CHAPTER 2: PROCESSING OF THE VNIR-SWIR MASTER DATA
2.4 Data Analysis
2.4.6 Determining the Likely Lithology of the Endmembers
number of bands, and determines the spectral similarity between two spectra by calculating the angle in radians between the spectra [Kruse et al., 1993].
Supervised classifications were produced for the MASTER images as well. A supervised classification has the advantage that the image does not need to be atmospherically corrected before classification. These classifications were made with classes of selected pixels within regions of known lithology. The classification of the remaining pixels was based on these classes. The results outlined surfaces that likely had similar geochemistry, but because they are based on an average of pre-selected pixels, they did not yield as much information about the surfaces as the classification scheme outlined above.
most likely matches for the VNIR and the SWIR wavelengths are listed in Appendix B.
The best fit is determined with a combination of the Spectral Angle Mapper algorithm and the Spectral Feature FittingTM (SFFTM). SFFTM is an absorption-feature-based methodology, where the unknown spectrum is scaled to match each library spectrum after continuum removal from both spectra. The output from the Spectral AnalystTM is a number from zero to 2, with two being a perfect match with both the SAM and SFFTM spectral matching algorithms, with each contributing one point. When assessing the degree of fitting from these algorithms, several things are important. For example, a good fit is relative. If an endmember matches a particular mineral with a very high score, it is only meaningful if that score is significantly higher than a score between that mineral and very different minerals. A “significant” difference is difficult to ascertain, however.
A flat spectrum, one devoid of absorption features, will fit other flat spectra very well, but this is not an indication of a good match. Whereas, a spectrum with very large features may not have a perfect fit to another spectrum with a large feature, but the common feature, varying just slightly, may indicate the endmember shares a similar chemical make-up to that of the known mineral. Additionally, some of the minerals in the USGS spectral library are known to have impurities, such that the spectrum of the rock may not be entirely attributed to the mineral family to which the sample is assigned [Clark et al., 2003]. Careful consideration has been given to all of these factors when determining the likely lithology of the endmembers.
Compared to the USGS Mineral and Vegetation Spectral Libraries, using bands 1-10 (.4589-.91 microns) n-d #1 Montmorillonite; Phyllosilicate; (Na,Ca)0.33(Al,Mg)2Si4O10(OH)2•nH20 1.883
n-d #2 Microcline (Feldspar group); Tectosilicate; KAlSi3O8 1.844
n-d #3 Microcline (Feldspar group); Tectosilicate; KAlSi3O8 1.851
n-d #4 Gypsum (Selenite); Sulfosalt; CaSO4•2H20 1.851
n-d #5 Augite (Pyroxene group); Inosilicate; (Ca,Mg,Fe2+,Fe3+,Ti,Al)2(Si,Al)2O6 1.784
n-d #6 Magnetite (Spinel group); Oxide; Fe+2Fe+32O4 1.874
n-d #7 Galena; Sulfide; PbS 1.833
n-d #8 Andradite (Garnet group); Nesosilicate; Ca3(Fe+3)2(SiO4)3 1.911
n-d #9 Juniper; Shrub 1.613
n-d #10 Hematite; Oxide; alpha-Fe2O3 1.814
n-d #11 Dickite (Kaolinite-Serpentine group); Phyllosilicate; Al2Si2O5(OH)4 1.957 n-d #12 Kaolinite/Smectite (50% Kaol.)(Kaolinite-Serpentine group); Nesosilicate; Al2SiO5 1.821 Basalt Staurolite HS188; Nesosilicate; Fe2+2Al9O6(SiO4)4(O,OH)2 1.837 Faroash Montmorillonite; Phyllosilicate; (Na,Ca)0.33(Al,Mg)2Si4O10(OH)2•nH20 1.969
Wttfgp Grossular; Nesosilicate; Ca3Al2(SiO4)3 1.916
LV-veg Rubber Rabbitbrush; Shrub 1.502
Table 2. Best matches to the USGS mineral and vegetation spectral libraries for the VNIR wavelengths to the endmembers determined from the MASTER image and selected field spectra.
