High resolution remote sensing and potential analysis of iron ore prospecting ——Taking Datong Township,West KunLun Area for example

Li, J Q; Hao, Yi; Ren, Ge; Gao, Ting; Yang, Min; Han, Haihui; Yang, Junlu

doi:10.1088/1755-1315/46/1/012006

Cited by 2 publications

(3 citation statements)

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“…The successful application of remote sensing in the exploration and mapping of iron-containing minerals has been carried out and reported by many researchers, e.g. Rajendran et al (2007), Raja et al (2010), andLi et al, 2016. A case in point is the detection of the lithological occurrence of iron ore in southwestern Algeria using Landsat Enhanced Thematic Mapper Plus (ETM+) data by (Ciampalini et al, 2013). Furthermore, the iron ore occurrence in the western part of the Wadi Shatti district, in Libya, was successfully discriminated and delineated in the work carried out by Abulghasem et al (2011), who used and processed ETM images by using a Maximum Likelihood supervised classifier and band rationing.…”

Section: List Of Tablesmentioning

confidence: 98%

“…Optical remote sensing provides quantitative observational parameters for large areas and hence, is an essential source of information for many geological investigations (Rajendran et al, 2007). In the last century, remote sensing has been extensively used in many geological applications (Abrams et al, 1983;Clark & Roush, 1984;Chung, & Rencz, 1994;Agar & Coulter, 2007;Rajendran et al, 2007;Li et al, 2016;Manuel et al, 2017;Joseph & Bamidele, 2018;Izawa et al, 2019). Most notably, it has been extensively applied in geological mapping, mineral exploration, and geotechnical investigations, where it saves both time and initial investments.…”

Section: Satellite Remote Sensing As An Augmentative Toolmentioning

confidence: 99%

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Use of multispectral remote sensing data to map magnetite bodies in the Bushveld Complex, South Africa: a case study of Roossenekal, Limpopo.

Twala¹,

Roberts²,

Munghemezulu³

2021

Preprint

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The use of remote sensing in mineral detection and lithological mapping has become a generally accepted augmentative tool in exploration. With the advent of multispectral sensors (e.g. ASTER, Landsat, Sentinel and PlanetScope) having suitable wavelength coverage and bands in the Shortwave Infrared (SWIR) and Thermal Infrared (TIR) regions, multispectral sensors have become increasingly efficient at routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this project sought to evaluate and discuss the detection and mapping of vanadium bearing magnetite, found in discordant bodies and magnetite layers, on the Eastern Limb of the Bushveld Complex. The Bushveld Complex hosts the world&#8217;s largest resource of high-grade primary vanadium in magnetitite layers, so the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes warranted the delineation of magnetite.&#160;The detection and mapping of the vanadium bearing magnetite was evaluated using specialized traditional, and advanced machine learning algorithms. Prior to this study, few studies had looked at the detection and exploration of magnetite using remote sensing, despite remote sensing tools having been regularly applied to diverse aspects of geosciences. Maximum Likelihood, Minimum Distance to Means, Artificial Neural Networks, Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope data in ENVI, QGIS, and Python. For each classification algorithm, a thematic landcover map was attained and the accuracy assessed using an error matrix, depicting the user's and producer's accuracies, as well as kappa statistics.&#160;The Maximum Likelihood Classifier significantly outperformed the other techniques, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user&#8217;s accuracy of 76.41% and a producer&#8217;s accuracy of 88.66%. The erroneous classification of some mining activity pixels as magnetite in the Maximum Likelihood was inherent to all classification algorithms. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially in iron oxide mineralization in the Eastern Limb of Bushveld Complex.&#160;&#160;

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Section: List Of Tablesmentioning

confidence: 98%

Section: Satellite Remote Sensing As An Augmentative Toolmentioning

confidence: 99%

Use of multispectral remote sensing data to map magnetite bodies in the Bushveld Complex, South Africa: a case study of Roossenekal, Limpopo.

Twala¹,

Roberts²,

Munghemezulu³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…spaceborne platforms (Drury, 2001;Agar and Coulter, 2007;Ngcofe and Van Niekerk, 2016;Joseph and Bamidele, 2018). Remote sensing provides quantitative observational parameters for large areas and hence, is an essential source of information for many geological investigations (Abrams et al, 1983;Clark and Roush, 1984;Chung and Rencz, 1994;Agar and Coulter, 2007;Rajendran et al, 2007;Li et al, 2016;Manuel et al, 2017;Joseph and Bamidele, 2018;Izawa et al, 2019). In the last century, remote sensing has been extensively used in many geological applications.…”

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confidence: 99%

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

Twala

Roberts

Munghemezulu

2020

South African Journal of Geology

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Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.

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High resolution remote sensing and potential analysis of iron ore prospecting ——Taking Datong Township,West KunLun Area for example

Cited by 2 publications

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Use of multispectral remote sensing data to map magnetite bodies in the Bushveld Complex, South Africa: a case study of Roossenekal, Limpopo.

Use of multispectral remote sensing data to map magnetite bodies in the Bushveld Complex, South Africa: a case study of Roossenekal, Limpopo.

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

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