In cooperation with U.S. Agency for International Development 

Landsat ETM+ False-Color Image Mosaics of 

Afghanistan 

By Philip A. Davis 

Open-File Report 2007-1029 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

Dirk Kempthorne, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 20192 

2007 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

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its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Davis, P. A., 2007, Landsat ETM+ False-Color Image Mosaics of Afghanistan, U.S. Geological 

Survey Open-File Report 2007-1029, Washington, D.C., 22 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

2
Contents 

Introduction .........................................................................................................................................................................5 

Spectral Basis for False-Color Images...........................................................................................................................5 

Processing of Landsat ETM+ to Produce False-Color Images ..................................................................................8 

Masking Vegetation, Snow/Ice, and Clouds..............................................................................................................8

Local-Area Histogram Stretch of Selected Landsat Band Images .......................................................................9

Reprojection of Landsat Map Tiles .............................................................................................................................9

References Cited ..............................................................................................................................................................10

Figures 

1. Landsat ETM+ natural-color image of an area in southeastern Afghanistan...................................................11 

2. Laboratory reflectance spectra for common iron-oxide minerals (Clark et al., 1993), resampled to 

the six Landsat ETM+ nonthermal bands, which are designated by “B” numbers................................12 

3. Laboratory reflectance spectra for minerals commonly found in sedimentary rocks and alluvium 

(Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, which are 

designated by “B” numbers..............................................................................................................................12 

4. Laboratory reflectance spectra for common phyllosilicate minerals that occur in many different 

rock types and in alluvium (Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal 

bands, which are designated by “B” numbers .............................................................................................13 

5. Laboratory reflectance spectra for snow of different grain sizes and for average deciduous 

vegetation (Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, which 

are designated by “B” numbers. .....................................................................................................................13 

6. Laboratory reflectance spectra for some common chemical and clastic sedimentary rocks (Clark 

et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, which are designated by 

“B” numbers. .......................................................................................................................................................14 

7. Laboratory reflectance spectra for different shales, which are fine-grained, fissile rocks (Clark et 

al., 1993), resampled to the six Landsat ETM+ nonthermal bands, which are designated by 

“B” numbers. .......................................................................................................................................................14 

8. Laboratory reflectance spectra for common siliceous igneous rocks (Clark et al., 1993), resampled 

to the six Landsat ETM+ nonthermal bands, which are designated by “B” numbers. ..........................15 

9. Laboratory reflectance spectra for common mafic and ultramafic (anorthosite and dunite) igneous 

rocks (Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, which are 

designated by “B” numbers..............................................................................................................................15 

10. Landsat ETM+ false-color image of same area shown in Figure 1...................................................................16 

11. Russian geology (Sborshchikov et al., 1972) superposed on the Landsat ETM+ false-color image 

shown in Figure 10..............................................................................................................................................17 

12. Landsat ETM+ natural-color image of the northeastern border of the Registan Desert where drift 

sand (orange) meets bright white alluvium along a river valley ................................................................18 

13. Landsat ETM+ false-color image of area shown in Figure 12 where drift sand (orange) meets 

alluvium (white and magenta), river (sinuous dark), and vegetation (green)..........................................19 

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14. Landsat ETM+ natural-color image of an area north of Registan Desert in southern Afghanistan 

where irrigated alluvial areas are adjacent to desert pavement ..............................................................20 

15. Landsat ETM+ false-color image of area shown in Figure 14 where irrigated alluvial areas 

(green) are adjacent to desert pavement (dark brown) ..............................................................................21 

16. Map tile scheme for the Afghanistan project (on a natural-color image base) with each 1o x 2o

map quadrangle designated by its lower left corner latitude and longitude...........................................22 

