Lithological and Structural Lineament Mapping from Landsat

자원환경지질, 제53권, 제4호, 425-440, 2020Econ. Environ. Geol., 53(4), 425-440, 2020http://dx.doi.org/10.9719/EEG.2020.53.4.425

425

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*Corresponding author: [email protected]

Lithological and Structural Lineament Mapping from Landsat 8 OLI

Images in Ras Kammouna Arid Area (Eastern Anti-Atlas, Morocco)

Brahim Es-Sabbar*, Mourad Essalhi, Abdelhafid Essalhi and Hicham Si Mhamdi

Moulay Ismail University, Faculty of Sciences and Techniques, Department of Geosciences, BP 509, Boutalamine,

Errachidia, Morocco, 52000

(Received: 8 February 2020 / Revised: 5 July 2020 / Accepted: 24 July 2020)

The study area is located in the southern part of the M’aider Paleozoic basin in the Moroccan Eastern Anti-Atlas. It

is an arid region, characterized by minimal vegetation cover, which can provide an ideal environment to apply remote

sensing. In this study, remote sensing and field investigations were integrated for lithological and structural lineaments

mapping. The Landsat 8 OLI data were processed in order to understand the role of lithology and geological structures

in the distribution of mineral deposits in the study area. To achieve this purpose, the Color Composite (CC), the Principal

Component Analysis (PCA) and Band Rationing transformation (BR) tests were performed. The results of remote sensing

techniques coupled with field investigations have shown that the zones of high lineaments densities are highly correlated

with the occurrences of barite mineralization. These findings depict a spatial relationship between structural lineaments

and the mineralization distribution zones. Therefore, the barite and Iron oxides mineralization veins, which occur mainly

in the Ras Kammouna district, seem to have a structural control. The methodological approach used in this study exam-

ining lithological mapping and lineament extractions can be used to explore mineral deposits in arid regions to a high

degree of efficiency.

Key words : remote sensing, geological mapping, Ras Kammouna, Eastern Anti-Atlas, Morocco

1. Introduction

Geological mapping including lithology and

fracture networks are fundamental for geological

studies, in particular mineral deposits exploration.

Many studies have emphasized the role of optical

remote sensing technology in geological mapping

(Sabins, 1999; Amer et al., 2010, 2012; Ali and

Pour, 2014; Pour et al., 2016). The concept of

lineament extraction from digital satellite images

has been performed by several authors over the

world for many purposes, for instance, structural

and tectonic analysis (Won et al., 1997; Kim et al.,

1999; Madani, 2001; Sedrettea and Rebaïb, 2016;

Si Mhamdi et al., 2017) , groundwater exploration

(Bruning, 2008; Alonso-Contes, 2011) and mineral

exploration (Lee et al., 2010; Rowan et al., 1991;

Mars and Rowan, 2006; Mathew and Ariffin, 2018;

Farahbakhsh et al., 2018 and references therein).

Due to high quality of its outcrops and its location

in the arid domain, the Anti-Atlas which comprises

the study area, have long attracted the interest of

field geologists to apply remote sensing for

lineament extractions and lithological mapping

(Vall and Badra, 2016; Adiri et al., 2016, 2017;

Bouramtane et al., 2017; Hejja et al., 2020 etc).

Most cited authors above except (Si Mhamdi et

al., 2017) and (Hejja et al., 2020) have limited

their works in automatic extraction of geological

lineaments. The reason that could make structural

lineaments mapping insufficient. To avoid this

gape, we combine the automatic method and manual

426 Brahim Es-Sabbar et al.

method that involves the digitizing of visually

identified lineaments after image processing.

The study zone is located in the southern part of

M’aider Paleozoic basin, in the Moroccan Eastern

Anti-Atlas. It is an arid region, where the vegeta-

tion cover is scarce or mostly absent, which can

provide an ideal environment for the application of

remote sensing (Harris et al., 2005) using Landsat

8 OLI data.

The Principal Component Analysis (PCA) and

Band Rationing (BR) transformations are the

principal remote sensing techniques used to

improvement of satellite images (Mars and Rowan,

2006; Amer et al., 2010; Ali and Pour, 2014;

Mathew and Ariffin, 2018a; Safari et al., 2018).

