“An Analysis of Surface Temperature in San Antonio, Texas”

Term Project12-3-07

EES5053/ES4093: Remote Sensing, UTSA

Student Name: Sean Cummings

Introduction and Background:Urban Heat Islands (UHI) are areas in urbanized locations that have an increase in temperature compared with the temperature of the surrounding region. In hot summer months the air temperature can be 6-8 degrees Fahrenheit higher than the surrounding region. Usually in urbanized areas the ground vegetation cover is less than rural areas due to development. Vegetation can help to shade buildings, intercept solar radiation, and cool the air by “evapotranspiration”and dark materials tend to absorb more Electromagnetic Radiation. Therefore, buildings and pavement made of dark materials absorb the sun’s radiation instead of reflecting them away, causing the temperature of the surfaces and the air around them to rise (http://eetd.lbl.gov/HeatIsland/). Using imagery from Landsat 7 ETM+, I will try to determine the major sources of Urban Heat Islands in the San Antonio area. This will be done by calculating the surface temperature, analyzing aerial photography, and creating a land cover map. San Antonio is located in south central Texas between the Edwards plateau to the northwest and the Gulf Coastal Plains to the southeast. Between these two regions lies an area of rolling terrain with vegetation of mainly oak trees, mesquite, and cacti (http://www.city-data.com/us-cities/The-South/San-Antonio). The city is in a humid sub-tropical climate (temperate), but is close in proximity to semi-arid regions to the west and southwest. Due to this location the region gets both humid and dry weather. The area experiences short winters with an average of 20 days that drop below the freezing point. Precipitation falls at approximately 28 inches per year with drizzle in the cooler months and thunder showers in the warmer months. This amount of rain and presence of a mild winter allows for the production of most crops. San Antonio is one of the hottest cities in the country, with an average of 111 days per year with temperatures over 90 degrees Fahrenheit (http://en.wikipedia.org/wiki/San_Antonio,_Texas).

With San Antonio’s long hot summer months; the area should be prone for several heat islands to develop. The city has witnessed rapid growth for the last four decades increasing its Metropolitan area population from 901,220 to 1,804.012 in 2006 (US Census Bureau). This doubling of population has also changed the geography of the land and the local climate. Stripping the land in new development removes vegetation, which helps cool the air naturally. In addition to stripping the land, the vegetation is being replaced by buildings and pavement that all contribute to the warming effects. We should expect to find hot spots in areas throughout major cities, and San Antonio is no different. Some typical major sources of UHI are dense urban areas such as downtown areas and other commercial centers and airports. Other areas such as suburban development contribute to the warming effect. In San Antonio areas such as the Lackland Air force base, Kelly USA, Fort Sam Houston, Randolph Air force base, downtown, San Antonio

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PrecipitationYear January February March April May June July August September October November December Total Year2001 2.85 0.7 2.77 2.29 2.48 3.39 0.5 7.83 4.05 2.07 4.36 3.43 36.72 20012002 0.37 0.42 1.19 3.82 2.26 1.48 16.92 0.54 7.02 7.64 2.08 2.53 46.27 2002

Average 1.59 1.69 1.83 2.73 3.53 3.19 2.23 2.39 3.31 2.82 2.11 1.79 29.02 Average

TemperatureYear January February March April May June July August September October November December Total Year2001 49.2 57.5 56.6 70.8 76.3 82.6 85.4 85.6 76.9 67.9 62.9 53.8 68.8 20012002 53.7 50.8 60.3 73.2 76.8 83.4 82.5 85.3 78.7 70.7 57.8 53.8 68.9 2002

Average 51.8 55.3 62.2 69.4 75.7 81.7 84.1 84.3 79.5 70.9 60.6 53.6 69.1 Average

International Airport, the South Texas Medical Center, Brooks City Base, and the IH-410/IH-35 corridor are areas that are suspected to be major sources of urban heating.

Data:The Imagery that was processed and analyzed was from the month of July, taken by LandSAT 7 ETM+ in 2001 and 2002. The imagery from 2001 was sensed on July 21st and the 2002 image was sensed on July 8th. The data was retrieved from the Texas View website (http://www.texasview.org/). The images are from the satellite’s pass-27 in row-40. Only four Bands will be used for analysis. Band sequence 7, 4, 2 was used as RGB for classifying the land cover and band 9 was used in the surface temperature analysis. Other data sets were used for reference, such as: aerial photography, roads, county boundary, etc.

