Australian Journal of Basic and Applied Sciences

Transcription

Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120
AENSI Journals
Australian Journal of Basic and Applied Sciences
ISSN:1991-8178
Journal home page: www.ajbasweb.com
Automated, manual lineaments extraction and geospatial analysis using remote sensing
techniques and GIS, Case Study: Gabal Shabrawet area, south Ismailia, Egypt
1
Safaa M. Hassan, 2Abdel Aziz A. Mahmoud, 2Munir El-Mahdy, 2Yahia A. El kazzaz
1
National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt.
Helwan University, Department of Geology, Faculty of Science, Cairo, Egypt
2
ARTICLE INFO
Article history:
Received 25 April 2014
Received in revised form
8 May 2014
Accepted 20 May 2014
Available online 17 June 2014
Keywords:
Lineaments extraction, Geospatial
analysis, Sobel filters, Land sat ETM,
Shabrawet.
ABSTRACT
Background: Geological structural features such as lineaments have been took much
interest in the geological studies, because it is considers as a very important structural
and geological indicator to determine general and local tectonic trends and fractures
zones in the rocks. Objective: The purposes of this study are to extract lineaments from
satellite images in order to contribute to the understanding of the faults. Panchromatic
Landsat ETM+ image with spatial resolution 15 m is used for the analysis which is
processed for both automated and manual extraction. Three geospatial analyses are
applied in order to evaluate the extracted lineaments, these are: length, density and
orientation analysis. Results: The results have indicated that, the Lineaments in the area
have two main trends with high density NE-SW & NW-SE trend. Geospatial analyses
of lineaments give a good correspondence with the arrangement of the main tectonic
forces of the studied area. Conclusion: The accuracy of the automatic lineaments
extracted map is different from the manually extracted map by about 30%, so the best
way for the identification of the lineaments of the area is by the correlation between the
lineaments extracted by both the manual & automatic techniques.
© 2014 AENSI Publisher All rights reserved.
To Cite This Article: Safaa M. Hassan, Abdel Aziz A. Mahmoud, Munir El-Mahdy, Yahia A. El kazzaz, Automated and manual
Lineaments extraction and geospatial analysis using remote sensing techniques and GIS. Aust. J. Basic & Appl. Sci., 8(10): 110-120, 2014
INTRODUCTION
Satellite remotely sensed data has been widely used as source of information for geologists to map
lineaments at district and regional scales. The lineament is a mappable linear or curvilinear feature of a surface
whose parts align in a straight or slightly curving relationship (Hung, 2005). They may be an expression of
faults, joints or other line weakness. The lineament may be has a geomorphological implication, i.e. major
structural ridges, cliffs, terraces and aligned segments of a valley are typical geomorphological expressions of
lineaments. Differences in vegetation, moisture content, and soil or rock composition account for most tonal
contrast which are used to extract the linear feature (O’Leary et al.,1976). Satellite images and aerial
photographs are extensively used to delineate lineaments for different purposes, such as defining geological
structures and tectonic fabrics (Neawsuparp and Charusiri, 2004).
The extraction of geological lineaments from remotely sensed data can be grouped into at least three main
approaches, namely: (i) manual extraction (Raj, 1989; Jordan and Schott, 2005), (ii) semi-automatic extraction
(Lim et al., 2001; Jordan et al., 2005), and (iii) automatic extraction (Mostafa and Bishta, 2005; Masoud and
Koike, 2011; M. Hashim et al, 2012, Anwar Abdullah et al, 2013; Rayan Ghazi, 2013). The extraction of the
information from manual and semi-automatic approaches has been greatly influenced by the experience of the
analyst, while automatic extraction, so far, depends on the algorithms efficiency as well as on the information
content in the image (Al Dossary and Marfurt, 2007).
The principle objective of this study is to compare between the extractions of the lineaments of the area by
automatically and by visual interpretation (manually) to determine the most suitable method for the
determination of the digital lineament analysis and then use the results in the tectonic evaluation. However, the
comparison of the extracted lineaments map with the geospatial analysis performing by Geographic Information
Systems (GIS) such as density, length and orientation will contribute to the understanding the tectonic
relationship between the lineaments and the structural elements in the study area.
Corresponding Author: Munir El-Mahdy , Helwan University, Department of Geology, Faculty of Science, Cairo, Egypt
Tel: 002-01000087369 E-mail: [email protected]
111
Safaa M. Hassan et al, 2014
Great
Bitter
Lake
G. El Shehabi
Wadi Abiad
Shabrawet east
Shabrawet
west
G. El-Goza
El-Hamra
Fig. 1: Location map of Shabrawet area.
