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<ul><li><p>JAG l Volume 2 - Issue 2 - 2000 </p><p>COMMUNICATION </p><p>Iron oxide and hydroxyl enhancement using the Crosta Method: a case study from the Zagros Belt, Fars Province, Iran </p><p>Majid Hashemi Tangestani and Farid Moore </p><p>Department of Geology, College of Sciences, Shiraz University, 71454 Shiraz, Iran </p><p>KEYWORDS: alteration mapping, principal component </p><p>analysis, Landsat-TM, eigenvalue, eigenvector, color </p><p>composite </p><p>ABSTRACT </p><p>Following preliminary reports on the probable occurrence of iron </p><p>ore in the Mashayekh-Nowdan area, west of Shiraz, principal com- </p><p>ponents analysis on 6 and 4 Landsat-TM bands was tested by the </p><p>Crosta method for the enhancement and discrimination of iron </p><p>oxide stained and hydroxyl-bearing areas in the region. Eigenvector </p><p>loadings of visible and infrared bands of TM bands 1, 3, 4, 5, and 7 </p><p>show that in each case the first principal component (PCI) indicates </p><p>albedo, PC2 indicates the difference between visible and infrared </p><p>bands, and PC3 indicates vegetation. Features with lower impor- </p><p>tance such as iron oxide or hydroxyl-bearing minerals are concen- </p><p>trated in subsequent principal components. PC4 of unstretched </p><p>data transformation on bands 1, 4, 5, and 7 indicates the hydroxyl- </p><p>bearing and carbonate exposures; and on bands 1, 3, 4, and 5, it </p><p>indicates iron oxides. Color composites of hydroxyl and iron oxide </p><p>images enhance the iron oxide exposures, but not as clearly in the </p><p>case of hydroxyls, because of some spectral behavior similarities </p><p>with carbonates. </p><p>INTRODUCTION </p><p>Landsat data have been used for a number of years in arid </p><p>and semi-arid environments to locate areas of iron oxides </p><p>and/or hydrous minerals [Abrams et a/, 1983; Kaufman, </p><p>1988; Ranjbar &amp; Roonwall, 19971 which might be associ- </p><p>ated with hydrothermal alteration zones. However, iron </p><p>oxides have a wide range of occurrences that are often </p><p>unrelated to alteration phenomena; these include sedi- </p><p>mentary red beds, volcanic rocks, and weathered alluvium. </p><p>In addition, there are types of alteration which are iron- </p><p>oxide free, such as advanced argillic and siliceous rocks </p><p>that are highly leached. These leached areas are character- </p><p>ized by the presence of hydrous minerals such as kaolinite, </p><p>sericite, montmorillonite, and alunite. </p><p>The second generation Landsats, launched in 1982, carry </p><p>a multispectral scanner called the Thematic Mapper (TM). </p><p>This instrument has seven channels and provides data </p><p>with 30-m spatial resolution. Spectral bands 5 and 7 of </p><p>the Thematic Mapper are located beyond 1 .O pm and are </p><p>situated in spectral regions that contain characteristic fea- </p><p>tures of hydrous minerals, and hence many hydrothermal- </p><p>ly altered rocks. The 1.65~ym band is located where </p><p>altered rocks have their highest reflectance; the 2.2~ym </p><p>band spans the region where hydrous minerals have a </p><p>strong absorption feature ( Figure 1). </p><p>The principal component transformation is a multivariate </p><p>statistical technique that selects uncorrelated linear com- </p><p>binations (eigenvector loadings) of variables in such a way </p><p>that each successively extracted linear combination, or </p><p>principal component (PC), has a smaller variance [Singh &amp; </p><p>Harrison, 19851. The statistical variance in multispectral </p><p>images is related to the spectral response of various surfi- </p><p>cial materials such as rocks, soils, and vegetation. The </p><p>methodology of this paper, which follows an earlier study </p><p>in the Mashayekh-Nowdan area [Hashemi Tangestani &amp; </p><p>Moore, 19971, relies specifically on the Crosta &amp; McM. </p><p>Moore methodology [Crosta &amp; McM. Moore, 19891 and </p><p>also on the selective input of only four image bands for </p><p>PCA [Loughlin, 19911. </p><p>A 1878 x 2034-pixel subscene of the Landsat TM </p><p>163/039/3 quarter image covers the