Urban Sprawl and UHI in Dallas and Minneapolis Matthew Welshans, MGIS Student, Penn State University April 11, 2014 – Association of American Geographers Annual Meeting
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
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+ ASTER
Satellite 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
0-10
10-2
0
20-3
0
30-4
0
40-5
0
50-6
0
60-7
0
70-8
0
80-9
0
90-1
00
-2
-1
0
1
2
3
4
5
6
7
8/6/20018/30/20049/10/2011
Percent Impervious Surface
Dep
art
ure
fro
m A
vera
ge
(°C
)
Results – Dallas
UHI
2001 1.59C -0.30C
1.89C
2005 1.71C -0.63C
2.34C
2013 1.22C -0.57C
1.78C
0-10
10-2
0
20-3
0
30-4
0
40-5
0
50-6
0
60-7
0
70-8
0
80-9
0
90-1
00
-2
-1
0
1
2
3
4
5
6
7
5/18/20013/10/20053/16/2013
Percent Impervious Surface
Dep
art
ure
fro
m A
vera
ge
(°C
)
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%
Barren0.06%
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
%
Wetlands1.43%
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