Prepared in cooperation with the Afghanistan Geological Survey, Ministry of Mines under the 

auspices of the Task Force for Business and Stability Operations, Department of Defense

Lineament Analysis of Mineral Areas of Interest in 

Afghanistan

By Bernard E. Hubbard, Thomas J. Mack, and Allyson L. Thompson

Open-File Report 2012–1048

USGS Afghanistan Project Product No. 233

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia 2012

For product and ordering information:

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

Telephone: 1–888–ASK–USGS

For more information on the USGS—the Federal source for science about the Earth,

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

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

Telephone: 1–888–ASK–USGS

Suggested citation:

Hubbard, B.E., Mack, T.J., and Thompson, A.L., 2012, Lineament analysis of mineral areas of interest in 

Afghanistan: U. S. Geological Survey Open-File Report 2012–1048, 28 p., available only at 

http://pubs.usgs.gov/of/2012/1048.

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

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
iii

Cover: Photograph of an example of a prominent curvilinear “geomorphic” feature (that is, lineament ) mapped using both ASTER and Landsat imagery, 

measured in the field by T. Mack on August 6, 2010, and resulting from localized microtographic relief within the Dusar-Shaida copper and tin area of 

interest, Afghanistan.
iv

Contents

Introduction.................................................................................................................................................................... 1

Automated Lineament Mapping..................................................................................................................................... 3

Manual Lineament Mapping........................................................................................................................................... 9

Results and Discussion ............................................................................................................................................... 11

References Cited......................................................................................................................................................... 13

Appendix 1. Rose Diagrams Summarizing Dominant Azimuth Directions for Lineaments Mapped Within the 

Mineral Areas of Interest (AOIs)................................................................................................................................... 16

Figures

1. Landsat ETM+ scene orbital path (blue labeled) and row (red labeled) coverage map of Afghanistan, 

showing the locations of 24 numbered mineral occurrence areas of interest.................................................... 4

2. Areas characterized by slopes less than 2° as measured using the 90-m resolution SRTM DEM (A), 

compared with Quaternary basin-fill deposits, lakes and playas mapped in Doebrich and Wahl (2006) (B).. ... 8 

3. Manually mapped lineaments within the 24 mineral occurrence areas of interest (see also figure 1 and 

table 2), superposed on the major tectonic regions and faults of Afghanistan for comparison ....................... 11 

Tables

1. Summary of names of mineral areas of interest, their highlighted commodities (after Peters and others, 

2007; 2011), and alpha/numeric designation used in this report....................................................................... 2 

2. Landsat ETM+ image data used in manual and automated lineament mapping for yellow-shaded 

paths/rows shown in figure 1 (after Davis, 2006).. ............................................................................................ 3

3. Summary of Natural-Color Landsat Mosaic Images of Afghanistan, orthorectified and pan-sharpended to 

15.25-m resolution by Davis (2006) and cut into the following 1:250,000-scale map quadrangles. ................ 10
1 

Lineament Analysis of Mineral Areas of Interest in 

Afghanistan

By Bernard E. Hubbard, Thomas J. Mack, and Allyson Thompson

Introduction 

During a preliminary mineral resource assessment of Afghanistan (Peters and others, 2007), 24 

mineralized areas of interest (AOIs) were highlighted as the focus for future economic development 

throughout various parts of the country. In addition to located mineral resources of value, development 

of a viable mining industry in Afghanistan will require the location of suitable groundwater resources 

for drinking, processing of mineral ores for use or for export, and for agriculture and food production in 

areas surrounding and supporting future mining enterprises. This report and accompanying GIS datasets 

describe the results of both automated and manual mapping of lineaments throughout the 24 mineral 

occurrence AOIs described in detail by Peters and others (2007; 2011) and summarized in table 1. For 

this study, we define lineaments as "mappable linear or curvilinear features of a surface whose parts 

align in a straight or slightly curving relationship that may be the expression of a fault or other linear 

zones of weakness" as derived from remote sensing sources such as optical imagery, radar imagery or

digital elevation models (DEMs) (Sabins, 2007). 
2 

Table 1. Summary of names of mineral areas of interest (AOI), their highlighted commodities (after Peters 

and others, 2007, 2011), and alpha/numeric designation used in this report. In order to facilitate crosscomparison with their respective geohydrologic summaries discussed in Peters and others (2011), our AOI 

numerical order follows that of Peters and others (2011) where AOIs numbered 2 through 15 highlight metallic 

and precious metal prospects, while AOIs numbered 17 through 26 highlight industrial minerals. Note that 

numbers 1 and 16 are not used.