Geological Setting of the Studied Area:
Shabrawet area covers about 300Km2. It is located midway between Suez and Ismailia cities. The studied
area is located between latitudes from 30° 13’ into 30° 19’ N and longitudes from 32° 12’ into 32° 20’ E. It is
bound to the east by the Ismailia Suez road and to the west by the tributaries of Wadi Abu Talh and Gebel Umm
Kethieb (Fig. 1). From north and south sides it is bounded by a wide plain that extends to the Great Bitter Lake
and Gebel Gharra respectively
The geology of Shabrawet area has been investigated by many authors (i.e. Faris and Abbass, 1961; Said,
1962; Al-Ahwani, 1982; Mohammad and Omran, 1991; Khalil, 1994; Moustafa and Khalil, 1995; Hegazy and
Omran, 1999; Wael Dardir, 2010).
Gabal Shabrawet area forms a conspicuous topographic feature, consisting of steeply dipping Cretaceous
rocks surrounded by gently dipping Eocene and younger rocks, midway between Ismailia and Suez. Being a part
of the Syrian Arc system, most of the topographic highs are structurally controlled. The northern Cretaceous hill
112
(Gabal Shabrawet east) reaches an elevation of about 220 m above sea level. Gabal El Goza El Hamra is 212 m
to the southeast, and its southward extension, Gabal Geneifa is 192 m, both are covered by Eocene rocks.
Northwards, the area descends gradually to a wide plain extending to the shoreline of the Great Bitter Lake. To
the south, the area is bounded by the tributaries of Wadi Hessa which runs east-west. Gabal El Shehabi forms
the northeastern part of the area and is composed of Miocene sediments, which are tilted mildly to the north.
The area is barren of vegetation though it has a considerable number of dry wadis. The Oligocene fluviatile
sediments, consisting of conglomerates, cross-bedded sandstones and sands, cover a wide plain to the south and
southwest of the area.
The exposed stratigraphic succession in the Shabrawet area includes Malha Fm, Galala Fm, Maghra El
Hadida Fm, Maghra El Baharia Fm, El-Goza El-Hamra Fm, Maadi Fm, Gebel Ahmar Fm, Ghara Fm and Hagul
Fm. Malha Formation represents the oldest exposed sedimentary rocks in the mapped area. It is introduced by
(Abdallah and Adindan, 1963) which include all the rocks which hang in age from Aptian to Albian. It is
composed of alternating beds of clastic and non clastic rocks. The base of the formation is composed mainly of
varicolored sandstone which is overlaid by a white to yellowish white Limestone intercalated with hard
dolostone. Upwards, the section grades into varicolored sandstone ranging from violet, purple to blackish. The
Galala Formation is first introduced by (El Akkad and Abdallah, 1971). It is composed mainly of Dolomite and
Limestone intercalated with shale & marl. The Maghara El Baharia Formation which is described for the first
time by (El Akkad and Abdallah, 1971) is represented in Shabrawet area by Dolomitized limestone and
limestone with intercalations of marl and chalky limestone bands.
(Hassan and Omran, 1991) have been used the term El-Goza-El-Hamra Formation to describe the Middle
Eocene beds in Shabrawet area. It is composed mainly of non-clastics at the base grading to clastics at its higher
layers. It can be easily detected in the field by its pinkish and reddish colors and the dark brown nature of their
erosion surface. The lower carbonate beds are composed mainly of pinkish limestone with some intercalations
of dolomite. The upper clastic beds are composed by reddish brown and yellowish brown sandy clays. The
Maadi formation conformably overlies El-Goza El-Hamra Formation, and has a Late Eocene age. (Said, 1962)
term Maadi Formation to describe the Upper Eocene brownish sandy limestone, shale and calcareous sandstone
exposed at east Maadi in Cairo. In the study area, It is consists mainly of sandy limestone, dolomitic limestone,
marl & clays. Oligocene sediments, which are termed Gebel Ahmar Formation by (Shukri, 1954) are widely
distributed in the Shabrawet area. They are un-fossiliferous continental facies, consist of varicolored sandstone
& conglomerates with some silicified woods. The Gharra Formation has an Early Miocene age (Omran, 1989).
It begins with massive, medium to coarse-grained sandstone at the base which varies into calcareous sandstone
and finally into limestone at the topmost part of the formation.
(Abdallah and Abdel Hadi, 1968) termed the Middle Miocene sediments by Hagul Formation. In the study
area, it is composed mainly of limestone, with a few sandstone and claystone interbeds. Generally, Shabrawet
region is structurally related to the Gulf of Suez region and Cairo Suez district, and it is affected by the Syrian
arc system that extends from Syria through Lebanon, Palestine and northern Egypt (Krenkel, 1925; Shukri,
1954).
