Connecting Urban Sprawl and Urban Heat IslandMatthew Welshans – GEOG 596A – Fall I 2013

Advisor: Dr. Jay Parrish

Project Summary

• 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?

Carrie Sloan (Flickr)

Kai Hendry (Flickr) Dr. Edwin Ewing/CDC

Urban Sprawl Example – Houston Area

1990 Census TractsPop_Density_Sq_Mi

0.000000 - 500.000000

500.000001 - 1000.000000

1000.000001 - 14750.155939

Counties in Study Area

Other Counties

2000 Census TractsPop_Density_Sq_Mi

0.000000 - 500.000000

500.000001 - 1000.000000

1000.000001 - 34276.985723

Counties in Study Area

Other Counties

2010 Census TractsPop_Density_Sq_mi

0.000000 - 500.000000

500.000001 - 1000.000000

1000.000001 - 55360.600747

Counties in Study Area

Other Counties

From 1990, 2000, and 2010 US Census SF1 Databases

Connecting Urban Heat Island to Urban Sprawl

Houston

2000 – 620km2 1990 – 450km2

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 AreasDallas-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

Year Area with Pop Density > 1000/sq mi

1990 953.9839 square miles2000 1247.7582 square miles2010 1566.5537 square miles

1990 2000

2010

Minneapolis-St. Paul, MN/WI Urban Sprawl

Year Area with Pop Density > 1000/sq mi

1990 613.5845 square miles2000 781.6853 square miles2010 856.5263 square miles

1990 2000

2010

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

stationsFrom NWS Minneapolis-St. Paul Office

Temperature DataSatellite 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): 30mTIR (1 band): 60m

Visible/NIR (3 Bands): 15mTIR (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 = –

– 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 IrradianceSTEP 1

• Filter out sky irradiance

• Estimate • Estimate T

Final Image• Temperature (+/-

1.5K)• Emissivity for five

bands

STEP 2• Calculate

spectrum of ratios of to average

STEP 3• Calculate

max-min diff in each band

• Predict • Recalculat

e T

STEP 4• Flag any

failures• Estimate

accuracy and precisions

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

• Unsupervised Land Cover Classification• Temperature Algorithms

Process Data – November - December

• Overlay Analysis• Statistical Analysis

Data Analysis – Late November to January

• AAG Annual Meeting – Tampa, FL – Climate Change Sessions• 21st Conference on Applied Climatology – Boulder, CO

Note and Present Findings – December - March

SourcesAbrams, 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%2018_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

SourcesOffice 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?