4
Landsat ETM+ False-Color Image Mosaics of 

Afghanistan 

By Philip A. Davis1

Introduction 

In 2005, the U.S. Agency for International Development and the U.S. Trade and 

Development Agency contracted with the U.S. Geological Survey to perform assessments of the 

natural resources within Afghanistan. The assessments concentrate on the resources that are related 

to the economic development of that country. Therefore, assessments were initiated in oil and gas, 

coal, mineral resources, water resources, and earthquake hazards. All of these assessments require 

geologic, structural, and topographic information throughout the country at a finer scale and better 

accuracy than that provided by the existing maps, which were published in the 1970’s by the 

Russians and Germans. The very rugged terrain in Afghanistan, the large scale of these 

assessments, and the terrorist threat in Afghanistan indicated that the best approach to provide the 

preliminary assessments was to use remotely sensed, satellite image data, although this may also 

apply to subsequent phases of the assessments. Therefore, the first step in the assessment process 

was to produce satellite image mosaics of Afghanistan that would be useful for these assessments. 

This report discusses the production of the Landsat false-color image database produced for these 

assessments, which was produced from the calibrated Landsat ETM+ image mosaics described by 

Davis (2006). 

Spectral Basis for False-Color Images 

Much of Afghanistan has a semi-arid to arid climate. The climate, together with years of 

clear cutting vegetation for heating fuel, has produced the present-day landscape that is relatively 

barren, except in the far northeast corner of the country. The sparse vegetation cover and long 

history of folding, faulting, and erosion presents optimal conditions for using remotely sensed data 

to map exposed geologic materials and structure. The natural colors of semi-arid and arid 

environments, such as Afghanistan, are generally quite bland, consisting of subtle shades of brown, 

red, and yellow (Figure 1) that are produced mainly by the different amounts of iron-oxide minerals 

in exposed materials and by the different degrees of iron oxidation of these minerals that produce 

absorptions of sunlight at different wavelengths of the visible spectrum. The Landsat ETM+ sensor 

was developed as a space platform that could be used to map major mineral differences using 

selected short-wave infrared wavelength bands, as well as the visible and near-infrared wavelength 

bands that are collected by other, less sophisticated spaceborne sensors (e.g., IKONOS, Quickbird). 

Landsat ETM+ provides six nonthermal wavelength bands within the 0.4-2.5 micrometer 

wavelength range. Previous statistical studies have shown that color composite images using 

 

1

 U.S. Geological Survey, 2255 North Gemini Drive, Flagstaff, AZ 86001 

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Landsat ETM+ bands 1 or 2, 3 or 4, and 5 or 7 provide the most geologic discrimination as threeband color composites (Chavez et al., 1982; Sheffield, 1985; Beauchemin and Fung, 2001). For 

this project, the three-band combination selected for the false-color image database was Landsat 

ETM+ bands 2, 4, and 7, displayed as blue, green, and red, respectively. Band 2 was used instead 

of band 1 because band 2 data were better tone matched in the low-contrast desert areas where 

local image enhancement accentuates very small image differences. The remainder of this section 

will discuss the geologic significance of the colors displayed in this false-color composite (FCC) 

database, as well as the chemical basis for the colors, which is extracted from the fundamental 

reflectance studies by Hunt and Salisbury (1970, 1971) and Hunt et al. (1971a, 1971b). 

Iron is a minor element in many common minerals on Earth (and other planets of the inner 

Solar System). In some common minerals, such as magnetite, iron is a major element. Rocks 

having as little as a few weight percent magnetite are generally gray to black because magnetite 

(Fe3O4 or FeO·Fe2O3) has both ferric (Fe+3) and ferrous (Fe+2) iron and each of these cations absorb 

light at several wavelengths in the visible and near-infrared wavelength regions, which results in 

magnetite’s characteristic flat, low reflectance spectrum (Figure 2). Manganese has a similar 

spectrum and, in desert areas, rocks coated with manganese (known as desert varnish) are black 

regardless of the substrate. Iron-oxide is usually only a few weight percent of a rock’s 

composition, but even this small amount can dramatically change the color of a rock. For example, 

sandstone generally has less than two weight percent iron oxide, but different degrees of iron 

oxidation can produce dramatic color variations ranging from brown, yellow, orange, to red. 

