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Hydrothermal alteration mapping using EO-1 Hyperion hyperspectral data in Kösedağ, Central-Eastern Anatolia (Sivas-Turkey)

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Abstract

The Kösedağ region, is located in Central-Eastern Anatolia, contains a lot of base metals (Pb–Zn, Cu) and Au occurrences. The region is explored by numerous mining companies and MTA. In this research, hydrothermal alteration mapping by hyperspectral Hyperion satellite data was carried out to contribute to these explorations in a part of the region. Hydrothermal alteration is one of the initial steps in the exploration of such metallic occurrences. This study area was chosen to test the accuracy of the hyperspectral data results with those of field and laboratory study results. The in situ alteration map was prepared during field surveys and numerous samples analyses. These samples were investigated by microscopy and XRD examinations. The minerals which are belonging to argillic, sericitic, propylitic, and FeOx (iron oxide) hydrothermal alterations were determined. These minerals were checked and confirmed on the surfaces of altered samples by using ASD fieldspec 4 hi-res. The mixture tuned matched filtering (MTMF), which is one of the widely used spectral classification methods, was applied on the Hyperion data to determine the distributions of these alteration minerals in the study area. The results have shown that the comparison of field and laboratory studies and MTMF results coincided with an overall accuracy of over 86% and a kappa coefficient of 0.80. The alteration map has been generated using the MTMF method, has been the first hyperspectral research of the hydrothermal alteration mineralogy in the region. Consequently, the generated map can be used as a basic alteration map during exploration studies of hydrothermal base metal mineralizations in the region.

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References

  • Abrams MJ, Brown D, Lepley L, Sadowski R (1983) Remote sensing for porphyry copper deposits in southern Arizona. Econ Geol 78:591–604. https://doi.org/10.2113/gsecongeo.78.4.591

    Article  Google Scholar 

  • Abubakar AJ, Hashim M, Pour AB (2019) Identification of hydrothermal alteration minerals associated with geothermal system using ASTER and Hyperion satellite data: a case study from Yankari Park, NE Nigeria. Geocarto Int. https://doi.org/10.1080/10106049.2017.1421716

  • Adiri Z, El Harti A, Jellouli A et al (2020) Mapping copper mineralization using EO-1 Hyperion data fusion with Landsat 8 OLI and Sentinel-2A in Moroccan Anti-Atlas. Geocarto Int. https://doi.org/10.1080/10106049.2018.1544287

    Article  Google Scholar 

  • Agar B, Coulter D (2007) Remote sensing for mineral exploration – a decade perspective 1997–2007. In: Proceedings of Exploration 07: Fifth Decennial International Conference on Mineral Exploration. pp 109–136

  • Akçay AE, Beyazpirinç M (2017) Sorgun (Yozgat)-Yıldızeli (Sivas) Önülke Havzasının Jeolojik Evrimi, Havzada Etkin Olan Volkanizmanın Petrografik, Jeokimyasal Özellikleri ve Jeokronolojisi. Maden Tetk Aram Derg 155:1–32

    Google Scholar 

  • Alpaslan M (2000) Pazarcık Volkanitinin (Yıldızeli - Sivas) Mineralojik Petrografik ve Jeokimyasal Özellikleri. Türkiye Jeol Bülteni 43:49–60

    Google Scholar 

  • Alpaslan M, Terzioğlu N (1998) Pontidlerde çarpışma sonrası volkanizmaya bir örnek: Sürmeli Volkaniti (Taşova-Amasya). Cumhur Üniversitesi Yerbilim Derg 15:13–20

    Google Scholar 

  • Asner GP, Heidebrecht KB (2003) Imaging spectroscopy for desertification studies: comparing AVIRIS and EO-1 Hyperion in Argentina drylands. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2003.812903

    Article  Google Scholar 

  • Atakay Gündoğdu E (2009) Çorum güneybatısındaki volkanik kayaçların jeolojik ve petrolojik özellikleri ve Alaca Höyük kazısında jeoarkeolojik çalışmalar. Ankara Üniversitesi, Fen Bilimleri Enstitüsü Doktora Tezi. 212s

