CHAPTER – 6 HYDROTHERMAL ALTERATION ZONES MAPPING …shodhganga.inflibnet.ac.in/bitstream/10603/74721/11/chapter 6.pdf · CHAPTER 6 HYDROTHERMAL ALTERATION ZONES MAPPING USING PRINCIPAL

CHAPTER 6 HYDROTHERMAL ALTERATION ZONES MAPPING USING PRINCIPAL COMPONENT ANALYSIS AND BAND RATIO TECHNIQUES

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CHAPTER – 6

HYDROTHERMAL ALTERATION ZONES MAPPING

USING PRINCIPAL COMPONENT ANALYSIS AND BAND

RATIO TECHNIQUES

6.1 INTRODUCTION

Hydrothermal alteration zones mapping is one of the most common applications

of remote sensing for mineral exploration in which the presence of altered rocks is the

main indicator of the possible ore deposit (Sabins, 1999; Rajesh, 2004). Many techniques

have been developed to get significant performance and image quality enhancement of

specific features (Achalakul and Taylor 2000; Novak and Soulakellis, 2000; Ferrier et al.,

2002; Moghtaderi et al., 2007; Tommaso and Rubinstein, 2007). Principal Component

Analysis (PCA) and band ratios are two examples of these techniques.

The use of PCA and band ratios during the early stages of mineral exploration has

been very successful in pointing to hydrothermally altered rocks. Many researchers have

proposed that PCA is an effective approach to delineate anomalous concentrations (e.g.

Chica-Olmo and Abarca, 2000; Tangestani and Moore, 2000; Ranjbar et al., 2004).

Initially PCA and band ratios were applied to different Landsat sensors by many

researchers in order to study the alteration zones. Band ratio images are generated from

bands in which specific geological materials have either relatively high or relatively low

total reflectance (Abdelsalam et al., 2000b). The band ratio technique is well proven to

identify geological materials through the detection of diagnostic absorption bands of the

component materials (Vincent, 1997). It has been used successfully since the advent of


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multispectral scanners in 1970. Science 2000 ASTER data enable alteration zones to be

identified before field work is undertaken.

Both ASTER and ETM+ data are suitable for mapping the altered rocks, but with

ETM+ data it is less effective comparing with the ASTER data because of its limited

spectral resolution. ASTER image is more suitable to map silica alteration because of its

5 thermal bands as compared to the single band of ETM+. The main objective of this

chapter to map the hydrothermal alteration zones in north east of Hajjah using PCA and

band ratios techniques.

6.2 Principal Component Analysis (PCA)

PCA, which is also called as Principal Component Transformation (PCT), is an

image processing technique which transforms the original remotely sensed data set into a

substantially smaller and easier to interpret set of uncorrelated variables that represent

most of the information present in the original data set (Fig. 6.1) (Jensen, 2005). The

main objective of PCA is to remove redundancy in multispectral data and extract new

information. It builds up a new set of axes orthogonal to each other (i.e. non-correlated

data) (Gupta, 2003), and can be performed on as many spectral bands as possible. PCA is

also widely used for mapping of alteration in metallogenic provinces (Abrams et al.,

1983; Kaufman, 1988; Loughlin, 1991; Bennett et al., 1993; Tangestani and Moore,

2001; 2002; Crosta et al., 2003; Ranjbar et al., 2004; Zhang et al., 2007). By applying

PCA a new data set with fewer variables is created (Lillesand and Kiefer, 2000). Crosta

technique is known as a feature of oriented principal component selection; it indicates

where the materials are represented as bright or dark pixels in the PC (Ranjbar et al.,

2004).


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The idea of applying PCA to derive mineral abundance maps using high spectral

resolution data was proposed by Crosta et al. (1996) and Prado and Crosta (1997). The

PC1 is generally the weighted average of all data and represents albedo and topographic

effects found in the scene (Drury, 1993; Ranjbar et al., 2004). The PC1 contains the

largest percentage of data variance and the PC2 contains the second largest data variance,

and it continuous like that. The last PC appears noisy because it contains very little

variance, much of which is due to noise in the original spectral data or uncertainty in the

data. It is possible to combine any three of different PCs in R-G-B to create a colour

image (Sabins, 1997).

