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GEOLOGY, ECOLOGY, AND LANDSCAPES 2020, VOL. 4, NO. 1, 59–70 INWASCON https://doi.org/10.1080/24749508.2019.1585657 RESEARCH ARTICLE Lithological mapping in Sangan region in Northeast Iran using ASTER satellite data and image processing methods a a a b Ali Rezaei , Hossein Hassani , Parviz Moarefvand and Abbas Golmohammadi a b Department of Mining and Metallurgy Engineering, Amirkabir University of Technology, Tehran, Iran; Geological Survey and Mineral Exploration of Iran, Tehran, Iran ABSTRACT ARTICLE HISTORY Received 25 April 2018 Lithological mapping using satellite images, particularly the Advanced Spaceborne Thermal Accepted 19 February 2019 Emission and Reﬂection Radiometer (ASTER) data help in eﬀectively deﬁning the best initial targets for regional exploration. ASTER data allow for the discrimination of rock units in the KEYWORDS broader region. This research work is focused on the use of remote sensing techniques for Lithological mapping; geological mapping using ASTER satellite image and generating a geological map of Sangan ASTER; Band ratio; spectral region. The study area is located in southeast of Khorasan-e-Razavi province and at the angle mapper; support eastern edge of the Khaf-Kashmar-Bardskan Volcano-Plutonic Metallogenic Belt in northeast vector machine; Sangan Iran. Band ratio (BR), spectral angle mapper (SAM) and support vector machines (SVMs) methods were used for classifying the main lithologic units in Sangan region. The results of BR, SAM and SVM techniques were quantitatively compared with geological boundaries mapped in the ﬁeld showing an accuracy of nearly 79 %. SVMs method, in comparison to conventional methods of classiﬁcation, was known to provide superior results. As the ﬁnal result of this research, integration of remote sensing and ﬁeld investigations led to generating high accuracy geological map in the Sangan region. Application of the methods has invalu- able implications for geological mapping and mineral exploration in inaccessible regions. 1. Introduction product when working on regional scale geological maps. Numerous research works have used ASTER The importance of recognizing the spatial patterns of data in geological mapping and discrimination of rock geological mapping makes remote sensing one of the units during the last decade mainly due to spectral standards and most successful procedures in explora- characteristics of the unique integral bands of ASTER tion geology. Remote sensing is a very powerful and in the visible/near-infrared (VNIR), shortwave infrared essential tool for geologists that can be applied to (SWIR) and thermal infrared (TIR) parts of the electro- improve regional geological mapping process (Pour & magnetic spectrum and the possibility of applying sev- Hashim, 2015). Remote sensing satellite imagery has eral image processing algorithms (Hewson, Robson, high potential to provide a solution to overcome the Carlton, & Gilmore, 2017). problems and limitations associated with geological In the Sangan region, due to harsh conditions and ﬁeld mapping and mineral exploration. Recent devel- logistic diﬃculties, many areas remain poorly studied opment of multi-spectral remote sensing systems such from geological mapping and mineral exploration as the Advanced Spaceborne Thermal Emission and point of view. Thus, the ASTER multispectral remote Reﬂection Radiometer (ASTER) have shown the appli- sensing data are used for geological mapping objec- cation of remote sensing satellite imagery for geological tives and improving the coverage and overall quality mapping with various purposes in the world (Di of geological information in this region. Tommaso & Rubinstein, 2007; Liu, Zhou, Jiang, In this research, we apply the ASTER remote sen- Zhuang, & Mansaray, 2014; Masoumi, Eslamkish, sing data because of speciﬁc characteristics of ASTER Abkar, Honarmand, & Harris, 2017; Pour, Hashim, bands along with some other conventional and Hong, & Park, 2017; Van Ruitenbeek, Cudahy, Van sophisticated image processing techniques to extract der Meer, & Hale, 2012). The interpretation of satellite geological information and lithologic units. Diﬀerent images for geological mapping is normally based on the image processing techniques such as band Ratio (BR), indirect evidence that can be visible at the surface (Pour, spectral angle mapper (SAM) and support vector Hashim, & Marghany, 2011). Remote spectral geology machines (SVMs) are used to ASTER bands for geo- images have been processed for enhancing and under- logical mapping and discrimination of the exposed standing the geology in a regional scale. Thus, applica- rock units based on satellite images in the study area. tion of ASTER images helps greatly in improving the CONTACT Hossein Hassani firstname.lastname@example.org Department of Mining and Metallurgy Engineering, Amirkabir University of Technology, Tehran, Iran This article has been republished with minor changes. These changes do not impact the academic content of the article. © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 60 A. REZAEI ET AL. These enhancement techniques combined with the ﬁeld observations and previous geological maps of the study area, allowed distinguishing between diﬀer- ent rock units. This research aims at prediction of geologically signiﬁcant patterns from ASTER data in support of ﬁeldwork planning and map compilation. 