Mapping of the Ronda peridotite massif (Spain) from AVIRIS spectro-imaging survey: A first attempt

In both AVIRIS and ISM data, through the use of mixing models, geological boundaries of the Ronda massif are identified with respect to the surrounding rocks. We can also yield first-order vegetation maps. ISM and AVIRIS instruments give consistent results. On the basis of endmember fraction images, it is then possible to discard areas highly vegetated or not belonging to the peridotite massif. Within the remaining part of the mosaic, spectro-mixing analysis reveals spectral variations in the peridotite massif between the well-exposed areas. Spatially organized units are depicted, related to differences in the relative depth of the absorption band at 1 micron, and it may be due to a different pyroxene content. At this stage, it is worth noting that, although mineralogical variations observed in the rocks are at a sub-pixel scale for the airborne analysis, we see an emerging spatial pattern in the distribution of spectral variations across the massif which might be prevailingly related to mineralogy. Although it is known from fieldwork that the Ronda peridotite massif exhibits mineralogical variations at local scale in the content of pyroxene, and at regional scale in different mineral facies, ranging from garnet-, to spinel- to plagioclase-lherzolites, no attempt has been done yet to produce a synoptic map relating the two scales of analysis. The present work is a first attempt to reach this objective, though a lot more work is still required. In particular, for the purpose of mineralogical interpretation, it is critical to relate the airborne observation to field work and laboratory spectra of Ronda rocks already obtained, with the use of image endmembers and associated reference endmembers. Also, the pretty rough linear mixing model used here is taken as a 'black-box' process which does not necessarily apply correctly to the physical situation at the sub-pixel level. One may think of using the ground-truth observations bearing on the sub-pixel statistical characteristics (texture, structural pattern, surface distribution and vegetation contribution (grass,..)) to produce a more advanced mixing model, physically appropriate to the geologic and environmental contexts.