ALTERATION MINERAL ZONATION MAPPING IN WESTERN PART OF CUDDAPAH BASIN ALTERATION MINERAL ZONATION...

ALTERATION MINERAL ZONATION MAPPING IN WESTERN PART OF CUDDAPAH BASIN

ALTERATION MINERAL ZONATION MAPPING IN

WESTERN PART OF CUDDAPAH BASIN USING

REMOTE SENSING TECHNIQUES

M. DEEPAK KAVIARUSU, S. JAYSREE, A. RUPAK CHANDAR, S. SURJITH, G. YALINI

Under the guidance of: Dr. N. Nagappan, Centre for Remote Sensing,

Bharathidasan University, Trichy-620023.


AKNOWLEDGEMENT

First and above all, we praise God, the almighty for providing us this opportunity

and granting us the capability to proceed successfully. This project appears in its current

form due to the assistance and guidance of several people. We would therefore like to

offer my sincere thanks to all of them.

We take immense pleasure in thanking Prof. Dr. C. J. Kumanan Head of Centre

for Remote Sensing (CRS), Dr. D. Ramesh Associate Professor, and all other members of

CRS family, Bharathidasan University for having permitted us to carry out this project

work.

We wish to express my deep sense of gratitude to our Guide Dr, N. Nagappan

Scientist, CRS for his able guidance and useful suggestions, which helped us in

completing the project work, in time.

We thank Dr. J. Saravanavel, Dr. C .Lakshmanan, Dr. K. Palanivel, Dr. S.

Gunasekaran, Dr. R. Sakthivel for helping us to carry out this project in a successful

manner.

We are greatly beholden to Mrs. D. Gayatri, Ms. A. Mary Vasanthakumari, and

Ms. Vasavi for their immense help during our study period.

And special thanks to our scholar Mr. N. Ramalingam for his timely help and

patients in supporting us to complete this project. We also thank Mr. N. Arunachalam ,

Mr. S. Karthick Subramanian , Mr S. Sugumaran, Mr. Jaiyendran, Mr. Rajasiman,

Mr. Thamaraikannan, Ms. Sandhiya, Mr. Krishna kumar, Mr. Vivekanandan, Mr.

Valavan, Ms. Kirthika for helping us to carry out this project in a successful manner.

We would also like to thank Mr. Suresh, Mr. Diwakar, Mrs. Gowri, Mr.

Arumugam, Mrs. Vijaya, Mr. Karthick and Mr. Charles for their helping hands in every

situation of our study.

Words are inadequate in offering my thanks to the Project Trainees and Project

Assistants, of CRS for their encouragement and cooperation in carrying out the project

work.

Importantly, we would like to express my heartfelt thanks to our beloved Parents

and Family Members for their love and blessings, our Friends for their help and wishes

for the successful completion of this project.


ABSTRACT

The Cuddapah basin was formed during the earliest period of Upper – Middle

Proterozoic and is endowed with rich mineral wealth; it is still being altered due to

several Structurally and Geomorphologically controlled factors. This study is made in

order to delineate the alteration zones and henceforth prepare the mineral zonation map.

Remote Sensing is the powerful tool in exploration of mineral. Along with the satellite

data and the Digital Image Processing software, the Alteration Mineral Zonation

Mapping is more effective. Here we use the Geology and Mineral map of GSI to prepare

the lithology and mineral map and to validate it with the processed images from the

Landsat ETM data. Digital Image Processing such as band ratio, PCA, MNF,

Classification helps to identify the Structurally and Geomorphologically controlled

alteration zones. These alteration Corridors are useful for further narrow downing the

study for mineral zonation mapping. Apart from the regular image processing, FLAASH

and SAM aids in dealing with the narrow band satellite data and produce more accurate

results. Here the Western part of the Cuddapah basin is taken under study which

comprises of various lithology like Limestone, Dolomite, Shale, Granite, Quartzite etc.,

and with minerals like Asbestos, Cooper, Uranium, Clay, Diamond, etc., When the study

area was processed they could give the hydrothermal alteration zones, structurally and

geomorhologically altered zones. These corridors narrowed down in zoning the minerals

like Talc, Steatite, Hornblende, Biotite, Hematite, Magnesite, Olivine, Butlerite, etc.,


