using remote sensing data to identify iron deposits in central

Abstract—The main objective of this paper is to use remote

sensing data to identify iron deposits occurring in the Wadi ash shati in the central part of western Libya. In this work, several advanced image processing techniques were applied and tested on a TM image of study area. By applying ER mapper image processing techniques, colour composites, band ratios, principal components PCA and supervised classification, images were created for enhancing mineral detection in this study. The method uses six of the Landsat TM bands for mapping iron (bands1,3&5) and hydroxyl (bands 1,5 & 7 )alteration, readily apparent in TM imagery. Final detection of the iron deposit was based on the difference in reflectance of the iron oxide content. The Wadi ash shati is an excellent environment to experiment with remote sensing for identifying iron ore. The ore deposits have iron-rich caps or gossans with distinct reflectance spectra. The large size of the gossans and associated alteration zones makes them easily detectable with orbital remote sensing platforms, such as Landsat TM. Orbital remote sensing data will consist of visible and near infrared data (VNIR) from 0.4µm to 2.5µm. Landsat Thematic Mapper (TM) will be the source of multispectral data for the exploration of minerals due a typing in bands.

Keywords— TM, PCA, RGB,OIF, ER.

I. INTRODUCTION HE Wadi ash Shati iron-ore deposit is apparently one of the largest in the world, suitable in considerable part for

strip mining. Its outcrops of ore underlies roughly eighty square kilometres of the valley. According to information in the mid-1980s, none of it was high-grade ore. Preliminary estimates suggest that it amount of 30 to 40 percent iron-content ore in the deposits which total anywhere between 700 million and 2 billion tons. The all introduction of Wadi ash shati iron was based on report of French group.

The occurrence of the iron ore at the surface was first reported by Desio (1936; 1943). The first description of the geology and of the deposit was made by Muller-Feuga (1954) who recorded an oolitic zone and estimated 5-10 mets of ore. Preliminary exploration work, followed by detailed studies and test drilling, was carried out during 1955 – 1958 by Goudarzi (1958, 1970, and 1971) and by Goudarz and Tschoepke (1968).

In this paper I will use Remote Sensing as a tool by applying some of image processing techniques to detect the iron deposits based on the differences of the reflectance of materials. Today Remote Sensing has become a good tool in

Amro F. ALASTA Faculty of Science - ALMARGIB University, Zliten, LIBYA

mineral exploration by using different satellites. In the early Landsat days, several geologic investigations were aimed at determining whether the MSS (and later, the TM) sensor could produce images (any mode: natural or false colour; ratio; PCA; Unsupervised Classification; others) in which tell-tale signs of alteration of minerals in near surface deposits could be detected. The mean reason to choose this topic, because the iron deposits represented good economic store, but there is too far from the factory of iron in the north, I spouse to the connect this area with electric line to electric power station to let building factory on area of ore.

II. OBJECTIVE OF THIS WORK The objective of this study’s to demonstrate how remote

sensing can used to detect iron deposits due to reflectance of radiation using six band of landsat TM imagery and different image processing techniques which are statistical by based on colour composite(R, G, B), principal component, clay ratio, iron ratio, Abram’s ratio, and classification, try to find other possible areas of ore deposit

III. STUDY AREA The Wadi Shatti area, where flat lying iron-bearing

sedimentary beds are exposed with remarkable regularity, is located in the central part of western Libya, in the Province of Fezzan, at about 270301N latitude. It occupies an E.NE W.SW trending depression, 200 km long and 20 km wide, between the 13°E and 15°E longitude.

Bordering the Shatti area in the north is the Jebel Fezzan, a massif along the 14th meridian which rises to an elevation of about 1,000 m above sea level. In the north and north west of Jebel Fezzan extends the Hamada el Hmra over an area of approximately 80 to 90,000 sq. km (figure 1. 1).

Using Remote Sensing data to identify iron deposits in central western Libya

Amro F. ALASTA

T

International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2011) Bangkok Dec., 2011

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Fig 1.1 Study area

Photographic IR (0.7 - 0.9 µm); (3) and (4) thermal bands at (3 - 5 µm) and (8 - 14 µm). We measure longer wavelength intervals in units ranging from mm to cm. to meters. The microwave region spreads across 0.1 to 100 cm, which includes the entire interval used by radar systems. These systems generate their own active radiation and direct it towards targets of interest. The lowest frequency-longest wavelength region beyond 100 cm is the radio bands, from VHF (very high frequency) to ELF (extremely low frequency). Within any region, a collection of continuous wavelengths can be portioned into discrete intervals called bands.

