related soil moisture stress with temperature differen
ces in cotton and potatoes. Wear (1966) found an in-’ —
crease in temperature in forest trees with roots dama
ged by insects. Myers and Allen (1968) realted soil —
salinity with high cotton leaf temperatures.
The Corn Blight Watch Experiment, demonstrated that
use of infrared remote sensing has possitive effects
in stress levels determinations (MacDonald, et al.,
1972; Kumar and Silva, 1973).
2.3 Airbone multispectral scanning thermography
Myers, et al. (1966) made use of pictorial and thermal
infrared data to determine differences in the tempera
ture of plants as an indicator of the relative subsur
face salinity and moisture conditions affecting crop -
production. They stated that the temperature contrasts
between salt affected and unaffected cotton plants are
likely to be greater than the temperature contrasts
between moisture stressed and unstressed cotton.
Wiegand, et al. (1968), using the UNiversity of Mi
chigan airborne thermal scanner in Texas, studied the
thermal behavior of several variables such as crop
species, plant spacing, tillage, irrigation regime and
special features, such as highways and water reservoirs.
They found that irrigated crops tend to be cooler that
non irrigated at midday conditions, but the opposite
results were obtained at early morning hours. Thermal
differences related to tillage were minimal.
The feasibility of using thermal imagery for land use
land cover studies has been demonstrated. Brown and
Holz (1976) following Anderson^s classification system
(Anderson, et al. ,1976), produced a land use/land cover
map of Oak Creek Lake, West Texas.
2.4 Thermal band of Landsat 3
The Landsat 3 MSS characteristics are in sense the same
as those of the previous Landsats, except that Landsat
3 acquired additional data in the thermal infrared por
tion of the spectrum (10.4 to 12.6 um) with a ground
resolution of 237 m. As a result, a single thermal
band measurements corresponds to an area represented
by nine measurements in each of the four reflective
spectral bands, a 9 to 1 ratio (Price, 1981).
The Landsat 3 thermal band did not function properly
due to several unexpected causes. The problems asso
ciated with the thermal sensing system were reflected
in the quality of the imagery. Both thermal and spa
tial resolution were affected and the thermal imaging
system was eventually turned off in the spring of 1979
(Price,1981; Lougeay,1982).
Despite the problems associated with the thermal band,
some analysis was performed to evaluate the contribu
tion and usefullness of this band. Price (1981), using
Principal Components analysis, assessed the statistical
correlation between the emissive band, and the four
reflective bands. He found that the thermal data ei
ther were not useful or were associated with a physical
parameter that is not directly related to surface type.
He found that thermal data made a limited contribution
to multispectral classifications. He concluded that its
use for classification is subject to ambiguities and
prone to error: "...an indiscrimante use of the thermal
data appears to be undesirable because of many possi
bilities for misinterpretation and the fact that the
thermal ’signature' is not a direct indicator of sur
face type."
Lougeay (1982) compared the Landsat 3 MSS band 5
(0.6 to 0.7 um) and the thermal MSS band 8 (10.4 to
12.6 um). He found the thermal imagery of MSS band 8
to be of limited use by itself due to its coarse spa
tial and thermal resolution. However it did provide
a rendition of gross topographic structure which was
not readily available from the other MSS spectral
bands.
2.5 Classifiaction and data compression techniqes
If the use of all available channels was not possible,
data compression techniqes have been used to represent
the large content of data into fewer components.
Principal Components or Karhunem - Loeve transforma
tion is an orthogonal linear transformation that com
presses multidimensional data into fewer dimensions
without significant loss of information content. This
transformation assigns the random variance or noise to
eigenvectors with lowest variance (Bartolucci, et al.,
1983).
Data compression is one result of the generation of
principal components. It is possible to describe the
relative influence or "pull" of the original ban-s on
each of the new components. This procedure allows us
to evaluate which of the original bands contains most
of the significant variance or information content for
a particular data set (ANuta, et al. , 1984)
3 METHODOLOGY
3.1 Landsat TM characteristics
The TM data utilized to carry out the present project
were gathered by Landsat 4 on 3 September 1982 over
the central Iowa. The NASA scene number is 40049-16264
accesion 182, path 27, row 31. The TM data used was
radiometrically and geometrically corrected, i.e. ,
P-tape or fully processed tape, and consisted of 5,965
scan lines with 6,976 pixels per line. The geomtric
correction of the TM thermal data requires special
consideration, since the spatial resolution of thermal
data is 129 m compared to 30 m for the other TM bands.
One image sample or pixel of raw thermal data repre
sents an area equivalent to 16 area units from any of
the reflective bands. The coarse resolution of the
thermal data is resaimpled to forma a registered grid
of 28.5 m by 28.5 m pixels. Thus all bands of the geo
metrically corrected TM data contain the same number
of pixels per unit area.
3.2 Description of the study area
A study area of 10 by 10 sections (approximatelly
26,000 hectares), was selected as representative of •
a great diversity of land use/land cover features.
This area is located in Polk County which is in south
central Iowa.
The area lies between latitudes 41°37'45" N and
41°46’15" N, and from longitude 93°37’ W to 93°45_' W.
The general topography is nearly level to undulating
with some steep areas along the streams and rivers.
The geology of the area consists mainly of a Wiscon-
sonian glacial till. The entire area is underlain by
a shale bedrock of the Des Moines Group.
The native vegetation of Polk County was praire gra
sses and hardwood forests. The forests grew along the
major streams, particularly along the Des Moines River.
The cover types in this area are water bodies, agri
cultural fields, urban areas (new and old developments)
industrial and commercial parks, and a dense road net
work (from gravel roads to four lane highways).
The Agricultural Stabilization and Conservation Ser
vice (ASCS) of the US Department of Agriculture in
Polk County collected 35 mm color aerial slides for
the entire county in August 1982. Each slide covers
two sections (approximately 520 ha) on the ground.
These slides were used in conjunction with aerial
infrared slides obtained by the Laboratory for Appli
cations of Remote Sensing (LARS) of Purdue University
in May 1983 over selected sites in the county as re
ference data.
The hardware and software used for the present re-
searcg resided at LARS/Purdue U. The software system
for digital analysis of multispectral data is LARSYS
(Phillips, 1973) and LARSYSDV (Mrcoczynski,1980). "
