The Use of Intensity-Hue-Saturation
Transformation of Landsat-5 Thematic
Mapper Data for Burned Land Mapping
Nikos Koutsias, Michael Karteris, and Emlllo Chuvieco
Abstract
Objectives
Several techniques have been developed to detect and map
Several kinds of image classification techniques have been
burned areas using Landsat Thematic Mapper data, ranging
developed and used to detect and map burned areas, ranging
from simple ones, such as visual interpretation and singlefrom simple ones, such as visual analysis, to more complex,
such as spectral mixture analysis. However, the Intensity-Hue- channel density, to more complex techniques, such as spectral
Saturation transformation, a method mainly used for merging mixture analysis and principal component analysis (Pereira et
al., 1997).Most of these techniques rely on enhancing the specmultiresolution and multispectral data and for contmststretching applications, has never been applied. In this study, tral signal of burned areas. We hypothesized that the IntensityHue-Saturation (IHS) transformation could help that enhancea method is resented bv which transforming the RGB values
ment, because it improves the analysis in the spectral domain
of a three-ch;mnel comiosite to IHS Galues, ;he mapping of
of color composites. Because burned vegetation shows a severe
areas affected by forest fires can be easily achieved. Specifireduction in the spectral contrast from healthy vegetation (Percally, the hue component of two RGB color composites,
respectively,
eira et al., 1997),we assumed that the Hue component should
consisting of TM7-TM4-TM1and TM4-TM7-TMI,
proved to be very useful in mapping of burned areas.
be the most efficient to discriminate scorched areas.
In this study, IHS transformation of different RGB color composites,
consisting of the original spectral channels of LandsatIntroduction
5 Thematic Mapper, has been applied to produce a more interForest fire occurrence, especially in the Mediterranean Basin,
pretable data set for burned area discrimination.
is a major ecological process, which has a profound influence,
positive or negative, on the natural cycle of vegetation succesBasis of IHS Transformation
sion and on the ecosystem's structure and function. Forest fires
Among the existing ways to represent color on electronic disinfluence ecosystem dynamism to a degree, which depends on
model and the Intenplay devices, the Red-Green-Blue [RGB)
the particular characteristics of fire such as intensity, type,
sity-Hue-Saturation (MS) model are widely applied. The RGB
periodicity, etc. However, the high number of forest fires
model is applied for producing three-channel color composoccuring every year, which amounts to thousands hectares of
burned land, constitutes one of the major degradation factors of ites on color monitors or other devices. The MS model defines
the color mathematically, using cylindrical or spherical coorforest ecosystems. Development, on a permanent basis, of
dinates (Carper et al., 1990; Edwards and Davis, 1994).In the
appropriate statistics of fire occurrence and a complete and
RGB
model, the coordinates range between 0 and 1on each axis.
accurate database of burned areas should be initiated and
In the MS the coordinates range for the hue component between
included in a well structured decision-making system regard0 and 360 degrees while, for the intensity and saturation coming the management of forest fires. Moreover, the protection
ponents, between 0 and 1 (Mather, 1987).Graphically,the geoof the areas affected by wildland fires and their restoration to
metrical representation of the RGB color cube and the Msmodel
pre-settlement status requires their accurate location and
are depicted in the Figures l a and lb, respectively (Mather,
mapping.
1987).
Satellite remote sensing data, acquired before and after the
Intensity refers to the total brightness or dullness of a color,
fire, have been used successfully to map burned areas, species
hue
refers
to what is perceived as color or the dominant waveaffected, and severity levels of damage, as well as to monitor the
vegetation regeneration status after the fire (Chuviecoand Con- length of light, and saturation refers to the purity of the color
(Mather, 1987; Carper et al., 1990).In general, the MS transforgalton, 1988; Lopez and Caselles, 1991; Pereira et al., 1997
mation utilizes a three-color composite image from the original
Koutsias and Karteris, 1998).
satellite data in a way that the original spatial information is
The purpose of this paper is to demonstrate an image
separated into the intensity component, while the spectral
enhancement technique for the rapid, easy, and effective mapinformation is separated into the hue and saturation compoping of burned areas, separating the needed spectral informanents (Carper et a]., 1990).
tion into one new component.
