Chapter 4: Materials and Methods -...

Materials and Methods

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Chapter 4:

This chapter briefly discusses the classification system for the landforms of fluvial origin, the methodology adopted as well as the materials used for carrying out the present study.

4.1: Introduction:

Main objectives of the present study is to interpret the fluvial geomorphology of the river

Ganga using remote sensing data and compile/integrate different datasets using

Geographical Information System (GIS), to understand the past/present status and

behavioral/evolutionary pattern of the Ganga river and to understand the probable causes

responsible for bringing the change in the morphology of the river in past few decades.

Various techniques, viz. remote sensing, digital image processing, visual interpretation

and delineation using Geographical Information System (GIS) were used for present

study. Interpretation and analysis of the satellite data, which includes the identification,

delineation and classification of the fluvial geomorphological features of the Ganga river

basin in the Uttar Pradesh state from the multispectral and multitemporal satellite data,

were carried out. Finally integration of different sets of database was carried out,

including the spatial and non-spatial data sets to generate the outputs required to meet the

objectives of the present study.

The classification system for the landforms of fluvial origin, the materials and the

methods used to carry out present study is further discussed in detail in this chapter.


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4.2: Materials:

Multidate satellite data acquired by the Indian Remote Sensing Satellites IRS 1D, IRS P6

(Resourcesat-1) and LANDSAT have been used in the present work. In addition, the

Survey of India topographical maps, Seismo-tectonic Atlas of India and thematic maps

on geology published by the Geological survey of India, other ancillary report and maps

on geomorphology , soil, etc. were consulted. Observations made from satellite data have

been supplemented with limited field checks in the study area.

4.2.1: Satellite data:

Multidate and multispectral satellite images of different satellites, viz. IRS, LANDSAT

covering the Ganga River course in Uttar Pradesh state was used for present work. An

area covered by two adjacent scenes taken during different times may show variation in

the appearance of features. This is essentially due to variation in its water spread. Hence

mosaics of satellite images of different dates has caused mismatch in stream pattern

between adjacent scenes. Non-consistency in the appearance of land use/land is also the

result of data of different dates over the Ganga basin.

Though the consecutive satellite passes over the same area are quite frequent, the cloud

free data just before monsoon and immediately after monsoon period is seldom available

over the entire Ganga basin in Uttar Pradesh. Therefore data acquisition during one

period through subsequent passes of the same satellite becomes a difficult task. In

particular, along the upper reach of the Ganga basin the region is invariably under clouds

for most part of the year. A certain amount of clouds are invariably present in the area

comprising the Ganga river basin in the plains of Uttar Pradesh also. This can well be


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noted from the mosaics of satellite data presented in the present work. After browsing

several data sets, satellite data with minimum cloud cover have been selected during the

project work.

Multidate, orthorectified LANDSAT MSS post monsoon data of 1970’s and multidate,

orthorectified LANDSAT TM postmonsoon data of 1990’s has been downloaded form

the website: http://www.landsat.org/ortho/index.htm. Multidate IRS-1 D LISS-III post-

monsoon satellite data of the year 2000, multidate IRS-P6 ( Resourcesat-I) LISS-III post-

monsoon satellite data of the year 2004 and Multidate IRS-P6 ( Resourcesat-I) LISS-III

pre- and post-monsoon satellite data of the year 2006 covering Uttar Pradesh, part of

Uttarakhand states of the country have been procured from the National Data Centre,

National Remote Sensing Agency Hyderabad, for the present study. The sensor details of

MSS, TM and LISS – III data is given in Table 4.1.

These raw data were geocorrected according to latitude-longitude information obtained

from the Survey of India (SOI) topographical maps and also using the ground control

points collected with the GPS during the field survey, which are considered as the

reference/base maps for the present study. Later on the geocorrected satellite data was re-

projected to the projection similar to the SOI topographical maps so as to maintain the

uniformity with in the data base generated there after. Salient features of MSS, TM and

LISS III data are as follows:

Table 4.1: Imaging Sensor Characteristics of MSS, TM and LISS III

Satellite Sensors Spectral Bands

Wavelength (mm)

Spatial Resolution

(m)

LANDSAT-1/2

MSS

0.50-0.60

0.60-0.70

0.70-0.80

0.80-1.10

79


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LANDSAT-5

TM

0.45 - 0.52

0.52 - 0.60

0.63 - 0.69

0.76 - 0.90

1.55 - 1.75

10.40 - 12.50

2.08 - 2.35

30

120

30

IRS – 1D

RESOURCESAT-I

LISS - III

0.52-0.59

0.62-0.68

0.77-0.86

1.55-1.70

23.5

70.5

The list of LANDSAT MSS data of 1970’s, LANDSAT TM data of 1990’s, IRS-1 D

LISS III data for year 2000 and IRS-P6 ( Resourcesat-I) LISS-III data for year 2004 and

2006 are listed below (Table 4.2, 4.3, 4.4, 4.5 and 4.6):

TABLE 4.2: LANDSAT MSS data OF 1970’S

Sr.No. Path Row Acquisition date of

RS data

1 153 42 14 Feb.1977

2 154 42 05 Nov, 1975

3 155 41 10 Jan, 1973

4 156 41 30 Jan, 1977

5 157 39 14 Nov, 1972

6 157 40 08 Mar, 1977


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TABLE 4.3: LANDSAT TM data OF 1990’S


