connecting urban sprawl and urban heat island matthew welshans – geog 596a – fall i 2013...
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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 Tracts
Pop_Density_Sq_Mi
0.000000 - 500.000000
500.000001 - 1000.000000
1000.000001 - 14750.155939
Counties in Study Area
Other Counties
2000 Census Tracts
Pop_Density_Sq_Mi
0.000000 - 500.000000
500.000001 - 1000.000000
1000.000001 - 34276.985723
Counties in Study Area
Other Counties
2010 Census Tracts
Pop_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
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 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
Year Area with Pop Density > 1000/sq mi
1990 953.9839 square miles
2000 1247.7582 square miles
2010 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 miles
2000 781.6853 square miles
2010 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 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): 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 Irradiance
STEP 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
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%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
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?