-
N. Koutsias and M Karteris are with the Department of Forestry
and Natural Environment, Aristotelian University of Thessaloniki, Box 248, GR-540 06, Thessaloniki, Greece
[[email protected]).
E. Chuvieco is with the Department of Geography, University
of Alcalk, Colegios 2 E-28801 Alcala de Henares, Spain.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Photogrammetric Engineering & Remote Sensing
Vol. 66, No. 7, July 2000, pp. 829-839.
0099-1112/00/6607-829$3.00/0
O 2000 American Society for Photogrammetry
and Remote Sensing
l u l y 2000
829
1
Magenta
/
Red
In Rcd-Gm-Bhumbrcube
-
Black
I b. Hue Sltuntian- Intensityhsxmc
(a)
(b)
Figure 1. Geometrical representation of the RGB color cube
(a) and the IHS model (b) (source: Mather, 1986).
Many different algorithms have been developed and used
to transform the values of RGB color model to the values of the
MS color model (Carper et al., 1990). These algorithms differ in
the way of calculating the intensity component (Carper et al.,
1990;Edwards and Davis, 1994), and in the RGB color used as
reference point for calculating the hue component (Edwards
and Davis, 1994). In this study, the algorithm of RGB-to-MS
transformation, supported in the ERDAS software version 7.5,
has been adopted. The output range of the values of the IHS
model is transformed directly to fit in the available dynamic
range of 8-bit image files (0 to 255).In detail, the algorithm used
consists of the following set of mathematical expressions
developed by Conrac (1980) and described in the ERDAS field
guide (ErdasInc., 1991):
-M-G
M-B
r=-M - R
M + R ' ~ M+G'~=M+B
where r, g, bare each in the range of 0 to 1; Mis the largest value
of R, G, and B; and m is the least value of R, G, and B.
Intensity ( I )
Saturation(S)
ifM=m,S=O
matic data (Thormodsgard and Feuquay, 1987), SPOT Panchromatic and Landsat MSS (Carper eta]., 1990),and Landsat TM
and SPOT Panchromatic (Chavez et al., 1991).These studies are
based on a forwardlinverse IHS transformation, in which the
intensity component is replaced with the higher spatial resolution image before applying the back-transformation to the original space (Chavez et al., 1991). m S has also been used as an
image enhancement technique (Haydn et al., 1982; Green,
1983; Gillespie et al., 1986; Edward and Davis, 1994), especially in the context of geological applications (Drury, 1986;
Mather, 1987). However, little work has been done using this
transformation for pure and applied research in fields like forestry, agriculture, etc.
Techniques for Burned Area Mapping
So far, a diverse set of methods has been developed and applied
to map burned areas using satellite data, especially those
derived from the Landsat Thematic Mapper. Pereira et al.
(1997) grouped these methods into the following general
categories:
Visual analysis
Single channel density slicing
Multitemporal thresholding of vegetation indices
Principal component analysis
Regression modeling
Supervised and unsupervised classification
Spectral mixture analysis
However the Intensity-Hue-Saturation transformation has
never been applied to detect and map burned areas using satellite data.
Pereira et al. (19971,in an extended review paper concerning remote sensing of burned areas, stated that, due to the
diverse and complex patterns of the spatial and temporal variability of the spectral response of burned areas, their detection
and mapping remains somewhat problematic, although a large
number of different classifiers have been developed and used.
These difficulties arise, to a large extent, from the classifiers
used and also from the burn age, as well as from the local ecoclimatic conditions. In the same work also, it is mentioned that
land-coverlland-use categories, which have been reported as
highly confused, are water bodies, urban areas, and shadows.
In this study, using the MS transformation, some of these confusions are eliminated, as for instance the confusion with
cloud shadows.