Also, they facilitate the discrimination of litho-

logical units and geological features. Being weakly

studied in terms of structural geology and metal-

logenic studies, the Ras Kammouna region con-

stitutes a good zone to apply remote sensing

techniques. In this study we focus on lithological

units and structural lineaments mapping. These

results can provide a valuable indication of occurr-

ences of ore deposits in the study area, and their

possible relationship with geological structures.

2. Geological Setting

The Anti-Atlas represents the oldest structural

domain of Morocco. In the eastern part, where the

study zone is located, two Precambrian inliers can

be recognized: Saghro and Ougnat inliers (Choubert,

1947) (Fig. 1). The Precambrian basement consists

of sedimentary, volcano-sedimentary and magmatic

rock.

The Precambrian basement is overlain by the

Paleozoic sedimentary sequences exposed around

the Saghro and Ougnat Precambrian inliers and

this sedimentary pile is broadly characterized by

terrigenous clastics and carbonates deposited in a

shallow-water environment (Hollard and Willefert,

1985; Destombes and Hollard, 1986; Villas et al.,

2006).

The area under investigation is located at the

Fig. 1. (a) Geological map showing the location of the study area. (b) Color composite RGB (4/5/3) extracted from theLandsat 8 OLI image of study zone.

Lithological and Structural Lineament Mapping from Landsat 8 OLI Images in Ras Kammouna … 427

southern part of M’aider basin between 30°37'13"N

and 30°49'19"N latitudes, and 4°25'2"W and

4°46'22"W longitudes. This area is occupied by

Paleozoic formations, which started with Ordovician

sequences, represented by the 1st Bani quartzite-

sandstone and the Ktaoua shales topped by 2nd

Bani sandstone. The Ordovician pile shows several

intercalations of ferruginous benches (Destombes

and Hollard, 1986). The Silurian deposits are

dominated by black-grey shales, whereas the

Devonian pile is composed mainly of a bluish-

gray limestone bars with intercalation of marls and

argillites (Hollard, 1974; Mounji, 1999). During

the Carboniferous time the sedimentation became

predominantly detrital with shales and sandstones

deposited in a deltaic environment (Robert-Charrue,

2006).

Structurally the study area underwent a sequence

of extensional and shortening events namely i)

extensional faulting events during the Cambrian to

Late Devonian times, represented by normal faults

manifested by two main directions; E-W and NE-

SW. ii) NE-SW late Variscan shortening (Robert-

Charrue, 2006; Burkhard et al., 2006; Raddi et al.,

2007; Baidder et al., 2008; Michard et al., 2008).

Later, these authors and (Malusà et al., 2007)

suggested that the Eastern Anti-Atlas was uplifted

during the Alpine shortening.

3. Data and Methodology

For the purpose of this study, the data set used in

the present work was a Landsat OLI 8 image

covering the M’aider basin was acquired on 1 July

2017, data available for free at the United States

Geological Survey (https://earthexplorer.usgs.gov).

This image is formed by 11 bands with different

wavelengths and resolutions (Table 1). The geo-

logical map of Toudgha Maider with a scale of

1:200000 (Destombes and Hollard, 1986; Du

Dresnay et al., 1988) served as a reference map.

This study was performed using ENVI, ARCGIS

and Rockworks software. The main steps of data

pre-processing and processing are summarized in

the flowchart (Fig. 2).

3.1. Digital Image pre-processing and pro-

cessing

In the pre-processing stage, the satellite image

radiometry is corrected; then, atmospheric correction

and the Dark Object Subtraction (DOS) technique

was applied to remove atmospheric noises and the

shadow in the image (Zhang et al., 2010; Adiri et

al., 2016).

Image processing “enhancement” was applied to

clarify the geological features; the techniques

include color composites (false color RGB), principal

component analysis (PCA), spatial filtering, and

band ratios. Based on the objectives the proposed

digital image processing has two parts: structural

lineaments extraction and lithological mapping,

such as illustrated in the flowchart of Fig. 2.

