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Remote sensing for resources development and environmental management
Damen, M. C. J.

Figure 2. Continued
classified as lawn areas but were primarily drier
grasses. There was same class confusion between
lawns and vegetation/soil mixes (sparse cover) and
healthy vegetation (long, thick grasses). Lawns were
also often associated with mixed pixels, either with
inert materials in residential areas or with trees
and shrubs in parks, cemeteries, or landscaped areas.
Healthy, frequently watered vegetation was
characterized on the signature plots by a steep rise
between channels 3 and 4 and a substantial drop in
channel 5, as shown in Figure 2(k). Oi the
discriminant function scatter plot (Figure 1) this
category was very high on the "greenness axis."
Class 40 showed the most vigorous vegetative response
of the spectral classes in CLUS67. Most of class 40
involves healthy alfalfa fields in the agricultural
areas, although some healthy lawn areas were combined
into this class. Classes 20 and 21 also represented
alfalfa fields of varying plant densities, while
class 46 usually indicated corn fields. Class 18 was
on the borderline between very thick and healthy
grasses (often found in lawns or golf course roughs)
and the slightly drier crops found in some
agricultural fields.
Trees and shrubs had a very similar spectral
pattern to both lawns and moist vegetation, except
that the response in the near infrared (channel 4)
was usually not as high. Since trees are not as
large as the TM sensor's IFOV (30 meters), there was
usually some mixing between tree canopies and the
understory materials, with both contributing to the
pixel's response. Often, the density of tree cover
was difficult to observe. Aerial photography that is
slightly off nadir will show oblique views of trees,
which are in turn hiding other surface cover
materials, making tree canopies appear as the
predominant land cover. With a completely vertical
view it became apparent that tree cover density in
the urban setting is actually quite a small
percentage, with other cover materials contributing a
major proportion of an individual pixel's response.
For this reason, the land cover in virtually all of
the tree and shrub category was mixed. However,
trees with shrubs or dense weedy materials were major
contributors to the spectral response. Classes 53
and 60 were the most representative signatures in
this category for densely wooded tree cover. Class
19 was a borderline class between trees and other
healthy vegetation. It often represented areas where
grass was showing through the trees, as in city
cemeteries or parks. Class 19 also represented
clumps of shrubby trees and marshy weeds. Classes 26
and 62 were primarily located in residential areas
and most often represented treelined streets or back
yards with large trees. Class 62 contained a
slightly higher proportion of inert material than the
other classes in this category.
In the past, very little use has been made of the
Thematic Mappers thermal band (channel 6) in land
cover analysis due to its coarser resolution (120
meters) and low range of spectral variation. This is
unfortunate, since the two parameters most
responsible for variability of surface temperatures
are surface moistness (moisture availability) and
diurnal heat capacity (Carlson & Boland 1978). These
two factors are highly related to the nature of
surficial materials in the urban setting.
It was observed in this study that many of the land
cover categories that were being confused in
multispectral classification were actually very
different in terms of thermal properties. For
example, coal and asphalt were classified
interchangeably as water, and cropped agricultural
fields were often confused with residential areas or
natural grass. For this reason, TM channel 6 was
used as an ancillary data layer to set thermal