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 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 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 Safaa M. Hassan et al, 2014 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 (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 Safaa M. Hassan et al, 2014 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 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 Safaa M. Hassan et al, 2014 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 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 Safaa M. Hassan et al, 2014 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 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. 116 Safaa M. Hassan et al, 2014 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 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 Safaa M. Hassan et al, 2014 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 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 Safaa M. Hassan et al, 2014 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 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 Safaa M. Hassan et al, 2014 Australian Journal of Basic and Applied Sciences, 8(10) July 2014, Pages: 110-120 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. 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