A Novel Spectral Index for Identifying Ferronickel (Fe–Ni) Laterites from Sentinel 2 Satellite Data

Anifadi, A.; Sykioti, Olga; Koutroumbas, Konstantinos; Vassilakis, Emmanuel

doi:10.1007/s11053-022-10055-6

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…A special case of SIs is represented by band ratios, where the ratio of two spectral bands is computed. Several SIs have been proposed using a range of wavelength combinations for mapping various mineral assemblages (Cloutis, 1996;Sabins, 1999;Cudahy and Hewson, 2002;Yamaguchi and Naito, 2003;Henrich et al, 2009;Shim et al, 2021), especially after the arrival of ASTER data with their SWIR spectral bands (Abrams et al, 1983;Abrams & Hook, 1995;Yamaguchi et al, 1998;Yamagushi & Naito, 2003;Kalinowski and Oliver, 2004;Gad and Kusky, 2007;Rajendran et al, 2011;Abrams et al, 2015;Ferrier et al 2016;Askari et al, 2018;Shuai et al, 2022) and/or Landsat 8 data (Anifadi et al, 2016;Ferrier et al 2016;Tompolidi et al, 2020;Anifadi et al, 2022;Sykioti et al 2022), spectral unmixing on (i) S-2, ASTER and Landsat 8 OLI data (Tompolidi et al, 2020) and (ii) EO-1 Hyperion hyperspectral data (Nikolakopoulos et al, 2007), Spectral Angle Mapping and Mixture-Tuned Matched…”

Section: Volume 59mentioning

confidence: 99%

“…During the last three decades, the use of satellite imagery in geological mapping and mineral exploration has grown rapidly in order to detect mineralization-related alterations and associated ore deposits (Van der Meer et al, 2012;Van der Meer and Van der Werff, 2014;Van der Meer, 2015, 2016;Ge et al, 2018(a), 2018(b); Hu et al, 2018;Van der Meer et al, 2018;Pour et al, 2019;Vasuki et al, 2019;Ferrier et al 2019;Tompolidi et al, 2020;Anifadi et al, 2022). Recent multispectral satellites such as the Landsat Operational Land Imager (OLI), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have been the most widely used multispectral satellite sensors for the detection and mapping of minerals related to alteration, such as iron oxides, hydroxyl-bearing minerals, and Volume 59 carbonates (Rowan and Mars, 2003;Rowan et al, 2006;Ferrier et al, 2010Ferrier et al, , 2016Mars and Rowan, 2010;Hosseinjani et al, 2011;Pour et al, 2011;Rajendran et al, 2017;Yang et al, 2018;El Kati et al 2018;Abubakar et al, 2019;Pour et al 2019;Ferrier et al 2019;Tompolidi et al 2020;Vural et al, 2021), especially, when the satellites provide spectral information in the shortwave infrared (SWIR) part of the electromagnetic spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…Since 2015, additional studies have been conducted in order to reveal the potential of Sentinel-2 (S-2) Multispectral Imager (MSI) in geological remote sensing, mineral exploration and discrimination (Mielke et al, 2014;Van der Meer and Van der Werff, 2014;Yokova et al, 2016;Hu et al, 2018;Adiri et al, 2020;Ge et al, 2020;Purwadi et al, 2020;Tompolidi et al, 2020;Lanfranchi et al, 2021;Soydan et al, 2021;Anifadi et al, 2022). Sentinel-2 provides spectral information in twelve spectral bands, namely ten (10) in the visible-near infrared (VNIR) and two (2) in the SWIR (Table 1).…”

Section: Introductionmentioning

confidence: 99%

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Investigating the capability of Sentinel-2 and Worldview-3 VNIR satellite data to detect mineralized zones at an igneous intrusion in the Koutala islet (Lavreotiki, Greece) using laboratory mineralogical analysis, reflectance spectroscopy and spectral indices

et al. 2023

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During the last decades, the rapid progress of remote sensing data processing for their utilization in detecting locations of possible sites linked to hydrothermal alteration and ores has gained increasing attention due to significant time and cost savings. In this study, we present the findings of a joint remote sensing and laboratory examination of a mineralization zone induced by a granitoid intrusion onshore the islet of “Koutala”, in Lavreotiki, central Greece. Our objective is to investigate the potential of Sentinel-2 and WorldView-3 VNIR satellite data to detect and map oxidized ore zones and alteration minerals that are detected from laboratory analysis of samples and could be linked to Fe-Mn mineralization. Two approaches are followed, namely reflectance spectroscopy and spectral indices. In reflectance spectroscopy, the spectral signatures of all minerals detected in the study area are retrieved from spectral libraries. The signatures are resampled to Sentinel-2 and to WorldView-3 VNIR spectral bands. Continuum-removal is then applied and the diagnostic absorption features of each mineral are detected for each spectral band configuration. The dataset with the best spectral configuration for mineral detection is then used for the production of mineral maps using the corresponding satellite image. The second approach involves the calculation of spectral indices, namely ferric, ferrous iron and hydroxyl-bearing alteration, on reflectance spectra. The ferric iron index is applied to both satellite datasets while the two other indices require the use of SWIR bands and therefore, they can be only calculated on Sentinel-2 data. All results show that laboratory and satellite data analyses results are consistent and complementary. WorldView-3 VNIR data seem to be sensitive only to the ferric and manganese phase. Sentinel-2 data seem to be capable to detect and map all alteration minerals that are potentially linked to Fe-Mn ore, including both ferric and ferrous phases. The mineral absorption and spectral indices maps show that in the investigated area, there is significant mineralization related to the granitoid intrusion. Hydrothermal alteration is observed on the entire surface of the islet but it seems to be stronger at the eastern part of the islet where the granitoid/schist contact is located. It is the first time that (i) minerals linked to a potential Fe-Mn ore are detected on the islet and (b) the corresponding alteration mineral maps are produced from satellite data, revealing their spatial distribution and providing indirect estimations of the degree of their presence.

