GISdevelopment.net --> Application --> Geology
Gholamreza Mirzavand
Civil Engineering Department,
Islamic Azad University Dezful Branch, Dezful Iran
Gholamreza Mirzavand
mirzavand@yahoo.com
Abstract
Principal component analysis is a well known method of orthogonalizing data. It converges very fast and the theoretical method is well understood. Basically image processing is used for extraction of required information. The present study begins with remote sensing techniques and PCA applying on enhance thematic mapper (ETM+) satellite image dated 2002 for central of Iran, west Esfahan city. The study presents two methods of enhancing igneous body consists of amphibolites and volcanic using optimum index factor (OIF) and Crosta. In this study the optimum index factor allows to use the multi-spectral data for understanding the best false color composite (FCC) image. It is also highlighted the required spectral range (bands) for classification of image to enhance the igneous bodies. The Crosta technique is based on PCA using eigenvector, enhanced the spectral behavior of minerals. The objects on the image are enhanced in terms of black and white with respect to eigenvector loading. 27 FCC images are produced and analyzed using OIF. It is seen that the classification on FCC images for bands 741 and 541 of Infra-red and FCC images for band 654 and 641 of Thermal infra-red are the most accurate composite color images to enhance igneous body, amphibole and clay minerals. The study reveals that the PC3 is increasing the enhancement for reorganization of amphibole minerals in orthoamphibolite rocks. It also shows that PC5 has suitable enhancement to identify the clay minerals in granite rocks. However the study also indicates the use of optimum index factor for identification of spectral bands to give more information about minerals on the image using classification techniques in arid and semi-arid environment where vegetation cover is scanty. This study also represents the use of remote sensing techniques for exploration.
1. Introduction
Principal component analysis is a linear procedure to find the direction in input space where most of the energy of the input lies. In other words, PCA performs feature extraction. The projections of these components correspond to the eigenvalues of the input covariance matrix. The principal component analysis is performed first, and then the eigenvector loading is trained to generate image that give the information of spectral bands for easy interpretation. The reason for this is that the PCA faster since eigenvalues are stable. Natural color and especially standard false color infrared images don't show much variation in rock colors. It is often difficult to discern rock contacts, let alone identify the rock type. Various computer based techniques have been developed that allow a greater variation in the observed colors in non-standard false-color renditions. Many workers have used OIF method such as Hunt and et al (1978), Abrams et al (1983), Duchscherer(1982), Srivastar and et al ( 1998) and Ranjbar and Honarman (2004). One of the statistical methods is optimum index factor (OIF) described by Chavez and et al, (1984). Landsat ETM+ data have been subjected to Crosta and OIF analysis. Igneous body, amphibole and clay minerals in images have been prepared according to OIF and crosta methods. The Crosta technique is based on PCA. Through the analysis of eigenvector values (Crosta and et al 1989), it allows identification of principal components that contain spectral information about specific minerals as well as contribution of each of the original bands to the components in relation to spectral response of the material interest. The PCA transformation (eigenvector and eigenvalues) is applied six ETM+ bands Viz. 1,2,3, 4,5 and 7 in table 3 for the study to enhance the spectral variability of content on the image. The purpose to use OIF and Crosta methods in the present study is: 1- to identify the suitable composite color to generate FCC that can enhance the igneous bodies and mineral content on the image, 2- to emphasize the suitable spectral range (bands) for classification on the image and 3- impact of environmental condition on the igneous body and its associated ecosystem.
2. Study area
The study area lies between latitudes 32º 33' 00" N to 32 º 42' 44" N and longitudes 50 º 52' 30" E to 51º 42' 00" E (Fig 1). It lies in 65 km west of Esfahan city and south of Tiran city. The Zayandeh river is a perennial and flowing north to south direction. The sinuosity or meandering is high in the southern part of the study area. The area has variable topography and geomorphological features, with gentle to moderate slope. Environmentally, the area lies in arid and semi-arid region with seasonal rain fall and some times snow fall during winter period. The area is having scanty vegetation cover except along the river course.
