Spectral data in geological mapping

Figure 2. Spectra of extracted endmembers compared with selected minerals from USGS spectral library. a) full spectral range b) SWIR range, (Salehi et al. 2017a)

Figure 3. Result of spectral unmixing for the main geologic endmembers in presence of abundant lichen and vegetation generated with the ISMA method, purple color outlining mafic-ultramafic supracrustal rocks, (Salehi et al. 2017a).

Fractional abundances of the endmembers within the scene are determined using an iterative implementation of spectral mixture analysis (ISMA) (Rogge et al. 2006). The result is a set of abundance fractions for the optimal endmembers (Figure 3). Analysis of the airborne data resulted in a high quality regional mapping product capable of discriminating geological materials of interest based on subtle spectral differences and resulting fractional abundance maps allow subsequent detailed geologic interpretation. (Figure 3).

Modeling and assessment of wavelength displacements of characteristic absorption features of common rock forming minerals encrusted by lichens

Lichens are the dominant autotrophs of polar and subpolar ecosystems commonly encrust the rock outcrops. Spectral mixing of lichens and bare rock can shift diagnostic spectral features of materials of interest thus leading to misinterpretation and false positives if mapping is done based on perfect spectral matching methodologies. The extent to which diagnostic rock features are preserved, despite the presence of lichens, is of major concern in remotely sensed geological studies and estimates of the critical level of lichen coverage, below which spectral features of the mineral substrate can still be identified, are needed. We investigated how surficial lichen cover affects the characteristics of shortwave infrared (SWIR) mineral absorption features and the efficacy of automated absorption feature extraction. Distinctive trends were identified that can be used in future analysis (Salehi et al. 2017b). By quantifying lichen cover effects on mineral absorption features, our study highlights the importance of being precautious in any interpretation in areas characterized by abundant lichen-covered outcrops. This can be of significant importance for mineral- and deposit vectoring as the presence of abundant lichen coverage causing slightly shifted features for a given spectra can be erroneously identified as a path to a deposit.

Identification of a robust lichen index for the deconvolution of lichen and rock mixtures

The ability to distinguish the lichen coverage from rock and decomposing a mixed pixel into a collection of pure reflectance spectra, can improve the applicability of hyperspectral methods for mineral exploration. The objective of this study is to propose a robust lichen index that can be used to estimate lichen coverage, regardless of the mineral composition of the underlying rocks. Laboratory spectroscopic data are obtained from lichen covered samples collected from Karrat, Liverpool Land, and Sisimiut regions in Greenland (Figure 4). The spectra are then resampled to Hyperspectral Mapper (HyMAP) resolution, in order to further investigate the functionality of the indices for the airborne platform. In both resolutions, a Pattern Search (PS) algorithm is used to identify the optimal band wavelengths and bandwidths for the lichen index. The results of our band optimization procedure revealed that the ratio between R894-1246 and R1110 (Figure 5) explains most of the variability in the hyperspectral data at the original laboratory resolution (R2=0.769). However, the normalized index incorporating R1106-1121 and R904-1251 yields the best results for the HyMAP resolution (R2=0.765).

Figure 4.Geology map of Greenland and the locations of the collected rock samples. a) Karrat region: quartzite [3 samples], b) Liverpool Land region: monzonite [3 samples], c) Sisimiut Kangerlussuaq region: kimberlite [9 samples], lamproite [4 samples], gneiss [3 samples], kersantite [3 samples], fenite [3 samples], granite [5 samples], carbonatite [4 samples], (Salehi et al. 2017b)

Figure 5. Schematic representation of the averaging concept used for index calculations, (Salehi et al. 2016)