Dale A. Quattrochi (firstname.lastname@example.org), NASA, Global Hydrology and Climate Center, Huntsville, AL
Jeffrey C. Luvall
, NASA, Global Hydrology
and Climate Center, Huntsville, AL
Project ATLANTA (ATlanta Land-use ANalysis: Temperature and Air-quality)
as a newly-funded NASA EOS Interdisciplinary Science (IDS) investigation
in 1996, seeks to observe, measure, model, and analyze how the
rapid growth of the Atlanta, Georgia metropolitan area since the
early 1970's has impacted the region's climate and air quality.
The primary objectives for this research effort are: 1) To investigate
and model the relationship between Atlanta urban growth, land
cover change, and the development of the urban heat island phenomenon
through time at nested spatial scales from local to regional;
2) To investigate and model the relationship between Atlanta urban
growth and land cover change on air quality through time at nested
spatial scales from local to regional; and 3) To model the overall
effects of urban development on surface energy budget characteristics
across the Atlanta urban landscape through time at nested spatial
scales from local to regional. Our key goal is to derive a better
scientific understanding of how land cover changes associated
with urbanization in the Atlanta area, principally in transforming
forest lands to urban land covers through time, has, and will,
effect local and regional climate, surface energy flux, and air
quality characteristics. Allied with this goal is the prospect
that the results from this research can be applied by urban planners,
environmental managers and other decision-makers, for determining
how urbanization has impacted the climate and overall environment
of the Atlanta area. It is our intent to make the results available
from this investigation to help facilitate measures that can be
applied to mitigate climatological or air quality degradation,
or to design alternate measures to sustain or improve the overall
urban environment in the future. Project ATLANTA is a multidisciplinary
research endeavor and enlists the expertise of 8 investigators:
Dale Quattrochi (PI) (NASA/Global Hydrology Center); Jeffrey
Luvall (NASA/Global Hydrology and Climate Center); C.P. Lo (University
of Georgia); Stanley Kidder (Colorado State University); Haider
Taha (Lawrence Berkeley National Laboratory); Robert Bornstein (San Jose State
University); Kevin Gallo (NOAA/NESDIS); and Robert Gillies (Utah State University).
Atlanta Urban Growth and Effects on Climate and Air Quality
In the last half of the 20th century, Atlanta, Georgia has risen
as the premier commercial, industrial, and transportation urban
area of the southeastern United States. The rapid growth of the
Atlanta area, particularly within the last 25 years, has made
Atlanta one of the fastest growing metropolitan areas in the United
States. The population of the Atlanta metropolitan area increased
27% between 1970 and 1980, and 33% between 1980-1990 (Research
Atlanta, Inc., 1993). Concomitant with this high rate of population
growth, has been an explosive growth in retail, industrial, commercial,
and transportation services within the Atlanta region. This has
resulted in tremendous land cover change dynamics within the metropolitan
region, wherein urbanization has consumed vast acreages of land
adjacent to the city proper and has pushed the rural/urban fringe
farther and farther away from the original Atlanta urban core.
An enormous transition of land from forest and agriculture to
urban land uses has occurred in the Atlanta area in the last 25
years, along with subsequent changes in the land-atmosphere energy
Air quality has degenerated over the Atlanta area, particularly
in regard to elevations in ozone and emissions of volatile organic
compounds (VOCs), as indicated by results from the Southern Oxidants
Study (SOS) which has focused a major effort on measuring and
quantifying the air quality over the Atlanta metropolitan region.
SOS modeling simulations for Atlanta using U.S. Environmental
Protection Agency (EPA) State Implementation Plan guidelines suggest
that a 90% decrease in nitrogen oxide emissions, one of the key
elements in ozone production, will be required to bring Atlanta
into attainment with the present ozone standard (SOS, 1995).
Project ATLANTA Science Approach
The scientific approach we are using in relating land cover changes
with modifications in the local and regional climate and in air
quality, is predicated on the analysis of remote sensing data
in conjunction with in situ data (e.g., meteorological
measurements) that are employed to initialize local and regional-level
numerical models of land-atmosphere interactions. Remote sensing
data form the basis for quantifying how land covers have changed
within the Atlanta metropolitan area through time from the mid-1970's,
when Atlanta's dramatic growth began in earnest, to the present.