  • Ayoobi I, Tangestani MH (2018) Evaluation of subpixel unmixing algorithms in mapping the porphyry copper alterations using EO-1 Hyperion data, a case study from SE Iran. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2018.03.009

    Article  Google Scholar 

  • Azizi H, Tarverdi MA, Akbarpour A (2010) Extraction of hydrothermal alterations from ASTER SWIR data from east Zanjan, northern Iran. Adv Sp Res. https://doi.org/10.1016/j.asr.2010.03.014

    Article  Google Scholar 

  • Barry P (2001) EO-1/ Hyperion Science Data User’s Guide, Level 1_B, TRW Space. Def Inf Syst

  • Barry PS, Pearlman JS (2001) National aeronautics and space administration. The EO-1 Misson: Hyperion data

  • Başıbüyük Z (2006) Hydrothermal alteration mineralogy-petrography and geochemistry of Eocene volcanics: An example from quadrangle of Zara - Imranli-Susehri-Serefiye (Northeast of Sivas. Cumhuriyet University, Central Eastern Anatolia, Turkey)

    Google Scholar 

  • Bhattacharya AR (2008) Basement rocks of the Kumaun-Garhwal Himalaya: ımplications for Himalayan tectonics

  • Bhattacharya S, Majumdar TJ, Rajawat AS et al (2012) Utilization of Hyperion data over Dongargarh. India, for Mapping Altered/weathered and Clay Minerals along with Field Spectral Measurements 33:5438–5450. https://doi.org/10.1080/014311612012661094

    Article  Google Scholar 

  • Boardman JW (1998) Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering. In: JPL Airborne Earth Science Workshop

  • Bouali M, Ladjal S (2011) Toward Optimal destriping of MODIS data using a unidirectional variational model. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2011.2119399

    Article  Google Scholar 

  • Boztuğ D (2008) Petrogenesis of the Kösedag Pluton, Suşehri-NE Sivas. Turkish J Earth Sci 17:241–262

    Google Scholar 

  • Brindley GW (1952) Identification of clay minerals by X-ray Diffraction analysis. Clays Clay Miner. https://doi.org/10.1346/ccmn.1952.0010116

    Article  Google Scholar 

  • Büyükonal G (1985) Yozgat yöresi volkanitlerinin asal ve iz Elementlerinin dağılımı. Maden Tetk Aram Derg 105:97–111

    Google Scholar 

  • Canbaz O, Gürsoy Ö, Gökce A (2017) Determination of hydrothermal alteration areas by aster satellite ımages: Ağmaşat Plato- Zara (Sivas) / Turkey Sample. Cumhur Sci J 38:419–426. https://doi.org/10.17776/csj.340473

  • Canbaz O (2018) Characteristics of geological, mineralogical, geochemical and metallogenic of hydrothermal alterations zones on surround of Ağmasat Plato (Zara - Sivas). PhD. Thesis. Cumhuriyet University

  • Canbaz O, Gökce A, Ekici T, Yılmaz H (2020) Geochemical characteristics of the Eocene Karataş volcanics (Northeast Sivas, Turkey) in the İzmir-Ankara-Erzincan Suture Zone. Bull Miner Res Explor 162:55–74

    Google Scholar 

  • Canbaz O, Gürsoy Ö, Gökce A (2018) Detecting clay minerals in hydrothermal alteration areas with integration of aster image and spectral data in Kösedag-Zara (Sivas), Turkey. J Geol Soc India. https://doi.org/10.1007/s12594-018-0882-1

  • Chattoraj SL, Sharma RU, Kumar C et al (2020) (2020) Identification and characterization of hydrothermally altered minerals using surface and space-based reflectance spectroscopy, in parts of south-eastern Rajasthan. India SN Appl Sci 24(2):1–9. https://doi.org/10.1007/S42452-020-2225-2

