The third principal component seems meaningless, except that the black spots probably correspond to certain alteration zones.

A glance at the fourth principal component shows the same dark hook-like pattern that was observed in the ratio image 7/5. White Mountain is set apart by its light tones, with similar tones north of the basalt deposits.

Earlier, a PCA composite (shown below) was made from a 24-channel Bendix aircraft flight over White Mountain. For this, they included eight non-thermal channels and combined components 1, 2, and 4 into the composite shown below. Most of the rock units mapped in the field and identified in the Landsat images seem to show up but in some instances occupy somewhat different areas and have dissimilar sizes of outcrop. But, we safely conclude that, using the Bendix image as a standard, the Thematic Mapper (TM) PCA composites match fairly well. At least, they are good enough to stand alone as successful guides to the principal rock and alteration types defined by field work.

5-8: All three above PCA products each seem to have useful information. Evaluate them beyond the information already offered in the preceding paragraphs. What in particular is separated rather well in the two TM images? ANSWER

We come now to a highlight of Section 5, a Supervised Classification of the Landsat TM data made by IDRISI with training sites based on the maps and on field observations by the author. Ten classes were established and then identified in training sites used to run a Maximum Likelihood classifier. Here is the end result (unfortunately, the legend initially created has been lost from the display program; see descriptions below).

This is a colorful and a believable product. The classes designated Basalt (dark blue) and Andesite (green) are largely where they should be in field terms. White Mountain is well separated but its legend color (whitish) also is found where additional limestone outcrops are postulated north of the basalts. The Kaolinite/Alunite zone (purple) coincides well with the map information. The class designated as Ironrich (brown) is broadly equivalent to the geologic map unit called Moderately Hematized, whereas the class called Hematite (red) matches at least some of the map units called Strongly Hematized. Arbitrarily, four different classes of alluvium were set apart, based on photointerpretative and geologic reasoning. The class MixAlluv (gray) is partly within the altered zone and we assume it is a mix of altered rock and volcanic rock debris. DrkAlluv (dark gray) is a differentiable deposit consisting mostly of weathered volcanic residue. The class LsAlluv (light blue), we presume contains considerable contributions from White Mountain and other limestone sources. BrtAlluv (yellow) refers to alluvium west of the western basalt hills, which probably received much of its input from the Wah Wah Mountains. Its brightness (in individual bands and color composites) implies a variety of light-colored detritus (fragmented debris) and clays.

We also classified the White Mountain data collected by the Bendix scanner, using 7 of its channels. We consulted the same map to pick training sites, but these sites were not necessarily the same as we selected for the above Supervised Classification. Plus, we employed the IDIMS program for the processing algorithm, once again applying the maximum likelihood classifier. In the image above is the resulting classification and a color code for the selected classes (note that these are not the same as for the first classification). The major differences between the two classifications are: 1) we subdivided the volcanic units in part by degree of vegetative cover, and 2) we treated the unconsolidated cover (alluvium) as a single unit. In general, the correspondence between the two classification images and between this classification and the published map is good in both instances.

5-9: Comparing the two classifications, how does the Bendix classification differ from the IDRISI one? Account for this. ANSWER

This case study with its accompanying images should convince you that satellite remote sensing has practical value. We already insinuated this idea in the Overview and the content of the first four Sections. Besides, remote sensing can lead to possibly sensational payoffs. We grant that the White Mountain example is almost a sure thing. The major types of alteration are distinctly different, so much so, that the color aerial photo is almost sufficient to produce an accurate map (remember, we said that the images seem superior to the pre-Landsat field map). But, the special processing products make these differences even more obvious.

Imagine that we have a one-time opportunity to stake several claims anywhere in Utah but must do so in 30 days. Its a big state! But, with Landsat and other space imagery, properly processed, we can shrink this huge area to just those small patches that apparently display abundant gossan. We can visit the most promising of these patches in brief trips, using rapid reconnaissance to seek signs of minerals. We can take samples for quick assay to determine grade or concentration (amounts of useful metals present per unit volume). Favorable results mean that we ought to file a claim by the deadline. Then, we must drill and map in detail to determine whether any mineralization we have detected is in enough gross volume to warrant developing and mining. With any luck, we’ll learn to fully realize the merits of prospecting from space.