Assessing the feasiblility of combining reflectance spectrometry with phytogeochemical exploration techniques for the discrimination of three geologies on the Witwatersrand basin gold field, South Africa

• Main Author: Wilson, Miranda Hanli
• Format: Others
• Language: en
• Published: 2018
• Online Access: https://hdl.handle.net/10539/24936

A Dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science School of Computer Science and Applied Mathematics, March 2017 === Mineral exploration is expensive, logistically challenging and can be detrimental to the environment. In addition to the physical disturbed of geological sampling, artisanal miners, charcoal burners and poachers follow in the wake of geological exploration teams, resulting in severe environmental degradation. The remote sensing of geological features is used in conjunction with geophysics to help refine the amount of ground based sampling where the surface geology is exposed (e.g. deserts, barren surfaces and rocky outcrops). However, it is not feasible to use these geological remote sensing techniques the earth’s surface is covered with vegetation. Studies have shown that plants respond to mineral nutrients or conversely toxicities in their growing environment, including metal concentrations in the soil, either through the presence or absence of particular species, or by exhibiting physiological or phenological changes in response to depleted or elevated substrate metal concentrations. The use of plant species composition and foliar elemental contents (methods known collectively as phytogeochemical exploration) have been successfully used to detect ore-bodies. Visible changes in leaf structure and chemical composition as a result of deficiencies in elemental nutrition or toxicities have been well-researched from botanical and soil science aspects, and are widely used for agronomic applications, but have yet to be exploited for mineral exploration. This study assessed the feasibility of using remotely-sensed spectral reflectance signatures of tree foliage to detect changes in substrate elemental concentrations across three geologies on the Witwatersrand Basin. The study comprises of an outcropping metal-rich ore body, the Black Reef (quartzite), flanked by dolomite to the South East and Ventersdorp Lavas to the North West. The soils of these three parent geologies can be expected to exhibit differences in plant nutrient availability, as well as deficiencies or toxicities. Each geology on the study site was characterised and classified into landscape functional types to account for aspect, position on the catena and soils characteristics, all of which could mask, conflict or auto correlate with any observed changes in vegetation stress spectral signatures associated with the changing geology. Three tree species with continuous across the study site were selected: Searsia lancea (L.f.) Moffet (previously Rhus lancea), Euclea crispa (Thunb.) Guerke var crispa and Acacia karroo Hayne. The study determined how the foliar and substrate elemental concentrations and uptake ratios differed between the three tree species, the three geologies and the landscape functional types. The study then related plant spectral response of three tree species to geology, landscape function type and to the foliar and substrate elemental content. Soil elemental concentrations were analysed and it was found that the three parent geologies could be classified by their relative concentrations of Mn, Cr, Ti, Cu Cr, Pb, Ba, Fe, and Zr in the soils. The findings revealed that the plants showed changes in physiological status associated with geology which were detectable through the use of vegetation indices. The study made use of eight different vegetation indices (NDVI, NDWI, PSRI, Red-edge NDVI, red-edge position, red-edge inflection point, and the 725/702 ratio of the first and second derivative), derived from handheld hyperspectral data. The three species differed in their spectral response to the changes in geology and in their stress response to elevated metal content on the Black Reef (p < 0.05). Regression (linear and non-parametric) was used to identify which foliar and substrate elemental concentrations most affected spectral response. The A. karroo samples were found to be most affected by Mn, Ti, Fe and Sr. The S. lancea samples were found to be most affected by As, Cu, Pb and Sn and the E. crispa response was found to be most affected by Cu, Mn, Na, Ni, Rb, Zn, and Zr (p < 0.01). In order to identify the changes in geology, it was found to be necessary to first classify the spectral response of the three species, and then detect spectral variations within each species class, as the species-specific spectral responses to changes in geology were significantly different (p< 0.05). The study successfully classified the three tree species according to their spectral response through the combined use of the eight vegetation indices. However, it was found that a subset of the samples which had either much higher or much lower elemental concentrations in the leaves and soils than the remaining samples for that species, showed a plant stress response which affected the spectral response of the plants sufficiently to result in an incorrect species classification. In conclusion, the finding of this study showed that VIs can be used to detect differences in spectral response between trees growing on different geologies. It was found that the combination of vegetation indices can be used to determine a “typical” spectral response per species, but that where the growing conditions were particularly stressful, the stress response could alter the plant spectral response sufficiently to result in a misclassification of the sample by species. Further work is required to validate this observation, and to investigate how more sophisticated spectral analysis could be used to distinguish between taxonomic and substrate induced spectral variation, before it would be possible to scale this work up to a canopy-scale remote sensing tool. === XL2018