    Article  Google Scholar 

  • Chen JY, Reed IS (1987) A detection algorithm for optical targets in Clutter. In: IEEE Transactions on Aerospace and Electronic Systems

  • Christian B, Krishnayya NSR (2007) Spectral signatures of teak (Tectona grandis L.) using hyperspectral (EO-1) data. Current Science, 1291–1296

  • Clark RN, King TVV, Klejwa M et al (1990) High spectral resolution reflectance spectroscopy of minerals. J Geophys Res Solid Earth 95:12653–12680. https://doi.org/10.1029/JB095IB08P12653

    Article  Google Scholar 

  • Clark RN (1999) Spectroscopy of rocks and minerals and principles ofspectroscopy. In: Rencz AN (ed) Remote Sensing for the Earth Sciences, 3rd edn. John Wiley & Sons, pp 3–58

    Google Scholar 

  • Clark RN, Swayze GA, Gallagher AJ, King TVV, Calvin W M (1993) The U.S. geological survey, digital spectral library: Version 1 : 0.2 to 3.0 μm. U.S. Geological Survey Open File Report 93–592.

  • Cŕosta AP, De Souza Filho CR, Azevedo F, Brodie C (2003) Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis. Int J Remote Sens. https://doi.org/10.1080/0143116031000152291

    Article  Google Scholar 

  • Cudahy TJ, Hewson R, Huntington JF, et al (2001) The performance of the satellite-borne hyperion hyperspectral VNIR-SWIR imaging system for mineral mapping at Mount Fitton, South Australia. In: International Geoscience and Remote Sensing Symposium (IGARSS)

  • Datt B, McVicar TR, Van Niel TG et al (2003) Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2003.813206

    Article  Google Scholar 

  • Demirkesen C, Leloglu UM (2015) Destriping of hyperion images. Signal Processing and Communications Applications Conference (SIU). Malatya, Turkey

  • Efe A, Gökce A (1999) Geology and fluid inclusion studies of the Maden Village (Imranli-Sivas) Pb-Zn deposits. Bull Fac Eng Cumhur Univ Ser S-Earth Sci 16:29–38

    Google Scholar 

  • Eyuboglu Y, Dudas FO, Thorkelson D et al (2017) Eocene Granitoids of Northern Turkey: Polybaric Magmatism in an Evolving Arc–slab Window System. Gondwana Res 50:311–345

    Article  Google Scholar 

  • Folkman M, Pearlman J, Liao L, Jarecke P (2001) EO-1/Hyperion hyperspectral imager design, development, characterization, and calibration. Hyperspectral remote sensing of the land and atmosphere. In: Proc SPIE. pp 4151:40–51

  • Gersman R, Ben-Dor E, Beyth M et al (2008) Mapping of hydrothermally altered rocks by the EO-1 Hyperion sensor. Northern Danakil Depression, Eritrea, Int J Remote Sens. https://doi.org/10.1080/01431160701874587

    Book  Google Scholar 

  • Ghrefat H, Al Mutairi Y, ElAraby H et al (2021) (2021) Using reflectance spectroscopy and Advanced Spaceborne Thermal Emission and Reflection Radiometer data to identify bauxite deposits in vicinity of Az Zabirah, northern Saudi Arabia. Arab J Geosci 149(14):1–10. https://doi.org/10.1007/S12517-021-07155-7

    Article  Google Scholar 

  • Göçmengil G, Karacik Z, Genç C, Billor MZ (2018) 40Ar-39Ar geochronology and petrogenesis of postcollisional trachytic volcanism along the İzmir-Ankara-Erzincan Suture Zone (NE, Turkey). Turkish J Earth Sci 27:1–31. https://doi.org/10.3906/yer-1708-4

    Article  Google Scholar 

  • Goetz AFH, Kindel BC, Ferri M, Qu Z (2003) HATCH: Results from simulated radiances, AVIRIS and hyperion. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2003.812905

  • Goodenough DG, Dyk A, Niemann KO et al (2003) Processing Hyperion and ALI for forest classification. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2003.813214

