Image Processing Reference
In-Depth Information
The vast array of remotely sensed data now available
to researchers allows for the analysis of the physical
environment in ways not always possible through
other methodologies. Remote sensing can serve as a
link between the physical environment and the social
lives of urban residents, and perhaps lead to improved
understanding of the relationship between the built
environment and numerous urban demographic and social processes.
Sociologists in the past seemed to overlook the potential of remote sensing for
use in deriving indicators of social change (Blumberg and Jacobson 1997 ).
However, a number of recent studies described here have integrated remote sensing
data with urban population, quality of life, health, poverty, crime, and other similar
socio-economic data types. These studies do much to promote the utility and inter-
disciplinary attractiveness of remotely sensed data.
Many regions of the world are rapidly becoming more urbanized and the analy-
sis of urban areas along with a greater understanding of quality of life is crucial to
the understanding of what the future may hold for urban populations (Vlahov and
Galea 2003 ). In a study that shows the utility of remote sensing for health and qual-
ity of life related research, Lo ( 1998 ) integrated environmental variables derived
from satellite imagery, such as land use/land cover, NDVI, and surface temperature,
with census data on population density, house value, education, and income. Using
this combined data set he assessed quality of life in Athens-Clarke County, Georgia
and found that remotely sensed data adds value to the study of urban quality of life.
The utility of remote sensing in famine early warning systems has also been
explored (Hutchinson 1998 ). The author showed how famine is a function of many
factors including economics, access to food, and environmental effects. Through
the use of remote sensing, environmental effects are quantifiable and can be input
into models designed to provide early warnings on famine. Although typically
applied to rural areas at the regional or national scale, some work has focused on
famine in urban areas and methods for adopting rural-centric models to urban envi-
ronments (Bonnard 2000 ) in recognition of the growth in urban poverty and under-
nutrition (Haddad et al. 1999 ).
The current pace of urbanization around the world calls for an appreciation of
the demands, quality, and affordability of housing. Another study in the socio-
economic realm sought to show the utility of remote sensing and GIS to identify
potential low-income housing sites (Thomson and Hardin 2000 ). The authors used
TM imagery of Bangkok, Thailand, to identify land cover and land use classes
using a two-stage hybrid classification technique. Through a set of criteria (i.e.,
flood risk, density) input into a GIS, they were able to identify land area most suit-
able for low-income housing. The authors note that planners need to consider more
than just the socio-economic factors of possible housing sites; they must also con-
sider the physical characteristics of the sites.
Walsh and Welsh ( 2003 ) describe a variety of approaches for linking data across
thematic domains in order to create data sets that extend across social, biophysical,
and geographical fields. Although they applied their techniques in a rural setting,
remote sensing can
serve as a link between
the physical
environment and the
social lives of urban
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