Zagros orogenic belt has developed on northern-eastern edge of Arabian plate from Northern-Western-Southern-Eastern Turkey to Strait of Hormuz with a length of over 2000 km. Thick sedimentary series of the Zagros (6 - 12 km) has maintained complex tectonic history of the region, which represents all stages of development of a basin from a passive continental shelf to a rift. This finally represents various stages of deformation in relation to ophiolite obduction and continental collision. The study area is located in the south and southeastern part of Iran in the range of 28 and 29 to 55 and 57. The study area includes Hormozgan and Kerman Provinces in national classification. Geographic position of this region at the intersection of three sedimentary structural zones of Zagros, Makran and Central Iran has revealed that Hormozgan Province has specific geological and structural features. Nowadays, remote sensing techniques and particularly structural analysis with satellite images are supplement to the observation and field interpretation. Landsat satellites can be noted in this regard, which has helped the scientists to interprete natural science since a long time ago. Landsat 8 is equipped with panchromatic band and thus has a high spatial resolution. Therefore, the images obtained from this satellite are used. The images are raw and after application of various filters and image processing operations by ER mapper and Arc GIS the lineaments that have remained unidentified are observed. The discoveries are then introduced to the realm of construction geology in the form of a new map of regional faults using the remote sensing technologies.
In general, remote sensing can be called acquiring information technology and land imaging using aeronautical equipment such as aircraft, space equipment or satellite [
A scene of the study area is required in order to use Landsat satellite images. Each scene is determined with a path and a row. The study zone was located in one scene in Hormozgan Zone (
The first stage in image processing is called pre-processing, which should be done before processing operations. Given that remote sensing data is mainly used as a background for other information embedded in the map, remote sensing data significantly shows surface of the earth with sufficient accuracy [
Usually, raster images are stored as raw images with geometric errors. Geometrically corrected images are needed for accurate coordinates, area and measurements [
Geometric correction of satellite images resolve errors and distortions in the image such as changes in height and speed of the satellite, displacement in terms of height and lowness and other factors, which are cited in the following.
The purpose of processing images lies in clarifying geographical data in digital images in order to extract certain information for the user. A digital image is stored in a two-dimensional array by small limited areas, which
are called pixels [
Each pixel spatially coincides with an area on surface of the Earth. This orderly network structure is called raster. Digital data are generally in raster from, which are stored in horizontal and vertical rows. Each pixel in the raster image is a numerical value, which is called digital number (DN). In satellite images such as Landsat, DN represents intensity of the reflected energy in the visible spectrum, infrared or other electromagnetic rays. Mathematical transformations on digital numbers with ER Mapper software are used to extract and interpret digital data. This technique is impossible in manual interpretations. For this purpose, image processing has become an interpretation tool for various fields of Geoscience [
One scene of digital data and ETM Landsat satellite were used for structural analysis. RAW images (
Three methods were used to detect fracture lines in satellite images as follows:
1) Detection of displacement in layers and sudden changes in lithologic boundaries
2) A review of direct valleys as fracture zones
3) A review of fault bluff or dikes [
In this section, remote sensing techniques were used for better visibility and drawing fracture lines using unique methods.
・ The techniques used in this project are as follows:
・ Using band ratios to eliminate the effects of topography and shadows
・ Color combination of RGB images to separate rock units based on color
・ Apply fusion in order to increase the spatial resolution
・ NDVI Vegetation to highlight begetation in the area
・ Apply Sunangle, Highpass and Edges filters to detect fractures lines, borders and sharp edges
・ Using DEM images for better visibility of valleys by the created shadows
・ Principal component analysis (PCA) in order to focus the data relevant to multiple bands in one band for better visibility from sharp edges
・ Band ratio to observe minerals and other complications associated with the fault
In this section, the techniques used in this project are discussed.
This technique is used for distinguishing various phenomena by the differences in absorption and reflection of light in different phenomena (
1) On grayscale images
2) On RGB color images
Grayscale images relevant to the above phenomenon are brighter due to maximum reflection and absorption properties compared to other image types. For example, vegetation and rocks in hydrothermal alteration appear brighter than other areas in grayscale images with 6/7 ratio.