2.1. Geological settings
The pioneer workers are divided Iran into ten regions of different history, structures and sedimentation ( Berberian 1976, Colman-Sadd 1978, Darvishzadeh 1992, Ali and Pirasteh 2004, Pirasteh et al, 2004). Ali and Pirasteh 2004 stated that the Zagros Mountains comprises five litho-tectonic units from northeast to southwest, are Urumieh Dokhtar Arc (UDA), Sanandaj Sirjan Zone (SSZ), Imbricate Zone (IZ), Zagros Fold Belt (ZFB) and Molasse Cover Sequences (MCS). Geologically, the study area belongs to Sanandaj Sirjan Zone which consists of igneous-metamorphic rocks, volcanics and sedimentary from Precambrian to Recent in age (Fig. 1). Structurally, the area is complex with almost rugged topography, that is due to tectonic activities and erosion processes (Pirasteh and Ali, 2005). The area is uplifted with fault zones in Sanandaj Sirjan Zone. It comprises alternative metamorphic rocks of Precambrian age and undergone to younger sedimentary and Paleozoic rocks. Alternative metamorphic rocks consists of phylite rocks, schists and igneous bodies ( meta basaltic and granodiorite). Structurally, the trend of these outcrops follow the Zagros Mountains direction ( north west to south east). Meta-igneous rocks are consists of green schists with schistosity, light gray schists and ortho-amphibolite in dark color with coarse texture and non-schistosity. Granodioritic series are cut by ortho-amphibolite rocks in the area. They generally are comprise medium grained to coarse alkali feldspar and plagioclase with some quarts grained. Due to weathering feldspar minerals are converted to clay minerals.
Fig.1. Geological map of the study area
3. Materials and Methodology
Methodology in brief is given in Figure 2. Geological map and digital topography maps are used for ground truth measurement and accuracy purposes. Landsat 7 satellite, sensor-ETM+ with spatial resolution is 28.5 meter in bands-1,2,3,4,5,6 and band 7 were used for digital analyses to extract the thematic information from the digital image. This allows to discriminate the additional information about the mineralogy and its associated features of the ground. With the use of the ETM+ sensor data and more sophisticated computer software, more insight is gained into the minerals and hence the types of rocks present. Ground-truth data is used to identify interesting targets (such as an existing igneous body) and the computer is instructed to search for regions that appear similar in all bands. The role of OIF contains high values of OIF and gives more spectral information of the object. In OIF method, seven bands of ETM+ data are used (Table 2), spectral range (8-14 micrometers) and gives rock and minerals ( Lillisand and Leild, 1994) information content. OIF is given as in equation 1. Field observations are taken in four steps as follows:
Fig. 2. Flowchart represents brief methodology adopted
3.1. Digital analysis
The analyses are carried out on satellite images for classification, enhancement of normalize differential vegetation index (NDVI), band ratioing and Principal Component. These enhancement techniques are differentiated thematic information and their associated features. Software packages have been used for digital analyses are namely ER-Mapper version 6.1 and ENVI version 4. Satellite image of ETM+ sensor is geometrically corrected using ground control points (GCPs) of digital topography map at scale 1:25000 applying ER-Mapper software. 27 FCCs are prepared to select the best images for differentiating the rock types and minerals. The OIF formula (Eq 1) is applied for different composite spectral (Table.1).
Table 1 OIF values for different composite spectral (FCC)
Principal component transformation (eigenvector and eigenvalues) are applied to six ETM+ bands (1,2,3,4,5 and 7) in Table 2. Crosta method (Wilson, 1992) is based on one of the statistical techniques. In this study Crosta method and OIF bands combination are compared to derive the optimum results.
Table 2 Homougeneous bands matrix
3.1.1. Supervised classification
Supervised classification procedure is performed using ENVI 4 software package. The training sets are identified on the image for extraction of thematic information. Six classes are chosen based on ground truth data evidences. The supervise classification is carried out using maximum probability algorithm (Wilson, 1992) and different FCC images are produced. With respect to Table 1, seven FCC images in different band combination of visible, Infra-red and Thermal infra-red with maximum OIF have been used for classification purposes. Post classification confusion matrix using ground truth images and region of interest (ROIs) are carried out for accuracy measure (Table 3). On the basis of OIF method, best band combination FCC images are selected for enhancing thematic information content and classifications.
Table 3 Accuracy of classification in different spectral composite
4. Data analysis and discussion
The use of ETM+ bands (Fig 3) for interpreting igneous and minerals which are providing more accurate delineation of the thematic area. Amphiboles and clay minerals are enhanced on digital images. To evaluate the enhancement capability of the ETM+ sensor, few test sites in west Esfahan city have been identified and studied as training window (areas) for image classification techniques.