The structural pattern of the area is characterized by two anticlines with a shallow syncline in between. The
Shabrawet anticlinal structures form a part of krenkel’s “Syrian Arc System”, a system of folds that cross the
northern part of Egypt. The anticlines are Shabrawet proper, known as Gebel Shabrawet and Shabrawet west 3
km to the west. Generally, the region is highly faulted being affected by both NE-SW and NW-SE faults. Faults
of different directions but with minor displacements are also encountered.
MATERIALS AND METHODS
The main flow chart which is applied for the lineament extraction and analysis is given in (Fig. 2). The
methodology of this research composed of five successive steps: 1) Applying Sobel- kernels directional filtering
on Landsat ETM band 8 with spatial resolution 15 m in order to enhance the edge and direction of lineaments.
2) Extraction of lineaments by manual method (visual interpretation) and automatic technique to compare
between them. 3) Evaluating the extracted lineament map with their directions by calculation geospatial analysis
like density and the lengths of the extracted lineaments features. Finally, evaluating the tectonic setting of the
study area depending on the results of the suggested method in this paper.
113
Fig. 2: Flowchart shows steps lineaments extraction and analysis.
Data used and Image Processing Techniques:
The data utilized in this research is the spectral panchromatic band 8 of Landsat-8 ETM+ data with special
resolution 15 m. This panchromatic band has chosen in lineament analysis as a consequence of its improved
spatial resolution 15 meter. It is geo-referenced to the UTM coordinate system; Zone 36 North and resampled
using nearest neighbor resample technique.
One of the characteristic features of the satellite images is a parameter called spatial frequency which is
defined as the number of changes in brightness value per unit distance for any particular part of an image.
Filtering operations are used to emphasize or deemphasize spatial frequency in the image. This frequency can be
attributed to the presence of the lineaments in the area. In other words, the filtering operation will sharpen the
boundary that exists between adjacent units (Sarp, 2005). In this study, Directional filtering has been used to
enhance, extract and classified the oriented lineaments of the study area. Directional filters are applied to image
using a convolution process by mean of constructing a window normally with a (3×3) pixel box of Sobelkernels filters (Table 1). This type of filter was used in order to get a high accuracy in extraction of oriented
lineaments because the directional nature of Sobel kernels generate an effective and faster way to evaluate
lineaments in four principal directions (Suzen et al.,1998). Four filtered images have been produced by
ERDASE Imagine software related to the directions N-S, E-W, NE-SW and NW-SE, which are used as an
inputs images for auto extraction methods
Table 1: Sobel - kernels in four principle directions
N-S
SOBEL
-1 0 1
-2 0 2
-1 0 1
NE-SW
-2 -1 0
-1 0 1
0
1 2
-1
0
1
E-W
-2 -1
0 0
2 1
NW-SE
0
1 2
-1 0 1
-2 -1 0
Manual and Automatic Lineaments Extraction:
Lineaments extraction in this study is performed by both manual and automatic lineaments extraction. In
manual extraction method, the lineaments are extracted from satellite image by using visual interpretation. The
lineaments usually appear as straight lines or “edges” on the panchromatic satellite images which in all cases
contributed by the tonal differences within the surface material. The knowledge and the experience of the user is
the key point in the identification of the lineaments particularly to connect broken segments into a longer
lineament (Wang et al., 1990). The results of manual extracted lineament map are shown in Figs (3 and 4).
Lineaments are extracted from satellite images using automated extraction techniques in order to compare
with the manually extracted lineaments. The main advantages of automated lineament extraction over the
manual lineament extraction are its ability to uniform approach to different images; processing operations are
performed in a short time and its ability to extract lineaments which are not recognized by the human eyes.
114
Fig. 3: Manually extracted Lineament overlapped over the filtered images of the study area at four directions
(A) N-S, (B) E-W, (C) NE-SW, (D) NW-SE.
Fig. 4: Total number of the manually extracted Lineament.
