Spatial Component Analysis to Improve Mineral Estimation Using Sentinel-2 Band Ratio: Application to a Greek Bauxite Residue

Abstract

Remote sensing can be fruitfully used in the characterization of metals within stockpiles and tailings, produced from mining activities. Satellite information, in the form of band ratio, can act as an auxiliary variable, with a certain correlation with the ground primary data. In the presence of this auxiliary variable, modeled with nested structures, the spatial components without correlation can be filtered out, so that the useful correlation with ground data grows. This paper investigates the possibility to substitute in a co-kriging system, the whole band ratio information, with only the correlated components. The method has been applied over a bauxite residues case study and presents three estimation alternatives: ordinary kriging, co-kriging, component co-kriging. Results have shown how using the most correlated component reduces the estimation variance and improves the estimation results. In general terms, when a good correlation with ground samples exists, co-kriging of the satellite band-ratio Component improves the reconstruction of mineral grade distribution, thus affecting the selectivity. On the other hand, the use of the components approach exalts the distance variability.

Imaging Spectroscopy for the Mining Life Cycle: A Guide for Drone Applications

“…The minerals of environmental concern such as asbestos and talc, which occur in ultramafic rock complexes and/or arise from anthropogenic sources such as mining activities, may be detectable using spaceborne imaging spectroscopic data [162,191]. In addition, spectral imaging can predict heavy metal content, and identify and map chemical/geochemical contents of wastes and residues [162,167,[192][193][194][195][196][197][198][199]. Spectral imaging has proved to be useful for quantitative measurements of dust emissions from mining activities in nearby areas [200][201][202].…”

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

“…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].…”

“…However, the distinction between Fe-Ni-laterite and bauxite ores using remote sensing techniques is a challenging task and, to our knowledge, until now it has been relatively poorly studied. The main methods proposed in the literature are BRs, LSU, or classification applied to Sentinel-2, ASTER and Hyperion data [31,32,34,37].…”

Multispectral Remote Sensing to Delineate the Distribution Area of Scandium-Bearing Minerals in Bauxite Mining Sites, West Kalimantan Province, Indonesia

“…Minerals and rocks exhibit different spectral patterns due to distinct spectral absorption features (Asadzadeh and de Souza Filho, 2016). Preliminary studies applied multispectral imaging to measure the spectrum of light in each pixel (Bruno et al, 2021;Calvini et al, 2019) Landsat-8 OLI is used as a sensor consisting of eight channels or bands and broad spatial resolution (Guerrero and Aleu, 2020). Landsat imagery is powerful for gaining an overview of the environmental condition in large-scale mining (Paull et al, 2006).…”