Spaceborne Mine Waste Mineralogy Monitoring in South Africa, Applications for Modern Push-Broom Missions: Hyperion/OLI and EnMAP/Sentinel-2

Abstract

Abstract:Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste sites have the potential to contain problematic trace element species (e.g., U, Pb, Cr). In our research, we aim to combine the mapping and monitoring capacities of multispectral and hyperspectral spaceborne sensors. This is done to assess the potential of existing multispectral and hyperspectral spaceborne sensors (OLI and Hyperion) and future missions, such as Sentinel-2 and EnMAP (Environmental Mapping and Analysis Program), for mapping the spatial extent of these mine waste surfaces. For this task we propose a new index, termed the iron feature depth (IFD), derived from Landsat-8 OLI data to map the 900-nm absorption feature as a potential proxy for monitoring the spatial extent of mine waste. OLI was chosen, because it represents the most suitable sensor to map the IFD over large areas in a multi-temporal manner due to its spectral band layout; its (183 km × 170 km) scene size and its revisiting time of 16 days. The IFD is in good agreement with primary OPEN ACCESS Remote Sens. 2014, 6 6791 and secondary iron-bearing minerals mapped by the Material Identification and Characterization Algorithm (MICA) from EO-1 Hyperion data and illustrates that a combination of hyperspectral data (EnMAP) for mineral identification with multispectral data (Sentinel-2) for repetitive area-wide mapping and monitoring of the IFD as mine waste proxy is a promising application for future spaceborne sensors. A maximum, absolute model error is used to assess the ability of existing and future multispectral sensors to characterize mine waste via its 900-nm iron absorption feature. The following sensor-signal similarity ranking can be established for spectra from gold mining material: EnMAP 100% similarity to the reference, ALI 97.5%, Sentinel-2 97%, OLI and ASTER 95% and ETM+ 91% similarity.

Sentinel-2 for Mapping Iron Absorption Feature Parameters

“…Left: Spectra of six minerals which contains ferrous ions, either in different crystal fields or in different sites, after Hunt [19]. Right: Spectra of iron mineralogy associated with mine tailings, selected following Mielke et al [29], and a "Grass" vegetation spectrum. The figures show the USGS library spectra (light grey shaded areas), the sentinel bands (dark grey dots), the fitted parabola (black lines) and deduced wavelength of maximum absorption (black dots).…”

“…The minerals Bronzite, Diopside, Goethite, Hematite and Jarosite were chosen, inspired by Mielke et al [29] who tested band ratios with synthetic Sentinel-2 data, amongst other sensors. In addition, a Grass vegetation spectrum was used to show the behaviour of our technique on vegetation.…”

“…Mielke et al [29] evaluated several current and next-generation satellite sensors for mapping iron feature depth. They were able to use multiple bands of Sentinel-2 in a complex band-ratio approach and found next-generation sensors such as Sentinel-2 to be better equipped for measuring iron feature depth than the current multi-spectral sensors.…”

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

“…EnMAP enables short-term geological exploration of minerals and metals and long-term mine waste monitoring using the EnGeoMAP software [16,65]. Sharp, distinctive absorptions of rare earth element oxides, such as neodymium oxide, partially modulated by broad iron absorptions, can be explored using high-pass and absorption feature techniques and semi-quantified to map local enrichment and its spatio-temporal distribution for mine waste monitoring.…”

“…The potential of airborne and spaceborne imaging spectrometers has long been exploited to monitor the Earth's surface and atmosphere and to provide valuable information for the better understanding of a large number of environmental processes (e.g., [3,4]). Those applications include, for example, vegetation monitoring and ecology (e.g., [5][6][7][8][9][10][11]), geology and soils (e.g., [12][13][14][15][16][17]), coastal and inland waters (e.g., [18][19][20][21]), mapping of snow properties [22,23] and archaeological prospection [24,25].…”