Numerical

Order of Discussion

Mineral AOI Name 

(in alphabetical order from Peters and others, 2007; 2011)

2

3 

4 

5 

6 

7 

8 

9 

10

11

12

13

14

15

17 

18 

19 

20

21

22

23

24

25

26

Aynak copper, cobalt and chromium

Badakshan gold

Balkhab VMS copper

Daykundi tin and tungsten

Dusar-Shaida copper and tin

Haji-Gak iron

Katawas gold

Kharnak-Kanja mercury

Kundalan copper and gold porphyry

Nalbandon lead and zinc

North Takhar gold-placer

Panjsher Valley emerald, iron and silver

Tourmaline tin-vein

Zarkashan copper and gold

Baghlan clay and gypsum

Bakhud fluorite

Dudkash industrial minerals

Ghunday-Achin magnesite and talc

Khanneshin carbonatite

Kunduz celestite

North Herat barium and limestone

Nuristan pegmatite

South Helman travertine

Takhar evaporate

Water wells in bedrock aquifers are generally more productive where boreholes intersect 

fractures or fracture zones. Lineament identification and analysis have long been used as a 

reconnaissance tool to identify such favorable conditions for groundwater resources in carbonate 

bedrock environments (Lattman and Parizek, 1964; Siddiqui and Parizek, 1971). More recently, 

lineament analysis has been used to identify areas of greater well yields in other bedrock settings, such 

as crystalline bedrock (Mabee and other, 1994; Moore and others, 2002). Lineaments provide an 

indication of bedrock areas that warrant further investigation for optimal water well placement. They

may also indicate areas of preferential flow and storage of groundwater, and, thus, areas with a greater 

density of lineaments may indicate greater secondary porosity. Lineaments may indicate structurally 

trending mineralized areas (for example, Mars and Rowan, 2007), or locations of near-surface water 

resources, especially when surface vegetation growth coincides with lineaments. 

The purpose of this report and accompanying GIS data is to provide lineament maps that give 

one indication of areas that warrant further investigation for optimal bedrock water-well placement

within 24 target areas for mineral resources (Peters and others, 2011). These data may also support the 

identification of faults related to modern seismic hazards (for example, Wheeler and others, 2005; 

Ruleman and others, 2007), as well as support studies attempting to understand the relationship between 
3 

tectonic and structural controls on hydrothermal fluid flow, subsequent mineralization, and waterquality issues near mined and unmined mineral deposits (for example, Eppinger and others, 2007). 

Automated Lineament Mapping

Table 2 summarizes the 27 Landsat-7 ETM+ scenes used for automated lineament mapping 

across much of Afghanistan, which are also depicted as overlapping, yellow-shaded path and rows 

within location figure 1. 

Table 2. Landsat ETM+ image data used in manual and automated lineament mapping for yellow-shaded

paths/rows shown in figure 1 (after Davis, 2006). Numeric designation for each mineral area of interest (AOI) is 

explained in table 1.

Landsat Scene ID Mineral AOIs Covered 

(#, all or in part)

Solar Elevation

Angle (degrees) Center Latitude Center Longitude

ELP151R036_7T20010928 20, 24 47.96 34.6110 N 71.6543 E 

ELP152R034_7T20010802 3, 12 60.52 37.4782 N 70.9474 E 

ELP152R035_7T20010802 13, 24, 26 61.11 36.0439 N 70.5284 E 

ELP152R036_7T20011005 2, 13, 20, 24 45.77 34.6130 N 70.1216 E 

ELP152R037_7T20001018 20 42.94 33.1784 N 69.7880 E 

ELP153R034_7T20010708 3, 12, 22, 26 64.07 37.4774 N 69.4083 E 

ELP153R035_7T20010708 13, 17, 19, 22, 26 64.49 36.0482 N 68.9902 E 

ELP153R036_7T20000822 2, 7, 17 58.19 34.6113 N 68.5849 E 

ELP153R037_7T20001025 8, 15 40.76 33.1808 N 68.2271 E 

ELP154R035_7T20010629 4, 17, 19, 22 65.26 36.0489 N 67.4779 E 

ELP154R036_7T20010629 4, 5, 7, 17 65.59 34.6182 N 67.0698 E 

ELP154R037_7T20000525 5, 10, 15, 18 66.41 33.1810 N 66.6676 E 

ELP154R038_7T20010629 10, 18 66.00 31.7496 N 66.2818 E 

ELP155R036_7T20010503 5, 9, 11 62.01 34.6160 N 63.9870 E 

ELP155R037_7T20010503 5, 9, 11, 18 62.63 33.1878 N 65.0803 E 

ELP155R038_7T20010503 18 63.24 31.7511 N 64.6912 E 

ELP155R039_7T20010503 21, 25 63.70 30.3176 N 64.3117 E 

ELP155R040_7T20010503 25 64.14 28.8790 N 63.9390 E 

ELP156R036_7T20010627 11, 23 65.72 34.6160 N 63.9870 E 

ELP156R037_7T20010627 9 65.96 33.1843 N 63.5895 E 

ELP156R038_7T20010510 none 64.52 31.7493 N 63.1516 E 

ELP156R039_7T20010510 21, 25 64.94 30.3158 N 62.7724 E 

ELP157R036_7T20010517 6, 23 64.62 34.6180 N 62.4094 E 

ELP157R037_7T20010517 6, 14 65.11 33.1864 N 62.0113 E 

ELP158R035_7T20010711 23 64.18 36.0457 N 61.2592 E 

ELP158R036_7T20000708 6, 23 65.23 34.6103 N 60.8681 E 

ELP158R037_7T20010625 6 66.09 33.1829 N 60.4928 E 
4 

Additional metadata and description of these 27 Landsat ETM+ scenes (including specific scene 

acquisition times in UTC) are provided by Davis (2006) and tabulated using scene path and row ID 

numerical order. All of the 27 scenes used here were acquired either during late spring (May), summer 

