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Integration of Sentinel-2 and ASTER Satellite Data for Gold Mineral Exploration in Northern Cameroon
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Resumo(s)
Africa is one of the richest continents in terms of mineral resources yet most of the people continue to live in poverty and technological advancement is very low. The mineral sector is one of the industries which the continent can take advantage of to haul itself out of poverty. The demand for mineral continues to increase. Searching for minerals to feed our sustainability goals in a sustainable and environmentally friendly manner is of utmost importance. Remote Sensing is one of the technologies which can be leveraged to achieve our goals. It is a multidisciplinary domain that is at the helm of innovation whose application span almost every industry. Unfortunately, it is still not utilized to its full potential despite all the benefits that accompany its usage. This thesis aimed at exploring the performance of remote sensing in mineral exploration in the North of Cameroon and attempted to derive a prediction model for gold exploration using data from Sentinel-2 and ASTER. Some studies had been done already in this direction in this area before, but the methodology applied was different. While image enhancement used was common (band ratios and principal component analysis), they were applied in different ways. We carried out PCA analysis not on the bands but on the band ratios. Moreover, there has not been any attempt to create a machine learning model that can be applied for gold exploration. Prediction maps were produced from the models, and they show a regional correlation with known gold occurrences in the region. This thesis presents an integrated approach of Sentinel-2 imagery, ASTER, DEM and field data through machine learning models that can be applied to set priority areas for field exploration thereby streamlining cost and effort in a complex geological terrain
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Remote Sensing