RS data

1 142 42 10 Nov,1990

2 142 43 10 Nov,1990

3 143 42 17 Nov,1990

4 145 40 15 Nov,1990

5 145 41 15 Nov,1990

6 145 42 15 Nov,1990

7 146 39 21 Oct,1990

8 146 40 24 Sep,1992

TABLE 4.4: IRS-1D LISS III data for year 2000


RS data

1 96 50 17 Dec.2000

2 97 50 2 Feb.2001

3 97 51 2 Feb.2001

4 98 51 16 Nov.2000

5 98 52 16 Nov.2000


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6 99 52 13 Nov.2000

7 99 53 13 Nov.2000

8 100 53 5 Dec.2000

9 101 53 7 Nov.2000

10 101 54 7 Nov.2000

11 102 53 4 Nov.2000

12 102 54 29 Nov.2000

13 103 53 26 Nov.2000

14 103 54 21 Dec.2000

TABLE 4.5: IRS-P6 (RESOURCESAT-1) LISS III data for year 2004


RS data

1 96 50 13 Nov.2004

2 97 50 12 Dec.2004

3 97 51 12 Dec.2004

4 98 51 10 Jan.2005

5 98 52 3 Feb.2005

6 99 52 4 Nov.2004

7 99 53 28 Nov.2004


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8 100 53 9 Nov.2004

9 101 53 14 Nov.2004

10 101 54 14 Nov.2004

11 102 53 13 Dec.2004

12 102 54 19 Nov.2004

13 103 53 24 Nov.2004

14 103 54 18 Dec.2004

TABLE 4.6: IRS-P6 (RESOURCESAT-1 data for year 2006


RS data

1 96 50 12 Feb.2006

03 Nov.2006

2 97 50 17 Feb.2006

15 Oct.2006

3 97 51 17 Feb.2006

15 Oct.2006

4 98 52 22 Feb.2006

1 3 Nov.2006

5 99 52 27 Feb.2006

18 Nov.2006

6 99 53 27 Feb.2006

18 Nov.2006

7 100 53 04 Mar.2006

23 Nov.2006


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8 101 53 13 Feb.2006

28 Nov.2006

10 101 54 13 Feb.2006

28 Nov.2006

11 102 53 18 Feb.2006

09 Nov.2006

12 102 54 18 Feb.2006

09 Nov.2006

4.2.2: Colateral data

The database consulted as reference for the present study, covering Ganga river channel

in Uttar Pradesh is as listed below.

Survey of India topographical maps on 1:250,000, as listed below:

Table 4.7: List of Topographical sheet

Toposheet Number Survey year

53 K 1966-67

63 G 1923-25, 1972

Geological map of India published by Geological survey of India (1972)


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Compilation of geological and geomorphological map of Ganga basin U. P. (GSI

records Khanna and Prasad, 1995).

Seismo-tectonic Atlas of India published by Geological survey of India (2001)

Ancillary reports and map (Muley et. al., 2006a, 2006b; Arya et al., 2008a, 2008b,

2008c)

The Survey of India topographical maps with the different survey years were used for the

study. Thus while mosaicing this topographical maps mismatch in stream pattern and the

river features between adjacent topographical maps occurred at some places. The Ganga

river is characterized by high rate of variation in its channel configuration, stream flow

and sediment transport at few locations. Its large alluvial channel is marked with intense

braiding and frequent changes in its banks at some of the location along the course. Due

to frequent changes in its river course tracing out river for its entire course leads to

mismatch in its channel pattern if it is done using different topographical maps on

1:250,000 scale, as their survey period differs from map to map.

4.2.3: In Situ Data:

Field data, including the field photographs and the ground control points collected during

the field survey carried out in the Uttar Pradesh state along the Ganga river from

Devprayag to Varanasi, primarily covering the selected windows all along the Ganga

river course in pre-monsoon season during March 2007. The ground control points

collected at certain places mainly, Devprayag, Rishikesh, Haridwar, Bijnor, Kanpur and

Allahabad covering UttaraKhand and Uttar Pradesh, were used for geocorrection of

satellite images. The list of ground control points collected during the field survey of the

study area in Uttar Pradesh and part of uttarakhand states is given below in Table 4.8 and

are plotted on the mosaic of satellite images of year 2004 in Figure 4.1.