TABLE OF CONTENTS

CHAPTER

NO

TITLE PAGE NO

ABSTRACT

LIST OF FIGURES

1 INTRODUCTION AND LITERATURE REVIEW

1.1 BACKGROUND 1

1.2 LITERATURE REVIEW 3

1.2.1 ATMOSPHERIC CORRECTION 3

1.2.2 MINIMUM NOISE FRACTION

TRANSFORMATION 4

1.2.3 PIXEL PURITY INDEX 5

1.2.3 n-D VISUALIZATION 5

1.2.4 SPECTRAL ANGLE MAPPER 5

2 METHODOLOGY

2.1 AIMES & OBJECTIVE 6

2.2 STUDY AREA 6

2.3 METHODOLOGY 6

2.4 DATA PRODUCTS 9

2.5 GEOLOGY OF STUDY AREA 10

3 PREPARATION OF THEMATIC LAYERS

3.1 GENERAL 13

3.2 BASE MAP 13


3.3 LITHOLOGY AND MINERAL MAPPING 13

3.4 STRUCTURAL MAPPING 13

3.5 LINEAMENT DENSITY 14

3.6 GEOMORPHOLOGY 14

4 DIGITAL IMAGE PROCESSING

4.1 LANDSAT ETM 17

4.2 PROCESSING TECHNIQUES 18

4.2.1 BAND RATIO 18

4.2.2 PRINCIPAL COMPONENT ANALYSIS 18

4.2.3 MINIMUM NOISE FRACTION 19

4.2.4 TARGET DETECTION 19

4.3 BAND COMBINATION FOR MINERALS 20

4.4 ASTER 21

4.5 APPLYING MINERAL INDEX 23

4.6 IDENTIFICATION AND EXTRACTION OF

ENDMEMBERS 23

4.6.1 PIXEL PURITY INDEX 24

4.6.2 IDENTIFICATON OF ENDMEMBERS 24

4.6.3 EXTRACTION OF ENDMEMBERS 24

4.6.4 SPECTRAL ANGLE MAPPER 24

5 RESULTS AND DISCUSSION 25

REFERENCES


LIST OF FIGURES

S.no Name

1 Study area

2 Methodology

3 Base map

4 Lithology map

5 Mineral map

6 Lineament map

7 Lineament Density map

8 Geomorphology map

LANDSAT ETM PROCESSED MAPS

9 Colour transformation map

10 Low Pass filter map

11 Unsupervised classification Isodata map

12 Unsupervised classification K-means map

13 Principal Component Analysis map

14 Minimum Noise Fraction map

15 Carbonate Index

16 Iron Index

17 Oxide Index

18 Silica Index

19 Vegetative Index

20 Alteration Zones

21 Hydrothermal Alteration

22 Band Combination (Iron)

23 Band Combination (Vegetation)

24 Band Combination (Hydroxyl)

25 Granite Index

26 Structurally Altered Zones

ASTER PROCESSED MAPS


27 Vegetative Index

28 Hydroxyl Bearing Mineral Index

29 Kaolinite Index

30 Calcite Index

31 Silica Index

32 Carbonate Index

33 Mafic Index

34 Ferric Oxide

35 Sulphide Index

36 Sericite Index

37 Gossans

38 Alteration Zones

MINERAL ZONATION MAPS

39 Quartz

40 Orthoclase

41 Pyrite

42 Olivine

43 Magnetite

44 Biotite

45 Talc

46 Haematite

47 Magnesite

48 Hornblende

49 Steatite

50 Silica

51 Buterlite


INTRODUCTION AND LITERATURE REVIEW

1.1 BACKGROUND

Remote Sensing is the science of making inferences about material objects from

measurements, made at distance, without coming into physical contact with the objects

understudy. Visual perception of objects is the best example of remote sensing. We see

an object by the light reflected from the object falling on the human eye. Here, eye is the

sensor and the nervous system carries information to the brain, which interprets the

information in terms of the identification and location of the object seen. Modern remote

sensing is an extension of this natural phenomenon. However, currently the term remote

sensing is used more commonly to denote identification of earth features by detecting the

characteristic electromagnetic radiation that is reflected and or emitted by the earth

surface. A remote sensing system consists of a sensor to collect the radiation and a

platform- an aircraft, balloon, rocket, satellite or even a ground- based sensor- supporting

stand- on which a sensor can be mounted. The information received by the sensor is

suitably manipulated and transported back to the earth- may be telemetered as in the case

of unmanned spacecraft, or brought back through films, magnetic tapes, etc., as in aircraft

or manned spacecraft systems. The data are reformatted and processed on the ground to

produce either photographs, or computer compatible magnetic tapes (CCT). The

photographs/ CCTs are interpreted visually/ digitally to produce thematic maps and other

resources information.

Remote Sensing technology has opened up new vistas for mapping planning,

management and monitoring of natural resources. Both aerial photographs and satellite

data-based studies have proved their capabilities in various fields like geological and

geomorphological, structural mapping, mineral targeting, groundwater targeting, forest

and soil mapping, land use planning, flood mapping, irrigation and water resource

management, etc., Detailed information can be extracted accurately from high resolution

satellite data and large scale aerial photographs. Further detailed digital information can

be generated using the above data in Geographical Information System (GIS). The

interpretation of aerial and satellite data for various resource application has been

successfully carried out in the country, and it has proven its utility with respect to time


and space. In this study, a brief account in remote sensing application in mapping of

various mineral resources has been dealt with.

Multispectral sensors measure multiple, wide, separated wavelength bands and

hyperspectral sensors measure multiple, narrow and contiguous wavelength bands.

Although most hyperspectral sensors measure hundreds of wavelength bands, and most

multispectral sensors measure only few, it’s not the number of measured wavelengths

that defines a sensor as multispectral or hyperspectral; it’s the narrowness and contiguous

nature of the measurements. For example, a sensor that is measured only 20 bands could

be considered hyperspectral if the bands were contiguous and 10nm wide. If a sensor

measured 20 wavelength bands that were 100 nm wide or separated by nonmeasured

wavelength ranges, the sensor could be considered multispectral.

The thematic mapper(TM) is an advanced, multispectral scanning, earth resources sensor

designed to achieve higher image resolution, sharper spectral separation, improved

geometric fidelity, and greater radiometric accuracy and resolution than that of the MSS

sensor. This sensor also images a swath that is 185km (115 miles) wide, but each pixel in

a TM Scene represents a 30 m x 30m ground area, except in the case of the far- infrared

band 7, which uses a larger 120 m x 120 m pixel. The TM sensor has seven bands that

simultaneously record reflected or emitted radiation from the earth’s surface in the blue-

green (band 1), green (band 2), red (band 3), near- infrared (band 4), mid- infrared (band

5 and 7), and the far- infrared (band 6) portions of the electromagnetic spectrum. It

collects image data in a 16 day repeat cycle. Landsat 5 satellites operate in sun-

synchronous, near- polar orbits of about 705 km above the earth.

The Enhanced Thematic Mapper/ Landsat 7 satellite is a multispectral scanning

radiometer developed by NASA, was launched on April 15th, 1999. A swath width of

Landsat 7 image data is 185 km. It consists of 8 bands having pixel resolution 30 m for

visible blue (0.45- 0.52 µm), green (0.53- 0.61 µm), red (0.63- 0.69µm), near infrared

(0.78- 0.90µm), infrared (1.55- 1.75µm), SWIR (2.09- 2.35µm) and panchromatic (0.52-

0.90 µm) with pixel resolution 15 m (all are reflected radiation from the earth’s surface).