A limitation of PCA is the gray-tone statistics of a PC image which are highly

scene dependent and can not be extrapolated to other scenes. Further, geologic

interpretation of PC image also requires great care as the surface information dominates

the variation (Gupta, 2003). In some cases different materials are enhanced with same

brightness as for example, in the study area vegetation cover and altered clays are

enhanced with the same brightness.

PCA technique was applied to VNIR and SWIR bands of ASTER (1, 2, 3, 4, 5, 6,

7, 8 and 9) and ETM+ (1, 2, 3, 4, 5 and 7) imagery using ERDAS Imagine 9.1 model

(Fig. 6.2).


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Fig. 6.1 Diagram showing principal component transformation technique. The creation of principal component by shifting and rotating the coordinate system a) scattering of two original highly correlated variables x1 and x2 with means µ 1 and µ 2. b) The new coordinate system x′′′′ found by shifting the original x axis system. c) the principal components (PC1-PC2) are the new coordinate system by rotating the shifted axis system x′′′′ about the point (µ 1, µ 2) (Jensen, 2005).

Fig. 6.2 ERDAS model which was used in PCA of the study area


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6.2.1 Principal Component Analysis of Landsat-7 ETM+

PCA of ETM+ data is shown in Table 6.1. The PC1 contains the largest amount

(93.817%) of total variance of six bands, which decreases until it reaches 0.108 % for PC

7. PC1 has positive loadings from all bands (1, 2, 3, 4, 5, and 7); with the highest loading

is in band 5 (0.621) and band 7 (0.575). The other components showed decreasing

variance caused by differences between spectral regions and individual bands.

PC1 does not contain spectral feature relevant to this analysis as it is a

combination of all bands (Fig. 6.3-PC1). It provides information mainly on albedo and

topographic effects. In the PC2, the spectral bands are separated into visible and infrared

bands with the negative sign for bands 1, 2, and 3 and positive sign for bands 4, 5, and 7.

This PC shows the contrast between the visible red and the near infrared (de Jong and van

der Meer, 2004).

PC2 contains 3.355% of the total variance of six bands. Vegetation cover, Amran

limestone, Kohlan sandstone and granitic rocks are enhanced in this component with

bright pixels. Akbra shale and quartz-graphite-biotite-sericitic schist are represented by

dark pixels (Fig. 6.3-PC2). The PC3, which contains 1.978% of the total variance of six

bands, has positive loading in band 7 (0.453) and low negative loading in band 4

(-0.875). In this component the vegetation cover is enhanced with dark pixels (Fig. 6.3-

PC3), because it has a higher reflectance in band 4 of ETM+ and lower reflection in band

3 (Fig. 4.1).


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Table 6.1 Principle Component Analysis of ETM+ data

In PC3, band 5 represents the negative loading and band 7 the positive loading so

that the altered clay minerals appear in this component as dark pixels. The PC4 contains

0.433 % of the total variance, and has positive loading in band 1 (0.690) and low positive

loading in band 3 (-0.467). This component shows areas with iron oxide as dark pixels

because they possess higher reflection in the red region of the spectrum and absorption in

the blue region (Fig. 6.3-PC4).

The PC5 explains 0.309% of the total variance, and has the highest positive

loading value in band 5 (0.550) and the lowest loading value in band 7 (-0.595). In this

component the hydroxyl bearing altered zones are seen as bright pixels (Fig. 6.4-PC5).

PC5 has higher loading of band 5 with positive sign and band 7 with negative sign so that

the hydroxyl bearing altered zones are enhanced with bright pixels. The spectral feature

of the clay minerals exhibits absorption feature in band 7 of ETM+, and higher

PC Band No. PC1 PC2 PC3 PC4 PC5 PC7

Band 1 0.159 -0.363 -0.036 0.690 -0.304 -0.523

Band 2 0.250 -0.424 -0.145 0.268 -0.123 0.806

Band 3 0.410 -0.646 0.012 -0.467 0.362 -0.254

Band 4 0.196 0.145 -0.875 -0.241 -0.324 -0.111

Band 5 0.612 0.450 -0.085 0.336 0.550 0.004

Band 7 0.575 0.217 0.453 -0.250 -0.595 0.010

variance % 93.817 3.355 1.978 0.433 0.309 0.108

Cumulative variance %

93.817 97.173 99.151 99.583

99.892

100


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reflectance in band 5 (Fig. 4.3). This explains the higher pixel values of the altered areas

in this PC. PC7 contains 0.108% of the total variance and has high positive and negative

loading in band 2 (0.806) and band 1 (-0.523), respectively. In this component the

features are not clear probably due to the noise (Fig. 6.4-PC7). The colour combination of

PC5, PC3 and PC2 as R-G-B, respectively highlights altered area represented by the red

color (Fig. 6.5).

