Spectral signatures and spectral mixture modeling as a tool for targeting aluminous laterite and bauxite ore deposits, Koraput, India

I. C. Das
Scientist, Geosciences Group,
National Remote Sensing Agency,
Balanagar, Hyderabad-500 037, India.
Tel: 91- 40 - 3884225(o), Fax: 91- 40 - 3884258
E-mail: icdas@rediffmail.com

Introduction:
The Eastern Ghats mobile belt is one of the oldest groups of rocks (average age of about 2900 million years) of Indian Peninsula. The rocks of this belt are mainly consisting of Charnockites, Khondalites, Granites, Granodiorites and unclassified Granulites. The belt shows a fairly consistent trend (NE-SW) for over 1000 kms from Parkasam district of Andhra Pradesh, to the south-eastern edge of the Talchir coalfield of Orissa. The Khondalites in the area generally form high linear ridges while the charnockites occur in the form of low and domal shaped hills. In contrast to the rugged topography presented by these rocks the laterite/bauxite cappings are characterised by wide and flat to gently sloping plateau-like tops and are marked by conspicuous vertical scarp faces around the peripheries. This is because of the presence of hard and compact laterite on the top of the hills with very thin or no soil covers. The hilltops are mostly barren with no major trees growing over it. Grass and dwarf date palm trees grow abundantly over these hills. The hills are well dissected and give rise to mesa like landforms. Laterite floats are also very common on the hilltops (Photo #1).


Imaging space-borne Satellite sensors acquire information about the earth materials in different ranges of electromagnetic spectrum (spectral resolution) with certain amount of energy coming out of a specific area (spatial resolution) in a particular direction. In nature, there are n-numbers of materials randomly distributed over the earth’s surface. Spectral signature of any material depends upon its composition and molecular structure. Hence spectral signatures are unique to each material. As remote sensing deals with spectral reflectance of the objects in a spatial domain (ground resolution or IFOV) there may be the chance that reflectance from the closer objects are mixed. Depending upon the spatial and spectral resolution of the imaging sensors the spectral information is modified in the images. Again geological Remote Sensing in most of the cases is based on indirect evidences, as natural rock exposures are scanty and patchy in occurrences. Therefore spectral unmixing is required to separate target material from the background by estimating approximate sub-pixel abundance in the images.

Study area:
The present study area, an Archaean metamorphic terrain is situated in the eastern part of India. It is located east of Koraput town in the southern part of Orissa province with latitude 18°45' N to 19° 05' N and longitude 82° 50' E to 83° 05' E (figure 1). The extent of the area is about 900 square kilometers. The area is well connected in all modes of transport with the city of Vishakhapatnam situated southeast of the study area.


Geology:
The Eastern Ghats mobile belt is one of the oldest Pre-Cambrian geosynclinal orogenic belts of Indian Shield. The major rock types of this belt are Khondalite, Charnockite and Granite gneiss. Khondalite is the oldest rock of the Eastern Ghat Super group mainly composed of quartz, feldspar, garnet, sillimanite and minor amounts of graphite at places. The Charnockite, another major group of rock is mostly composed of quartz, feldspar and hypersthene and shows intrusive relationship towards the Khondalite. Other type of rock that is found in this region is granite gneiss, which is mostly quartzo-feldspathic in composition with some amount of pyroxene and hornblende.

The aluminous laterite and bauxite deposits of this area constitute a part of East Coast Bauxite deposits developed by residual weathering of Eastern Ghats Group of rocks (mainly Khondalite and Charnockite) in a series of plateaus and hill ranges spread over the states of Orissa and Andhra Pradesh (Rao and Ramam, 1979). Laterite and bauxite ore deposits occur as blanket cover over highly dissected plateaus. At Panchpatmali hill, the Bauxite occurs as a blanket cover over Khondalite in a plateau of about 15 square kilometers. The average thickness of the bauxite deposits in panchpatmali hill is around 14 meters. Laterites are highly porous in nature and are formed as capping over the bauxite deposits and their thickness varies from 3 to 5 meters. The hills containing laterite and bauxite deposits are normally devoid of vegetation with thin or no soil cover. A particular plant species, Phoenix acaulis (dwarf date) grows abundantly over the plateau. This plant species is not recorded in the low altitude areas surrounding Panchpatmali, Kodingamali and other bauxite bearing hills. This plant may be a geobotanical indicator of laterite/bauxite deposits of this region (Photo # 2).


Mineralogical study carried out by X-ray diffraction, thermal analysis, electron microscopy and heavy mineral analysis of bauxites derived from different parent rocks reveal gibbsite as the main aluminous mineral. Other associated minerals include hematite, goethite, kaolinite and anatase etc. The bauxites are very hard and massive on the top and moderately hard and spongy below. The pale buff to creamy white colored bauxite is mostly of high grade variety and is recorded mostly on the upper part of the bauxite zone and pale pink to reddish brown bauxite with a clayey appearance, is found to occur towards the lower part of the bauxite zone. The porosity and moisture contents of bauxite are highly variable and specific gravity ranges between 2.1 and 2.5.

