The second Section concentrates on applications of remote sensing to geological studies. A list of principal uses begins this page. Special attention is given to ways in which remote sensing (especially through image classification) can aid in making geologic maps. The notion of "formation" is discussed and reasons given as to why this standard geologic map unit cannot be recognized as such in imagery alone. One of the pitfalls of making these maps solely from imagery - namely, the presence of soil and/or vegetation cover - is mentioned. Some typical spectral signatures of different rock types are displayed.
Geologists have used aerial photographs for decades to serve as databases from which they can do the following:
1. Pick out rock units (stratigraphy)
2. Study the expression and modes of the origin of landforms (geomorphology)
3. Determine the structural arrangements of disturbed strata (folds and faults)
4. Evaluate dynamic changes from natural events (e.g., floods; volcanic eruptions)
5. Seek surface clues (such as alteration and other signs of mineralization) to subsurface deposits of ore minerals, oil and gas, and groundwater.
6. Function as a visual base on which a geologic map is drawn either directly or on a transparent overlay.
With the advent of space imagery, geoscientists now can extend that use in three important ways:
1) The advantage of large area or synoptic coverage allows them to examine in single scenes (or in mosaics) the geological portrayal of Earth on a regional basis
2) The ability to analyze multispectral bands quantitatively in terms of numbers (DNs) permits them to apply special computer processing routines to discern and enhance certain compositional properties of Earth materials
3) The capability of merging different types of remote sensing products (e.g., reflectance images with radar or with thermal imagery) or combining these with topographic elevation data and with other kinds of information bases (e.g., thematic maps; geophysical measurements and chemical sampling surveys) enables new solutions to determining interrelations among various natural properties of earth phenomena.
While these new space-driven approaches have not yet revolutionized the ways in which geoscientists conduct their field studies, they have proven to be indispensable techniques for improving the geologic mapping process and carrying out practical exploration for mineral and energy resources on a grand scale.
We now consider several examples of geologic applications using these new approaches. We concentrate initially on how Landsat Thematic Mapper (TM) data for a local region in Utah are manipulated to identify different rock types, map them over a large area using supervised classification, and correlate their spatial patterns with independent information on their structural arrangement. Next, our focus changes to examination of geologic structures, particularly lineaments, as displayed in regional settings in the U.S., Canada, and Africa. Then, in Section 5, we will look at how space-acquired data fit into current methods of exploring for mineral and hydrocarbon deposits by considering a case study of a mineralized zone and at a large-area Landsat scene in Oklahoma. In Section 18, we will return to a geologic theme by examining landforms at regional scales, (so-called mega-geomorphology), as the principal subject in considering how remote sensing is used in basic science studies.
Most geologic maps are also stratigraphic maps, that is, they record the location and identities of sequences of rock types according to their relative ages. The fundamental rock unit is the formation (abbreviated as Fm or fm), defined simply as a distinct mappable set of rocks (if sedimentary, then usually layered) that has a specific geographic distribution. A formation typically is characterized by one or two dominant types of rock materials.
Any given formation is emplaced over some finite span of geologic time. We can approximate its age by the fossils (evidence of past life) that were deposited with it during the time in which these life forms existed. Age dating by determining the amounts of radioactive elements and their decay-daughter products can usually produce even more accurate age estimates. Another, less precise, approach to fixing the age (span) of a rock unit is to note its position in the sequence of other rock units, some of whose ages are independently. We can correlate the units with equivalent ones mapped elsewhere that have had their ages worked out. This method tends to bracket the time in which the sedimentary formation was deposited but erosional influences may lead to uncertainties.
Remote-sensing displays, whether they are aerial photos or space-acquired images, show the surface distribution of the multiple formations usually present and, under appropriate conditions, the type(s) of rocks in the formations. The formations show patterns that depend on their proximity to the surface, their extent over the surveyed area, their relative thicknesses, their structural attitude (horizontal or inclined layers), and their degree of erosion. Experienced geologists can recognize some rock types just by their appearance in the photo/image. The identify others types from their spectral signatures. Over the spectral range covered by the Landsat TM bands, the types and ages of rocks show distinct variations at specific wavelengths. This is evident in the following spectral plots showing laboratory-determined curves obtained by a reflectance spectrometer for a group of diverse sedimentary rocks from Wyoming:
2-1 From these spectra, predict the general color of these four rock units: Niobrara Fm; Chugwater Fm; Frontier Fm; Thermopolis Fm. ANSWER
2-2: What spectrally distinguishes the Mowry Fm from the Thermopolis Fm; the Jelm Fm from the White River Conglomerate? ANSWER
A common way of mapping formation distribution is to rely on training sites at locations within the photo/image. Geologists identify the rocks by consulting area maps or by visiting specific sites in the field. They then extrapolate the rocks' appearance photographically or by their spectral properties across the photo or image to locate the units in the areas beyond the site (in effect, the supervised classification approach).
In doing geologic mapping from imagery, we know that formations are not necessarily exposed everywhere. Instead they may be covered with soil or vegetation. In drawing a map, a geologist learns to extrapolate surface exposures underneath covered areas, making logical deductions as to which hidden units are likely to occur below the surface. In working with imagery alone, these deductions may prove difficult and are a source of potential error. Also, rock ages are not directly determined from spectral data, so that identifying a particular formation requires some independent information (knowledge of a region's rock types and their sequence).
In exceptional instances, such as those to be shown on the next three pages, when geologic strata are turned on their side (from folding; discussed on page 2-5) so that the successive geologic units are visible as a sequence, the changes within and between each discrete unit can be measured in terms of some spectral property, as for example, variations in the reflectance of a given band, or a ratio of bands. When plotted as shown below the result are tracings that resemble (analogously) those made from well logging of such properties as electric resistivity, permeability, magnetic intensity and other geophysical parameters. Here are two figures, the top showing the succession of sedimentary strata exposed along the Casper Arch in central Wyoming; the bottom being reflectance "logs" derived from spectral traverses along one of the lines in the upper image:
In the lower diagram, the bottom unit is the Permian Phosphoria Formation, extending upward from the Triassic Chugwater Formation to the Frontier sandstone (Cretaceous) at the top. On the right the left tracing is of TM band 3 (red), with 0% reflectance on the right extending to 70% on the left, and the right tracing goes from 0% on left to 50% on the right.
Collaborators: Code 935 NASA GSFC, GST, USAF Academy