Advances in optical earth observation for geological mapping: A Review

Ngcofe, Luncedo; Niekerk, Adriaan van

doi:10.4314/sajg.v5i1.1

Cited by 6 publications

(4 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remote sensing is the acquisition of information and the identification of Earth-surface features or phenomenon using reflected and emitted electromagnetic radiation (from the surface features), assessed and measured by sensors on airborne or spaceborne platforms (Drury, 2001;Agar & Coulter, 2007;Ngcofe & Van Niekerk, 2016;Joseph & Bamidele, 2018). 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).…”

Section: Satellite Remote Sensing As An Augmentative Toolmentioning

confidence: 99%

“…Most notably, it has been extensively applied in geological mapping, mineral exploration, and geotechnical investigations, where it saves both time and initial investments. In the aforementioned fields, remote sensing gives a synoptic view of the sites of interest, which are challenging to obtain from merely field-based observations (Ngcofe & Van Niekerk, 2016;Manuel et al, 2017).…”

Section: Satellite Remote Sensing As An Augmentative Toolmentioning

confidence: 99%

See 1 more Smart Citation

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

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;

show abstract

Section: Satellite Remote Sensing As An Augmentative Toolmentioning

confidence: 99%

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).…”

mentioning

confidence: 99%

“…In the last century, remote sensing has been extensively used in many geological applications. Most notably in geological mapping, mineral exploration, and geotechnical investigations, where it saves both time and initial investments, by giving a synoptic view of the sites of interest, which are often difficult to obtain from field-based observation alone (Ngcofe and Van Niekerk, 2016;Manuel et al, 2017).…”

mentioning

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

View full text Add to dashboard Cite

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.

show abstract

Integrating Remotely Sensed and GIS Data for the Detailed Geological Mapping in Semi-Arid Regions: Case of Youks les Bains Area, Tebessa Province, NE Algeria

et al. 2019

View full text Add to dashboard Cite

Advances in optical earth observation for geological mapping: A Review

Abstract: Recent increases in the availability of earth observation (EO) data and the advances made in its processing

Cited by 6 publications

References 64 publications

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

Integrating Remotely Sensed and GIS Data for the Detailed Geological Mapping in Semi-Arid Regions: Case of Youks les Bains Area, Tebessa Province, NE Algeria

Contact Info

Product

Resources

About