2. Location and geological setting of the study area The study area is located in southeast of Khorasan- e-Razavi province in Iran and in the end section of the Khaf-Kashmar-Bardskan Tertiary magmatic belt of Central Iran blocks that lies between longitudes 60° 10′ 00″ to 60° 50′ 00″ E and latitudes 34° 10′ 00″ to 34° 50′ 00″ N, NE Iran. The geology of this belt mainly includes Cenozoic silicic to maﬁc volcanic rocks, which have been intruded by granitoid units of granitic to dioritic composition (Figure 1(a)). There are a number of iron ore deposits with con- siderable reserve amounts in the study area. The Sangan iron skarn is one of the most important deposits. Information on the regional geology of the Sangan Magmatic Complex (SMC) area has been presented by Golmohammadi, Karimpour, Malekzadeh Shafaroudi, and Mazaheri (2014); Sepidbar, Mirnejad, and Mi (2018). The oldest rocks in the Sangan region include a thick pile (1500 m) of Late Neoproterozoic volcano – sedimentary rocks with limestone – marble interlayers, overlain strati- graphically by Devonian and Carboniferous dolo- mites, shales and calc–schists in the southern parts of the SMC (Figure 1(b)). These sedimentary rocks Figure 1. (a) The Khaf-Kashmar-Bardskan Volcano-Plutonic Belt are unconformably covered by the SMC volcanic and and Doruneh fault (Malekzadeh Shafaroudi et al., 2013); and (b) pyroclastic rocks and/or intruded by granitoids. The geological map of the study area and Sangan Magmatic carbonate rocks of Jurassic consist of crystalline and Complex (modiﬁed after 1:250,000 geological map of Taybad, less dark dolomite limestone that spread all over the Geological Survey of Iran) (Sepidbar et al., 2018). area (Mazhari, Malekzadeh Shafaroudi, & Ghaderi, 2017). The SMC includes 2000 m of volcanic, pyro- (BA) and Dardvey (D)) anomalies and eastern anom- clastic rocks cross-cut by plutonic rocks (Sepidbar aly (subdivided Senjedak 1, 2, 3; Madanjoo, Som– et al., 2018). It is subdivided into two major units, Ahanai and Ferezneh) (Rezaei et al., 2019). The according to the geographical position and rock Sangan iron skarn deposits are distributed along the types: (1) the southern SMC unit, including volcanic contact zone between the Jurassic clastic rocks or and volcanoclastic rocks with minor intrusive rocks; Cretaceous carbonates and the Eocene igneous rocks and (2) the northern SMC unit, predominantly com- (Malekzadeh Shafaroudi, Karimpour, & prising granitoid intrusions (Figure 1(b)). The Golmohammadi, 2013). The structural features of depressions between the ranges are ﬁlled in by thick the region such as faults and foldings, as well as the Neogene sediments (Fauvelet & Eftekhar-Nezhad, strike of the formations follow the E–WtoNW–SE 1990). Occurrences of iron mineralization are present direction of the major Doruneh fault in several places in the study area, including small (Golmohammadi et al., 2014). and large bodies of magnetite in relationship with skarn-type rocks (Rezaei, Hassani, Moarefavand, & Golmohammadi, 2019). The SMC includes several 3. Material and methods ore bodies in east-west direction. Based on character- istics of ore deposits this mining region is divided Data sources for this study included remote sensing into western (subdivided into A, Aʹ, B, CS (C-South), data (ASTER image), regional geological material and CN (C-North)), central (subdivided into Baghak (geological map at a scale of 1:250,000 and relevant GEOLOGY, ECOLOGY, AND LANDSCAPES 61 documentation) and ground survey data. Also, 3.2 Pre-processing steps of ASTER data Geoscience dataset used in the compilation of The ASTER images used in this study have been a remote predictive geological map of the study area pre-georeferenced to UTM zone 41 North projec- includes SMC geological map at a scale of 1:50,000. tion using the WGS-84 datum. Pre-processing steps ENVI (Environment for Visualizing Images) ver- of ASTER data mainly include removing crosstalk sion 4.8 and ArcGIS 10.3 software packages were eﬀects, re-sampling and stacking, radiometric cali- used to process the ASTER imagery and preparation bration and atmospheric correction. Crosstalk cor- of GIS layers, respectively. rection was performed with the ASTER SWIR