As discussed in PC3 the vegetation cover is displayed with dark pixels, but after

inversing (inverse PC3) using the following equation it displays the vegetation cover with

bright pixels (Fig. 6.6).

)1.6()7(453.0)5(085.0

)4(875.0)3(012.0)2(145.0)1(036.03

bandband

bandbandbandbandPC

−++−+=


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Fig. 6.3 PC1, PC2, PC3, PC4 of ETM+ of the study area


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Fig. 6.5 Band color combination of ETM+ PC (5-3-2) as R-G-B of the study area

Fig. 6.6 Result of inversing PC3 of ETM+ of the study area

Fig. 6.4 PC5 and PC7 of ETM+ of the study area


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6.2.2 Principal Component Analysis of ASTER data

PCA of subsystems VNIR and SWIR bands (1, 2, 3, 4, 5, 6, 7, 8 and 9) of ASTER

data is shown in Table 6.2. The PC1 explains the largest amount (90.456%) of total

variance among nine bands. It has positive loadings of all bands with the highest loading

in band 2 (0.460). This component generally represents the albedo and the topographic

effects (Fig. 6.7-PC1). PC2 accounts for 6.081% of the total variance, and has the highest

positive loading in band 4 (0.314), and the negative loading in band 1 (-0.526). In this

component the sedimentary and granite rocks appear with bright pixels and the

Precambrian basement rocks with the dark pixels (Fig. 6.7-PC2). PC3 accounts for

1.655% of the total variance, and has the highest positive loading in band 3 (0.907) and

high negative loading in band 2 (-0.277).

Vegetation cover is enhanced in this component and appears as bright pixels, as

this PC3 has higher loading of band 3 with the positive value and lower loading with

negative value in band 2 (Fig. 6.7-PC3). The spectral feature of vegetation cover (Fig.

4.1), exhibits strong absorption feature at 0.45µ m and 0.65µ m and maximum

reflectance at 0.55µ m, and as such it appears in this component as bright pixel. The PC4

explains 0.585% of the total variance, and has the highest positive loading in band 4

(0.722) and negative loading in band 7 (-0.401). Hydroxyl bearing area is enhanced in

this component by bright pixels, as this PC has positive loading of band 4 and negative

loading of bands 7, 8 and 9 (Fig. 6.7-PC4). The spectral feature of different types of clay

minerals (Fig. 4.3) shows, that clay minerals exhibit higher reflectance in band 4 and

higher absorption in bands 7, 8 and 9.


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PC5 explains 0.384% of the total variance, and has the highest positive loading in

band 6 (0.563) and negative loading in band 2 (-0.454). Iron oxides are enhanced in this

component with dark pixels, because this PC has higher loading in band 2 with negative

sign and positive loading in band 1 (Fig. 6.8-PC5). Iron oxide with bright pixels can be

seen when the PC5 has been inversed as mentioned in equation 6.1. PC6 explains 0.339%

of the total variance, and has the highest positive loading in band 6 (0.513) and negative

loading in band 1 (-0.475). Iron oxide is enhanced in this component with bright pixels

(Fig.6.8-PC6). PC7, PC8, and PC9 explain 0.248%, 0.126% and 0.125%, respectively of

the total variance. PC7, PC8 and PC9 are predominantly noisy images and do not

discriminate any features (Fig. 6.8-PC7, PC8; Fig. 6.9-PC9). Rocks rich in massive

sulfide materials are represented in PC9 by the bright pixels.

