moisture. Time-lapse satellite images allow mapping of seasonal or longer-term changes or of subsidence over a reservoir.
Several satellites have surveyed the Earth’s surface, with a variety of frame or viewing sizes and resolutions. Resolution varies both by satellite and by portion of the spectral band sampled. Although the resolution of most satellites is insufficient to discriminate indivi - dual features such as bushes or boulders, remote-sensing maps can differentiate vegetation-covered regions from boulder fields because of their different spectral reflections. Since satellite images can encompass an entire land seismic survey area, this technology is a useful tool for hazard screening and for planning deployment and acquisition logistics. The most important factor affecting how a remote-sensing evaluation proceeds is the terrain: whether it is flat, rocky, sandy, popu lated, farmed, covered with vegetation or icy (previous page). The type of maps produced can differ greatly by survey location because different combinations of spectral bands optimize discrimination of different specific risks. In a land seismic survey, the most efficient and repeatable acoustic source is a vibrator, such as a vibroseis truck. However, vibrator trucks are large and heavy; their deployment requires careful logistical planning. In steep terrain, there is a danger of rolling over, and in soft terrain, of the truck getting stuck in sand or mud. Other risks arise from the contact and coupling between a vibrator pad and the surface. Although a vibrator truck might be supported in a sabkha or a dry riverbed, the crust might appear stable yet not sustain the additional force from the vibrator, causing the truck to fall through.1
Also, soft sediments may attenuate the
acoustic signal strongly. At the other textural extreme, a hard, rock-strewn surface may not allow proper coupling because the vibrator pad contacts only a few high points on the rocks— point loading.
Evaluating risk of poor source and receiver coupling to the Earth’s surface and of energy losses related to seismic-wave propagation in the near surface is important for planning a seismic survey. These two factors account for the majority of the degradation of the seismic signal intended for hydrocarbon exploration and reservoir characterization. Remote sensing can help develop a risk assessment for data acqui - sition by densely characterizing the near surface using optical and radar data.
Blue, Green, Red VNIR NIR SWIR TIR
VIS
0 Pan
Wavelength range
Surface feature interpretation
Visible to very near infrared
Water
Infrastructure; terrain feature mapping
Vegetation
Seismic application
Logistics planning; environmental- impact estimate
alluvial and eolian deposits
Burned vegetation Sedimentary rocks;
Metamorphic, volcanic and magmatic rocks
Data-quality estimation; near-surface modeling
VIS–visible; VNIR–very near infrared; Pan–panchromatic; NIR–near infrared; SWIR–short-wave infrared; TIR–thermal infrared
> Landsat 7 Enhanced Thematic Mapper Plus (ETM+) spectral bands and selected uses of band information. The Landsat 7 satellite has sensors for three visible-spectrum bands and four infrared bands, plus a panchromatic, or pan, band spanning the visible and very near-infrared bands (top). Since the detected bands respond either strongly or weakly to different surface features, combining them is useful in discriminating such features (bottom).
This article describes remote sensing and includes two case studies in very different types of geography. The first, from a desert environ - ment in Egypt, shows the general approach to remote sensing, describing how the various spectral bands combine to deliver useful plan - ning information. The second involves the determination of glacial features in Austria. These field examples illustrate the broad but not exhaustive scope of the utility of remote imaging today.
RGB and Beyond Television screens and computer monitors deliver an impressive variety of color to the human eye by combining only three colors: red, green and blue (RGB). Based on just the RGB portion of the spectrum, people commonly perform the kind of discrimination done by satellite sensing. We tend to associate green with vegetation and blue with water, and many rocks are shades of tan and gray.
Some satellites capture sunlight reflected from the surface of the Earth in these three spectral bands; the intensity of each band— given as a gray-scale value—can be assigned as intensity values for each respective color and recombined to generate a familiar color image. Most satellites designed for remote sensing have sensors for additional bands in other parts of the electromagnetic spectrum; these bands add a wider range of information (above). As an example, sensors on the Landsat 7 satellite capture intensity data from seven spectral bands plus a panchromatic, or pan, band.2
Three bands
in the visible (VIS) spectrum roughly cover red, green and blue colors. A very near-infrared (VNIR) band helps differentiate types of vegetation, while one in the near infrared (NIR) is sensitive to the amount of water in plants, or turgidity. Surface geology is discriminated by using a short-wave infrared (SWIR) band. In addition, the Landsat 7 pan sensor covers most of
1. A sabkha is a salt flat.
2. Landsat satellites are launched by NASA and operated by the USGS. For more information: http://landsat.
usgs.gov/ (accessed February 11, 2009).
1
2 Wavelength, µm Near to short-wave infrared Thermal infrared Moisture in ground and voids
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Normalized amplitude
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