The two images represent different weather settings in San Antonio. Even though, they were both from the month of July, the weather was considerably different. In 2001 the month of July was slightly warmer than average with less precipitation than average. On the other hand, in 2002 the month of July was cooler than average and set a record for precipitation. These two sets of data allow for analysis of surface temperature under two completely different circumstances.

Methods:

To classify surface temperature and land cover to identify and analyze Urban Heat Islands, there are several data processing steps that need to be taken. I will be using three different software packages to complete my processing and analysis; ENVI, IDL, and ArcGIS 9.2. The ENVI software was used to classify the land cover, IDL was used to process the surface temperature from the image and then classified in ENVI, and ArcGIS 9.2 was used to analyse the data and create maps for the project. For land cover several ENVI tools were used to classify the area. The first step was to process the data

by converting the DN to radiance and resize the LandSat image to a smaller area surrounding San Antonio. The radiance is calculated using the equation:

offsetDNgainL += *� (Landsat 7 Science User Data Handbook Chap.11, 2002)

The gain and offset are embedded in the header file associated with the image. At this point, the radiance has been calculated and the next step is to classify the image using the ‘Region of Interest’ tool in ENVI. I used three classes: Urban, Vegetation, Water, and Barren to describe the land cover. The Urban class incompasses roads, buildings, and other build landscapes. Vegetation represents live growth that has a green color in the 742 RGB. Water represents water bodies that show up blue. The barren land class can conflict with some built areas because it contains some pinkish/lavender values, but for the most part it is browns, pinks, and sandy. Classifying the land cover is important so that relationships between land cover and surface temperature can be analyzed.

Two different ‘Supervised Classification’ methods were used for the two images. The ‘Maximumum likeleyhood’ method was used for the 2001 data and the ‘Spectral Angle Mapper’ method was used for the 2002 data. The ML method would not work for the 2002 data so the ‘Spectral Angle Mapper’ method had to be implemented. A low probability threshold was used to get as complete coverage as possible. Calculating the surface temperature from the digital number will be performed using the IDL with ENVI software. Using the software the DN is converted to radiance (4), radiance to brightness temperature (5), and finally brightness temperature to surface temperature (6) in Kelvin:

These equations can be included in a script used in IDL to convert the DN to surface tmperature:

PRO temper; calculate temperature of Landsat image

envi_select, title=’Choose multispectral image’, fid=fid, dims=dims, pos=posif (fid EQ -1) THEN BEGIN PRINT, ‘cancelled’ RETURNENDIF

map_info = envi_get_map_info(fid=fid)

image=envi_get_data(fid=fid, dims=dims, pos=pos[0]) ;pos[0] is the first band, pos[1] the second band, ...

num_cols=dims[2]-dims[1]+1 ; get the number of columns (x)num_rows=dims[4]-dims[3]+1 ; get the number of rows (y);num_bands=n_elements(pos);num_pixels=num_cols*num_rows

L=temporary(0.0370588*image+3.2) ;calculate radiance of high gain image;L=temporary(0.066823*image) ;calculate radiance of low gain imageTB=temporary(1282.71/(alog((666.09/L)+1))) ;calculate brightness temperatureRT=temporary(TB/(1+(0.0007991666*TB)*alog(0.988))) ;supposing the same emissivity of 0.988

fname=‘tmperHG.img‘;fname=‘temperLG.img‘openw, unit, fname, /get_lunwriteu, unit,RTfree_lun, unit

ENVI_SETUP_HEAD, fname=fname, ns=num_cols, nl=num_rows, nb=1, interleave=0, data_type=4, offset=0, map_info=map_info,/write, /open

END

The method used is supposing that all surfaces have an emissivity of 0.988. A more complete analysis could be done if different emissivities are used for the different surfaces. But for this analysis we assume that they are all

the same. The image that has been output from the original loses its geographic referencing, therefore, needed to be registered. An image to image registration was performed by opening the two images in different windows and assigning control points. A total of 15 control points were used for each image (2001 and 2002). The geographic referencing was assigned from the original image file. All four images were saved as geotif files in order to open the images in ArcMap, part of the ArcGIS 9.2 software package. In ArcMap the Land Cover raster was converted to a vector polygon file, containing five regions: Urban, Vegetation, Water, Barren, and unclassified. The tool “Raster to Polygon’ was used from the Data Management tools.

Also in ArcMap, the surface temperature geotif was used as a Digital Temperature Model (DTM). First the raster data was converted from Kelvin to degrees Fahrenheit, using the ‘Raster Calculator’ tool from the Spatial Analyst extension.