Fig. 3. Showing FCC of 7,4&1 ETM image
Clay minerals are more enhanced using band ration 5/7. PC3 has high positive loading in band 7 and negative loading in band 1 respectively. This PC enhances amphibole minerals and amphibolite rocks. The bright pixel on the image (Fig 4) is showing amphibole minerals and amphibolite rocks. The spectral reflectance curve diagram of amphibole (Fig 5) indicates that the amphibole minerals reflects in band 7 and absorb in band 1. Thus the amphibole minerals are showing in bright pixels on the image. For amphibole the digital image (Fig 4) is prepared using eigenvector loading of PC3 (Eq 2) as follows:
{ ( PC3= 0.48(B1)-0.39(B2)-0.32(B3)-0.22(B4)-0.48(B5)+0.49(B7)}………Eq.2
Fig. 4. Represents amphibole minerals using band ratio (7/3) image in PC3
Fig. 5. Represents behavior of spectral reflectance in the study area
Band ratios were also applied for the study which shows that the band ratio: (7/1) and (7/3) on the image is also enhanced the amphibole minerals. In PC3 the granite body is seen in dark color on the image while orthoclase and quartz minerals visible band and Infra-red band (IR) have homogeneous and uniform reflection.
The combination of Band Vs Principal component contains 86.8 percentage of variance of six bands.
PCA analysis shows that PC1 does not contain spectral feature relevant to enhancing the igneous body and amphibole minerals as it is combination of all bands and contain 86.8 % of the variance of six bands. This PC provides information mainly on albedo and topography (Ranjbar and Honarmand, 2004). Vegetation cover is enhanced in PC2 (Fig 6) as this has higher loading of band 4 (0.001). Thus, in PC2 vegetation cover display in light color with moderate tone on the image due to maximum negative values (Table 4).
Fig. 6. Showing NDVI (PC2)
Table 4 Main components 6 band (Values and vectors)
PC5 enhance the clay minerals (Fig 7). This PC has higher loading of band 7 (0.68) and band 5 ( -0.71) but in negative sign (Table 4). Clay minerals are absorbed in band 7 and reflected in band 5. Analysis of PC5 shows that the most important contribution come in band 5 (- ve) and band 7 (+ve). Therefore, darker color in the image represents pixel content that contain clay minerals and shales. But highlight clay minerals and bright pixel (Fig. 7) on the image, the reversed of the component is obtained using the following equation 3.
PC5=-0.07 (B1) -0.014 (B2) +0.014 (B3) +0.13(B4)-0.71(B5)+0.68(B7)…………………………eq.3
Fig. 7. Explicit the clay minerals using band ratio ( 5/7) image in PC5
PC3 has four negative values (B1 to B4) and two positive values in band 5 and 7. The higher loading is in band 7 (0.49) and loading value in band 1 (-0.48) but in negative sign. It shows that amphibole minerals are more enhanced and can be seen in bright appearance on the image. By applying formula (Eq.2), it reveals that band ratio 7/1 and 7/3 are the best for enhancing amphibole minerals. In PC3 the granitic minerals appear dark color having low pixel values. The analyses indicate that separation of the minerals can be possible using above techniques.
One of the best is called principal components analysis. It is still difficult to identify the rocks without on-site information "ground-truth". Usually, color anomalies are difficult to explain from the imagery alone and warrant examination on the ground. However, different spectral behavior on objects is given in Fig 8. The classification on different FCC images beside OIF method for understanding the best image combination using Crosta method is becoming more realizable for discrimination of the minerals. Classification processing on the best FCC images (Fig 9) derived from OIF method shows that applying PCA Crosta method in the same images gives more accurate information. Hence, it has become much easier to identify contacts and even variations within a particular rock unit. The above analyses indicated that the minerals variability in the area on account of the environmental impact and chemical weathering on the surface of the landscape which are responsible changing the geochemical characteristics and its reflective properties.