These remotely sensed data will be used to provide input to numerical
models that relate land cover change through time with surface
energy flux and meteorological parameters to derive temporal models
of how land cover changes have impacted both the climatology and
the air quality over the Atlanta region. Current remote sensing
data (i.e., data obtained during 1997) will be used to calibrate
the models and as baseline data for extending the models to predict
how prospective future land cover changes will effect the local
and regional climate and air quality over the Atlanta-north Georgia
region. Additionally, remote sensing data will be used as an
indirect modeling method to describe urbanization and deforestation
parameters that can be used to assess, as well as predict, the
effects of land use changes on the local microclimate.
In concert with the remote sensing-based analysis and modeling
of land cover changes is an extensive numerically-based modeling
effort to better understand the cause-and-effect relationships
between urbanization and trends in climatology and air quality.
Sophisticated numerical meteorological models can complement
extensive field monitoring projects and help improve our understanding
of these relationships and the evolution of the urban climate
on a location-specific basis. Measured data alone cannot resolve
the relationships between the many causes of urban heat islands/urban
climates and observations. For example, measured data cannot directly
attribute a certain fraction of temperature rise to a certain
modification in land use patterns, change in energy consumption,
or release of anthropogenic heat into the atmosphere. These are
aspects that numerical modeling can help resolve. Similarly,
monitored air quality data cannot be used to establish a direct
cause-and-effect relationship between emission sources, activities,
or urbanization and observed air quality (e.g., smog). In this
sense, photochemical models can be used in testing the sensitivity
of ozone concentrations to changes in various land-use components,
emission modifications and control, or other strategies. Thus,
we are incorporating an assessment of land cover/land use change
as measured from remote sensing data, with temporal numerical
modeling simulations to better understand the effects that the
growth of Atlanta has had on local and regional climate characteristics
and air quality.
ATLAS Data: Role and Characteristics
To augment the quantitative measurements of land cover change
and land surface thermal characteristics derived from satellite
data (i.e, Landsat MSS and TM data for assessment of land cover
change; Landsat TM thermal, and AVHRR and GOES data for land surface
thermal characteristics), we are employing high spatial resolution
airborne multispectral thermal data to provide detailed measurements
of thermal energy fluxes that occur for specific surfaces (e.g.,
pavements, buildings) across the Atlanta urban landscape, and
the changes in thermal energy response for these surfaces between
day and night. This information is critical to resolving the
underlying surface responses that lead to development of local
and regional-scale urban climate processes, such as the urban
heat island phenomenon and related characteristics. (Quattrochi
and Ridd, 1994, 1997). These aircraft data will also be used
to develop a functional classification of the thermal attributes
of the Atlanta metropolitan area to better understand the energy
budget linkages between the urban surface and the boundary layer
atmosphere. This will be performed using the Thermal Response
Number (TRN) (Luvall and Holbo, 1989; Luvall, 1997) which is expressed
Where Rn is total net radiation and T
change in surface temperature for time period t1
Because urban landscapes are very complex in composition, the
partitioning of energy budget terms depends on surface type. In
natural landscapes, the partitioning is dependent on canopy biomass,
leaf area index, aerodynamic roughness, and moisture status, all
of which are influenced by the development stage of the ecosystem.
In urban landscapes, however, the distribution of artificial or
altered surfaces substantially modifies the surface energy budget.
Thus, one key component of Project ATLANTA is to measure and
model surface energy responses in both space and time, to better
understand the processes-responses of urban climate and air quality
interactions across the Atlanta metropolitan area.
The airborne sensor used to acquire high spatial resolution multispectral
thermal infrared data over Atlanta is the Advanced Thermal and
Land Applications Sensor (ATLAS), which is flown onboard a Lear
23 jet aircraft operated by the NASA Stennis Space Center. The
ATLAS is a 15-channel multispectral scanner that basically incorporates
the bandwidths of the Landsat TM (along with several additional
channels) and 6 thermal IR channels similar to that available
on the airborne Thermal Infrared Multispectral Scanner (TIMS)
sensor (Table 1). Of particular importance to the Atlanta study
is the multispectral thermal IR capability of the ATLAS instrument.
ATLAS thermal IR data, collected at a very high spatial resolution,
have been used to study urban surface energy responses in a previous
study over the Huntsville, Alabama metropolitan area with excellent
results (Lo et al., 1997).
