CHAPTER-II

MATERIALS AND METHODS

2.1 INTRODUCTION

The quality and accuracy of resource and environmental assessment using remote sensing

and GIS techniques depends upon the data products used. proper interpretation and

analysis of the data and extent of field validation. Though the physical verification can be

done to a certain extent by the field survey, but the chemical characteristics of earth

materials require careful analysis in the laboratory. The present chapter deals with the data

used and the details of the methodology used.

2.2 DATA USED:

2.2.1 Remote Sensing data:

(a) Analog: IRS IB LISS-ll FCC(432); path: 23 row: 51 on I :50,000 scale

(DOP : 08-03-I994)

(b) Digital: IRS lB USS-1 CCT; path: 23 row: 51 (DOP: 01-03-1992)

IRS IB USS-ll CCT; path: 23 row: S I (DOP: 09-I O-I996)

2.2.2 AnciUary data:

2.2.3 Field data:

(a) Survey oflndia toposheet no. 63 UI3, 63 Ul4, 63 UIS, 63 P/2, 63 P/3 and 63 P/4 on I :50,000 scale

(b) Published work on geology (GSI), groundwater (CGWB), UP JaiNigam

(c) Lithology and other aquifer parameters (UP Jal Nigam, unpublished report)

(I) Lithological and structural information

(2) Landform/Landuse

(3) WeD inventory, mainly water level data

( 4) Water and soil samples

Materials and Methods

2.2.4 Laboratory data:

( 1) Visual interpretation of satellite data to prepare thematic maps

(2) Various enhanced and classified products of digital satellite data

(3) Chemical analysis of water and soil samples

2.3 WORK PLAN

The whole process of the study carried out under the present research work can be broadly

grouped under three stages, viz., Pre-field stage, Field work and post field stage. The

details of work under each stage are listed below:

a) Pre-field stage: -Visual interpretation of satellite data and preparation of various

b) Field work:

thematic maps.

-Digital enhancement of satellite data and improvement upon the

thematic maps.

-Preparation of slope map using toposheet.

-Field verification of interpreted maps.

-Collection of additional field information on various themes.

-Collection of soil and water samples for analysis (Fig. 2.1 ).

-Collection of well-inventory data.

c) Post field stage: -Finalisation of thematic maps incorporating field information.

-Chemical analysis of soil and water samples.

-Data integration and GIS analysis.

-Compilation of the results.

2.4 VISUAL INTERPRETATION OF SA TEL LITE DATA:

Visual interpretation of analog data (hard copy FCC of IRS IB) was carried out taking into

account of different image elements and associated features such as tone, texture, size,

shape, pattern, shadow, association etc. together with the Survey of India toposheets to

10

Materials and Methods

prepare tentative geological, geomorphological. landuse and lineaments maps (Barret and

Curtis. 1982~ Drury. 1987 and Sabins. 1987).

These maps were transferred on the base map prepared from Survey of India toposheet.

Doubtful areas and the interpreted features were selected for ground verification to confirm

the interpreted data and also to have a better understanding of the terrain and terrain

features.

2.5 DIGITAL IMAGE PROCESSING

Digital image processing was carried out in Regional Remote Sensing Service Centre

(RRSSC), Dehradun, using following hardware and software

Hardware: ffiM 6000 workstation

Sojtwllre: EASI-PACE (version 7) and window based ERDAS (8.2)

The digital image processing included image rectification and image enhancement.

2.5.1 Image Rectification/Registration: Digital data both LISS-I and LISS-II from

computer compatible tapes (CCT) were downloaded to the computer and converted to

appropriate format of software. A small window of 340 x 560 and 565 x 1200 pixels of

image from LISS-I and LISS-11 respectively, covering the area of study were extracted

from the data.

LISS-II data was also geometrically corrected with reference to Survey of India (SOl)

topographical maps in order to achieve positional accuracy during landusellandcover

classification. Various image processing techniques were applied on this sub-scene.

2.5.2 Digital image enhancement: Image enhancement is the modification of an image

to alter its impact on the viewer. The purpose of the enhancement was to improve image

interpretation by amplification of the desired spectral or spatial characteristics while

suppressing non-essential characteristics.