Fig. 8. Showing spectral trends of objects
Fig. 9. Showing classification of image
5. Conclusions
Environment condition is one of the factors to hinder right information at right time by conventional techniques. This method provides real information through digital analysis of satellite data. Geologic maps (assuming that they are even available) may be inaccurate and, at best, are usually generalizations. The geologist doing the mapping may have painstakingly mapped the boundaries of a particular formation and missed an obvious mineralization zone within it. Similarly, geologic structures and mineral information can be quite vivid on satellite imagery. Subtle color variations that would go unnoticed on the ground can be made quite bold in false-color renditions, begging to be assayed, as it were. Known mining areas can sometimes be identified by using satellite imagery.
However, this study shows that Crosta method with applying PCA is useful to identify a PC in which give more information about minerals and rock types. It is seen that PC3 enhance the amphibole minerals and amphibolite rocks. PC5 also enhances the clay minerals on the image. OIF is useful to identify the specific spectral bands which give more information about minerals. Thus combining of two methods is highly increasing the accuracy to identify the minerals and rock type in arid and semi arid environment having scanty vegetation. Field observations have suggested the combination of OIF and Crosta methods is more accurate than use in separate form.
While capable of producing images similar to the ETM+ scanners, their real advantage is providing a spectral signature for each pixel in the image. By matching laboratory-derived reflection spectra of minerals against the individual pixel spectra, the dominant mineral and rock type (Igneous body). Under ideal conditions, even mixtures of two or three minerals can be identified. However, there is the potential for prospecting directly for key minerals associated with economically interesting mineralization.
This study reveals that I various processes of rock discrimination and mineral identification in arid and semi-arid environment, satellite data across field observation is preferable. It also shows that applying PC analysis could be used for specific minerals. Thus, comparative study shows that arid and semi-arid environment condition is responsible for fluctuation in the spectral variability in the area due to fluctuating of environmental condition. This area required high frequency analysis and multi angle high resolution data to manage and monitor the impact of the environment in the area.
Acknowledgements
The authors are thankful to staffs of the Islamic Azad University of Dezful Iran for their help and continue encouragement during this work. The authors are thankful to Leila Farzinpor for her kind support.
Reference:
Using Principal techniques on ETM+ 2002 for arid and semi-arid environment Central Iran
Gholamreza Mirzavand
Civil Engineering Department,
Islamic Azad University Dezful Branch, Dezful Iran
Gholamreza Mirzavand
mirzavand@yahoo.com
Abstract
Principal component analysis is a well known method of orthogonalizing data. It converges very fast and the theoretical method is well understood. Basically image processing is used for extraction of required information. The present study begins with remote sensing techniques and PCA applying on enhance thematic mapper (ETM+) satellite image dated 2002 for central of Iran, west Esfahan city. The study presents two methods of enhancing igneous body consists of amphibolites and volcanic using optimum index factor (OIF) and Crosta. In this study the optimum index factor allows to use the multi-spectral data for understanding the best false color composite (FCC) image. It is also highlighted the required spectral range (bands) for classification of image to enhance the igneous bodies. The Crosta technique is based on PCA using eigenvector, enhanced the spectral behavior of minerals. The objects on the image are enhanced in terms of black and white with respect to eigenvector loading. 27 FCC images are produced and analyzed using OIF. It is seen that the classification on FCC images for bands 741 and 541 of Infra-red and FCC images for band 654 and 641 of Thermal infra-red are the most accurate composite color images to enhance igneous body, amphibole and clay minerals. The study reveals that the PC3 is increasing the enhancement for reorganization of amphibole minerals in orthoamphibolite rocks. It also shows that PC5 has suitable enhancement to identify the clay minerals in granite rocks. However the study also indicates the use of optimum index factor for identification of spectral bands to give more information about minerals on the image using classification techniques in arid and semi-arid environment where vegetation cover is scanty. This study also represents the use of remote sensing techniques for exploration.