bands, aimed at removing the eﬀects of energy overspill from band 4 into bands 5 and 9 (Iwasaki 3.1 Satellite remote sensing data & Tonooka, 2005;Mars&Rowan, 2011). The The ASTER data are commonly used in geological ASTER SWIR data with a 30-m spatial resolution application. ASTER is a multispectral imaging sensor was re-sampled at 15-m to ﬁtwithVNIRdata that measure reﬂected and emitted electromagnetic applying a bilinear method. Thus, the VNIR and radiation from Earth’s surface and atmosphere in 14 SWIR bands were combined to form nine bands at bands. Consisting of three separate instrument sub- 15-m spatial resolution. Afterwards, radiometric systems, ASTER provides observation in three diﬀer- calibration and atmospheric correction were exe- ent spectral regions of the electromagnetic spectrum, cuted. At the Radiometric calibration, the digital including the VNIR, SWIR and TIR bands (Table 1). numbers (DN) in the pixel of the original image ASTER swath width is 60 km (each individual scene were 16-bit integers. Radiometric calibration con- covers 60 × 60 km area of land) making it a useful verted the data of the observed surface into physical tool for regional geological mapping (Abrams, Hook, radiance. While the wavelength range is an atmo- & Ramachandran, 2004). ASTER provides more spec- spheric window, there is atmospheric inﬂuence, tral bands in the SWIR, which therefore increases its including scattering, absorbing, attenuating energy, ability to spectrally discriminate minerals and rocks or changing the spectral distribution, which needs on the Earth’s surface. Reﬂectance measurements in to be compensated for, especially for quantitative the VNIR till the SWIR wavelength region provide applications. Atmospheric correction was applied by diagnostic information that can be used to identify Fast- Line-of-sight Atmospheric Analysis of Spectral rocks and their constituent minerals (Hunt, 1977). Hypercubes (FLAASH) algorithm (Cooley et al., These data enhance the capability of lithologic dis- 2002) to the VNIR and SWIR bands of the ASTER. crimination between diﬀerent rock units. Spectral bands of ASTER have great ability to map hydrother- 3.3 Image processing methods for geological mal alteration mineral zones and lithologic units mapping (Pour, Hashim, Park, & Hong, 2017a, 2017b; Safari, Maghsodi, & Pour, 2017). In this research, six spec- Geological mapping involves identiﬁcation of types of tral bands of the SWIR region and three spectral rock and geologic structures that can contribute to bands of the VNIR region were selected for the pro- each type of geological maps through analysis of cessing. As a result and starting from 2000, ASTER imagery from one or more systems or instruments. data has been successfully used for lithologic map- It is useful to map the presence and distribution of ping in well-exposed areas. rocks in a region for geological purposes. Lithologic units of exposed rocks are mapped using ASTER Table 1. ASTER spectral passband characteristics (Di data, laboratory spectral reﬂectance measurements Tommaso & Rubinstein, 2007). of rock samples and ﬁeld observation in the study Spectral Spatial Radiometric area. Four diﬀerent image processing methods are Band’s Bandwidth Resolution Resolution applied: BR, FCC, SAM and SVMs. Characteristics Number (µm) (m) (bits) VNIR 1 0.52–0.60 15 8 2 0.63–0.69 3.3.1 Band ratio method 3N 0.78–0.80 BR is one of the most important and suitable techniques 3B 0.78–0.86 SWIR 4 1.650–1.700 30 8 for lithological mapping, while can be useful for high- 5 2.145–2.185 lighting certain features or materials that cannot be seen 6 2.185–2.225 7 2.235–2.285 in raw bands. In this method, the choice of bands 8 2.295–2.395 depends on their spectral reﬂectance and positions of 9 2.360–2.430 TIR 10 8.125–8.475 90 12 the absorption bands of the mineral being mapped 11 8.475–8.825 (Rajendran, Thirunavukkarasu, Balamurugan, & 12 8.925–9.275 Shankar, 2012). The false color composites (FCCs) of 13 10.25–10.95 14 10.95–11.65 band combinations and ratios are applied to enhance 62 A. REZAEI ET AL. discriminations between diﬀerent rock units. The inter- is for processing a speciﬁc type of imagery while their pretation of FCC images depends on how these bands ability to successfully handle small training data sets, are assigned to the three principal colors RGB used for often producing a higher classiﬁcation accuracy than the image display which in turn depends on the spectral the traditional methods (Mantero, Moser, & Serpico, characteristics of rocks (Ibrahim, Watanabe, & Yonezo, 2005). The SVM uses a set of data vectors with 2016). The spectral signature analysis is helpful to select known class labels acquired by a-priori knowledge the best bands in the ratio techniques. The choice of the to design a linear hyperplane for