Multi- and hyperspectral spaceborne remote sensing of the Aggeneys base metal sulphide mineral deposit sites in the Lower Orange River region, South Africa

Mielke, Christian; Muedi, Thomas; Papenfuß, Anne; Boesche, Nina Kristine; Rogaß, Christian; Gauert, Christoph; Altenberger, Uwe; Wit, Maarten J. de

doi:10.2113/gssajg.119.1.63

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…The geometrical transformation is applied at the end on the results to prevent resampling effects on the spectral analyses. The pre-processing chain was used previously for geological applications, e.g., by Mielke et al [85,86] proving that it can deal with the sensor limited quality and still delivers reflectance data that are adequate for mineralogical identification analyses.…”

Section: Eo-1 Hyperion Mineralogical Mappingmentioning

confidence: 99%

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

2017

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This study combines spaceborne multitemporal and hyperspectral data to analyze the spatial distribution of surface evaporite minerals and changes in a semi-arid depositional environment associated with episodic flooding events, the Omongwa salt pan (Kalahari, Namibia). The dynamic of the surface crust is evaluated by a change-detection approach using the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) based on the Landsat archive imagery from 1984 to 2015. The results show that the salt pan is a highly dynamic and heterogeneous landform. A change gradient is observed from very stable pan border to a highly dynamic central pan. On the basis of hyperspectral EO-1 Hyperion images, the current distribution of surface evaporite minerals is characterized using Spectral Mixture Analysis (SMA). Assessment of field and image endmembers revealed that the pan surface can be categorized into three major crust types based on diagnostic absorption features and mineralogical ground truth data. The mineralogical crust types are related to different zones of surface change as well as pan morphology that influences brine flow during the pan inundation and desiccation cycles. These combined information are used to spatially map depositional environments where the more dynamic halite crust concentrates in lower areas although stable gypsum and calcite/sepiolite crusts appear in higher elevated areas.

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Section: Eo-1 Hyperion Mineralogical Mappingmentioning

confidence: 99%

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

2017

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“…So far, simulated EnMAP data has been tested by researchers who specialise in the identification of minerals (N. Yokoya et al 2016, C. Mielke et al 2016, N.K. Bösche et al 2014) and vegetation.…”

Section: Introductionmentioning

confidence: 99%

Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains

Jędrych

Zagajewski

Marcinkowska-Ochtyra

2017

Polish Cartographical Review

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Effective assessment of environmental changes requires an update of vegetation maps as it is an indicator of both local and global development. It is therefore important to formulate methods which would ensure constant monitoring. It can be achieved with the use of satellite data which makes the analysis of hard-to-reach areas such as alpine ecosystems easier. Every year, more new satellite data is available. Its spatial, spectral, time, and radiometric resolution is improving as well. Despite significant achievements in terms of the methodology of image classification, there is still the need to improve it. It results from the changing needs of spatial data users, availability of new kinds of satellite sensors, and development of classification algorithms. The article focuses on the application of Sentinel-2 and hyperspectral EnMAP images to the classification of alpine plants of the Karkonosze (Giant) Mountains according to the: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood (ML) algorithms. The effects of their work is a set of maps of alpine and subalpine vegetation as well as classification error matrices. The achieved results are satisfactory as the overall accuracy of classification with the SVM method has reached 82% for Sentinel-2 data and 83% for EnMAP data, which confirms the applicability of image data to the monitoring of alpine plants.

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“…The band ratio the 660/490 nm (B4/B2 with Landsat 8) from [3] and the ratio 1610/865 nm (B6/B5) from [21] were tested. The use of the IFD index from [22,23] was also explored. This index highlights the absorption feature of iron in the near infrared (NIR) band (positive IFD values indicate an absorption).…”

Section: Discussionmentioning

confidence: 99%

“…As gossans are Fe-oxides/hydroxides rich, the main absorption peaks are causes by electronic processes between 0.45 µm and 0.9 µm for Fe2+, between 0.9 µm and 1.2 µm for Fe3+, and vibrational processes for Fe-OH bond around 2.2 µm [16][17][18]. Mapping Fe rich formation or gossans has been done for over four decades using Landsat multispectral scanners [19,20], or more recently, using Landsat 7 [3] or Landsat 8 [18,21,22]. The methods used in these studies consist mostly of Principal Components Analysis and bands ratios.…”

Section: Introductionmentioning

confidence: 99%

“…This study also shows that gossans can be highlighted in Landsat 8 imagery following a principal component (PC) analysis, using a color composite of PC2 as red, PC3 as green, and PC4 as blue or by using the components 2, 3, and 4 of a minimum noise fraction (MNF) analysis. An iron feature depth (IFD) index, built on different ratios around Fe2+ and Fe3+ absorption features, was developed to map gossans [22,23].…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

et al. 2020

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Gossans are surficial deposits that form in host bedrock by the alteration of sulphides by acidic and oxidizing fluids. These deposits are typically a few meters to kilometers in size and they constitute important vectors to buried ore deposits. Hundreds of gossans have been mapped by field geologists in sparsely vegetated areas of the Canadian Arctic. However, due to Canada’s vast northern landmass, it is highly probable that many existing occurrences have been missed. In contrast, a variety of remote sensing data has been acquired in recent years, allowing for a broader survey of gossans from orbit. These include band ratioing or methods based on principal component analysis. Spectrally, the 809 gossans used in this study show no significant difference from randomly placed points on the Landsat 8 imageries. To overcome this major issue, we propose a deep learning method based on convolutional neural networks and relying on geo big data (Landsat-8, Arctic digital elevation model lithological maps) that can be used for the detection of gossans. Its application in different regions in the Canadian Arctic shows great promise, with precisions reaching 77%. This first order approach could provide a useful precursor tool to identify gossans prior to more detailed surveys using hyperspectral imaging.

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Multi- and hyperspectral spaceborne remote sensing of the Aggeneys base metal sulphide mineral deposit sites in the Lower Orange River region, South Africa

Cited by 8 publications

References 28 publications

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains

A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

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