(June through August), or early fall (September) with high sun angles greater than 55–60°. Although 

high sun-angle scenes are preferable for spectral classification methods used to derive mineral and 

lithologic maps (for example, Mars and Rowan, 2007), low sun-angle scenes (for example, latest fall, 

winter, and earliest spring) that accentuate large-scale topographic differences in illumination are 

preferable for lineament mapping (Sabins, 2007). In this study, no attempt was made to correct 

erroneous lineaments, such as ridgelines and catchment boundaries that are usually enhanced under high 

sun-angle lighting conditions (for example, Hung and others, 2005). However, linearly shaped sceneedge boundaries were the most obvious erroneous lineaments and, thus, were eliminated where possible. 

Figure 1. Landsat ETM+ scene orbital path (blue labeled) and row (red labeled) coverage map of Afghanistan, 

showing the locations of 24 numbered mineral occurrence areas of interest (Peters and others, 2007, 2011). 

Yellow-shaded scenes are listed in table 2. Also shown for clarity are overlapping scene boundaries and portions of 

scenes outside of the boundaries of Afghanistan. 

The "Lineament Extraction" (LINE) algorithm of PCI Geomatica software was used to 

automatically extract lineaments from both the 30-m resolution Landsat-7 ETM+ multispectral band 

images and 15-m resolution Landsat-7 ETM+ panchromatic band images. Although several methods of 

pre-processing and image enhancements have been applied to multispectral data, such as Landsat and 

ASTER for enhancing geomorphic and tonal contrast related to lineaments (for example, principal 

components analysis, histogram-based stretch tools, band averages and, numerous filters; Walsh and 
5 

Mynar, 1986), the most effective method was found to be simple band average of all radiance values 

stretched to an 8-bit gray-scale range (Walsh and Mynar, 1986). Notably, band ratios were not used here 

because they tend to highlight spectral contrast at the expense of geomorphic and topographic contrast 

(Sabins, 2007). For the individual panchromatic band images of Landsat-7 ETM+, the original radiance 

values were used instead, though expanded to their full 8-bit monochrome dynamic display range. 

The following input parameters were used and are documented in both the software user's 

manual (PCI, 2009) and recent supporting application references such as Hung and others (2005) and 

Abdullah and others (2009), who both successfully used the same PCI Geomatica software:

RADI = 24

GTHR = 94

LTHR = 50

FTHR = 7

ATHR = 40

DTHR = 30

RADI specifies the pixel distance of edge detection filtering such that smaller values can be used 

to detect more detail (that is, linear edges), but larger values can be used to minimize the detection of 

noise. RADI values can range from 0 to 8,192 pixels, but the default value is 10 pixels, whereas a value 

of 12 pixels was used by Abdullah and others (2009) and a value of 5 pixels was used by Hung and 

others (2005). GTHR specifies the minimum threshold change in brightness, which defines an edge 

pixel. GTHR values can range from 0 to 255 on an 8-bit pixel gray-scale. The default value is 100, 

though 90 was used by Abdullah and others (2009) and 10 was used by Hung and others (2005). LTHR 

specifies the minimum threshold of curvature (in pixel distance) used for mapping curved features as 

valid lineaments. Values of LTHR can range from 0 to 8,192 pixels, but the default value used by 

Abdullah and others (2009) is 30 pixels, and values of 7 and 3 pixels were used by Hung and others 

(2005) for ASTER and Landsat imagery, respectively. FTHR specifies the maximum error (in pixel 

distance) allowed in fitting a vector GIS arc or polyline to pixels defining a curved feature, such that 

lower values yield better fits using a number of shorter line and arc segments, while higher values yield 

coarser fits using longer lines and arcs with fewer segments. Values of FTHR can range from 0 to 8,192 

pixels, but the default value used by Hung and others (2005) is 3 pixels, whereas a value of 10 pixels 

was used by Abdullah and others (2009). ATHR specifies the maximum angle (in degrees) between two 

neighboring polyline or arc segments, below which they can be linked into a single vector polyline or 

arc. Values of ATHR can range from 0 to 90°, with a default value of 30° as was used by Abdullah and 

others (2009), whereas a lower value of 7° was used by Hung and others (2005). DTHR specifies the 

minimum distance (in pixels) between the end points of two polyline or arcs for them to be linked, with 

possible values ranging from 0 to 8,192. The default value of DTHR is 20 pixels as was used by 

Abdullah and others (2009), whereas a lower value of 3 pixels was used by Hung and others (2005). The 

values for all of these parameters are used in a three-step execution process consisting of: edge 

detection, thresholding, and feature extraction (LINE module – PCI, 2009). 