Oxidation of ferrous iron produces ferric iron, which transforms FeO-bearing minerals 

(such as magnetite) into other iron-oxide minerals, such as hematite (Fe2O3 - red), goethite 

(FeO·OH – reddish and yellowish), and limonite (FeO(OH)·nH2O – brown). Laboratory spectra of 

these minerals, which were convolved to the wavelength bands of Landsat ETM+ data, are shown 

in Figure 2. As FeO is oxidized to Fe2O3 to form hematite, the numerous absorptions of Fe+2

diminish and the major absorption of Fe+3 at 0.40 micrometers dominates the reflectance spectrum, 

which produces increasingly higher reflectance above the blue wavelength (centered at 0.45 

micrometers). Hematite’s higher reflectance in band 7 (displayed as red) relative to that in bands 2 

and 4 produces red on the FCC image. Some sandstones and drift sand derived from sandstone, 

most basaltic pyroclastic cinders, and iron-ore deposits have bright red signatures on the FCC 

image. Hydrothermal systems and low-temperature hydration of iron-bearing minerals can produce 

goethite, which has an OH-

 molecule that absorbs at 1.4 micrometers and secondarily at 1.9 

micrometers. These absorptions produce a reflectance spectrum that is lower than that of hematite 

within the short-wave region (Figure 2). Complete hydration of iron-bearing minerals can produce 

limonite (common in soils), which has a water molecule that absorbs at 1.9 micrometers, which 

further reduces the reflectance within band 7 (Figure 2). 

Quartz is a major mineral constituent in many sedimentary rocks and in certain igneous 

rocks. Igneous rocks also have various proportions of pyroxene and plagioclase, both of which 

have reflectance spectra similar to that of quartz (Figure 3). Therefore, rocks with these minerals 

that have not been weathered (which is rare) are generally white to gray in color. Calcite and 

dolomite are common carbonate (CO3

-2) minerals in sedimentary rocks and alluvium derived from 

sedimentary rocks. The CO3

=

 molecule causes absorptions between 1.6 and 2.5 micrometers 

wavelength (within ETM+ bands 5 and 7) due to lattice vibrations (Hunt and Salisbury, 1971). 

Sulfate minerals also commonly occur in sedimentary and alluvial environments. The sulfate 

molecule does not produce absorptions within the short-wave region, but some common sulfate 

6
minerals (such as gypsum and jarosite) contain OH-

 and H2O, respectively, which do produce 

absorptions in the short-wave region (Hunt and Salisbury, 1971b; Figure 3). Therefore, without 

significant amounts iron oxide, both of these mineral groups generally have high reflectance values 

in the visible (band 2) and near-infrared (band 4) wavelength regions and low reflectance in band 7 

(Figure 3), thus producing FCC colors that are shades of blue. The much larger band-7 absorption 

in gypsum relative to carbonate minerals would suggest that these two mineral groups could be 

distinguished with Landsat data, but that is generally not the case. Jarosite, generally formed in ore 

deposits by oxidation of iron sulfide minerals, is an exception. In addition, there are some 

phyllosilicate minerals (kaolinite, montmorillonite; Figure 4) that also produce bluish FCC 

signatures, but these are generally more cyan than the dark blues produced by carbonates and 

sulfates. Water bound in these clay minerals produce H-O-H dipolar stretching and OH-

 bending, 

whose overtones and combination tones cause absorptions within band 5 and band 7 (Hunt and 

Salisbury, 1970). Rocks containing muscovite or biotite, such as shale, are generally green on the 

FCC image, as a result of the depression of bands 1 and 7 reflectance relative to that of band 4. 

Rocks containing the phyllosilicates illite and chlorite are more difficult to distinguish because they 

should produce shades of red similar to that produced by iron oxides. Despite some of these 

mineral ambiguities, Landsat FCC images do allow mapping of geologic rock groups and provide 

wide-area coverage in relatively inaccessible terrain such as Afghanistan. 