The most widely used software for the automatic lineament extraction is the LINE module of the PCI
geomatica. It provides different algorithms for automated extraction. Three common algorithms are applied,
Hough transform, Haar transform and Segment Tracing Algorithm (STA) (Koçal, 2004). The Hough transform
is a technique which can be used to separate features of specific shape within an image. The main advantages of
this transform are that it is relatively unaffected by gaps in lines and by noise (Wang et. al. 1990). While Haar
transform used by (Majumdar and Bahattacharya, 1988) for extraction of linear and anomalous patterns in the
image. This transform has both low and high frequency components and therefore can be used for image
enhancement (Koçal, 2004). The Segment Tracing Algorithm (STA), which is developed by (Koike et al.,
1995), is a method to automatically detect a line of pixels as a vector element by examining local variance of the
gray level in a digital image. The automated lineament extraction in this study is performed by the LINE module
115
of Geomatica software. A brief explanation of the algorithm of this module will be given here. This information
is provided from the Geomatica users’ manual (2001). LINE module of Geomatica extracts linear features from
an image and records the polylines in vector segments by using six parameters. The description of six
parameters used in the algorithm is as follows:
RADI (Filter radius): This parameter is used in the first step of the first stage of the process for the “Canny
edge detection”. It specifies the radius of the edge detection filter (in pixels). It roughly determines the smallestdetail level in the input image to be detected. The data range for this parameter is between 0 and 8192.
1. GTHR (Gradient threshold): This parameter is used in the second stage of the process for the “Canny edge
detection”. It specifies the threshold for the minimum gradient level for an edge pixel to obtain a binary image.
The data range for this parameter is between 0 and 255.
2. LTHR (Length threshold): This parameter is used in the third stage of the process. It specifies the minimum
length of curve (in pixels) to be considered as lineament or for further consideration (e.g., linking with other
curves). The data range for this parameter is between 0 and 8192.
3. FTHR (Line fitting error threshold): This parameter is used in the second step of the third stage. It specifies
the maximum error (in pixels) allowed in fitting a polyline to a pixel curve. Low FTHR values give better fitting
but also shorter segments in polyline. The data range for this parameter is between 0 and 8192.
4. ATHR (Angular difference threshold): This parameter is used in the last step of the third stage of the
process. It specifies the maximum angle (in degrees) between segments of a polyline. Otherwise, it is segmented
into two or more vectors. It is also the maximum angle between two vectors for them to be linked. The data
range for this parameter is between 0 and 90.
5. DTHR (Linking distance threshold): This parameter is used in the last step of the third stage of the process.
It specifies the minimum distance (in pixels) between the end points of two vectors for them to be linked. The
data range for this parameter is between 0 and 8192.
The extraction process is manipulated changing the six parameters. The most suitable threshold values are
selected (below) considering for the lineament extraction. General properties of faults are taken into
consideration such as the length, curvature, segmentation, separation and so on in order to determine the
threshold values Table (2).
Table 2: Suggested parameters values.
Parameter
Suggested values
RADI
5
GTHR
75
LTHR
10
Parameter
FTHR
ATHR
DTHR
Suggested values
2
20
1
Fig. 5: Automatically extracted Lineament overlapped over the filtered images of the study area at four
directions (A) N-S, (B) E-W, (C) NE-SW, (D) NW-SE.
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Fig. 6: Total number of the automatically extracted Lineament.
Geospatial Analysis of the Extracted Lineament:
The extracted lineaments are analyzed by three process of geospatial analysis in order to extract further
information related to distribute and nature of these structures. Geospatial analysis processes are including:
length, density and orientation analysis.
Length analysis has been applied on the both manual and automatic extracted linements of the study area.
Figures (7 and 8) show the relationship between the lineaments frequencies and lengths at both manual and
automatic lineaments map.
Fig. 7: Frequency distribution and basic statics of the manually & automatically extracted lineaments at ifferent
directions.
117
Fig 8: Histogram and basic statistics of the total automatically & manually extracted lineament map.
In manually and automatic extracted maps, total of 224 and 795 geologic lineaments for all directions were
identified respectively. Length per unit area for each line is completely calculated digitally and then represented
the value of length in meter by attributes table in the data base as a new field. As shown in (Figs. 7 and 8), it has
been noticed that the maximum number of lineaments are in the NW-SE direction (120) in the manual map and
in NE-SW direction (428) in automatic map. The minimum number of lineaments is in the N-S direction (13
lineaments about 10% of the total number of the lineaments) in the manual map and in NW-SE direction (44
lineaments about 4.5% of the total number of the lineaments) using the automatic map. The maximum length of
the lineaments is (3831m) recorded in the (NE-SW) direction in the manual map which affected the Miocene
sediments at G. El Shehabi north east Gabal Shabrawet. In the automatic map, the maximum length of the
lineaments is (1928 m) recorded in the (NW-SE) direction which affected the Eocene sediments at Wadi Darbet
El Houity, south west Gabal Shabrawet.
Density analysis has been applied on the both manual and automatic extracted linements of the study area.