Material and Methods
Study Area
ifM=m,H=O
ifR=M,H=60(2+ b-g)
The MS transformation has been extensively applied for
merging satellite data acquired from different sources, such as
Landsat MSS with Return Beam Vidicon (RBV) and Heat Capacity Mission data (Haydn et al., 1982),Landsat MSS and SAR
images (Blom and Daily, 1982), SPOT HRV and Landsat TM
(Welch and Ehlers, 1987), SPOT Multispectral and Panchro-
Two large forest fires that occurred in 1992 and 1995 in the prefecture of Attica in central Greece, one of the areas most
affected by forest fires, were the study cases for the development and application of IHS transformation in burned area
mapping. For this purpose, two Landsat-5 Thematic Mapper
images (Figures 2 and 31, taken a few days after both fires, were
acquired and constituted the basic source of information. The
study area experiences a Mediterranean type climate, with
vegetation composed mainly of conifers and shrublands. The
bioclimate of the study area is characterized as semiarid, with
high temperatures and low relative humidity during the fire
season. As a result, the forested land in the study area is composed of conifers (Pinus halepensis, Pinus brutia) and various
Mediterranean shrublands (Maquies)that are well adapted to
such climatic conditions.
Mathods
The detection and mapping of the areas affected by forest fires
is accomplished either by using single post-fire satellite data or
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Figure 2. Spectral channel 4 of the Landsat-5 Thematic Mapper image acquired a few days after the 1992 fire. The black area
in the middle of the image corresponds to the burned area.
1
multitemporal data acquired before and after the fire event,
although the multitemporal approach has been the most frequently used technique. Depending on the kind of available satellite information, a diverse set of methods have traditionally
been applied to successfully detect and map the burned areas.
In general, the spectral resolution of the sensor influences the
types of methods applied more than does the spatial or temporal resolution (Pereira eta]., 1997).
It has been proven that multitemporal satellite data are
more advantageous than single post-fire satellite data, because
the multitemporal data reduce the likelihood of confusion with
permanent land-cover types (Pereira et al., 1997).However,
single post-fire methods are superior to multitemporal methods
because of the cost for the acquisition of the data and the effort
required for the registration and processing of the multitemporal data set. One of the most critical issues in the multitemporal
approach is the radiometric and geometric matching of &e
imaees used. Misreeistration
on both the radiometric and the
"
geometric dimension can produce undesirable and unpredictable errors, which in turn may result to either under-estimation
or over-estimation of the burned areas.
Several methods that utilize both multitemporal or single
post-fire satellite data require a thorough knowledge of the
V
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
spectral properties of the burned areas and other land-cover1
land-use categories presented in the satellite images, as well as
a complete and accurate location of training areas as in the case
of supervised classification. It is obvious that the development
of a simple method that will not require sophisticated and
costly algorithms and, at the same time giving accurate results
is highly desired.
In this study, several RGB composites consisting of the original spectral channels of Landsat-5 Thematic Mapper data
were transformed to the HScolor model. The latter were used
to map the burned areas by applying single-channel thresholding, using the hue component of the MS color model. The
choice of RGB color composites derived from the original spectral channels of Landsat TM data was based on previous work by
Koutsias and Karteris (1998).In that work, it was concluded
that TM4 was the most sensitive in alteration of the spectral
response of the burned pixels followed by TM7 which proved
to be the second best channel. Regarding the visible channels,
although all three presented a similar performance, TM1
proved to be the most valuable among them. Consequently, in
this study, the RGB color composites, which were transformed
to the M S color model, were the TM7-TM4-TM1, TM7-TM4-TM2,
TM7-TM4-TM3, TM7-TM5-TM4, and TM5-TM4-TM3.
/uly 2000
831
Figure 3. Spectral channel 4 of the Landsat-5 Thematic Mapper image acquired a few days after the 1995 fire. The black area
in the middle-right of the image corresponds to the burned area.
of the spectrum ( T M ~of
) the post-fire satellite image. This reduction is due to the destruction of the leaf cell structure within the
Spectral Behavlor of Burned Areas
vegetation, which reflects a large part of the incident solar radiTwo critical aspects associated with the post-fire situations are
ation in this spectral region. In addition, a strong increase in
responsible for the spectral characteristics of burned areas (Rob- reflectance of "burned category pixels" is observed in the midinson, 1991):(1)the deposition of charcoal as the direct result
infrared region of the post-fire satellite image ( T M ~ ) The
.
of the burning and (2) the removal of photosynthetic vegetareplacement of the vegetation layer with charcoal reduces the
tion. In addition to the removal of vegetation which may be
water content, which absorbs the radiation in this spectral
caused also from factors such as cutting, grazing, water stress,
region. As a consequence, burned areas are expected to have
diseases, etc., the deposition of charcoal constitutes a unique
higher reflectances than those of a healthy vegetation (Pereira
consequence of the fire burning (Pereira et al., 1997).
et al., 1997;Koutsias and Karteris, 1998).
