Urban Sprawl and UHI in Dallas and MinneapolisMatthew Welshans, MGIS Student, Penn State University

April 11, 2014 – Association of American Geographers Annual Meeting

Project Summary• Define Urban Heat Island (UHI) and Urban

Sprawl• Explore Data Used in Project• Methodology for Project• Results• Conclusions and Next Steps

Urban Heat Island Definition

Image Source: US EPA (2012)

Why is Urban Heat Island a Concern?

Carrie Sloan (Flickr)

Kai Hendry (Flickr) Dr. Edwin Ewing/CDC

Urban Sprawl – NE of Dallas

The Problem• Urban Heat Island is affected by the growth of

metropolitan areas– Size of heat island– Increase in temperature difference between

rural/urban areas• What is the correlation between increased

urban sprawl and the change in urban heat island?

Study AreasDallas-Ft. Worth-Arlington, TX MSA• 12 counties in northeast Texas• 2010 Population: 6,426,214• 9,286 square miles (~690/sq mi)

Minneapolis-St. Paul, MN/WI MSA• 11 counties in southeast Minnesota and

2 in western Wisconsin• 2010 Population: 3,759,978• 6,364 square miles (~590/sq mi)

Data Sets– Land Use/Land Cover Data (2001, 2006, 2011

Draft)• National Land Cover Database (Landsat 7)• Split into 15 land cover categories• Percent Impervious Surface (%IS) calculated per

each pixel– Temperature Data

• Derived from ASTER Imagery from the MODIS Satellite

• Three swaths per study area were chosen based within 2 years of the LULC Data.

Why ASTER For Temperature Data?  LANDSAT 7 ETM+ ASTERSatellite Landsat 7 (1999) Terra EOS Satellite (1999)

Resolution Visible/NIR (4 bands): 30mTIR (1 band): 60m

Visible/NIR (3 Bands): 15mTIR (5 bands): 90m

From ASTER User Handbook Version 2 (2002)

Deriving Temperatures from ASTER• Temperature calculated using Gillepsie et al

(1998)’s Temperature Emissivity Separation (TES) Method for each image.– Atmospheric Scattering effects filtered out– Max and min pixel emissivity calculated– Surface temperature ± 1.5°C calculated using

Planck’s Law

Methodology• Split each study area into eastern and

western sections• Sampled each swath extent with ~10,000

points• Averaged temperatures in each land cover

category• Averaged temperatures based on 10-percent

intervals in percent impervious surface (IS)• Calculated average Urban (>15% IS) and

Rural (<15% IS) to produce UHI calculation

Results – Minneapolis (West) UHI

2001 2.28C -0.41C

2.68C

2004 3.23C -0.56C

3.79C

2011 3.17C -0.78C

3.95C

0-1010

-2020

-3030

-4040

-5050

-6060

-7070

-8080

-9090

-100

-2-101234567

8/6/20018/30/20049/10/2011

Percent Impervious Surface

Dep

artu

re fr

om A

vera

ge

(°C)

Minneapolis (West)

Results – DallasUHI

2001 1.59C -0.30C

1.89C

2005 1.71C -0.63C

2.34C

2013 1.22C -0.57C

1.78C

0-1010

-2020

-3030

-4040

-5050

-6060

-7070

-8080

-9090

-100

-2-101234567

5/18/20013/10/20053/16/2013

Percent Impervious Surface

Dep

artu

re fr

om A

vera

ge

(°C)

Dallas

Collin County, TX

Pop 2000

Pop 2010

491,675 782,351

Why The Difference?• Daytime Surface Albedo (reflectivity)

– Higher in cleared areas versus water, wetlands, and forest

– Proportional to surface temperature– Differs depending on time of year

Why The Difference?

Water 5.78%Urban

27.64%

Barren 0.10%

Forest 16.04%Shrub 1.39%Grass 2.96%

Ag36.66%

Wet-land-

s9.44%

Minneapolis (West) - 2006

Water5.30%

Urban21.66% Barren

0.06%Forest15.28%

Shrub1.63%

Grass2.78%

Ag44%

Wetlands8.79%

Minneapolis (West) - 2001

Water6.68%

Urban30.74%

Barren0.14%

Forest14.99%

Shrub1.32%

Grass2.55%

Ag,36%

Wetlands7.74%

Minneapolis (West) - 2011

Water3.73%

Urban22.71% Bar-

ren0.11%

For-est

11.63%

Shrub0.49%

Grass29.04%

Ag30.38%

Wetlands1.91%

Dallas - 2001Water4.25%

Urban35.22%

Bar-ren0.33%

Forest10.91%

Shrub0.06%

Grass25.17%

Ag21.96%

Wetlands2.10%

Dallas - 2006Water5.14%

Urban41.56%

Barren0.38%

Forest10.21%

Shrub

0.03%

Grass23.00%

Ag18.26

%Wetlands

1.43%

Dallas - 2011

Conclusions• Generally good link between temperature

and percent impervious surface• Land cover type plays key role in daytime

surface temperature patterns– Lower temperatures around water, forests– Highest temperatures in urban, agriculture,

grassland

Next Steps• Compare 2011 and upcoming 2016 land

cover data to newer ASTER imagery• See if trends continue to hold up• Compare to nighttime imagery if possible to

see how UHI patterns differ. • Reverse Migration and Green Initiatives

Acknowledgements• Dr. Jay Parrish – Advisor• Beth King and Dr. Doug Miller – Penn State MGIS Program• Jon Dewitz, Joyce Fry, Dr. Jim Vogelmann – USGS EROS

Center

Questions?maw323@psu.edu