    Article  Google Scholar 

  • H Govil G Mishra N Gill 2021 Mapping Hydrothermally altered minerals and gossans using hyperspectral data in Eastern Kumaon Himalaya Appl Comput Geosci India https://doi.org/10.1016/j.acags.2021.100054

  • Green AA, Berman M, Switzer P, Craig MD (1988) A transformation for ordering multispectral data in terms of ımage quality with ımplications for noise removal. IEEE Trans Geosci Remote Sens DOI 10(1109/36):3001

    Google Scholar 

  • Gürsoy Ö, Kaya Ş (2016) Detecting of lithological units by using terrestrial spectral data and remote sensing ımage. J Indian Soc Remote Sens 452(45):259–269. https://doi.org/10.1007/S12524-016-0586-1

    Article  Google Scholar 

  • Gürsoy Ö, Kaya Ş, Çakir Z, et al (2017) Determining lateral offsets of rocks along the eastern part of the North Anatolian Fault Zone (Turkey) using spectral classification of satellite images and field measurements. https://doi.org/10.1080/19475705.2017.1318794

  • Harsanyi JC, Chang CI (1994) Hyperspectral ımage classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/36.298007

  • Hosseinjani Zadeh M, Tangestani MH, Roldan FV, Yusta I (2014) Sub-pixel mineral mapping of a porphyry copper belt using EO-1 Hyperion data. Adv Sp Res. https://doi.org/10.1016/j.asr.2013.11.029

    Article  Google Scholar 

  • Hu B, Xu Y, Wan B, et al (2018) Hydrothermally altered mineral mapping using synthetic application of Sentinel-2A MSI, ASTER and Hyperion data in the Duolong Area, Tibetan Plateau, China. Ore Geol Rev. https://doi.org/10.1016/j.oregeorev.2018.07.017

  • Hunt G, Salisbury J (1971) Visible and near infrared spectra of minerals and rocks. II. Carbonates. Mod Geol

  • Hunt GR (1977) Spectral sıgnatures of particulate minerals ın the visible and near infrared. Geophysics. https://doi.org/10.1190/11440721

    Article  Google Scholar 

  • Hunt GR, Ashley RP (1979) Spectra of altered rocks in the visible and near infrared. Econ Geol. https://doi.org/10.2113/gsecongeo.74.7.1613

    Article  Google Scholar 

  • Jafari R, Lewis MM (2012) Arid land characterisation with EO-1 hyperion hyperspectral data. Int J Appl Earth Obs Geoinf. https://doi.org/10.1016/j.jag.2012.06.001

    Article  Google Scholar 

  • Kalkancı Ş (1978) Suşehri güneyinin jeolojik ve petrokimyasal etüdü. kösedağ siyenitik masifinin jeokronolojisi (NE Sivas-Türkiye). In: TJK 32. Bilimsel ve Teknik Kurultayı. pp 33–34

  • Ke Y, Shi Z, Li P, Zhang X (2018) Lithological classification and analysis using Hyperion hyperspectral data and Random Forest method. Yanshi Xuebao/Acta Petrol Sin

  • Kayadibi O, Aydal D (2013) Quantitative and comparative examination of the spectral features characteristics of the surface reflectance information retrieved from the atmospherically corrected images of Hyperion. 101117/1JRS7073528 7:073528. https://doi.org/10.1117/1.JRS.7.073528

  • Keskin M, Genç ŞC, Tüysüz O (2008) Petrology and geochemistry of post-collisional Middle Eocene volcanic units in North-Central Turkey: evidence for magma generation by slab breakoff following the closure of the Northern Neotethys Ocean. Lithos 104:267–305. https://doi.org/10.1016/j.lithos.2007.12.011

    Article  Google Scholar 

  • Koçbulut F, Yılmaz Şahin S, Tatar O (2001) Akdağmadeni (Yozgat)- Yıldızeli (Sivas) arasındaki Kaletepe volkanitinin mineralojik-petrografik ve jeokimyasal incelenmesi. İstanbul Üniversitesi Yerbilim Derg 14:77–91

