Multi and Hyperspectral Remote Sensing for Geology

1. Multi and Hyperspectral Remote Sensing for Geology Lithology, Structure, Geomorphology

3. Brandenberg Massif, Namibia Granitic intrusion in desert

4. Landsat 8 image

5. Geologic Applications -- Overview • The strength of multi and hyperspectral data for geology lies in its spectral resolution • Minerals have distinct spectral 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

6. Spectral analysis of minerals • 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

7. Slide from J.R. Harris and P. Budkewitsch – Canada Natural Resources

11. How does hyperspectral mineral identification work? • Acquire hyperspectral imagery • 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

12. Or… • 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 • Do fieldwork to label the spectral classes—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.

13. Atmospheric correction and calibration • 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

14. First step…correction and calibration Remember: Light affected by atmosphere Each wavelength (satellite band) affected differently

15. Many sophisticated programs available for atmospheric correction • MODTRAN: MODerate resolution atmospheric TRANsmission – models transmission of light through the atmosphere • ACORN: Atmospheric CORrection Now • ATREM: ATmosphericREMoval (modeled after MODTRAN) • Many others…

16. Procedure "removes" atmospheric interference from the satellite radiance AVIRIS data: Kansas City Water vapor image “removed” by ATREM

17. Kansas City image

18. Field spectra collection using Analytical Spectral Devices (ASD) radiometer

19. Spectral Matching Procedure • Image acquisition and preprocessing • Careful atmospheric correction of each band • Generate spectral curves from image pixels • Each corrected image pixel has a reflectance based on it’s digital number in each of the many hyperspectral bands • Compare pixel spectra to spectral libraries • Many minerals have been spectrally examined in laboratories and their spectral curves are stored in online libraries (and in RS software)

20. Spectral matching: Software looks for best match of unknown spectra (from image) to known spectra (from libraries)

21. Mineral and petroleum exploration • 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

22. Case Study: SW Utah ore prospecting(Modified from the NASA RS Tutorial) • 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)

23. Baffin Island, Canada (J.R. Harris slide)

24. Landsat-based prospecting in Utah Part of a Landsat image covering SW Utah (near Zion NP) White Mountain Classic gossan staining Basin deposits WahWah Mts. (block fault) Prospectors look for telltale gossan and enhance imagery to make it stand out

25. Natural color Landsat zoomed in: Dark areas are volcanics; White Mt. is blue-gray limestone; Gossan patches are brownish areas west of White Mt.

26. Band ratios – enhancement of spectral features 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

27. 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.

28. 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.

29. Supervised classification (map) of the area created using some of the enhancements previously discussed. Gossan areas are the brown and red classes.

30. SW Utah prospecting -- benefits • 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

31. Other examples: Copper prospecting in Nevada with ASTER imagery

32. Hyperspectral mapping of a mining district in Utah

33. Landsat data – San Rafael Swell, Utah Enhanced to show different lithologies in this uranium-rich area

34. Case study 2: Petroleum prospecting (Modified from the NASA RS Tutorial) • Petroleum requires source – hydrocarbons and usually some kind of trapping formation

35. Contribution of multispectral satellite imagery? • 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

36. 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

37. 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

38. Similar leak of hydrocarbon gas in Wind River Basin, Wyoming caused the alteration of rocks in tan oval area in center of this image.

39. Vegetation effects • 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

41. Summary • 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