Degrees Fahrenheit = 1.8 * (Kelvin – 273.15) + 32

Now the data is in a more familiar format. From the DTM contour lines can be drawn in order to define possible Heat Island source regions. Now the two new files can be used to analyze spatial relationships of the surface temperature and land cover.

Analysis and Conclusions:There are several areas around the city of San Antonio that contribute considerably to the Urban Heat Island Effect. All areas picked out from aerial photography turned out to be “Hot Spots” on the surface temperature from LandSat 7 ETM+ imagery: Downtown San Antonio, Brooks City Base, Kelly USA, lackland Air Force Base, Fort Sam Houston, Randolph Air Force Base, the San Antonio International Airport, and the South Texas Medical Center. The most striking of these areas was Kelly USA for magnitude and Downtown San Antonio in size. The Brooks City Base area did not register as high as hypothesized, but was still above average.

There were areas that had very high temperature that were not part of the hypothesis. On the south side of town, Southwest Military between Interstate 35 and Kelly USA was one of these areas. There is very little vegetation in the area and there are a lot of asphault surface parking lots. The area had some of the hottest temperatures on the image and an average surface temperature more than ten degrees above the surounding rural areas. The area just east of Kelly USA also registered hot. This is an area that is industrial with a lot of rail yards.

On the west central side of town there were two areas that stood out with the hypothesized areas. These areas were North Fredericksburg and the Northwest IH-410 corridor. These areas were left out of the hypothesis because the area is not as densely populated as others closer to the city. The whole area is made up of shopping centers, officespace, and pavement, causing the high surface temperture. Both of these areas feed into the Medical Center area.

On the east side of town the IH35/40 corridor was a lot larger than the aerial photography showed. It extended south of the IH-10 interchange and as far west as the AT&T Center. This whole area should have originally been included in the hypothesis, but was left out due to the area being lush with vegetation. Though the surface temperature was high in this area it may not result in that much of an increase in air temp due to the heavy vegetation in the area.

With the addition of these areas to the ‘major contributors to Urban Heat Islands,’ the distribution can be analyzed. Temperature magnitude withstanding, assumptions can be made from these areas. These areas as well as there surroundings would most likely be subject to substantial air temperature increases.

Further Study:This analysis is only part of what the entire extent of the project should be. The next part would be to analyze the air temperature using data from a satellite such as MODIS, for the same days. Then the spatial relationships between the surface temperature and the air temperature could be analyzed in order to conclude if these “Major Hot Spots” are contributing greatly to San Antonio’s Urban Heat Island. It would also be beneficial to use the same methods to analyze data from July that is under normal (average) conditions (ie. temperature and precipitation). Then there would be three samples of data; one under normal conditions, another milder and wetter, and finally hot and dry. Not only would this give insight into what the surface heat temperature distribution would be, but also help normalize the data even more. The pixels that have high magnitudes in all images will stand out in the output “mean pixel” image. This analysis is key because as was concluded in this analysis different moisture levels can affect surface temperature greatly. Another project that would relate to this one would be to take these methods of analysis and track surface temperature and air temperature. From this analysis there are many conclusions that could be made:

• What is the signature of the Urban Heat Island and how has it changed?• How does development affect the Urban Heat Island?• What are the temperature variances seasonally for the Urban Heat Island?

Tracking the heat island seasonally for a span of at least five years would be a large enough sample size to get a real understanding of the Urban heat Island and how we affect it, or how it affects us.

References:

Hashem Akbari. “Heat Island Group.” Homepage. last modified by Sheng-chieh Chang on August 30, 2000. 2 Nov. 2007<http://eetd.lbl.gov/HeatIsland/>.

“San Antonio, Texas,” Homepage, 2003-2007 Advameg, Inc. 25 Oct. 2007 <http://www.city-data.com/us-cities/The-South/San-Antonio>.

“San Antonio, Texas,” Homepage. 25 Oct. 2007<http://en.wikipedia.org/wiki/San_ Antonio,_Texas>.

“VERY HOT EARLY SEPTEMBER 2000 WEATHER.” National Weather Service. Retrieved on 2007-03-19.

“LandSat 7 ETM+,” http://www.city-data.com/us-cities/The-South/San-Antonio. 10 November 2007. <http://www.texasview.org/>.

“Chap.11,” Landsat 7 Science User Data Handbook. 2002