3.2.1. Band Ratio (BR)

This remote sensing technique is largely used in

geological studies (Amer et al., 2010; Ali and

Pour, 2014; Si Mhamdi et al., 2017; Yousefi et al.,

2018). Band ratio involves a transformation in

which the digital number value of a band is

divided by the digital number value of another

band (Mars and Rowan, 2006; Pour and Hashim,

2012). The ratio of the bands is chosen according

to the reflectance and absorption features of a

given target (Amer et al., 2010).

3.2.2. Principal Component Analysis (PCA)

The PCA is a multivariate statistic method

which transforms a set of linked (or correlated)

variables into uncorrelated ones called principal

components (PC). It reduces the number of

variables and attenuates information redundancy

(Wold et al., 1987; Pour et al., 2016; Mathew and

Table 1. Spectral bands of the Landsat 8 satellite (Irons et

al., 2012; Roy et al., 2014)

Bands Wavelengths

(µm)

Spatial resolution

(m)

Band 1- Coastal aerosol 0.43 to 0.45 30

Band 2- Blue 0.45 to 0.51 30

Band 3- Green 0.53 to 0.59 30

Band 4- Red 0.64 to 0.67 30

Band 5- NIR 0.85 to 0.88 30

Band 6- SWIR 1 1.57 to 1.65 30

Band 7- SWIR2 2.11 to 2.29 30

Band 8- Panchromatic 0.50 to 0.68 15

Band 9- Cirrus 1.36 to 1.38 30

Band 10- TIRS 1 10.60 to 11.19 100

Band 11- TIRS 2 11.50 to 12.51 100


Ariffin, 2018b) This technique was widely applied

to geological studies; it can give an efficient

discrimination of lithological unites and geological

structures (Amer et al., 2010; Pour et al., 2016;

Gasmi et al., 2016). Previous studies have suggested

that the first three bands of PCA contain over 90%

of spectral information (Amer et al., 2010, 2012;

Richards, 2013; Adiri et al., 2016).

3.2. Lithological mapping

After the image preprocessing numerous com-

binations of bands were tested in RGB based on

the spectral signature and the contrasts of litho-

logical units. in addition, the color composite 257

(RGB; Ali and Pour, 2014), PC1PC4PC3 (RGB;

suggested in this study) was found to be the best

for accurate lithological mapping in the study area

(Fig. 2).

Many studies used ratios combinations for

mapping lithological units, using Landsat 8 OLI

image (Table 3). These combinations were tested

and applied in this study, and two combinations

were chosen, due to their good discrimination of

rock units in the study area.

3.3. Lineaments extraction

Lineaments are defined as straight line structures

observed on the surface of earth. They include

Fig. 2. Flowchart showing the main steps of the methodology approach used.

Table 2. Parameters values applied for automaticlineaments extraction (Geomatica software, version 2012)

Parameters Applied Values

RADI (filter radius) 15

GTHR (Edge Gradient Threshold) 55

LTHR (Curve Length Threshold) 10

FTHR (Line Fitting Threshold) 4

ATHR (Angular Difference Threshold) 20

DTHR (Linking Distance Threshold) 20


natural structures (geologic and topographic features)

and anthropogenic features (roads, railways etc.).

In order to have a better identification of struc-

tural lineaments, we have proceeded to improve

the spatial resolution (Alonso-Contes, 2011) of the

OLI image applying panchromatic band which has

15m in spatial resolution. In addition, the spectral

bands were resampled to 15m using “Pan Shar-

pening BGram-Schmid” method such as that

suggested in literature (Laben and Brower, 2000;

Amer et al., 2012; Maurer, 2013).

In this study we carried out a visual inspection

to select the band which can show a better identi-

fication of features. Based on numerous tests of

several combinations, the combination of Ratio

band as RGB (6/5, 7/6 and4/7) are used for

lineament mapping using the visual interpretation

(manual tracing). Then the band 6 (SWIR1) and

PC1 are used to extract lineaments by automatic

extraction and applied to the directional filters of

the band 6 and PC1 in different orientations (NW-

SE, N-S, NE-SW and E-W) (Fig. 3). The 3×3

neighborhood convolution mask (kernel) was chosen

in order to extract the maximum of lineaments at

different scales.

