“URBAN HOT ISLAND PHENOMENON OF SAN
ANTONIO TEXAS USING TIME SERIES OF
TEMPERATURE FROM MODIS IMAGES”
Sandra Ytuarte
Noreen O. Castellano
Hot Island Phenomenon Project Proposal
Urban Heat Islands have been created over time in the United States and around
the world. Scientific data has shown that July’s maximum temperature during the last 30
to 80 years has been steadily increasing at a rate of one-half to one degree Fahrenheit
every ten years.
The urban heat island phenomenon occurs when the air in the city is 2-8°F hotter
than the surrounding countryside.1 There are several factors that contribute to a city
becoming a heat island. One scenario is when urban areas have fewer trees, and the lack
of other natural vegetation to shade buildings. The vegetations purpose is to block solar
radiation and cool the air by evapotranspiration. Evapotranspiration occurs when plants
transpire water through pores in their leaves. The water draws heat as it evaporates,
cooling the air. Another contributor of heat islands is the low reflectivity of roofs and
paving materials. Buildings and pavements made of dark materials absorb the sun’s rays,
causing the temperature of the surface and the air around them to rise. Pavements and
dark materials such as a black surface in the sun can become up to 70°F hotter than a
reflective white surface. The energy of the sunlight is converted into thermal energy and
the pavement gets hot and causes the air to be heated around it. The pavement “Albedo”
or reflectivity is the ratio of the amount of light reflected from a material to the amount of
light shone on the material.2 For pavements a lower albedo implies that more sunlight is
absorbed by the pavement. As opposed to pavements with a higher albedo less sunlight is
absorbed by the pavement and it is cooler. In addition, when the sun beats down on
houses with dark shingle roofs, some of the heat collected by the roof is transferred inside
which causes heat to be unnecessarily stored.
Smog is a serious effect created from an urban heat island. Smog is created when
photochemical reactions occur due to pollutants in air that creates ozone in the lower
atmosphere. The formation of smog is highly sensitive to temperature, the higher the
temperature, the higher the formation and the concentration of the smog. These reactions
are more likely to occur and intensify at higher temperatures.3 Higher ambient
temperatures in hot islands also increase the use of air conditioning. This increases the
use of electrical power tremendously in large cities. As a power plant burns more fossil
fuels, pollution levels, smog and energy costs are all increased.
Results Expected
Each city’s urban heat island varies based on the city structure and thus the range
of temperatures within the island vary as well.4We expect that the downtown area of San
Antonio will have a higher temperature than the hill country as a result from the
absorption and storage of solar energy by the urban artificial substratum, and also from
the heat released into the atmosphere from industrial and communal processes. In many
large cities satellite sensed temperatures are 10-15C warmer than the surrounding rural
1
M. G. Estes, Jr., V. Gorsevski, C. Russell, Dr. D. Quanttrochi, Dr. J Luvall, Approaching The Millenium,
1999 APA National Planning Conference, “The Urban Heat Island Phenomenon and Potential Mitigation
Strategies”.
2
B. Pon, D. Moses Stamper-Kurn, C. Kenton Smith, and H. Akbari, “Existing Climate Data Sources and
Their Use in Heat Island Research”, April 27, 2002.
3
M.Pomerantz, B. Pon, H. Akbari, and S.-C Chang. “The Effect of Pavements’ Temperatures On Air
Temperatures in Large Cities” Heat Island Group, Lawerence Berkeley National Laboratory, Berkeley CA
4
Rosenberg, Matt T.. Urban Heat Islands http://geography.about.com
2
areas. 5Another contributor to the increase in downtown temperature will be the lack of
vegetation such as trees and shrubs. In summer, the symptoms of diurnal heating begin
to appear by mid-morning and can be warmer than nearby downtown and the amount of
less vegetation compared to the hill country.6
Methods
Data was inputted using NASA Earth Observing System Data Gateway. The test data
was ordered through Land Processes DAAC (LP DAAC) User Services from the U.S.
Geological Survey. The following parameters were needed: data set, data search type,
area – latitude / longitude, date and time range. The following parameters were used: data
set - MODIS Aqua (MOD 11-Land Surface Temperature & Emissivity), data search type
– primary search, area -33 N 29S latitude -102 W -97E longitude, date range- July 1,
2004 through August 1, 2004, and time range - 2:00 p.m.
MODIS/Aqua (EOS PM) (MOD 11-Land Surface Temperature & Emissivity)
satellites orbit around the earth and passes south to north over the equator in the
afternoon (2pm). Aqua MODIS can view the entire earth’s surface every 1 to 2 days,
acquiring data in 36 spectral bands, or groups of wavelengths.