A. The Concept of Remote Sensing Imaging spectroscopy is a new mapping technique and

represents a part of the next generation in remote sensing technology. The narrow spectral channels of an imaging spectrometer form a continuous reflectance spectrum of the Earth's surface, which contrasts with the 4 to 7 channels of the previous generation of imaging instruments, for example the Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) instruments. Systems like Landsat can distinguish general brightness and slope differences in the reflectance spectrum of a surface. However, imaging spectroscopy has the advantage of providing compositional information based on the presence and position of absorption bands, as well as contributing data on brightness and slope. For a full description see L & K (1994).

Using spectroscopes and other radiation detection instruments, over the years scientists have arbitrarily divided the EM spectrum into regions or intervals and applied descriptive names to them. At the very energetic (high frequency and short wavelength) end are gamma rays and x-rays (whose wavelengths are normally measured in angstroms [Å], which in the metric scale are in units of 10-8 cm). Radiation in the ultraviolet extends from about 300 Å to about 4000Å.

It is convenient to measure the mid-regions of the spectrum in one of two units: micrometers (µm), which are multiples of 10-6 m or nanometres (nm), based on 10-9 m. The visible region occupies the range between 0.4 and 0.7 µm, or its equivalents of 4000 to 7000 Å or 400 to 700 NM The infrared region, spanning between 0.7 and 100 µm, has four

B. Transmittance, Absorptance, and Reflectance Any beam of photons from some source passing through

medium 1 (usually air) that impinge upon an object or target (medium 2) will experience one or more reactions that are summarized in this diagram (figure 1.2).

Fig 1.2 Various in behaviour of radiation.

Some objects are capable of transmitting the light through without significant diminution (note how the beam bends twice at the medium 1/medium 2 interface but emerges at the same angle as entry). Other materials cause the light energy to be absorbed (and in part emitted as longer wavelength radiation). Or, the light can be reflected at the same angle as it formed on approach. More commonly the nature of the object's surface (owing to microscopic roughness) causes it to be scattered in all directions.

C. Data analysis and results Orbital remote sensing at visible and near infrared (VNIR)

and short wave infrared is based on the spectral reflectance of objects on the Earth’s surface. Rocks are collections of minerals, and their reflectance spectra are composites of the individual spectra of the constituent minerals (Hook et al., 1999). Mineral spectra exhibit diagnostic features at various wavelengths, which provide a means for their remote discrimination and identification. These features are produced by electronic or vibrational-rotational processes resulting from the interaction of electromagnetic energy with the atoms and molecules, which comprise the minerals that make up a rock. In general, reflectance is defined as the ratio of the intensity of the electromagnetic radiation scattered from a surface to the intensity of the radiation incident upon it. When measured as a function of wavelength, reflection spectra exhibit specific albedo, continuum, and absorption features, which are a function of the material properties of the surface measured (Mustard and Sunshine, 1999). Absorption features are related to the chemical composition and mineralogy of the surface, while the continuum and overall albedo are a function of nonselective absorption and scattering, which are partially controlled by both physical properties of the surface (particle size, roughness, texture, etc.) and the chemical composition (Mustard and Sunshine, 1999). Processes and oxidized forming hematite and goethite.

The metals such as iron often appear as abnormalities or anomalies at the Earth's surface. Most widespread among these guides is "gossan", a miner's term for rust.

It is, in fact, a mix of several forms of hydrated iron oxides, including the mineraloid group limonite (FeO(OH) nH2O), that develop most commonly when iron-bearing minerals react


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with water and other chemicals during the natural weathering or alteration processes. The brownish to yellow-brown soil colour and yellows and oranges of many sandstones owe those distinctive tints to these secondary iron compounds. The reds in rocks usually result from the presence of hematite, a non-hydrated iron oxide (Fe2O3). The chief reason that gossan forms in areas where useful metals are concentrated is that pyrite, (FeS2).

Data analysis was conducted in the image processing laboratory , APEME of Dundee University, operating an ER-Mapper system for image processing of landsat TM imagery. The raw data was received from Biruni Remote Sensing Centre. See Richards (1993) for a discussion of IP.

Before starting analysis of data, the raw data needed some of processing to be ready to apply different analysis.

D. Pre-processing of data Image data which is received by a user has travelled a long

way. Along this way at several places changes to the data have occurred, in the atmosphere from the sun to the earth, at the surface of earth, in the atmosphere from the earth to the satellite, in the satellite to the ground station, from the ground station to the storage media and from the storage media to the user. Thus the data used by the user is not necessarily equal to what is supposed to have been reflected by earth surface. Errors occur and have to be corrected before information extraction can take place.

Data conversion to a suitable format for the user and correction of errors due atmospheric interaction and system and transferaction can be combined in a phase called Pre-processing.