Thermal band having pixel resolution 60 m, emitted band with range (10.40- 12.50µm).


Landsat satellite sensors collect image data in a 16- day repeat cycle. It operates in sun-

synchronous, near- polar orbits at about 705 km above the earth.

In the present study, mineral discrimination is carried out using ASTER, Landsat- 7/

ETM.

1.2 LITRETURE REVIEW

Multispectral remote sensing involves the collection of visible, near infrared, and short-

wave infrared images in several broad wavelength bands. Since different materials reflect

and absorb differently at different wave lengths, it is possible to differentiate among

materials by their spectral reflectance signature as observed in these remote sensing

images, whereas direct identification is usually not possible. Developed for remote

sensing applications in the mid- 1980s, hyperspectral sensors acquire images in over one

hundred contiguous spectral bands. Well-developed application areas include geology;

forestry; marine, coastal zone and inland waters; agriculture; ecology; urban; snow and

ice; and atmosphere. There are also numerous military applications in camouflage,

littoral zone mapping and land mine detection.

1.2.1 ATMOSPHERIC CORRECTION

Introduction

The nature of remote sensing requires that solar radiation pass through the atmosphere

before it is collected by the instrument. Because of this, remotely sensed images include

information about the atmosphere and the earth’s surface. For those interested in

quantitative analysis of surface reflectance, removing the influence of the atmosphere is a

critical pre- processing step. To compensate for atmospheric effects, properties such as

the amount of water vapour, distribution of aerosols, and seen visibility must be known.

Because direct measurements of these atmospheric properties are rarely available, there

are techniques that infer them from their imprint on satellite data. These properties are

then used to constrain highly accurate models of atmospheric radiation transfer to

produce an estimate of the true surface reflectance. Moreover, atmospheric corrections of

this type can be applied on a pixel- by- pixel basis because each pixel in hyperspectral


image contains an independent measurement of atmospheric water vapour absorption

bands.

ENVI’s Fast Line- of- sight Atmospheric Analysis of Spectral Hypercubes (FLAASH)

module is a first- principles atmospheric correction modelling tool for retrieving spectral

reflectance from hyperspectral radiance reflectance. With FLAASH, you can accurately

compensate for atmospheric effects. FLAASH corrects wavelengths in the visible

through near- infrared and shortwave infrared regions, up to 3 mm (For thermal regions,

use the Basic-Tools- Pre-processing- Calibration Utilities- Thermal Atmospheric

Correction menu option). Unlike many other atmospheric corrections that interpolate

radiation transfer properties from a pre- calculated database of modelling results,

FLAASH incorporates the MODTRAN4 radiation transfer code. Correction for the

adjacency effects (pixel mixing due to scattering of surface- reflected radiance). An

option to compute a scene – average visibility (aerosol/ haze amount). FLAASH uses the

most advanced technique for handling particularly stressing atmospheric conditions, such

as the presence of clouds.

1.2.2 Minimum Noise Fraction Transformation

The minimum noise fraction (MNF) transformation is used to determine the inherent

dimensionality of image data, to segregate noise in the data and to reduce the

computational requirements for the subsequent processing (Boardman and Kruse, 1994).

The inherent dimensionality of the data is determined by examination of the eigenvalues

and associated eigenimages. The data space is divided into two parts: one associated with

large Eigen values and coherent Eigen images and a second with near unity Eigen values

and noise- dominated images. By using only the coherent portions in subsequent

processing, the noise is separated from the data, thus improving spectral processing

results. In a common practise, MNF components with Eigen values less than 1 are

usually excluded from the data as noise in order to improve the subsequent spectral

processing results (Qui et al. 2006).


1.2.3 Pixel Purity Index

The Pixel Purity Index (PPI) is a means to determine automatically the relative purity of

the pixels from the higher order MNF Eigen images (Boardman, 1993; Boardman et al.,

1995). The Pixel Purity Index is computed by repeatedly projecting n- dimensional

scatter plots into a random unit vector. The extreme pixels in each projection are

recorded and total number of time each pixel is marked as extreme is noted. A ‘pixel

purity image’ is created in which the digital number (DN) of each pixel corresponds to

the number of times that pixel was recorded as extreme.

1.2.4 n- Dimensional endmember visualization:

When spectral end members are defined from a hyperspectral image, each image

endmember should have the maximum abundance of the physical material that it

represents. (Ideally, each endmember would be a single pure material, but pure pixels of

each member may not be present in the image). If image spectra are represented as points

in an end- dimensional, scatter plot, endmembers should correspond to cusps at the edge

of the cloud of spectral points.

1.2.5 Spectral Angle Mapper:

The Spectral Angle Mapper (SAM) developed by Kruse et al., (1993) is a physically

based spectral classification that evaluates similarity between image and end member

spectra. This algorithm determines spectral similarity between the two spectra by

calculating the angle between them. Smaller angles represent closer matches to the

reference spectrum. The result is a classification image and a rule image for an each

endmember. Darker pixels in the rule images represent smaller spectral angles, and thus

spectra that are more similar to the reference spectrum. The SAM- score is compared in

terms of the angular difference, measured in radiance ranging from zero to π/2. The

smaller the spectral angle, the more similar is the unknown pixel to the reference (target)

spectra.


METHODOLOGY

2.1 AIMS AND OBJECTIVES

To identify the mineral zones

To broadly classify the mineral accumulation

To prepare the lithology map using image processing

2.2 STUDY AREA

The study area falls between 77o 45’ E and 79o 5’ E longitude and 14o 15’ N and 15o 40’

N in the western part of Cuddapah Basin. It covers an area of about 10,355 sq.km. Major

district covering the study area are Cuddapah, Kurnool and Anantapur (Fig: 1)

Cuddapah basin was formed during the earliest period of Middle Proterozoic. It has a

highly metamorphosed lithology. The sediments in the Cuddapah region are due to the

upturned edges of gneissic and schistose rocks of Archean age. With a profound

unconformity. The bucking effect of the earth produced the concave side facing the east

and the thinning of the crescent along the tip. The Western part of the Cuddapah basin

has a number of radial factures around the boundary which is due to the release forces

(release factures). The western part is comparatively less metamorphosed than the eastern

side.

