The Mesozoic ophiolitic Mélange, north of Nain in the Central-East Iran Microplate (CEIM) comprises serpentinized ultramafic rocks, harzburgites, dunite, gabbro, peridotite, pelagic limestone and other carbonate rocks. The excellent and vast exposure of this desert region is well suited for geologic mapping of this rock suite using remote sensing, especially using data from the satellite-borne advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) imaging system which was designed for mapping mineral information. In this study, data processing methods like Method Minimum noise fraction (MNF), Feature Oriented Principal Components Selection (FPCS), Band Ratios (BR) and Optimum Index Factor (OIF) were used to process ASTER data to optimize the mapping of ophiolite rock types. For example, a simple color composites of OIF (Red: B3, Green: B4, and Blue: B8) and Band ratios (e.g. Red: (B2 + B4)/B3, Green: (B5 + B7)/B6, Blue: (B7 + B9)/B8) were useful for discriminating serpentinite, meta-basalt and granite rock types. It is concluded here that proposed ASTER data has the potential for mapping similar ophiolites elsewhere using the global archive of ASTER imagery.
Nain Ophiolitic Complex with an area of approximately 500 square kilometers is located approximately 100 km northeast of Esfahan in Iran (
from the city of Nain in the south to the villages of Seprabvand Paco-Sohail in the north and northwest respectively [
Today, applying aerospace technologies for terrestrial data collection is very common to investigate and identify the sources without physical contact [
The study area, Nain ophiolite is located within the 53˚00'00'' to 53˚07'00'' East length and 32˚52'00'' to 33˚00'00'' northern latitudes (
There are three main steps in the implementation of the identify Ophiolite Mélange using band ratio techniques, composite band and Feature Oriented Principal Components Selection projects,
・ Satellite data of ASTER Sensor (LEVEL 1T) derived from TERRA satellite has “AST_L1T_00303082001073110_20150415023833_85285” ID number and 2015/04/15 DATE ACQUIRED.
Aster Level 1T is modified by Aster Level 1. In fact, AST_L1T is a product that including formatting code AST_L1B (Aster LEVEL 1B), radiometric and geometric corrections (AST_L1B is used) and crosstalk correction coefficients [
Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) | |||||||
---|---|---|---|---|---|---|---|
Nr | Name | Start WL | Middle WL | End WL | Sp. Rg | Spat. Res. | Comment |
1 | VNIR Band 1 | 520 | 560 | 600 | 80 | 15 | - |
2 | VNIR Band 2 | 630 | 660 | 690 | 60 | 15 | - |
3 | VNIR Band 3N | 760 | 810 | 860 | 100 | 15 | Nadir |
4 | VNIR Band 3B | 760 | 810 | 860 | 100 | 15 | Backward |
5 | SWIR Band 4 | 1600 | 1650 | 1700 | 100 | 30 | - |
6 | SWIR Band 5 | 2145 | 2165 | 2185 | 40 | 30 | - |
7 | SWIR Band 6 | 2185 | 2205 | 2225 | 40 | 30 | - |
8 | SWIR Band 7 | 2235 | 2300 | 2365 | 130 | 30 | - |
9 | SWIR Band 8 | 2295 | 2330 | 2365 | 70 | 30 | - |
10 | SWIR Band 9 | 2360 | 2395 | 2430 | 70 | 30 | - |
11 | TIR Band 10 | 8125 | 8300 | 8475 | 350 | 90 | - |
12 | TIR Band 11 | 8475 | 8650 | 8825 | 350 | 90 | - |
13 | TIR Band 12 | 8925 | 9100 | 9275 | 350 | 90 | - |
14 | TIR Band 13 | 10250 | 10600 | 10950 | 700 | 90 | - |
15 | TIR Band 14 | 10950 | 11300 | 11650 | 700 | 90 | - |
At last we will make the necessary corrections including geometric correction and atmospheric correction. AST_L1T images have basic correction, geometric correction was not made. Due to the lack of sufficient meteorological information such as water vapor pressure; visibility and temperature of the air, the IARR (Internal Average Relative Reflectance) atmospheric correction method has been used. Finally, MNF (Minimum Noise Fraction) is used for noise fraction and the variation of noise in all bands is the same. When the noise is in a clause, we reduce it with multiple linear regressions. By changing the MNF space, we remove the noise, and then the bands become the original space [
・ We select considered spectrum rocks from ASTER spectral library and resample it according to band spectrum sensor. we should identify absorption and reflection band, then characterize band ratio and OIF (Optimum Index Factor) of best composite band; Finally feature oriented principal components selection analysis is done.
