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

Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986
Assessment of TM thermal infrared band contribution in land
cover/land use multispectral classification
José A.Valdes Altamira
ICI, Mexico
Marion F. Baumgardner
Purdue University, Laboratory for Applications of Remote Sensing, West Lafayette, Ind, USA
Carlos R.Valenzuela
ITC, Enschede, Netherlands
ABSTRACT :. Thermal data from Landsat 4 TM were used in conjunction with the six reflective TM bands to assess
the contribution of the thermal band in eight multispectral classifications using four different data sets.
Despite its coarse resolution and differences in radiometric measurements, the thermal data provided an
additional informational plane in the generation of Principal Components. This informational plane did not
appear when the thermal band was excluded from the linear transformation. The use of all seven TM bands for
cluster statistics generation provided greater statistically separability between pairs of spectral classes
than when only reflective bands were used. Classification with subsets of selected bands gave better results
than classification performed without the use of the thermal band for statistics generation. Classifications
with Principal Components reduced the number of spectrally separable classes, but with a significant reduction
in computer time.
The present paper is an abbreviated version of a Master of Science thesis (Valdés, 1984) , as part of the LIQDA
NASA contract NAS5-26859 conducted by LARS/Purdue University.
Thematic Mapper sensor era started with the launch of
Landsat 4, the first of the second generation of Land)
sat satellites. This sensor has better spatial reso-~
lution than the earlier Multispectral Scanner onboard
Landsats 1,2 £ 3 (30 m -vs- 80m), seven spectral
bands instead of four, and four the number of Quanti
zation levels (256 -vs- 64).
The T.M. also has a band in the thermal infrared re
gion of the spectrum, this band differs from the re
flective bands in its spatial resolution (120 m) and
the type of electromagnetic measurements. This band
has not been used often by the scientific community
either in the experiments with T.M. simulators or in
the first analysis conducted by NASA on the Landsat
Image Data Quality Analysis.
The hypothesis of this study is that the use of the
T.M. thermal infreres band in conjunction with the
six reflective bands will provide better discrimina
tion of agricultural and urban features than does
classifications with the six reflective bands only.
The hypothesis can be expressed as:
Ho = P(7 TM bands) ^P(6 TM reflective bands)
HI = P(7 TM bands) ^rP(6 TM reflective bands)
Where P = goodness of classification.
Principal Components analysis (data compression tech
nique) was also performed to evaluate the contribu- ~
tion of each band to the informational content of the
T.M. data.
2.1 Agricultural mapping with remote sensing data
The specialized literaure in remote sensing contains
many examples of the detection and quantification of
crops using techniques of digital analysis. Many of
these applications are considered either experimental
systems (Bauer', et al. ,1971 ;Bauer, 1977; Valdes ,1981)
or quasi-operational systems (McDonald and Hall,1978)
The results of some of these experiments show diffe
rent degrees of accuracy in the identification and
quantification of crop resources. However, all these
results demonstrate a great potential for surveying
crops due to the characteristics of.the data obtained
by the Landsat sensors, and the computer processing,
for monitoring the vegetative resources in large geo
.graphic areas.
There is a great amount of documentation available
related to the physiological, physical and spectral
behavior of vegetation. These must be considered in
understanding how solar energy interacts with the ve
getation and in order to interpret data from multi
spectral sensors.
In 1963 , Hoffer and Johannsen working with different
vegetative species (com, soybeans and 3 timber spe
cies) , found that the spectral response of all those
species have the same typical vegetation curve. They
also found significant differences in the response —
at certain wavelengths, mainly in the visible and
near infrared portions of the spectrum.
To discriminate crop species by means of remote sen
sing, several factors related to the cultural practi_
ces for each crop must be considered, such as plant
and row spacing, geometric arrangement of the plants,
fertilization and irrigation practices, and growth
cycles. The differences in reflectance wich allows us
to discriminate between vegetative species, are due
to the characteristics of the leaves and canopies of
different species. All these internal and external
factors influence the optical properties of the leves
and canopies. The spectral patterns sensed by the scan
ners represent the integration of all of them.
2.2 Thermal and environmental effects of incoming
solar energy
In order to interpret remote. sensing data of vegeta
tion, it is important to comprehend the interaction
of the plant with its environment. A plant is exposed
to electromagnetic radiation from its surroundings,
such as soil, rocks, plants, sun, sky, clouds and
atmosphere. All objects above.absolute zero radite
energy by virtue of their tempreature and emittance.
At temperatures normally exhibited by objects at or
near the earths surface, this radiation is almost en
tirely in the infrared wavelength region from 4 urn to
100 urn approximately (Swain and Davis, 1978),
Plants in stress caused by insects, diseases, physio
logical disorders, nutrient deficiency and adverse en
vironmental effects suffer detectable temperature and
or emittance changes (Kumar and Silva, 1973).
Several authors have presented the potential use of
thermal change detection on plants in order to evalua
te stress causal agents. Clum (1926) and Curtis (19357)