2.3 METHODOLOGY

For the zonation of the mineral potential of Cuddapah Basin, satellite data ETM, SRTM,

ASTER were used.

Survey of India (SOI) Toposheet was used to prepare the Base Map, while the Geological

Survey of India (GSI) resource map was used to prepare the Lithology map and the

Mineral map.

Geomorphology map and Structure map were prepared using the ETM data.

The Structure map was further overlaid on the SRTM data for the detailed delineation of

the lineaments.

Image processing techniques such as Band Rationing, Minimum Noise Fraction, Principal

Component Analysis, Supervised Classification, and Unsupervised Classification were


done in order to identify structurally and Geomorphologically controlled alteration

zones.

ASTER data was processed for applying the mineral indices such as for Quartz, Calcite,

Mafic Minerals, Kaolinite, Alunite, OH bearing Minerals, Carbonate etc.

Further the processed image of the ETM and ASTER were compared to narrow down the

regions of the Alteration zones.

Those alteration corridors were taken and the Spectral Angle Mapper (SAM) is done

using the FLAASH module.

After the atmospheric correction of the ASTER data, radiance is converted to reflectance

followed by the Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). n-D

visualization is done and the end members are selected for Resampling using the USGS

mineral spectral Library.

Finally the SAM is run and the minerals of the selected corridors are mapped.


Figure 2


2.4 DATA PRODUCTS

Two digital data are used in this study, especially for digital image processing, such as,

ASTER

Landsat ETM


ABSTRACT

2.5 GEOLOGICAL SETTINGS OF CUDDAPAH

The crescent shaped Cuddapah basin, on the eastern fringe of the Eastern Dharwar

Craton (EDC), is surrounded by granitic and gneissic terrain including thin, linear N–S

trending K-granitic plutons and greenstone belts towards the north, west and south (Fig:4).

The semi-circular boundary on these three sides is an undisturbed geological contact

representing an Eparchean unconformity. The metamorphosed sandstones, shales, dolomite,

quartzites and limestones are the main rock types of the basin. The lithostratigraphy of the

basin is divided into the Cuddapah super group and the Kurnool group.

The Cuddapah supergroup of middle Proterozoic (1600– 1300 Ma) consists of three

sub groups, namely Papagni, Chitravati and Nallamalai. Eastern part of the basin is occupied

by the Nallamalai group of rocks which are highly disturbed, folded and faulted. They are

overlain by the Kurnool group of rocks of middle to late Proterozoic (980–520 Ma) period

in the central part. The Cuddapah supergroup is composed dominantly of argillaceous and

arenaceous sequences with subordinate calcareous sediments. Each of the three subgroups

starts with quartzite and ends with a shale unit representing an overall shallow marine shelf-

environment with cyclic transgression and regression in the basin. Contemporaneous igneous

activities are manifested as sills, flows and other intrusives along the western periphery of the

basin and the eastern Nallamalai group (Nagaraja Rao et al., 1987). Plausible explanations for

the evolution of more than 10-km thick sedimentary column in the Cuddapah basin are

equivocal. A crustal sag of more than 10 km was envisioned through vertical tectonic

movements along pre-existing faults.

The basic sills exposed in the south-western part led to the proposal that the western

Cuddapah basin evolved under a thermal regime. The eastern Cuddapah basin, with a linear

shape and complex fold patterns, seems to correspond well with a stress based model in

which a graben structure accounts for subsidence. Pulses of thermally driven crustal sagging,

punctuated by events of extensional stretching are supported by the multiple transgressive

and regressive cycles in the basin. A tectonic setting behind a collision belt is considered as

an alternative mechanism where such high order of crustal sagging is possible (Ramam and

Murty, 1997). This foreland basin is the Proterozoic analogue of the Phanerozoic Himalayan

orogeny with its foredeep Ganga basin in front of the rising mountain chain (Radha Krishna

and Naqvi, 1986). The adjoining, EGMB is mostly characterized by high-grade charnockite,

khondalite and leptinite of middle to late Proterozoic period. Sialic basement of orthogneisses


ABSTRACT

and grandiorites, which are known collectively as the peninsular gneiss, and limited

occurrences of K-granite, carbonatite, syenite and an orthosite form the next most important

lithologic unit in the EGMB. Though the number and ages of the metamorphic events in the

EGMB are debatable, the most widespread episode, when most of the granulites in the belt

are formed, was around 1000 Ma (Paul et al., 1990). The Nellore schist belt of early

Proterozoic period is located between the Cuddapah basin to the west and the granulite

terrane of the EGMB to the east.

The gneisses of 252 A.P. Singh, D.C. Mishra / Journal of Geodynamics 33 (2002)

249–267 Khammam schist belt, further north, exhibits arrested charnockitization features

and interpreted as the lower crustal assemblages of a collision (Sarvothaman, 1994). The

EGMB is further characterized by a series of sills and dykes with minor proportion of

volcanic rocks of the middle to late Proterzoic period known as Kandra volcanics or Kandra

igneous complex (Rao, 1992). The petrographic and petrochemical characteristics of this

volcanic belt led to the proposal that they belong to an ophiolite complex (Leelanandam,

1990). The largest Archean metamorphosed anorthositic complex (of about 200 km2) near

Kanigiri is another significant feature associated with the Nellore schist belt in this area. This

association of the Nellore greenstone belts and the Kanigiri anorthosite complex further

vindicate the tectonic setting of Proterozoic continental collision (Ramam and Murty, 1997).


ABSTRACT


ABSTRACT

PREPARATION OF THEMATIC LAYERS

3.1GENERAL

The thematic maps were preparation was done by visual interpretation using Landsat ETM

data. Maps like base map, lithology were prepared by the hard copy interpretation.