However, both the deposition of charcoal and the removal
A comparison of the spectral signatures between the
of photosynthetic vegetation modify greatly the spectral
burned areas and the other land-covertland-use categories was
behavior of the "burned category pixels" compared to the preaccomplished in order to achieve a better understanding of
fire situation. In particular, a strong decrease in reflectance of
their spectral behavior and potential discriminator ability. The
"burned category pixels" is observed in the near-infrared region spectral signatures of the burned areas were compared against
Results and Discussion
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Figure 4. The spectral signatures of the burned areas were compared against four major groups of landcover/landuse categories which have been reported, in literature, to generate spectral confusion within the burned areas. The
superior performance of the infrared channels of the Thematic Mapper over the visible channels to distinguish the
burned areas is clearly observed.
four major groups of land-coverlland-use categories: vegetation, water bodies, barellow vegetated areas, and urban and
cloud shadows. These categories were chosen because they
have been reported to generate spectral confusion with burned
areas (Pereira et al., 1997). The comparisons were accomplished by the graphical evaluation of the spectral signature
plots and by the use of the Jeffries-Matusita separability index
(Swain and Davis, 1978).
The superior performance of the infrared channels of the
Thematic Mapper as compared to the visible channels to distinguish the burned areas is clearly obsemed in the spectral signature diagrams in Figure 4. This finding is also verified by the
Jeffries-Matusitaindex provided in Table 1, where TM4, TM5,
SEPARABILITY
INDEX BETWEEN THE BURNEDAREAS
TABLE1. JEFFRIES-MATUSITA
AND THE OTHER LANDCOVER/LANDUSE
CATEGORIES
PRESENTED
IN THE
STUDYAREAFOR EACHSPECTRAL
CHANNEL.
Burned Areas
TM1
TM2
TM3
TM4
TM5
Clouds
Cloud Shadows
Sea
Forest
Agricultural Crops
L o w Vegetated
Bare L a n d
Urban Areas
Average
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
-
-
TM7
and TM7 have an average value of 1393.5,1209.5,and 1235.4,
respectively. However, with respect to the discrimination
between the burned areas and the forest, channels TM4, TM7,
and TMI are superior because they provide a separability index
of 1407,1245, and 1007, respectively.
IHS Color Model
Initially, three RGB color composites of the 1992 fire were transformed to the IHS color model, consisting of the spectral channels TM4, TM7, and one of the TM1, TM2, and TM3. The spectral
combinations TM4-TM7-TM1, TM4-TM7-TM2, and TM4-TM7-TM3
proved to be the most well performing three color composites
ibr burned area mapping in-this studfarea (Koutsias &d Karteris. 19981. Table 2 summarizes the results achieved from the
appiicatidn of the IHs transformation of the three RGB color
composites.
For all three-color composites, it is evident that the burned
areas are highly differentiated from the other land-coverllanduse categories in the hue component. The burned areas are
depicted with very low values, while the majority of the other
land-coverlland-use categories are depicted with very high values. In the TM741 RGB color composite, this separation between
the burned areas and the other categories is greater than with
the other color composites. The mean value of the burned areas
in the hue component is 19.15 while, for the other categories, it
ranges between 286 and 353. A confusion with the barellow
vegetated category is likely to happen because its mean value in
the hue component is 18.07. However, taking into consideration both the intensity and the saturation component, this confusion can be eliminated because barellow vegetated areas are
TABLE2. AVERAGEVALUES OF THE LANDCOVER/LANDUSECATEGORIES
IN THE INTENSIM,
COMPOSITESUSED
HUE, AND SATURATION
COMPONENTS
OFTHE THREERGB COLOR
Clouds
Cloud Shadows
Sea
Burned Areas
Forest
Agricultural Crops
BareILow
Vegetated
Bare Land
Urban Areas
depicted quite differently from the burned areas. Their mean
values in the intensity and the saturation component are 0.31
and 0.20, respectively, while those of the burned areas are 0.20
and 0.47.
