Geological Controls and Prospectivity Mapping for Manganese Ore Deposits Using Predictive Modeling Comparison: An Integration of Outcrop and Remote Sensing Data, Sinai Microplate, Egypt

Zamzam, Sara

doi:10.1007/s12583-021-1583-z

Cited by 6 publications

(2 citation statements)

References 57 publications

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“…In the future, S34I will (i) assess the usefulness of supervised classification algorithms; (ii) test hyperspectral data and very high-resolution data such as PRISMA and Worldview-3, respectively; and (iii) characterize in detail the hydrothermal alteration associated with the epithermal Co-Cu-Ni deposits. Moreover, to fully exploit radar data, Sentinel-1 will be used to perform lineament extraction and structural mapping (i.e., structural controls of the mineralization) as done in previous works 31,32 . The integration of Sentinel-1 and Sentinel-2 could be considered using AI, specifically machine learning algorithms.…”

Section: Aramomentioning

confidence: 99%

Sentinel data for critical raw materials (CRM) exploration: first results of the S34I project

Cardoso-Fernandes,

Carvalho,

Azzalini

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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To achieve sustainable development goals, it is crucial to ensure the best practices through the whole mining cycle of Critical Raw Materials (CRM) used in high-tech, low-carbon goods (batteries, wind turbines, electronics). This is the aim of the Secure and Sustainable Supply of Raw Materials for EU Industry (S34I) Horizon Europe (HE) project that began on January 1st, 2023. S34I is investigating new data-driven methods to analyze Earth Observation (EO) data, supporting systematic mineral exploration (and other) activities to increase European autonomy regarding CRM resources. The S34I project is based on satellite data, airborne, Unmanned Aerial Vehicle (UAV), ground-based conventional in-situ techniques/methods, and fieldwork. This study presents the first preliminary results based on Sentinel-1 and Sentinel-2 data using classical image processing techniques (RGB combinations, band ratio, Principal Component Analysis – PCA, and unsupervised classification) for mineral exploration in Spain, either: (i) on land (Aramo area), to gain knowledge on cobalt (Co) deposits (and associate CRMs) by hydrothermal alteration mapping; and (ii) offshore/shallow waters (Ria de Vigo area), to update the knowledge on coastal metallic placers including CRMs such as Ti, Sn, Li, Rare Earth Elements (REEs) and Au. Among the techniques, PCA proved most effective, followed by band ratios, and self-proposed algorithms for detecting placer minerals showed promise, warranting further investigation. In the future, S34I will exploit Copernicus Contributing Missions (CCM) and other EU hyperspectral satellite sensors such as PRISMA and EnMAP. Different Artificial Intelligence (AI) techniques will, in the future, promote advances and innovative methods that mining stakeholders can use to address the challenges faced in different phases of the mining life cycle.

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Section: Aramomentioning

confidence: 99%

Sentinel data for critical raw materials (CRM) exploration: first results of the S34I project

Cardoso-Fernandes,

Carvalho,

Azzalini

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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“…Since orbiting 2016, many parties have used it for monitoring rice fields, drought, oil spill detection, and hydrological analysis [2]- [4]. Most of the utilization is used in agriculture and land use, which is associated with the spectral that can be captured by the camera [5], [6]. Land use and land cover classification is among the most widely applied satellite images [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of Land Use Classification Using Multi-spectral Camera From LAPAN-A3, Landsat-8 and Sentinel-2 Satellite: A Case Study in Probolinggo-East Java

Anggari,

Herawan,

Hakim

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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The LAPAN-A3 is the third microsatellite generation developed by the Research Center for Satellite Technology. The satellite can be used for land classification, agriculture monitoring, drought monitoring, and land use change. This study aims to classify land use and land cover in the research area. The main image used is LAPAN-A3; the compared images are Landsat-8 and Sentinel-2. Three images were taken on the same day and selected on cloud-free terms. The classification process starts with determining the region of interest (ROI) and the class. The classification is divided into six classes: water, forests, rice fields, settlements, open land, and coastal areas. The classification technique uses supervised learning with the maximum likelihood method. This study used Landsat 8 and Sentinel-2 data to compare the results obtained from LAPAN-A3. The accuracy test results for the LAPAN-A3 and Landsat-8 are 84.7042% and 0.783, respectively. While the accuracy test of LAPAN-A3 and Sentinel-2 is 72.2313%, the kappa value is 0.6394. The classification of two comparisons is quite accurate, with an accuracy of more than 70%. The LA3 classification successfully identifies water and coastal areas. The producer and accuracy is substantiated by comparing the results with both Landsat-8 and Sentinel-2 satellite data, which exhibit an accuracy rate exceeding 85%. Finally, LAPAN-A3 has great potential for classifying land use and land cover when compared to Landsat 8 and Sentinel-2 images, but future research should increase the number of datasets and vary the research area to improve the results.

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A New Vector for Mapping Gold Mineralization Potential and Proposed Pathways in Highly Weathered Basement Rocks using Multispectral, Radar, and Magnetic Data in Random Forest Algorithm

Eldosouky,

Othman,

Saada

et al. 2023

Nat Resour Res

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Geological Controls and Prospectivity Mapping for Manganese Ore Deposits Using Predictive Modeling Comparison: An Integration of Outcrop and Remote Sensing Data, Sinai Microplate, Egypt

Cited by 6 publications

References 57 publications

Sentinel data for critical raw materials (CRM) exploration: first results of the S34I project

Sentinel data for critical raw materials (CRM) exploration: first results of the S34I project

Assessing the Accuracy of Land Use Classification Using Multi-spectral Camera From LAPAN-A3, Landsat-8 and Sentinel-2 Satellite: A Case Study in Probolinggo-East Java

A New Vector for Mapping Gold Mineralization Potential and Proposed Pathways in Highly Weathered Basement Rocks using Multispectral, Radar, and Magnetic Data in Random Forest Algorithm

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