Best Practices for Mapping Environments Across the Mining Lifecycle with Hyperspectral Remote Sensing

Abstract

Hyperspectral remote sensing has become an increasingly powerful tool in geological and mining applications, due to its ability to acquire high-resolution spectral and spatial data across hundreds of contiguous bands in the visible to shortwave infrared (VNIR/SWIR) (400 ¨C 2500 nm) range. While numerous studies have demonstrated the potential of hyperspectral imaging for mineral classification and mapping, there is still a lack of consolidated guidance on practical implementation across the mining value chain. This dissertation aims to address that gap by integrating this technique at different stages of the mining lifecycle.This dissertation is based on author¡¯s previous work related to ground- and drone- based hyperspectral imaging systems, expanding the scope to include satellite-based hyperspectral sensors, with a particular focus on tailing detection and mineral exploration. Collectively, the research covers key stages of the mining life, from mineral exploration to ore processing, and to reclamation. Figure 1 shows the different stages and main tasks of a copper mine lifecycle. Three chapters provide best practices for mineral exploration, mapping/monitoring leach pads, and tailings detection. Chapter 1 integrates non-negative least squares (NNLS) unmixing, minimum wavelength mapping, and mineral index techniques to increase the accuracy of interpretation of hyperspectral information into hydrothermal alteration zones in the Yerington district, Nevada. Chapter 2 shifts the focus to operational mapping/monitoring leach pads. The study proposes a streamlined workflow that avoids endmember extraction and instead uses spectral references. Fully Constrained Least Squares (FCLS) spectral unmixing is suggested to use to map leach pads. Chapter 3 addresses mine reclamation by introducing the Arizona Tailing Index (AZTI), a new spectral index for the detection of tailings storage facilities (TSFs) using hyperspectral data from NASA's Earth Surface Mineral Dust Source Investigation (EMIT) sensor. AZTI is designed to quickly map tailings throughout Arizona and provide the area of mining affect area, which is useful for environmental management.