Table 6.2 Principle Component Analysis of ASTER data

PC Bands

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9

Band1 0.425 -0.526 -0.206 0.193 0.438 -0.475 0.214 0.009 0.026

Band2 0.460 -0.461 -0.277 -0.109 -0.454 0.488 -0.207 -0.021 0.010

Band3 0.318 -0.177 0.907 -0.203 0.014 -0.003 -0.049 0.032 -0.011

Band4 0.337 0.314 0.123 0.722 -0.437 -0.149 0.125 -0.100 0.111

Band5 0.268 0.272 -0.081 0.066 0.203 -0.014 -0.487 0.748 0.072

Band6 0.320 0.302 -0.016 0.158 0.563 0.513 -0.021 -0.373 -0.248

Band7 0.289 0.302 -0.132 -0.401 -0.039 -0.376 -0.379 -0.472 0.371

Band8 0.297 0.273 -0.124 -0.345 -0.225 -0.243 0.276 0.117 -0.710

Band9 0.216 0.223 -0.061 -0.281 0.040 0.226 0.660 0.232 0.528

Variance % 90.456 6.081 1.655 0.585 0.384 0.339 0.248 0.126 0.125

Cumulative Variance %

90.456 96.537 98.192 98.777 99.161 99.501 99.749 99.875 100


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Fig. 6.7 PC1, PC2, PC3, PC4 of ASTER data of the study area


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Fig. 6.8 PC5, PC6, PC7, PC8 of ASTER data of the study area


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6.3 BAND RATIO

Band ratio is a multispectral image processing method and is a powerful

technique in remote sensing. It is prepared by dividing the DN of one band by the

corresponding DN in another band for each pixel and displaying the new DN value as

grayscale image (Sabins, 1987; 1997; 1999; Drury, 2001; Jensen, 2005). The band-ratio

of the images generated from orbital multi-spectral optical remote sensing data can

distinguish different rock units better than when only bands are used in band colour

combination (R-G-B) image (Abrams et al., 1983; Abrams, 1984; Jensen, 1996;

Abdelsalam et al., 2000a; b; Jensen, 2005). It highlights the spectral difference between

materials and reduces the variable effects of solar illumination and topography. Band

Fig. 6. 9 PC9 of ASTER data of the study area


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ratio is also helpful in enhancing the spectral information of images (Gupta, 2003), and

provide unique information not available in any single band which is useful for

discrimination of the surface materials (Satterwhite, 1984; Jensen, 2005). According to

Lillesand and Kiefer (2000) band ratio is often useful in discriminating subtle spectral

variations that is masked by the brightness variations in images from individual spectral

bands or standard colour compositions.

Band ratio technique has been widely utilized for visual interpretation and

thematic classification of multi-spectral remote sensing data, especially for geological

mapping. It has also been widely used to extract information about hydrothermal

alteration zones in the analysis of different satellite sensors data (Perry, 2004; Tommaso

and Rubinstein, 2007). The selection of bands for use in the development of band ratio

images depends on the spectral characteristics of the surface material to be analyzed and

the abundance of this material relative to the surrounding features of the surface

(Thurmond et al., 2006).

The band ratio is expressed mathematically as

lji

kjirji vB

vBBV

,,

,,,, = (6.2)

Where, BV rji ,, is the output ratio for the pixel at row ;i column j ; BV kji ,, is the

brightness value at the same location in band k, and BV lji ,, is the brightness value in

band l. Theoretically the range of BVrji ,, is from 0 to ∞, but actually, the range is from

1/255 to 255. Sometimes differences in brightness values from identical surface

materials may be obtained because of the topographic slope and aspect, shadows or

seasonal changes in sunlight illumination angle and intensity (Sabins, 1997; Jensen,


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2005); these variations affect the viewer’s interpretations and may lead to misguided

results. In band ratio images, the black and white extremes represent the areas with the

greatest difference in the spectral reflectance of the two bands (Sabins, 1997). In the ratio

denominator will be greater than the numerator for the areas with the darkest signatures

of a band ratio image, and the brightest signatures will be for areas where the

denominator is smaller than the numerator (Sabins, 1987; 1997). Deciding which two

bands to ratio is not always a simple task (Jensen, 2005).

Band ratios are employed to map and identify altered rocks (Rowan and Kahle,

1982; Ford et al., 1990; Rowan and Mars, 2003; Hellman and Ramsey, 2004; Rowan et

al., 2006; Thurmond et al., 2006). It is also commonly used to detect the presence and to

identify the vegetation cover (Asrar, 1989; Peng, 1991; Kariuki et al., 2004; Zhang et al.,

2007). Ratio images can be combined to produce a colour image of any three

monochromatic ratio datasets as G-R-B (Sabins, 1997; 1999; Lillesand and Kiefer, 2000):

this technique provides more geological information and shows greater contrast between

rock units from as many bands in a single image and very easy for visual interpretation.