Mashayekh-Nowdan </p><p>area, west of Shiraz and north of Kazerun. The image </p><p>was acquired on 10 September 1990. The area is semi- </p><p>arid; the vegetation type and amount are influenced by </p><p>elevation, aspect, and availability of soil moisture. The </p><p>results of the examinations are illustrated for an area that </p><p>covers the northern part of the subscene, called </p><p>Mashayekh-Nowdan. </p><p>GEOLOGY </p><p>The Mashayekh-Nowdan area, which is about 50 km long </p><p>and 40 km wide, lies within the southern margin of the </p><p>Zagros Mountain Range (29 36 - 30 03 N , 51 31 - </p><p>51 56 E) in an area generally known as Simply Folded </p><p>Belt. The detailed geology of the Zagros Mountain Range </p><p>has already been described in the literature [Alavi, 1980; </p><p>140 </p></li><li><p>Iron oxide and hydroxyl enhancement JAG l Volume 2 - Issue 2 - 2000 </p><p>a4 a6 0.6 IO u I.4 lk 1;s 2:o i2 2:4 wad~lh h microns </p><p>-.._.._.. 9resn w9stdon -.-.-. carbonale- bwlnp roil or rock </p><p>km- bearing soil or rock _--_ hydmxyl- bearing soil or rock </p><p>Darvishzadeh, 1992; Falcon, 1974; James &amp; Wynd, 19651. </p><p>The most prominent structural feature of the area is the </p><p>presence of three anticlines, namely the Dashtak anti- </p><p>cline, the Nowdan anticline, and the Anar anticline, </p><p>which trend parallel to the general trend of the Zagros </p><p>Mountain Range, that is, NW-SE (Figure 2). </p><p>The exposed formations, in order of oldest to youngest, </p><p>are the marly Kazhdumi Formation (Albian), the calcare- </p><p>ous Sarvak Formation (Cenomanian-Turonian), the shaly </p><p>Gurpi and Pabdeh Formations (Santonian-Campanian), </p><p>the calcareous Asmari Formation (Oligocene-Miocene), </p><p>and the evaporitic Gachsaran Formation (Miocene). The </p><p>contacts between all these formations are conformable. </p><p>FIGURE 2 Geological map of the western part of the Mashayekh- Nowdan Area (29 36 - 30 03N, 51 31 - 51 56E modified from NIOC. Map No.2051 2, 1:250,000); the area covered by the subscene of the Landsat TM 163/039/3 quarter image extends some 8 km further eastwood </p><p>FIGURE 1: Diagrammatic SpeCtra illus- trating the position of diagnostic iron, clay, carbonate and chlorophyll absorp- tion bands. (after Kaufman, 1988) </p><p>Structurally, the Mashayekh-Nowdan area has hardly </p><p>been disturbed and several small normal faults of local </p><p>importance occur in the anticlines. These small local faults </p><p>and some huge slides in the Asmari Formation can be </p><p>related to the Kazerun lineament activity. This lineament </p><p>is part of the N-S Qatar-Kazerun lineament and passes to </p><p>the west of the study area. </p><p>Carbonatic formations comprise a large part of the areal </p><p>surface, and among these, exposures of Asmari </p><p>Formation are distinctly prominent. This carbonatic for- </p><p>mation also exerts an influence on the morphology of the </p><p>anticlines. Despite the widespread distribution of Asmari </p><p>exposures, it is only on the northern flank of the Anar </p><p>141 </p></li><li><p>Iron oxide and hydroxyl enhancement JAG l Volume 2 - Issue 2 - 2000 </p><p>anticline that anomalous quantities of iron oxides are cent of the total variance for the unstretched data PCA. reported. Close field observations have revealed that iron Overall scene brightness, or albedo, is responsible for the oxides occur mainly as a thin veneer of Quaternary sedi- strong correlation between multispectral image channels. ments on top of Asmari limestone, giving the impression PCA has effectively mapped this into PC1 of the transfor- </p><p>of an authochthonous origin. mation (Figure 3). </p><p>PRINCIPAL COMPONENTS ANALYSIS OF SIX </p><p>TM BANDS </p><p>Table 1 lists the image eigenvalues (which give an indica- </p><p>tion of decreasing variance in successive principal compo- </p><p>nents) and eigenvector loadings (linear combinations of </p><p>weighted input images in the principal components) for a </p><p>principal components transformation, using the covari- </p><p>ante matrix, on all six reflective bands of TM on the </p><p>Mashayekh-Nowdan subscene. The