Connecting Urban Sprawl and Urban Heat Island
Matthew Welshans – GEOG 596A – Fall I 2013
Advisor: Dr. Jay Parrish
Project Summary
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Define Urban Heat Island (UHI) and Urban Sprawl
Outline Prior Research
Highlight Planned Methodology for Project
State Anticipated Results
Show Project Timeline
What is the Urban Heat Island?
Image Source: US EPA (2012)
Why is Urban Heat Island a Concern?
Kai Hendry (Flickr)
Dr. Edwin Ewing/CDC
Carrie Sloan (Flickr)
Urban Sprawl Example – Houston Area
1990 Census Tracts
2000 Census Tracts
2010 Census Tracts
Pop_Density_Sq_Mi
Pop_Density_Sq_Mi
Pop_Density_Sq_mi
0.000000 - 500.000000
0.000000 - 500.000000
0.000000 - 500.000000
500.000001 - 1000.000000
500.000001 - 1000.000000
500.000001 - 1000.000000
1000.000001 - 14750.155939
1000.000001 - 34276.985723
1000.000001 - 55360.600747
Counties in Study Area
Counties in Study Area
Counties in Study Area
Other Counties
Other Counties
Other Counties
From 1990, 2000, and 2010 US Census SF1 Databases
Connecting Urban Heat Island to Urban Sprawl
1990 – 450km2
2000
1990
2000 – 620km2
Houston
1953631
1630553
From Streutker (2002)
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?
• How can it be measured objectively?
Previous Research
• Studies from several metropolitan areas
– Atlanta, Houston, New York City, Toronto, Hong Kong, just to
name a few!
• Differing satellite data sources
– AVHRR
– Landsat 5 TM /Landsat 7 ETM+
– ASTER
• Similar results:
– As infrastructure increases, size and strength of UHI
increases
Study Areas
Dallas-Ft. Worth-Arlington, TX MSA
• 12 counties in northeast Texas
• Humid Sub-Tropical Climate
• 2010 Population: 6,426,214
Minneapolis-St. Paul, MN/WI MSA
• 11 counties in southeast Minnesota and 2
in western Wisconsin
• Humid Continental Climate
• 2010 Population: 3,317,308
Proposed Methodology
• Comparing 2000 to 2010 data
– Census data for population and density in those study areas
– Land use/land cover changes from those periods
– Satellite imagery to measure skin (surface temperature)
Proposed Methodology
• Population Data
– US Census defines urban areas as those having a population
density of 1000 per sq mile and surrounding blocks of at
least 500 per sq mile.
– How has buildup changed over time?
Dallas-Ft. Worth-Arlington Urban Sprawl
1990
2010
2000
Year
Area with
Pop Density > 1000/sq mi
1990
953.9839 square miles
2000
1247.7582 square miles
2010
1566.5537 square miles
Minneapolis-St. Paul, MN/WI Urban Sprawl
1990
2010
2000
Year
Area with
Pop Density > 1000/sq mi
1990
613.5845 square miles
2000
781.6853 square miles
2010
856.5263 square miles
Proposed Methodology
• Land use/land cover
– Unsupervised classification
• Urban infrastructure
• Green cover (trees/grass/etc)
• Water
– How much green cover has disappeared over time?
Temperature Data
NWS Cooperative Network
 Potentially long climate
record (100+ years)
 Standard data available
 Generally no urban obs
 Missing data at many stations
From NWS Minneapolis-St. Paul Office
Temperature Data
Satellite Data
 Coverage Area
 Measures Surface Temp
 Requires Cloud Free Days
 Relatively short climatology
(~30 years for Landsat)
 Some potential error due to
atmospheric effects
Comparing Satellite Sources
LANDSAT 7 ETM+
ASTER
Satellite
Landsat 7 (1999)
Terra EOS Satellite (1999)
Resolution
Visible/NIR (4 bands): 30m
TIR (1 band): 60m
Visible/NIR (3 Bands): 15m
TIR (5 bands): 90m
From ASTER User Handbook Version 2 (2002)
Proposed Methodology
• Satellite Data
– Separate Urban/Rural Land Cover Pixels and calculate mean
temperature in each to determine strength of UHI (Jin,
2012).
U H I = T urban –
T rural,LC
– Temperature calculated using Gillepsie et al (1998)’s
Temperature Emissivity Separation (TES) Method for each
image.
