Image Processing Reference
In-Depth Information
How Will the Variables Be Measured?
Composition refers to the proportional abundance in a region of particular land cover
classes that are of interest to the researcher. We employed Ridd's ( 1995 ) V-I-S
(vegetation, impervious surface, soil) model to guide the
spectral mixture analysis (SMA) of medium-resolution
multi-spectral images for Cairo for 1986 and 1996, in a
manner similar to methods used by Phinn and his col-
leagues for Brisbane, Australia (Phinn et al. 2002 ), and by
Wu and Murray ( 2003 ) for Columbus, Ohio. The classifi-
cation methods are described elsewhere (Rashed and
Weeks 2003 ; Rashed et al. 2001 , 2003, 2005 ; Roberts et al.
1998 ) and so will not be discussed here in any detail. The
V-I-S model (see Chapter 6) views the urban scene as being composed of combina-
tions of three distinct land cover classes. An area that is composed entirely of bare
soil would be characteristic of desert wilderness, whereas an area composed entirely
of vegetation would be dense forest, lawn, or intensive fields of crops. At the top
of the pyramid is impervious surface, an abundance of which is characteristic
of central business districts, which are conceptualized as the most urban of the
built environments.
We added another component to Ridd's physical model - shade/water - following
the work of Ward et al. ( 2000 ) suggesting that the fourth physical component
improves the model in settings outside of the United States. When combined with
impervious surfaces in urban areas it becomes a measure of the height of buildings
(based on the shadows cast by buildings). When combined with vegetation it pro-
vides a measure of the amount of water in the soil and the shade cast by tall vegeta-
tion (largely trees that may serve as windbreaks in agricultural areas). In combination
with bare soil it is largely a measure of any shadows cast by trees, although there
could be some component of shade from large buildings in heavy industrial areas.
Spectral mixture analysis permits a “soft” classification of a pixel into the likely
fraction of the pixel that is composed of each of the four physical elements of
vegetation, impervious surface, soil, and shade. By summing up these fractions over
all pixels contained within each area of interest, we have a composite measure of
the fraction (the “proportional abundance”) of the area that is covered by each of the
four land cover types.
These compositional metrics build on the qualitative sense that each of us has
about what an urban place “looks like.” Even today in highly urbanized countries
in Europe and North America it is visually very evident when you move from a
largely rural to a predominantly urban place and, of course, the change in the built
environment is the principal index of that. Even within non-urban areas it is usually
quite evident when you have passed from a wilderness area into a largely agricul-
ture area. Once again, it is the configuration of the environment that provides the
clue. Figure 3.1 shows this in a schematic way. Wilderness areas can, at the
extreme, be expected to be composed especially of bare soil, since deserts tend to
the built environ-
ment is quantified
by measures of
composition and
configuration of
land cover within
an area
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