separating various most suitable combination of three bands can be carried classes. The data vectors are used as a training set, out statistically (through an optimum index factor) or and every data vector within the set is characterized by referring to the extensive literature and taking into by unique features upon which the classiﬁcation is account lithologies in the region and ﬁnally provide an based (Bishop, 2006). The SVM ﬁnds the optimal adequate base map (Patel & Kaushal, 2011). separating hyperplane between classes by focusing BRs (4/7, 3/4, 2/1 as RGB) of ASTER images are on the training cases. Once the optimal classiﬁer is selected for mapping metasediments, volcaniclastics found, new data with unknown class information and granitoids lithologic units in this research as (test data) can be classiﬁed by the trained SVM recommended by Abdeen, Allison, Abdelsalam, and based on their features (Smirnoﬀ, Boisvert, & Stern (2001). BRs (7/6, 6/5, 6/4 as RGB) are used for Paradis, 2008). SVMs have been used successfully in mapping gneiss domes and granites (Wolters, Goldin, the mapping of similar lithological classes (Sahoo & Watts, & Harris, 2005). BRs (8/5, 5/4, 7/8 as RGB) are Jha, 2016) mostly due to its ability to generate good used for distinguishing alkali granites (younger gran- classiﬁcation results. The SVM approach enables us itoids), granodiorites and quartz diorite (old grani- to discriminate between volcanic and sedimentary toids) (Madani & Emam, 2009). rocks (Othman & Gloagen, 2014). Training data in the study area are deﬁned based on a combination of ﬁeld observations, geological maps, BR and SAM 3.3.2 Spectral angle mapper method results. The SAM has been widely used for lithological map- ping (Chen, Warner, & Campagna, 2007; Murphy, Monteiro, & Schneider, 2012). SAM algorithm is 4. Discussion and results a supervised approach which identiﬁes the various classes in the image based on the calculation of the In this research, lithologic mapping in the study area spectral angle. The algorithm determines the angle is carried out using an integrated interpretation of between the spectra and treats them as vectors in remote sensing data and geological ﬁeld data. The n-dimensional space with dimensionality equal to ﬁrst stage of image interpretation and mapping the number of bands (Kruse et al., 1993). In the included the integrated empirical analysis of ASTER SAM method, image classiﬁcation is performed image and geological map data. Using the 1:250,000 based on calculation of the angle between image scale geological map and image processing, spectra and reference spectra. Reference spectra can a provisional geological map compilation was pre- be taken from the available spectral libraries and ﬁeld pared before new ﬁeldwork commences as remote observations (Kruse et al., 1993). The spectral angle is predictive mapping. In this section, BR method used an average ﬁt over the entire spectral range (or for geological mapping and FCC image are created to a subset of it) of the dataset used in classiﬁcation discriminate the various lithologies in the Sangan (Hecker, Van der Meijde, Van der Werﬀ, & Van region. We applied various BRs that have previously der Meer, 2008). The SAM classiﬁcation enhances been conﬁrmed as useful ratios for detecting geologi- the target reﬂectance characteristics and discriminates cal mapping. The BR images were integrated into one rock units. In lithological mapping studies using FCC image and were used to produce a geological SAM method, each lithological class is assumed to map of the study area. Thus, using diﬀerent combi- have a unique spectral signature (Debba, Carranza, nations of BRs has updated the geological map of the van der Meer, & Stein, 2006; Rowan, Crowley, Sangan region. Schmidt, & Mars, 2000) and the mean spectra of The BR (4/7, 3/4, 2/1 as RGB) results show that the training samples of the class, which is considered as granites are well separated from the adjacent rocks the representative of spectral feature of the class, is (Figure 2). As displayed in Figure 3, BRs (7/6, 6/5, 6/4 used as the reference spectra. as RGB) were used for mapping granites. The 8/5, 5/4 and 8/7 BRs were also used in mapping and discri- mination the diﬀerent rock units in the study area. 3.3.3 Support vector machine method The metasedimentary rocks and granites are discri- The SVM, a supervised classiﬁcation method, was minated on ASTER image with 8/5 and 7/8 proposed by Vapnik (1979) as a data-driven techni- BRs respectively. Also, the 5/4 BR of ASTER data is que. One application of SVM in remote sensing ﬁeld GEOLOGY, ECOLOGY, AND LANDSCAPES 63 Figure 2. FCC image based on band ratios (4/7, 3/4, 2/1 as RGB) of ASTER data. useful for discrimination of muscovite granites. als was also used to evaluate the results of ASTER image