    Google Scholar 

  • Kruse FA, Boardman JW, Huntington JF, et al (2002) Evaluation and validation of EO-1 Hyperion for geologic mapping. In: International Geoscience and Remote Sensing Symposium (IGARSS)

  • Kruse FA, Boardman JW, Huntington JF (2003) Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2003.812908

    Article  Google Scholar 

  • Kumar MV, Yarrakula K (2017) Comparison of efficient techniques of hyper-spectral image preprocessing for mineralogy and vegetation studies. Indian journal of geomarine sciences.

  • Liao, LB, Jarecke PJ, Gleichauf DA, Hedman TR (2000) Performance characterization of the Hyperion Imaging Spectrometer instrument. Earth Observing Systems V. https://doi.org/10.1117/12.494253

  • Li D, Gong J, Shan J (2009) Geospatial technology for earth observation

  • Liu L, Feng J, Rivard B, et al (2018) Mapping alteration using imagery from the Tiangong-1 hyperspectral spaceborne system: example for the Jintanzi gold province, China. Int J Appl Earth Obs Geoinf. https://doi.org/10.1016/j.jag.2017.03.013

  • Liu L, Zhou J, Han L, Xu X (2017) Mineral mapping and ore prospecting using Landsat TM and Hyperion data, Wushitala, Xinjiang, northwestern China. Ore Geol Rev. https://doi.org/10.1016/j.oregeorev.2016.10.007

    Article  Google Scholar 

  • Lunzer JB, Hunt SD (2013) Fully automatic destriping of Hyperion hyperspectral satellite imagery using local window statistics. In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX

  • Magendrana T, Sanjeevi S (2014) Hyperion image analysis and linear spectral unmixing to evaluatethe grades of iron ores in parts of Noamundi, Eastern India. International Journal Appl Earth Obs Geoinf. https://doi.org/10.1016/j.jag.2013.09.004

  • Mishra G, Govil H, Srivastava PK (2021) Identification of malachite and alteration minerals using airborne AVIRIS-NG hyperspectral data. Quat Sci Adv 4:100036. https://doi.org/10.1016/J.QSA.2021.100036

    Article  Google Scholar 

  • Mitchell JJ, Glenn NF (2009) Subpixel abundance estimates in mixture-tuned matched filtering classifications of leafy spurge (Euphorbia esula L.). International Journal of Remote Sens. https://doi.org/10.1080/01431160902810620

  • Moeinzadeh H (2015) Hyperspectral mapping of Iranian east ophiolite mélanges using neutral network classification method. Arab J Geosci. https://doi.org/10.1007/s12517-014-1333-y

    Article  Google Scholar 

  • Moghadam HJ, Oskouei MM, Nouri T (2020) Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran. Arab J Geosci. https://doi.org/10.1007/s12517-020-06070-7

  • MTA (2002) 1/500.000 Türkiye Jeoloji Haritası, Sivas Paftası. Maden Tetkik ve Arama Genel Müdürlüğü. Ankara.

  • Mundt JT, Streutker DR, Glenn NF (2007) Partial unmixing of hyperspectral imagery: theory and methods. Am Soc Photogramm Remote Sens - ASPRS Annu Conf 2007 Identifying Geospatial Solut

  • Noori L, Pour AB, Askari G et al (2019) Comparison of different algorithms to map hydrothermal alteration zones using ASTER remote sensing data for polymetallic vein-type ore exploration: Toroud-Chahshirin Magmatic Belt (TCMB), North Iran. Remote Sens 11:495. https://doi.org/10.3390/RS11050495

    Article  Google Scholar 

  • Oluwadebi A, Hecker C (2013) Mapping of hydrothermal alteration in mount berecha area of main Ethiopian Rift using hyperspectral data. J Environ Earth Sci 3:12.