The reflectance spectra of rocks are generally more subdued than that of their mineral 

constituents, which produces ambiguities in rock identification using the six nonthermal Landsat 

band data. Figure 6 shows the reflectance spectra of some common clastic sedimentary rocks. The 

spectra for these rocks are depressed in the visible wavelength bands, relative to their major 

mineral constituent (quartz in the sandstones and calcite in the limestones), due to the presence of 

iron oxide. The reflectance spectra of shales (Figure 7) appear to be dominated by the reflectance 

of chlorite and illite clay minerals. The reflectance spectra of siliceous igneous rocks (Figure 8) are 

very similar to those of sandstones and siltstones (Figure 6), which make their discrimination very 

difficult using Landsat nonthermal image data, unless bedding can be seen in the imagery. A 

similar ambiguity exists with respect to mafic and ultramafic igneous rocks (Figure 9); most of the 

mafic igneous rocks have relatively flat spectra due to the presence of magnetite. However, 

ultramafic rocks are frequently altered because the surface environment is very different from their 

(deep) environment of formation. Thus, the reflectance spectrum of dunite (composed mostly of 

olivine – a high-temperature mineral) shows the Landsat-band absorptions (at bands 4 and 7) due to 

the conversion of olivine to hydrous clay minerals. 

Figure 10 is a Landsat false-color image of an area near the southeastern border between 

Afghanistan and Pakistan. The area consists dominantly of sedimentary rock formations that have 

been intensely folded and faulted. This image shows much more geologic information than the 

natural-color image (Figure 1). The false-color image better depicts individual strata, allows 

regional correlations of strata, and better depicts structure, such as anticlines, synclines, and faults, 

compared to the natural-color image. When the Russians mapped Afghanistan in the early 1970’s, 

the Landsat satellite did not exist. Thus, the most detailed Russian mapping (superposed on the 

Landsat false-color image in Figure 11) was very general. For example, the Russians mapped two 

sedimentary rock units in this area; one labeled Eo (Eocene) and the other labeled Ol (Oligocene). 

The Eocene unit is described as stratified clay, shale, siltstone, sandstone, limestone, marl, gypsum, 

and conglomerate. The Oligocene unit is described as stratified sandstone, siltstone, clay, 

conglomerate, limestone, marl, and siliceous and mafic volcanics. The units labeled with a Q are 

Late Quaternary and Recent detrital sediments. The colors shown in Figure 4 or 5 suggest that 

7
there are sandstone units, either limestone, marl, or gypsum-rich units, and probably clay-rich 

layers throughout the area, but igneous rocks do not appear to exist in this particular area. Remote 

sensing data cannot determine the age of rocks; this may be inferred from stratigraphic position, if 

the age of at least one unit is known, but age differences can be very subtle, even in the field. 

Processing of Landsat ETM+ to Produce False-Color Images 

The false-color image database described in this report was produced from a three-band 

subset (ETM+ bands 2, 4, and 7) of the calibrated, six-band Landsat image data that were produced 

as 1o x 2o map tiles (Davis, 2006). Three steps were required to produce the false-color images, 

namely (1) the creation of digital masks for vegetation, snow and ice, and clouds; (2) the 

application of a local-area histogram stretch to selected Landsat bands; and (3) the transformation 

of the FCC images from Universal Transverse Mercator (UTM) to a geographic coordinate 

reference system. 

Masking Vegetation, Snow/Ice, and Clouds 

In order to preferentially enhance the geologic material within each map tile it was 

necessary to create image masks of vegetation, snow and ice, and clouds where they occur within 

any particular image map tile. The use of the different masks during image enhancement is 

discussed in the next section. The vegetation mask was constructed using a combination of three 

criteria applied to the six-band Landsat data, based on the general spectral characteristics of 

vegetation (Figure 5). The three criteria for vegetation consisted of the Normalized Difference 

Vegetation Index (NDVI), which is the ratio of (band 4-band 3)/(band 4 + band 3); the band 4/band 

5 ratio; and the band 3*/band 3 ratio, where band 3* is the interpolated reflectance of bands 2 and 4 

at wavelength of band 3. A picture element was classified as vegetation if its NDVI was greater 

than or equal to 0.325, its band 4/band 5 ratio was greater than 1.0, and its band 3*/band 3 ratio was 

greater than 1.0. 