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Table 4.8: List of GCP’s collected from Uttar Pradesh using GPS

Sr. No. Lattitudes Longitudes Altitudes (Ft) Above MSL

1 N30°00.558' E78°11.502' 1102

2 N30°06.126' E78°17.615' 1033

3 N30°07.574' E78°19.824' 1155

4 N30°08.687 E78°35.765' 1309

5 N30°04.483' E78°30.092' 1450

6 N30°05.378' E78°26.073' 1302

7 N29°22.370' E78°02.475' 682

8 N28°11.612' E78°24.224' 528

9 N27°55.813' E78°51.291' 479

10 N27°36.616' E79°15.949' 456

11 N27°38.022' E79°17.821' 446

12 N26°30.468' E80°19.018' 328

13 N26°30.738' E80°19.244' 335

14 N26°31.178' E80°19.622' 341

15 N26°31.488' E80°19.883' 331

16 N26°32.022' E80°20.318' 338

17 N26°37.634' E80°19.048' 308

18 N26°38.368' E80°19.779' 308

19 N26°39.993' E80°22.447' 318

20 N26°40.597' E80°24.722' 331

21 N26°28.327' E80°22.475' 328

22 N25°26.202' E81°53.340' 266

23 N25°25.938' E81°54.024' 266

24 N25°26.087' E81°53.621' 249

25 N25°26.365' E81°52.883' 272

26 N25°31.616' E81°55.695' 266

27 N25°31.031' E81°52.005' 262

28 N25°30.717' E81°51.962' 272

29 N25°30.221' E81°51.960' 259


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4.3: Methods

Digital satellite data on 1:250,000 scale for the entire river course and on 1:50,000 scales

for selective reaches/windows were interpreted at the first instance. First, georeferencing

of the satellite data was carried out with reference to ground coordinates referred from the

Survey of India topographical maps and using the ground control points collected with

the GPS during the field survey, to minimize errors in mapping landforms. A standard

classification system and legend, given in section 4.4, was adopted for the fluvial

geomorphological mapping. Database consists of the Ganga river course and surrounding

fluvial geomorphological features were generated using SOI toposheets which was

considered as the reference/base map through out the study. Prior to the preparation of

the thematic maps the river morphological features to be shown on the maps presenting

channel configuration were identified keeping in view the objectives of the present study.

ERDAS Imagine software was used for image processing and analysis purpose. Visual

interpretation and digitization of the fluvial geomorphological features of the Ganga river

in Uttar Pradesh state as discerned from the satellite data procured in the digital form of

1970’s, 1990, 2000, 2004 and 2006 for selected reaches/windows on 1:50,000 scale for

was followed by integration of multidate satellite data analysis using ARC GIS. The

various thematic layers have been generated adopting the method of onscreen analysis of

multidate satellite data.

Bank line, main and secondary channels courses, sandbars, agriculture/grass land, island,

flood plains, palaeochannels (meander scars, abandoned channels) were identified and

delineated. In addition, fluvial features like the oxbow lakes, waterbodies, major land use

classes like forests and cultural features like the townships/cities, railway line, national

highways and approach roads in the plains of Uttar Pradesh were also identified from the

satellite data. However, in order not to add clumsiness to the maps only major cities have


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been shown on the maps for knowing the reference location on the ground. These maps

were then compared with the map prepared using SOI data to understand/identify the

river channel changes of the Ganga river and its morphological changes.

Ground truth was carried out from Devprayag to Varanasi, all along the Ganga river

course in pre-monsoon season during March 2007. The field data was collected at certain

places mainly, Devprayag, Rishikesh, Haridwar, Bijnor, Kanpur and Allahabad covering

UttaraKhand and Uttar Pradesh. Global Positioning System (GPS) was used to locate test

site on the ground with respect satellite image by registering latitude and longitude of the

test site. The landforms namely, sandbars, terraces, paleochannel, ox-bow lake,

floodplain, meanders and straight river course were studied on the ground. The river

deposits like course to fine sand, pebbles, gravels and boulders were seen on the bank and

within riverbed.

In addition to the generation of data on various fluvial geomorphological features of the

Ganga river bed areas of erosion and aggradation along both the banks were also

formulated. Areas eroded away by the river and the newly silted up areas during the

period under consideration have been worked out. The studies also revealed the stable

and unstable river banks, rate of erosion, islands indicating presence of agriculture/ grass,

etc. The analysis also brought out significant data on the trend of changes in channel of

the rivers which will lead to help understanding likely behavior of the river channels in

due course thus helping to define suitable flood / erosion protection measures. Relation

between the river behavior and the structures present in the river basin as well as the

evolutionary trend of the river was also identified.

This database generated using the remote sensing and Geographical Information System

form basic background information useful for various types of development activities

such as water resources, flood control measures, agriculture, land use, geoengineering,

etc. Flow chart depicting the methodology deployed for carrying out present study and

meeting the objectives set is as shown below (Fig 4.2):


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Figure 4.2: Flow chart showing the methodology


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4.3.1: Digital Image Processing:

In today’s world of advanced technology where most remote sensing data are recorded in

digital format, virtually all image interpretation and analysis involves some element of

digital processing. Digital image processing may involve numerous procedures including

formatting and correcting of the data, digital enhancement to facilitate better visual

interpretation, or ever automated classification of targets and features entirely by

computer (some times referred to as Image analysis system) with the appropriate

hardware and software to process data. Most of the common image processing functions

available in image analysis systems can be categorized into the following four categories;

(1) Preprocessing, (2) Image Enhancement, (3) Image transformation and (4) Image

classification and Analysis.

4.3.1.1: Preprocessing:

Preprocessing functions are those operations that are intended to correct for sensor- and

platform- specific radiometric and geometric distortions of data and are normally

required prior to main data analysis and extraction of information, commonly named as

radiometric and geometric corrections.