ATLAS Data Collection
ATLAS data were collected over a 48 x 48 km2 area,
centered on the Atlanta Central Business District (CBD) on May
11 and 12, 1997. An early May data acquisition window was selected
to facilitate the collection of ATLAS data during the spring when
vegetation canopy was filled out, surface temperatures were high
enough to permit substantial heating of the urban landscape, and
there was a high probability that cool fronts would still be moving
through the Atlanta area to permit clear skies, as opposed to
later in the spring or summer when increased cloud cover or convective
storms become limiting factors in obtaining aircraft data. ATLAS
data were collected at a 10m pixel spatial resolution during the
daytime, between approximately 11:00 a.m. and 3:00 p.m. local
time (Eastern Daylight Time) to capture the highest incidence
of solar radiation across the city landscape around solar noon.
ATLAS 10m data were also obtained the following morning (May
12) between 2:00-4:00 a.m. local time (Eastern Daylight Time)
to measure the Atlanta urban surface during the coolest time of
the diurnal energy cycle. Eleven flight lines were required to
cover the 48 x 48 km2 area at a 10m spatial resolution.
To permit the derivation of TRN values, all 11 daytime flight
lines were flown and then repeated later at about a 2 hour interval.
Nighttime overflights were not repeated because of the relative
invariance in thermal energy fluxes at night which obviated the
need to calculate TRNs.
Sky conditions at the time of the daytime overflights were mostly
clear with some cirrus clouds present. The Lear jet aircraft flew
at an altitude of 5,063m above mean terrain to achieve a 10m pixel
resolution which was well below the cirrus clouds. Cirrus clouds
covered the entire Atlanta metropolitan area during the night
flights. The presence of cirrus cloud cover at night did, to some
extent, dampen the cooling effect of thermal energy release to
a clear sky, but air temperatures were still sufficiently cool
to provide ample difference with daytime heating. Maximum air
temperatures during the daytime overflights were approximately
25oC, while air temperature during the nighttime flights
was around 10oC. Sample surface temperatures for tree-shaded
grass, tree canopy, and asphalt in full sunlight recorded with
a hand-held infrared thermometer (8-14 lm)
during the afternoon were 28oC, 21oC, and
50oC, respectively. Daytime temperatures for a commercial
building roof comprised of rock/membrane coating ranged from 49oC
to 52oC. This illustrates that although air temperatures
were cooler than optimal for development of the urban heat island
effect, there was still significant heating by artificial urban
surfaces to permit good contrast with nighttime cooling.
Atmospheric radiance must be accounted for in order to obtain
calibrated surface temperatures. Although the ATLAS thermal channels
fall within the atmospheric window for atmospheric longwave transmittance
(8.0-13.0 m), the maximum transmittance is only about 80%. The
amount of atmospheric radiance in the atmospheric window is mostly
dependent on the atmospheric water vapor content, although there
is an ozone absorption band around 9.5 m. To assist in obtaining
accurate thermal surface energy response measurements from the
ATLAS data, radiosonde launches were made concurrently with both
the daytime and nighttime overflights. The atmospheric profiles
obtained from these radiosonde data are then incorporated into
the MODTRAN3 model for calculation of atmospheric radiance (Berk
et al., 1989). The output from MODTRAN3 is combined with calibrated
ATLAS spectral response curves and blackbody information recorded
during the flight, using the Earth Resources Laboratory Applications
Software (ELAS) module TRADE (Thermal Radiant Temperature) (Graham
et al., 1986), to produce a look-up table for pixel temperatures
as a function of ATLAS values (Anderson, 1992).
One pyranometer and one pyrgeometer were also stationed on a rooftop
within one of aircraft flight lines for use in measuring incoming
shortwave and longwave radiation within the study area. Additionally,
two shadowband radiometers were placed in strategic locations
within the flight path for use in measuring shortwave visible
radiation for determining visibility parameters for input into
MODTRAN3. The output from MODTRAN3 is combined with calibrated
ATLAS spectral response curves and onboard calibration lamp information
recorded during the flight in TRADE to produce calibrated at-sensor
radiance for the visible wavelengths.
ATLAS Data: Some Examples
Approximately 5 Gb of raw (unprocessed) ATLAS data were collected
during the May 11-12 aircraft overflights. In addition to the
digital ATLAS data, color infrared aerial photography at 1:32,000
scale was obtained during daytime mission.
daytime thermal (channel 13 - 9.60-10.2 m) ATLAS data collected
over the Atlanta CBD area.
provides an example of ATLAS
data (channel 13) acquired during the night over the Atlanta CBD.