ASTER data have 14 bands by which more ratio images, more accurate results and

more lithology indicates can be derived (Zhang et al., 2007). Band ratios can enhance the

minerals response and reduce the vegetation response, but it does not work well in

regions that are densely covered by vegetation.

The band ratio 5/7 of ETM+, which is equivalent to the ratio 4/6 of ASTER data,

is used for mapping clay minerals (Sabins, 1997; 1999; Abdelsalam et al., 2000a). Clay

minerals normally exhibit high reflectance feature in band 5 of ETM+ and band 4 of

ASTER. The absorption feature is clear in band 7 of ETM+ and 6 in ASTER data (Fig.

4.3). The band ratio 5/7 ETM+ is sensitive to vegetation cover as it filters out the

vegetation cover band ratio 4/3 that has to be subtracted from it and both are then


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weighted to optimize the result. The other method to separate the vegetation cover is

inversing of the image.

Band ratio 3/1 of ETM+ is used for mapping iron minerals because they exhibit low

reflectance in band 1 and high reflectance in band 3 of ETM+ data (Fig, 4.4) (Sabins,

1999). This ratio is equivalent to the ratio 2/1 of ASTER data. Band ratio 4/5 of ETM+

emphasizes hydroxyl and silicate minerals rather than FeO-rich minerals (Abrams et al.,

1983; Abrams, 1984; Ruiz-Armenta and Prol-Ledesma, 1998). FeO-rich Alumino-silicate

minerals have reflectance feature in bands 3 and 5 and absorption feature at band 4. Band

ratio 5/1 of ETM+ is used for discrimination of opaque minerals, because magnetite and

other opaque minerals as they exhibit spectral reflectance feature in band 5 and

absorption in band 1 (Sultan et al., 1987).

Ninomiya (2003) applied different ratios to SWIR bands of ASTER data for

mapping the alteration minerals and these include:

OHI= (band 7/ band 6)×(band 4/ band 6) (6.3)

where OHI is the index for O-H bearing minerals.

KLI= (band 4/ band 5) × (band 8/ band 6) (6.4)

ALI = (band 7/ band 5) × (band 7/ band 8) (6.5)

CLI = (band 6/ band 8) × (band 9/ band 8) (6.6)

Where, KLI is the kaolinite index, ALI is the alunite index and CLI is the calcite

index. Ninomiya et al. (2005) also proposed three lithologic indices from the five ASTER

TIR bands which are:

QI= (band 11× band 11)/ (band 11/ band 12) (6.7)

CI= band 13/ band 14 (6.8)

MI= band 12/ band 13 (6.9)


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Where, QI is the quartz index, CI is the carbonate index and MI is the mafic

index. MI reversely correlates to bulk SiO2 content in silicate rocks and it increases as the

bulk SiO2 content decreases. QI is expected to be high for quartz and low for K-feldspars

and gypsum (Ninomiya et al., 2005). Siliceous rocks typically show intense Si-O

absorption in band 12 relative to bands 13 and 14 of ASTER image, therefore band 13/

band 12 or band 14/ band 12 ratio images are particularly useful for mapping of

sandstone, quartzite, and silicified rocks (Rowan and Mars, 2003). ETM+ data are not

suitable for discrimination of these types of rocks.

Band ratio combination of 4/7-3/4-2/1 of ASTER image is equivalent to 5/7- 4/5-

3/1 of ETM+ image (Abram’s combination) and 4/7-4/1-2/3×4/3 of ASTER image is

equivalent to 5/7-5/1-3/4×5/4 ETM+ (Sultan’s combination) (Abdeen et al., 2001)

6.3.1 Band Ratio of ETM+ and ASTER data of Study Area

The band ratio technique was applied to all subsystems (VNIR, SWIR and TIR)

of ASTER image and to VNIR and SWIR bands (1, 2, 3, 4, 5 and 7) of ETM+ image.

