Image Processing Reference
In-Depth Information
Uttar Pradesh showed dramatic decreases. The abundant yellow areas through-
out the image demonstrate the electrification of India during the 1993-2000
period. A cursory examination of the change detection image depicted in
Fig. 17.4 showed many areas in the former Soviet Union with significantly
reduced nocturnal emissions. Most areas of the world showed substantial
increase in areas where new lights were seen. This kind of change detection
was used to identify new exurban areas in the state of Colorado that were sub-
ject to high fire risk (Cova et al. 2004 ). The potential of these time series data
products for mapping, estimating, and measuring changes to land use from
urbanization and perhaps even changing levels of economic development is
just beginning to be exploited. Future nighttime satellite systems such as the
National Polar Orbiting Environmental Satellite (NPOES) will have finer spec-
tral and spatial resolution which will likely make these kinds of studies even
more useful and exciting.
Case Studies
Case Study#1 (Developed Country): Mapping Exurbia
in the Conterminous United States
It has been shown how the DMSP OLS imagery can be used to identify urban areas.
However, there is much more to the low-gain data products than simply identifying
urban areas. In developed countries there are vast expanses of land that emit a low
light signal that would not be classified as urban by finer resolution imagery such
as Landsat (Vogelmann et al. 2001 ) or be classified as urban by the standards of
most census bureaus. In many cases these low-light areas could be called Exurban
or areas beyond the suburban fringe of the urban areas that have lower population
densities. This case study is an exploration of the exurban areas of the conterminous
United States.
As an example inspect the southwest corner of the Denver, Colorado metropolitan
area (Fig. 17.5 ). The areas of low-light in the DMSP OLS image that show up as
vegetation in the 30 m resolution classification of Landsat imagery have significant
populations and include three high schools and the second busiest 'Safeway' super-
market in the state of Colorado. These low light areas capture significant popula-
tions not identified as urban by the census bureau and identified as vegetation by
30 m resolution Landsat imagery. This area outside of Denver is populated by people
who commute longer distances to Denver, tend to have private water and sewage
systems and do not have or want street lighting. The inset of the 1 m resolution
Ikonos imagery shows that these 'vegetated' areas according to the NLCD classifi-
cation of Landsat imagery are in fact significantly impacted by human develop-
ment. It is of interest to map these exurban areas because they often represent the
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