The lineaments were automatically extracted

from the filtered images using Line Module PCI of

Geomatica software and we also apply six para-

meter values as suggested in the Table 2. The

processing of extracted lineaments, establishment

Table 3. Some combinations of band ratios used in literature

RGB scale R G B Discriminated rock Reference

Ratio

combinations

4/2 6/5 6/7 Iron oxides and clay minerals (Ali and Pour, 2014)

5/4 6/5 7/6 Alluvium in red color(Adiri et al., 2016b)

6/5 7/6 4/7 Sandstone, Limestone and shales respectively

5/4 6/5 7/2 Alluvium (Yang et al., 2018a)

6/5 7/6 4/7 Sandstone, Limestone and shales respectively

(4:2)/(6:7) 6/7 4/2 Quartz-rich zones and argilites (Yousefi et al., 2018)

Fig. 3. Example of directional filters of the PC1.


of lineament map and rose diagrams were made

using ArcMap10.2.2 and Rock Work16 software.

The extracted lineaments were validated through

several fieldwork trips and by overlaying the

lineaments on high-resolution Google Earth image

and topographical maps. The aim of this step is to

remove anthropogenic linear/curvilinear features

of the non-geologic origin.

4. Results and Discussion

4.1. Lithological mapping

The work of Ali and Pour (2014) have shown

that 257 and 657-color composites as RGB images

are useful in the lithological mapping. They

suggest that the Clay and carbonate minerals have

absorption features from 2.1 to 2.4 µm and reflec-

tance from 1.55 to 1.75 µm. On the same basis,

and visual examination of several RGB band

combinations we found that the 2,5 and 7 yield a

good discrimination between limestone, sandstone

and alluvium.

In this investigation many color composites have

been tested. Therefore, the 257-color composite as

RGB image was used (Fig. 4). The geological

interpretations of this image show the limestone in

brown color, which is mostly exposed in the north

part of the study area. The sandstone-mudstone

appears with purple color, whereas the alluvium

exhibits a white color. In addition, those band

combinations allow us to identify the sand by

azure color. In order to enhance the contact between

sandstone and mudstone, we use the principal

component analysis (PCA) and Band ratio.

Based on reflectance features, this study suggests

that PC4 is reliable for identifying sand; it is

shown by the bright pixel in gray scale. In addition,

the PC3 is efficient to map the alluvium because of

its high reflectance. The PC1 exhibits a better

discrimination between limestone and sandstone.

The color composite of the results of PCA (PC1,

PC4 and PC3) displays a good difference between

reflectance of lithological units (Fig. 5). The

sandstone has a pink color, limestone appear in

blush color, and sandy-pelite which is intercalated

in limestone in the El Marakeb Mountain shows a

purple color. The alluvium in the form of fluviatile

deposits are represented by a blue color and sand

by a green color. This latter covers some sandstone

outcrops in the area.

Fig. 4. color composite image (band 2, band 5 and band 7 as RGB).


Concerning band ratios, the 4/2 and 4/5 ratios

effectively discriminate sandstone because of its

content of iron oxides (Ali and Pour, 2014). Pelites

and clay are rich in mica and clay minerals could

be identified by the ratio 7/5, due to its high

reflectance in band 7 and absorption in band 5

(Harris et al., 2005). The band ratio 3/2 is also

used to identify iron oxides-bearing rocks (Mahan

Fig. 5. Color composite combination (PC1, PC4, and PC3 as RGB) obtained from PCA technique.

Fig. 6. Color composite of 7/5, 6/4 and 4/2 as RGB images obtained from band ratio technique.


and Arfania, 2018). Based on our observations, the

6/4 ratio shows limestone with a distinctive reflec-

tance in band 6 and absorption in band 4.”The

RGB image of band ratios 7/5, 6/4 and 4/2 show

the limestone in a plum color and it mostly covers

El Mrakib mountain (the north part of the study

area) and the Zirg massif (the south of studied

area) (Fig. 6). The sandstone has a yellow color; it

crops out in the Ras Kammouna district to the east,

in El Mziouda to the west and tops the limestone

formation in the Zirg Massif. Pelitic rocks are

distinguished by an olive color. A sandy pelites

unit is revealed in El Mrakib mountain by a

greenish color.