11

Materials and Methods

Various digital enhancement and information extraction teclmiques have been applied on

the digital data of the study area, by taking into account variance in data statistics and the

different objectives of study.

a. Data Statistics: As a preliminary step before starting any processing of the four bands of

IRS LISS-I and LISS-11 (Band 1,2,3,4) were displayed and studied individually by simple

linear stretching. Statistics for all the above mentioned four bands were generated to

understand data distribution. variance, covariance and correlation among them. The

statistics are given in Table 7.1 & 7.2 in chapter- VII.

b. FCC Generation by Raw Band Combinations: From variance-covariance and

correlation matrices, the Optimum Index Factors (OIF) (Rathore and Wright, 1992) for

each possible raw band combination were taken into consideration. Details of OIF

calculation has been mentioned in Chapter- VII (Table 7.3).

2.5.2.1 Contrast Enhancement: For the study area both linear stretching (min.-max.

contrast stretch) and histogram equalisation were applied (Sabins, 1987). It is often

observed that the processed data falls towards higher side of 0-255 range and easily gets

saturated colours, thus obscuring scene details. In order to avoid this problem, the image is

often complemented, thus shifting the histogram towards lower end of 0-255 range. This

has been used in the present analysis in order to get all the finer details from all the bands in

false colour composite (FCC) mode (Ray et al., 1995).

2.5.2.2 Edge Enhancement: In edge enhancement, not only the edges are enhanced, but

also the background details are preserved. It is being achieved by high pass filtering along

two directions and combining the results in a vector calculation. In the present study area,

Laplacian non-directional filters were found to be useful (Jensen, 1986). The following 3x3

vector was used for Laplacian filter.

0 -1 0

-1 5 -1

0 -1 0

12

1.5.2.3 Ratioiog: Ratio images (Sabins, 1986) were prepared by dividing the DN value of

one band by the corresponding DN value in another band for each pixel. Different

combination of ratios were tried in the study area. Due to higher atmospheric noise (Drury,

1987) in the scene, none of the ratio image was found suitable in the study area.

1.5.2.4 Principal Coalponeat Analysis (PCA): Principal component analysis bas proven

to be a very powerful technique for the analysis of correlated multidimensional data

(Jensen. 1986). The n-dlannel multispectral data can be considered as n-dimensional data.

The transformation of the raw remotely sensed multi band data using PCA can result in

new principal component images, which are more interpretable as these are having

compressed uncorrelated bands information.

For this analysis, the original four bands of IRS data were projected on 3 new principal

components as linear, additive combinations by using eigen vectors. The highest eigen

value is associated with the first principal component and progressively lower eigen values

with each higher order principal component. The result is that the first principal component

is generally a weighted average of all band data and has the greatest contrast. The next

higher order components (PC2) express deviations of various kinds from this average and

contain information relating to geological and vegetation in the scene. Any nOise in the data

is contained in the higher order component. Various statistical parameters ofPCA has been

described in Chapter- Vll (Table 7.4).

1.5.1.5 Intensity, Hue and Saturation (ffiS) Transformation: After combining any

three spectral bands in RGB mode for FCC, the colour images generally lack saturation

which is due to the high degree of correlation between two spectral bands. To correct this,

a method of enhancing saturation is used by transfonning any three bands of satellite data

from RGB to HIS system in which the three components of image represents Intensity (I),

Hue (H) and Saturation (S).

Intensity relates to the total brightness of a colour. It is a measure of the total energy

involved in all wavelength and is akin to albedo.

13

Materials and Methods

Hue refers to the dominant or average wavelength of the (Electro-magnetic) EM energy

contributing to a colour. It is the average wavelength of energy reflected by the object.

Saturation specify the purity of colour relative to grey. Spectrally pure colour results in

high value of saturation. Low saturation means more mixture of different wavelength.