Both images are oriented with north at the top. Excluding the
effects of the highly variable emissivites of urban building materials,
an empirical observation of the images presented in Figures 1
and 2 illustrates the wide range of thermal energy responses present
across the Atlanta city landscape, as well as the detail that
can be discerned from the 10m data. The Georgia Dome, an enclosed
football stadium, appears as the large square-shaped structure
due west of the Atlanta city center. Interstate highways 75/85
which traverse in a north-south direction around the city center,
are seen as a dark "ribbon" on the day data (Figure
1) just to the east of downtown Atlanta. Just south of the city
center, is the junction of Interstate Highways 75/85 and 20.
Shadows from tall buildings located in the Atlanta city center
can also be observed on the daytime data. In Figure 1, the intense
thermal energy responses from buildings, pavements and other surfaces
typical of the urban landscape, as well as the heterogeneous distribution
of these responses, stand in significant contrast to the relative
"flatness" of Atlanta thermal landscape at night (Figure
2). Also, the damping effect that the urban forest has on upwelling
thermal energy responses is evident, particularly in the upper
right side of the daytime image where residential tree canopy
is extensive. In Figure 2, there is still evidence, even in the
very early morning, of elevated thermal energy responses from
buildings and other surfaces in the Atlanta CBD and from streets
and highways. It appears that thermal energy responses for vegetation
across the image are relatively uniform at night, regardless of
vegetative type (e.g., grass, trees).
ATLAS Data Analysis: The Next Steps
From the images in Figures 1 and 2, it is apparent that high resolution
ATLAS data offer a unique opportunity to measure, analyze and
model the state and dynamics of thermal energy responses across
the Atlanta metropolitan landscape. In addition to deriving energy
balance measurements for day and night, and TRN values for specific
urban surfaces to better understand the thermal characteristics
that drive the development of the urban heat island phenomena
and the overall Atlanta urban climate, these multispectral ATLAS
data also exist as database record of current land cover/land
use conditions for the Atlanta metropolitan area. Along with
the extensive meteorological data available via a network of mesonet
stations that are currently operating across the Atlanta area,
the ATLAS data will be used to initialize and calibrate the meteorological
and air quality models that will be run for the time period when
the airborne data were collected. Moreover, one of the key facets
from Project ATLANTA is to work with local planning agencies,
such as the Atlanta Regional Commission (ARC), to model how the
continued growth of Atlanta will impact the climate and air quality
of the north Georgia region. The ARC is currently developing
a 20-year growth plan for a 10 county area around Atlanta. Using
the ATLAS data obtained in May, 1997 as a baseline for land cover/land
use, our objective is to perform some "prospective"
modeling on how meteorological conditions and air quality will
change, predicated on the ARC's 20-year plan. By doing so, we
hope to provide the ARC and other planning or decision-making
bodies, with model output that can be used to modify or revise
growth plans for the Atlanta metropolitan area, and to help mitigate
or ameliorate the expansion of the urban heat island effect or
the further deterioration in air quality.
Anderson, J. E., 1992. Determination of water surface temperature
based on the use of thermal infrared multispectral scanner data.
Geocarto International 3:3-8.
Berk, A., L. S. Bernstein, and D. C. Robertson., 1989: Modtran:
A Moderate Resolution Model for Lowtran 7. U.S. Air Force
Geophysics Laboratory, Environmental Research Papers GL-TR-89-0122,
Hanscom Air Force Base, MA, 37 pp.
Graham, M.H., B.G. Junkin, M.T. Kalcic, R.W. Pearson and B.R.
Seyfarth, 1986. ELAS - Earth resources laboratory applications
software. Revised Jan.1986. NASA/NSTL/ERL Report No. 183.
Lo, C.P., D.A. Quattrochi, and J.C. Luvall, 1997. Application
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Figure 1. ATLAS daytime thermal image (channel 13 -- 9.60-10.2
m) of the Atlanta central business district area. These data
have not been geometrically or atmospherically corrected.
Figure 2. ATLAS nighttime thermal image (channel 13 -- 9.60-10.2
m) of the Atlanta central business district area. These data
have not been geometrically or atmospherically corrected.
Global Hydrology and Climate Center
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