Cover- Land Cover Change- Forest Fragmentation- Urban Growth- Impervious Surfaces

Impervious SurfacesImpervious Surfaces

Materials like:- cement,- asphalt,- roofs,

that prevent percolation of runoff into the ground.


Why should we care?

Local impacts to water quality


Adapted from Schueler, et al., 1994

8080

7070

6060

5050

4040

3030

2020

1010

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NES

SW

ATER

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ED

IM

PER

VIO

US

NES

S(%

)(%

)

DEGRADED

PROTECTED

IMPACTED

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ED

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Water Quality Impacts

25

Impervious SurfacesImpervious SurfacesUrbanization

Adapted from Hall, 1984

Population DensityIncreases

Building DensityIncreases

Urban ClimateChanges

Waterborne WasteIncreases

Water DemandRises

Water ResourceProblems

Base FlowReduces

GroundwaterRechargeReduces

StormwaterQuality

Deteriorates

Receiving WaterQuality Deteriorates

Pollution ControlProblems

Peak Runoff Rate Increases

Flood ControlProblems

Runoff VolumeIncreases

Drainage SystemModified

Lag Time and TimeBased Reduced

Flow VelocityIncreases

Impervious AreaIncreases

Quantifying Imperviousness


• Previous examples of impervious surface estimation in Connecticut:• Impervious Coefficients estimates are

calculated by multiplying a land use specific percent impervious coefficient by the total area of that land use within an area.

• Results in an estimate, does not identify specific locations if impervious surfaces.

• Does not account for small scale variance within a particular land use.

• Direct Estimation from Remote Sensing Imagery • Neural Networks• Sub-pixel Classifier

Technical Session 97: Wed., May 26th

4:30 - 5:30Room: Governor’s Square 14.



• Software: ERDAS IMAGINE’s Subpixel Classifier®

• Add-on module developed by Applied Analysis Inc.

• Able to detect features smaller than the spatial resolution of the sensor.

• Provides the percentage of the material within each pixel.

• Output: raster image overlay depicting per pixel IS occurrences greater than 20%.

Pixel Reflectance ValuesPixel Reflectance Values100% ImperviousPixel ValueB1 - 141B2 - 70B3 - 255B4 - 155B5 - 210B7 - 80

50% ImperviousPixel ValueB1 - 102B2 - 45B3 - 161B4 - 169B5 - 171B7 - 47

50% ImperviousPixel ValueB1 - 113B2 - 47B3 - 174B4 - 138B5 - 131B7 - 40

0% ImperviousPixel ValueB1 - 98B2 - 42B3 - 165B4 - 148B5 - 206B 7- 53

Landsat ETM 4,3,2ADAR

Sub-pixel Classifier Procedures

Sub-pixel Classifier Procedures

Automated process. - performed prior to signature development and classification.

Image PreprocessingEnvironmental Correction

Process that compensates forunwanted spectral variations(i.e. haze and clouds).- In scene- Scene to scene

Signature SelectionSignature Selection

Material of Interest(MOI) is impervioussurfaces.

Bradley Int’l. AirportWindsor Locks, CTApril 26, 1985

Impervious PixelSpectral Variability

Impervious PixelSpectral Variability

bright

medium

dark

very darkPrincipal Components 1

0

50

100

150

200

250

300

B1 B2 B3 B4 B5 B7

TM Band

Mea

n R

efle

ctan

ce

very dark

dark

medium

bright

0

50

100

150

200

250

300

B1 B2 B3 B4 B5 B7

TM Band

Mea

n R

efle

ctan

ce

very dark

dark

medium

bright

Signature SelectionSignature Selection

Bradley Int’l. AirportWindsor Locks, CTApril 26, 1985

Select pixels thatRepresent 100% IS:-Very dark pixels-Dark pixels-Medium pixels-Bright pixels

Signature DerivationSignature Derivation

Signature CombinerSignature Combiner

MOI ClassificationMOI Classification

Initial Subpixel Classification

Initial Subpixel Classification

20% - 29%30% - 39%40% - 49%50% - 59%

60% - 69%70% - 79%80% - 89%90% - 100%

Manchester, CTAugust 30, 1995

Signature RefinementSignature Refinement

Signature RefinementSignature Refinement


20% - 29%30% - 39%40% - 49%50% - 59%

60% - 69%70% - 79%80% - 89%90% - 100%

Supervised ClassificationSupervised

ClassificationManchester, CTAugust 30, 1995

20% - 29%30% - 39%40% - 49%50% - 59%

60% - 69%70% - 79%80% - 89%90% - 100%

Create signatures thatrepresent 100% IS.

Used Parallelpipeddecision rule to identifyonly those pixels withinupper and lower limits.

3 x 3 Majority Filter3 x 3 Majority Filter

20% - 29%30% - 39%40% - 49%50% - 59%

60% - 69%70% - 79%80% - 89%90% - 100%


Used 3x3 majority filter.