The Band ratio 5/7 of ETM+ and its equivalent 4/6 of ASTER image highlighted altered

rocks containing clays. These rocks are concentrated around Sauq Sharis, Alharirah, and

Bab Muris (Fig. 6.10 a, b). The sedimentary rocks; Kohlan sandstone, Akbra shale and

Amran limestone appeared as dark coloured. This ratio 5/7 is very sensitive to vegetation

areas, which have enhanced with bright colour same as clay. The vegetation cover was

separated by inversing the ratio (Fig. 6.11). The result of band ratio image 4/5 of ETM+

and its equivalent 3/4 of ASTER are shown in Figures 6. 12-a,b. These ratios highlight

the regions which are dominated by the hydroxyl and silicate-bearing rocks.


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Fig. 6.11 Result of convert ratio 5/7 of ETM+ data

Fig. 6.10 a) Ratio 5/7 of ETM+ and b) ratio 4/6 of ASTER data

b

a b


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Band ratio 3/1 of ETM+ and its equivalent 2/1 of ASTER highlight regions

covered by the iron-bearing rocks as bright pixels compared to other rock units (Fig.

6.13-a, b). The combination band ratios of 5/7, 5/4 and 3/1 of ETM+ as R-G-B,

respectively is very effective in mapping the different rock units in the study area (Fig.

6.14). Altered rocks containing clay appeared as red to pink colour, iron oxides bearing

rocks as blue, shale as deep red, metavolvanic rocks as light green, and serpentinite rocks

as orange to yellow colour. In this combination, the vegetation cover appeared as the

same colour as that of the altered clay minerals. The band ratio combinations 5/7, 4/5 and

3/1 of ETM+ (Fig. 6.15) shows clay minerals-bearing alterations as greenish blue to light

blue, the iron oxides as red, sperpentinite rocks as pink, metavolcanic rocks as bluish

pink to deep red, sedimentary rocks (Amran limestones, Kohlan sandstones) as pink

colour and granitic rocks as red. Vegetation cover appeared with the same colour as that

of the altered clay minerals.

Fig. 6.12 (a) Ratio 4/5 of ETM+ and its equivalent (b) 3/4 of ASTER data

a b


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Fig. 6.14 Combination ratios 5/7,

5/4 and 3/1 of ETM+

Fig. 6.15 Combination ratios 5/7, 4/5 and 3/1 of ETM+

Fig. 6.13 (a) Ratio 3/1 of ETM+ and (b) 2/1 of ASTER of the study


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In the combination of ASTER data 4/7, 4/1 and 2/3 metavolcano-sedemintary

rocks, Akbra shale and dolomitic limestons appeared as bluish red colour and

metavolcanic as light bluish green to light yellowish brown (Fig. 6.16). Granitic rocks,

Kohlan sandstone, and Amran limestone appeared as bluish green to light green colour.

Vegetation cover shown as yellowish red to yellow colour. The combinations of band

ratio 4/8, 3/4 and 2/1 of ASTER, which are equivalent to 5/7, 4/5 and 3/1 of ETM+ image

show altered rocks containing clay minerals appearing as green (Fig. 6.17). Kohlan

sandstones, Amran limestone and granitic rocks appeared as blue to reddish blue colour

and matavolcanic rocks as bluish red to yellow red colour. The vegetation cover was

shown as yellow colour. The ratio 14/12 of ASTER is used for mapping rocks rich in

silica and sulfides (Fig. 6.18). These rocks appeared as white colourd. The dark colour of

Wadi Sharis indicated as highly wetted or flowed water.


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Fig. 6.16 Combination ratios 4/7, 4/1 and 2/3 of ASTER

Fig. 6.17 Combination ratios 4/8, 3/4 and 2/1 of ASTER

Fig. 6.18 Ratio 14/12 of ASTER data shows rocks containing silica


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6.3.2 Normalized Differences Vegetation Indices (NDVI)

Normalized Difference Vegetation Index is the ratio of near infrared reflectance

to visible red light reflectance. NDVI is the most widely used among all vegetation

indices to show the presence/absence of green vegetation and its condition. It can be

applied virtually to all multispectral data types. NDVI is least affected by topographic

features (Lyon et al., 1998). Vegetation shows a strong infrared reflection and low visible

reflectance, so that vegetation areas generally yield high positive for their NDVI

(Lillesand and Keifer, 2000). NDVI is calculated using the following formula:

NDVI = (near-infrared - red)/ (near-infrared + red) (6.10)

ETM+ = (band 4 - band 3)/ (band 4 + band 4) (6.11)

NDVI (ASTER) = (band 3 - band 2)/ (band 3 + band 2) (6.12)

The possible range of values of NDVI are between -1 and 1 (Sabins, 1997,

Barbosa et al., 2006). Vegetated regions generally fall within 0 to +1 range - higher

values indicate more active growth and productivity. Snow, water, and cloud have large

visible reflectance than near-infrared reflectance, so that they yield negative index value.