Figure 4 depicts the FCC image (8/5, 5/4, 7/8 as spectral signature of the lithologic units in the study RGB) of ASTER data. area. In addition, the mean spectra of all sample spectra The SAM method was applied to ASTER scene cov- of a lithology type was used as the reference spectra. For ering in the Sangan region. The United State Geological SAM, the spectra of the training samples of each litho- Survey (USGS) spectral library of rock forming miner- logic unit class were averaged to construct a ﬁnal class Figure 3. FCC image based on band ratios (7/6, 6/5, 6/4 as RGB) of ASTER data. 64 A. REZAEI ET AL. Figure 4. FCC image based on band ratios (8/5, 5/4, 7/8 as RGB) of ASTER data. spectrum. The training and test samples were selected and red classes, respectively. Comparison of these according to a published 1:250,000 geological map and classes with the ﬁeld observation and geological map SMC geological map. The ﬁeld data were used together indicates that the blue class includes dacite, andesite, with the results of visual interpretation of diﬀerent green tuﬀ, granitic rocks and associated colluvial and ASTER images to prepare a geological map for the alluvial deposits. The intrusive rocks with northwest- Sangan region. The SAM classiﬁcation result of litholo- southeast trend are often included in this class primar- gical mapping is shown in Figure 5, where four classes ily, which is consistent with their granitic and grano- are distinguished and represented by blue, white, green dioritic compositions (biotite granite, diorite and Figure 5. SAM classiﬁcation result for lithological mapping in the Sangan region. GEOLOGY, ECOLOGY, AND LANDSCAPES 65 Figure 6. Geological map at a scale of 1:20,000 of SMC based on ASTER data and ﬁeld observations Figure 7. The ﬁeld photographs of the study area during ﬁeld investigation. (a) Granite; (b) Sandstone and shale; (c) Dolomite; (d) Limestone. 66 A. REZAEI ET AL. Figure 8. Classiﬁcation results achieved by SVM of ASTER data in the study area. Table 2. Each class producer accuracy, user’ s accuracy and average accuracy of ASTER in the study area. sandstone classes, complex of meta-volcanosediment Producer’s User’s Overall rocks and mixtures of carbonate and silicate rock col- Class Accuracy Accuracy Accuracy Kappa luvium and alluvium. The green category includes the Recent alluvium 70.10 72.30 79.03 0.75 Gravel fans and terraces 71.20 72.10 conglomerate, schist, quartzite rocks and sedimentary/ Conglomerate 78.30 80.25 metamorphic rocks. The red class, referred to younger Red conglomerate 78.90 80.25 gravel fans and terraces and recent alluvium classes Sandstone, shale, marl, 80.20 84.30 gypsum (including debris of granite and volcanic). Several litho- Tuﬀ, volcano- 79.20 80.10 logic units of diﬀerent ages exposed in the study area are sedimentary, dacite, andesite composed of similar rocks, such as siltstone, shale and Granite, biotite granite 82.10 83.15 conglomertare, and thus show very similar spectral Quartzite, schist 80.05 81.60 Limestone, dolomite 80.30 82.35 properties. Also the spectra of limestone and dolomite Debris of granite(Placer) 83.05 84.20 are closer than other rock types to the shale and sand- Debris of volcanic 73.20 75.35 Mud ﬂat 82.30 81.90 stone spectrum in the study area. The classiﬁcation map generated by the SAM method for ASTER data shows that this method could be eﬀectively used for geological granodiorite), whereas they are not easily distinguished mapping and exploration in unexplored areas. from the volcanic category, such as dacite, andesite and A local-scale geological map of the SMC, based on green tuﬀ rocks. This class also includes a broad area of the previous geological map and ASTER data by colluvial and alluvial material in northeastern and visual interpretation updated with ﬁeld investigations southeastern parts, which is derived from granitic– (Figures 6 and 7). granodioritic rocks in the study area. The white cate- The preliminary analysis of remote sensing image gory, which is the carbonate rocks (limestone, dolo- interpretation, 1:250,000 scale geological map, and mite), matamorphed conglomerate, shale and SMC geological map of the study area, reveals that GEOLOGY, ECOLOGY, AND LANDSCAPES 67 the lithologies in this region can be identiﬁed on the highest classiﬁcation accuracy. Training and test satellite images. The results show that the SWIR samples used in classiﬁcation and accuracy assess- bands have higher discrimination power for lithologic ment are independently selected with the support of identiﬁcation. geological map and ﬁeld observations. Training sam- In the second stage, the results of BR and SAM ples were carefully selected corresponding to 12 litho- techniques and ﬁeld observations were used to stra- logic classes. In the SVM classiﬁcation of the study tegically