・ We perform the validation using geological map that was produced in 1972 by Davoudzadeh [
A gray level image with equal amount of red, green and blue per pixel. A false color conversion is done by changing colors in the RGB display [
MNF Eigenvalue Number | Band | B1 | B2 | B3 | B4 | B5 | B6 | B7 | B8 | B9 |
---|---|---|---|---|---|---|---|---|---|---|
ASTER | 1104.0 | 152.2 | 52.1 | 24.9 | 13.3 | 11.6 | 9.5 | 6.0 | 3.2 | |
OIF | ASTER | - | - | R* | G* | - | - | - | B* | - |
*The highest OIF is 0.37210387 for RGB: (B3, B4, B8).
Spectral rationing is a multi-spectral image processing method which includes dividing one band to another one, (usually after initial corrections for atmospheric path radiation or sufficient movement imposed by the multi-spectral sensor) [
and Serpentine are shown. In this paper, we use the composite band of band ratio (R: (B2 + B4)/B3, G: (B5 + B7)/B6, B: (B7 + B9)/B8) proposed by Amer in 2010 and from now on we call it Amer band ratio [
ASTER sensor’s spectral library includes data from three spectral library like Johns Hopkins University (JHU), NASA Jet Propulsion Laboratory’s spectral library (JPL) and United States geological Survey Headquarters (USGS). For more information you can refer to the website (https://speclib.jpl.nasa.gov/search-1) which contains the whole Spectrum, Stone, Soil, Mineral, Vegetation and a comprehensive collection of more than 2300 covering products with a wavelength range of 0.4 - 15.4 micrometers [
brary; after that we should specify reflection, absorbtion band according to the spectrum. Lisweanit’s reflection and the absorption band are taken from Sankaran Rajendran article [
Generally, it is a linear transformation maximizing the variance by transferring data to a new coordinate system. In Feature Oriented Principal Components Selection (FPCS) we make use of good and well-known bands with appropriate information. In this way, only basic information about minerals, stone and vegetation properties are required and it’s based on principal component conversion capabilities in mapping and data variance details in consecutive components. This method is known as Crosta [
Serpentine | Peredotit-4 | ||||||||
---|---|---|---|---|---|---|---|---|---|
*EV | 4 | 6 | 7 | 8 | *EV | 1 | 3 | 4 | 5 |
PC 1 | 0.999 | −0.047 | −0.024 | −0.00 | PC 1 | −0.992 | 0.087 | 0.089 | −0.012 |
PC 2 | −0.047 | −0.999 | −0.012 | 0.009 | PC 2 | 0.072 | 0.984 | −0.16 | −0.023 |
PC 3 | 0.023 | −0.013 | 0.999 | −0.03 | PC 3 | −0.102 | −0.153 | −0.98 | 0.033 |
PC 4 | 0.007 | 0.009 | 0.032 | 0.999 | PC 4 | −0.007 | 0.029 | 0.03 | 0.999 |
Pyroxenite | Peredotit-2 | ||||||||
*EV | 2 | 4 | 5 | 9 | *EV | 1 | 3 | 4 | 8 |
PC 1 | −0.999 | 0.024 | −0.031 | 0.025 | PC 1 | 0.99 | −0.14 | −0.04 | −0.06 |
PC 2 | 0.021 | 0.994 | 0.111 | 0.007 | PC 2 | 0.13 | 0.95 | 0.16 | −0.22 |
PC 3 | 0.034 | 0.11 | −0.993 | 0.003 | PC 3 | −0.01 | 0.18 | −0.98 | 0.05 |
PC 4 | 0.025 | −0.008 | 0.003 | 0.999 | PC 4 | −0.09 | −0.2 | −0.08 | −0.97 |
Lisweanit | |||||||||
*EV | 4 | 6 | 7 | 8 | |||||
PC 1 | 0.999 | −0.035 | 0.0159 | −0.016 | |||||
PC 2 | −0.036 | −0.998 | 0.012 | −0.031 | |||||
PC 3 | −0.016 | 0.015 | 0.997 | −0.066 | |||||
PC 4 | 0.014 | −0.031 | 0.066 | 0.9971 |
*EV: Eigenvector.