• Temperature can be determined from radiance reflected, but
only if the surface emissivity is known.
Proposed Methodology
ASTER TES Method (Gillepsie et al, 1998)
ASTER Image:
• Reflected Radiance
• Sky Irradiance
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STEP 1
Filter out sky
irradiance
Estimate
ε𝑚𝑎𝑥
Estimate T
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STEP 2
Calculate
spectrum of
ratios of
ε𝑚𝑎𝑥 to
average
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STEP 3
Calculate
max-min diff
in each band
Predict ε𝑚𝑖𝑛
Recalculate
T
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STEP 4
Flag any
failures
Estimate
accuracy
and
precisions
Final Image
• Temperature (+/-1.5K)
• Emissivity for five bands
Example of ASTER Image – July 18, 2000
Correlating UHI and Urban Sprawl
• Overlay Analysis – Temperature patterns (isotherms)
compared to land use and/or pop density maps
– Measure size changes
– Compare to land use change over time
• Sample point data for different land use types
– Correlate changes in temperature between two time frames
– Plot regression lines to determine relationships
Anticipated Results
• Expecting to find strong correlation between urban sprawl
patterns and urban heat island patterns (Overlay Analysis)
• Statistical analysis should show that temperature
increases are somewhat dependent on the land cover over
an area.
Project Timeline
Obtain and Review Data – October - November
Process Data – November - December
•Unsupervised Land Cover Classification
•Temperature Algorithms
Data Analysis – Late November to January
•Overlay Analysis
•Statistical Analysis
Note and Present Findings – December - March
•AAG Annual Meeting – Tampa, FL – Climate Change Sessions
•21st Conference on Applied Climatology – Boulder, CO
Sources
Abrams, M., Hook, S. & Ramachandran, B. (2002). ASTER User Handbook (Version 2). Pasadena, CA:
NASA Jet Propulsion Laboratory. Obtained from
http://asterweb.jpl.nasa.gov/content/03_data/04_Documents/aster_user_guide_v2.pdf
Jin, M. (2012). Developing an Index to Measure Urban Heat Island Effect Using Satellite Land Skin
Temperature and Land Cover Observations. Journal of Climate, 25, 6193-6201.
doi:http://doi.org/10.1175/JCLI-D-11-00509.1
Land Processes Distributed Active Archive Center (2013). ATSER SWIR User Advisory. Retrieved from
https://lpdaac.usgs.gov/sites/default/files/public/aster/docs/ASTER_SWIR_User_Advisory_July%20
18_08.pdf
Mallick, J., Rahman, A., & Singh, C.K. (2013). Modeling urban heat islands in heterogeneous land surface
and its correlation with impervious surface area by using night-time ASTER satellite data in highly
urbanizing city, Delhi-India. Advances in Space Research, 52, 639-655.
doi:http://dx.doi.org/10.1016/j.asr.2013.04.025
Nichol, J., Fung, W.Y., Wong, M.S. (2009). Urban heat island diagnosis using ASTER satellite images and ‘in
situ’ air temperature. Atmospheric Research, 94, 276-284.
doi:http://dx.doi.org/10.1016/j.atmosres.2009.06.011
Sources
Office of Management and Budget (2009). OMB Bulletin Number 10-02: “Update of Statistical Area
Definitions and Guidance on Their Uses.” Retrieved from
http://www.whitehouse.gov/sites/default/files/omb/assets/bulletins/b10-02.pdf
Rajasekar, U. & Weng, Q. (2009). Spatio-temporal modeling and analysis of urban heat islands by using
Landsat TM and ETM+ imagery. International Journal of Remote Sensing, 30(13), 3531-3548.
doi:http://dx.doi.org/10.1080/01431160802562289
Rinner, C. & Hussain, M. (2011). Toronto’s Urban Heat Island—Exploring the Relationship between Land
Use and Surface Temperature. Remote Sensing, 3, 1251-1265.
doi:http://dx.doi.org/10.3390/rs3061251
Streutker, D. (2003). Satellite-measured growth of the urban heat island of Houston, Texas. Remote
Sensing of Environment, 85, 282-289. doi:http://dx.doi.org/10.1016/S0034-4257(03)00007-5
United States Environmental Protection Agency (2012). “Heat Island Effect.” Retrieved from
http://www.epa.gov/heatisld/about/index.htm
Questions?