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

Zimmermann, Robert; Brandmeier, Melanie; Andreani, Louis; Mhopjeni, Kombada; Gloaguen, Richard

doi:10.3390/rs8080620

Cited by 37 publications

(32 citation statements)

References 41 publications

(70 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hyperspectral sensors have become a key tool for a large range of applications in remote sensing and are now widely used in geology, mineral mapping and exploration (e.g., van der Meer et al [1], Laakso et al [2], Jakob et al [3], Zimmermann et al [4]). During the last few years, lightweight hyperspectral imaging (HSI) sensors have been increasingly developed for use on unmanned aerial systems (UAS) (i.e., Cubert GmbH [5] or Rikola Ltd. [6]).…”

Section: Introductionmentioning

confidence: 99%

The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data

2017

Self Cite

View full text Add to dashboard Cite

Drone-borne hyperspectral imaging is a new and promising technique for fast and precise acquisition, as well as delivery of high-resolution hyperspectral data to a large variety of end-users. Drones can overcome the scale gap between field and air-borne remote sensing, thus providing high-resolution and multi-temporal data. They are easy to use, flexible and deliver data within cm-scale resolution. So far, however, drone-borne imagery has prominently and successfully been almost solely used in precision agriculture and photogrammetry. Drone technology currently mainly relies on structure-from-motion photogrammetry, aerial photography and agricultural monitoring. Recently, a few hyperspectral sensors became available for drones, but complex geometric and radiometric effects complicate their use for geology-related studies. Using two examples, we first show that precise corrections are required for any geological mapping. We then present a processing toolbox for frame-based hyperspectral imaging systems adapted for the complex correction of drone-borne hyperspectral imagery. The toolbox performs sensor-and platform-specific geometric distortion corrections. Furthermore, a topographic correction step is implemented to correct for rough terrain surfaces. We recommend the c-factor-algorithm for geological applications. To our knowledge, we demonstrate for the first time the applicability of the corrected dataset for lithological mapping and mineral exploration.

show abstract

Section: Introductionmentioning

confidence: 99%

The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…One way to tackle this problem is through the integration of different sensors at different resolution levels. Recent work aims at improving the identification of targets for mineral exploration by combining information from multisensor sources as well as geomorphological data in the spectral mapping scheme [13,14].A way to mitigate the problem is potentially provided by the development of Remotely Piloted Airborne Systems (RPAS). Imaging spectroscopy (IS) using RPAS represents a novel, still developing field in modern remote sensing technologies.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case Study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia

et al. 2019

View full text Add to dashboard Cite

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. 2 of 28 increased demand for Rare Earth Elements (REEs). As a result, the exploration of new and promising REE deposits is of economic importance in view of the current dependence of the world market on imports from a limited number of countries.Carbonatites, rocks comprising more than 50 modal-% carbonate minerals, are the main source of REEs [1]. The typical ore minerals for light rare earth elements (LREEs) are monazite, bastnaesite, and parisite, and for the heavy rare earth elements (HREEs), is xenotime [1]. Hyperspectral remote sensing has been recently suggested to detect and identify carbonatites as potential REE deposits [2][3][4], due to the distinctive absorption feature of carbonates (CO 3 ) around 2300 to 2350 nm [5]. At a sufficient grade, REEs can be detected directly due to their characteristic narrow absorption features in the VNIR and SWIR [3,6]. Neodymium...

show abstract

“…SOFMs can be transformed and formalized into the form of a Kohonen neural network [42,43]. Kohonen neural networks have been successfully applied in image segmentation tasks, the detection of specific areas in geographic maps [44][45][46], and the detection of cracks and corrosion in materials [47]. The results obtained from these applications represent a solid foundation and an inspiration for this approach to be applied in the segmentation of infrared images for the detection of anomalies in the temperature fields of different objects.The results of pattern recognition and image segmentation come in the form of classes present in the image.…”

mentioning

confidence: 99%

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

et al. 2019

View full text Add to dashboard Cite

There are only a few applications of infrared thermal imaging in aviation. In the area of turbojet engines, infrared imaging has been used to detect temperature field anomalies in order to identify structural defects in the materials of engine casings or other engine parts. In aviation applications, the evaluation of infrared images is usually performed manually by an expert. This paper deals with the design of an automatic intelligent system which evaluates the technical state and diagnoses a turbojet engine during its operation based on infrared thermal (IRT) images. A hybrid system interconnecting a self-organizing feature map and an expert system is designed for this purpose. A Kohonen neural network (the self-organizing feature map) is successfully applied to segment IRT images of a turbojet engine with high precision, and the expert system is then used to create diagnostic information from the segmented images. This paper represents a proof of concept of this hybrid system using data from a small iSTC-21v turbojet engine operating in laboratory conditions.

show abstract

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

Cited by 37 publications

References 41 publications

The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data

The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data

Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case Study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

Contact Info

Product

Resources

About