4.3.1.1.1: Geometric corrections:

All remote sensing images are inherently subjected to geometric distortions. These

distortions may be due to several factors, including; the perspective of the sensor optics;

the motion of the scanning system; the motion of the platform, the platform attitude and

velocity; the terrain relief and the curvature and rotation of the earth. Geometric

corrections are intended to compensate for these distortions so that the geometric

representation of the imagery will be as close as possible to real world.

The geometric registration process involves identifying the image coordinates (i.e. row,

column) of several clearly discernible points called ground control points (or GCPs), in

the distorted image (Fig 4.3: A-A1 to A4) and matching them to their true positions in


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ground coordinates (e.g. latitude, longitude). The true ground coordinates are typically

measured from a map (Fig 4.3: B-B1 to B4), either in paper or digital format. This is

image to map registration and similarly registration may also be performed by registering

images to another image, instead of geographic coordinates, which is called image to

image registration.

Figure 4.3: Ground control points

(Source: http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.ph)

In order to geometrically correct the original distorted image, a procedure called

resampling was used to determine the digital values to place in the new pixel locations of

the corrected output image (Fig 4.4). There are three methods for resampling: nearest

neighbour, bilinear interpolation and cubic convolution. Nearest neighbour resampling is

the simplest method and used in present study (Fig 4.5). It uses the digital value from the

pixel in the original image which is nearest to the new pixel location in the corrected

image and hence it does not alter the original values.

Figigure 4.4: Nearest Neighbour resampling method

(Source: http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.ph)


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Figure 4.5: Image to Image Geometric corrections using ERDAS Imagine software

(Source: ERDAS Imagine Software)

4.3.1.1.2: Mosacking:

Mosaicking is the process of joining georeferenced images together to form a larger

image or a set of images (Fig 4.6). The input images must all contain map and projection

information, although they need not be in the same projection or have the same cell sizes.

Calibrated input images are also supported. All input images must have the same number

of layers.


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Figure 4.6: Mosacking operation using ERDAS Imagine software


4.3.1.2: Image Enhancement:

Enhancements are used to makes visual interpretation and understanding of imagery

easier. The advantage of digital imagery is that it allows us to manipulate the digital pixel

values in an image. With large variations in spectral response from a diverse range of

targets (e.g. forest, deserts, snowfields, water, etc.) no generic radiometric correction


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could optimally account for and display the optimum brightness range and contrast for all

targets. Thus, for each application and each image, a custom adjustment of the range and

distribution of brightness values is usually necessary. In raw imagery, the useful data

often populates only a small portion of the available range of digital values (commonly 8

bits or 256 levels). Contrast enhancement involves changing the original values so that

more of the available range is used, thereby increasing the contrast between targets and

their backgrounds.

4.3.2.1.1: Image Histogram:

The key to understanding contrast enhancements is to understand the concept of an image

histogram. A histogram is a graphical representation of the brightness values that

comprise an image (Fig 4.7). The brightness values (i.e. 0-255) are displayed along the x-

axis of the graph. The frequency of occurrence of each of these values in the image is

shown on the y-axis. By manipulating the range of digital values in an image, graphically

represented by its histogram, we can apply various enhancements to the data.

Figure 4.7: Image Histogram as seen using ERDAS Imagine software


There are many different techniques and methods of enhancing contrast and detail in an

image; but the only linear contrast stretch is the one which is used for present work.


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4.3.2.1.2: Linear contrast stretch:

The simplest type of enhancement is a linear contrast stretch. This involves identifying

lower and upper bounds from the histogram (usually the minimum and maximum

brightness values in the image) and applying a transformation to stretch this range to fill

the full range (Fig 4.8). This enhances the contrast in the image with light toned areas

appearing lighter and dark areas appearing darker, making visual interpretation much

easier.

Figure 4.8: Linear contrast stretch (Source: http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.ph)

Figure 4.9: Transfer function used in linear contrast stretch (Source: Lecture note, EDUSAT program 2007, Department Of Space, Govt. of India)

The equation y = ax + b (eq. 1) performs the linear transformation in a linear contrast

stretch method (Fig 4.9). The values of ‘a’ and ‘b’ are computed from the equations.


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a = (ymax - ymin)/ xmax - xmin and b = (xmax ymin - xmin ymax)/ xmax - ymin

Where, x = Input pixel value and y = Output pixel value. xmin, xmax, ymin and ymax are the min and max input and output values. To stretch the data between 0-255, eq. 1 takes the form:

Figure 4.10: Min and Max brightness curves before and after linear contrast stretch (Source: Lecture note, EDUSAT program 2007, Department Of Space, Govt. of India)

This graphic illustrates the increase in contrast in an image before (left) and after (right) a

linear contrast stretch (Fig 4.10, 4.11).

Figure 4.11: Image display before and after linear image stretch (Source: http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php)


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4.3.1.3: Image Transformation:

Image transformations typically involve the manipulation of multiple bands of data,

whether from a single multispectral image or from two or more images of the same area

acquired at different times (i.e. multitemporal image data). Either way, image

transformations generate "new" images from two or more sources which highlight

particular features or properties of interest, better than the original input images.