  • Oskouei MM, Babakan S (2016) Detection of alteration minerals using hyperion data analysis in Lahroud. J Indian Soc Remote Sens. https://doi.org/10.1007/s12524-016-0549-6

  • Pal MK, Porwal A (2015) Destriping of Hyperion images using low-pass-filter and local-brightness-normalization. In: International Geoscience and Remote Sensing Symposium (IGARSS)

  • Pal MK, Porwal A, Rasmussen TM (2020) Noise reduction and destriping using local spatial statistics and quadratic regression from Hyperion images. J Appl Remote Sens. https://doi.org/10.1117/1.jrs.14.016515

    Article  Google Scholar 

  • Pearlman JS, Barry PS, Segal CC et al (2003) Hyperion, a space-based imaging spectrometer. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2003.815018

    Article  Google Scholar 

  • Perkins T, Adler-Golden S, Matthew M, et al (2005) Retrieval of atmospheric properties from hyper- and multi-spectral imagery with the FLAASH Atmospheric Correction Algorithm. In: Proceedings of SPIE: Remote Sensing of Clouds and the Atmosphere X. pp 5979: 59790E-1–59790E–11

  • Petropoulos GP, Kalaitzidis C, Prasad Vadrevu K (2012) Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Comput Geosci. https://doi.org/10.1016/j.cageo.2011.08.019

    Article  Google Scholar 

  • Pirajno F (2009) Hydrothermal processes and mineral systems

  • Pour AB, Hashim M (2012) The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits. Ore Geol Rev. https://doi.org/10.1016/j.oregeorev.2011.09.009

    Article  Google Scholar 

  • Pour AB, Hashim M (2015) Evaluation of Earth Observing-1 (EO1) Data for lithological and hydrothermal alteration mapping: a case study from Urumieh-Dokhtar Volcanic Belt, SE Iran. J Indian Soc Remote Sens. https://doi.org/10.1007/s12524-014-0444-y

  • Pour AB, Hashim M (2014) ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration. Springerplus

  • Pour AB, Hashim M (2011) The Earth Observing-1 (EO-1) satellite data for geological mapping, southeastern segment of the Central Iranian Volcanic Belt, Iran. Int J Phys Sci. https://doi.org/10.5897/IJPS11.910

  • Pour AB, Park Y, Park T-YS et al (2019) Evaluation of ICA and CEM algorithms with Landsat-8/ASTER data for geological mapping in inaccessible regions. Geocarto Int 34:785–816

    Article  Google Scholar 

  • Ramakrishnan D, Bharti R (2015) Hyperspectral remote sensing and applications. Current Science.

  • Rajendran S, Nasir S (2018) Mapping of hydrothermal alteration in the upper mantle-lower crust transition zone of the Tayin Massif, Sultanate of Oman using remote sensing technique. J African Earth Sci. https://doi.org/10.1016/j.jafrearsci.2018.10.001

  • Research Systems Inc ENVI Tutorial (2003) ENVI Software Package Version 4.0.

  • Rowan LC, Mars JC (2003) Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sens Environ 84:350–366. https://doi.org/10.1016/S0034-4257(02)00127-X

    Article  Google Scholar 

  • Rowan LC, Schmidt RG, Mars JC (2006) Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data. Remote Sens Environ. https://doi.org/10.1016/j.rse.2006.05.014

  • Sabins FF (1999) Remote sensing for mineral exploration. Ore Geol Rev. https://doi.org/10.1016/S0169-1368(99)00007-4

    Article  Google Scholar 

  • Salaj SS, Srivastava SK, Dugal R et al (2019) Application of ASTER remote sensing for lithological mapping in the Udaipur District of Rajasthan, India. Appl Challenges Geospatial Technol Potential Futur Trends 99–119. https://doi.org/10.1007/978-3-319-99882-4_7

  • Salem SM (2017) ASD field hyperspectral measurements for discrimination of the ferruginous rocks and the iron ore types at El Gedida-Ghorabi area, Bahariya Oasis, Western Desert. Egypt Arab J Geosci 10:166. https://doi.org/10.1007/s12517-017-2944-x