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Section: Volume 59mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating the capability of Sentinel-2 and Worldview-3 VNIR satellite data to detect mineralized zones at an igneous intrusion in the Koutala islet (Lavreotiki, Greece) using laboratory mineralogical analysis, reflectance spectroscopy and spectral indices

et al. 2023

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“…In particular, concerning Fe-Ni-laterite and/or bauxite detection and characterization, several processing techniques have been proposed using Earth Observation (EO) data, such as band ratios (BRs), Principal Component Analysis (PCA) applied to ASTER and Sentinel-2 data [31][32][33], sub-pixel-based classification techniques on hyperspectral data such as Linear Spectral Unmixing (LSU) [32,34], and various regression methods on Sentinel-2 data [21,35,36]. Other studies have focused on the use of reflectance spectroscopy techniques and Spectral Indices in order to recognize general categories of Fe-Ni-laterite or bauxite-related mineral assemblages, such as iron-bearing minerals and/or iron oxides [19,21,22].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data

Anifadi,

Sykioti,

Koutroumbas

et al. 2024

Remote Sensing

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Currently, the global shift towards green energy is at the forefront of efforts introducing a new era, thus rendering exploration for critical raw materials essential. To this purpose, the utilization of advanced machine learning methods in remote sensing has emerged as a rapid and cost-effective approach. This study proposes a new methodology, utilizing Sentinel-2 satellite data, to distinguish ferronickel (Fe-Ni-) laterite from bauxite across pre-mining, mining, and post-mining occurrences worldwide. Both ores contain mineral raw materials such as nickel, iron, cobalt, and alumina and their discrimination is generally macroscopically challenging, especially when their locations are often in geographical proximity. The proposed method is based on Support Vector Machines (SVM) classification using spectral signatures of known Fe-Ni-laterite and bauxite-bearing pixels in Greece, Cuba, and Jamaica. The highest classification accuracies are obtained by combining b12 with b6 or b7 spectral bands. Comparisons with specific ore mineralogies show that b6 and b7 are strongly linked to the ferric phase, while b12 is mainly associated with the argillic mineralogies, the latter probably being the key discriminating factor between the two ores. From laboratory chemical analyses, we also establish that b12 and b6 or b7 are strongly associated with Al2O3 and Fe2O3 content correspondingly. The proposed method is accurate, it has reduced prospection costs, and it can facilitate the initial screening of broad areas by automatically characterizing whether an ore is bauxite or Fe-Ni-laterite. This underscores the methodology’s significance in ore differentiation and exploration within the context of green energy endeavors.

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Capitalising on the Floristic Survey as a Non-Destructive Line of Evidence for Mineral Potential Modelling: A Case Study of Bauxite in South-Western Australia

Trotter,

Wardell-Johnson,

Grigg

et al. 2024

Land

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While geobotanists have long used plant occurrence to locate subsurface resources, none have utilised floristic surveys as evidence in models of mineral potential. Here, we combine plant species distributions with terrain metrics to produce predictive models showing the probability of bauxite presence. We identified nineteen taxa with statistically significant associations with known bauxite deposits and identified eleven terrain metrics from previous studies. We grouped variables into three variable sets (floristic, topographic, and topo-flora) and produced mineral potential models for each using four algorithms or approaches: (a) a generalised linear model (GLM); (b) random forest (RF); (c) maxent (ME); and (d) a heterogenous stacking ensemble (GLM-RF-ME). Overall, the random forest model outperformed all algorithms including the ensemble based on the area under the curve (AUC) metric. The floristic set of variables outperformed the topographic set (AUC: 0.86 v 0.82). However, together they had the greatest predictive capacity (AUC: 0.89). Six taxa, including Banksia grandis, Leucopogon verticillatus, and Persoonia longifolia, were indicators of bauxite presence, while five other taxa, including Xanthorrhoea preissii and Hypocalymma angustifolium, were associated with bauxite absence. Important topographic variables were topographic wetness, landscape position, and valley depth, which characterised bauxite locations as being well drained, in the upper slope positions of subdued hills, and at some distance from valleys. The addition of floristic surveys provides a new line of evidence about the overlying botanical life that tolerates, accumulates, or avoids bauxite or associated minerals. As opposed to drilling, both datasets can be collected and interrogated at low cost and without impact to the surrounding environment. These data are valuable additions to future applications of mineral potential modelling.

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A Novel Spectral Index for Identifying Ferronickel (Fe–Ni) Laterites from Sentinel 2 Satellite Data

Cited by 3 publications

References 43 publications

Investigating the capability of Sentinel-2 and Worldview-3 VNIR satellite data to detect mineralized zones at an igneous intrusion in the Koutala islet (Lavreotiki, Greece) using laboratory mineralogical analysis, reflectance spectroscopy and spectral indices

Investigating the capability of Sentinel-2 and Worldview-3 VNIR satellite data to detect mineralized zones at an igneous intrusion in the Koutala islet (Lavreotiki, Greece) using laboratory mineralogical analysis, reflectance spectroscopy and spectral indices

Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data

Capitalising on the Floristic Survey as a Non-Destructive Line of Evidence for Mineral Potential Modelling: A Case Study of Bauxite in South-Western Australia

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