For this study, this set of parameter values best replicated the patterns of linearly trending 

vegetation, phyllic-mineralized areas, and mapped faults using the ASTER imagery within the 

Argandab igneous intrusive complex of Afghanistan, as rendered by Mars and Rowan (2007). These 

parameter values were also tested on ASTER scenes covering portions of the “Dushar-Shaida copper 

and tin” AOI (AOI 6, table 1 and figure 1) and "tourmaline tin-vein"-bearing AOI (AOI 14, table 1 and 

figure 1), both south of the city of Herat, Afghanistan. The resulting maps of lineament and vegetation 

features were then field checked by both aerial reconnaissance and field reconnaissance by Thomas 

Mack on August 6, 2010. 
6 

Hung and others (2005) tested the PCI algorithm using both Landsat ETM and ASTER imagery 

and found that ASTER 15-m resolution visible and near-infrared (VNIR) bands produced less noisy and 

more accurate lineament patterns than the equivalent Landsat 30-m resolution bands. Despite their lower 

30-m resolution, the ASTER SWIR bands are still useful for mapping linearly trending spectral-tonal 

contrast related to epithermal phyllic alteration (for example, Mars and Rowan, 2007). In evaluating the 

spectral-tonal and geomorphic contrast characteristics of each of the six reflective bands of Landsat, 

Abdullah and others (2009) found that Landsat TM band 4 (as well as SPOT band 3) yields the highest 

number of lineaments, which is not surprising since it overlaps with the spectral range of the near 

infrared (NIR) reflective peak of vegetation. In contrast, Smith and Wise (2007) demonstrated enhanced 

numbers and increasing accuracy of lineaments mapped by using the higher spatial resolution 

panchromatic band (15 m for ETM+), instead of pre-processed gray-scale versions of the lower spatial 

resolution multispectral bands (30 m for ETM+). Therefore, using this understanding, we create 

automated lineament maps using both the multispectral and panchromatic Landsat ETM+ datasets. 

Results from both image sets were compared to our preliminary field-checked ASTER mapping 

results that we obtained for the Dusar-Shaida area. The results derived from the 15-m panchromatic 

Landsat data appear to best match (based on visual inspection) the pattern and density of lineaments 

mapped using ASTER 15-m VNIR and 30-m SWIR band images (level 2 atmospherically corrected “at 

surface” radiance product, AST09), both of which were pre-processed to single monochrome images. 

The most obvious caveat with this parameterization approach is that lineament mapping results are 

being compared between two different multispectral image data types with differing spectral and spatial 

resolution characteristics, both acquired at two completely different times of year (May 17, 2001, for the 

ASTER imagery covering Dusar-Shaida and July 20, 2001, for the Landsat ETM+ imagery covering 

part of the same area). The consistency in the results between Landsat and ASTER for both this study 

and previously cited studies lends support for this approach. 

Because of ASTER’s limited swath-width (60 km) compared to that of Landsat (185 km) and 

variable off-nadir pointing, which produces inconsistent overlap between successive orbital swaths, the 

use of ASTER data for country-scale mapping of Afghanistan was impractical. Thus, we used both the 

30-m resolution multispectral band and 15-m resolution panchromatic band images of Landsat-7 ETM+ 

to derive our country-scale lineament map coverages. Usage of low sun-angle Landsat scenes from the 

EROS Data Center archives was also considered impractical due to time constraints, as well as snowcover and cloud-cover considerations that are more prevalent in low sun (winter) images. 

The final seamless country-wide coverage was produced by first removing redundant lineaments 

from image overlap areas (fig. 1). For example, lineaments mapped in northern and eastern overlapping 

scenes were eliminated in favor of lineaments mapped in southern and western overlapping scenes. The 

merge tool of ARCGIS software was then used to reassemble all lineaments from both overlapping and 

nonoverlapping portions of Landsat swaths (fig. 1) back into country-scale coverages based on the two 

image datasets. 

Secondly, lineaments mapped over flat basin and valley-fill areas represented by slope angles 

less than 2° (for example, Jayko and others, 2005), using slope data derived from ~90-m resolution 

Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), were also removed as 

explained further below. Figure 2 compares the distribution of areas characterized by less-than-2° slope 

angles with mapped Quaternary basin-fill deposits, lakes, and playas. Correlation between the two maps 

was calculated as 91.0586 percent (kappa coefficient = 0.7162) without including lakes and other 

sizable bodies of water, and 91.2260 percent (kappa coefficient = 0.7226) with lakes and other sizable 

bodies of water included as shown in figure 2B. The less-than-2° slope areas were then gap filled using 

a median filter with a seven SRTM pixel kernel, and used to exclude lineaments derived from the 30-m 
7 

multispectral and 15-m panchromatic band images of Landsat. The two resulting vector GIS shapefile 

coverages reflect lineaments mapped in higher slope (greater than 2°) bedrock areas, with most flatbasin and valley-fill areas containing anthropogenic features, such as roads and agricultural landuse 

patterns, for example, removed. These “higher sloping bedrock areas” should also include alluvial fans 

and pediments (for example, Jayko and others, 2005), which can also exhibit surface expressions of 

deeper-seated faulting (for example, Hooper and others, 2003; Ruleman and others, 2007), perhaps even

related to buried aquifers. Finally, all other lineaments either outside the boundary of Afghanistan or 

outside the coverage of our SRTM data (from Peters and others, 2007) were eliminated. 
8 

Figure 2. Areas characterized by slopes less than 2° as measured using the 90-m resolution SRTM DEM (A), 

compared with Quaternary basin-fill deposits, lakes, and playas mapped in Doebrich and Wahl (2006) (B).
9 