Rock and bare soil areas have similar reflectance in the two bands of Near-Infrared and

Red, which result in vegetation index approximately to zero (Lillesand and Kiefer, 2000).

NDVI is stable and sensitive enough for studying vegetation, if it is applied to

atmospherically corrected reflectance data. However, it is not stable if it is applied to

radiance at the sensor data without atmospheric corrections (Ninomiya, 2003). To

minimize scattering and atmospheric absorption effects, the DN values have to be

converted into surface reflectance (Galvao et al., 2005).


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Gangopadhyay et al. (2005) classified the emission value from the NDVI in to

three classes:

(a) NDVI < 0.2: pixels with NDVI values less than 0.2 are considered as bare soil.

(b) NDVI > 0.5: in this case, the pixel is considered as mostly vegetated.

(c) 0.2 < NDVI < 0.5: in this case, the pixel is composed of a mixture of bare soil

and vegetation.

NDVI is applied to visible and near-infrared channels of ETM+ and ASTER data

with atmospheric corrected reflectance using the equations 6.11 and 6.12, respectively

using ERDAS 9.1 model (Fig. 6.19). The result of NDVI reveals that the vegetation cover

in the study area is very sparse ranging between -0.246 and 0.500 of ASTER and -0158

and 0.488 of ETM+ (Fig. 6.20-a,b) (Appendix-B). Moreover the NDVI has been applied

to ETM+ and ASTER with DN data. The values are ranging between -0.376 to 0.291

(ETM+) and -0.379 to 0.400 (ASTER) and this result confirms that NDVI of reflectance

data are more sensitive and shows good results.

Comparing this result with Gangopadhyay et al. (2005) classification also shows

that the vegetation cover in the study area is sparse.


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Fig.6.20 NDVI from a) ETM+ and b) ASTER data after atmospheric correction

Non vegetation vegetation

Non vegetation vegetation

Fig. 6. 19 Model used in NDVI


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PCA and band ratio have played an important role in remote sensing for mapping

hydrothermal alteration zones and different rock units. They are also effective for

studying vegetation cover. PC1 in both ASTER and ETM+ data contains the largest

amount of total variance. It is an indication of the albedo and the topography effects.

There are no clear features in the PC7 and PC8 of ASTER data and PC7 of ETM+

because of noise. The features are displayed in the other PCs with different levels. Rocks

containing altered clays were enhanced with bright pixels in both PC5 of ETM+ and PC4

of ASTER data and iron oxide with dark pixels. It is difficult to discriminate between

vegetation cover, Amran limestone, Kohlan sandstone and granitic rocks in PC2 of

ETM+ because they are enhanced with the same bright pixels. This is one of the

disadvantages of PCA in the study area.

The classic band ratio 5/7 of ETM+ and its equivalent 4/6 of ASTER data have

highlighted the clay minerals-bearing altered rocks and the ratio 3/1 of ETM+ and 2/1 of

ASTER data highlighted the areas of iron oxide. The band ratio 4/5 of ETM+ and its

equivalent 3/4 are useful for mapping the hydroxyl and silicate-bearing rocks compared

to FeO-rich minerals. Combinations of band ratio images 5/7, 4/5 and 3/1 of ETM+ and

its equivalent 4/8, 3/4, and 2/1 of ASTER data are important for mapping the

hydrothermal alteration zones, different rock units and vegetation cover.

NDVI is sensitive to reflectance data and reveals sparse vegetation cover in the

study area.

Both PCA and band ratio are more effective for mapping hydrothermal alteration

zones and different rock units in areas with a poor vegetation cover and dense exposed

rocks.

Based on the different analyses of the PCA and band ratio, it is concluded that the

rocks of the study area are affected by hydrothermal alteration and are a promising field

for mineralization.

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