Introduction

Modern work stations based on high-speed processors have the computational power of former mainframe computers. High-capacity hard-disk drives and other compact data-storage devices greatly aid in the processing of digital images. Display devices have likewise improved dramatically. Large-screen, high-resolution monitors allow true-color display of processed images. Sophisticated software packages for combined image processing and GIS are readily available for various computer operating systems. It is safe to say that research based on Landsat imagery is now possible at moderate cost for almost any academic or governmental organization.

Careful selection of Landsat scenes and attention to special seasonal conditions are important for acquiring the best images of landscapes. Several image-processing techniques are also useful for enhancing images and creating dramatic displays of certain environmental conditions and landform assemblages. Seasonal selection and digital processing of Landsat imagery for geomorphology are described in more detail in the following sections.

Variations in image tone (or color) indicate the kinds of surficial sediments and landforms on which the soils have developed (Morrison 1976a). This is also a good time of year to see surface drainage and lake patterns clearly. The standard false-color composite image is quite effective for displaying geobotanical aspects of the landscape. Landsat images from later in the growing season (July and August) may be almost completely dominated by vegetation, which blankets the landscape. Geomorphic features are usually less distinct on such summer images.

Sun elevation is significantly lower, compared to spring images, which imparts a moderate topographic shadow effect. These factors working in combination can result in excellent synoptic displays of geomorphology on Landsat images--see Devils Lake. As with spring imagery, timing is important, as determined by local climate, vegetation activity, and latitude.

Standard false-color composite of Devils Lake, North Dakota. Autumn scene showing strong variation in vegetation cover and water bodies. Low sun elevation creates some shadowing of terrain. Landsat MSS bands 1,2,4; acquired 9/88; image processing by J.S. Aber.

1. Low sun elevation: Very low sun angle produces a strong shadowing and highlighting of terrain features. Valleys, escarpments, lineaments, dune fields, end moraines, drumlins, and other elongated geomorphic elements may be emphasized.

2. Snow cover: Snow smooths the landscape and provides a homogeneous cover with uniform spectral properties, which mask distracting variations in soils and vegetation. This tends to accentuate the effect of shadows on low-relief terrain. The presence of snow usually means that lakes and streams are also frozen over.

3. Lack of active vegetation: Vegetation is an important, often dominant, aspect of Landsat images during the growing season. Certain subtle topographic features may be more apparent when vegetation is not active.

Single-band, winter image of southern Nain province, eastern Canada. Snow cover combined with a low sun elevation (11°) gives strong emphasis to landscape topography. Glacial erosion has etched out crustal fractures of several sizes and orientations. Landsat MSS band 7, acquired 1/73; image from NASA GSFC.

Continuity of snow cover is extremely important; variations in thin, patchy, or irregular snow cover may so dominate the appearance of an image that any topographic impression is lost. The character of vegetation cover is another important variable. Good topographic expression is most apparent in regions with cropland, grassland or sparse deciduous forest cover. Regions with heavy forest, especially conifer trees, retain a vegetated character even in winter. It should be noted, finally, that winter images may suffer from the relatively low level of solar illumination.

Table 3. Vegetation indices for Landsat MSS and TM.

Formula	Type of Index	Reference
NIR ÷ Red = RVI	Ratio vegetation index	Tucker 1979
(NIR - Red) ÷ (NIR + Red) = NDVI	Normalized difference vegetation index	Tucker 1979
(NIR - Green) ÷ (NIR + Green) = GNDVI	Green normalized difference vegetation index	Lymburner et al. 2000
MIR ÷ NIR = RDI	Ratio drought index	Pinder & McLeod 1999
NIR ÷ (Red + MIR) = SLAVI	Specific leaf area vegetation index	Lymburner et al. 2000

Creating these vegetation indices involves one or more raster overlay operations (+, -, *, ÷), which may result in output of fractional real numbers. Real-number data storage is a significant limitation when analyzing large data sets. To overcome this problem, the real-number output may be scaled and converted into byte-binary format (0-255). As an example, raw NDVI values range from +1 (maximum vegetation) to -1 (no vegetation). The standard conversion technique is: (NDVI + 1) x 100. The resulting range of values is zero to 200, which can be converted to byte-binary format. A vegetation index may be combined with other spectral bands to create striking false-color images of land vegetation.

Special composite image of Gdansk Bay region, northern Poland. Field of view is about 150 km across. The image incorporates green and NIR bands plus a ratio vegetation index (RVI). Fields with crops and pasture appear light yellow; fallow fields are light blue; deciduous forest is orange; conifer forest and peat bogs are dark brown/green. Landsat MSS bands 1, 3, 4/2; acquired 10/86; image processing by J.S. Aber.

Devils Lake, North Dakota depicted in a false-color composite that includes a ratio vegetation index (RVI). Active vegetation shown by bright green and yellow colors; zones within Devils Lake indicate floating mats of algae. Field of view about 50 km across. Landsat MSS bands 1, 4/2, 3; acquired 10/86; image processing by J.S. Aber.

Water strongly absorbs infrared energy and weakly reflects red light. Water is the only common surface material with this spectral signal. Thus, the infrared/red ratio also has the effect of depicting all surface water bodies, regardless of water depth or turbidity. The only exceptions in water are floating mats of algae or other emergent aquatic plants that produce spectral signals like terrestrial vegetation. The combination of surface drainage and vegetation patterns, depicted on infrared/red ratio images, may give valuable information about geomorphology.

The state of the world's vegetation has become a major subject of research during the past two decades. Vegetation is a key indicator for overall environmental conditions, and changes in vegetation are useful means for recognizing changes in other environmental factors. Regional and global analysis of both terrestrial and marine vegetation is now possible based on satellite remote sensing. The status of vegetation and vegetation changes over multiyear periods can be documented with Landsat imagery.

NBR = (TM4 - TM 7) ÷ (TM4 + TM 7)

High-contrast images

For example, a land mask would be applied to black out water areas. The remaining land portion of the image could be processed then to enhance only land features. The water portions of the image could be handled in a similar manner, and finally the land and water portions could be recombined for a complete image. Equivalent techniques may be applied to other kinds of high-contrast images to treat relatively light and dark portions separately. The resulting dual-processed images can be quite impressive (Lillesand and Kiefer 1987).

Lake Van, Turkey showing enhancement of lake features. Lake and land portions of the image were processed separately to improve display of suspended sediment in surface water. Note several circular eddies within the lake. Compare with normal composite image of Lake Van. Landsat TM special processing; acquired 9/84; image from NASA GSFC.