Application of the IHS Color Model
The first RGB color composite that was transformed to the MS
color model consisted of T M ~ - T M ~ - T of
M ~the 1992 fire. The
output range of the values of the msmodel was transformed
directly to fit with the available dynamic range of 8-bit image
files. The three components of the MS transformation are presented in Figure 5, while their histogram data plots are presented in Figure 6. Based on a graphical evaluation of both the
output images and their histogram data plots, it is obvious that
in the hue component there is an evident discrimination
between the burned land and other land-coverlland-use categories. It is very clear that, in the histogram data plot of the hue
component, there are two very distinct groups of pixels; the
group which ranges between 0- and 40 corresponds to burned
category pixels, while the other group ranges between 196 and
255 and corresponds to other land-coverlland-use categories.
The gap, in the histogram data plot, between the burned and
the unburned category pixels of 156 values indicates that the
hue component can be used successfully to detect and map the
burned areas by applying a simple thresholding.
It is quite evident from the visual evaluation of the results,
that the majority of the land-coverlland-use categories presented in the study area are well discriminated from the burned
areas. Confusion with some of the categories still remains a
problem, although it is not so extensive. Among the confusing
categories, we can distinguish some areas dominated by sparse
vegetation of which some were affected by forest fires in the
past, the coastal line, and some segments of the road which
passes through the forested area. However, applying spatial
techniques such as "clumping" and "sieving" can easily eliminate most of these classification errors.
The second and third RGB color composite that was transformed to the Ms color model differs from the first only in the
spectral channel used for the blue color. Thus, replacing TM1
with TMZ and T M correspondingly,
~
two new RGB color composites were transformed to MS, those of TM7-TM4-TM2 and TM7TM4-TM3. Evaluation of these two RGB color composites gave
the chance to propose the most suitable three-channel color
composite to enhance the discrimination between burned land
and other land-cover/land-use categories. The output images,
as well as the histogram data plots of the hue component of
these two RGB color composites, are displayed in the Figures 7a
and 7b. Although the hue component of these composites offers
a high discrimination between the burned and the unburned
category pixels, it is not as evident as in the case with the T M ~ TM4-TM1 composite (Figure 5b). The various land-coverllanduse categories,including the burned areas, occupy almost all
the dynamic range (0to 255) in the histogram data plots of the
hue components, without forming clearly separated groups, as
~ . burned areas occupy the lower
in the case of T M ~ - T M ~ - T MThe
values in the histogram and appear darker than the unburned
areas, which occupy the higher values.
Two more RGB color composites, those of TM7-TM5-TM4 and
TM5-TM4-TM3, were also transformed to the IHS color model.
Evaluation of both composites, based on the graphical evaluation of the output images and the histogram data plots of the
IHS components, showed that both composites are not suitable
to be used for burned area mapping.
To explore more and also to verify the acquired results of
MS transformation using the TM7-TM4-TM1 RGB color composite, another fire that occurred in 1995 was also analyzed. A Landsat-5 Thematic Mapper image (Figure 3) acquired a few days
after the fire was employed in this study. Without applying any
kind of radiometric correction or enhancement, the RGB color
composite consisting of TM7-TM4-TMlwas transformed to the
IHScolor model. As in the previous case, the hue component
differentiated the fire scar from other land-coverlland-use categories (Figure 8). Two very distinct groups of pixels were presented in the hue component. One, which corresponded to the
fire scar, occupied the range between 0 and 67 and appeared
dark on the image. The second group, which corresponded to
other land-coverlland-use categories, occupied the range
between 183 and 255 and appeared white. Again, the 116-value
gap between the burned and the unburned category pixels
demonstrate how well the hue component can be used successfully to detect and map the burned areas by applying a simple
thresholding. Other RGB color composites that were transformed to the MS color model provided results similar to those
analyzed for the 1992 fire.