guide new ﬁeldwork and newly acquired area, the parameters of radial and polynomial kernels, ﬁeld data were coregistered and jointly interpreted two commonly used kernel functions in remote sen- in another stage for compilation of geological maps, sing applications, are tested. More details are avail- which facilitated tracing geological units between the able in the classiﬁcation result of ASTER data using visited ﬁeld stations and processing images. SVM method (Figure 8). This ﬁgure depicts the dis- We present the application of SVM method in tribution of lithologic units in the Sangan region classifying lithology from the ASTER data in the while volcanic rocks are well separated from sedi- Sangan region in order to update the former litholo- mentary rocks. gical map of the study area. Various combinations of The classiﬁcation results were evaluated by a detailed surface reﬂectance were processed using SVM classi- accuracy assessment and visual interpretation. The clas- ﬁcations to determine the optimal layers that create siﬁcation accuracy was quantitatively evaluated by Figure 9. The ﬁnal geological map with ﬁeld survey points distribution of the study area. 68 A. REZAEI ET AL. testing samples via a confusion matrix, and the kappa investigations conﬁrmed with the highest classiﬁcation coeﬃcient. Accuracy assessment was performed using accuracy of the main rock units in the Sangan region. overall accuracies and confusion matrices derived the Results also indicated that the image processing meth- producer’s and user’s accuracies. The kappa coeﬃcient ods can provide detailed information for discriminat- of agreement was derived. Table 2 indicates that the ing the diﬀerent rock types using ASTER data. producer’s accuracy and user’s accuracy for each class Combining the spectral discrimination via remote sen- using SVM method. The overall accuracy of ASTER sing data resulted in speeding up the survey in the data classiﬁcation is 79% and the kappa coeﬃcient is Sangan region with sparse detailed geological map- 0.75. The results show a good accuracy and represents ping. The ﬁnal lithological map derived from image the measurement of agreement between the classiﬁed analysis can be helpful to identify the economic depos- map and the true reference data. its, such as skarn types in the study area. The ﬁeld observations show that rock units present in the region are consistent with results obtained from Acknowledgments the remote sensing techniques. The higher classiﬁca- tion accuracy of the main rock units (nearly 79 %) was This paper is a part of the ﬁrst author’sPh.D. thesisat also conﬁrmed by visual interpretation in the Sangan Amirkabir University of Technology (AUT), Tehran, Iran. The authors would like to thank the Amirkabir University of region against analytical work results. Lithological clas- Technology (Polytechnic Tehran), Iranian Mines and siﬁcation map of ASTER data after the post- Mining Industries Development and Renovation classiﬁcation processing is shown in Figure 9.The Organization (IMIDRO), and Sangan Iron Ore Mines accuracy of the lithological map is evaluated by inde- Complex (SIOMC) for supporting this research (Project pendent validation samples, ﬁeldwork, and a geological NO.95-3-9372). The contributions of Nima Jabbari, Adonis Fard Mousavi and Samira Rezaei are highly appreciated. The map. It is considerable that former geological maps authors would like to thank the reviewers for their very were used to select the validating and training polygons helpful and constructive reviews of this manuscript. for drawing and accuracy assessment of output classi- ﬁed map. A visual comparison of the classiﬁed inte- grated results with the geologic map shows an overall Disclosure statement good correspondence between predicted occurrences No potential conﬂict of interest was reported by the authors. and the map units. The results indicate that is helpful the productivity of ASTER data for lithological map- ping and classiﬁcation of rock units in the study area. Funding This work was supported by the Amirkabir University of 5. Conclusions Technology [95-3-9372]. The purpose of this study was to use ASTER data and integrated interpretation of remote sensing techniques References to map the lithologic units in Sangan region and to Abdeen, M. M., Allison, T. K., Abdelsalam, M. G., & conﬁrm the results through a series of ﬁeld investiga- Stern, R. J. (2001). Application of ASTER band-ratio tions and former lithological maps. Various image images for geological mapping in arid regions; the neo- processing techniques such as BR, SAM and SVM proterozoic Allaqi Suture, Egypt. Abstracts with were applied to produce derivative data sets that con- Programs - Geological Society of America, 3, 289. Abrams, M., Hook, S., & Ramachandran, B. (2004). ASTER tained