Applied to class values0, 5, 6 ,7, 8 only.

Inclusion of Land CoverInclusion of Land Cover


10% - 19%20% - 29%30% - 39%

40% - 49%50% - 59%60% - 69%

70% - 79%80% - 89%90% - 100%

Extract pixels classifiedas developed from land cover.

Mask the sub-pixelclassification to identifyonly pixels from thedeveloped category.

Pixels classified asdeveloped but notidentified as containingIS make 10% class.

Classification of 10% IS Pixels

Classification of 10% IS Pixels

Extract pixels identifiedas 10% IS.

Perform sub-pixelAnalysis on extractedpixels.

Overlay on previoussub-pixel classification.


10% - 19%20% - 29%30% - 39%

40% - 49%50% - 59%60% - 69%

70% - 79%80% - 89%90% - 100%

Initial 1995 IS EstimateInitial 1995 IS Estimate


10% - 19%20% - 29%30% - 39%

40% - 49%50% - 59%60% - 69%

70% - 79%80% - 89%90% - 100%

Subsequent Dates(1985, 1990, 2002)

Subsequent Dates(1985, 1990, 2002)

Perform same analysis operations on1985, 1990, and 2002 Landsat images.

1985 1990 2002

10% - 19%20% - 29%30% - 39%

40% - 49%50% - 59%60% - 69%

70% - 79%80% - 89%90% - 100%

Deriving Final IS estimates

(1985, 1990, 1995, 2002)


(1985, 1990, 1995, 2002)

• A fundamental principal in developing a temporal IS dataset was the need for consistency between dates.• Needed to eliminate the possibility of

fluctuation in IS% between dates.• Needed to eliminate the decrease in IS%

over time.• Combine IS results from adjacent dates by using

the difference between dates.


(1985, 1990, 1995, 2002)


(1985, 1990, 1995, 2002)For Example to generate the final 1990 IS estimate:

There are nineIS classes1 = 10% - 19%2 = 20% - 29%3 = 30% - 39%4 = 40% - 49%5 = 50% - 59%6 = 60% - 69%7 = 70% - 79%8 = 80% - 89%9 = 90% - 100%

Take the difference1995 IS – 1990 IS

(add constant of 10 tomaintain positive values)

Pixel 2: (5 – 1) + 10 = 14

Pixel 3: (5 – 4) + 10 = 11

If the difference value was 11, then the 1990 IS value was maintained, otherwise the 1995 IS value was used. If the 1990 IS value was 0 then the 0 value was maintained.• The result is a final 1990 IS estimate that

maintains the 1995 IS estimate levels unless the 1990 pixel was one class value below the 1995 level.• Same procedure used between 1990 and 1985.• Reverse procedure used between 1995 and 2002.

Pixel 1: (5 – 8) + 10 = 71995 1990


(1985, 1990, 1995, 2002)


(1985, 1990, 1995, 2002)

• Examination of results with validation data still resulted in under-estimate of IS.• Needed to increase the overall IS levels.

• Beginning with the 1985 initial IS estimate and adjusted 1985 IS estimate, if IS values per pixel were higher in the initial 1985 estimate, then maintained higher values.

• Applied this 1985 IS estimate to the 1990 adjusted estimate.

• Continued for 1995 and 2002.

Final IS Estimates(1985, 1990, 1995, 2002)Final IS Estimates

(1985, 1990, 1995, 2002)

10% - 19%20% - 29%30% - 39%

40% - 49%50% - 59%60% - 69%

70% - 79%80% - 89%90% - 100%

Manchester, CT1985Manchester, CT1990Manchester, CT1995Manchester, CT2002

Validation AreasValidation Areas

1:140,000

West HartfordGrotonMarlborough

Planimetric DataPlanimetric DataWest Hartford, CT

Generate 30-meter grid that corresponds to Landsat pixel grid. Calculate actual IS.

Validation ResultsValidation Results

West Hartford, CTcirca 1990

Planimetric Derived IS Landsat Derived ISReference IS - Estimated IS

62 68 121

293

552

887

1179

1680

23442214

1963

1641

1097

583

22286 34 31 27

0

500

1000

1500

2000

2500

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

Difference (% impervious)over estimate under estimate

Nu

mb

er

of

Pix

els

Excluding 0 value pixelsin both dates.