The transformation ftom RGB to HSI colour space is performed by a rotation of axis ftom

those representing RGB in Cartesian co-ordinate to Spherical co-ordinates representing

HSI (Drury, 1987). The polar axis of the HSI sphere is the line where red, green and blu~

DN are equal. This defines all pixels units are grey and is known as grey axis, along which

intensity increases ftom black to white. Saturation at any value of intensity increases in

concentric circles away from the grey axis. Hues are represented as a radial sequence

around these circles of saturation.

For the present study IRS 1B band 2,3.4 were used for RGB-IHS transformation.

2.5.2.6 Hybrid FCC: Different combination of FCC in RGB mode were generated taking

all the above parameters of raw bands.

Different features get highlighted in individual bands and combination of bands. So, the

slides were prepared for all the unique digitally enhanced products and the highlighted

features were interpreted and transferred on the common base maps of respective thematic

maps using PROCOM optical enlarger to get composite thematic maps.

All the digitally enhanced products has- been given in Plates 2.1 & 2.2.

2.6 INTERPRETATION OF TOPOGRAPHIC MAPS

From Survey of India toposheets drainage map and slope map were prepared.

Morphometric parameters of sub-basins were calculated. These have been described in

respective sections in detail.

14

Plate l .la: FCC ofiRS lB LISS-1 bmdJ 431 (RGB) Plate l .lb: FCC ofiRS lB LISS-1 PC 113 (RGB) (Unear atret~)

Plate 2.2a: B&W PC2 Plate 2.2b : FCC of ffiS I B LISS-11 Band 432 (RGB) .

Materials and Methods

2.7 FIELD WORK

• All the interpreted thematic maps were checked in the field along selected traverses and

interpretation keys for the scene was prepared for different features for the

modification of map.

• The lineaments and faults interpreted from images were checked in the field on the basis

anomalies, moisture zones, topographical breaks, linearity of stream courses etc.

• Training sites for different landuse/landcover classes were selected for all over the area.

• Well and handpump inventory was done.

• Groundwater and surface water samples were collected in pre-washed polyethylene

bottles ( 500 ml) from different places (Fig.2.1 ).

• Soil samples were collected from different places (Fig.2.1) covering different landuse

classes taking 0-25 em of top soils. These were stored and marked in polyethylene bags.

• Lithological and structural characteristics of all the lithounits with respect to

groundwater occurrence and erosion status were observed.

2.8 POST FIELD WORK

2.8.1 Finalisation of thematic maps: All the thematic maps were finalised

incorporating all information and transferred to a base map made from Survey of India

toposheets.

2.8.2 Supervised classification of digital satellite data: On the basis of signature

statistics (mean, standard deviation, correlation) of different training sites were studied and

supervised classification using maximum likelihood algorithm was performed.

2.8.3 Chemical analysis of water samples: The water samples were filtered in the

laboratory through o.45 J.lm Whattman membrane filters using a vacuum filtration set up.

2.8.3.1 pH and Electrical Conductivity (EC): pH determinations of the collected samples

were made in the laboratory at 25°C using a pH electrode calibrated with standard buffers.

For the pH measurements, the pH electrode was immersed in the filtered sample and stirred

17

WATER AND SOIL SAMPUNG LOCAllON MAP

+W2 +S1,2; w1.DALA w2., +s7,a

W28

+W9

-+W16

W14 +S15,16 ++w15

W18 W: -q. 517,18,19,20; 20

w19 • HATHAWANI

+W25

• RENUKUT

Scale

Fig. 2·1

W.f +W21 S21,22

+ W - Water samples

+ S - Soil samples

N

Materials and Methods

using a magnetic bead, the calibration of the sample with the atmospheric C02 took about

20 minutes and the reading taken when the drift was less than 0. 05 pH units.

Measurements of electrical conductivity was made by a conductivity meter, which

simultaneously measure temperature and conductivity. Final reading was taken after a

temperature correction.

2.8.3.2 Determination of Cations: The determination of cations (Ca++, Mg++, Na+ and K+)

was made using flame spectroscopy methods on a GBC 906 Atomic Absorption

Spectrophotometer. The instrument was calibrated using different chemical standards.