1. Introduction
Principal component analysis is a linear procedure to find the direction in input space where most of the energy of the input lies. In other words, PCA performs feature extraction. The projections of these components correspond to the eigenvalues of the input covariance matrix. The principal component analysis is performed first, and then the eigenvector loading is trained to generate image that give the information of spectral bands for easy interpretation. The reason for this is that the PCA faster since eigenvalues are stable. Natural color and especially standard false color infrared images don't show much variation in rock colors. It is often difficult to discern rock contacts, let alone identify the rock type. Various computer based techniques have been developed that allow a greater variation in the observed colors in non-standard false-color renditions. Many workers have used OIF method such as Hunt and et al (1978), Abrams et al (1983), Duchscherer(1982), Srivastar and et al ( 1998) and Ranjbar and Honarman (2004). One of the statistical methods is optimum index factor (OIF) described by Chavez and et al, (1984). Landsat ETM+ data have been subjected to Crosta and OIF analysis. Igneous body, amphibole and clay minerals in images have been prepared according to OIF and crosta methods. The Crosta technique is based on PCA. Through the analysis of eigenvector values (Crosta and et al 1989), it allows identification of principal components that contain spectral information about specific minerals as well as contribution of each of the original bands to the components in relation to spectral response of the material interest. The PCA transformation (eigenvector and eigenvalues) is applied six ETM+ bands Viz. 1,2,3, 4,5 and 7 in table 3 for the study to enhance the spectral variability of content on the image. The purpose to use OIF and Crosta methods in the present study is: 1- to identify the suitable composite color to generate FCC that can enhance the igneous bodies and mineral content on the image, 2- to emphasize the suitable spectral range (bands) for classification on the image and 3- impact of environmental condition on the igneous body and its associated ecosystem.
2. Study area
The study area lies between latitudes 32º 33' 00" N to 32 º 42' 44" N and longitudes 50 º 52' 30" E to 51º 42' 00" E (Fig 1). It lies in 65 km west of Esfahan city and south of Tiran city. The Zayandeh river is a perennial and flowing north to south direction. The sinuosity or meandering is high in the southern part of the study area. The area has variable topography and geomorphological features, with gentle to moderate slope. Environmentally, the area lies in arid and semi-arid region with seasonal rain fall and some times snow fall during winter period. The area is having scanty vegetation cover except along the river course.
2.1. Geological settings
The pioneer workers are divided Iran into ten regions of different history, structures and sedimentation ( Berberian 1976, Colman-Sadd 1978, Darvishzadeh 1992, Ali and Pirasteh 2004, Pirasteh et al, 2004). Ali and Pirasteh 2004 stated that the Zagros Mountains comprises five litho-tectonic units from northeast to southwest, are Urumieh Dokhtar Arc (UDA), Sanandaj Sirjan Zone (SSZ), Imbricate Zone (IZ), Zagros Fold Belt (ZFB) and Molasse Cover Sequences (MCS). Geologically, the study area belongs to Sanandaj Sirjan Zone which consists of igneous-metamorphic rocks, volcanics and sedimentary from Precambrian to Recent in age (Fig. 1). Structurally, the area is complex with almost rugged topography, that is due to tectonic activities and erosion processes (Pirasteh and Ali, 2005). The area is uplifted with fault zones in Sanandaj Sirjan Zone. It comprises alternative metamorphic rocks of Precambrian age and undergone to younger sedimentary and Paleozoic rocks. Alternative metamorphic rocks consists of phylite rocks, schists and igneous bodies ( meta basaltic and granodiorite). Structurally, the trend of these outcrops follow the Zagros Mountains direction ( north west to south east). Meta-igneous rocks are consists of green schists with schistosity, light gray schists and ortho-amphibolite in dark color with coarse texture and non-schistosity. Granodioritic series are cut by ortho-amphibolite rocks in the area. They generally are comprise medium grained to coarse alkali feldspar and plagioclase with some quarts grained. Due to weathering feldspar minerals are converted to clay minerals.
Fig.1. Geological map of the study area
3. Materials and Methodology
Methodology in brief is given in Figure 2. Geological map and digital topography maps are used for ground truth measurement and accuracy purposes. Landsat 7 satellite, sensor-ETM+ with spatial resolution is 28.5 meter in bands-1,2,3,4,5,6 and band 7 were used for digital analyses to extract the thematic information from the digital image. This allows to discriminate the additional information about the mineralogy and its associated features of the ground. With the use of the ETM+ sensor data and more sophisticated computer software, more insight is gained into the minerals and hence the types of rocks present. Ground-truth data is used to identify interesting targets (such as an existing igneous body) and the computer is instructed to search for regions that appear similar in all bands. The role of OIF contains high values of OIF and gives more spectral information of the object. In OIF method, seven bands of ETM+ data are used (Table 2), spectral range (8-14 micrometers) and gives rock and minerals ( Lillisand and Leild, 1994) information content. OIF is given as in equation 1. Field observations are taken in four steps as follows:
- Before applying remote sensing techniques
- During applying OIF method
- During applying Crosta method
- After three above steps to study the accuracy separately as well as combination of OIF and Crosta methods.