 Linear features mapped by automated (or visual) interpretation of optical remote-sensing data, 

such as Landsat TM imagery, can be caused by either highlights and shadows due to localized 

topographic relief (that is, geomorphic) or surface spectral discontinuities such as those caused by 

vegetation growth or soil moisture differences (that is, tonal). Additional spectral discontinuity may 

result from epithermal mineralization, and is best detected using sensors (for example, ASTER) with 

enhanced spectral resolution capability that can detect ferric-iron and phyllic [and (or) propylitic] 

alteration, in addition to other spectral discontinuities (for example, Mars and Rowan, 2007). Some 

linear features may represent cultural features, such as roads or canals, and may not represent a fracture 

zone. As such, automated-derived lineaments may require cross-comparison with other data sources, 

such as the manually derived lineaments as described in the following section, as well as follow-up field 

investigations to confirm the validity of the lineaments and their relation to water-filled bedrock fracture 

zones. 

Manual Lineament Mapping

Lineaments were mapped using one observer, drawn at a map scale of 1:100,000, and using two 

datasets: (1) 30-m Shuttle Radar Topography Mission (SRTM) DEM data (Philip Davis, USGS, written 

commun., 2009) and (2) 14.25-m natural-color orthorectified Landsat mosaics (table 3) cut into 

1:250,000 quadrangles by Davis (2006), and derived from the scenes listed in table 2 and shown in 

figure 1. Shaded-relief images were generated from the 30-m DEM using a 45° solar illumination angle 

at azimuth angles of 180°, 225°, 270°, and 315°. Linear features were initially identified on each of the 

four resulting SRTM shaded-relief images and then further refined using natural-color mosaic images. 

Features identified at one azimuth angle or using one sensor may not be apparent in another angle or 

using another sensor. Similarly, features identified using natural-color mosaics, particularly linear 

vegetation patterns, may not be apparent using the DEM data. Figure 3 shows the resulting distribution 

of lineaments derived manually and rendered for each AOI in Afghanistan, which are included in the 

accompanying vector GIS shapefile layer. Manually derived lineaments are stored in separate 

accompanying GIS shapefiles from those two sets derived automatically from Landsat multispectral 

band and panchromatic band images. 
10

Table 3. Summary of Natural-Color Landsat Mosaic Images of Afghanistan, orthorectified and pan-sharpended 

to 15.25-m resolution by Davis (2006) and cut into the following 1:250,000-scale map quadrangles. Individual 

Landsat scenes covering all 24 mineral areas of interest and used in the following quadrangle map series are 

reported in table 2 after Davis (2006).

Landsat Natural-Color Image Mosaic Quadrangle Name

Q2962_14m_UTM_NC

Q2964_14m_UTM_NC

Q3062_14m_UTM_NC

Q3064_14m_UTM_NC

Q3162_14m_UTM_NC

Q3164_14m_UTM_NC

Q3262_14m_UTM_NC

Q3264_14m_UTM_NC

Q3266_14m_UTM_NC

Q3268_14m_UTM_NC

Q3360_14m_UTM_NC

Q3362_14m_UTM_NC

Q3364_14m_UTM_NC

Q3366_14m_UTM_NC

Q3368_14m_UTM_NC

Q3460_14m_UTM_NC

Q3462_14m_UTM_NC

Q3464_14m_UTM_NC

Q3466_14m_UTM_NC

Q3468_14m_UTM_NC

Q3470_14m_UTM_NC

Q3560_14m_UTM_NC

Q3564_14m_UTM_NC

Q3564_14m_UTM_NC

Q3568_14m_UTM_NC

Q3570_14m_UTM_NC

Q3664_14m_UTM_NC

Q3666_14m_UTM_NC

Q3668_14m_UTM_NC

Q3670_14m_UTM_NC

Q3764_14m_UTM_NC

Q3770_14m_UTM_NC

We applied our lineament identification criteria in a uniform manner during digitization within 

the 24 AOIs in an effort to improve identification objectivity. Field reconnaissance trips by Thomas 

Mack (USGS) in 2009 and 2010 provided additional familiarity with lineaments in various terranes and 

in several of the mineral areas of interest throughout Afghanistan. However, visual lineament 

identification is inherently subjective, particularly with respect to one observer, and in some locations it 

is possible that cultural features, such as irrigation canals and roads, topographic ridges, or other 

nonfracture-related features, may be mistaken for lineaments. As such, mapped lineaments require 

comparison with other data and field investigation to confirm their nature and relation to water-filled 

bedrock fracture zones. 
11

Figure 3. Manually mapped lineaments within the 24 mineral occurrence areas of interest (see also fig. 1 and 

table 2), superposed on the major tectonic regions and faults of Afghanistan for comparison. Modified after Wheeler 

and others (2005) and Ruleman and others (2007), the latter of which provides abbreviated fault names for dashed, 

red-colored fault boundaries shown above. 

