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Mapping From Space
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Applying Newly Developed Calibrated Radiance DMSP/OLS Data
for Estimation of Population
6. Estimating population density
The coefficient of correlation for pixels with population, between population
density and radiation, was 0.75. Multiple regression models were experimentally
developed to find out if a better model could be attained or not. The result showed that
neither altitude nor slope improves the accuracy in estimating the population.
Figure 3 shows those "radiated" pixels without population around the local
capital of Sapporo city, which deteriorate the correlation between radiance and
population. Such pixels mostly exist in either inland areas around major cities or coastal
areas around major ports.
Figure 3: "Radiated" pixels without population around Sapporo city
The former is mainly due to road lights and facilities along roads (e.g.
shopping malls with parking lots). The latter is due to lights in the port facilities and the
same from fishing boats. Fishing boats for squid-fishing, weighting between 60 and 100
tons, are especially equipped with as many as 50 incandescent lamps with an average
power of 3,500 watts per lamp in order to attract squids in the dark (Croft, 1978). This
way of fishing squids is commonly practiced in the Japan Sea and the coastal zone of
Hokkaido. These findings suggest, as Elvidge et. al. (1997b) implied, that the radiance
data by DMSP/OLS represent power consumption of a given area much well than
population density.
Contrary, some "not radiated" pixels in fact had population. Such pixels are
mostly found in remote areas with relatively low population density. The radiance from
such remote areas is apparently insufficient to be detected by DMSP/OLS when the
sensor is in low gain mode.
Figure 4 shows the probability of a pixel being dark (i.e. no radiation detected)
as a function of population within the same pixel. The “radiation data set” is slightly
more sensitive to the presence of population than the “city light data set”. A pixel with
50 residents has 50% of chance to have a radiation value. Since one pixel has the area of
6 km 2 , a pixel with 50 residents corresponds to population density of 8 people per km 2 .
It was even a surprise for authors that nocturnal light due to human activities could be
detected even in such low population density area.
Figure 4: Probability of a pixel being dark as a function of population
7. Conclusion
The newly elaborated DMSP/OLS "radiance data set" was found a better
indicator of population density in a small region, as compared with previously
developed "city lights data set". It was owing to the broader dynamic range of the
"radiation data set".
The “radiance data set” could detect nocturnal light emitted by human
activities even in low population density area of 8 residents per km 2 . Further study
should be carried out to see if the "radiance data set" could be useful to estimate
population density in the developing world.
Acknowledgment
This research was supported by the Grant-in-Aid for Scientific Research on
Priority Areas from the Japanese Ministry of Education, Science, Sports and Culture No.
08241105.
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