By now, hundreds of missions designed to test and use imaging spectrometers have flown, with many impressive images generated from the data. Still one of the most successful and instructive among these was the NASA AVIRIS flights over Cuprite, Nevada. The JPL AVIRIS team and the Spectroscopy Group at the U.S. Geological Survey in Denver reduce and manipulate the data. Be advised to check out the USGS page that will come up as it will offer the option of clicking on "Maps" which then brings up the Cuprite site as a choice. The images you will find at that site are superior to those below (the legends are readable), as these latter are degraded in downloading.
The Cuprite mining district lies near Tonopah, NV, in the southwestern part of the state. Gold and copper have been mined from here for more than a century. This area is a valuable geological study site to evaluate remote sensing for mineral exploration, in particular with hyperspectral data sets, because of the wide variety of telltale alteration and other mineralization. This is a Landsat view from space.
Here is a view made from three AVIRIS channels in pseudotrue color of the main area displaying significant alteration. The area shown is approximately 17 by 10 km.
By way of comparison, we got the next image from the HYDICE sensor. This false color combination consists of Red = Channel 175 (2,200 nm), Green = Ch. 125 (1,650 nm), and Blue = Ch. 50 (650 nm). The area here includes part of the AVIRIS scene but also includes other features.
Cuprite was selected in part because previous field work at ascertained the variety of alteration minerals at that site, as shown in the map below. The alteration has affected several types of volcanic deposits of Tertiary age.
Laboratory spectra of three of the principal alteration minerals are reproduced here; spectra constructed from AVIRIS data are similar:
Here is a SWIR bands image made during the first field trials (1990) of AVIRIS in which the colors represent the iron minerals Hematite, Goethite, and Jarosite. The Goethite occurs mainly as stain or pigmentation in the alluvial materials in the valley and along slopes. (Black is unclassified).
By judiciously selecting channels extending into absorption bands that are indicators of particular mineral species or groups, we have identified and mapped most of the alteration-related minerals at Cuprite. This next map extends the location of iron-bearing minerals, which have many of their diagnostic bands in the 400 to 1,200 nm range.
This map differs from the previous one (above) in subdividing Hematite and Goethite according to crystallinity and in showing other iron minerals as distinguishable chemical phases.
We can recognize an even more diverse assembly of minerals using bands within the 2,000-2,500 nm range. AVIRIS images have shown a large group of silicates, carbonates, and sulphates, as shown in this map.
Of special interest is the mineral Buddingtonite, shown in fuchsia (a peach pink), which occurs in a few patches on the map. Buddingtonite is a rare form of the common potash-feldspar group. The ammonium ion, NH3+, partially replaces the potassium ion, K+. This map is an exciting example of high resolution hyperspectral data to reveal a notable diversity of minerals in alteration zones and fresh rock, as well, at a detail that could require years of field mapping to duplicate.
The Cuprite site has now been overflown at low and high altitudes to see how height (hence resolution) affects the ability to distinguish mineral distributions. In the image pair below, the left shows a part of the Cuprite scene at 2.3 x 7 m resolution; the right, obtained from a higher altitude, is the same area at 18 x 18 m resolution. Judge for yourself from the patterns the extent of better or new information obtained at low altitude/high resolution.
Because of the variety of alteration present at Cuprite, that site has become a standard for testing the ability of hyperspectral sensors to discriminate its characteristic mineralogy. Terra's ASTER has enough bands in the SWIR region of the spectrum to indicate how well that spaceborne sensor can pick up the diagnostic minerals. This first SWIR color composite shows that the overall anomaly at Cuprite can be spotted as evidence of an alteration zone:
Having established this general detectability, ASTER bands considered especially sensitive to mineralogy were used to produce this image.