Calcium and Magnesium were analysed in the absorption mode and Sodium and Potassium

in the emission mode.

2.8.3.3 Determination of anions:

(a) Bicarbonate: Bicarbonate determinations were made by the potentiometric titration

method (APHA, 1982). Standard solutions were prepared in the range of 25-500 ppm. In

this method no indicator was used, dilute HCI (0.03 N) was added to both standard and

samples. The end point was taken at 4.5 pH.

(b) Sulphate: The determination of sulphate was done by the turbidimetric method

(APHA, 1982). S042- ions precipitated in an acetic acid medium with barium chloride form

barium sulphate crystals of uniform size. Light absorption of the BaS04 suspension was

measured at 420 nm on a spectrophotometer and the sol- concentration determined by

comparison with a standard curve.

(c) Dissolved Silica: The dissolved silica content was determined by the heteropoly method

(APHA, 1982). The yellow molybdosilicic acid (produced by the molybdosilicate method)

is reduced by means of aminonaphthol-sulphonic acid to heteropoly blue. The blue colour is

more intense than the yellow colour produced by the molybdo-silicate method, and

provides increased sensitivity. The absorbance is measured at 812 nm and the concentration

measured by comparison with a standard curve.

19

Materials and Methods

(d) Phosphate: Phosphate was determined by the Ascorbic acid method (APHA, 1985).

Phosphate standard solution of different concentration were prepared from the potassium

dihydrogen phosphate (KH2P04). 40 ml of each standard and water samples were pipetted

out into a 50 ml volumetric flask and 5 ml of molybddate antimony solution and 2 ml of

ascorbic acid solution was added and mixed well. The mixture was diluted to 50 ml and

absorbance was measured at 640 nm using UVMS spectrophotometer. Molybdate

solution was prepared by dissolving 4.8 gm of ammonium molybdate and 0.1 gm of sodium

antimony tartrate in 400 ml of 4N sulphuric acid and making the total volume to 500 ml

with the same acid. Ascorbic acid was prepared by dissolving 2 gm of ascorbic acid in 100

ml water.

(e) Chloride: Chloride content was determined by 'Radelkis' chloride ion selective

electrode in combination with a double junction reference electrode and 'Racho' pH/mV

meter. 25 ml of each sample and a series of chloride standards was mixed with equal

volume of Ionic Strength Adjustment Buffer (ISAB) (prepared by dissolving 15.1 gm

Sodium bronate in 800 ml of distilled water. 75 ml cone. HN03 was added and solution was

stirred well before diluting to 1 litre by distilled water). The electrodes were conditioned

and dipped into sample to note stable reading in m V. The standard graph was plotted

between known concentration and m V values and final reading was recorded from this

graph.

(f) Flouride: Flouride concentration was determined by 'Radiometer' Ion 85 ion analyser.

25 ml of each sample and standard solution (0.1 mg/1 - 10 mg/1) was mixed with equal

volume of Total Ionic Strength Adjustment Buffer (TISAB) (prepared by mixing 1M

Sodium chloride, 0.23 M Ascetic acid, 0.75 M Sodium acetate and 0.001 M Sodium

citrate). The instrument was calibrated with standards after compensation for temperature

had been done.The concentration of fluoride was directly recorded by instrument.

(g) Nitrate: Nitrate was determined by Consort ion meter using standard nitrate electrode.

100 mi of each sample and standard solution was mixed with 0.2 ml of 2M ammonium

sulphate solution. The electrodes were conditioned and dipped into sample to note stable

20

Materials and Methods

reading in mV. The standard graph was plotted between known concentration and mV

values and the final reading was recorded from the graph.

2.8.4 Analysis of soil samples

2.8.4.1. Preparation of samples:

• Air dried soil samples were gently crushed

• Samples were passed through 2mm sieve, and the crumbs were gently rubbed through

the mesh so that only gravels and roots etc. remain in the sieve.

• 250 gm of representative samples were kept separately by coning and quartering

• Soils were further ground in a mortar in order to pass through a 60 mesh screen for

total N, C and P analysis.