Lineament, geomorphology and structure maps were prepared using the soft copies of SRTM and

ETM in ArcGIS.

3.2 BASE MAP

The base map was prepares from the Survey of India Toposheet and it was further matched with the

Google earth data and the Landsat ETM data for updation. The major districts covering the study

area are Cuddapah, Kurnool and Anantapur (Fig 3)

3.3 LITHOLOGY AND MINERAL MAP

The lithology map was prepared using the Geology Map of Geological Survey of India (GSI).

The GSI maps of Kurnool, Cuddapah, Anantapur districts were manually interpreted and traced. It

was later scanned and digitized in ArcGIS. The area is almost equally covered by Limestone,

Dolomite, Shale, and Quartzite. Granite, Basic Sill, Basic Flow, Gneiss, Meta Andesite, were also

vectorized using GSI map (Fig 4). Minerals such as Asbestos, Barite, Clay, Copper, Diamond,

Limestone, High Grade Limestone, Ochre, Talc, Steatite, and Uranium were mapped (Fig 5).

3.4 STRUCTURAL MAP

In general the lineaments can be defined as a long linear or curvilinear feature which formed

by tectonism. According to Ramasamy (1986) lineaments id defined as a long linear or curvilinear

features which are structurally or tectonically controlled and can be seen only through aerial

photographs or satellite imageries. The remotely sensed data provides a panoramic view and are

once again a very good tool for mapping the pattern ad orientation of the hill ranges and the folded

structures.

The lineaments were first mapped on the basis of the morphological expression and

anomalies such as straight and sharp linear ridges, lithological straightness, sharp lithological

boundaries and contacts, offset lithologies and structural trend lines break in slopes, long ad linear

fracture valleys, soil tone linearity’s etc. using Landsat ETM data. It was then overlaid on the SRTM

data for detailed delineation of lineaments (Fig 6).

3.5 LINEAMENT DESITY


ABSTRACT

The total length of lineaments per unit area is called as lineament density. These types of

lineament density diagrams were used to find the structurally controlled alteration zones (Fig 7).

3.6 GEOMORPHOLOGY

The size, shape, pattern configuration and the spatial networks of the various landform

elements have been interpreted using ETM data ad various landform which have been formed due

to different geomorphic process have been interpreted such as Structural hills, Pediments, Fracture

Fills, Colluvial fills, Flood Plains etc. These different landforms express distinct morphogenetic

expression of their own in the satellite imagery (Fig 8). The following are the geomorphological units

demarcated from the study area by onscreen interpretation.

Bazada

Colluvial fills

Deep pediments

Dissected lateritic upland

Flood plain

Fracture fills

Lateritic uplands

Linear ridges

Moderate pediments

Plateau

Reservoir

River

Shallow pediments

Structural hills

Valley fills

3.6.1 Structural Hills

These are the hills formed due to the regional tectonics showing structural trends of

the region. The hill ranges in general have shown anticlinal hills, synclinal valleys and the

ring shaped hills which could be easily delineated from the satellite imagery.

3.6.2 Linear Ridges


ABSTRACT

Linear ridges are also the structural hill which are linear i shape trending few

kilometres, the features are delineated in the same way as like structural hills with the same

tone, texture, shape and linearity in addition

3.6.3 Plateau

Plateau is a flat topped hill is an area of high land usually with a flat terrain. It is also

called as high plain or tableland.

3.6.4 Fractured Valley

Valleys are deep depression found in between two hills formed as a result of rapid

erosion. A valley formed due to fracture are called fracture valley, in the study area they are

filled with debris and vegetation. In satellite imagery the barren and filled fracture valley

could be easily differentiated with the tone. A filled fracture valley will be fully loaded with

good vegetation hence giving good red tone.

3.6.5 Pediments

These are generally sloping bedrock surface terminating against a mountain front,

usually by a sharp breaking slope. Pediments are identified as gently undulating rock folded

surface without weathered mantle, but often partly mantled with thin veneer of soil obtained

from the upland masses bordering the hills. This is formed by combination of process, such

as back weathering, sheet wash and lateral erosion. These zones are generally low permeable

with poor infiltration and are considered to be the run-off zones. However, the structural

features such as fractures, joints, which transverse this unit could act as a good recharge

zones in LANDSAT satellite imagery pediments are identified by their grey to yellow tone,

medium texture, highly irregular pattern and close association with foothills indicates the

pediments where the slope is low to moderate, generally the left out rocky materials preen

over here.

a) Buried Pediment shallow: This unit is characterised by very high run-off and poor

groundwater recharge region. Consists of very low weathered zone with thickness of

materials varying up to 5m. In the Imagery Shallow Pediment is identified by pale yellowish

tone lacks vegetation.


ABSTRACT

b) Buried Pediment deep: The thickness of this zone varies from 10 to 20m and favours a good

amount of water to circulate within the zone before reaching the deeper fracture zone. In

this zone infiltration is moderately good. Deep pediments are identified in the imagery by

dark reddish tone and coarse texture.

c) Buried Pediment medium: These areas are in the mid category of deep ad shallow, these

could be located by its irregular and coarse texture having the mixed tone found in deep and

shallow pediments.

3.6.6 Colluvial Fills

Colluvial fills are the eroded material brought down the hill by the drainages. In any

satellite imagery this fills could be located by its finger shaped flow in the foot hills, good

results can be made by further image processing like PC Analysis.

3.6.7 Flood Plain

Flood plains are found along the course of any river in the mature stage, it is to be

noted that water can reach the reach the maximum extent of the flood plain when the same

river is flooded. The flood plains are potential areas of ground water thus having good

vegetation showing deep red tone in FCC.


ABSTRACT


ABSTRACT

DIGITAL IMAGE PROCESSING

4.1 LANDSAT ETM

In order to take advantage of and make good use of remote sensing data, we must be

able to extract meaningful information from the imagery. Much interpretation and

identification of targets in remote sensing imagery is performed manually or visually, i.e. by

a human interpreter. Recognising targets in the key to interpretation and information

extraction. Observing the differences between targets and their backgrounds involves

comparing different targets based on any, or all, of the visual elements of tone, shape, size,

pattern, texture, shadow and association.