Based on these results and taking also into consideration
the results produced by the work of Koutsias and Karteris
(1998),it is evident that the spectral information contained in
,
TMIis the most suitable to
the spectral channels T M ~T, M ~and
detect and map the burned areas in these two study cases.
The results acquired from the Ms transformation are influenced first by the specific spectral channels used in the RGB
color model and second by the correspondence of them with
the red, green, and blue color planes. Thus, the results of the
Mstransformation are quite different if the RGB color composite
I TM4-TM7-TM1. For that reason, the
consists of T M ~ - T M ~ - T Mor
MS transformation was applied using six RGB color models,
,
and TMi
which include all possible combinations of T M ~1x17,
displayed in the red, green, and blue color planes.
The output images and the histogram data plots of the hue
component of these six color composites are presented in Figure 9. It is obvious that the MS model is effective only in the
cases of the TM4-TM7-TM1 and TM7-TM4-TMl RGB color models.
Between the two RGB color composites, TM7-TM4-TM1 is preferred because the radiometric values of the fire scar inthe hue
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
0
50
100
150
200
250
Intensity
a. Intensity component
20000
F 17500
= 1 m
E
q 12500
:
;
I
:
U 10000 t
N
7500
0
50
100
150
200
250
Hue
b. Hue component
c. Saturation component
Figure 5. The Intensity, Hue, and Saturation component of
the IHS color model of the TM~-TM~TMI RGB color composite.
In the hue component the burned areas are well discriminated from the majority of the landcover/land-use categories. Confusion with some of the categories still remains as
a problem, although it is not so extended. Among these
categories we can distinguish some areas dominated by
sparse vegetation, the coastal line, and some segments of
the road which passes through the forested area.
Figure 6. Histogram data plot of the IHS transformation of
TM~-TM~TMI RGB color model. In the histogram data plot
of the hue component, there are two very distinctive groups
of pixels: the one occupies the range between 0 and 40
(burnedcategory), while the other group occupies the range
between 196 and 255 (unburned category). The gap
between the radiometric values of these two groups indicates that the hue component can be used successfully
in burned area mapping.
a. Hue component of TM7-TM4-TM2
b. Hue component of TM7-TM4-TM3
Figure 7. lmage and histogram data plot of the hue component of the T M ~ - T M & T M
and
~ ( T~M) ~ - T M & T M(b)
~ RGB color
composite. Although the hue component of these composites offers a high discrimination between the burned and unburned
category pixels, this is not as apparent as it was in the case with the TM7-TM4-TM1composite. In the histogram data plot,
the landcover/land-use categories, including the burned areas, occupy almost all the dynamic range (0to 255) without
forming clearly separated groups.
Figure 8. lmage and histogram data plot of the hue component of the T M ~ TM~-TMI RGB color composite of the 1995 fire. As with the previous case, the
fire scar is well differentiated from the other landcover/land-use categories.
In the histogram data plot, the gap between the two groups, burned-unburned,
strongly indicates that the hue component can be used successfully in burned
area mapping.
836
l u l y 2000
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Hue component of TMl-TM7-'.
0
LI.l.rrnI.mb111
Hue component of TM7-TMQTMI RGB color model
.....--J color model
I l r n t P 9 1 r n
m"I.ICI.rIYrnM
Hue component of TM4-TM7-TMl RGB color model
Figure 9. Image and histogram data plot of the hue component of all possible combination of TM4, T M ~ and
,
TM1 displayed
in the red, green, and blue color plane. It is evident that the IHS model is effective Only in the case of TM4-TM7-TM1 and
T M ~ - T M ~ - T RGB
M ~ color models in which there are two welldiscriminated groups of pixels. Between the two RGB color
composites, TM~-TM~-TMI is preferred because the radiometric values of the fire scar in the hue component correspond
to higher values than other landcover/land-use categories, which results in a discrimination from areas with no data.
component correspond to higher values than do the other landcoverlland-use categories. In addition to TM7-TM4-TM1, the
radiometric values of the fire scar in TM4-TM7-TM1 are depicted
with low values which results in confusion with areas of no
data.
