Abstract:Reflection spectra of particulate samples of minerals that commonly occur in hydrothermally altered rocks and soils were recorded to display their features at their natural spectral bandwidths in the near-infrared from 1.3 to 2.4 pm. Atmospheric transmission spectra were recorded over limited wavelength segments in the same region to demonstrate the availability of some of the diagnostic mineral bands that occur close to regions of intense absorption. Changes occur in the appearance of all these spectra caused… Show more
“…Numerous references discuss the relationship between physical/biophysical variables and EMR, including: the way in which minerals absorb photons and their associated wavelength dependency (for example, see Hunt and Salisbury, 1970;Hunt et al, 1971a and b;Rowan et al, 1977;Hunt and Ashley, 1979;Hunt, 1977 and1979;Goetz and Rowan, 1981); the spectral reflectance of vegetation (Collins, 1978;Horler et al, 1983;Milton and Mouat, 1989;Boochs et al, 1990;Elvidge, 1990;King et al, 1995;Campbell, 1996;Dawson and Curran, 1998;Datt, 1999 and2000); and, characteristics of soils (Baumgardner et al, 1985;Irons et al, 1989).…”
Section: Target and Wavelength Dependencymentioning
“…Numerous references discuss the relationship between physical/biophysical variables and EMR, including: the way in which minerals absorb photons and their associated wavelength dependency (for example, see Hunt and Salisbury, 1970;Hunt et al, 1971a and b;Rowan et al, 1977;Hunt and Ashley, 1979;Hunt, 1977 and1979;Goetz and Rowan, 1981); the spectral reflectance of vegetation (Collins, 1978;Horler et al, 1983;Milton and Mouat, 1989;Boochs et al, 1990;Elvidge, 1990;King et al, 1995;Campbell, 1996;Dawson and Curran, 1998;Datt, 1999 and2000); and, characteristics of soils (Baumgardner et al, 1985;Irons et al, 1989).…”
Section: Target and Wavelength Dependencymentioning
“…The spectral curve for rhyolite tends to be flat until a broad increase in absorption is visible around 1000 nm (Figure 3a). This absorption is attributed to the effect of ferrous iron and electronic transitions in discrete ions in crystal field absorption (CFA) [37,38]. Weak absorption is visible at 480 nm, 1900 nm, and 2200 nm.…”
Section: Spectral Features Of Rhyolitementioning
confidence: 99%
“…The weathered surface spectrum is not as strongly affected by ferric iron absorption bands as the fresh spectrum. Relatively, a broad band is centered near 1090 nm, which shows the presence of ferrous rather than ferric iron in the fresh spectrum [37,46].…”
Section: Spectral Features Of Basaltmentioning
confidence: 99%
“…A local reflectance peak is present at 2200 nm, followed by a decrease in reflectance with absorption features superimposed at 2250 nm, 2300 nm, 2380 nm, and 2450 nm. Cor- relation of these bands with specific minerals is hampered by the observation that different samples of the same mineral can have absorption bands at different wavelength positions and of different relative strengths [36,37,[49][50][51]. The absorption bands in the sample spectrum appear to be caused by weathering products that consist of both Al-OH and Mg-OH absorption bands.…”
Surfaces weathering of rocks in which mineral materials may be similar to or quite different from the minerals in the underlying parent rock completely control the reflectance spectra of the terrain. Our study of typical weathered and fresh rock samples from the Xiemisitai metallogenic belt, Western Junggar region, Xinjiang, found that weathering results in the formation of new materials that cause differences in the spectral features of fresh and weathered rock surfaces. Alterations induce variations in spectrum brightness, presence and intensity of characteristic absorption features, and spectral slope. Spectral differences between weathered and fresh rock surfaces are small for rhyolite, granite, and tuffaceous sandstone, but large for andesite, basalt, and diorite. Spectral changes in the 350-1000 nm wavelength region are attributed to alteration of iron oxides by atmospheric processes or secondary alteration of iron-rich minerals. Spectral features between 1000-2500 nm are caused by O-H vibrations, with features at 2200-2500 nm solely attributed to hydroxyl groups. The strongest Al-OH bands appear near 2200 nm, while Mg-OH bands are found near 2300 nm and 2350 nm. Results from this study can be used to better characterize and discriminate lithological units and potential mineral zones using hyperspectral and multispectral remote sensing techniques.
“…Carbonate registers a moderately wide absorption at 2300 nm, plus several minor minima between 1800 and 2000 nm (Hunt and Salisbury 1971a, Hunt 1979, Lyon and Honey 1979 (figure 2(B)). Such absorptions allow the differentiation between siliceous and carbonated areas.…”
Abstract. Weathering processes are responsible for slight surface mineralogical differences allowing the distinction between lithologically similar geological units using Thematic Mapper (TM) data. Two different stages throughout time of overlying iron alteration are notoriously distinctive on the imagery and laboratory spectra. Their diverse spectral behaviour follows the dominant iron hydroxide with kaolinite and carbonate crusts on the Pliocene Ochre Alteration typical of a humid warm climate, compared with the dominant nonhydratated iron oxides with smectite on the Miocene Red Alteration developed under a mediterranean dry climate. Iron materials with carbonate hinder appearance of the typical iron absorption features in the visible wavebands. Therefore, the iron weathering alteration coatings will be obscured on the imagery when it is developed on carbonate sediments or detritic sediments with carbonate cement or matrix. The presence of carbonate within the sediment as cement or alteration product decreases the overall reflectance of laboratory nonconsolidated rocks and the clay size fraction from rocks, apart from smoothing the 2200 nm absorption typical of OH-bearing minerals. The presence of carbonate cement and carbonate crusts favours the differentiation of some units. Digital mapping through image pro cessing of different series of digital data leads to a sequential masking of classes to produce a final map. The sequence of masking produces different maps which can be used as a tool to model aspects of the sedimentary basin and geological processes throughout time.
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