An e-mail from CM Shared with the subject “ECS Notification” was received with five
days. Within this e-mail was a URL link where you just clicked onto and the information
was ready to be downloaded. This information was downloaded to the C drive of the
computer. The MODIS Tool MRT was used the transfer the HDF files to tif files. See
figure 1 on page 7 for MRT Tool. The input files (hdf) were opened one at a time. These
file were give a new output file name (tif), output projection type: Geographic/WGS 84,
projection parameter: of UTM Zone 14, and an output pixel size of 1000 m. Each tif file
was opened in the ENVI software and these files were cut into sub sets for the San
Antonio Area. The spatial data was set and these 30 tif files were stacked into one image
file for night and day. The night and day image files were opened and only certain bands
were selected for night and day. From this process four day and nine night were chosen
after overlaying roads and creating a density slice for each. See figures 2-5 of July 13th
on pages 8- 11 for the process used for selecting these bands for each night and day.
The four days that were chosen were July 4th, July 5th, July 13th, and July 18th.
The nine night that were chosen were July 13th , July 14th , July 15th , July 16th , July
19th, July 20th , July 24th , July 27th , and July 31st. See the figure 6 on page 12 for 3dimensional produced for further study.
Excel software was used to input formulas for converting DN numbers to degrees
Kelvin. Degrees Kelvin was then converted to degrees Celsius and then finally to
Fahrenheit. Tables were created to show the range of temperature results for each day
and night. Average temperatures were then calculated for each day and night and
graphed.
Price, J.C.. Monthly Weather Review 107, no. 11 (1999) pp 1554-1557. “Assessment of the urban heat
island effect through the use of satellite data”.
6
Kim, H.H.. International Journal of Remote Sensing Vol 13, no.12 1992, pp 2319-2336. “ Urban heat
Island “
5
3
Results
Not all days or nights could be used to show the Hot Island Phenomenon. Some
reasons for this were that there was simply no data available and the weather conditions
were not favorable. Weather conditions that could have affected the MODIS/Aqua
satellite are the following overcast or cloudy conditions, rain storms, and heavy humidity
levels. The National Oceanic and Atmospheric Administration (NOAA) was used to
locate weather data from July 2004. However, NOAA was not acquired due to charge for
data requested.
The results from this data acquisition were not what we expected. The results that
were observed from this data acquisition showed that July 13th had the hottest average
day temperature of 118.4F for the month of July 2004. July 10th had the hottest average
night temperature of 85.2F for the month of July 2004.
The July 16th and July 19th night images the showed the hottest temperature to be
mostly in the downtown center of San Antonio. The July 4th, July 13th day images the
showed the hottest temperature to be mostly in the downtown center of San Antonio. All
the other images showed different areas that were hotter than the downtown center. Some
of these areas are the following: Northwest - Medina Lake, Southeast - Lake Calaveras,
Northeast - Canyon Lake, further out Northeast - Lake Travis, Northeast - where 410 and
35 intersect/ Interchange Parkway (Distribution Center), Northwest- IH 10 Interchange,
Northwest - Medical Center Area, North - 281 airport area, and North - Canyon Springs
Golf course.
Future Research
After one year using these mitigation strategies acquire new data using the same
methods. Record and evaluate this new data to see if there has been any change in
temperature for the San Antonio Urban Area. Data should be collected from NOAA for
daily weather details and see if there is any correlation with the weather and the satellites
ability to acquire data. This data will help improve our understanding of global dynamics
of hot islands and the processes occurring in these urban areas. MODIS is playing a vital
role in the development of validated, global, interactive Earth system models able to
predict global change accurately enough to assist policy makers in making sound
decisions concerning the protection of our environment. 7
Conclusion
Mitigation strategies are needed to decrease the urban heat phenomenon. Some of
theses strategies are the following: plant programs, installing reflective materials for roofs
and pavements in urban areas.
Planting programs can help reduce the urban temperatures, for example one
mature, properly watered shaded tree with a crown of 30 feet can “evapotranspire” up to
40 gallons of water in a day. Within ten to fifteen years a properly placed tree can grow
to a useful size a can reduce heating and cooling costs by an average of 10-20%. Having
7
Remote Sensing of Vegetation Lecture 9
http://ioc.unesco.org/oceanteacher/resourcekit/M3/data/datasets/earthdatapubs/Servers/MODIS.htm
4
trees in a city over period of time can be much less expensive than air conditioners and
the energy needed to run them. To achieve the best results from the trees it is important to
have a correct selection and location of the trees. Grouped trees can protect each other
from the sun and wind, making them more likely to grow to maturity and live longer.8
Deciduous trees shading the south and west sides of a building block the summer sun.
