text: Water, which has a very high heat capacity due to its intermolecular hydrogen bonds, converts electromagnetic energy into thermal energy, often evaporating in the process. Mammals take advantage of this chemistry in using sweat to thermoregulate. Although urban materials like asphalt and steel also have high heat capacities, they are unable to evaporate. As a result, they retain heat for long stretches of time without any direct process to transport heat away.
  Urban geometry also increases temperatures. The canyon-like architecture of many downtown areas cause greater scattering and reduce the likelihood that radiation will reflect back into space. This lowers the effective albedo of cities, increasing absorbance and therefore temperatures. Furthermore, these urban canyons make it harder for heat to effectively radiate out at night, reducing the amount of cooling that can occur between sunset and sunrise.
  The net effect of these three factors are city temperatures generally 1-5°C warmer than rural temperatures. This additional warmth incurs a number of side effects. During heat waves, the lack of relief from scorching temperatures causes hundreds of deaths every year in the United States, “more than hurricanes, lightning, tornadoes, floods, and earthquakes combined.” Energy usage also increases with temperature. For every 0.55°C (1.0°F) of additional summertime heating degrees, electricity usage increases 1.5 to 2.0 percent. Additional electricity generation from fossil fuel sources worsens air quality and emits more greenhouse gases. The takeaway from these side effects is that mitigating the urban heat island is a priority for public health officials, urban planners and policymakers.
  The best proven strategy for mitigating the urban heat island is increasing the coverage of vegetation. A greater number of trees lining streets, more parks, thicker forests and an overall larger coverage of well-watered vegetation diminish the warming potential. However, such planting strategies take time and considerable funding to implement. The Forestry Division in Providence’s Department of Parks has made an effort recently to plant or replant street trees, partially for this reason.
  Remote sensing is useful in analyzing the urban heat island. The coverage of vegetation can be inferred from visible and near-infrared reflectance, while temperatures can be inferred from thermal infrared radiation. These data help us answer critical questions:
  Three main data sources are needed for this project: vegetation, surface temperature, and air temperature. In brief, Landsat 5 TM data were used for vegetation, ASTER data were used for temperature, and NCDC data were used for air temperature.
  Vegetation cover and intensity for Providence can be determined using the Normalized Difference Vegetation Index (NDVI). The chlorophyll in green photosynthetic plants preferentially reflects electromagnetic radiation at near-infrared wavelengths—an effect known as the red edge. Knowing this, one would assume the only bands needed to map chlorophyll concentrations are in the near-infrared. However, this does not take into account shadows, terrain features, or linear perspective. These factors incur artifacts in the measurement of even a homogenously vegetated area. The NDVI normalizes these values with reflectance from a separate band to correct for these anomalies. The end result is a proven description of vegetative intensity.
  Data from Landsat 5’s Thematic Mapper (TM) sensor were used to calculate NDVI. Seven cloud-free scenes from 1995 to 2009 were used. After atmospheric calibration and dark subtraction, the NDVI was calculated by Wumeng.
  Thermal data was acquired from Landsat 5’s Thematic Mapper (TM) and Terra’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). These two sensors have strengths and weaknesses (see right). While Landsat has larger swaths, more complete coverage, and is available from the easier-to-use GLOVIS repository, ASTER has more thermal bands, better spatial resolution, higher-bit quantization, and collects more imagery at night. We decided to utilize both data sources. Devon focused on the Landsat thermal imagery for daytime scenes, while I focused on ASTER for nighttime scenes.
  Unlike visible and near infrared bands where sunlight is reflected off of the surface and into the sensor, thermal infrared radiation comes from the surface’s blackbody radiation. Planck’s Law states that the wavelength where a blackbody’s radiance peaks depends precisely on its temperature. Thus, one can work backwards from radiance using an inverted form of Planck’s Law to determine the surface temperature.
  ASTER collects thermal infrared radiation in five discrete bands (see right). Three of these bands are between 8.125 and 9.275 microns, with two more between 10.250 and 11.650 microns. They are split due to an ozone absorption band. The radiance from each of the five thermal bands can be processed in a similar workflow as Landsat to determine temperature. This was done manually using a similar inverse Planck’s Law with different sensor-specific constants. In addition, another product is directly available from the WIST repository. JPL (Jet Propulsion Laboratory) created a lookup table for each ASTER band containing “values that represent observed radiance as a function of brightness temperature” based on the sensor’s specifications. This table was inverted so that temperature is represented as a function of radiance. When temperature needs to be calculated, one simply looks up the radiance value. JPL claims that this method is more precise because the inversion of Planck’s Law “becomes increasingly inaccurate for temperatures farther away from [the sensor’s] standard temperature.” The lookup and processing is done on-demand by LP-DAAC (Land Processes Distribution Active Archive Center, ASTER’s data repository) to output temperatures for each band in product known as AST_04. AST_04 and the manually computed temperature were compared as a quality assurance check.
  Both the manual temperature calculation and AST_04 incorporate a significant assumption: emissivity. The earth’s surface is not a perfect blackbody. Instead, it emits only a portion of the radiation that a blackbody of the same thermodynamic temperature would emit due to the chemical properties of that material. Therefore, the Planck inversions described above used an underestimated value for radiance, thus their temperature estimation is colder than in reality. Fortunately, an advanced ASTER product exists that takes emissivity into account when calculating temperature.