Results and Discussion

Accompanying this report are two sets of GIS lineament shapefiles automatically mapped using 

both the 30-m resolution multispectral Landsat ETM+ bands and the 15-m resolution panchromatic 

Landsat ETM+ bands, as well as a shapefile of lineaments derived manually using shaded-relief DEMs 

and pan-sharpened Landsat ETM+ quadrangle mosaics. Appendix 1 shows rose diagrams comparing the 

azimuth direction statistics for lineament populations derived manually and those derived automatically 

within the 24 mineral occurrence AOIs (figs. 1 and 3; table 2). Also shown are rose diagrams showing 

azimuth statistics of other previously mapped faults, fractures, folds and other structures mapped over 

Afghanistan (for example, Chmyriov and Mirzad, 1971; Abdullah and Chmyriov, 1977; Wheeler and 

others, 2005; Doebrich and Wahl, 2006) for which digital GIS versions were compiled by Peters and 

others (2007). 

Most of the rose diagrams show an agreement in orientation azimuth. Azimuthal direction

averages 45° NE–225° SW for lineaments derived automatically from the 30-m Landsat multispectral 

data, 41° NE–221° SW for lineaments derived automatically from the 15-m Landsat panchromatic data, 
12

and 51° NE–230° SW for the manually derived lineament sets. These directions are within 25° of the 

dominant structural trends of the country, which parallel many of the faults bounding accreted 

lithotectonic terranes shown in figure 3. This level of agreement can be anticipated given the NE–SW 

orientations of most of the mineral occurrence AOIs (fig. 3), whose individual deposit locations are 

strongly controlled by prevailing regional tectonic trends (for example, Peters and others, 2007). 

Because Landsat images are acquired over Afghanistan between 9:53 a.m. and 10:53 a.m. local 

time (for example, Davis, 2006), their solar azimuths are typically from the SE direction, regardless of

the solar elevation angle variation with season. Therefore, subtle topographic variations associated with 

faults and fractures trending in the NW–SE directions are not accentuated by sunlight, which makes 

them difficult to detect visually by a human operator and even more difficult to detect by automated 

mapping algorithms (Sabins, 2007). An example of this is illustrated by the Balkhab (AOI4, fig. 3; 

appendix 1.4), Ghunday-Achin (AOI20, fig. 3; appendix 1.20), Khanneshin (AOI21, fig. 3; appendix 

1.21), Kunduz (AOI22, fig. 3; appendix 1.22), and North Herat (AOI23, fig. 3; appendix 1.23) AOIs, 

which all contain large populations of mapped NW–SE-trending geologic structures. In all of these 

cases, except for Ghunday-Achin (AOI20), which was imaged with steep (less than 48°, table 2) sunangle Landsat data, automated lineament mapping using both Landsat ETM+ dataset resolutions failed 

to capture many populations of NW–SE lineaments depicted in geologic maps. In the case of the 

Khanneshin and Kunduz AOIs, visual identification of lineaments successfully captured the NW – SE 

lineaments, perhaps due to the addition of DEM data represented by various solar azimuth light angles. 

Manual lineament plotting was somewhat successful in capturing NW – SE- trending lineaments in the 

North Herat AOI, but just as unsuccessful as automated methods in mapping such lineament trends 

within the Balkhab and Ghunday-Achin AOIs. 

Based on similarities in mean azimuth and (or) the shape of the distribution of azimuth 

directions, the best agreement between the manual and automated lineament mapping occurs within the 

Kharnak-Kanja (AOI9, fig. 3; appendix 1.9), Nuristan (AOI24, fig. 3; appendix 1.24), and Zarkashan 

(AOI15, fig. 3; appendix 1.15) mineral occurrence AOIs. The latter AOI (Zarkashan, AOI 15, fig. 3) 

was partly mapped using Landsat imagery acquired at the lowest sun angle of 40.76° (table 2). Other 

notable areas of interest that yielded good agreement between linear structures depicted in geologic 

maps and those detected by either or both automated or manual image analysis are Aynak (AOI2, fig. 3; 

appendix 1.2), Katawas (AOI8, fig. 3; appendix 1.8), Nuristan (AOI24, fig. 3; appendix 1.24), and 

Panjsher Valley (AOI13, fig. 3; appendix 1.13). These four mineral occurrence AOIs were also imaged 

with the lowest sun angle (less than 48°, table 2) Landsat ETM+ data available. 

Based on similarities in mean azimuth and (or) the shape distribution or number of lineaments 

with closely matching azimuths directions, good agreement between geologic map lineaments and 

manually derived lineaments also occurs within the Daykundi (AOI5, fig. 3; appendix 1.5), Dudkash 

(AOI19 – fig. 3; appendix 1.19), Takhar-evaporite (AOI26, fig. 3; appendix 1.26), and Tourmaline-tin 

(AOI14, fig. 3; appendix 1.14) mineral AOIs. Alternately, good agreement between mapped geologic 

lineaments and either or both of the automated mapped lineament sets occurs within the Bakhud 

(AOI18, fig. 3; appendix 1.18); Dusar-Shaida (AOI6, fig. 3; appendix 1.6); Haji-Gak (AOI7, fig. 3; 

appendix 1.7); Kundalan (AOI10, fig. 3; appendix 1.10); and Nalbandon (AOI11, fig. 3; appendix 1.11) 

mineral AOIs. Notably, the Dusar-Shaida AOI was field assessed for this study, with an example of a 

north–south (N–S)-trending lineament shown on the cover page. 