West Hartford, CTcirca 1990


12 21 28104

287

464634

942

1883

1680

2163

1642

1083

524

17552 22 21 7

0

500

1000

1500

2000

2500

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90


Nu

mb

er

of

Pix

els



Marlborough, CTcirca 1995

PlanimetricDerived IS

LandsatDerived IS

Reference IS - Estimated IS

6 1 6 26 35 50132

264

11551193

1887

849

328

77 16 12 3 2 20

400

800

1200

1600

2000

2400

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90


Nu

mb

er

of

Pix

els



Waterford, CTcirca 1995

Planimetric Derived IS

Landsat Derived IS


33 17 26 43100

187276

385

1521

614

1078

794

391

19780 33 8 7 11

0

200

400

600

800

1000

1200

1400

1600

1800

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

Differenceover estimate under estimate

Nu

mb

er

of

Pix

els



Waterford, CTcirca 1995


LandsatDerived IS


107 52 81 132227

427

630789

2409

1244

1567

1178

701

347

136 56 36 24 220

400

800

1200

1600

2000

2400

2800

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

Differenceover estimate under estimate

Nu

mb

er

of

Pix

els



Woodbridge, CTcirca 1995


5 5 12 42 77202

343511

1935

1093

3039

2026

981

348122 31 22 11 9

0

500

1000

1500

2000

2500

3000

3500

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90


Nu

mb

er o

f P

ixel

s



Milford, CTcirca 2002


102 77 125207

364

562

760

1010

1577

13141458

1123

694

391

21592 45 37 12

0

400

800

1200

1600

2000

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90


Nu

mb

er o

f P

ixel

s



Suffield, CTcirca 2002


LandsatDerived IS


2 5 16 2353

91130

198

366

206

474

350

227

8951

26 20 14 23

0

100

200

300

400

500

600

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90


Nu

mb

er o

f P

ixel

s



Groton, CTcirca 2002


LandsatDerived IS


41 30 40 58102

168230

348

750

467509

407

286

165

8536 19 15 6

0

100

200

300

400

500

600

700

800

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90


Nu

mb

er o

f P

ixel

s


Validation ResultsMisregistration

Validation ResultsMisregistration

100%

100%

0%

0%

50%error

50%error

30 m

30 m

½ pixel shift

100%

0%

0%

0%

75%error

½ pixel x ½ pixel shift

100%

100%

0%

0%

100%error

100%error

1 pixel shift

can significantly affect classification accuracy even at small scales.


Town

Planimetric %

Impervious Surface

Estimated %

Impervious Surface

Difference

Sample Area(acres)

Groton 21.68 23.65 (year 2002)

-1.97 1,412

Marlborough 3.85 2.95 (year 1995)

0.90 6,667

Milford 24.60 27.39 (year 2002)

-2.79 3,529

Suffield 7.46 5.94 (year 2002)

1.52 1,985

Waterford(area 1)

4.07 3.91(year 1995)

0.16 6,697

Waterford(area 2)

8.61 9.31 (year 1995)

-0.70 7,052

Woodbridge 6.74 4.27 (year 1995)

2.47 7,394

West Hartford(area 1)

34.06 37.75 (year 1990)

-3.69 4,122

West Hartford(area 2)

16.51 14.99 (year 1990)

1.52 4,467

ConclusionsConclusions

• The ERDAS Imagine Sub-pixel Classifier was designed to identify sub-pixel contributions of MOIs, therefore is suitable for deriving this type of product.

• Care needs to be taken in generating appropriate signatures due to the complex spectral variability of impervious surfaces.– Identify signature pixels, classify, refine,

classify, refine, etc….

ConclusionsConclusions

• Areal results of IS estimates compare favorably with actual IS.

• Per-pixel accuracy is less then desirable with:– 1990 overall accuracy 31%, n = 37,416

• 52% if include +/- 1 class increment

– 1995 overall accuracy 76%, n = 121,140• 88% if include +/- 1 class increment

– 2002 overall accuracy 36%, n = 39,334• 57% if include +/- 1 class increment

• There is still work to be done…

Future ResearchFuture Research

• Want to improve per-pixel accuracy.• Want to create a more uniform IS

estimate.

WestboundI-84

3 lanes

EastboundI-84

3 lanes

Access road

Rest area

Decrease variabilityalong interstate

Future ResearchFuture Research

• Focus sub-pixel analysis on groupings of pixels from previous IS estimate.

Extract IS class values 7, 8, 9Extract IS class values 4, 5, 6Extract IS class values 1, 2, 3

Expanded AreaExpanded AreaPhase 1,Greater Connecticut

Phase 2,Long Island &Westchester County

Land CoverLand Cover ChangeForest FragmentationUrban GrowthImpervious Surfaces

AcknowledgementsAcknowledgements

• Funding from the NOAA Coastal Services Center (CSC). Phase 1.

• Funding from the EPA Office of Long Island Sound Programs. Phase 2.



James D. Hurd

[email protected]

Center for Land use Education And Research (CLEAR)

Department of Natural Resources Management & EngineeringThe University of Connecticut

U-4087, Room 308, 1376 Storrs RoadStorrs, CT 06269-4087

May 26, 2004

Temporal Characterization of Impervious Surfaces for the State of Connecticut James D. Hurd & Daniel...

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