Fig. 2. Flowchart represents brief methodology adopted
3.1. Digital analysis
The analyses are carried out on satellite images for classification, enhancement of normalize differential vegetation index (NDVI), band ratioing and Principal Component. These enhancement techniques are differentiated thematic information and their associated features. Software packages have been used for digital analyses are namely ER-Mapper version 6.1 and ENVI version 4. Satellite image of ETM+ sensor is geometrically corrected using ground control points (GCPs) of digital topography map at scale 1:25000 applying ER-Mapper software. 27 FCCs are prepared to select the best images for differentiating the rock types and minerals. The OIF formula (Eq 1) is applied for different composite spectral (Table.1).
Supervised classification procedure is performed using ENVI 4 software package. The training sets are identified on the image for extraction of thematic information. Six classes are chosen based on ground truth data evidences. The supervise classification is carried out using maximum probability algorithm (Wilson, 1992) and different FCC images are produced. With respect to Table 1, seven FCC images in different band combination of visible, Infra-red and Thermal infra-red with maximum OIF have been used for classification purposes. Post classification confusion matrix using ground truth images and region of interest (ROIs) are carried out for accuracy measure (Table 3). On the basis of OIF method, best band combination FCC images are selected for enhancing thematic information content and classifications.
The use of ETM+ bands (Fig 3) for interpreting igneous and minerals which are providing more accurate delineation of the thematic area. Amphiboles and clay minerals are enhanced on digital images. To evaluate the enhancement capability of the ETM+ sensor, few test sites in west Esfahan city have been identified and studied as training window (areas) for image classification techniques.
Fig. 3. Showing FCC of 7,4&1 ETM image
Clay minerals are more enhanced using band ration 5/7. PC3 has high positive loading in band 7 and negative loading in band 1 respectively. This PC enhances amphibole minerals and amphibolite rocks. The bright pixel on the image (Fig 4) is showing amphibole minerals and amphibolite rocks. The spectral reflectance curve diagram of amphibole (Fig 5) indicates that the amphibole minerals reflects in band 7 and absorb in band 1. Thus the amphibole minerals are showing in bright pixels on the image. For amphibole the digital image (Fig 4) is prepared using eigenvector loading of PC3 (Eq 2) as follows:
{ ( PC3= 0.48(B1)-0.39(B2)-0.32(B3)-0.22(B4)-0.48(B5)+0.49(B7)}………Eq.2
Fig. 4. Represents amphibole minerals using band ratio (7/3) image in PC3
Fig. 5. Represents behavior of spectral reflectance in the study area
Band ratios were also applied for the study which shows that the band ratio: (7/1) and (7/3) on the image is also enhanced the amphibole minerals. In PC3 the granite body is seen in dark color on the image while orthoclase and quartz minerals visible band and Infra-red band (IR) have homogeneous and uniform reflection.
The combination of Band Vs Principal component contains 86.8 percentage of variance of six bands.
PCA analysis shows that PC1 does not contain spectral feature relevant to enhancing the igneous body and amphibole minerals as it is combination of all bands and contain 86.8 % of the variance of six bands. This PC provides information mainly on albedo and topography (Ranjbar and Honarmand, 2004). Vegetation cover is enhanced in PC2 (Fig 6) as this has higher loading of band 4 (0.001). Thus, in PC2 vegetation cover display in light color with moderate tone on the image due to maximum negative values (Table 4).
Fig. 6. Showing NDVI (PC2)
PC5=-0.07 (B1) -0.014 (B2) +0.014 (B3) +0.13(B4)-0.71(B5)+0.68(B7)…………………………eq.3
Fig. 7. Explicit the clay minerals using band ratio ( 5/7) image in PC5
PC3 has four negative values (B1 to B4) and two positive values in band 5 and 7. The higher loading is in band 7 (0.49) and loading value in band 1 (-0.48) but in negative sign. It shows that amphibole minerals are more enhanced and can be seen in bright appearance on the image. By applying formula (Eq.2), it reveals that band ratio 7/1 and 7/3 are the best for enhancing amphibole minerals. In PC3 the granitic minerals appear dark color having low pixel values. The analyses indicate that separation of the minerals can be possible using above techniques.