Material and method: Image acquisition:
Landsat TM data of 3rd march, 2000 (mid dry season) was acquired for this study. All bands were used in this study except band number 6. All the image processing and spectral processing were done in ENVI 3.2 and ILWIS 2.2.

Image analysis:

1. Logical approach:

Digital images from space borne imaging sensors can be visualized either as single band images or as additive color composites using the three primary colors; Red, Green and Blue (RGB). The RGB displays are used extensively in digital processing to display normal color, false color and hybrid color composites. Landsat TM images are acquired over three different range of Electro magnetic spectrum (EMS) such as visible (band 1,2 and 3), near infrared (band 4) and mid infrared (band 5 and 7). For the purpose of geological feature extraction Landsat TM band 7 (spectral range: 2.08 to 2.35 mm) has already proved to be most suitable for lithological discrimination. TM band 4 (spectral range: 0.76 to 0.90 mm) is the only band representing near infrared region and gives good information about the vegetation cover. Visible bands mostly give similar signatures for many of the earth objects. But band 3 (spectral range: 0.63 to 0.69 mm) is proved to be useful for soil-boundary and geological- boundary delineations. Band 3 also exhibits more contrast than bands 1 and 2 because of the reduced effect of atmospheric attenuation. Therefore a colour composite of bands 7(red), 4(green), 3(blue) was prepared to delineate the laterite and bauxite cappings as well as the extent of vegetation and soil cover in this region (figure 2). In this figure; all the red patches on the hilltops mostly correspond to aluminous laterite and bauxite deposits where as green patches are vegetation cover and pink colour corresponds to bare soil even though specific separation of endmembers are not possible in this process.







2. Spectral approach:



• Image calibration:

Digital data acquired from satellites are provided to the user in the form of quantified and calibrated values (QCALs) for individual picture elements (pixels). These post-calibration QCAL values are in units of digital numbers, which have a full range of 8 bits. Conversion from calibrated QCAL values of the raw image data to spectral radiance, Ll, was done using the following equation (Markham and Barker 1986).


Where
QCAL = Calibrated and quantified scaled radiance values in digital numbers
L_min(l) = Spectral radiance value at QCAL = 0
L_max(l) = Spectral radiance value at QCAL = QCAL_max
QCAL_max = Range of rescaled radiance values in DN
Ll = Spectral radiance

Values for L_max and L_min vary for each of the Landsat satellites and for different period of their use. Often the L max and L min and Ll values published for a given sensor are expressed in units of milliwatts per square centimeter per steradian per micrometer (mW cm-2 ster-1 mm-1). Subsequently, spectral radiance was correlated for solar irradiance by converting the values obtained from the above equation to effective at satellite reflectance or planetary TM albedo, rp, by


Where
r_p = Effective at satellite planetary reflectance composed the combined surface and atmospheric reflectance of the earth (unitless).
L_l = Spectral radiance at sensor (in mW cm^-2 ster^-1mm^-1).
d = Earth-Sun distance in astronomical units.
Esun(l) = Mean solar exoatmospheric irradiances (in mW cm^-2 ster^-1mm^-1).
q_s = Solar zenith angle (in degrees), and p = 3.1416.
The updated mean solar exoatmospheric irradiances, Esun (l), from Markham and Barker (1985) are given in table 1.




The Earth-Sun distance, d, was approximated as




Where D is the day number of the year (Van der Meer 1996).
In the present study one pre-defined algorithm in ENVI (Environment for image analyst) was run to convert DN values into raw radiance values. Necessary parameters for calibration are given in table 2.




A further nominal calibration to reflectance using a standard atmospheric model was not done because the necessary input data and software facilities for such model were not available. Alternately, a haze correction formula by means of dark-object subtraction with band minimum was applied, thus disregarding atmospheric absorption and assuming that atmospheric scattering is an additive component that has the effect of adding a constant value to each pixel in a spectral band. This calibration was applied uniformly to each of the TM bands, thus assuming a constant atmosphere across the image.