The purpose of the lineament density analysis is to calculate frequency of the lineaments per unit area. This is
also known as lineament-frequency (Greenbaum, 1985). This analysis will produce a map showing
concentrations of the lineaments over the area. The lineament density is created by spatial analyst tool in
(ArcGIS10.2.1) program by counting lines digitally per unit area (number/km2) and then plotted in the
respective grid centers and contoured using the same tool.
Fig. 9: Lineaments density map of the manually extracted lineaments
Figures 9 and 10 show the lineaments density map of the overall lineaments in the main four directions of
the manually and automatically extracted maps.
118
The manually extracted map shows that lineaments are highly concentrated at G. El Goza El Hamra
(Eocene sediments) and decreases gradually westwards. Also the lineaments density increase at G. El Shehabi
(Miocene sediments) North West of the studied area. While the automatic map shows an increase in the density
of lineaments at G. El Goza El Hamra (Eocene sediments), Wadi Darbet El Houity, south west Gabal
Shabrawet, G. El Shehabi (Miocene Sediments) and slightly concentration of lineaments at Gabal Shabrawet.
Lineaments orientations are usually analyzed by rose diagram in all researches which are dealing with these
structures. Figure (11) shows the directional frequency of the extracted lineaments over the specific area. A rose
diagram tool from the (Rock works 14) was used to derive lineament directions in the selected part in the
studied area.
Fig. 10: Lineaments density map of the automatically extracted lineaments
Fig. 11: Rose diagrams prepared from manually (A), & automatically (B) extracted lineaments.
Discussions:
Generally, Shabrawet region is affected by two phases of deformations: Gulf of Suez rift and the Syrian arc
system that extends from Syria through Lebanon, Palestine and northern Egypt (Krenkel, 1925; Shukri, 1954).
Faults in the Shabrawet area are more important than the other structural features. As a matter of fact, faulting is
the controlling factor of the topographic configuration of the area. Throughout this study, panchromatic band 8
was analyzed for the lineaments extraction under user visual interpretation. The extracted lineaments showing
all possible linear features that can be represented fractures zones.
119
Comparison between the manual and automatic maps can yield the following observations:
1. Frequency of automatically extracted lineaments is more than 4 times of the manually extracted ones (795
versus 224).
2. Spatial distribution of the lineaments in both maps is considerably different. In the automated one the
frequency of the lines seems to be higher in the G. El Goza El Hamra (Eocene sediments), Wadi Darbet El
Houity, south west Gabal Shabrawet, G. El Shehabi (Miocene Sediments) and slightly concentration of
lineaments at Gabal Shabrawet. In the manual one, on the other hand, lineament frequency is higher only at G.
El Goza El Hamra (Eocene sediments) and at G. El Shehabi (Miocene sediments) North West of the studied
area.
3. The pattern of the two maps although in general look similar, there are some minor differences among
them. For examples G. El Goza El Hamra southeast of the G. Shabrawet is totally different in both maps. This is
lacking lineaments in the manual one but is full of lines in the second. Other examples of such areas are at Gabal
Shabrawet east anticline and Gabal Shabrawet West anticline.
4. Orientation of the lineaments for both lineament maps are compared using the rose diagrams (Figure 11).
The diagrams are prepared using the frequencies of the lineaments and therefore are not length-weighted. The
two diagrams are differing in that lineaments are highly concentrated at the NE-SW direction at the automatic
map, while they are widely distributed at both direction NE-SW & NW-SE at the manual one.
Conclusions:
Landsat panchromatic imaging system has very useful characteristics. In optimum conditions these
characteristics enhance the image interpretation especially in structure geology. In order to get the maximum
emphasizing of the three main sets of faults affecting the study area, Sobel kernels edge enhancement technique
was applied on the Landsat image with different kernel (windows).
Briefly, the study area was affected by two phases of deformations. The first phase of deformation has
taken place in Late Cretaceous event, which is known by the “Syrian Arc System belt”. Faults of NE-SW trend
are the more frequent fault trend (using manual or automatic method) in the Shabrawet area. They are formed by
rejuvenation of the Shabrawet- Um Qamar E-W oriented deep-seated normal fault by dextral transpression,
during late Cretaceous-Early Tertiary time. The second phase of deformation is the opening of the Suez rift
which took place due to the extension in the Afro-Arabian plate leading to the separation of the African and
Arabian plates. Extensive faulting took place and most of the NW-SE faults were formed. The accuracy of the
automatic lineaments extracted map is different from the manually extracted map by about 30% that is because
the parameters used in the automatic technique affected by many different factors in the studied area such as
drainage system, and man-made roads. So the best way for the identification of the lineaments of the area is by
the correlation between the lineaments extracted by both the manual & automatic extracted techniques.
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