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.
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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
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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)
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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
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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
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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
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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.
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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).
Blom, R.G., and M. Daily, 1982. Radar Image Processing for Rock-Type Discrimination, IEEE Zkansactions on Geoscience Electronics, GE-203343-351. Carper, W.J., T.M. Lillesand, and R.W. Kiefer, 1990. The Use of IntensityHue-Saturation Transformations for Merging SPOT Panchromatic and Multispectral Image Data, Photogrammetric Engineering b Remote Sensing, 56(4):459-467. Chavez, P.S., Jr., S.C. Sides, and J.A. Anderson, 1991. Comparison of Three Different Methods to Merge Multiresolution and Multispectral Data: Landsat TM and SPOT Panchromatic, Photogrammetric Engineering b Remote Sensing, 57(3):295-303. Chuvieco, E., and R.G. Congalton, 1988. Mapping and Inventory of Forest Fires from Digital Processing of TM Data, Geocarto International, (4):41-53. Conrac Corp., Conrac Division, 1980. Raster Graphics Handbook, Conrac Corp., Covina, California. Drury, S., 1986. Geological Structures on Landsat TM Images of Southern Britain, Proceedings, ISPRS/Remote Sensing Society Symposium, Mapping from Modern Imagery, Edinburgh, Remote Sensing Society, University of Nottingham, pp. 710-718. Edwards, K., and P.A. Davis, 1994. The Use of Intensity-Hue-Saturation Transformation for Producing Color Shaded-Relief Images, Photogrammetric Engineering b Remote Sensing, 60(11):1369-1374. Erdas Inc., 1991. Erdas Field Guide, Second Edition, Erdas Inc., Atlanta, Georgia, 628 p. Gillespie, A.R., A.B. Kahle, and R.E. Walker, 1986. Color enhancement of highly correlated images. I: Decorrelation and HIS contrast stretches, Remote Sensing of Environment, 20:209-235. Green, W.B., 1983. Digital Image Processing-A Systems Approach, Van Nostrand Reinhold Co., New York. Haydn, R., G.W. Dalke, and J. Henkel, 1982. Application of the HIS Color Transform to the Processing of Multisensor Data and Image Enhancement, Proceedings, International Symposium on Remote Sensing of Arid and Semi-Arid Lands, Cairo, Egypt, pp. 599-616. Koutsias, N., and M. Karteris, 1998. Logistic Regression Modeling of Multitemporal Thematic Mapper Data for Burned Area Mapping, International Journal of Remote Sensing, 19(18):3499-3514.
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1
Lopez, M.J.G., and V. Caselles, 1991.Mapping Burns and Natural Reforestation Using Thematic Mapper Data, Geocarto International,
Swain, P.H., and S.M. Davis, 1978.Remote Sensing: The Quantitative Approach, McGraw-Hill, Inc., New York, 396 p. Thormodsgard, J.M., and J.W. Feuquay, 1987.Larger Scale Image Mapping with SPOT, Proceedings, SPOT-1 Utilization and Assessment Results, Paris, France. Welch, R., and W. Ehlers, 1987. Merging Multiresolution SPOT HRV and TM Data*Photogrommetric Remote Sensing, 53(3):301-303.
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Mather, P.M., 1987. Computer Processing of Remotely-Sensed Images. An Introduction, John Wiley & Sons, New York, pp. 227-231. Pereira, J.M.C., E. Chuvieco, A. Beaudoin, and N. Desbois, 1997. Remote Sensing of Burned Areas, A fieview ofRemote Sensing Methods for the Study of Large Wildland Fires (E.Chuvieco, editor), University of Alcala, Alcala de Henares, Spain, pp. 127-183. Robinson, J.M., 1991. Fire from Space: Global Fire~valuationUsing Infrared Remote Sensing, International Journal of Remote Sensing, 12:3-24.
The Photogrammetric Society, London
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(Received 15 February 1999;revised and accepted 23 July 1999)
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