The logic required to accurately mask snow and ice was most difficult because the spectral 

characteristics of these two materials vary greatly within a map tile due to differences in grain size 

(shown in Figure 5), compaction, the snow/ice ratio, and the relative proportion of dust, sediment, 

or boulders to snow/ice on the surface of the ice flows. A picture element was classified as snow or 

ice if it satisfied one of four criteria: (1) the reflectance of band 5 was less than 1.3 percent of the 

reflectance sum of bands 1 through 4 and the reflectance sum of bands 1 through 4 was greater than 

20 percent; (2) the reflectance of each of the first four bands was at least 10 percent and the band 

4/band 5 reflectance ratio was greater than 3.0; (3) the reflectance of band 4 was at least 12 percent 

and the band 4/band 5 reflectance ratio was greater than 8.0; and (4) the reflectance of band 5 was 

greater than 12 percent, the reflectance of each of the first four bands was at least 16 percent, and 

the band 4/band 5 reflectance ratio was at least 1.7. These rules were developed by trial and error 

and visual inspection of the results; the rules were found to be as accurate as performing an 

unsupervised classification on the image data. The rules were designed to detect the lower limits of 

snow and ice, without including other geologic materials. 

The development of a cloud mask was also difficult because of the gradation in opacity of 

the clouds toward their margins. Clouds were the smallest populated category of the three 

materials to be masked and, therefore, it was easier to construct the cloud masks using an 

unsupervised classifier on the six-band image data than it was to develop accurate spectral logic. 

8
Each of the three masks were assigned a unique number and combined into a single image 

mask for use during the image enhancement step. 

Local-Area Histogram Stretch of Selected Landsat Band Images 

The algorithm used to locally stretch the image band data was developed by Pizer et al. 

(1987) and is referred to as adaptive histogram equalization. The picture-element area used to 

enhance the 28.5-m image data was 401 image rows x 401 image lines; the area used for the 14.25-

m image data was twice those dimensions. The image band enhancement was performed on each 

of the three Landsat bands separately. The enhancement consisted of four steps. First, the entire 

image tile was locally enhanced and the enhanced image was stored in an image file and the 

vegetation and snow and ice picture elements were then removed from the original band image. 

Second, the remaining picture elements were locally enhanced and the cloud picture elements were 

then superposed onto the stored enhanced image file and then the cloud elements were removed 

from the original image. Third, the remaining picture elements were locally enhanced, and where 

these picture elements were adjacent to snow or ice, the enhanced value of each border picture 

element was averaged with its coincident picture element from the stored enhanced image. This 

averaging reduced sharp brightness discontinuities along geologic and snow/ice margins. The last 

step superposed the stored cloud, vegetation, and snow/ice picture elements onto the enhanced 

geologic image base produced in the third step. The results from this enhancement on bands 7, 4, 

and 2 were stored as a three-band image file, to be displayed as red, green, and blue, respectively. 

This procedure was performed on both the 28.5-m and 14.25-m Landsat image quads. 

There are two situations where the false-color images have color inconsistencies. The first 

situation is along the border of the Registan Desert where drift sand adjacent to very bright 

alluvium (Figure 12) was stretched less than the drift-sand areas that are well within the desert. 