enhanced information according to lithologic Users Guide V2. NASA JPL. Accessed on 10 July 2015. discrimination. FCCs images were used based on dif- https://asterweb.jpl.nasa.gov/content/03data/04_ ferent BRs for mapping metasediments, volcaniclastics Documents/aster_user_guide_v2.pdf. and granitoids lithologic units. Using diﬀerent combi- Bishop, C. M. (2006). Pattern recognition and machine learn- nations of BRs has prepared the preliminary geological ing (pp. 738). Singapore, Malaysia: Springer Science. map of the Sangan region. In this research work, SAM Chen, X., Warner, T. A., & Campagna, D. J. (2007). Integrating visible, near-infrared and shortwave infrared method did not consider the spectral illumination and hyperspectral and multispectral thermal imagery for only consider the spectral similarity between the refer- geological mapping at Cuprite, Nevada. Remote Sensing ence and image spectra, therefore it introduces poten- of Environment, 110, 344–356. tial commission errors in the results. The results Cooley, T., Anderson, G. P., Felde, G. W., Hoke, M. L., obtained from the application of the SVM method Ratkowski, A. J., Chetwynd, J. H., . . . Bernstein, L. S. (2002). FLAASH, a MODTRAN4-based atmospheric showed the producer’s accuracy and user’s accuracy correction algorithm, its application and validation. of classiﬁcation were 78.24% and 79.8%, respectively. Geoscience and Remote Sensing Symposium IEEE The kappa coeﬃcient was computed as 0.75, showing International, 3, 1414–1418. high overall and categorical accuracies. The results Debba, P., Carranza, E. J. M., van der Meer, F. D., & showed the geological map deriving from SVM classi- Stein, A. (2006). Abundance estimation of spectrally ﬁcation has an overall accuracy of nearly 80%. Field similar minerals by using derivative spectra in simulated GEOLOGY, ECOLOGY, AND LANDSCAPES 69 annealing. Geoscience and Remote Sensing, IEEE Mazhari, N., Malekzadeh Shafaroudi, A., & Ghaderi, M. Transactions on, 44, 3649–3658. (2017). Detecting and mapping diﬀerent types of iron Di Tommaso, I., & Rubinstein, N. (2007). Hydrothermal mineralization in Sangan mining region, NE Iran, using alteration mapping using ASTER data in the inﬁernillo satellite image and airborne geophysical data. porphyry deposit, Argentina. Ore Geology Reviews, 32, Geosciences Journal, 21(1), 137–148. 275–290. Murphy, R. J., Monteiro, S. T., & Schneider, S. (2012). Fauvelet, E., & Eftekhar-Nezhad, J. (1990). Explanatory text Evaluating classiﬁcation techniques for mapping vertical of the taybad quadrangle map1:250000, Geological geology using ﬁeld-based hyperspectral sensors. IEEE Survey of Iran, Tehran, Iran. trans. Geoscience and Remote Sensing, 50, 3066–3080. Golmohammadi, A., Karimpour, M. H., Malekzadeh Othman, A. A., & Gloagen, R. (2014). Improving litholo- Shafaroudi, A., & Mazaheri, S. A. (2014). Alteration- gical mapping by SVM classiﬁcation of spectral and mineralization, and radiometric ages of the source plu- morphological features: The discovery of a new chromite ton at the sangan iron skarn deposit, northeastern Iran. body in the Mawat ophiHolite complex (Kurdistan, NE Ore Geology Reviews, 65(2), 545–563. Iraq). Remote Sensing, 6(8), 6867–6896. Hecker, C., Van der Meijde, M., Van der Werﬀ, H., & Van Patel, N., & Kaushal, B. (2011). Classiﬁcation of features der Meer, F. D. (2008). Assessing the inﬂuence of refer- selected through Optimum Index Factor (OIF) for ence spectra on synthetic SAM classiﬁcation results,” improving classiﬁcation accuracy. Journal of Forestry IEEE trans. Geosci. Remote Sens., 46(12), 4162–4172. Research, 22,99–105. Hewson, R. D., Robson, D., Carlton, A., & Gilmore, P. Pour, A. B., Hashim, M., Park, Y., & Hong, J. K. (2017a). (2017). Geological application of ASTER remote sensing Mapping alteration mineral zones and lithological units within sparsely outcropping terrain, Central New South in Antarctic regions using spectral bands of ASTER Wales, Australia. Cogent Geoscience, 3, 1319259. remote sensing data. Geocarto International. Hunt, G. (1977). Spectral signatures of particulate minerals doi:10.1080/10106049.2017.1347207 in the visible and near infrared. Geophysics, 42, 501–513. Pour, A. B., Hashim, M., Park, Y., & Hong, J. K. (2017b). Ibrahim, W. S., Watanabe, K., & Yonezo, K. (2016). Lithological and alteration mineral mapping in poorly Structural and litho-tectonic controls on neoproterozoic exposed lithologies using Landsat-8 and ASTER satellite base metal sulﬁde and gold mineralization in North data: North-eastern Graham Land, Antarctic Peninsula. Hamisana shear zone, South Eastern Desert, Egypt: Ore Geology Reviews. doi:10.1016/j.oregeorev.2017.07.018 The integrated ﬁeld, structural, landsat 7 ETM+and Pour, A. B., & Hashim, M. (2015). Structural mapping ASTER data approach. Journal of Ore Geology Reviews, using PALSAR data in the central gold belt peninsular 79,62–77. Malaysia. Ore Geology Reviews, 64,13–22. Iwasaki, A., & Tonooka, H. (2005). Validation of Pour, A. B., Hashim, M., Hong, J. K., & Park, Y. (2017). a crosstalk correction algorithm for ASTER/SWIR. Lithological and alteration mineral mapping in poorly IEEE Transactions on Geoscience and Remote Sensing, exposed lithologies using landsat-8 and ASTER satellite 43(12), 2747–2751. data: North-eastern Graham Land, Antarctic Peninsula. Kruse, F. A., Lefkoﬀ,A.B.,Boardman,J.B.,Heidebrecht,K.B., Journal of Ore Geology Reviews. doi:10.1016/j. Shapiro,A.T.,Barloon,P.J.,& Goetz,A.F.H.