4.3.1.3.1: Change Detection (Image subtraction):

Basic image transformations apply simple arithmetic operations to the image data. Image

subtraction is often used to identify changes that have occurred between images collected

on different dates (Fig 4.12, 4.13). Typically, two images which have been geometrically

registered are used with the pixel (brightness) values in one image (1) being subtracted

from the pixel values in the other (2). Scaling the resultant image (3) by adding a constant

(127 in this case) to the output values will result in a suitable 'difference' image. In such

an image, areas where there has been little or no change (A) between the original images,

will have resultant brightness values around 127 (mid-grey tones), while those areas

where significant change has occurred (B) will have values higher or lower than 127 -

brighter or darker depending on the 'direction' of change in reflectance between the two

images . This type of image transform can be useful for mapping changes in any

geomorphological features.

Figure 4.12: Image Subtraction (Source: http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php)


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Figure 4.13: Change detection model used in ERDAS Imagine Software (Source: ERDAS Imagine Software)


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4.3.2: Introduction to Interpretation and Analysis:

Interpretation and Analysis of remote sensing imagery primarily involves the

identification and/or measurement of various targets in an image in order to extract useful

information about them and those targets may be natural or man-made features. Targets

may be defined in terms of the way they reflect or emit radiation. This radiation is

measured and recorded by a sensor, and ultimately is depicted as an image product such

as an air photo or a satellite image. To interpret any remote sensing data, the interpreter

should have the knowledge about the ‘behavior of the target with electromagnetic

radiation, i.e. spectral behavior of any object. Moreover the ground investigations are

very necessary as it gives the realistic picture of the area under study and one can

establish relationships between radiative and physical properties of any object over

electromagnetic spectrum.

Imagery displayed in a pictorial or photograph-type format, independent of what type of

sensor was used and how the data were collected is refered to the data as being in analog

format. Remote sensing images represented in a computer as arrays of pixels, with each

pixel corresponding to a digital number, representing the brightness level of that pixel in

the image are in a digital format. Both analogue and digital imagery can be displayed as

black and white images, or as colour images by combining different channels or bands

representing different wavelengths. When remote sensing data are available in digital

format, digital processing and analysis may be performed using a computer. 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.

However, rarely is digital processing and analysis carried out as a complete replacement

for manual interpretation. Often, it is done to supplement and assist the human analyst. In

most cases, a mix of both methods is usually employed when analyzing imagery. In fact,


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the ultimate decision of the utility and relevance of the information extracted at the end of

the analysis process still must be made by humans.

The present section discusses the image interpretation, including the identification and

delineating the fluvial geomorphological features of the Ganga river basin in Uttar

Pradesh state from multispectral and multitemporal data provide by the LANDSAT and

IRS satellites, using the digital image processing and the geographical information

system for generating the required results to meet the objectives of the present study (Fig

4.14).

4.3.3: Sequence of Interpretation and Analysis:

The sequence in the image interpretation and analysis begins with the detection and

identification of objects followed by delineation after that deduction, then classification

using satellite data and the ancillary data to generate different themes, which forms the

essential part of the database and finally integration of two or more data sets or themes

from the generated database and the ancillary data to generate new themes, features and

quantities and computation of the newly generated quantities, giving statistics, as well as

the output in one or more desired formats. Image is considered in terms of information

and proceedings starts from general considerations to specific details and from know to

unknown features.

4.3.3.1: Detection

Detection means selectively picking out an object or element of importance for the

particular kind of interpretation in hand. It is often coupled with recognition, in which

case the object is not only seen but also recognized.


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4.3.3.2: Recognition and Identification Recognition and identification together are sometimes termed photo-reading. However,

they are fundamentally the same process and refer to the process of classification of and

object means of specific or local knowledge within a known category upon objects

detection in an image.

4.4.3.3: Delineation It is a process of separating and delineating a set of similar objects. In this step boundary

lines are drawn separating the groups and the degree of reliability of these lines may be

indicated.

4.3.3.4: Deduction Deduction may be directed to the separation of different groups of objects or elements

and the deduction of their significance based on converging evidence. The evidence is

derived mainly from visible elements, which give only partial information on the nature

of certain correlation indications.

4.3.3.5: Classification Classification establishes the identity of a surface or an object delineated. It includes

modification of the surface into a pertinent system for use in field investigation.

Classification is made in order to group surfaces or objects according to those aspects

that, for a certain point of view, bring out their most characteristic aspects.

4.3.3.6: Integration and Output Integration is the process of integrating the database generated from above mentioned

steps and the ancillary data available, including the spatial and non-spatial data, to

generated new datasets which includes features and/or quantities.


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Figure 4.14: Flow chart showing the process of Interpretation and Analysis


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4.4: Classification System:

A classification system was defined and was adopted for carrying out the fluvial

geomorphological studies of the Ganga river in the selected area of Uttar Pradesh state of

India. The following classification system was followed to interpret the landforms of

fluvial origin and was used for mapping fluvial geomorphological units and forms using

satellite data in present work.

This classification system contains three major devision, i.e.

1. The Feature: The regional feature, which in present case is the Gangetic plain,

2. The Units: Two major fluvial geomorphic units were identified in the study are,

viz., the River bed and the flood plain.

