{"title":"利用 ASTER 和 Sentinel-2A 图像对 Hasançelebi 地区(土耳其马拉蒂亚)及其附近的含铁岩石进行识别和实地验证","authors":"Sedat İnal, Kaan Sevki Kavak","doi":"10.1007/s12665-024-11962-y","DOIUrl":null,"url":null,"abstract":"<div><p> In this study, image processing has been applied to ASTER and Sentinel-2A satellite images, and obtained data is used to reveal Fe-bearing rocks in the vicinity of Hasançelebi (Malatya), close to Divriği (Sivas) which is one of the most important iron provenances in the Central-Eastern Anatolia region of Türkiye. Remote sensing images, particularly the visible-near-infrared (VNIR) and partially shortwave infrared (SWIR) bands, have been employed to identify Fe-bearing minerals and rocks. With the purpose of identifying Fe-bearing minerals and rocks, various band rationing processes have been applied. Supervised classification which utilizes a parallelepiped algorithm has been employed on the resulting ratio images to create classification distributions for Fe-bearing minerals. According to the classification results; ferrous iron (Fe<sup>2+</sup>) and ferric oxides are more associated with ophiolitic rocks, ferrous silicates and ferric iron (Fe<sup>3+</sup>). The distributions are generally associated with clastic lithologies, and laterite and gossan appear to be associated with volcanic and plutonic rocks. Because of the different band widths in the VNIR range, Sentinel-2A classifications have the highest pixel count when compared to ASTER classifications for the same surface areas. During fieldwork, rock samples representing the lithologies and Fe-bearing minerals in the region have been collected and mineralogical-petrographic, geochemical, and XRD analyses have been conducted on these samples. Additionally, for spectral mineral identification and to compare Fe-bearing minerals with other analysis results, spectral signatures have also been obtained from the same samples via Analytical Spectral Device (ASD). In extracting features such as lineaments and faults, which play a crucial role in the development of ore deposits along the structural discontinuities, digital elevation models (DEM) have been preferred instead of optical images. When lineament analysis results and iron deposits, which had been identified in previous studies, were overlapped, it has been detected that revealed iron deposits are predominantly associated with the Ciritbelen-Otmangölü Fault (COF) which is an approximately east-west trending strike-slip fault located in the study area, along with other related fault systems. They are generally distributed within an ophiolitic slice and the surrounding magmatic intrusions.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"83 22","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification and field verification of Fe-bearing rocks in the Hasançelebi region (Malatya, Türkiye) and its vicinity using ASTER and Sentinel-2A images\",\"authors\":\"Sedat İnal, Kaan Sevki Kavak\",\"doi\":\"10.1007/s12665-024-11962-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p> In this study, image processing has been applied to ASTER and Sentinel-2A satellite images, and obtained data is used to reveal Fe-bearing rocks in the vicinity of Hasançelebi (Malatya), close to Divriği (Sivas) which is one of the most important iron provenances in the Central-Eastern Anatolia region of Türkiye. Remote sensing images, particularly the visible-near-infrared (VNIR) and partially shortwave infrared (SWIR) bands, have been employed to identify Fe-bearing minerals and rocks. With the purpose of identifying Fe-bearing minerals and rocks, various band rationing processes have been applied. Supervised classification which utilizes a parallelepiped algorithm has been employed on the resulting ratio images to create classification distributions for Fe-bearing minerals. According to the classification results; ferrous iron (Fe<sup>2+</sup>) and ferric oxides are more associated with ophiolitic rocks, ferrous silicates and ferric iron (Fe<sup>3+</sup>). The distributions are generally associated with clastic lithologies, and laterite and gossan appear to be associated with volcanic and plutonic rocks. Because of the different band widths in the VNIR range, Sentinel-2A classifications have the highest pixel count when compared to ASTER classifications for the same surface areas. During fieldwork, rock samples representing the lithologies and Fe-bearing minerals in the region have been collected and mineralogical-petrographic, geochemical, and XRD analyses have been conducted on these samples. Additionally, for spectral mineral identification and to compare Fe-bearing minerals with other analysis results, spectral signatures have also been obtained from the same samples via Analytical Spectral Device (ASD). In extracting features such as lineaments and faults, which play a crucial role in the development of ore deposits along the structural discontinuities, digital elevation models (DEM) have been preferred instead of optical images. When lineament analysis results and iron deposits, which had been identified in previous studies, were overlapped, it has been detected that revealed iron deposits are predominantly associated with the Ciritbelen-Otmangölü Fault (COF) which is an approximately east-west trending strike-slip fault located in the study area, along with other related fault systems. They are generally distributed within an ophiolitic slice and the surrounding magmatic intrusions.