ASTER mineral indices and abundance maps

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4 Results and Discussion

4.1 ASTER mineral indices and abundance maps

Mineral abundance maps created using ENVI software highlight volcanic alteration zones. The mapped areas are particularly promising for locating alteration minerals such as kaolinite, smectites or alunite. All digital maps are available upon request from the first author as GeoTIFF or as ArcGIS project. The maps were used, together with geological maps, ground-truth information, DEMs, and geochemical and XRD analysis for geological interpretation and classification.

Data quality was assessed by plotting band ratios derived from ASTER reflectance data against ratios from laboratory ASD hyperspectral data (Fig. 5). Except for samples of iron oxide crusts which are not representative of ASTER 30x30 m pixels, results were generally good. Quality of the mineral abundance maps and indices are different for various reasons.

Fig. 5: ASTER Quality assessment. Ratios from ASTER reflectance data are plotted with ASD ratios. Correlation is good except for samples rich in iron oxides that are not representative for an ASTER 30x30 m pixel.

The results can be summarized as follows: We found no proof of propilitization (Mg-OH and Fe-OH group minerals) which would be typical for deeper alteration of porphyry copper and altered mafic intrusions. Mg-OH and Fe-OH content maps also appear to be very sensitive to moisture, shadowed areas, and residual vegetation. This is illustrated in Fig. 6D,

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showing the Mg-OH content map overlain on ASTER false color image. High (red) values are considered false positives and delineate steep, shadowed areas.

Fig. 6: A: Cerro Carhuarazo (ASTER RGB:321) with sample locations. This subset is an example of results for mineral abundance maps and spectral indices. B: Cerro Carhuarazo subscene with Al-OH abundance map as overlay. Red color means high abundance, blue is low abundance. Al-OH abundance maps are calculated using ASTER band ratio (b5+B7)/b6 with the composite mask applied. C: Cerro Carhuarazo subscene with ferric oxide abundance map as overlay. High contents (red) near the samples in the northern part were confirmed in the field.

D: Mg-OH content map overlain on ESRI imagery. High values (red) highlight steep, shadowed areas (black arrows). There is no evidence for propilitization. E: Cerro Carhuarazo subscene with Matched Filtering results for a mixture of kaolinite and smectites as overlay (Brandmeier, 2010). The “Kaosmectite spectra” used for matched filtering is from USGS spectral library.

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The high scores of kaolinite and smectite and high values in Al-OH abundance maps are highly correlated and confirmed by our ground-truthing observations. Furthermore, the spectral unmixing results in Brandmeier (2010) of natrolite, kaolinite-group minerals and smectites is supported by ground-truthing and mineral analysis. We thus conclude that our approach and ground-truthing of the ASTER spectral data should result in meaningful mineral distribution maps.

Results for mineral maps and spectral indices will be further discussed in the context of subscene maps for Cerro Carhuarazo (Fig. 6) and Lomada Atansa (Fig. 7). For the first subscene, five different mineral abundance maps are shown in Fig. 6A-E. Fig. 6B shows results for Al-OH mineral group abundance mapping. Anomalies were confirmed in the field and are in accordance with matched filtering results for kaolinite/smectite mixtures (Fig. 6E, Brandmeier, 2010). In comparison to matched filtering results, Al-OH abundance maps are less reliable as they are more sensitive to areas with shadows and moisture. This problem was circumvented following Brandmeier (2010) by (a) using crosstalk-corrected reflectance data instead of level 1b data and (b) using a selected subset for mapping instead of only creating a mask. The drawbacks of Brandmeier's (2010) approach are that 1) the spatial and spectral resolution of ASTER is still not good enough to reliably identify minerals and 2) that the spectral unmixing employed was more time consuming due to the need to identify and isolate suitable spectrally mixing endmembers. This is why matched filtering results shows high values for kaolinite, dickite, nacrite and kaolinite/smectite mixtures, which cannot be distinguished (some are polymorphs of the same mineral) or quantified using ASTER data with limited spectral resolution as compared to hyperspectral data. Nevertheless, these results indicate the general type of alteration and the presence of clay minerals very well (Fig. 6).

Results for ferric oxide content maps following the approach of Cudahy et al. (2008) are shown in Fig. 6C. The high values near the samples in the north of Carhuarazo volcano (CCAR-11-13, CCAR-11-14) were confirmed in the field. This data product proved to be very sensitive to residual, dry vegetation as also observed by Cudahy et al. (2008).

The second example for ASTER mineral mapping is Lomada Atansa, an ignimbrite sheet cut by minor faults, with typical zeolite alteration of the glass-rich ignimbrite rocks. The area (Fig. 7) was chosen according to the results of Brandmeier (2010) based on anomalous high Pixel Purity Index (PPI) values and corresponding Matched Filtering (MF) results. Natrolite MF results are shown in Fig. 7B. Field measurements and electron microprobe (EMP) analysis confirm this finding. Although ASTER spectral resolution could not distinguish natrolite, the different alteration type with minerals of the zeolite group in this ignimbrite setting was successfully mapped.

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Fig. 7: A: Lomada Atansa with sample locations. This subset will be used to examplify results for mineral abundance maps and spectral indices. B: MF results for natrolite (Brandmeier, 2010) (1.6% to 19.8% linearly stretched image) overlain on ASTER RGB:321 at Lomada Atansa. The rainbow colors (green: medium abundance to red: high abundance) highlight areas with high abundance of zeolites.

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4.2 Geochemical analysis- major elements and mineral content (RFA, EMP and