• Spectral processing method:

Finally spectral processing of Landsat TM dataset was carried out by converting the digital data from quantized and calibrated values (QCALs) to reflectance values. All the reflectance values of six TM bands were used interactively to locate pure pixels within the dataset. The pure pixels are derived through Pixel Purity Index (PPI) which is a means of finding the most ‘spectrally pure’ (extreme) pixels in multispectral and hyperspectral images (Boardman et al. 1995). The pixel purity index is computed by repeatedly projecting n-dimensional scatterplots onto a random unit vector. The PPI is typically run on a Principal Component transformation image excluding noise bands. The extreme pixels in each projection are recorded and the total number of times each pixel is marked as extreme is noted. A ‘pixel purity image’ is created in which the DN of each pixel corresponds to the number of times that pixel was recorded as extreme. These spectrally pure pixels derived from the multispectral images are plotted in an n-dimensional Scatterplot. The n-Dimensional scatterplot allows for interactive rotation of data in n-D space, selection of groups of pixels into different classes (Boardman and Kruse 1994). The Scatterplot of the pure pixels of TM bands enabled to locate, identify, and cluster the purest pixels and most extreme spectral responses in the data set (figure 3). Here in this study this procedure was followed to isolate different groups of pixels representing different materials. Several groups of pixels such as redsoil, vegetation, laterite/bauxite, Khondalite and mud water (red mud slurry coming out of alumina plant) were isolated and mostly projected at the corners of the scatter plot or completely isolated in the interactive scatterplot (figure 3). It was observed during the field visit that forest concentration is found mostly in the hilly parts. Sal (Shorea robusta) forests are abundant in the eastern and southern part of the study area. Other areas are either cultivated or covered by scrubs. Soil is mostly red in colour due to high iron content in the soil. The hills containing bauxite ores are covered with hard and compact laterite cappings with little or no soil cover on the top and are generally devoid of vegetation cover. The sequence of bauxite deposit in this area is as follows: weathered khondalite or lithomerge at the bottom of the sequence, above that bauxite ore and on the top of the bauxite horizon thick laterite cover of about 5 to 6 meters are present. Soil cover of about ½ to 1 meter is found in patches and at some places laterite is exposed on the surface. Out of the five classes (endmembers) separated in the above process, mud water, vegetation and red soil could be considered ‘pure’ in the sense that they would only comprise single surface material. But khondalite and laterite/bauxite cannot be considered ‘pure’ in the similar manner. On the contrary, we selected those areas that show a representative mix of different ground materials (e.g., green grass, dry grass, soil and lithology) that are characteristic for the laterite/bauxite cappings and khondalite.



Figure 3: n-D Scatterplot of the pure pixels. The pixels market in the corners
correspond to their position in the TM image


Fig 3: Predictive mineral potential map outlying high potentiel areas

Spectral processing method applied here is Mixture Tuned Matched Filtering (MTMF) which performs a partial unmixing - finding the abundances of user defined materials (endmembers). All the endmembers in the image need not to be known for applying this technique. This technique maximizes the response of the known endmember and suppresses the response of the composite unknown background, thus "matching" the known signature. The matched filtering results appear as gray-scale images with value ranging from 0.0 to 1.0 (Zero to One). These images provide a means of estimating relative degree of match to the reference spectrum and approximate sub-pixel abundance, where 1.0 is a perfect match with the reference spectrum and 0.0 is no match situation. It provides a rapid means of detecting specific materials based on matches to library or image endmember spectra and does not require knowledge of all the endmembers within an image scene (Boardman et al., 1995). Here three classes (endmembers) such as laterite/bauxite, vegetation and red soil were considered for the spectral processing of the image. Spectra of these three groups of pixels were plotted in figure 4. Khondalite and mud water endmembers were not considered for the spectral processing, not because their occurrence is very less at pixel resolution but because laterite/bauxite, vegetation and red soil describe about 95% of the variance in the image.

Results and discussion:
From the composite image of bands 743 (figure 2) it is clear that all the laterite and bauxite cappings on the hilltops are displayed in red to magenta colour. But there are some rock exposures and rocky pavement in the valleys, which are also displayed, in magenta colour. They are either laterite floats on the hill slopes or khondalite and charnockite exposures in the area. Vegetation is displayed in green colour as band 4 giving highest reflectance in NIR bands is assigned green filter. Dry red soil is displayed in pink to bluish pink colour, may be because of the contribution from band 3 (projected in blue filter) is more. In general iron oxide gives high reflectance in band 3, which corresponds to the iron reflection peak, and red soil in this region is the weathered product of khondalites and charnockites, which are rich in iron.

On the otherhand spectral processing results are displayed in the form of separate images corresponding to each group of pixels (endmembers). MTMF method applied on Landsat TM images gave three score (abundance) images for three different classes (endmembers) such as Laterite/bauxite, vegetation and red soil respectively (figure 5a, 5b and 5c). The laterite/bauxite score image showed the target areas of laterite and bauxite very nicely (figure 5a). All the laterite and bauxite cappings in the study area are represented by bright pixels in this image, which was confirmed during the fieldwork. Vegetation score image showed the areas dominated by vegetation cover (figure 5b). Bare redsoil areas are nicely detected in the score image of redsoil (figure 5c). A colour composite of these three output images was made for better interpretability (figure 6). In this colour composite image laterite and bauxite deposits are shown in red colour, vegetation dominated areas are shown in green colour and redsoil in blue colour. The areas not categorized in these three classes are shown as dark pixels in the colour composite image. This is because the MTMF method does not require all the endmembers to be known in an image for classification. Laterite/ bauxite score image showed number of hills having signatures of laterite and bauxite cappings including the Panchpatmali and Kodingamali hills where presently mining is being carried out. Other hills such as Hatimali, Kakirimali, Gusramali and numerous other small hills also showed the signature of presence of laterite and bauxite deposits.