3. The Forms: Different fluvial geomorphological landforms were identified using

the satellite data as described in the classification system.

The standard legend for fluvial geomorphological mapping was also defined for the

representation of the different fluvial geomorphological landforms as shown in the

classification system below.

Interpretation key, with definitions of the fluvial geomorphological features (Fairbridge,

1968; Bates and Jackson, 1980) was also formulated for identifying and delineating each

and every landforms of fluvial origin along with the present classification system. These

interpretation keys were used to interpret the fluvial geomorphological landforms in the

study.


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Level I Level II Level III FEATURE UNIT FORM LEGEND INTERPRETATION KEY

GANGA RIVER PLAIN

RIVER BED

BANKLINE

Bank of a stream can be defined as margin of the ground bordering a river/stream with water during normal course of flow. Thus it is the line separating the Riverbed and flood plain. Here river bed is identified by river channels (water show dark tone), in association with the sandbars (bright tone) in an image. The flood plain has high moisture content, vegetation (high reflectance in infrared region) and cultivation pattern. Hence it becomes distinguishable from the riverbed on an image.

RIVER CHANNEL

River channel absorbs most of the EMR incident on it, when it is turbid it exhibits different shades of blue in the FCC image as the green band is assigned the blue colour. Moreover streams can also be distinguished by the other form of water bodies by its spatial extent and pattern, i.e. shape, size, riverine patterns and its association with the other fluvial features

SANDBAR

Single stranded streams with straight or sinuous pattern forms the sandbars which are usually formed on the inner side of the meander. Its spectral characteristics includes a very bright tone with almost white to dull white colour as the sand has a very low moisture content and hence a very high reflectance in almost all the region of EM spectrum. Its orientation, form and association with the river channels make its identification and distinction from other fluvial features very easy


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CHANNEL BAR

Multi stranded or braided streams gives rise to the channel bars which separates two channels from each other. It exhibits bright to dull white colour in an image pertaining to its higher reflectance. Small channel bars are water submerged partially or completely in water and remain water laden hence their reflectance decreases. As it is interlaced with the braided streams and it is much smaller in size then compared to islands it can be identified easily

SANDBAR/ CHANNEL BAR

WITH VEGETATION

Cultivation/grass partly covering the sandbars/channel bars and can be identified on the satellite image FCC in different tone of red colore as vegetation highly reflects the near infrared radiations and the red colour is assigned to the near infrared band. Its size is much smaller and is relatively more prone to erosion as compared to islands. Its pattern and association with the river channels makes its identifiable on an image

ISLAND

By definition an island is a land, completely surrounded by water under normal condition in river. A braided channel forms islands between two or more channels. Mature or relatively stable islands covers fairly large area in the river bed, do not get completely submerged frequently as they are upto flood plain level, do not get eroded by river flow because of well-developed vegetation cover as compared to sandbars/channel bars. It is seen in the different shades of red colour in FCC image because the vegetation highly reflects the near infrared radiations and the red colour is assigned to the near infrared band. As it is surrounded on all the sides by the river channels its easily gets separated from the floodplains on a standard FCC satellite image


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FLOOD PLAIN

ABANDONNED CHANNEL

Abandoned channel is a channel along which runoff no longer occurs or is cut off from the main stream. As there is no runoff, sandy riverbed is exposed, exhibiting the bright tone on an image as the sand is highly reflective. But It is distinguished from sandbars by its drainage pattern, size and its association with the active river channels

PALAEOCHANNEL/ PALAEO

MEANDER SCAR

A remnant of a stream channel cut in the older rock and filled by the sediments of younger overlying rock is called palaeochannel. It is discernible and distinguishable from flood plain on an image mainly because of its drainage pattern. It has high moisture content and a vegetation cover, which shows dull tone with different shades of red in standard FCC image. Its presence in the flood plain/interfluves, in proximity with the active stream also helps in its identification

OXBOW LAKE

It is crescent-shape body of standing water, situated near by the side of the river/stream in the abandoned channel of the meander. Thus it can be identified on a a standard FCC image by its dark tone, dark blue colour, its crecent shape, comparatively smaller size than a meandering stream, drainage pattern and association with an active stream

WATERBODY

A water body can be identified on a standard FCC image by its dark tone, dark blue colour, its shape depending on its origin (natural or man-made), comparatively much smaller size than a stream and absence of any kind of drainage pattern

DRY/SILTED LAKE

A silted up waterbody can be identified on a FCC image by its bright tone, shape depending on its origin (natural or man-made) and comparatively much smaller size


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4.5: Interpretation & Analysis of Fluvial

Geomorphological Features from satellite data:

Different features can be identified and distinguished on the basis of their characteristics,

i.e. difference in its spectral, spatial and temporal behavior. Different features exhibits

unlike and unique behavior in different bands and hence in a multi spectral images

different bands stacked together and assigned different colours, forming a False Colour

Composits (FCC), gives a complete information of an area under study. Fluvial

geomorphological features as defined (Fairbridge, 1968; Bates and Jackson, 1980) and

listed in previous section have been identified and delineated as mentioned below in

details:

4.5.1: Bank line:

Bank of a stream can be defined as margin of the ground bordering a river/stream with

water during normal course of flow. The bank line is, primarily, the rising ground

bordering the river. River courses straight, meandering or braided seldom change their

bank line, in particular, if the rivers are flowing through hard rocks. But the river flowing

through the alluvial plains, which is under dynamic equilibrium with fluvial processes as

well as seismotectonic events hardly, shows stable river morphology.

