Next Article in Journal
Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements
Previous Article in Journal
Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(5), 358; doi:10.3390/rs8050358

Remote Sensing-Based Exploration of Structurally-Related Mineralizations around Mount Isa, Queensland, Australia

1
Helmholtz Zentrum Dresden Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Division “Exploration Technology”, Chemnitzer Str. 40, 09599 Freiberg, Germany
2
CSIRO Mineral Resources, 26 Dick Perry Avenue, Kensington, WA 6151, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Ruiliang Pu, Magaly Koch and Prasad S. Thenkabail
Received: 25 February 2016 / Revised: 20 April 2016 / Accepted: 21 April 2016 / Published: 25 April 2016
View Full-Text   |   Download PDF [41270 KB, uploaded 25 April 2016]   |  

Abstract

Hyperspectral imaging is a powerful tool for mineral mapping and increasingly used in poorly-accessible areas. It only requires a limited amount of validation sample points, but can fail to discriminate spectrally-similar features. In this manuscript, we show that we improve the identification of interesting targets by including geomorphological data in the spectral mapping scheme. We jointly use geomorphic and spectral features to locate gossanous ironstone ridges as an indicator for possible Pb-Zn-Ag-mineralization and provide an application around Mount Isa and George Fisher/Hilton mine, Queensland, Australia. We combine hyperspectral HyMap data using mixture tuned matched filtering with topographical indices, such as maximum curvature and the topographical position index. As it is often the case with structurally-controlled mineralization, the amount of training sites is limited, and supervised classification methods cannot be implemented. Therefore, we implement expert knowledge in a decision tree to take advantage of the relationship between mineralization, alteration and structure. Optimized rock sampling and spectral measurements provided data for validation. We are able to map sets of gossanous ridges with a minimum of validation points, not only within the Mount Isa mining area itself, but also outside the commonly-accepted host rocks. The ridges are parallel to north-south trending geomorphological features and probably associated with the Paroo fault zone. Similarities between the ridges were confirmed by field observations, spectral measurements and a qualitative rock sample analysis. We identified new mineralized ridges that we could subsequently attribute to a poorly-known and sub-economic deposit known as the Mount Novit Pb-Zn-deposit. View Full-Text
Keywords: HyMap; hyperspectral; mineral mapping; geomorphological features; alteration mapping HyMap; hyperspectral; mineral mapping; geomorphological features; alteration mapping
Figures

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jakob, S.; Gloaguen, R.; Laukamp, C. Remote Sensing-Based Exploration of Structurally-Related Mineralizations around Mount Isa, Queensland, Australia. Remote Sens. 2016, 8, 358.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top