Justification of the Results
In remote sensing applications, especially those dealing with
multidimensional data such as in the case of the Landsat-5
Thematic Mapper, multivariate statistical methods are widely
applied to extract the desired information. However, in most
cases the desired information is distributed as spectral information in all spectral channels. Consequently, these statistical
methods, especially those dealing with the reduction of the
dimensionality such as principal component analysis, vegetation indices, etc., try to separate this spectral information into a
smaller set of new components which are more interpretable.
If the reduction in the dimensionality and the separation of the
information is accomplished successfully, then by applying a
simple thresholding the desired information can be easily
acquired.
In this study, a method was presented by which transforming the spectral information of a three-channel composite
to intensity-hue-saturation, the burned area mapping can be
easily achieved. The hue component has been proven to be
very useful for burned area mapping, because the spectral
behavior of the burned category pixels is well differentiated
from other land-covertland-use categories. Thus, by applying a
simple thresholding, the burned area mapping can be easily
achieved.
The question that should be answered is why the burned
areas are well discriminated in the hue component but not in
the intensity or saturation components. The answer to this
question should take into consideration two points. The first is
the set of mathematical expressions that are behind this transformation and how each component utilizes the initial spectral
information, and the second is what actually represents each
component of the transformation. As previously discussed, the
intensity component is related to the spatial information of the
RGB color composite, while hue and saturation apply to the
spectral information. Thus, the burned areas are expected to be
found in the hue or saturation component, because they constitute a spectral, rather than a spatial, pattern.
On the other hand, both intensity and saturation components take into account only the minimum and maximum
value of the RGB color composites. Thus, in the case of burned
areas, if the RGB color composite consists of the TM4-TM7-TM1,
then the information that is utilized from both components is
taken from the spectral channels TM7 and TM1. It should be
noticed that, if we do not remove the haze from the original
spectral channels, then the maximum radiometric value of the
burned areas will be observed in TM1. However, the discrimina~ TM1) has not been
tor ability of this combination ( T M and
proven to be the most suitable for burned area mapping (Chuvieco and Congalton, 1988; Lopez and Caselles, 1991; Koutsias
and Karteris, 1998; Pereira et al., 1997). Instead, it has been
found that both TM4 and TM7 are the best inputs for burned area
mapping (Lopez and Caselles, 1991; Koutsias and Karteris,
1998), and the hue component utilizes this relationship if the
RGB color composites is TM4-TM7-TM1.
Conclusions
I
In this study, a method was presented by which transforming
the RGB values of a three-channel composite to IHS values, the
spectral information of the original spectral channels, which is
needed to map the burned areas, is efficiently separated in the
hue component of MS color model. Among the original spectral
channels of Landsat-5 Thematic Mapper, the spectral informa-
tion contained in TM4, TM7, and TM1 proved to be the most vduable in mapping burned areas. The reduction of the dimensionality and the elimination of the spectral information in one
new component was accomplished successfully so that two
very distinct groups of pixels, one belonging to the burned category and the other one to the unburned category, appeared in
the hue component. Thus, by applying a simple thresholding
approach, the burned area mapping can be easily achieved.
Among the six possible combinations which arise by corresponding the three spectral channels TM4, TM7, and TM1 to the
red, green, and blue color planes, TM7-TM4-TM1 and TM4-TM7TM1 were the most suitable to be transformed to the IHS color
model. However, between the two, TM7-TM4-TM1 was preferred.
Finally, the M s transformation proved to be superior to
other methods in the following aspects:
it does not require radiometric corrections or radiometric
enhancements;
it does not require the assessment of training areas;
it produces a new data set in which the burned areas are well
discriminated; and
confusion between burned areas and other land-coverlland-use
categories such as shadows, urban areas, and water bodies
is eliminated
Acknowledgments
This research was supported by the EC Environment and Climate Research Programme (contact ENV4-CT95-0256 Climatology
and Natural Hazards).
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The Photogrammetric Society, London
oRAM*
i=&5
d
o*
(Received 15 February 1999;revised and accepted 23 July 1999)
w
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