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Remote Sens. 2016, 8(8), 620;

Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia)

Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Division “Exploration Technology”, Chemnitzer Str. 40, 09599 Freiberg, Germany
TU Bergakademie Freiberg, Institute of Geology, Bernhard-von-Cotta-Str. 2, 09599 Freiberg, Germany
Geological Survey of Namibia, 1 Aviation Road, Windhoek, Namibia
This paper is an extended version of our paper published in Geophysical Research Abstracts, Volume 17, EGU2015-8836-1, 2015.
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao and Prasad S. Thenkabail
Received: 3 June 2016 / Revised: 20 July 2016 / Accepted: 22 July 2016 / Published: 28 July 2016
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On the example of the Epembe carbonatite-hosted Nb-Ta-LREE deposit, we demonstrate the use of hyperspectral reflectance data and geomorphic indicators for improving the accuracy of remote sensing exploration data of structurally-controlled critical raw material deposits. The results further show how exploration can benefit from a combination of expert knowledge and remotely-sensed relief, as well as imaging data. In the first stage, multi-source remote sensing data were used in lithological mapping based on Kohonen Self-Organizing Maps (SOM). We exemplify that morphological indices, such as Topographic Position Index (TPI), and spatial coordinates are crucial parameters to improve the accuracy of carbonate classification as much as 10%. The resulting lithological map shows the spatial distribution of the ridge forming carbonatite dyke, the fenitization zone, syenite plugs and mafic intrusions. In a second step, the internal zones of the carbonatite complex were identified using the Multi-Range Spectral Feature Fitting (MRSFF) algorithm and a specific decision tree. This approach allowed detecting potential enrichment zones characterized by an abundance of fluorapatite and pyroxene, as well as dolomite-carbonatite (beforsite). Cross-validation of the mineral map with field observations and radiometric data confirms the accuracy of the proposed method. View Full-Text
Keywords: carbonatite; REE; self-organizing maps; decision tree; spectral feature fitting; geomorphology; Namibia; HyMap; SRTM carbonatite; REE; self-organizing maps; decision tree; spectral feature fitting; geomorphology; Namibia; HyMap; SRTM

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zimmermann, R.; Brandmeier, M.; Andreani, L.; Mhopjeni, K.; Gloaguen, R. Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia). Remote Sens. 2016, 8, 620.

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