EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Mthokozisi Twala1, James Roberts1, and Cilence Munghemezulu2
Mthokozisi Twala et al.
  • 1University of Pretoria, Geology, South Africa (
  • 2Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW), Division of Geoinformation Science, Pretoria

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’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.


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.


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’s accuracy of 76.41% and a producer’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. 


How to cite: Twala, M., Roberts, J., and Munghemezulu, C.: Use of multispectral remote sensing data to map magnetite bodies in the Bushveld Complex, South Africa: a case study of Roossenekal, Limpopo., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7932,, 2021.


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