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Environment Reporting
to the communities along their
paths.
Visible radiation is often ideal
for observing hurricanes, clouds
or, under clear conditions,
many phenomena at the earth’s
surface. Satellite instruments
measuring only visible radiation,
however, have the same
limitations that our eyes have.
They are unable to measure phenomena
that our eyes can’t see,
and any data they collect on
surface phenomena are obscured
if there’s a substantial
cloud cover intervening between
the surface and the instrument.
Fortunately, many phenomena
that cannot readily be measured
with visible radiation can
be determined through the use
of other types of radiation. Temperatures,
whether atmospheric,
land or oceanic, can be
calculated (at least approximately)
from infrared data. The
ozone amounts in the atmosphere
can be calculated (again
at least approximately) from ultraviolet
data. Many surface variables,
including snow cover, sea
ice cover, and vegetation cover,
can be monitored even in the
presence of a substantial cloud
cover through the use of microwave
data. The list goes on and
on, with many dozens of variables
being able to be calculated
through satellite observations,
using one type of
radiation or another.
Using Information
Collected By Satellites
Of course, no matter how good
they are, for the satellite data to
be useful the user must be able
to understand them. For much of the
data, scientists and computers greatly
aid this process by converting the
streams of numerical information into
maps, often color-coded, of the relevant
geophysical quantities. The color
codes used by scientists in research
publications are often intimidating to
Shrinking of the Aral Sea, in west-central Asia, as viewed in
three Landsat images, from May 29, 1973, August 19, 1987,
and July 29, 2000, respectively. The increased land area
(gray) and reduced sea area (black) is clear as one progresses
from the 1973 to 1987 to 2000 image. The shrinkage of the
sea, caused by humans siphoning off millions of gallons of
water from the inflowing rivers, has destroyed the sea’s fish
population, eliminated the former profitable commercial
fishing industry, and caused thousands of tons of salty soil
from the former lake bed to be blown across the region,
damaging crop yields and air quality. Original images in
color, courtesy of the United States Geological Survey EROS
Data Center, based on data provided by the Landsat Science
Team.
the general public because of having
too many colors, sometimes seemingly
randomly selected. This is not a difficulty
for people used to color-coded
images, and it often increases the
amount of information relayed. However,
if the same color scale is used to
present the image to readers or viewers
who are intimidated by the
scale, the amount of information
relayed could sink to zero.
Therefore, it is often appropriate
to simplify the color scale,
sometimes to the point of doing
away with the numerical
color bar altogether and instead
presenting just the image and a
statement in the caption along
the lines of: Warm temperatures
(or high ozone amounts,
etc.) are indicated in shades of
red and cold temperatures in
shades of blue.
Fortunately, by now computers
are so advanced that, in
general, changing color scales
is relatively minor. Journalists
who want to use a satellite image
to illustrate a story but find
the available image too complicated
should not hesitate to ask
the image provider to adjust
the color scale (even to a gray
scale for a black-and-white publication).
Also, since most scientists
are not astute regarding
color choices, it would not be
out of line for a journalist to
make suggestions.
As with all sources of information,
satellite data can be
misused. A very important aspect
of environmental issues
concerns changes over time,
such as increasing atmospheric
pollution, decreasing water
quality, global warming, or sea
level rise. Satellite data can very
effectively monitor and help to
illustrate environmental
changes in many variables.
However, like statistics, they can
be misused to give a distorted
picture of the change that has
occurred.
Consider, for instance, a satellite
record that reveals a particular
variable increasing from 1980
to 1985, then decreasing back to the
1980 level by 1990, increasing back to
the 1985 level by 1995, and decreasing
back to the 1980 level by 2000. In this
case, the full record clearly shows a
systematic 10-year cycle; but if someone
presents only the 1985 and 2000
88 Nieman Reports / Winter 2002