Identification of a Robust Lichen Index for the Deconvolution of Lichen and Rock Mixtures Using Pattern Search Algorithm (Case Study: Greenland)

Abstract

ABSTRACT: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. Therefore, 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. The performance of three index structures of ratio, normalized ratio and subtraction have been investigated using synthetic linear mixtures of pure rock and lichen spectra with prescribed mixing ratios. Laboratory spectroscopic data are obtained from lichen covered samples collected from Karrat, Liverpool Land, and Sisimiut regions in Greenland. 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 R 894-1246 and R 1110 explains most of the variability in the hyperspectral data at the original laboratory resolution (R 2 =0.769). However, the normalized index incorporating R 1106-1121 and R 904-1251 yields the best results for the HyMAP resolution (R 2 =0.765).

Reflectance Spectroscopy in Geology and Soil Sciences: Literature Review

“…Identification of spectral indices that can directly reflect the ratio of the rock and lichen in hyperspectral data was a focus of studies performed in Greenland by Salehi et al (2016Salehi et al ( , 2017Salehi et al ( , 2020 and Salehi (2018). By analysing HyMAP images, they investigated how lichen cover affects the characteristics of SWIR mineral absorption features and developed an automated absorption feature extraction algorithm.…”

Image analysis for automatic measurement of crustose lichens

“…Due to the characteristics of lichen and the rocky surfaces where it grows this technique is not effective. One other work directly applied to lichen [37] defines of a vegetation index for lichens based on hyperspectral measurements in the visible to mid-infrared spectrum using samples as training and validation data sets to find the optimal values by minimizing the RMSE. By using bands narrower that those of RGB digital cameras and in the infrared spectrum it is possible to distinguish lichen from the background.…”