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

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.

[1]  J. K. Crowley,et al.  Mapping mine wastes and analyzing areas affected by selenium-rich water runoff in southeast Idaho using AVIRIS imagery and digital elevation data , 2003 .

[2]  A. Bebbington,et al.  Assessing impacts of mining: Recent contributions from GIS and remote sensing , 2019, The Extractive Industries and Society.

[3]  P. Ferro,et al.  Materials selection in a critical raw materials perspective , 2019, Materials & Design.

[4]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[5]  Yasushi Yamaguchi,et al.  Overview of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) , 1998, IEEE Trans. Geosci. Remote. Sens..

[6]  G. Matheron.  The intrinsic random functions and their applications , 1973, Advances in Applied Probability.

[7]  Waldir Renato Paradella,et al.  Multi-scale approach using remote sensing images to characterize the iron deposit N1 influence areas in Carajás Mineral Province (Brazilian Amazon) , 2011, Environmental Earth Sciences.

[8]  Tsehaie Woldai,et al.  Multi- and hyperspectral geologic remote sensing: A review , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Stefano Pignatti,et al.  Using imaging spectroscopy to map red mud dust waste: The Podgorica Aluminum Complex case study , 2012 .

[10]  Isao Sato,et al.  Aster early image evaluation , 2001 .

[11]  M. Jurado-Expósito,et al.  Using geostatistical and remote sensing approaches for mapping soil properties , 2005 .

[12]  Freek D. van der Meer,et al.  Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain , 2008 .

[13]  E. Balomenos,et al.  Selective leaching of rare earth elements from bauxite residue (red mud), using a functionalized hydrophobic ionic liquid , 2016 .

[14]  Un Desa.  Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .

[15]  Roberto Bruno,et al.  Mapping of Aluminum Concentration in Bauxite Mining Residues Using Sentinel-2 Imagery , 2021, Remote. Sens..

[16]  Arindam Guha,et al.  nalysis of ASTER data for mapping bauxite rich pockets within high altitude ateritic bauxite , Jharkhand , India , 2012 .

[17]  F. D. van der Meer,et al.  Extraction of mineral absorption features from high - spectral resolution data using non - parametric geostatistical techniques , 1994 .

[18]  Guofeng Cao,et al.  Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach. , 2018, Environmental Pollution.

[19]  Multivariate Geostatistics , 2004 .

[20]  G. Ferrier,et al.  Application of Imaging Spectrometer Data in Identifying Environmental Pollution Caused by Mining at Rodaquilar, Spain , 1999 .

[21]  Ayad M. Fadhil Al-Quraishi,et al.  Mapping of ferric ( and ferrous ( iron oxides distribution using band ratio techniques with ASTER data and geochemistry of Kanjamalai and Godumalai, Tamil Nadu, south India , 2020 .

[22]  G. Corder,et al.  The Role of the Mining Industry in a Circular Economy: A Framework for Resource Management at the Mine Site Level , 2017 .

[23]  B. Tokarska-Guzik,et al.  Geostatistical models with the use of hyperspectral data and seasonal variation – A new approach for evaluating the risk posed by invasive plants , 2021 .

[24]  L. Mancini,et al.  Social impact assessment in the mining sector: Review and comparison of indicators frameworks , 2018, Resources Policy.

[25]  J. Teixeira,et al.  Integrated approach to assess the environmental impact of mining activities: estimation of the spatial distribution of soil contamination (Panasqueira mining area, Central Portugal) , 2015, Environmental Monitoring and Assessment.

[26]  Harald van der Werff,et al.  Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing , 2016, Remote. Sens..