Trees grouped together create a refreshing oasis in a city and also cool nearby
neighborhoods. The leaves of a tree filter dangerous pollutants from the air. By
introducing the use of canopy coverage of downtown sidewalk buildings, the temperature
in the downtown areas can be reduced.
It has been discovered that buildings with light-colored roofs that reflect the sun’s
rays use up to 40% less energy for cooling than buildings with darker roofs. A new rating
system called the solar reflectance index (SRI) is being developed to measure how hot
materials are in the sun. Traditional roofing materials have an SRI of 5% for brown
shingles and 20% for green shingles.9 The surface temperature of a roof is mainly
determined by the vigorous heat flows at the outside surface. There are four different
types of instruments used to characterize roofing samples: the Fourier-Transform InfraRed (FTIR) Spectral Emissometer, the UV-VIS-NIR Spectrometer with an integrating
sphere, the Solar Spectrum Reflectometer and the Emissometer.10The following three
shingles are defined by their reflectance and temperature as the sun beats down on them.
“Black Shingles” have a reflectance of 5% and can reach a temperature as high as 180°F,
“Conventional White Shingles” have a reflectance of 29% and can reach a temperature as
high as 157°F, and “Advanced White Shingles” have a reflectance of 60% and can reach
a temperature of 128°F. 11The advanced white shingle would lead to a cooler roof and, in
turn, a cooler building. Light colored roofing materials have an increased albedo and
therefore lower surface temperatures than dark colored materials.12
Increasing the albedo of buildings and pavement surfaces through the use of
reflective paving materials will help to cool down the surrounding ambient air
temperature. The reduction in temperature of the pavement due to a higher albedo would
contribute to a reduced heat island effect. Dark fresh asphalt has an albedo of 0.05 and
can reach a temperature of 123°F, a light aged asphalt has and albedo of 0.15 and can
reach a temperature of 115°F, and an asphalt coated pavement has an albedo of 0.51 and
a temperature of 88°F. More reflective pavements with higher albedos create a cooler
atmosphere which would reduce the heat island effect.
M. Pomerantz, B. Pon, H. Akbari, and S. – C Chang. “Vegetation”, Heat Island Group, Lawrence
Berkeley National Laboratory, Berkeley, CA.
9
Ibid
10
Ibid
11
Ibid
12
A. Apprill, “Energy Emissivity Analysis of Georgia State University Building Rooftops”, p. 13, 1998.
8
5
Figure 1: MRT TOOL
6
Figure 2: July 13th Hottest Day Temperature
Without Road Overlay or Density Slice Range
7
Figure 3: July 13th Hottest Day Temperature
Road Map overlaid
Without Density Slice Range
8
Figure 4: July 13th Hottest Day Temperature
Density Slice Range
9
Figure 5: July 13th Hottest Day Temperature
Road Map overlaid
Density Slice Range Applied
10
Figure 6: 3 Dimensional - July 13th Hottest Day Temperature
11
References
M. G. Estes, Jr., V. Gorsevski, C. Russell, Dr. D. Quanttrochi, Dr. J Luvall, Approaching
The Millenium, 1999 APA National Planning Conference, “The Urban Heat Island
Phenomenon and Potential Mitigation Strategies”.
B. Pon, D. Moses Stamper-Kurn, C. Kenton Smith, and H. Akbari, “Existing Climate
Data Sources and Their Use in Heat Island Research”, April 27, 2002.
2
M.Pomerantz, B. Pon, H. Akbari, and S.-C Chang. “The Effect of Pavements’
Temperatures On Air Temperatures in Large Cities” Heat Island Group, Lawerence
Berkeley National Laboratory, Berkeley CA
3
4
Rosenberg, Matt T.. Urban Heat Islands http://geography.about.com
Price, J.C.. Monthly Weather Review 107, no. 11 (1999) pp 1554-1557. “Assessment
of the urban heat island effect through the use of satellite data”.
5
6
Kim, H.H.. International Journal of Remote Sensing Vol 13, no.12 1992, pp 2319-2336.
“Urban heat Island”.
M. Pomerantz, B. Pon, H. Akbari, and S. – C Chang. “Cool Roofs Instrumentation”,
Heat Island Group, Lawrence Berkeley National Laboratory, Berkeley, CA
7
8
Ibid
9
Ibid
10
Ibid
A. Apprill, “Energy Emissivity Analysis of Georgia State University Building
Rooftops”, p. 13, 1998.
11
12 Remote Sensing of Vegetation Lecture 9
http://ioc.unesco.org/oceanteacher/resourcekit/M3/data/datasets/earthdatapubs/Servers/M
ODIS.htm
12