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Urban Sprawl and UHI in Dallas and Minneapolis. Matthew 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

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slide1
Urban Sprawl and UHI in Dallas and Minneapolis

Matthew Welshans, MGIS Student, Penn State University

April 11, 2014 – Association of American Geographers Annual Meeting

slide2
Project Summary

Define Urban Heat Island (UHI) and Urban Sprawl

Explore Data Used in Project

Methodology for Project

Results

Conclusions and Next Steps

slide3
Urban Heat Island Definition

Image Source: US EPA (2012)

slide4
Why is Urban Heat Island a Concern?

Kai Hendry (Flickr)

Dr. Edwin Ewing/CDC

Carrie Sloan (Flickr)

slide6
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?

slide7
Study Areas
  • Dallas-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
  • 6,364square miles (~590/sq mi)
slide8
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.
slide9
Why ASTER For Temperature Data?

From ASTER User Handbook Version 2 (2002)

slide10
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
slide11
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

slide17
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
slide19
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
slide20
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

slide21
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