The bank line is the linear feature separating the riverbed and flood plain. River bed is

identified principally by its drainage pattern (Fig 4.15, 4.16). In the standard FCC image


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the property of water to show darkest tone, as it absorbs most of the part of the EMR

which is incident upon it, thus the reflectance is quite low, and its association with the

bright toned sandbar deposits along the banks as well as between the braided channels

helps detect/identify the river bed on an image. The flood plain shows comparatively

darker tone because of the presence of the high moisture content, but because of the high

reflectance of vegetation in infrared region it appears in different shades of red on the

standard FCC image. Moreover the flood plain is highly fertile land thus it is used for

cultivation and shows cultivation pattern in the image which makes it becomes

distinguishable from the riverbed on an image. Thus the river bed and flood plains are

detected/identified and the line separating both units delineated and deducted from the

other fluvial features and classified as the bank line of the river.

The catchment area of the Ganga valley comprises friable rocks and silty soil. In addition

the region is known for its seismic instability. The entire course of the river Ganga flows

through the alluvial plains and shows braided channel almost throughout its course except

at a stretch downstream of Allahabad and extends up to the Uttar Pradesh and Bihar state

border where river exhibits typical meandering pattern with the well defined banks, again

forming braiding pattern in the plains of Bihar state.

The excessive sediment load is the principal cause for shifting of channels and formation

of sandbars in its course. In addition, tributaries also carry high sediment load to the

Ganga River further enhancing the braiding pattern. High sediment load and continuous

flattening of slopes in the downward direction has resulted in the instability of the river

which can be noted from continuous changes in river channels and river banks, in

particular.

At several places the riverbed has come very close to the ground level. During floods the

river spreads quite a large quantity of sediments brought by its currents on its banks thus

diffusing its identity. Wherever the river bank gets merged with the sediments for

delineation of bank line on satellite data, available maps were referred to and also the

general trend of the river as observed from the synoptic view provided by the imagery


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was taken into consideration. The river is joined by many small and large tributaries on

both the banks. Many of the tributaries originate from the Himalayan ranges. Yamuna is

among those and is one of the major rivers joining the Ganga River in Uttar Pradesh. In

addition to few major tributaries, number of streams drain directly in to the river. Due to

bank erosion the outfall locations of these tributaries keep on changing. This results in

quite large-scale variations in the locations of the riverbank at the sites of confluence of

rivers. It is rather difficult to define bank line of the Ganga in such locations.

Figure 4.15: IRS P6 (Resourcesat-I) post-monsoon image (FCC) showing the Ganga

river with braided pattern and diffused bank line


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Figure 4.16: IRS P6 (Resourcesat-I) post-monsoon image (FCC) showing the Ganga

river with meandering pattern and well defined bank line

4.5.2: River channel:

The braided nature of the Ganga river presents intense multiple interlaced channels

separated by channel bars and islands of varying size, shape and orientation. The main


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channel along with its subsidiary stream channels are marked by their constantly shifting

nature with almost every monsoon period (Fig 4.7, 4.18).

The river wherever gets constricted due to the presence of the competent formation which

is resistant to erosion shows a single channel without any change in its course over the

ages. For instance the course downstream of Allahabad which extends up to the Uttar

Pradesh and Bihar state border, where the river is flowing as a single channel, exhibit a

typical meandering pattern and does not show any change in its stream pattern as well as

banks.

The river pattern, in particular, main channels and subsidiary channels can very well be

discerned by its spectral and spatial characters from the synoptic view provided by the

satellite images. As the water of the streams absorbs most of the EMR hence shows a

darker tone on the images. Only the visible light is reflected from the streams and most of

it is in the green region of visible spectrum hence the streams exhibits different shades of

blue in the FCC as the green band is assigned the blue colour, hence can be

detected/identified on an image. Moreover streams can also be deducted by the other

form or waterbodies by its spatial extent, i.e. its shape, size and riverine patterns viz. the

braided and meandering. Thus the water bodies with the reverine pattern can be classified

as the river channels and further as the main and the secondary channels using the

satellite data.

The Ganga river exhibits both the patterns typically in different area through out its

course in the Uttar Pradesh state. The dynamic changes in the main stream can best be

monitored from the satellite images. In the present work an attempt has been made to

highlight the variation in the main channel pattern. The shifting of the main channel in

the riverbed is not uniform. The shifting of the main channel varies from small to large-

scale shift. There is quite a large variation in the channel width of the main channel

throughout the course of the Ganga River.


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Figure 4.17: IRS P6 (Resourcesat-I) post monsoon image (FCC) showing the main

and the secondary channel courses of the Ganga river

Figure 4.18: Field photograph showing the main and the secondary channel courses

of the Ganga river


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4.5.3: Sandbars and Channel bars:

The braided channel of the Ganga River is characterized by sandbars and channel bars.

