The strength of multi and hyperspectral data for geology lies in its spectral resolution Minerals have distinct reflectance signatures with spectrally narrow features Lithologic mapping depends largely on discrimination of minerals Broad-scale structural mapping can also benefit from satellite RS Mineral and petroleum exploration is more efficient with imagery
Hyperspectral data are particularly powerful for untangling mineral spectra Can “see” subtle reflectance features Can “unmix” within-pixel mixtures of different minerals Hyperspectral data require careful processing to allow comparison of satellite data to ground data Atmospheric correction Calibration to ground test sites Common hyperspectral technique is called “spectral matching” for identifying materials
Slide from J.R. Harris and P. Budkewitsch – Canada Natural Resources
Acquire hyperspectral imagery The usual requirements – cloud free, geometrically corrected Acquire ground-based reflectance signatures of key minerals of interest Perform careful atmospheric correction and calibration of imagery so that it can be compared to ground spectra Use computer to find the best match of image-generated spectra to ground-measured spectra for each pixel Convert match information into a map
Choose bands that are most appropriate for the mineralogy of the region of interest Use classification techniques to group image pixels into classes based on those bands Label the spectral classes using fieldwork—associate mineral types with each class based on what you find on the ground Combine or split classes as needed to make a map of mineralogy/lithology.
Atmospheric correction of hyperspectral data should eliminate the effects of both absorption and scattering in the atmosphere Usually accomplished with combination of radiative transfer models (models of the effects of atmosphere on particular wavelengths of light) and ground calibration
MODTRAN: MODerate resolution atmospheric TRANsmission – models transmission of light through the atmosphere ACORN: Atmospheric CORrection Now ATREM: ATmospheric REMoval (modeled after MODTRAN) Many others…
AVIRIS data: Kansas City Water vapor image “removed” by ATREM
Field spectra collection using Analytical Spectral Devices (ASD) radiometer
1. Image acquisition and preprocessing 2. Careful atmospheric correction of each band 3. Generate spectral curves from image pixels 1. Each corrected image pixel has a reflectance based on it’s digital number in each of the many hyperspectral bands 4. Compare pixel spectra to spectral libraries 1. Many minerals have been spectrally examined in laboratories and their spectral curves are stored in online libraries (and in RS software)
Spectral matching: Software looks for best match of unknown spectra (from image) to known spectra (from libraries)
Spectral data allow exploration geologists to quickly narrow down search areas and eliminate unproductive ground work Geologists can map large structures, diagnostic mineralogy and lithology, and outcrop locations quickly with a satellite image Mineral clues can point geologists to areas that might be associated with gold, silver, copper and other metal- bearing minerals Petroleum is usually more deeply buried and requires structural analysis but often there are surface clues
Oxidized iron ores called “gossan” by prospectors can indicate areas of mineralization associated with ores Gossan has a distinctive look on the landscape (left). Often iron oxides occur with other minerals like copper (below)
Part of a Landsat image covering SW Utah (near Zion NP) White Mountain Classic gossan staining Basin deposits Wah Wah Mts. (block fault) Prospectors look for telltale gossan and enhance imagery to make it stand out
Natural color Landsat zoomed in: Dark areas are volcanics; White Mt. is blue-gray limestone; Gossan patches are brownish areas west of White Mt.
This image created by taking the ratio of two spectral bands to highlight gossan, which appears as yellow/brown area Ratio of TM bands 7/5 (Mid-IR bands) are often good for enhancing mineralogy
Another ratio image, this time using 3 separate ratio “bands” to create a 3-ratio color image. Good differentiation of different rock types in gossan area.
Another enhancement: Principal Components Analysis (PCA) – statistically reorganizes the satellite data to capture the greatest amount of information. In this image the gossan zone is well subdivided into iron-dominated (red/yellow) and kaolinite/alunite (purple). The red/yellow areas are most likely to be productive.
Supervised classification (map) of the area created using some of the enhancements previously discussed. Gossan areas are the brown and red classes.
Landsat imagery cheap to free Spectral information in Landsat sufficient to create believable map of gossan Significantly narrows the search area to constrain ground-based prospecting Potentially increases profitability
Hyperspectral mapping of a mining district in Utah
Landsat data – San Rafael Swell, Utah Enhanced to show different lithologies in this uranium-rich area
Petroleum requires source – hydrocarbons and usually some kind of trapping formation
Focus is on identifying appropriate trapping structures or rock formations Satellite imagery allows rapid survey of large areas at low cost Lithology mapping as previously discussed allows identification of key formations Mapping of fracture patterns useful for understanding traps – fractures let hydrocarbons migrate through rock Satellite surveys must be followed up by surface exploration and usually drilling to understand buried structures
Landsat MSS image on which geologists have marked anomalous features, such as circular patterns (tops of anticlines?) and “hazy” tones that they linked to know hydrocarbons This area is the Andarko Basin in Oklahoma
Ratio image (composite of three band ratios) of area A from previous slide. Oil-bearing formation looks reddish. Turns out that hydro- carbons leaking through surface rocks were altering them spectrally Rocks associated with key formations are spectrally different
Similar leak of hydrocarbon gas in Wind River Basin, Wyoming caused the alteration of rocks in tan oval area in center of this image.
Geologists and remote sensing scientists in Michigan have found stressed vegetation in vicinity of hydrocarbon gas leaks Shows up in the “red edge” of the vegetation spectral curve Even without leaks, vegetation can be associated with particular formations or it can follow structural features and fractures
Satellite data advantages for geologic studies include: Ability to survey large areas quickly Spectral resolution for mineral discrimination Spectral enhancement for broad scale mapping Geologic work almost always requires follow-up on ground and/or exploration of the subsurface geology Multispectral imagery like Landsat is good for quick assessments Hyperspectral imagery allows fine discrimination of minerals but is more labor intensive to process