transformation was </p><p>carried out on unstretched data. </p><p>In this transformation, the first principal component (PC 1) </p><p>is composed of a positive weighting of all total bands. As </p><p>indicated by the eigenvalues, PC1 accounts for 91.65 per- </p><p>Eigenvector loadings for PC2 in Table 1 indicate that PC2 </p><p>describes the difference between the visible channels </p><p>(TMI, 2, and 3) and the infrared (IR) channels (TM5 and 7). </p><p>Eigenvector loading for PC2 of TM4 is not considered </p><p>because it is very close to zero. </p><p>Eigenvector loadings for PC3 (in Table 1) indicate that </p><p>PC3 is dominated by vegetation, which is highly reflective </p><p>in TM4; the positive loading of TM4 in this PC (0.9091) </p><p>also indicates that strongly vegetated pixels will be bright </p><p>in this PC image (Figure 4). The percentage of variance </p><p>mapped into this vegetation PC is only 2.55 percent, </p><p>which is not a measure of vegetation abundance in the </p><p>Mashayekh-Nowdan area, where most pixels will contain </p><p>some vegetation. </p><p>TABLE I: Principal components analysis on 6 TM bands of Mashayekh-Nowdan area. </p><p>input bands TM1 TM2 </p><p>PC1 0.3433 0.2536 PC2 0.6778 0.3063 PC3 -0.2427 -0.0562 PC4 -0.5411 0.1349 PC5 -0.0498 -0.0265 PC6 -0.2614 0.9053 </p><p>TM3 TM4 Eigenvector matrix </p><p>0.3883 0.2844 0.2984 0.0913 -0.0386 0.9091 0.8013 -0.1234 -0.0490 0.2610 -0.3333 -0.0280 </p><p>TM5 </p><p>0.6533 -0.5089 -0.0215 -0.1621 -0.5359 -0.0037 </p><p>TM7 Eigenvalues (%) </p><p>0.3988 91.65 -0.3003 4.83 -0.3307 2.55 -0.0483 0.53 0.7993 0.40 0.0000 0.04 </p><p>FIGURE 3 PC1 image (albedo image) from 6-band PCA, FIGURE 4 PC3 image from 6-band PCA. Vegetated areas are MashayekhNowdan area. enhanced in bright pixels. </p><p>142 </p></li><li><p>Iron oxide and hydroxyl enhancement JAG l Volume 2 - Issue 2 - 2000 </p><p>Having mapped albedo to PC1 and visible to IR differ- </p><p>ences, and vegetation to PCs 2 and 3, respectively, the </p><p>remaining three PCs can be expected to contain informa- </p><p>tion due to the varying spectral response of iron oxides </p><p>(absorption in visible bands 1 and 2 and higher reflectance in TM3) and hydroxyl-bearing minerals </p><p>(absorption in TM7, higher reflectance in TM5) ( Figure </p><p>1). By looking for moderate or large eigenvector loadings </p><p>for TM1 and TM3 in PCs where these loadings are also </p><p>opposite in sign, we can predict that iron oxides will be </p><p>distinguished by bright pixels in PC4 of Table 1. </p><p>(0.7924) and moderate negative loading for TM5 (- </p><p>0.5467) can be considered as an H image for the </p><p>Mashayekh-Nowdan area. </p><p>Hydroxyl-bearing minerals are mapped as drak pixels in </p><p>PC5 due to the fact that the contribution is negative </p><p>from TM5 and positive from TM7 in this PC (Table 1). If </p><p>the number of input channels is reduced to avoid a par- </p><p>ticular spectral contrast, the chances of defining a </p><p>unique PC for a specific mineral class will be increased </p><p>[Loughlin, 19911. </p><p>Table 3 describes the principal components transforma- </p><p>tion on unstretched TM bands 1, 3, 4 and 5 of the </p><p>Mashayekh-Nowdan subscene. TM7 could be substituted </p><p>for TM5 in this analysis with little effect on the result; </p><p>one SWIR band is omitted deliberately to avoid hydroxyl </p><p>mapping. The PCs can be interpreted as albedo in PCI, IR </p><p>versus visible in PC2, vegetation in PC3, and iron oxide as </p><p>dark pixels in PC4 (eigenvector loading for TM3 = - </p><p>0.8457 and for TM1 = +0.4825). This PC image (F) can </p><p>be negated to show iron oxide stained areas as bright </p><p>pixels ( Figure 6). </p><p>The rules for iron oxide mapping are similar to those for </p><p>hydroxyl mapping. The magnitude of eigenvector load- </p><p>ings for TM1 and TM3 in either PC3 or PC4 should be </p><p>moderate or strong and opposite in sign. </p><p>PCA FOR HYDROXYL AND IRON OXIDE MAPPING Table 2 describes the principal components transforma- </p><p>tion on unstretched TM bands 1, 4, 5, and 7 of the </p><p>Mashayekh-Nowdan subscene. TM bands 2 and 3 have </p><p>been deliberately