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Journals / International Journal of Environment and Geoinformatics / 2020 / Cilt: 7 - Sayı: 2

Discrimination of Iron Deposits Using Feature Oriented Principal Component Selection and Band Ratio Methods: Eastern Taurus /Turkey

Senem TEKİN
Tolga ÇAN
Mamadou TRAORE
Pages
147–156
Publication Date
2022-10-19
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We conclude that the Landsat-8 OLI and Aster images present good precision for iron ore discrimination in low vegetation lands.

📌 3 Key Takeaways

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Objective & Hypothesis:Kayseri and Adana region of Taurus mountain belt is considered in the second range for iron mineralization in Turkey.
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Key Findings & Data:The objective of this research is to assess the potential of Landsat-8 OLI and Advanced Spacebοrne Thermal Emission and Reflection Radiometer (ASTER) data formapping iron oxide zones hosted by carbonate units in low vegetation cover in Tufanbeyli (Adana) and surrounding region.
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Conclusion & Impact:We conclude that the Landsat-8 OLI and Aster images present good precision for iron ore discrimination in low vegetation lands.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Kayseri and Adana region of Taurus mountain belt is considered in the second range for iron mineralization in Turkey. The region is characterized by high-grade iron οre deposits under exploitation, development and exploration stage. The objective of this research is to assess the potential of Landsat-8 OLI and Advanced Spacebοrne Thermal Emission and Reflection Radiometer (ASTER) data formapping iron oxide zones hosted by carbonate units in low vegetation cover in Tufanbeyli (Adana) and surrounding region. The methods used in this study for the detection of the iron oxides are Band Ratio (BR) and Feature-Oriented Principal Component Selectiοn (FOPCS). The obtained results have good correlation with 92% confidence intervals in BR method for Ferric iron (2/1),ferric oxide (4/3) and ferrous iron (5/4 +1/2) of Aster and iron oxide (4/2) and ferrous iron (6/5) οf the Landsat-8. We note that the results obtained in this study using PC4 of FOPCS depict a good spatial correlation by comparison with the previously mapped iron deposits. The significant lithologic groups Carbonate (limestone) be extracted well from ASTER data however alluvium is mappedwell in the Landsat -8 OLI image using Band Ratio Color Composite (BRCC). We conclude that the Landsat-8 OLI and Aster images present good precision for iron ore discrimination in low vegetation lands.

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Cite This Article

TEKİN, S., ÇAN, T., TRAORE, M. (2022). Discrimination of Iron Deposits Using Feature Oriented Principal Component Selection and Band Ratio Methods: Eastern Taurus /Turkey. International Journal of Environment and Geoinformatics, Cilt: 7 - Sayı: 2, 147–156. https://ajindex.com/articles/540999

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