E. Atmospheric correction When the sun light enters the atmosphere many interactions

happen to radiation such as scattering of radiation. In the lower portion of electromagnetic spectrum scattering of radiation is highest while in visible and infrared part of the spectrum. This results in haze in the image of satellites. Rayleigh scattering, one of the primary causes of haze in the image, is inversely proportional to the fourth power of wavelength, so the blue wavelength is more effected by this scattering than red and green wavelengths. In order to correct the haze, the data contributed by haze has be known. An assumption is made that no reflectance value should be obtained from the shadow area. Any value, which is grater than zero is assumed to be contributed from haze. The correction is done by following steps:

a) Plot the frequency distribution of reflectance data in each spectral band.

b) Note the offset of the data from the origin, this is assumed to come from the scattering of radiation.

c) Correction procedure is simply shifting of the histogram toward the origin. This can be done by subtracting the haze estimate from raw data as follows formula:

Output = Raw data – Haze estimate Then the aim of this correction is to produce ultimate good

quality image for applying the different analysis.

F. Geometric correction Geometric errors can be result from two types, systematic

or non- systematic. Systemic errors are corrected by establishing the error model and inverting it, and non-systematic errors can be corrected by establishing an approximation of the error model or by referencing the scene to existing ground truth ( image map, or terrain).

In our case the error results from earth rotation , while the satellite advances over a scene the earth rotates eastward, thus neighbouring scene elements (on different lines) will be shifted in the image data set. The shift or skewness depends on the latitude, rotational speed of the earth and satellite.

The image can be corrected by using other correct image by using ground control points method using ER mapper package.

There are various methods of finding the value of a new grid cell from the original numerical image. The procedure is called interpolation; there are three commonly used interpolation methods in the table 1.

TABLE I 1INTERPOLATION METHODS (RESAMPLING)

Geometric

Accuracy

Smoothing/

Sharpening

DN Value

Nearest neighbour

Fair No change

No change

Bilinear interpolation

Good Smoothing

Change

Cubic convolution

Excellent

Sharpening

Change

IV. IMAGE RATIOING (BAND RATIO) Ratioing is an enhancement process in which the DN value

of one band is divided by that of any other band in the sensor array. If both values are similar, the resulting quotient is a number close to 1. If the numerator number is low and denominator high, the quotient approaches zero. If this is reversed (high numerator; low denominator) the number is well above 1. These new numbers can be stretched or expanded to produce images with considerable contrast variation in a black and white display . Certain features or materials can produce distinctive grey tones in certain ratios; TM Band 3 (red) divided by Band 1 tends to emphasize red- or orange-colured features or materials, such as natural hydrated iron oxide, as light tones. Three band ratio images can be combined as colour composites which highlight certain features in distinctive colours. Ratio images also reduce or eliminate the effects of shadowing. For each pixel, we divide the DN value of any one band by the value of another band. This quotient yields a new set of numbers that may range from zero (0/1) to 255 (255/1) but the majority are fractional


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(decimal) values between 0 and typically 2 - 3 (e.g., 82/51 = 1.6078...; 114/177 = 0.6440...). We can rescale these to provide a grey-tone image. One effect of ratioing is to eliminate dark shadows, because these have values near zero in all bands, which tends to produce a "truer" picture of hilly topography in the sense that the shaded areas are now expressed in tones similar to the sunlight sides.

Three pairs of ratio images can be co-registered (aligned) and projected as colour composites. In individual ratio images and in these composites, certain ground features tend to be highlighted, based on unusual or anomalous ratio values. For example, an ore deposit may be weathered or altered so that a diagnostic surface staining, called gossan, develops. This stain consists of hydrated iron oxide (rust) that is normally yellow-brown. In Band 3, this material reflects strongly in the red but it is apt to be dark in Band 4. The ratio quotient values for this situation tend, therefore, to exceed 2-3, giving rise to a bright spot pattern in a 3/4 image. Three band ratio were tried.

a) Iron ratio b) Clay ratio c) Abram’s ratio

A. Iron ratio This ratio is formed from band 3 / band 1. Hydroxyl

alteration is highlighted by the presence of limonitic bearing phyllosilicates which causes strong absorption in TM band 7 and limonitic iron oxide alteration, which causes absorption in bands 1 and higher reflectance in band 3. The result is shown in (figure 2).

Fig 2– Iron ratio band 3/ band 1 Here the ratio of TM Band 3 to Band 1 (3/1) renders

most of the area in rather dark greys, but several areas are whitish (brighter). These probably correspond to zones of strong iron alteration (very reflective in band 3 but dark in band 1)

B. Clay ratio This ratio is obtained from band 7 / band 5, the result is

shown in (figure 3).

A A A

Fig 3 – Clay ratio band 7 / band 5. The 7/5 image has a unique pattern, in which a hook-shaped

dark area (sand duns)within a scene otherwise light-toned coincides closely to the general altered zone. Band 7 is an excellent detector of hydrous minerals such as the clays, alunite, etc., because these absorb radiation (hence significantly reduce reflectance).