The resulting colors are associated with non-iron minerals as follows: blue = Kaolinite; cyan = Montmorillonite; purple = Unaltered; light green = Calcite; dark green = Alunite + Kaolinite; yellow = Dickite; red = Alunite
The great improvement in information content resulting from hyperspectral data acquired both from airborne and spaceborne sensors has generated renewed interest in using such data for both mineral exploration and other applications. A number of private companies that fly remote sensors commercially have added hyperspectral capabilities to their services; in fact, several new ones were formed to specialize in this approach. Cuprite has been used as an established test base. One company, Borstad Associates, has released their results (SFSI sensor) on the Internet. Here are two images taken from their website:
Another Cuprite hyperspectral product is this image made by the HyMap sensor:
Based on the above evidence and other studies of alteration as telltale guides to ore deposits, it seems safe in asserting that Cuprite is the definitive mineralized site for demonstrating not only the value of hyperspectral remote sensing but remote sensing in general (see Section 5) as a powerful tool in searching for mineral and petroleum deposits. It seems likely that hyperspectral remote sensing for will become a prime means in future exploration programs. The strategy will be to use space imagery to look anomalies and then overfly promising ones with airborne sensors. However, if this is done systematically, most potential sites worldwide will be overflown in the 21st Century. To keep the companies viably operational, other uses need to be developed. To show that this is happening, we display several images touching on these additional uses, with minimal comment.
Determining vegetation to the species level is a major goal in remote sensing that hyperspectral sensors are helping to address. This next AVIRIS image, which we first saw on page 3-1, shows a group of crops in both circular and rectangular fields. The area is near Summitville, Colorado; a false color image of this site was included on page I-24. Note that the identified crops correspond to those shown on page 13-6, as a plot displaying spectral curves for a series of crops, first, and just below it, a continuum-removal plot of the same series. These came from the same data set used to generate this image.
13-30: What do you think is meant by "nothing mapped"? ANSWER
Both vegetation and non-vegetation classes are present in this hyperspectral image of the Kunugawa River valley in Japan:
The themes shown are: Medium blue = watermelon; Dark blue = Marigold; Purple = Pumpkin; Wine Red = Grass; Green = Maize; Brown = Trees; Yellow, Dark Red, Dark Peach = 3 soil types; Dark Gray = Concrete: Red = Built up.
Forest types also are amenable to individual species identification:
Categorizing the classes found in shallow water off a coral reef coastline is another application of hyperspectral data:
Before closing this subsection that highlights the rapidly growing use of imaging spectrometers, we briefly mention another burgeoning, and loosely related, field of remote sensing. We now call it "multisensor analysis." The term refers to combining data obtained by more than one type of sensor on a spacecraft or, more commonly, by sensors on different spacecraft. For example, we may image a study area at various times by Landsat, SIR-C, TIMS, SPOT, MOMS, and AVIRIS. Of course, we may independently examine each data set, and imagery derived therefrom. Or we can lay visual products side by side. From this multiset, we can interpret the scene by simply looking at the various aspects of scene content. This process is standard procedure in conventional photointerpretation.
Or instead, as we have seen in several images shown elsewhere in this tutorial, we can merge, or register, two or more data sets to form a single image, composed of combined images. Landsat and radar data are good examples. Landsat provides a color rendition of the surface cover and radar provides a sense of topography or relief. Another example uses Digital Elevation Map topographic data, in digital format, to create a quasi-3D, perspective view of a SPOT scene.
Somewhat more sophisticated is the approach that uses each sensor data set as input to classification. Thus, we can combine visual and SWIR bands from AVIRIS with TIMS thermal data, so that 10 to 12, or more, band values contribute to the multivariate analysis that leads to a classified scene or map, likely to have improved accuracy. And, of course, we can digitize and combine other kinds of data by using aerial photography or thematic maps (described in the review of Geographic Information Systems in Section 15).
The trick to doing meaningful multisensor analysis lies in properly registering a variety of data sets. These data sets may come from sensors mounted on several platforms, which leads to multidimensional data, characterized by different pixel sizes, viewing geometries, orbital or flight line paths, times of year, angles of illumination, etc. Techniques for registering images have evolved over the years. One example superposes multitemporal Landsat data. Another combines day and night HCMM images (from orbits inclined to each other). We can now do Automated Multisensor Registration conveniently, using computer-based algorithms that register to a common geocoded base, integrate tie-point features, make geometric corrections (rectification), and resample pixels to a common size.
We clearly observe that, with the proliferation of sensors and their platforms (satellites) in space, the systematic combining of data, acquired over a wide range of wavelengths, scales, and temporal conditions, will result in a strongly synergistic use of the valuable data sets each operating system provides. Plans are well along for putting hyperspectral remote sensing routinely into space. This has already been started with the orbit of EO-1 (see first page of the Overview). ASTER, which acts like a broader band hyperspectral system, was launched with Terra in 1999 (see page 16-10).