The ratios RGB image of band ratios 7/5, 3/2

and 4/5 were used, considering the high contrast

between different rock units (Fig. 7), in particular

the contact between sandstone and limestone. They

are shown by blue and pink color respectively. The

pelitic rocks have cerulean color. The sand that

invades a large part of the area is represented by

green color. Also, alluvium which are deposited

along the M’aider and Ziz Rivers, appear with

olive color.

In this work, the principal component analysis

and band ratios technique were evaluated in litho-

logical mapping. In addition, in order to have a

good and more exhaustive mapping of geological

outcrops it’s necessary to combine the results of

these two methods. In comparison between these

two methods, the PCA gave better results concern-

ing discrimination of sand and alluvium. Whereas,

the bands ratios combination shows a better

discrimination of limestone, sandstone and sandy

pelites

In comparison with the conventional geological

map (Destombes and Hollard, 1986; Du Dresnay

et al., 1988), our results show a good compatibility.

The study area is characterized by a pile of

sedimentary rocks that consist of Ordovician

sequence, which begins with 1st Bani sandstone

that crops out at the south of El Mziouda mountain

(Fig. 8a). A sequence mostly composed of pelites

in alternation with sandstone forms the Ktaoua

formation in El Mziouda mountain. This formation

is topped by the 2nd Bani sandstone (Fig. 8c) and

mainly occupies the eastern part of the study area.

To the north, tiny mounds of Silurian white-grey

shales are exposed and overlie the 2nd Bani

sandstone. The El Mrakib Mountain which is

Fig. 7. Color composite (5/7, 3/2 and 4/5 as RGB) image obtained from band ratio technique.


located at the north and the Zirg massif to the

south of the study area is mainly composed of the

Devonian limestone with intercalation of sandy

pelites (Fig. 8b). The summit of this sedimentary

pile is marked by sandstone with intercalation of

thin limestone benches.

4.2. Structural lineaments mapping

In this work, we are interested in lineaments

which reflect geological structures such as faults,

Fig. 8. Field photographs showing different lithological units of study area. (a) Panoramic view of the southern flank of theEl Mziouda mountain and First Bani sandstone. (a1) First Bani sandstone. (b) panoramic view showing limestone ant sandypelites in alternating layer at the El Mrakib mountain. (b1) and (b2) Limestone and sandy pelites respectively. c View to thenorth showing the pelits of Ktaoua formation topped by 2nd Bani sandstone.


fractures and veins. The study area hosts several

iron oxides veins (Hematite and/or Goethite) and

industrial mineral deposits represented by the

barite. These mineral deposits form a network of

filled fractures that are hosted in the Ordovician

sedimentary pile.

Two methods have been adopted in this study to

map filled fractures and faults; the visual inter-

Fig. 9. (a) Manually digitized lineaments from band ratios (6/5, 7/6, 4/7 RGB) image. (b) rose diagram of digitizedlineaments.

Fig. 10. (a) Map of fractures extracted automatically from the filtered images of PC1 and band 6 of OLI image. (b) Rosediagram of extracted lineaments from spatial filtering of the PC1 and band 6.


pretation applied to band ratios (6/5, 7/6, 4/7 RGB),

and automatic extraction applied to directional

filters of the PC1 and band 6.

i) The visual interpretation allows to identify

157 fractures, with variable length that can reach

three kilometers (Fig. 9a). Rose diagram of line-

aments highlights three fracture systems, which

are oriented N-S, NE-SW and E-W (Fig. 9b). The

NE-SW and E-W fracture systems are the pre-

dominant trends. The N-S system is weakly re-

presented.

ii) The automatic extraction allows extracting a

number of 875 fractures (Fig. 10a). Rose diagram

of these lineaments shows three principal fracture

systems with orientations N-S, NE-SW and E-W

(Fig. 10b). We note a remarkable dominance of the

NE-SW and E-W systems with respect to the N-S

trend.

In comparison between the visual interpretation

and automatic extraction, the second one permits

to extract a higher number of lineaments, and also,

it allows us to define small fractures which their

length in average doesn’t exceed 150m. however,

automatic extraction needs to be applied with

precaution before considering lineaments as struc-

tural lineaments. Therefore, both automatic and

visual interpretation must be coupled in order to

map the maximum of significant lineaments.

The lineaments obtained from four filtered

images, and those extracted from visual inspection,

were combined for performing synthetic map of

lineaments (Fig. 11).

