Image Processing Reference
Look at Fig.
17.1 or find a similar image on the web (http://dmsp.ngdc.noaa.gov/
html/night_light_posters.html, or search the internet for DMSP, Nighttime lights
of the world or something like that). Study the image and answer the following
How are city lights, fires, lightning, lantern fishing, and gas flares separated
from one another systematically?
What percentage of the earth's land surface appears to have city light emanat-
ing from it?
Do you think your 'eyeball' guess would correspond reasonably with an
analytic inquiry using a GIS or remote sensing package?
Does the map of city lights correspond directly with population density?
How do varying levels of economic development around the world influence
the amount of light seen in the image (compare U.S. to India, check out North
Korea and Afghanistan)?
Consider the concept of 'ambient' population density which the LandScan data-
set attempts to represent. Ambient population density is a temporally averaged
conception in which the population density of any particular area is a function
of the mobility of the human population. Census data records population density
on the basis of where people live. Typically census data records low population
density for places like airports where many people work and travel through on a
daily basis. Does the nighttime imagery provide a way for measuring ambient as
opposed to residential population density? How? Why?
In case study #1 the DMSP OLS imagery was used to map 'exurbia'. Surprisingly
the coarse resolution of the DMSP OLS imagery (1 km) could 'see' human
development that finer resolution Landsat imagery (30 m) could not.
How is the nighttime imagery able to 'leapfrog' this 'disadvantage' in spatial
scale of measurement to capture human development and activity?
Do you think the nighttime imagery used in this way truly measures the areal
extent of 'exurbia' (Explain Why or Why not)?
Do you think developing countries would have a similar phenomena of 'exur-
bia' (Explain Why or Why not)?
In case study #2 the DMSP OLS imagery was used to explore various aspects of
population and income distribution in Guatemala. Guatemala is one of the least
developed countries in the western hemisphere.