    Article  Google Scholar 

  • Scheffler D, Karrasch P (2013) Preprocessing of hyperspectral images: a comparative study of destriping algorithms for EO1-hyperion

  • Scheffler D, Karrasch P (2014) Destriping of hyperspectral image data: an evaluation of different algorithms using EO-1 Hyperion data. J Appl Remote Sens. https://doi.org/10.1117/1.jrs.8.083645

    Article  Google Scholar 

  • Schowengerdt RA (2012) Remote sensing: Models and methods for image processing: Second edition

  • Shaik I, Begum SK, Nagamani PV, Kayet N (2021) Characterization and mapping of hematite ore mineral classes using hyperspectral remote sensing technique: a case study from Bailadila iron ore mining region. SN Appl Sci 3:1–13. https://doi.org/10.1007/s42452-021-04213-3

  • Sheikhrahimi A, Pour AB, Pradhan B, Zoheir B (2019) Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: a case study from the Sanandaj-Sirjan Zone, Iran. Adv Sp Res. https://doi.org/10.1016/j.asr.2019.01.035

  • Spatz DM, Wilson RT (1995) Remote sensing characteristics of porphyry copper systems, western America Cordillera. FW Pierce, JG Bolm (Eds), Arizona Geol Soc Dig 20:94–108

  • Stocker A, Reed IS, Yu X (1990) Multidimensional signal processing for electrooptical target detection. In: Proc. SPIE Int. Soc. Opt. Eng., V. p 1305

  • Sun L, Neville R, Staenz K, White HP (2008) Automatic destriping of Hyperion imagery based on spectral moment matching. Can J Remote Sens. https://doi.org/10.5589/m07-067

    Article  Google Scholar 

  • Tiryaki C, Ekici T (2012) Çarpışma sonrası kalk-alkalin Yozgat volkaniklerinin petrolojisi. Türkiye Jeoloji Bülteni 55:19–42

    Google Scholar 

  • Tripathi MK, Govil H (2019) Evaluation of AVIRIS-NG hyperspectral images for mineral identification and mapping. Heliyon. https://doi.org/10.1016/j.heliyon.2019.e02931

    Article  Google Scholar 

  • Tsai F, Philpot W (1998) Derivative analysis of hyperspectral data. Remote Sens Environ. https://doi.org/10.1016/S0034-4257(98)00032-7

    Article  Google Scholar 

  • Tüysüz O, Dellaloğlu AA (1992) Çankırı havzasının tektonik birlikleri ve havzanın tektonik evrimi. In: Proceedings of 9th Turkish Petroleum Congress Turkey, Ankara. Turkish Association of Petroleum Geologists. pp 333–349

  • Xu Y, Chen J, Meng P (2019) Detection of alteration zones using hyperspectral remote sensing data from Dapingliang skarn copper deposit and its surrounding area, Shanshan County, Xinjiang Uygur autonomous region, China. J Vis Commun Image Represent. https://doi.org/10.1016/j.jvcir.2018.11.032

  • Xu Y, Ma H, Peng S (2014) Study on identification of altered rock in hyperspectral imagery using spectrum of field object. Ore Geol Rev. https://doi.org/10.1016/j.oregeorev.2013.07.004

    Article  Google Scholar 

  • Van der Meer FD, de Jong SM, Bakker WH (2001) Imaging spectrometry: basic analytical techniques. In: Van Der Meer FD, de Jong SM (eds) Imaging Spectrom: Basic Principles and Prospective Applications. Springer, Dordrecht, The Netherlands, pp 17–61

    Google Scholar 

  • Vibhute AD, Kale KV, Dhumal RK et al (2019) Spectral feature extraction and classification of soil types using EO-1 hyperion and field spectroradiometer data based on PCA and SVM. Lect Notes Electr Eng 521:525–533. https://doi.org/10.1007/978-981-13-1906-8_54