Feature Oriented Principal Components Selection (VNIR+SWIR) | **A | ***R | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Ratio | MAX | MIN | Mean | SD | *THV | Conf | *THV | Con | *THV | Con | L | H | L | H |
Peredotit-2 | 0 | 255 | 113.4 | 50.9 | 266.3 | 98% | 215.3 | 95% | 164.4 | 92% | 8 | 3 | 1 | 4 |
Peredotit-4 | 0 | 255 | 145 | 56.6 | 314.9 | 98% | 258.2 | 95% | 201.6 | 92% | 3 | 5 | 1 | 4 |
Pyroxenite | 0 | 255 | 116.4 | 57.5 | 289 | 98% | 231.5 | 95% | 174 | 92% | 5 | 4 | 2 | 9 |
Serpentine | 0 | 255 | 111.7 | 52.6 | 269.6 | 98% | 216.9 | 95% | 164.3 | 92% | 5 | 8 | 6 | 4 |
Lisweanit | 0 | 255 | 140.3 | 67.3 | 342.1 | 98% | 274.9 | 95% | 207.6 | 92% | 4 | 8 | 7 | 6 |
*Thershold Value, **Absorption, ***Reflection, Conf: Confidence, L: Low, H: High.
After identifying stones using FPCS method, determining the images accuracy is absolutely essential because indicates that how well the resulting map corresponds with the ground truth. In this project Davoudzadeh map is used to verify the validation [
・ Sampling: Generally, in this study, random sampling pattern was used. In addition to random mode maintenance, this pattern solves the nonuniform distribution of points [
The amount of A is divided by effect area to determine the population. The maximum number of samples is obtained according to sample size. The Equation (6), Cochran’s rule, is one of the most widely used methods for sample size calculation.
Using Cochran formula is the easiest way determining the sample size [
・ Validation: No method is invoked yet its accuracy has been tested. Therefore, accuracy assessment is done to ensure the variety of performed procedures [
・ Error matrix and Kappa coefficient: Error matrix is an efficient tool for the preparation and presentation of accuracy assessment information. Sometimes, it’s called contingency matrix or probability table. Error matrix compares the known relation between reference data (ground truth) and the relevant results of the automated classification [
Beside, Kappa coefficient or statistical kappa coefficient is an accuracy evaluation criteria. KAPA index or K is the result of Kapa analysis shown as an index to measure the classification accuracy based on the difference between error matrix accuracy and accuracy changes by sum of rows and columns [
In fact, Equation (7) K index indices the disorder between real consistency in reference data and an automated classifier or possible consistency between reference data and random classifier.
When the actual agreement is close to one and expected agreement is close to zero, K value comes to one, so it is the ideal mode [
To identify the ophiolitic Mélange, we used the band ratio techniques, composite band, and feature-oriented principal components selection projects. The research results are as follows: the results of the Optimum Index Factor (OIF)
Lithology Unit | Area (Km2) | ASTER |
---|---|---|
ASTER (VNIR + SWIR) | ||
Peredotit-2 | 58.5 | 50 |
Peredotit-4 | 79.15 | 65 |
Listvenite | 15 | 13 |
Pyroxenite | 48.1 | 42 |
Serpentine | 97.122 | 77 |
Overall Acc | Kapa | Kapa % (Kapa × 100) |
---|---|---|
0.97 | 0.92 | 92 |
showed the best combination of bands (R: B3, G: B4, B: B8). The maximum number obtained from the OIF for RGB is 0.37210387. The results of Amer band ratio (R: (B2 + B4)/B3, G: (B5 + B7)/B6, B: (B7 + B9)/B8) are shown as dark pink Chromite, yellow pink Gabbro, orange and pale green Carbonates and dark green vegetation. According to
The Justice and the Townshend formulas were used for estimating spatial accuracy based on the sampling pixels (
Khaneghah, A.R.N. and Arfania, R. (2017) Lithological Analysis of Nain Ophiolitic Zone Using ASTER Data. Open Journal of Geology, 7, 1200-1214. https://doi.org/10.4236/ojg.2017.78080