omitted to avoid mapping iron oxides, </p><p>and it should be noted that TM2 or TM3 could substi- </p><p>tute for TM1 in this transformation. Following the rea- </p><p>soning process described above, we can predict that </p><p>PC1 is the albedo image, PC2 describes the contrast </p><p>between the short wave infrared (SWIR) and the visible </p><p>region, PC3 is brightest for vegetation, and PC4 high- </p><p>lights hydroxyl-bearing minerals as dark pixels. This </p><p>Hydroxyl (H) image is therefore negated in Figure 5 </p><p>to show anomalous concentrations of H as brightest </p><p>zones. </p><p>The methodology for hydroxyl mapping by PCA on TM </p><p>bands 1, 4, 5 and 7 is to examine the eigenvector load- </p><p>ings for bands 5 and 7, in the PC3 and PC4 images. The </p><p>PC image that best discriminates hydroxyl-bearing miner- </p><p>als is that with a high or moderate eignvector loading, </p><p>irrespective of sign, for TM7 and a high or moderate </p><p>eignevector loading of opposite sign for TM5. Negation </p><p>of those PCs in which the TM7 loading is positive makes </p><p>the anomalous pixels brightest in all cases. PC4 in Table </p><p>2 with a relatively strong positive loading for TM7 FIGURE 5 PC4 image from 4-band (1, 4, 5, 7) PCA. Hydroxyl- bearing exposures are in bright pixels (after negation). </p><p>TABLE 2 Principal components analysis for hydroxyl mapping of Mashayekh-Nowdan area. </p><p>Input bands TM1 </p><p>PC1 0.3767 PC2 0.8509 PC3 -0.3549 PC4 -0.0888 </p><p>TM4 TM5 Eigenvector matrix </p><p>0.3195 0.7428 0.2510 -0.3863 0.8772 -0.0008 0.2554 -0.5467 </p><p>TM7 </p><p>0.4517 -0.2520 -0.3232 0.7924 </p><p>Eigenvalues (%) </p><p>91.51 4.77 3.21 0.51 </p><p>143 </p></li><li><p>Iron oxide and hydroxyl enhancement JAG l Volume 2 -Issue 2 - 2000 </p><p>TABLE 3 Principal components analysis for iron oxide mapping of Mashayekh- Nowdan area. </p><p>Input bands TM1 </p><p>PC1 0.3893 PC2 0.7615 PC3 -0.1886 PC4 0.4825 </p><p>TM3 TM4 Eigenvector matrix </p><p>0.4401 0.3278 0.2929 0.0064 -0.0720 0.9401 -0.8457 0.0928 </p><p>TM5 Eigenvalues (%) </p><p>0.7397 91.34 -0.5780 5.15 -0.2745 2.82 0.2081 0.68 </p><p>FIGURE 6 PC4 image from 4-band (1, 3, 4, 5) PCA. Iron oxide stained areas are in bright pixels ( after negation). </p><p>DISCUSSION AND RESULTS </p><p>The monochrome hydroxyl and iron oxide images produced </p><p>by PCA on four bands (such as those in Figures 5 and 6) </p><p>are easy to interpret in that anomalous concentrations of </p><p>each mineral category are represented by the brightest pix- </p><p>els on each image (after negation in some cases). There is </p><p>no need to consult the eigenvector matrices after the </p><p>images have been created to understand and interpret </p><p>these images; this would be necessary for PC images from </p><p>a six-band principal component transformation. </p><p>The Crosta images have another advantage in that they </p><p>can be added together to produce an image (an H+F </p><p>image ) on which pixels with anomalous concentrations </p><p>of both hydroxyls and iron oxides are the brightest. The </p><p>H+F image is produced simply by adding the H and F </p><p>images and resealing the resultant image to 256 gray lev- </p><p>els. An alternative can be a pairwise PCA using the H and </p><p>F images as the two input bands. One of the two PCs </p><p>from this is the H+F image. Care should be taken during </p><p>this transformation to equalize the statistics of the input </p><p>images such that the eigenvector loadings are approxi- </p><p>mately equal in the output PCs [Loughlin, 19911. </p><p>The color composite image is created by stretching the H, </p><p>H+F, and F images so that the brightest pixels in each are </p><p>favorably enhanced, and the darkest portion of each dis- </p><p>tribution is clipped to a certain extent. These three </p><p>images can then be combined in various ways to suit the </p><p>personal preferences of individual photogeologists. </p><p>Different combinations of Crosta images have been empir- </p><p>ically assessed, and the combination of H, H+F, and F in </p><p>red-green-blue (R...</p></li></ul>

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