Fig. 11. (a) Synthetic map of fractures extracted by remote sensing and those of geological map. (b) Rose diagram ofextracted lineaments. (c) Rose diagram of field measurement (faults/veins).


The validation of lineaments was made on the

basis of existing map and field work. The extracted

lineaments are efficiently correlated with the geo-

logical map (Destombes and Hollard, 1986; Du

Dresnay et al., 1988) (Fig. 11a). In other words,

the number of obtained lineaments by remote

sensing processing is higher than the number of

faults which are mapped in conventional geological

map. This result can be explained by the large

scale of the geological map (1/200000).

The mapped lineaments correspond to faults and

veins. In the western part, we observed parallel E-

W strike-slip faults, where the movement is

indicated by horizontal striated faults (Fig. 12).

Whereas, the Eastern part is marked by NE-SW

faults, which filled by barite and/ or Iron oxides

mineralization (Fig. 13). Note that the thickness of

these veins varies from 0,4 m to 2,5m.

The analyzing of the obtained lineament maps

and rose diagram (Fig. 11b, c) shows that the

predominant structure trends are E-W and NE-SW

direction, and with little importance of N-S di-

rection. Notice that the NE-SW direction is highly

pronounced in the eastern part of the area (Ras

Kmmouna, Fig. 13a) where the main barite and

iron oxides mineralization veins network is occurred

(Fig. 13c). Whereas, the western part (El Mziouda

and western El Mrakib), is predominantly affected

by an E-W fault direction, which is represented

mainly by numerous strike-slip faults with important

length (1 to 4 km). These E-W systems of faults

were divided Jbel Elmarakib into blocks (Fig.

12a). The N-S fractures are less marked in the

area, and represented mainly by veins filled fractures

of iron oxides (Fig. 13c).

The density map of lineaments was carried out

from extracted trends, in order to reveal the zones

of high structural density. Therefore, the western

part of EL Mrakib mountain and Ras Kammouna

district depict the high density of fractures (Fig.

14). These results are highly correlated with faults

distribution depicted in the conventional geological

map, and those measured in the fieldwork (Fig.

13).

Fig. 12. Field photographs illustrating some examples of fractures mapped by remote sensing, at different scales. (a)Fractured zone showing different fracture directions and a right strike-slip fault at the western part of the study area. (a1) twonormal faults. (b) A striated fault mirror indicates the horizontal displacement of this fault.


The superimposition of the obtained density

map of extracted lineaments, geological map, and

field observations, proves that mostly fractured

areas are characterized by competent outcrops

(sandstone in Ras Kammouna and limestone in El

Mrakib). Moreover, the interpretation of results

obtained from extracted lineaments, band ratios

and PCA enhanced images, clearly depicted that

the barite and iron oxides mineralization zones are

concentrated in intensely faulted and fractured

areas.

Fig. 13. (a) Synthetic map of fractures obtained from OLI image processing (Eastern part of the study area). (b) Iron oxidesfilled fractures. (c) Intersection of two veins; a NE-SW trend of barite and a N-S trend with Iron oxides filled fractures. (d)and (e) Two barite veins associated with iron oxides at the eastern part of the study area.


5. Conclusion

Using composite color, PCA and band ratios

techniques allowed us to effectively determine

lithological units outcrop in the study area. In

addition, analyzing the extracted lineaments indi-

cates that the predominant direction is the NE-SW

and the E-W ones, and with lesser importance the

N-S direction. They are the main structural feature

directions which affected the area and they seem to

be the major tectonic features that control the

structuration of the studied zone. Combining results

of applied remote sensing techniques and field

investigations, show a close spatial relationship

between fractured zones and mineralization occurr-

ences. According to these findings, we can assume

that the deposition of mineralization has a struc-

tural control. The methodology approach used in

this paper has depicted high efficiency in terms of

lithological mapping, structural lineaments extrac-

tion and can be used in other similar regions.

Acknowledgements

The authors express profound gratitude to Noble

LaRocco Masi and Anna LaRocco Masi for revising

the English of this manuscript. The authors thank

the reviewers for their insightful comments and

recommendations.

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Lithological and Structural Lineament Mapping from Landsat