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Ronda peridotite massif: Methodology for its geological mapping and lithological discrimination from airborne hyperspectral data
Authors:S. Chabrillat  P. C. Pinet  G. Ceuleneer  P. E. Johnson  J. F. Mustard
Affiliation:1. UMR 5562/CNRS-Observatoire Midi-Pyrenees , 14 Ave Edouard Belin, Toulouse, F-31400, France;2. Department of Physics and Astronomy , University of Wyoming , Laramie, Wyoming, 82070, USA;3. Department of Geological Sciences , Brown University , Providence, Rhode Island, 02912, USA
Abstract:The Ronda peridotite massif in the Sierra Bermeja, southern Spain, was imaged in July 1991 by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) during the Europe 1991 Multispectral Airborne Campaign. Principal component analysis was used (i) to extract the most spectrally extreme pixels for empirical line calibration; (ii) to physically interpret the image spectral information; and (iii) to define an optimal endmember selection based on both spatial and spectroscopic characteristics. Two successive spectral mixture analyses that allow one to focus on subtle spectral variations related to bedrock and soil lithology were applied. The first spectral mixture analysis was used to identify the major surface constituents in the image and extract the geological target to be investigated, i.e. the peridotite massif; and the second one was used to model the spectral variability within the designated target. Although mineralogical variations observed in the rocks were at a sub-pixel scale for the airborne survey, spatially organized units could be identified within the major outcrops of the peridotite massif from their spectral variations. A mineralogical interpretation of these spectral variations is proposed in relation with the field observations, in terms of relative abundance variations in the pyroxene/olivine ratio among the pixels. This work demonstrates that the proposed methodology makes possible the spectral distinction of lithological units within an ultramafic body, despite the occurrence of partial vegetation cover and multiple sub-pixel mixtures. Furthermore, it shows that hyperspectral data can provide such information, resulting in a very cost-effective method of petrologic mapping.
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