In the North Takhar gold-placer AOI (AOI12, fig. 3; appendix 1.12), both sets of automated 

lineaments agree with each other and contain the most dominant azimuth trend (45° NE–SW) that is 

depicted by the manual mapping technique. However, both manual and automated sets of lineaments did 

not detect the predominant 11°–191° N–S orientations that are depicted in geologic maps. As expected, 
13

the least reliable lineament datasets are those within the South Helman travertine AOI (AOI25, fig. 3; 

appendix 1.25) near the Pakistan border. The two automated (that agree with each other) and one 

manually derived lineament datasets disagree with the 75°–255° E–W directional trend of linear 

structures depicted on geologic maps of the area. This is not surprising since only high sun-angle

Landsat ETM+ imagery (greater than 63.7°, table 2) was available, and the area is dominated by eolian

deposits of the Helmand desert, as well as high clouds and dust streaks (or sand storms) within the 

imagery. 

Both sets of automated mapped lineaments and the manually mapped lineaments provide 

complementary datasets for future GIS fracture density analysis and groundwater exploration (for 

example, Kim and others, 2004). For example, human visual interpretation is often the most effective 

way of identifying fault and fracture patterns at various solar illumination angles and image acquisition 

conditions. However, accurate fracture density analysis requires a statistically significant number of 

nonduplicated but representative sets of lineaments that most likely reflect the actual fracture patterns of 

subsurface rocks (Kim and others, 2004). As such, human operators do not always make reliable 

positive identification of lineaments, and different workers will often interpret linear features 

differently. In contrast to automated algorithms, manual digitizing of lineaments is time-consuming and 

rarely can produce the number of statistically representative lineaments needed for thorough and 

accurate fracture density analysis. 

Problems with mapping lineaments oriented in a variety of directions both manually and 

automated can be reduced using imagery acquired during the seasons of lowest possible sun angle (if 

available and with minimal cloud- and snow-cover), and acquired with both morning and afternoon 

satellite passes that provide solar azimuths from the SE in the morning and the SW in the afternoon. 

However, because the most useful and highest spatial resolution optical sensors are in sun synchronous 

orbit, it is difficult to acquire morning imagery except in northern polar areas. Ancillary use of high 

quality (that is, noise-free) DEMs, shaded from various artificial light angles, may also assist in both 

manual and automated lineament mapping. Radar data sources, such as ALOS PALSAR, may provide 

additional opportunities to map lineaments using active remote sensing imagery acquired at different 

look or incident angles, and under all weather and lighting conditions. Finally, the lineament datasets 

generated in this study are being used to collectively assess overlapping areas of highest fracture density 

and potential as groundwater-aquifer resources across all 24 mineral AOIs in Afghanistan (for example, 

Peters and others, 2011). Two of these datasets (automated lineament mapping results) provide the 

statistical basis needed for typical fracture density analysis but contain more erroneous results due to 

limitations in current computer mapping algorithms. The other dataset (manual lineament mapping 

results) provides higher accuracy lineament maps due to superior abilities of the human visual cortex 

over computer algorithms but does not provide sufficient statistical basis for fracture density analysis 

due to the time-consuming and labor-intensive efforts required in digitizing them. 

References Cited

Abdullah, Anwar, Akhir, J.M., and Abdullah, Ibrahim, 2009, A comparison of Landsat TM and SPOT 

data for lineament mapping in Hulu Lepar area, Pahang, Malaysia: European Journal of Scientific 

Research, v. 34, no. 3, p. 406–415. 

Abdullah, S.H., and Chmyriov, V.M., 1977, Map of mineral resources of Afghanistan: Ministry of 

Mines and Industries of the Democratic Republic of Afghanistan, Department of Geological and 

Mineral Survey, scale 1:500,000. 
14

Chmyriov, V.M., and Mirzad, S.H., eds., 1971, Tectonic map of Afghanistan: Kabul, Afghanistan, 

Ministry of Mines and Industries of Royal Afghanistan, Department of Geology and Mines, 4 sheets, 

scale 1:1,000,000. 

Davis, P.A., 2006, Calibrated Landsat ETM+ Nonthermal-band image mosaics of Afghanistan: U.S. 

Geological Survey Open-File Report, OFR 2006–1345, 16 p., accessed September 15, 2011, at 

http://pubs.usgs.gov/of/2006/1345/.) 

Doebrich, J.L., and Wahl, R.R., 2006, Geologic and mineral resource map of Afghanistan: U.S. 

Geological Survey Open-File Report 2006–1038, 1 pl. (Also available at 

http://pubs.usgs.gov/of/2006/1038/.) 

Eppinger, R.G., Briggs, P.H., Dusel-Bacon, Cynthia, Giles, S.A., Gough, L.A., Hammarstrom, J.M., and 

Hubbard, B.E., 2007, Environmental geochemistry at Red Mountain, an unmined volcanogenic 

massive sulphide deposit in the Bonnifield district, Alaska Range, east-central Alaska: 

Geochemistry—Exploration, Environment, Analysis, v. 7, p. 207–233. 

Hooper, D.M., Bursik, M.I., and Webb, F.H., 2003, Application of high-resolution, interferometric 

DEMs to geomorphic studies of fault scarps, Fish Lake Valley, Nevada-California, USA: Remote 

Sensing of Environment, v. 84, p. 255–267. 