Compared to the USGS Mineral and Vegetation Spectral Libraries, using bands 20-24 (2.0-2.33250 microns) Mineral Name/Plant Name; Mineral Type/Plant Type; Formula Score
n-d #1 Pinnoite; Hydrous Borate; MgB2O4•3H2O 1.886
n-d #2 Beryl; Cyclosilicate; MgB2O4•3H2O 1.876
n-d #3 Alunite; Sulfate; KAl3(SO4)2(OH)6 1.888
n-d #4 Kaolinite/Smectite; Phyllosilicate; Al2Si2O5(OH)4+ (Na,Ca)0.33(Al,Mg)2Si4O10(OH)2•n 1.929
n-d #5 Diaspore; Hydroxide; AlO(OH) 1.993
n-d #6 Gypsum; Sulfosalt; CaSO4•2H2O 1.921
n-d #7 Hematite; Oxide; alpha-Fe2O3 1.926
n-d #8 Chlorite Mg-rich; Phyllosilicate; (Mg,Fe)3(Si,Al)4O10(OH)2-(Mg,Fe)3(OH)6 1.98
n-d #9 Dry Long Grass; Grass 1.95
n-d #10 Oligoclase (Plagioclase); Tectosilicate; (Na,Ca)Al(Al,Si)Si2O8 1.973 n-d #11 Labradorite HS105 Plagioclase; Tectosilicate; (NaAlSi,CaAl2)Si2O8 1.986
n-d #12 Manganite; Hydroxide; MnO(OH) 1.827
Basalt Augite (Pyroxene group); Inosilicate; (Ca,Na)(Mg,Fe,Al,Ti)(Si,Al)2O6 1.966
Faroash Spessartine; Nesosilicate; Mn3Al2(SiO4)3 1.96
Wttfgp Alunite; Sulfate; (Na,K)Al3(SO4)2(OH)6(Na82) 1.802
LV-Veg Dry Long Grass 1.941
Table 3. Best matches to the USGS mineral and vegetation spectral libraries for the SWIR wavelengths to the endmembers determined from the MASTER image and selected field spectra.
As examples, Figures 23 and 24 show a range in quality of matches to the spectral library data. A poor match between endmember “n-d-10” and the mineral malachite is shown in Figure 23, with the continuum removed to highlight the spectral features. The match between “n-d-10” and malachite yields a score of 0.338 in the Spectral AnalystTM, with a combination of the SAM and SFFTM algorithms. A good match between endmember “n- d-10” and the mineral Hematite is shown in Figure 24, with the continuum removed to highlight the spectral features. This match yields a score of 1.814 in the Spectral AnalystTM for comparison of the VNIR wavelengths.
- 1.0
- 0.4
Figure 23. Reflectance spectrum of the endmember "n-d-10" and the mineral malachite. The continuum has been removed to highlight the spectral features.
- 1.0
-0 .7
Figure 24. Reflectance spectrum of the endmember "n-d-10" and the mineral hematite. The continuum has been removed to highlight the spectral features.
The Spectral AnalystTM yields higher matches for spectra when compared for the SWIR wavelengths, since there are only 5 available MASTER bands for this comparison. In this study, a score of 1.8 for the VNIR spectrum and a score of 1.9 for the SWIR wavelengths are considered good matches. However, careful consideration must be given to the factors influencing apparent matches mentioned above. For vegetation spectra in the VNIR wavelengths, a very good match is one above 1.5. This lower score is due to the mixing of the VNIR features from the underlying rock surface with the vegetation spectrum and the vast differences among vegetation spectra in the VNIR.
While the spectral signature for chlorophyll present in photosynthesizing plants is very distinctive, the reflection from vegetation in the near infrared and in the visual ranges of
the spectrum varies considerably with how much chlorophyll is in the plant [Labovitz and Masuoka, 1984].