The bright alluvial borders increased the brightness range along the desert border, which caused the 

stretch on the drift sand to be less than that on the drift sand that is within the desert interior. This 

produced a yellow rim along the desert border (Figure 13). Attempts to isolate the bright alluvium 

and to stretch it and the drift sand separately produced more problems within the bright alluvium 

than the yellow color discontinuity. The second situation is where large vegetated areas (mostly 

irrigated fluvial valleys) are adjacent to old, darkened desert pavement (Figure 14). The vegetation 

mask algorithm did not remove the sparse vegetation and therefore vegetation picture elements, 

which have very low band 7 values (as shown in Figure 5), were included in the local histogram 

stretch of the adjacent desert pavement, which raised the pavement’s band 7 (displayed in red) 

stretch values to produce magenta (Figure 15). Care was exercised in the vegetation masking 

process to not include geologic material. Within sparsely vegetated (shrub) alluvial areas, the 

distinction between vegetation and alluvium is not distinct and can change throughout large areas 

due to changes in vegetation species and alluvial substrate. 

Reprojection of Landsat Map Tiles 

Map tiles in UTM map projection provide very good local positional accuracy, but map 

tiles from adjacent UTM zones (Afghanistan has three zones) cannot be joined into a single image 

file unless the data are either reprojected to a common zone, which produces very poor positional 

accuracy within the other two zones, or reprojected to a geographic coordinate reference system. 

Reprojection of the UTM map tiles to a geographic reference system was performed according to 

the process described in Davis (2006), which used a second-order polynomial transformation and 

an x and y control-point grid of 90 x 90 (i.e., 8100 control points) for the 28.5-m map tiles. The 

9
enhanced, 14.25-m map tiles required a 180 x 180 control-point grid. Once completed, each tile 

was trimmed to provide a 100 meter outside border in order to overlap adjacent map tiles. Each 

three-band enhanced map tile was then converted to an embedded geotiff image with a world file. 

The map tile geotiff and world file were compressed into a single digital file whose file name 

corresponds to the map tile number, image resolution, and projection. Therefore, each map tile has 

four compressed files. 

All of the 28.5-m and 14.25-m map tiles containing the enhanced false-color Landsat 

images for Afghanistan can be downloaded from http://gisdata.usgs.net/website/afghan. An index 

map for the tile designations is shown in Figure 16. 

References Cited 

Beauchemin, M., and Fung, K. B., 2001, On statistical band selection for image visualization. 

Photogrammetric Engineering and Remote Sensing, v. 67, p. 571-574. 

Chavez, P. S., Berlin, G. L., and Sowers, L. N., 1982, Statistical method for selecting Landsat MSS 

ratios. Journal of Applied Photographic Engineering, v. 8, p. 23-30. 

Clark, R.N., Swayze, G. A., Gallagher, A. J., King, T. V. V., and Calvin, W. M., 1993, The U. S. 

Geological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 microns, U.S. Geological 

Survey Open File Report 93-592, 1340 p. 

Davis, P. A., 2006, Calibrated Landsat ETM+ nonthermal-band image mosaics of Afghanistan. 

U.S. Geological Survey Open File Report 2006-1345, 18 p. 

Doebrich, J. L., and Wahl, R. R., 2005, Geologic and Mineral Resource Map of Afghanistan, 

1:850,000 scale, U.S. Geological Survey Administrative Report 2005. 

Hunt, G., R., and Salisbury, J. W., 1970, Visible and near-infrared spectra of minerals and rocks: I. 

Silicate minerals. Modern Geology, v. 1, p. 283-300. 

Hunt, G. R., and Salisbury, J. W., 1971, Visible and near-infrared spectra of minerals and rocks: II. 

Carbonates. Modern Geology, v. 2, p. 23-30. 

Hunt, G. R., Salisbury, J. W., and Lenoff, C. J., 1971a, Visible and near-infrared spectra of 

minerals and rocks: III. Oxides and hydroxides. Modern Geology, v. 2, p. 195-205. 

Hunt, G. R., Salisbury, J. W., and Lenoff, C. J., 1971b, Visible and near-infrared spectra of 

minerals and rocks: IV. Sulphides and sulphates. Modern Geology, v. 3, p. 1-14. 

Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Romeny, H., 

Zimmerman, B., and Zuiderveld, K., 1987, Adaptive histogram equalization and its variations. 

