Abstract

Traditional exploration techniques usually rely on extensive field work supported by geophysical ground surveying. However, this approach can be limited by several factors such as field accessibility, financial cost, area size, climate, and public disapproval. We recommend the use of multiscale hyperspectral remote sensing to mitigate the disadvantages of traditional exploration techniques. The proposed workflow analyzes a possible target at different levels of spatial detail. This method is particularly beneficial in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally noninvasive surveying. After a satellite regional reconnaissance, a target is characterized in more detail by plane-based hyperspectral mapping. Subsequently, Remotely Piloted Aircraft System (RPAS)-mounted hyperspectral sensors are deployed on selected regions of interest to provide a higher level of spatial detail. All hyperspectral data are corrected for radiometric and geometric distortions. End-member modeling and classification techniques are used for rapid and accurate lithological mapping. Validation is performed via field spectroscopy and portable XRF as well as laboratory geochemical and spectral analyses. The resulting spectral data products quickly provide relevant information on outcropping lithologies for the field teams. We show that the multiscale approach allows defining the promising areas that are further refined using RPAS-based hyperspectral imaging. We further argue that the addition of RPAS-based hyperspectral data can improve the detail of field mapping in mineral exploration, by bridging the resolution gap between airplane- and ground-based data. RPAS-based measurements can supplement and direct geological observation rapidly in the field and therefore allow better integration with in situ ground investigations. We demonstrate the efficiency of the proposed approach at the Lofdal Carbonatite Complex in Namibia, which has been previously subjected to rare earth elements exploration. The deposit is located in a remote environment and characterized by difficult terrain which limits ground surveys.

Highlights

  • The increasing demand for raw materials worldwide conflicts, with the growing difficulties to find new mineral deposits, outlines the need for innovative approaches in mineral exploration

  • We demonstrate the efficiency of the proposed approach at the Lofdal Carbonatite Complex in Namibia, which has been previously subjected to rare earth elements exploration

  • Multisource and multiscale field exploration integrates the full range of available remote sensing data sets, from space-borne imaging over airborne data, to high-resolution Remotely Piloted Aircraft System (RPAS)-based hyperspectral data acquisition as well as ground validation

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Summary

Introduction

The increasing demand for raw materials worldwide conflicts, with the growing difficulties to find new mineral deposits, outlines the need for innovative approaches in mineral exploration. The available HyMap data features a spatial sampling distance of 4.5 m, which does not allow detection of very small geological structures, such as the typical dykes, or lenses known to host REE deposits. There have recently been some multi- and hyperspectral cameras made available for the scientific community [17,18,19] These multi- and hyperspectral lightweight sensors cover the visible to near-infrared (VNIR) spectral range (400–1000 nm) with tens of narrow bands and provide images with high radiometric quality [18,19,20]. This study area provides unique conditions to demonstrate the benefits of RPAS surveys for the remote sensing mapping of fine geological structures and to evaluate the synergic potential of multisensor techniques

Geological Setting
Data Acquisition and Preprocessing
Satellite and Airborne Approach
Extraction of structural features
Reesults from Lofdal
4: Vectorization of Ridges
Discussion
Conclusions and Outlook
Conversion to Radiance
Lens correction and co-registration
Orthorectification and georeferencing
Findings
Topographic correction
Radiometric correction
Full Text
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