Mapping of Ferric (Fe3+) and Ferrous (Fe2+) Iron Oxides Distribution Using Aster and Landsat 8 Oli Data in Negash Lateritic Iron Deposit Northern Ethiopia.

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Addis Ababa University


Iron play important role in the industrial and engineering development of a country and there is a rapid demand for iron in Ethiopia. However, the search for valuable and means of primary mineral resource exploration remains challenging and costly. Therefore, this study aims to map iron oxide minerals using Landsat-8 Operational Land Imager (OLI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite imagery in Negash Lateritic iron deposit, Northern Ethiopia. Different image processing techniques such as band ratio, selective principal component analysis, linear spectral unmixing and mixture tuned matched filter were used to produce iron oxide maps. Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and N-dimensional visualizer were also applied to extract endmembers in Automated spectral hourglass wizard. First, Normalized Difference Vegetation Indices (NDVI) were calculated and values greater than 0.4 and 0.3 for ASTER and Landsat 8 OLI were used to mask vegetation and, reduce the effects of vegetation on the processed image, respectively. Band ratio of band-2/band-1 and band-5/band-3 + band-1/band-2 of ASTER, band-4/band-3 and band-6/band-4 of Landsat 8 OLI was used to map ferric (3+) and ferrous (2+) respectively. In addition to this, the enhanced image thresholding (from ratio, selective PCA and unmixing) through a varying percentage of confidence level and scatter plot (from MTMF) were used to map anomalous (potential) areas. Ferric iron oxide band ratio of ASTER mapped maximum area of 62.1 km2 followed by a laterite band ratio of ASTER covering 57.8 km2. The result shows a high correlation between results obtained using selective PCA with r = 0.59, and r = 0.3 moderate correlation between ferric iron, a poor correlation for ferrous iron with r = 0.22. The correlation result shows that iron oxides mapped from the selective principal component analysis have a better correlation with one another. The result was validated using existing iron oxide polygons and results obtained from selective PCA show a strong match with the existing iron oxide polygons. The sub-pixel mapping techniques show poor accuracy in mapping goethite and hematite relative to the pixel level. Results obtained from ASTER images show a better match with the existing iron oxide polygons. The comparison shows ASTER mapped better than Landsat 8 OLI for band ratio selective PCA, unmixing and MTMF. In poor countries like Ethiopia applying these techniques is a good option to map and use as preliminary exploration tools.



Endmembers Extraction, Selective PCA, Band Ratio, LSU, MTMF