The digital image processing involves the manipulation and interpretation of digital

images with the aid of a computer. The computer is programmed to insert the data into an

equation, or series of equations, and then store the results of the computation for each pixel.

These results from a new digital image that may be displayed or recorded in pictorial format

or additional programs may further manipulate itself. Image processing applications software

normally resides as an executable command module on the hard disk of a system. These

characteristic of softwares is essential and helpful to users.

Digital processing may be used to enhance data as a prelude to visual interpretation. Digital

processing and analysis may also be carried out to automatically identify targets and extract

information completely without manual intervention by a human interpreter. Digital image processing

may involve numerous procedures including formatting and correcting of the data, digital

enhancement to facilitate better visual interpretation, are even automated classification of targets and

features entirely by computer. In order to process remote sensing imagery digitally, the data must be

recorded and available in the digital form suitable for storage on a computer tape or disk.

At last but not the least, an important element in the image analysis is integration of data. In the early

days of analog, remote sensing when the only remote sensing data source are aerial photography, the

capability for integration of data from different sources was limited. Today, with most data available

in digital format from as wide array of sensors, data integration is a common method used for

interpretation and analysis. Data integration fundamentally involves the combining or merging of data

from multiple sources in an effort to extract better and/ or more information. This may include data

that are multi temporal, multi resolution, multi sensors or multi data type in nature.


ABSTRACT

The various techniques of digital image processing are image rectification techniques,

enhancement techniques, spatial fixture techniques, band combination techniques,

classification techniques, spectral wizard techniques and also statistical analysis. The image

processing techniques prefer the greatest potential for preserving the correct radiometry and

the maximum resolution of the images. It is fast and accurate and flexible to cater to the

analyst requirement. This helps the scientists in precise image interpretation.

Image Processing Software

For the present study, the digital image processing were done with the help of ENVI

(ENvironment for Visualizing Images) software and its run by IDL (Interactive Data

Language) program. ENVI strengths lies in its unique approach to image processing. It

combines file-based and band-based techniques with interactive functions. This software is

usefully interactive, prompting the user for specified inputs.

4.2 PROCESSING TECHNIQUES APPLIED

4.2.1 Band Ratio

Band rationing is powerful technique for extracting spectral information from multi spectral

imagery and is widely used for lithologic and alteration mapping. Band ratio images enhance

spectral differences between rocks suppress topography affects. Atmospheric corrections should

be made carefully to provide meaningful ratio image. Multispectral remote sensing is useful for

recognising altered rocks because their reflectance spectral differ from those of country rock

(Sabins 1987).

4.2.2 Principal Component Analysis

Principle Component Analysis (PCA) is used in various applications including remote sensing

(Blodget et al 1978) PCA is used to compress multichannel image data by calculating a new

coordinate system, so as to condense the scene variance in the original data into a new set of

variables which are called principal component (PCs). These data are decorrelated and most of the

image variance I confined within the first few channels. In the study, after transformation, the data

were scaled or linearly contrast stretched. This procedure increases the spectral discrimination

capacity among terrestrial materials (Qari 1991) (Fig 13).

4.2.3 Minimum Noise Fraction

The Minimum Noise Fraction (MNF) transformation is used to determine the inherent

dimensionality of image data, ti segregate noise in the data, and to reduce the computational

requirements for subsequent processing (See Boardman and Kruse, 1994). The MNF transform

has modified from Green et al. (1998) and implemented in ENVI is essentially two cascaded


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Principal Component’s transformations. The first transformation, Based on an estimated noise

covariance matrix, decorrelates and rescales the noise in the data. The first step results in

transformed data in which the noise as unit variance and no band-to-band correlations. The

second step is the standard principal components transformation of the noise whitened data. For

the purpose of further spectral processing, the inherent dimensionality of the dta is determined by

the examination of the final eigen values and the associated images. The data space can be

divided into two parts: one part associated with large eigen values and coherent eigen images, and

a complimentary part with near-unity eigen values and noise- dominated images. By using only

the coherent portions, the noise is separated from the data, thus improving spectral processing

results (Fig 14).

4.2.4 Target Detection

Target detection is the process of searching an input image from specific images (Termed the

Target) suspected to be present in very low concentration. In typical analysis scenario, the analyst

knows the spectral signatures of low probability materials of interest (Target), but does not have

background spectra and does not wish to generate them. This is a good task for the target

detection work flow.

4.3 BAND COMBINATION FOR MINERALS

Digital image processing play a major role in this study by bringing out many hidden

resources and thereby making way for future generation for fast and reliable processing technique to

pull out the resources. There are number of processing techniques each ends with different valuable

outputs.

The data product used for processing are Landsat ETM and ASTER. It is not possible locate

all the minerals in wide bands, the ETM data used for the study were also having wide band widths,

hence those minerals that have a good surface exposure were closely spotted.

The RGB combination of 4/7, 2/7, 1/7 enhances the carbonates of the study area, blue tone of

the image shows the region that has a carbonate alteration (Fig 15).

Iron oxides can be distinguished well in the ETM band 3(Fig 16).

Hydroxyl minerals can be located easily in the band combination of 5/7, 3/2, 4/5. The pink

shade represents the OH bearing minerals which is widely present in the study which is

further narrow downed by the ASTER processing (Fig 17).


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Silica can be narrowed down in the band rationing of ETM band 3 and band 5. The coarse

texture and the bright tone of the image helps to identify the silica rich region in the study

area (Fig 18).

The vegetation can be clearly mapped in the Band 1 of the ETM data. The bright tone

represents the vegetation zones in the study area. They are mostly present in the flood plains

(Fig 19).