Computer Vision, Graphics and Image Processing, v. 39, p. 355-368. 

Sheffield, C., 1985, Selecting band combinations from multispectral data. Photogrammetric 

Engineering and Remote Sensing, v. 51, p. 681-687. 

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Figure 1. Landsat ETM+ natural-color image of an area in southeastern Afghanistan. 

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Reflectance

magnetite hematite goethite limonite

B1 

B2 

B3 

B4 

B5 B7 

Figure 2. Laboratory reflectance spectra for common iron-oxide minerals (Clark et al., 1993), 

resampled to the six Landsat ETM+ nonthermal bands, which are designated by “B” numbers. 

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calcite dolomite quartz gypsum jarosite

B1 B2 B3 B4 B5 B7 

Figure 3. Laboratory reflectance spectra for minerals commonly found in sedimentary rocks and 

alluvium (Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, which are 

designated by “B” numbers. 

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chlorite illite kaolinite montmorillonite muscovite

B1 

B2 B3 B4 B5 

B7 

Figure 4. Laboratory reflectance spectra for common phyllosilicate minerals that occur in many 

different rock types and in alluvium (Clark et al., 1993), resampled to the six Landsat ETM+ 

nonthermal bands, which are designated by “B” numbers. 

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coarse snow medium snow fine snow vegetation

B1 B2 B3 B4 

B5 

B7 

Figure 5. Laboratory reflectance spectra for snow of different grain sizes and for average 

deciduous vegetation (Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, 

which are designated by “B” numbers. 

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limestone limestone sandstone red mica sandstone siltstone

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B2 

B3 

B4 

B5 B7 

Figure 6. Laboratory reflectance spectra for some common chemical and clastic sedimentary 

rocks (Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, which are 

designated by “B” numbers. 

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shale illite shale carbonaceous shale

B1 

B2 B3 B4 

B5 B7 

Figure 7. Laboratory reflectance spectra for different shales, which are very fine-grained, fissile 

rocks (Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, which are 

designated by “B” numbers. 

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Reflectance

granite rhyolite granodiorite m onzonite quartz m onzonite

B1 

B2 B3 B4 

B5 

B7 

Figure 8. Laboratory reflectance spectra for common siliceous igneous rocks (Clark et al., 1993), 

resampled to the six Landsat ETM+ nonthermal bands, which are designated by “B” numbers. 

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0.50

0.60

0.70

0.80

0.90

1.00

0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40

Wavelength (micrometers)

Reflectance

anorthosite dunite diabase diorite gabbro

B1 

B2 B3 

B4 

B5 B7 

Figure 9. Laboratory reflectance spectra for some common mafic and ultramafic (anorthosite and 

dunite) igneous rocks (Clark et al., 1993), resampled to the six Landsat ETM+ nonthermal bands, 

which are designated by “B” numbers. 

15
Figure 10. Landsat ETM+ false-color image of same area shown in Figure 1

16
Figure 11. Russian geology (Sborshchikov et al., 1972) superposed on the Landsat ETM+ falsecolor image in Figure 10.

17
Figure 12. Landsat ETM+ natural-color image of the northeastern border of the Registan Desert 

where drift sand (orange) meets bright white alluvium along a river valley. 

18
Figure 13. Landsat ETM+ false-color image of area shown in Figure 12 where drift sand (orange) 

meets alluvium (white and magenta), river (sinuous dark), and vegetation (green). 

19
Figure 14. Landsat ETM+ natural-color image of an area north of Registan Desert in southern 

Afghanistan where irrigated alluvial areas are adjacent to desert pavement.

20
Figure 15. Landsat ETM+ false-color image of area shown in Figure 14 where irrigated alluvial 

areas (green) are adjacent to desert pavement (dark brown). 

21
Figure 16. Map tile scheme for the Afghanistan project (on a natural-color image base) with each 

1o x 2o map quadrangle designated by its lower left latitude and longitude. 

22