Satellite-based spectral mapping (ASTER and landsat data) of mineralogical signatures of beach sediments: a precursor insight

Rejith, R. G.; Sundararajan, M.; Gnanappazham, L.; Loveson, Victor J.

doi:10.1080/10106049.2020.1750061

Cited by 19 publications

(11 citation statements)

References 59 publications

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“…It is also notable that there are similar overall spectral signatures of all samples (Figure 5), irrespective of their depositional environment (Table 2), which means that the depositional environment of any one sediment sample cannot be resolved by their spectral signature alone. One potential reason for this is that all of the field sediment samples examined here are quartz-dominated (data not shown) and thus, variations in mineralogy cannot be considered as a significant control on their spectral signatures, unlike in previous studies, e.g., [29,47,54]. Although the spectral signature of interstitial water is a dominant feature in other previous studies of coastal sediments e.g., [25,26,32], we deliberately excluded this by drying the samples prior to analysis.…”

Section: Discussionmentioning

confidence: 80%

“…The same research approach was used by Ibrahim et al [19] along the Belgian coast. Grain size properties along beaches were examined using Landsat visible, near infrared (VNIR) and thermal infrared bands in SE India [29], but these bands may have been influenced by a high concentration of heavy minerals (50-80%) at this site. Using IKONOS imagery, Park et al [30] showed that all spectral bands have a good correlation (>0.8) with grain size, and Williams and Greeley [31] showed that different spectral bands from synthetic aperture radar imagery are affected by surface moisture.…”

Section: Introductionmentioning

confidence: 99%

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Characterisation of Coastal Sediment Properties from Spectral Reflectance Data

Knight

Elbasit

2022

Applied Sciences

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Remote sensing of coastal sediments for the purpose of automated mapping of their physical properties (grain size, mineralogy and carbonate content) across space has not been widely applied globally or in South Africa. This paper describes a baseline study towards achieving this aim by examining the spectral reflectance signatures of field sediment samples from a beach–dune system at Oyster Bay, Eastern Cape, South Africa. Laboratory measurements of grain size and carbonate content of field samples (n = 134) were compared to laboratory measurements of the spectral signature of these samples using an analytical spectral device (ASD), and the results interrogated using different statistical methods. These results show that the proportion of fine sand, CaCO3 content and the distributional range of sediment grain sizes within a sample (here termed span) are the parameters with greatest statistical significance—and thus greatest potential interpretive value—with respect to their spectral signatures measured by the ASD. These parameters are also statistically associated with specific wavebands in the visible and near infrared, and the shortwave infrared parts of the spectrum. These results show the potential of spectral reflectance data for discriminating elements of grain size properties of coastal sediments, and thus can provide the baseline towards achieving automated spatial mapping of sediment properties across coastal beach–dune environments using hyperspectral remote sensing techniques.

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Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Characterisation of Coastal Sediment Properties from Spectral Reflectance Data

Knight

Elbasit

2022

Applied Sciences

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“…The geometric correction, re ectance calibration, and atmospheric corrections were carried out using Crosstalk correction software (for SWIR bands of ASTER data) and FLAASH (Fast line-of-sight Atmospheric Analysis of Spectral Hypercubes) modules [29][30][31] . In the present study, hyperspectral data analysis was carried out to bands 1-5 and band 7 of Landsat 8 whereas in ASTER the bands 1-9 are used.…”

Section: Satellite Data and Pre-processingmentioning

confidence: 99%

“…In the present study, hyperspectral data analysis was carried out to bands 1-5 and band 7 of Landsat 8 whereas in ASTER the bands 1-9 are used. Integration of hyperspectral analysis techniques and multispectral data facilitate accurate mapping of mineral deposits 29,30,32,33 .…”

Section: Satellite Data and Pre-processingmentioning

confidence: 99%

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Remote Sensing Exploration of Fullerene Bearing Shungite in Mangampet Baryte Mines, India

Rejith

Sundararajan

Gnanappazham

et al. 2021

Preprint

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The fullerene (C60) is economically significant due to its significant applications in diverse areas like nanochemistry, superconductivity, materials science, etc. The carbon allotrope fullerene C60 and C70 are identified within the thick sequence of black carbonaceous units belonging to the shungite suite of rock at the baryte mine, Mangampet, Andhra Pradesh. Since this is the first of its kind from India's geological material, the exploration of fullerenes enriched barytes from the mines at Mangampet in the Kadapa district is essential. In the present study, remote sensing techniques such as hyperspectral analysis followed by Mixture Tuned Matched Filtering (MTMF) and Support Vector Machine (SVM), and Principal Component Analysis (PCA) were used for exploring the presence of fullerene bearing baryte deposits in the Mangampet mine. The spectra measured for baryte samples were used as reference spectra for deriving true endmember from Landsat OLI and ASTER satellite data. The detailed characterisation of structure and chemistry of the baryte samples were carried out using techniques like Energy Dispersive X-ray Fluorescence (EDXRF), X-ray Powder Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), and Fourier-Transform Infrared Spectroscopy (FTIR) techniques. The High-Performance Liquid Chromatography (HPLC) and Matrix-Assisted Laser Desorption/Ionization (MALDI) mass spectrometry analyses confirm fullerene presence in baryte deposits. The retention time at 11.783–11.822 min obtained from HPLC and high-intensity rate m/z at 720.178 obtained from MALDI spectra suggest fullerene in baryte mine deposits. The remote sensing exploration of fullerene bearing shungite suite of rocks in baryte mineral deposits of Mangampet mine has opened up new research areas for converting this material into value-added products.

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