imagery, especially using the traditional linear relationship,
it is convenient to map CO
2
emissions at large scales and at
relatively fine spatial resolution. On the other hand, when
relatively high accuracy is required, more auxiliary
GIS
/re-
mote sensing data (e.g., transportation networks, land-cover
and land-use) are needed to assist stable light image products
to map CO
2
emissions.
Conclusions
The present study explores the overall quantitative correla-
tions between the
DN
values of a stable light annual image
product and the amount of CO
2
emissions that are closer to
exponential curves than linear functions. In regions where
the
DN
values of nighttime light imagery vary from 0 to 50, the
amount of CO
2
emissions increases linearly with the rise in
brightness of nighttime lights. In regions where the
DN
values
of nighttime light imagery are larger than 50, the amount of
CO
2
emissions increases exponentially with the rise in bright-
ness of nighttime lights. Particularly in urban core regions
where the
DN
values of nighttime light imagery are larger than
60, the exponential increase becomes very sharp. The above
correlations are invariant across different geographic scales.
Thus, piecewise functions are more appropriate to describe
the relationships between the
DN
values of stable lights im-
agery and the amount of CO
2
emissions than only linear or
exponential functions and consequently can more accurately
map CO
2
emissions.
However, additional gridded CO
2
emissions datasets are
needed to calculate such piecewise functions so uses of the
piecewise functions are greatly limited in practical applica-
tion. By contrast, although relatively large errors are produced
by the linear functions, the linear functions can be obtained
without any other gridded CO
2
emissions data. Consequently
the approach of linear disaggregation (i.e., total CO
2
emissions
being disaggregated to each pixel in proportion to the
DN
val-
ues of nighttime light imagery) is still valuable in practical ap-
plication, particularly given lack of other relevant geographic
or socioeconomic data.
This study also highlights that over-distributions are pro-
duced in suburban and urban areas, when the linear-disaggre-
gation-approach is used to map CO
2
emissions. In urban core
regions large under-distribution errors exist. CO
2
emissions
strongly correlate to other socioeconomic factors (e.g.,
GDP
and electric power consumption) and brightness of night-
time lights can be used as a proxy for many socioeconomic
factors (Doll
et al.
, 2000; Lo
et al.
, 2002; Zhao
et al.
, 2012).
Consequently, it can be expected that such socioeconomic fac-
tors should also have similar correlations to the
DN
values of
nighttime light imagery as CO
2
emissions. At present it is hard
to obtain other
GDP
and electric power consumption data with
relatively fine spatial resolution as baseline data. Therefore,
this study helps us indirectly understand accuracy and errors
of using nighttime light image data to map or disaggregate the
socioeconomic indicators in the hope of more practical ap-
plication in the future.
Regardless of the uses of linear or piecewise functions,
certain limitations exist that: (a) fail to show CO
2
emissions
in unlit areas, (b) lack variation in regions with the same
DN
values of nighttime light imagery, and (c) give large under-
estimation in urban core areas. It can be expected that small
population exists in some unlighted areas. Population has
small differences in the regions with the same
DN
values of
nighttime light imagery where larger population should usu-
ally contribute the larger amount of CO
2
emissions. In urban
core areas
DN
values of stable light image composites are
incommensurate with the amounts of CO
2
emissions which
leads to under-estimation. Large population in the urban core
regions can decrease the under-estimation derived from the
nighttime light data if population data and stable light im-
age data are jointly used. LandScan population dataset and
Socioeconomic Data and Applications Center’s (
SEDAC
) Global
Rural-Urban Mapping Project, Version One (
GRUMPv1
) data
have the same spatial resolution with nighttime light image
products and also cover the whole world. Therefore, in the
future we will employ the LandScan population or
GRUMPv1
data to assist nighttime light images to ease the above summa-
rized limitations of failure to show CO
2
emissions in unlight-
ed areas, lack of variation in regions with the same
DN
values,
and large under-estimation in urban core areas.
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