C. Abram’s ratio This is obtained by using bands 5/7 in red, 3/2 in green and

4/3 in blue. The result is shown in (Figure 4).

Fig 4 - Abram’s ratio, color composite bands 5/7 in red, 3/1 in green and 4.3 in blue

The ratio 3/2 is commonly used in the green channel and may be more useful when there is high level of noise or atmospherics in band 1. In this enhancement the clays will have high 5/7 ratios and clays should either be red or yellow if there is also high iron with light green.

D. Supervised Classification Supervised classification can be used to assign pixels to

classes based on the spectral signature of the pixels. The spectral signature is the relative reflectance, or brightness, recorded in each of the bands. Supervised classification is much more accurate for mapping classes, but depends heavily on the cognition and skills of the image specialist.

The strategy is simple: the specialist must recognize


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conventional classes (real and familiar) or meaningful (but somewhat artificial) classes in a scene from prior knowledge, such as personal experience with the region, by experience with thematic maps, or by on-site visits.

This familiarity allows the specialist to choose and set up discrete classes (thus supervising the selection) and the, assign them category names. The specialists also locate training sites on the image to identify the classes. Training sites are areas representing each known land cover category that appear fairly homogeneous on the image (as determined by similarity in tone or colour within shapes delineating the category). Specialists locate and circumscribe them with polygonal boundaries drawn (using the computer mouse) on the image display. For each class thus outlined, mean values and variances of the DNs for each band used to classify them are calculated from all the pixels enclosed in the site. More than one polygon can be established for any class. When DNs are plotted as a function of the band sequence (increasing with wavelength), the result is a spectral signature or spectral response curve for that class. In reality the spectral signature is for all of the materials within the site that interact with the incoming radiation. Classification now proceeds by statistical processing in which every pixel is compared with the various signatures and assigned to the class whose signature comes closest. A few pixels in a scene do not match and remain unclassified, because these may belong to a class not recognized or defined).The classification was carried out and produced 6 classes dependent on content on the image as shown in (Figure 5)

Fig 5 Supervised classifications

V. THE RESULTS The main results of this work: A colour composite of a Landsat image, that shows the iron

district with brown to brownish colour in figure 3. Hydroxyl alteration is highlighted by the presence of iron

bearing material which causes strong absorption in TM band 7 and iron oxide alteration, which causes absorption in bands 1 and higher reflectance in band 3, suitable band ratios detected

areas of iron very well. Band 7 is also an excellent detector of hydrous minerals

such as the clays, alunite, etc., because these absorb radiation (hence significantly reduce reflectance).

4. Principal component analysis has been used in several ways to enhance imagery; the first three principal components can be displayed as an RGB composite.

5.Classification now proceeds by statistical processing in which every pixel is compared with the various signatures and assigned to the class whose signature. The result is difficult to interpret.

From the above results which were produced during this study, We can see that the detection of iron deposit by using remote sensing data is very useful. The best method to identify iron deposit is by using the iron ratio and Abram’s ratio, other ratios were not so successful.

1 ) Image R G B, 7, 4, 1 2) Iron ratio 3/1 3) Abram’s ratio 4) PC 2 5) PCA 1, 2, and 3 as RGB 6) Supervised

classifications

Fig 6 comparison between results

Here in the first plates all the line indicator for the iron reflectance w

ith different brightness intensity , but in plates 2 & 5 is the best.

In plate 6 is difficult to interpretation


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VI. CONCLUSIONS The differences in spectral reflectance caused by variations

in mineralogy can be used to identify iron deposits in Wadi ash shati. The improved spectral and spatial resolution of landsat TM can better identify the massive iron deposit and alteration zones using band ratios. The colour composites of bands limits the utility of these bands for mineral exploration applications. The best results were achieved by examining the bands in R-G-B combinations of bands and band ratios, principal component and supervised classification.. The OIF uses the statistics calculated from the scene and can be applied to any multispectral data set making it a very flexible and useful technique. The above techniques are generally very important in providing information on the mineralization and possible alteration from TM imagery prior to field visits. It is however important that the spectral techniques be applied properly and that common sense is used in their interpretation. The image processing results obtained show the importance of spectral characteristics of iron despite in study area. The identification of ore deposits from these results is possible, and iron is also derived from oxidation of sulphide rich rock that occur as mined material discarded on surface, mineralized rock exposed within mined workings, or mineralized rock exposed through erosion. To get more detailed information about this ore, I suggest the useful methods such geophysics (gravity and magnetic) to assist this study to determine and mapping the ore.

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using remote sensing data to identify iron deposits in central

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