Using multiple spectral feature analysis for quantitative pH mapping in a mining environment

The pH is one of the major chemical parameters affecting the results of remediation programs carried out at abandoned mines and dumps and one of the major parameters controlling heavy metal mobilization and speciation. This study is concerned with testing the feasibility of estimating surface pH on the basis of airborne hyperspectral (HS) data (HyMap). The work was carried on the Sokolov lignite mine, as it represents a site with extreme material heterogeneity and high pH gradients. First, a geochemical conceptual model of the site was defined. Pyrite, jarosite or lignite were the diagnostic minerals of very low pH (<3.0), jarosite in association with goethite indicated increased pH (3.0-6.5) and goethite alone characterized nearly neutral or higher pH (>6.5). It was found that these minerals have absorption feature parameters which are common for both forms, individual minerals as well as parts of the mixtures, while the shift to longer wavelengths of the absorption maximum centered between 0.90 and 1.00 μm is the main parameter that allows differentiation among the ferric minerals. The multi range spectral feature fitting (MRSFF) technique was employed to map the defined end-members indicating certain pH ranges in the HS image datasets. This technique was found to be sensitive enough to assess differences in the desired spectral parameters (e.g., absorption shape, depth and indirectly maximum absorption wavelength position). Furthermore, the regression model using the fit images, the results of MRSFF, as inputs was constructed (R2 = 0.61, Rv2 = 0.76) to estimate the surface pH. This study represents one of the few approaches employing image spectroscopy for quantitative pH modeling in a mining environment and the achieved results demonstrate the potential application of hyperspectral remote sensing as an efficient method for environmental monitoring.