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Identification of Clay mineral deposits in the South-Eastern region of Uttar Pradesh, India, using Remote Sensing


Affiliations
1 CSIR-Recruitment and Assessment Board, CSIR Complex, Library Avenue, New Delhi-110 012, India
2 Presently working at Department of Chemistry, West Bengal State University, Berunanpukuria, Kolkata-700 126, India

Finding new mineral deposits is very important for the economic growth of a country. Recent advancements have made exploration of minerals very easy with the help of satellite imagery. Several inaccessible areas can be explored for the presence of deposits. The present article focuses on the analysis of the Landsat imagery of 2018 using datasets from the Landsat 8 (OLI/TIRS) satellite. The unsupervised classification with maximum likelihood algorithm is applied to bring out probable classes. It is found that the major classes are wasteland and forest followed by vegetation, water body, sand, built-up, limestone, kaolin and rocky land. The main objective is to find the kaolin rich zones which accounted for ~1.08% of the study area. To validate the findings, field survey have been carried out, 15 clay samples are collected from the study area in Ramgarh-Naudiha region of Sonbhadra district and have been characterised for mineral content. The mined mineral is fine-grained, off-white, siliceous, ball clay which can be beneficiated to make it acceptable to Indian ceramic industries. The remote sensing study is useful in identifying clay mineral deposits in the study area in Sonbhadra district and brightens the hope of findings pristine mineral deposits in the other parts of the country also.

Keywords

Remote sensing, Landsat, Unsupervised classification, Field survey, Clay
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  • Identification of Clay mineral deposits in the South-Eastern region of Uttar Pradesh, India, using Remote Sensing

Abstract Views: 35  | 

Authors

Debiprasad Karmakar
CSIR-Recruitment and Assessment Board, CSIR Complex, Library Avenue, New Delhi-110 012, India
Swapankumar Ghosh
Presently working at Department of Chemistry, West Bengal State University, Berunanpukuria, Kolkata-700 126, India

Abstract


Finding new mineral deposits is very important for the economic growth of a country. Recent advancements have made exploration of minerals very easy with the help of satellite imagery. Several inaccessible areas can be explored for the presence of deposits. The present article focuses on the analysis of the Landsat imagery of 2018 using datasets from the Landsat 8 (OLI/TIRS) satellite. The unsupervised classification with maximum likelihood algorithm is applied to bring out probable classes. It is found that the major classes are wasteland and forest followed by vegetation, water body, sand, built-up, limestone, kaolin and rocky land. The main objective is to find the kaolin rich zones which accounted for ~1.08% of the study area. To validate the findings, field survey have been carried out, 15 clay samples are collected from the study area in Ramgarh-Naudiha region of Sonbhadra district and have been characterised for mineral content. The mined mineral is fine-grained, off-white, siliceous, ball clay which can be beneficiated to make it acceptable to Indian ceramic industries. The remote sensing study is useful in identifying clay mineral deposits in the study area in Sonbhadra district and brightens the hope of findings pristine mineral deposits in the other parts of the country also.

Keywords


Remote sensing, Landsat, Unsupervised classification, Field survey, Clay