2.8.4.2 Mechanical Analysis and Chemical analysis

The analytical methods adopted for the physico-chemical analysis has been tabulated in

Table 2.1.

Table 2.1: Methodology Adopted for the Physico-chemical Analyses of Soil samples

Reference metbodolo_gy Mecbanical ... 0

pH and Electrical conductivity (on 1:5 soil in distilled water)

Total C3lbon. organic carbon and inorganic carbon Total Nitrogen

Total ...... ·~

Sodium bicarbonate extractable soil phosphorus Ammonium acetate extractable exchangeable (Cal+,~. Na+ and K) " ,

S'!V~"''

.. ·~.)·· > .

... i',.

,!/' ,. ~· :f . .

_!lydrometer method~ 1966_1 pH and conductivity meter (Okalebo et al. 1993)

Elen 2000 Total carbon analyser (working manual for Elen 2000 TCA) Kjeldahl oxidation method (Anderson and Ingram, 199~ Ascorbic acid method J..APHA, 1982_1 Olsen method J..Okalebo et.al. 1993_1 Ca2+ and~+ (GBC 906 AAS) and Na+ and K+ (Flame photometer) (Allen 1974)

21

Materials and Methods

2.9 DATA INTEGRATION & ANALYSIS USING GEOGRAPmCAL INFORMATION SYSTEM (GIS)

This was done at the Regional Remote Sensing Service Centre. Dehradun.

2.9.1 Hardware and Software used:

ffiM 6000 workstation

FSS 8000 Scanner

Sojtwtlre: ARC/INFO 7.02 and ARC VIEW GIS package

2.9.2_Preparation of Data base:

• Boundaries of all the thematic maps were redrawn on myler sheets using 0.2 nun

rotoring drafting pen to minimise the noise error during scanning. Twelve 'TIC' points

of known latitude/longitude value from Survey of India Toposheets were marked on all

the redrawn maps of for the use of registration.

• These were scanned using FS 8000 scanner

• Scanned maps were input into ARC/INFO environment for further editing

• Unique attribute were assigned for all the features (polygon. line, point) of different

thematic maps.

• Topology of polygon. line, and point W$ made using 'CLEAN' and 'BUH.D'

command in 'ARC/INFO'.

2.9.3 Data integration and analysis:

• Knowledge based ranking and recoding of the thematic maps depending upon the

objectives of analysis were done using 'TABLE' command which maintains the data

base in ARC/INFO GIS package.

• Overlay analysis was done using 'UNION' command in the package. Further

manipulation was done using 'TABLE' command for the composite map.

Generalised process has been shown in flow chart (Fig. 2.2) and the detailed descriptions

are given in respective chapters.

22

SOl TO PO SHEET

1 I

F'ag. 2.2

Working Approach in GIS

GEOCODED FCC ~ FIELD ANCILLARY \-lSUAL 11111ERPRETATION DATA DATA IP

I T

LABORA TOR AL ANALYTIC

DATA

L DRAINAGE MAP I I 4. GEOLOGICAL MAP I 2. WATERSHED M_I\P 3. SLOPE M...o\P

5. GEOMORPHOLOGICAL M..I\P 6. LA..liiDUSEILANDCOVER M..o\P II

7. LINEAMENT MAP 8. LINEA.\1ENT DENSITY Ml'..P I 9. Lll\.'EA.ME"tl-"'1' Il\."'TERSECTION DENSITY MAP 10. SOIL PIIYSIOGRAPillC l\.1AP 1

l SCAN"N1NG I

FS 8(j(j() SCANNER I L

INPUT TO GIS ARC/INFO

l EDITING I

l I

I TOPOLOGY CREA TIONJ

TRANSFORMATION & PROJECTION

* CLASSIFICATION

OVERLAYING INfEGRA TION BBUFFERING

LAND CAPABILITY MAP GROUNDWATER PROSPECT MAP

SOIL EROSION HAZARD SUSCEPTIBILITY MAP AIR POLLUTION HAZARD INfENSITYY MAP

I SURFACF ANO GROUNOWATF.R POLI.lmON HAZARD MA