</p></div>\",\"PeriodicalId\":542,\"journal\":{\"name\":\"Environmental Earth Sciences\",\"volume\":\"83 22\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Earth Sciences\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12665-024-11962-y\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Earth Sciences","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s12665-024-11962-y","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Identification and field verification of Fe-bearing rocks in the Hasançelebi region (Malatya, Türkiye) and its vicinity using ASTER and Sentinel-2A images
In this study, image processing has been applied to ASTER and Sentinel-2A satellite images, and obtained data is used to reveal Fe-bearing rocks in the vicinity of Hasançelebi (Malatya), close to Divriği (Sivas) which is one of the most important iron provenances in the Central-Eastern Anatolia region of Türkiye. Remote sensing images, particularly the visible-near-infrared (VNIR) and partially shortwave infrared (SWIR) bands, have been employed to identify Fe-bearing minerals and rocks. With the purpose of identifying Fe-bearing minerals and rocks, various band rationing processes have been applied. Supervised classification which utilizes a parallelepiped algorithm has been employed on the resulting ratio images to create classification distributions for Fe-bearing minerals. According to the classification results; ferrous iron (Fe2+) and ferric oxides are more associated with ophiolitic rocks, ferrous silicates and ferric iron (Fe3+). The distributions are generally associated with clastic lithologies, and laterite and gossan appear to be associated with volcanic and plutonic rocks. Because of the different band widths in the VNIR range, Sentinel-2A classifications have the highest pixel count when compared to ASTER classifications for the same surface areas. During fieldwork, rock samples representing the lithologies and Fe-bearing minerals in the region have been collected and mineralogical-petrographic, geochemical, and XRD analyses have been conducted on these samples. Additionally, for spectral mineral identification and to compare Fe-bearing minerals with other analysis results, spectral signatures have also been obtained from the same samples via Analytical Spectral Device (ASD). In extracting features such as lineaments and faults, which play a crucial role in the development of ore deposits along the structural discontinuities, digital elevation models (DEM) have been preferred instead of optical images. When lineament analysis results and iron deposits, which had been identified in previous studies, were overlapped, it has been detected that revealed iron deposits are predominantly associated with the Ciritbelen-Otmangölü Fault (COF) which is an approximately east-west trending strike-slip fault located in the study area, along with other related fault systems. They are generally distributed within an ophiolitic slice and the surrounding magmatic intrusions.
期刊介绍:
Environmental Earth Sciences is an international multidisciplinary journal concerned with all aspects of interaction between humans, natural resources, ecosystems, special climates or unique geographic zones, and the earth:
Water and soil contamination caused by waste management and disposal practices
Environmental problems associated with transportation by land, air, or water
Geological processes that may impact biosystems or humans
Man-made or naturally occurring geological or hydrological hazards
Environmental problems associated with the recovery of materials from the earth
Environmental problems caused by extraction of minerals, coal, and ores, as well as oil and gas, water and alternative energy sources
Environmental impacts of exploration and recultivation – Environmental impacts of hazardous materials
Management of environmental data and information in data banks and information systems
Dissemination of knowledge on techniques, methods, approaches and experiences to improve and remediate the environment
In pursuit of these topics, the geoscientific disciplines are invited to contribute their knowledge and experience. Major disciplines include: hydrogeology, hydrochemistry, geochemistry, geophysics, engineering geology, remediation science, natural resources management, environmental climatology and biota, environmental geography, soil science and geomicrobiology.