• Chemical analysis results:

Rock samples from one of the non-mining locations (Kakirimali hills) were collected during the fieldwork and analysed chemically. The chemical analysis result for five samples collected from Kakirimali hills showed the presence of high-grade bauxite in this hill (table 3).

Summary and conclusions:
Spectral signatures, being unique to each material, can be used for differentiating various materials present in an image depending on its spatial extent and contrast. It was found from the study that laterite and bauxite cappings could be very well identified in the satellite images with the help of spectral processing techniques. This is because of their unique spectral signature and high contrast with the surrounding region. Analysis of spectral signatures of laterite/bauxite, vegetation and redsoil (figure 4) showed that in TM band 7 vegetation gives low reflectance and laterite/ bauxite gives high reflectance where as in band 4 it is vice versa. Hence vegetation can be very well separated from laterite and bauxite deposits. Redsoil has reflectance pattern similar to that of laterite and bauxite, which makes it sometimes difficult to distinguish between these two materials. But aluminous laterite and bauxite cappings occur only on the flat-topped barren hills with sharp escarpment at the periphery, which makes it easier to distinguish them from red soil areas. Spectral processing and classification results have helped in identifying number of new aluminous laterite and bauxite ore deposits in the study area. Therefore it is concluded that this technique can be extrapolated to similar areas for identification of aluminous laterite and bauxite ore deposits.


Acknowledgement:
The author is grateful to Dr. R.R. Navalgund, Director, National Remote Sensing Agency, Hyderabad, India for his kind help. Author is also thankful to Dr. P. S. Roy, Dean, Indian Institute of Remote Sensing, Dehradun for all his support to carry out this research. Prof. V. K. Jha, Head Geosciences Division, IIRS, is also greatly acknowledged for his scientific suggestions and recommendations.

References:

• Boardman, J.W., Kruse, F.A., 1994, Automated spectral Analysis: A geologic example using AVIRIS data, north Grapevine Mountains, Nevada: in Proceedings, Tenth Thematic Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan, Ann Arbor, MI, p. 407,418.
• Boardman, J.W., Kruse, F.A., and Green, R.O., 1995, Mapping target signatures via partial unmixing of AVIRIS data: in summaries, Fifth JPL Airborn Earth Science Workshop, JPL Publication 95-1,v. 1, pp. 23-26.
• Das, I.C., 1999, Spectral characterization of early Pre-Cambrian rocks, Balaghat, Central India, M.Sc. thesis submitted in ITC, Netherlands.
• Geology and Mineral Resources of the states of India-Miscellaneous publication no. 30, part-III-Orissa. by DG, GSI, Feb, 1974.
• Markham, B.L. and Barker, J.L., 1985, Spectral characterization of the LANDSAT Thematic Mapper Sensor. Int. J. Remote Sensing, 6(2): pp.697-716
• Markham, B.L. and Barker, J.L., 1986, Landsat MSS and TM post-calibration ranges, exoatmospheric reflectences and at-satellite temparatures. EOSAT Landsat Tech. Notes, 1:3-8.
• Rao, M.G. and Raman, P.K. (1979) The East Coast Bauxite deposits of India, Bull. Geol. Surv. Ind. Series A no.46. 24p.
• Sahu, S.K. and Pandian, M.S., 1993, Dwarf Date (Phoenix acaulis) : A possible Botanical indicator of Bauxite in Panchpatmali plateau, Indian minerals, vol 47, no 3, pp 249-252.
• Som, S.K. and Mishra, R.N., 1987, Gibbsitisation of K-feldspar, Garnet and Sillimanite of East Coast Bauxite- A case study from Panchpatmali Bauxite deposit, Koraput district, Orissa, Chemical Geology, vol 63, pp 232-278.
• Van der Meer, F., 1996, Spectral mixture modeling and spectral stratigraphy in carbonate lithofacies mapping, ISPRS journal of Photogrammetry and Remote Sensing, Vol 51, pp.150-162.
• Van der Meer, F., Vazquez-Torres, M. and Van dijk, P.M., 1997, Spectral characterization of ophiolite lithologies in the Troodos Ophiolite complex of Cyprus and its potential in prospecting for massive sulphide deposits. Int. J. Remote Sensing, vol. 18, No. 6, pp. 1245-1257.