The RGB band combination of 5/7, 5/4, 3/1 and 5/7, 3/1, 4/3 will help to delineate the

alteration zones on the whole from the imagery (Fig 20).

5/7, 5/4, 3/1 gives the region where the iron oxides have undergone hydrothermal alterations

(Fig 21).

Similarly 7/4, 4/3, 5/4 shows the iron ions in red tone, vegetation in green tone and hydroxyl

minerals in blue tone (Fig 22 ).

Granite is visible in the band 3 of Landsat ETM is processed with MNF (Fig 25).

4.4 ASTER

The advanced space brone thermal emission and reflection radiometer (ASTER) is a

high spatial, spectral and radiometric resolution multispectral remote sensing sensor on

national aeronautics and space administration (NASA) earth observing system AM-1 (EOS

AM-1) polar orbiting spacecraft. It was launched in December 1999. EOS AM-1 spacecraft

operates near polar orbits at 705 km altitude. The recurrent cycle is 16 days. The ASTER

instrument is a cooperative effort between the Japanese Ministry of Economic Trade and

Industry (METI) and NASA. It has three separate instrument subsystems which provide

observations in three different spectral regions of electromagnetic spectrum, including visible

and near infrared radiation (VNIR), shortwave infrared radiation (SWIR) and thermal

infrared radiation (TIR). The VNIR subsystem has three recording channels between 0.52 and

0.86 µm and an additional backward-looking band for constructing digital elevation models

(DEMs) with spatial resolution of 15 m. The SWIR subsystem has six recording channels

from 1.6 to 2.43 µm, at a spatial resolution of 30 m, while the TIR subsystem has five

recording channels, covering the 8.125 to 11.65 µm wavelength region with spatial resolution

of 90 m. ASTER swath width is 60 km (each scene is 60 × 60 km ) and is useful for regional

mapping, but cross-track pointing capability extends the total viewing to 232 km. ASTER


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sensor can acquire approximately 600 scenes daily (Fujisada, 1995; Abrams and Hook,

1995; Yamaguchi et al. 1999; Abrams, 2000; Yamaguchi et al., 2001).

ASTER provides data useful for studying the interaction among the geosphere,

hydrosphere, cryosphere, lithosphere and atmosphere of the earth from the geophysical point

of view. To be more specific, wide range of science investigations and applications includes:

(1) geology and soil; (2) land surface climatology; (3) vegetation and ecosystem dynamics;

(4) volcano monitoring; (5) hazard monitoring; (6) carbon cycle and marine ecosystem; (7)

hydrology; (8) aerosol and cloud; (9) evapotranspiration rate; and (10) land surface and land

cover change (Fujisada, 1995). ASTER standard data products are available ‘on-demand’

from the Earth Remote Sensing Data Analysis Center (ERSDAC, Japan) and the EROS data

center (EDC, USA). Basically, all the ASTER captured data are processed to generate

Level1A data product. It consists of unprocessed raw image data and coefficients for

radiometric correction. The Level-1B (radiance-at-sensor) data product is a resampled image

data generated from the Level-1A data by applying the radiometric correction coefficients.

Level-2B data products of physical parameters including surface radiance data with nominal

atmospheric corrections (Level-2B01), surface reflectance data contains atmospherically

corrected VNIR-SWIR data (Level-2B07 or AST-07), surface emissivity data with

MODTRAN atmospheric correction and a temperature-emissivity separation (TES) algorithm

(Level-2B04) are generated on the basis of user request. Level-4B data product is also

generated under the user request from the alongtrack stereo observation in the near infrared

channel (band 3N and 3B) in order for the construction of digital elevation models (DEMs).

Level-3A is a geometrically well-corrected orthorectified ASTER standard data product with

ASTER-driven DEM, which is radiometrically equivalent to Level-1B radiance-at-sensor

data (Gillespie et al., 1998; Yamaguchi et al., 1999; Abrams, 2000; Yamaguchi et al., 2001;

Ninomiya, 2005, 2003a, b, c).

Recently, two new crosstalk-corrected ASTER SWIR reflectance products had been

released, which include: (1) AST-07XT SWIR reflectance data product available ‘on-

demand’ from ERSDAC and EDC (Iwasaki and Tonooka, 2005) and (2) RefL1b SWIR

reflectance data product complied and introduced (Mars and Rowan, 2010). The AST-07XT

SWIR reflectance data product is similar to AST-07 surface reflectance data that consist of

VNIR and SWIR bands, except that the crosstalk correction algorithm and atmospheric


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correction (nonconcurrently acquired MODIS water vapor data) have been applied (Iwasaki

and Tonooka, 2005; Biggar et al., 2005; Mars and Rowan, 2010). RefL1b and AST-07XT

reflectance datasets have crosstalk correction; differences between these datasets should be

caused by the addition of the radiometric correction factors and use of concurrently acquired

water vapor data in the atmospheric correction of the RefL1b data (Mars and Rowan, 2010).

During scene acquisition of ASTER data, there is optical ‘crosstalk’ effect caused by stray of

light from band 4 detector into adjacent band 5 and 9 detectors on SWIR subsystem. Such

deviations from correct reflectance result in false absorption features and distortion of

diagnostic signatures and consequently misidentification of spectroscopic results.

4.5 APPLYING MINERAL INDEX

The mineral indexes were done by using the Band Math tool from the ENVI software. The

formula used in this mineral index enhances the specific mineral according to their response

to each band.