Retina in human eyes is composed of a large number of rod and cone cells. The rod cells are sensitive to light and cone cells are sensitive to three primary colors of red, green and blue. In fact, the cone cells are commonly sensitive to a part of the electromagnetic rays. In color images, each phenomenon is assigned to one of the three primary colors. Each color represents a phenomenon in the image. The images were displayed in RGB space in order to separate phenomena (vegetation, rocks with hydrothermal alteration) from each other (
Since three bands are needed to construct an RGB image, it should be considered how many unique combination modes are possible using 2, 3, 4, 5, 7, 8, 10 and 11 bands.
n is the number of total bands
r is the number of applied bands
Then, 56 combination modes are possible by inserting the numbers in the formula. However, more information is needed to select the best combination among possible ones. Thereby, statistical parameters should be used.
Optimum Index Factor (OIF) should be used to calculate statistical parameters of the images [
band = 741 was selected as the optimum combination (
Sk = Standard Deviation
Abs (rj) = correlation matrix value
Fusion integration of ordinary images with panchromatic images or any other image improves spatial resolution. At this stage, RGB images were converted into HSI. The band with high special resolution was replaced with intensity. As shown in
Normalized difference vegetation index (NDVI) is one of the oldest and most widely used vegetation index.
According to
For example, total values of this index for sparse vegetation are (0.05, 0, 2), for regular and semi-dense vegetation are (0.2, 0.6), for very dense and rich vegetation are (0.6, 0.8), for water, snow and ice are negative. Values of this index are less than 0.05 in soil. Value of this index in overcast (cloudy) areas is equal to zero. This method is used to identify faults in the region.
Spectral values are modified in filtering operation in which value of each pixel changes with respect to value of the neighbor pixel. Contrast is changed as filtering is used on the original image. A matrix is filtered with odd cells (this is because a pixel should be located at the center). Depending on the application, some changes are made in the number of central cells with respect to the surrounding cells. Filters are in 3 × 3 (
(
Filtering is one important method for better visibility and detection of phenomena.
The filters used in this section are as follows:
・ High-pass filters
・ Edge detector filters
・ Sunangle filters
These filters are briefly discussed here.
High-pass filters act as edge detector and only allow passage of high frequency gray pixels. The filter not only detects the edges, but also does not obliterate other phenomena [
The edge detector filters as the name suggests detect edges in images such as structural lineaments, roads and rivers (
Sun-shading technique is used in DEM images to identify topography of the region. This technique can make
the sun shine in the desired direction to review the created shadows [
Principal Components Analysis (PCA) is one technique used to detect phenomena. There is a usually high correlation between different bands of satellite images. This duplicates a series of information. PCA method reduces duplicate data or disturbing phenomena such as shadows, effects of topography and angle of solar radiation (
In this equation,
Band Ratio is one common method in image processing. This method either increases or decreases a series of noises. This method also eliminates the effects of topography and shadows. This method also highlights the borders. Thereby, this method is used to separate the boundary between rock units and detect the rocks [
Ratio of the third band to the fourth band = separating vegetative from non-vegetative areas
Ratio of the seventh band to the fifth band = detecting hydrothermal altered rocks
Ratio of the first band to the third band = separation of iron oxide
A three-dimensional view of the study area can be created using Digital Elevation Model (DEM) (
The most useful types of data are SRTM sensor images used in this project. ER Mapper Software was also used to create three-dimensional image of the region with various RGB. The images suggest that the northern part of the study area has greater topography than southern part of the study area.
Tectonic lineaments in the study area were drawn using images obtained from the above techniques including RGB = 345 image and grayscale image and filtered images and PC1 single-band image and DEM images with directed shadows and images with certain band ratios. The final map was prepared using structural map of fractures with RGB = 345 (optimal combination) (Figures 19-22).
Following results are obtained according to remote sensing studies using satellite images of geo-magnetic aerial impression
1) Remote sensing studies with ER-mapper software being relevant to extraction of lineament represents that Sharpen
11 filter is the best filter, which shows the lineament.
2) The main structure extracted by remote sensing shows the same trend in accordance with the main zone of Zagros in direction of Khavari Bakhtari area.
3) Deformations and ophiolites are located in Zagros main thrust zone.
4) According to geomagnetic studies, slope of the main thrust is towards the North East.
5) The most important phenomenon in deformation in the area is Arabian Plate subduction beneath central Iran. Therefore, the farther from the main thrust is the more simplified structure. Farther faults have greater distances from the thrust.
All extracted faults are recommended for further studies because some of these lineaments and faults are hidden and are located near residential areas and some of them are beneath the cities. Geophysical studies and regional seismicity are recommended in general fault map of Iran.