    Article  Google Scholar 

  • Vignesh KM, Kiran Y (2020) Comparative analysis of mineral mapping for hyperspectral and multispectral imagery. Arab J Geosci 13. https://doi.org/10.1007/s12517-020-5148-8

  • Yalcin M, Kilic Gul F, Yildiz A et al (2020) (2020) The mapping of hydrothermal alteration related to the geothermal activities with remote sensing at Akarcay Basin (Afyonkarahisar), using Aster data. Arab J Geosci 1321(13):1–17. https://doi.org/10.1007/S12517-020-06083-2

    Article  Google Scholar 

  • Yılmaz A, Okay A, Bilgiç T (1985) Yukarı Kelkit Çayı Yöresi ve Güneyinin Temel Jeoloji Özellikleri ve Sonuçları. Maden Tetkik Arama Rapor No 7777:124

    Google Scholar 

  • Yılmaz A, Uysal Ş, Yusufoğlu H, et al (1994) Akdağmasifi (Sivas) dolayının jeolojik incelemesi. Maden Tetkik ve Arama Genel Müdürlügü, Ankara

  • Yılmaz Y, Tüysüz O (1984) Kastamonu-Boyabat Vezirköprü-Tosya arasindaki bölgenin jeolojisi (Ilgaz-Kargi-masiflerinin etüdü). Maden Tetkik ve Arama Genel Müdürlügü, Ankara

  • Yuan J, Niu. Z. (2007) Fuzzy Systems and Knowledge Discovery. In: FSKD 2007. Fourth International Conference, Volume: 3

  • Zadeh MH, Tangestani MH, Roldan FV, Yusta I (2014) Mineral exploration and alteration zone mapping using mixture tuned matched filtering approach on ASTER data at the central part of dehaj-sarduiyeh copper belt, SE Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2013.2261800

  • Zeng Q, Ma Q, Liu C et al (2013) Research on ımproved destriping algorith with spectral moment matching for hyper-spectral ımages. Proc 1:1–5

    Google Scholar 

  • Zhang L, Du B (2012) Recent Advances in hyperspectral image processing. Geo-Spatial Inf Sci 15:143–156

    Article  Google Scholar 

  • Zhang X, Pazner M (2013) Comparison of lithologic mapping with ASTER, Hyperion, and ETM data in the Southeastern Chocolate Mountains, USA. Photogramm Eng Remote Sens. https://doi.org/10.14358/pers.73.5.555

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Funding

This study is supported by The Research Foundation of Sivas Cumhuriyet University (Project No: M-613 and M-754). Laboratory studies were carried out in the Departments of Geological and Geomatics Engineering, Sivas Cumhuriyet University.

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Authors and Affiliations

  1. Department of Geological Engineering, Faculty of Engineering, Sivas Cumhuriyet University, Sivas, Turkey

    Oktay Canbaz & Ahmet Gökce

  2. Department of Geomatics Engineering, Faculty of Engineering, Sivas Cumhuriyet University, Sivas, Turkey

    Önder Gürsoy

  3. Department of Geological Engineering, Faculty of Mines, Istanbul Technical University, Istanbul, Turkey

    Muhittin Karaman

  4. Department of Architecture and Urban Planning, Vocational School of Technical Sciences, Hitit University, Çorum, Turkey

    Ayşe Betül Çalışkan

Authors
  1. Oktay Canbaz
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  2. Önder Gürsoy
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  3. Muhittin Karaman
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  4. Ayşe Betül Çalışkan
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  5. Ahmet Gökce
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Correspondence to Oktay Canbaz.

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Responsible Editor: Biswajeet Pradhan

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Canbaz, O., Gürsoy, Ö., Karaman, M. et al. Hydrothermal alteration mapping using EO-1 Hyperion hyperspectral data in Kösedağ, Central-Eastern Anatolia (Sivas-Turkey). Arab J Geosci 14, 2245 (2021). https://doi.org/10.1007/s12517-021-08665-0

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  • DOI: https://doi.org/10.1007/s12517-021-08665-0

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