Hung, L.Q., Batelaan, Okke, and De Smedt, Florimond, 2005, Lineament extraction and analysis, 

comparison of Landsat ETM and ASTER imagery: Case study—Suoimuoi tropical karst catchment, 

Vietnam. in Remote Sensing for Environmental Monitoring, Volume V: GIS Applications and 

Geology, edited by Manfred Ehlers and Ulrich Michel,Proceedings of SPIE, v. 5983, 12 p. 

Jayko, A.S., Menges, C.M., and Thompson, R.A., 2005, Digital method for regional mapping of 

surficial basin deposits in arid regions, Example from Central Death Valley, Inyo County, California: 

U. S. Geological Survey Open-File Report 2005–1445, 43 p., accessed September 15, 2011, at 

http://pubs.usgs.gov/of/2005/1445/. 

Kim, G.B., Lee, J.Y., and Lee, K.K., 2004, Construction of lineament maps related to groundwater 

occurrence with ArcView and Avenue scripts: Computers and Geosciences, v. 30, p. 1117–1126. 

Lattman, L.H., and Parizek, R.R., 1964, Relationship between fracture traces and the occurrence of 

ground water in carbonate rocks: Journal of Hydrology, v. 2, p. 73–91. 

Mabee, S.B., Hardcastle, K.C., and Wise, D.U., 1994, A method of collecting and analyzing lineaments 

for regional-scale fractured-bedrock aquifer studies: Groundwater, v. 32, no. 6, p. 884–894. 

Mars, J.C., and Rowan, L.C., 2007, Mapping phyllic and argillic-altered rocks in southeastern 

Afghanistan using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 

data: U.S. Geological Survey Open-file Report 2007–1006, single-page online poster format, accessed 

September 15, 2011, at http://pubs.usgs.gov/of/2007/1006/. 

Moore, R.B., Schwarz, G.E., Clark, S.F., Jr., Walsh, G.J., and Degnan, J.R., 2002, Factors related to 

well yield in the fractured-bedrock aquifer of New Hampshire: U.S. Geological Survey Professional 

Paper 1660, 2 pls., 51 p., accessed September 15, 2011, at http://pubs.usgs.gov/pp/pp1660/. 

PCI, 2009, Geomatica Version 10.3 Users Manual: PCI Geomatics Enterprises, Inc., Ontario, Canada, 

accessed September 29, 2011, at http://www.pcigeomatics.com/. 

Peters, S.G., Ludington, S.D., Orris, G.J., Sutphin, D.M., Bliss, J.D., and Rytuba, J.J., 2007, Preliminary 

Non-Fuel Mineral Assessment of Afghanistan: U.S. Geological Survey Open-File Report 2007–1214, 

p. 810, accessed September 15, 2011, at http://pubs.usgs.gov/of/2007/1214/. 

Peters, S.G., and others, 2011, Summaries of Important Areas for Mineral Investment and Production 

Opportunities in Afghanistan – Non-Fuel Minerals: U. S. Geological Survey Open-File Report 2011–

1204, 1810 p. appendixes on DVD. (Also available at http://pubs.usgs.gov/of/2011/1204/.) 
15

Ruleman, C.A., Crone, A.J., Machette, M.N., Haller, K.M., and Rukstales, K.S., 2007, Map and 

database of probable and possible Quaternary faults in Afghanistan: U.S. Geological Survey OpenFile Report 2007–1103, 39 p., 1 pl, accessed September 15, 2011, at 

http://pubs.usgs.gov/of/2007/1103/. 

Sabins, F.F., 2007, Remote Sensing: Principles and Interpretation (3d ed.): Long Grove, IL, Waveland 

Press, 512 p. 

Siddiqui, S.H., and Parizek, R.R., 1971, Hydrogeologic factors influencing well yields in folded and 

faulted carbonate rocks in central Pennsylvania: Water Resources Research, v. 7, no. 5, p. 1295–1312. 

Smith, M.J., and Wise, S.M., 2007, Problems of bias in mapping linear landforms from satellite 

imagery: International Journal of Applied Earth Observation and Geoinformation, v. 9, p. 65–78. 

Walsh, S.J., and Mynar, Frank, III, 1986, Landsat digital enhancements for lineament detection: 

Environmental Geology and Water Science, Springer-Verlag, v. 8, no. 3, p. 123–128. 

Wheeler, R.L., Bufe, C.G., Johnson, M.L., and Dart, R.L., 2005, Seismotectonic map of Afghanistan, 

with annotated bibliography: U.S. Geological Survey Open-File Report 2005–1264, 31 p., accessed 

September 29, 2011, at http://pubs.usgs.gov/of/2005/1264/].
16

Appendix 1

The following 12 pages are rose diagrams summarizing dominant azimuth directions for 

lineaments mapped within the mineral areas of interest (AOIs), numbered from 2 through 15 for

metallic and precious metal prospects and 17 through 26 for industrial minerals, following the 

chapters describing geohydrologic summaries for each of the AOIs within Peters and others 

(2011). Note that numbers 1 and 16 are not used because they do not correspond to 

geohydrologic summary chapters within Peters and others (2011): 
17
18
19
20
21
22
23
24
25
26
27
28