Image Processing Reference
In-Depth Information
of high elevation mountains. Temporally unstable lights are identified as lightning, fires,
or lantern fishing on factors such as where they are (land or sea), how long they last,
and spatial context (with clouds or not). A DMSP OLS mosaic of firs would consist
of lights, on land, that were ephemeral. However, it is important to realize that all the
fires you see in a DMSP OLS fire data product were not occurring at the same time.
The dataset is in this sense more of a 'climatology' of fire.
These earlier datasets are referred to as the 'high-gain' data products. The primary
drawback of these data was the issue of saturated pixels in urban areas. Both high-
gain datasets were virtually binary with mostly black or
dark pixels valued at 0 and brightly lit 'urban' pixels with a
value of 63 (the DMSP sensor has a 6-bit quantization).
These data lent themselves to aggregate estimation of urban
'cluster' populations but were not good at estimating popu-
lation density variation within the urban areas. This draw-
back was identified and resulted in a special request to the
Air Force regarding the use of the DMSP OLS platform.
As mentioned before, the DMSP OLS platform was
designed to observe reflected lunar radiation at night (pri-
marily reflected from clouds). During the days just prior to
and after a new moon there is very little lunar radiation striking the earth.
Consequently, the sensor has its gain set to its maximum possible value. The
NGDC requested that the Air Force turn down the gain on several orbits near the
new moon. This request was honored by the Air Force and resulted in what is now
referred to as the 'low-gain' data product. Turning down the gain produced dra-
matic results with respect to the saturation of the 'urban' pixels. The low-gain data
show dramatic variation of light intensity within the urban areas, and it can be cali-
brated to at-sensor radiances. Hyper-temporal datasets similar to the previous data
were made using the low-gain orbits (Elvidge et al.
1998
). Another problem identi-
fied in the processing of these 'low-gain' data products is the problem of 'over-
glow' associated with snow cover on the land outside urban areas. The snow
reflects light from urban areas and can increase the signal of light in areas outside
urban centers that had snow on the ground during observation. This problem has
been addressed by using observations in warmer months.
Presently global, low-gain DMSP OLS image products
are available for 1992-1993, and 2000. Eventually prod-
ucts will be available for most of the years since 1992. The
DMSP platform will eventually be retired and be replaced
by the National Polar Orbiting Environmental Satellite
(NPOES) which will obtain nighttime imagery at finer
spatial and spectral resolution.
This perhaps overly detailed description of the process-
ing of the DMSP OLS nighttime satellite image data prod-
ucts is provided to give the reader an appreciation of the
challenges, obstacles, and issues associated with 'simply'
preparing remotely sensed imagery for further analysis.
low-Gain DMSP
OLS Data shows
a much greater
diversity of light
intensity and can
be radiance
calibrated to
measure energy
output
virtually all other
remotely sensed
data have
challenges,
obstacles, and
issues pertaining
to acquisition
and processing
that must be
addressed prior
to using the data