Stabilized Vegetative Index (StVI) = [𝑏𝑎𝑛𝑑 3

𝑏𝑎𝑛𝑑 2] [

𝑏𝑎𝑛𝑑 1

𝑏𝑎𝑛𝑑 2] (Fig 26)

OH bearing Alteration Mineral Index (OHI) = [𝑏𝑎𝑛𝑑 7


𝑏𝑎𝑛𝑑 4


Kaolinite Index (KLI) = [𝑏𝑎𝑛𝑑 4


𝑏𝑎𝑛𝑑 8


Alunite Index (ALI) = [𝑏𝑎𝑛𝑑 7


𝑏𝑎𝑛𝑑 7

𝑏𝑎𝑛𝑑 8]

Calcite Index (CLI) = [𝑏𝑎𝑛𝑑 6


𝑏𝑎𝑛 𝑑9


Quartz Index (QI) = [𝑏𝑎𝑛𝑑 10∗𝑏𝑎𝑛𝑑 10

𝑏𝑎𝑛𝑑 10∗𝑏𝑎𝑛𝑑 12] (Fig 30)

Carbonate Index (CI) = [𝑏𝑎𝑛𝑑 13


Mafic Index (MI) = [𝑏𝑎𝑛𝑑 12



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4.6 IDENTIFICATION AND EXTRACTION OF ENDMEMBERS

Any spectral mapping methods require an end member value to accurately map a particular

feature and differentiate it from other. Hence the accuracy of the result depends upon the purity of the

endmember. The endmember values can be obtained as follows:

1. From the spectra corresponding to pixels within the image,

2. From library of endmembers spectra either measured in the laboratory or in the field,

3. With the automatic methods of constructing end members from higher order PCA (Principal

Component Analysis) eigenvalues (Boardman, 1993), Pixel Purity Index (PPI).

4.6.1 PIXEL PURITY INDEX (PPI)

The Pixel Purity Index (PPI) is a means of identifying spectrally pure in multi spectral

images. These pure pixels typically correspond to the mixing endmembers. The PPI is

computed by repeatedly projecting n- dimensional scatter plots onto a random vector. The

extreme pixels in each projection recorded and the number of times that pixel was recorded

as being extreme. PPI plot shows the number of extreme pixels satisfying the threshold

criteria found by the PPI processing as a function of the number of iterations.

4.6.2 IDENTIFICATION OF ENDMEMBER

The choice of endmember is very important in unmixing analysis; hence, it has to be

chosen with careful analysis. As stated above PPI method was used to identify the

endmembers.

In order to extract the purest pixels, PC 1 band was chosen since it consists maximum

information in it. About 50,000 iterations were given and the PPI output image is generated when the

PPI curve reached a stable straight line. The pixel with higher PPI score indicates higher purity and

hence, appears brighter in the image. Pixel with lower score is less ‘spectrally pure’ and appears

darker in the PPI image. Pixels with higher PPi score are usually taken as endmembers for using

spectral unmixing (Boardman 1993). The PPI images were then linked with the FCC of the

corresponding images to obtain the pixels which were brighter and the feature that corresponds to

particular pixels were identified to form 3 classes namely vegetation , hill, sand.


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4.6.3 EXTRATION OF ENDMEMBERS

The endmembers for 3 classes were chosen from the plot window of the spectra of the image

pure pixel. Pixels with high PPI score were taken as endmembers.

4.6.4 SPECTRAL ANGLE MAPPER

The Spectral Angle Mapper (SAM) is a physically based spectral classification that uses an n-

dimensional angle to match pixels to reference spectra. The algorithm determines the spectral

similarity between two spectra by calculating the angle between the spectra, treating them as vectors

in a space with dimensionally equal to the number of bands. This technique, when used on calibrated

reflectance data, is relatively insensitive to illumination and albedo effects. (Kruse et. al, 1993) SAM

compares the angle between the end member spectrum vector and each pixel vector in n- dimensional

space. Smaller angles represent closer matches to the reference spectrum. Pixels away than the

specified maximum angle threshold in radiance are not classified.

The end members for the SAM classification is also derived from the PPI approach. The pixels with

the highest score is taken as the end member.

The endmembers thus selected are compared with the FCC and is then given as an input to SAM

classifier. SAM for each individual image is run by selecting number of endmember for specific

minerls. It is then overlaid on the various ASTER single bands inorder to find the minerls occurances.

The output is then compared with the Lithology map, Mineral map and the Mineral Index maps

prepared by the Digital Image Processing and the Minerals are mapped along with the alteration

zones.


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RESULTS AND DISCUSSION:

The digital image processing has given good results especially in finding out the

altered minerals. Band ratios highlighted various altered minerals whereas the Principal

Component Analysis discriminated the different lithological characters. The digital image

processing of LANDSAT ETM data when compared and correlated with the Lithology and

Mineral map prepared from the Geology and Mineral Resource map of Geological Survey of

India could give us the broad classification of the region that has been altered.

When these maps are overlaid on the Lineament density map, the identification of the

structurally controlled alteration zones is quite easy and accurate. Similarly the

Geomorphologically controlled are also identified and this gives the corridors of the

alteration zones. These corridor or alteration windows can still be narrowed down using the

ASTER data processing for specific altered mineral zone mapping.

Mineral index applied on the various band math combinations of the ASTER data

helps us to delineate the region that has a unique kind of alteration or the accumulation of the

specific mineral type. (Eg. OH bearing alteration mineral index, Calcite index, Mafic index,

Quartx index, etc.,)

The FLAASH (Fast Line of Sight Atmospheric Analysis of Spectral Hypercubes)

module of Digital Image Processing uses the meta data of the particular image and processes

it for atmospheric correction modelling for retrieving spectral reflectance from thee radiance

images. SAM module of the Digital image processing is a powerful tool for the narrow band

data like ASTER to target the mineral zones that are altered. The end members of the pure

pixel index that is derived from atmospherically corrected FLAASH image is imported to

SAM module. SAM measures the similarity of unknown and reference spectra in n-

dimensions. Thus the altered minerals of the selected corridors are found.

Finally the overlaying of SAM output on the single bands of ASTER data could zone

minerals altered in the Cuddapah Basin namely Hornblende, Magnetite, Orthoclase, Apatite,

Olivine, Pyrite, Biotite, Butlerite, Quartz, Steatite, Talc, Hematite, Illite, Hypersthene, Silica,

Magnesite and Barite.

Accurate alteration mapping can be done by further extensive field study.


ABSTRACT

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