(1993). The oregeorev.2017.07.018 Spectral Image Processing System (SIPS)-interactive visua- Pour, B., Hashim, M., & Marghany, M. (2011). Using lization and analysis of imaging spectrometer data, remote spectral mapping techniques on short wave infrared sens. Environtal, 44, 145–163. bands of ASTER remote sensing data for alteration Liu, L., Zhou, J., Jiang, D., Zhuang, D., & Mansaray, L. mineral mapping in SE Iran. International Journal of (2014). Lithological discrimination of the the Physical Sciences, 6, 917–929. maﬁc-ultramaﬁc complex, Huitongshan, Beishan, Rajendran, S., Thirunavukkarasu, A., Balamurugan, G., & China: Using ASTER data. Journal of Earth Science, 25, Shankar, K. (2012). Discrimination of iron ore deposits 529–536. of granulite terrain of Southern Peninsular India using Madani, A., & Emam, A. A. (2009). SWIR ASTER band ASTER data. Journal of Asian Earth Sciences, 41,99–106. ratios for lithological mapping and mineral exploration: Rezaei, A., Hassani, H., Moarefavand, P., & A case study from El Hudi area, southeastern desert, Golmohammadi, A. (2019). Determination of unstable Egypt. Arabian Journal of Geosciences, 4,45–52. tectonic zones in C–North deposit, Sangan, NE Iran Malekzadeh Shafaroudi, A., Karimpour, M. H., & using GPR method: Importance of structural geology. Golmohammadi, A. (2013). Zircon U–Pb geochronology Journal of Mining and Environment, 10(1), 177–195. and petrology of intrusive rocks in the C-North and Rowan, L. C., Crowley, J. K., Schmidt, R. G., & Mars, J. C. Baghak districts, Sangan iron mine, NE Iran. Journal of (2000). Mapping hydrothermally altered rocks by ana- Asian Earth Sciences, 64, 256–271. lyzing hyperspectral image (AVIRIS) data of forested Mantero, P., Moser, G., & Serpico, S. B. (2005). Partially areas in the Southeastern United States. Journal of supervised classiﬁcation of remote sensing imges Geochemical Exploration, 68(3), 145–166. through SVM-based probability density estimation. Safari, M., Maghsodi, A., & Pour, A. B. (2017). Application IEEE Transactions on Geoscience and Remote Sensing, of landsat-8 and ASTER satellite remote sensing data for 43(3), 559–570. porphyry copper exploration: A case study from Mars, J. C., & Rowan, L. C. (2011). ASTER spectral analysis Shahr-e Babak, Kerman, south of Iran. Geocarto and lithologic mapping of the Khanneshin carbonate International. doi:10.1080/10106049.2017.1334834 volcano, Afghanistan. Geosphere, 7, 276–289. Sahoo, S., & Jha, M. K. (2016). Pattern recognition in Masoumi, F., Eslamkish, T., Abkar, A. A., Honarmand, M., lithology classiﬁcation: Modeling using neural networks, & Harris, J. (2017). Integration of spectral, thermal, and self-organizing maps and genetic algorithms. textural features o f ASTER data using random forests Hydrogeology Journal, 25, 311–330. classiﬁcation for lithological mapping. Journal of African Sepidbar, F., Mirnejad, H., & Mi, C. (2018). Mineral chem- Earth Science, 129, 445–457. istry and Ti in zircon thermometry: Insights into 70 A. REZAEI ET AL. magmatic evolution of the Sangan igneous rocks, NE hyperspectral, geochemical and geothermometric data. Iran. Journal of Chemie Der Erde. Ore Geology Reviews, 45,33–46. Smirnoﬀ, A., Boisvert, E., & Paradis, S. J. (2008). Support Vapnik, V. (1979). Estimation of dependences based on vector machine for 3D modeling from sparse geological empirical data (pp. 5165–5184). Moscow: Nauka. 27 (in information of various origins. Computers & Russian) (English translation: Springer Verlag, Geosciences, 34, 127–143. New York, 1982). Van Ruitenbeek, F. J. A., Cudahy, T. J., Van der Wolters, J. M., Goldin, L., Watts, D. R., & Harris, N. B. W. Meer, F. D., & Hale, M. (2012). Characterization of the (2005). Remote sensing of gneiss and granite in southern hydrothermal systems associated with archean VMS- Tibet. Abstracts with programs. Geological Society of mineralization at Panorama, Western Australia, using America, 37, 93.
Geology Ecology and Landscapes – Taylor & Francis
Published: Jan 2, 2020
Keywords: Lithological mapping; ASTER; Band ratio; spectral angle mapper; support vector machine; Sangan
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