Senior Design Expo 2019

The Impact of Climate Change on Cities:
Investigating the Urban Heat Island Effect in New
York, Chicago, and Durban
Priscilla Bell, Amit Krishnan, Eleni Reynolds, Nicole
Romola
Advisors: Dr. Christian V. Braneon, Goddard Inst. for
Space Studies and Robert Farrauto Professor of
Professional Practice
As the world becomes increasingly urbanized, rising
regional temperatures are experienced in expanding cities
due to a phenomenon known as the Urban Heat Island
(UHI) effect. Urban areas are prone to these UHIs as
man-made construction materials facilitate rapid heat
transfer and have different thermal conductivities than
the natural environment. Additionally, densely packed
buildings create a surface roughness that traps radiation
and slows airflow, causing increased heating in urban air
[1]. Water flow is also significantly altered in urban
spaces, as cities are engineered to efficiently remove
surface water from impervious surfaces. The evaporation
rate decreases and a larger portion of radiation is instead
utilized to heat the land surface and the above air [2].
Yuan and Bauer published a 2006 study detailing the
effects of Normalized Difference Vegetation Index
(NDVI) and percent impervious surface (eg. pavement,
roads, industrial areas) as contributors to the UHI effect
[3]. The results gathered in the study showed that land
surface temperatures (LST) very strongly correlate with
percent impervious surface. This was studied further by
Liu and Zhang, who attempted to correlate LST with
NDVI and found a correlation between “green”, well
vegetated land and lower land surface temperatures [4].
LST was calculated for New York City, Chicago, and
Durban using two methods for years 1988-2018. One day
from a 100-hottest day range was analyzed for each year.
Method 1 was adopted from the USGS website on
Landsat data use [5]. Thermal bands from Landsat and
constants from the metadata were used to calculate top of
the atmosphere spectral radiance, L λ, and sensor
brightness temperature, T sensor. Emissivity, ε, is derived
from NDVI, calculated using from spectral bands. Lastly,
L λ, T sensor, and ε were pulled together in a final LST
calculation. Method 2 was adopted from Jiminez-Munoz
et al [6]. The LST equation used the same three values
calculated in Method 1 with the addition of atmospheric
correction functions, Ψ 1, Ψ 2, and Ψ 3. These functions
were calculated using the total atmospheric water vapor
content, w, which was gathered from the NOAA
Reanalysis-1 dataset.
singular weighted sum visualizations, showing areas that
are most frequently temperature hotspots (Figures 1-3).
Large parks, such as Central Park in NYC and Lincoln
Park in Chicago, and bodies of water such as Natal Bay
in Durban all consistently registered LSTs in the bottom
10 th percentile. Conversely, dense urban areas such as the
Financial District, Brooklyn, Queens, and Harlem in New
York, and Near West Side in Chicago registered as the
hottest areas. This analysis shows the impact of the built
environment on temperature hot spots.
References
[1] de Faria Peres, Leonardo, et al., "The urban heat
island in Rio de Janeiro, Brazil, in the last 30 years
using remote sensing data." International Journal of
Applied Earth Observation and Geoinformation 64
(2018): 104-116.
[2] Imhoff, Marc L., et al., "Remote sensing of the urban
heat island effect across biomes in the continental
USA." Remote sensing of environment 114, no. 3
(2010): 504-513.
[3] Yuan, Fei, and Marvin E. Bauer. "Comparison of
impervious surface area and normalized difference
vegetation index as indicators of surface urban heat
island effects in Landsat imagery." Remote Sensing of
environment 106, no. 3 (2007): 375-386.
[4] Liu, Lin, and Yuanzhi Zhang. "Urban heat island
analysis using the Landsat TM data and ASTER data: A
case study in Hong Kong." Remote Sensing 3, no. 7
(2011): 1535-1552.
[5] "Using the USGS Landsat Level-1 Data Product."
Landsat Missions Timeline | Landsat Missions.
Accessed November 29, 2018.
https://landsat.usgs.gov/using-usgs-landsat-8-product.
[6] Jiminez-Munoz, Juan C., et al., “Revision of the
Single-Channel Algorithm for Land Surface
Temperature Retrieval from Landsat Thermal-Infrared
Data.” IEEE Transactions on Geoscience and Remote
Sensing 47, no. 1 (2009): 339-349.
There were two maps created for each year, for each city.
GIFs were produced using maps from all years to show
the change in hotspot locations, defined as the top 10 th
percentile in temperature, over time. The LST maps from
1988-2018 for the analyzed cities were combined into
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