Abstract
Hyperspectral high spatial resolution HyMap data are used to map mine waste from massive sulfide ore deposits, mostly abandoned, on the Iberian Pyrite Belt (southwest Spain). Mine dams, mill tailings and mine dumps in variable states of pyrite oxidation are recognizable. The interpretation of hyperspectral remote sensing requires specific algorithms able to manage high dimensional data compared to multispectral data. The routine of image processing methods used to extract information from hyperspectral data to map geological features is explained, as well as the sequence of algorithms used to produce maps of the mine sites. The mineralogical identification capability of algorithms to produce maps based on archive spectral libraries is discussed. Trends of mineral growth differ spectrally over time according to the geological setting and the recovery state of the mine site. Subtle mineralogical changes are enhanced using the spectral response as indicators of pyrite oxidation intensity of the mine waste piles and pyrite mud tailings. The changes in the surface of the mill tailings deserve a detailed description, as the surfaces are inaccessible to direct observation. Such mineralogical changes respond faithfully to industrial activities or the influence of climate when undisturbed by human influence.
Highlights
Geology-based geoenvironmental mineral deposit models point to the need for “useful geophysical techniques to identify, delineate, and monitor, environmental signatures associated with mined and unmined mineral deposits” [1]
Hyperspectral image processing provides a fast and accurate diagnosis of the secondary minerals growing on mine waste and their mineralogical changes mapped from HyMap flights in the test site in the summers of 2005, 2008 and 2009 [13,14,15]
The areas identified in the imagery as a single or double mineral presence should be interpreted as indicators of a mineralogically dominant trend displayed by the HyMap imagery with a 5-m spatial resolution
Summary
Geology-based geoenvironmental mineral deposit models point to the need for “useful geophysical techniques to identify, delineate, and monitor, environmental signatures associated with mined and unmined mineral deposits” [1]. Imaging spectroscopy using spectral libraries has been developed as a reliable technique for quick mineralogical analysis of mine wastes, which saves both time and costs versus conventional sample collection [2,3,4,5,6]. It permits the mineralogical diagnosis of ephemeral thin crusts concentrating heavy metals on inaccessible surfaces [1], providing an invaluable tool for environmental evaluation and information. The waste removal and movement of machinery during the recovery activities has a great influence on the mineralogy of the dust throughout the area [13]
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