One of the best is called principal components analysis. It is still difficult to identify the rocks without on-site information "ground-truth". Usually, color anomalies are difficult to explain from the imagery alone and warrant examination on the ground. However, different spectral behavior on objects is given in Fig 8. The classification on different FCC images beside OIF method for understanding the best image combination using Crosta method is becoming more realizable for discrimination of the minerals. Classification processing on the best FCC images (Fig 9) derived from OIF method shows that applying PCA Crosta method in the same images gives more accurate information. Hence, it has become much easier to identify contacts and even variations within a particular rock unit. The above analyses indicated that the minerals variability in the area on account of the environmental impact and chemical weathering on the surface of the landscape which are responsible changing the geochemical characteristics and its reflective properties.
Fig. 8. Showing spectral trends of objects
Fig. 9. Showing classification of image
5. Conclusions
Environment condition is one of the factors to hinder right information at right time by conventional techniques. This method provides real information through digital analysis of satellite data. Geologic maps (assuming that they are even available) may be inaccurate and, at best, are usually generalizations. The geologist doing the mapping may have painstakingly mapped the boundaries of a particular formation and missed an obvious mineralization zone within it. Similarly, geologic structures and mineral information can be quite vivid on satellite imagery. Subtle color variations that would go unnoticed on the ground can be made quite bold in false-color renditions, begging to be assayed, as it were. Known mining areas can sometimes be identified by using satellite imagery.
However, this study shows that Crosta method with applying PCA is useful to identify a PC in which give more information about minerals and rock types. It is seen that PC3 enhance the amphibole minerals and amphibolite rocks. PC5 also enhances the clay minerals on the image. OIF is useful to identify the specific spectral bands which give more information about minerals. Thus combining of two methods is highly increasing the accuracy to identify the minerals and rock type in arid and semi arid environment having scanty vegetation. Field observations have suggested the combination of OIF and Crosta methods is more accurate than use in separate form.
While capable of producing images similar to the ETM+ scanners, their real advantage is providing a spectral signature for each pixel in the image. By matching laboratory-derived reflection spectra of minerals against the individual pixel spectra, the dominant mineral and rock type (Igneous body). Under ideal conditions, even mixtures of two or three minerals can be identified. However, there is the potential for prospecting directly for key minerals associated with economically interesting mineralization.
This study reveals that I various processes of rock discrimination and mineral identification in arid and semi-arid environment, satellite data across field observation is preferable. It also shows that applying PC analysis could be used for specific minerals. Thus, comparative study shows that arid and semi-arid environment condition is responsible for fluctuation in the spectral variability in the area due to fluctuating of environmental condition. This area required high frequency analysis and multi angle high resolution data to manage and monitor the impact of the environment in the area.
Acknowledgements
The authors are thankful to staffs of the Islamic Azad University of Dezful Iran for their help and continue encouragement during this work. The authors are thankful to Leila Farzinpor for her kind support.
Reference:
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- Crosta, A., Moor,J,Mcm.,(1989)Enhanement of landsat thematic mapper imagery for residual soil mapping in SW Minais Gerais State, Brazil: a prospecting case history in Greenstone belt terrain. In: Proceedins of the Seventh ERIM Thematic Conference:Remote Sensing for Exploration Geology, 1173-1187.
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- Hunt, GR., Salisbury, J.,W., Lenhoff G.J.,(1978), Viible and near infrared spectra of minerals and rocks:III Oxides and hydroxide. Modern Geology, 195-205.
- Lillesand, T.M., and Kiefer,R.W,(1994)Remote sensing and image interpretation,(4th edition), New york, John willey and Sons.
- Pirasteh Saied, Ali Syed Ahmad, Hussaini Saiedeh, 13 and 14 December (2004), Use of RS and GIS to assess lithological & geomorphological controls on lineaments and tectonic processes in the Zagros Mountains, SW Iran, GRSE Annual meeting, Mapping hazardous Terrain using remote sensing, Burlington House, London, UK, http://www.grsg.org/meeting.html
- Pirasteh Saied, Ali Syed Ahmad, 2005, lithostratigraphic study from Dezful to Brojerd-Dorood areas SW Iran using digital topography, remote sensing and GIS, Indian Petroleum Geology Journal, Vol. 13, No. 1, 1-13
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