The excessive sediment load is the principal cause for the formation of sand bars in the

Ganga River. The sand bars, in general, are elongated in shape with their longitudinal

axis parallel to the course of the river. They vary in their length and breadth. The local

inhabitants use some of the large sandbars for cultivation to meet their livelihood. Large-

scale grass growth is also quite common on some of the sandbars. Quite often during

succeeding floods these sand bars once again get inundated and many a times completely

get shifted due to floodwaters. Erosion of sandbars occurring upstream cause deposition

of these eroded sediments further downstream and as a result of these the new sand bars

are formed in the downstream direction.

Figure 4.19: IRS P6 (Resourcesat-I) post monsoon image (FCC) showing sandbars

and channel bars in the Ganga river course

There are mainly two different types of sandbars associated with two different types of

patterns exhibited by the Ganga river. Single-stranded streams with straight or sinuous

pattern forms the sandbars (Fig 4.19, 4.20) which are usually formed on the inner side of


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the meander. Multi-stranded or braided streams gives rise to the channel bars (Fig 4.19,

4.21, 4.22) or braid bars which separates two channels from each other and are

surrounded by river braided channels on all the sides.

Synoptic view provided by the satellite data has made it possible to map and monitor the

development /movement and changes occurring in the sandbars/channel bars of the

Ganga River. Its spectral characteristics includes a very bright tone with almost white to

dull white colour as the sand has a very low moisture content and hence a very high

reflectance in almost all the region of Electromagnetic spectrum and thus it can easily be

detected/identified on the standard FCC image. Small channel bars are water submerged

partially or completely in water and remain water laden most of the time and hence their

reflectance highly decreases, thus it can be detected/identified because of its dull white to

grayish appearance on the standard FCC image. Depending on its occurance, orientation

and form in relation with the active streams, as described formerly, comprises its spatial

property, which makes its deduction from other fluvial features very easy and are

classified as sandbars and channel bars.

Figure 4.20: Field photograph showing sandbars in the Ganga river course


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Figure 4.21: Field photograph showing the channel bar in the Ganga river course

Figure 4.22: Field photograph showing the channel bar (water laden) in the Ganga

river course


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4.5.4: Sandbars and Channel bars with vegetation:

The riverbed of the Ganga River is spotted with enormous sand bars. These are of various

sizes and have been formed by fine sand and silt depositions. The annual flooding

influences the vegetation growth on the sand bars. If the local people find the large size

sandbars and the islands suitable for habitation purpose, they occupy these sand bars and

river islands and use the land for cultivation purpose. Vegetation growth increases the

stability of the sandbars/channel bars because it becomes more resistant to erosion.

The sandbars/channel bars partly under vegetation cover can be detected and identified

on the satellite image FCC because of the property of the cultivation/grass present on the

sandbars/channel bars to highly reflect the near infrared radiations (Fig 4.23). Thus it can

be seen in different tone of red colour because red colour is assigned to the near infrared

band in the standard FCC image. Its size is much smaller and is relatively more prone to

erosion as compared to islands. More over its pattern and association with the river

channels makes its deductible on an image and this set of land form is classified under the

present head and can also be considered as the stable sandbars/channel bars.

Figure 4.23: IRS P6 (Resourcesat-I) post monsoon image (FCC) showing vegetation

(cultivation) on the sandbars


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Figure 4.24: Field photograph showing vegetation (cultivation) on the sandbars

The soils of the river island support the crops for sustainability of the habitation residing

on the river islands. The agriculture and grass occupy a considerable area in the Ganga

River almost throughout its course in Uttar Pradesh (Fig 4.24). The agriculture in the

riverbed is an indicative of sizable population residing in the riverbed of the Ganga River.

4.5.5: Island: Bar growth and island development are integral parts of the process of sediment transfer.

The principal cause for the formation of island in the Ganga River again is the excessive

sediment load carried along with the flow of the river. An island can be defined as a body

of land surrounded by water. Islands may occur in oceans, seas, lakes, or rivers (Brauer et


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al, 2005). An island in a river or lake may also be called an eyot (It is especially used to

refer to islands found on the River Thames in England) or ait. Mature or relatively stable

islands are the fairly large channel bars which are constructed up to floodplain level, with

well-developed vegetation (cultivation and/or grass) cover. On islands, soils are formed

and hence supports vegetation.

Many times, the change in river courses also carves out and island. A multi-stranded river

system exhibiting two different types of pattern, namely the braided pattern (Fig 4.25)

and the anabranching/anastomosing pattern (Fig 4.26), forms islands between two or

more channels. A side channel/smaller channel results from a river being divided into two

or more channels by an island. Like channel bars the islands in general, are elongated in

shape with their longitudinal axis parallel to the course of the river and vary in their

length and breadth. But unlike sandbars, river islands are a comparatively stable feature,

which gets inundated by the flood waters but does not often change its shape and

dimensions.

Figure 4.25: IRS P6 (Resourcesat-I) post monsoon image (FCC) showing braided

pattern and the river island


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Satellite data provides a synoptic view which helps in understanding and monitoring the

river behavior and the change in the morphology of the river islands. River islands, as the

definition goes, have extensive vegetation cover which makes it relatively resistant to

erosion. It is detected/identified on the standard FCC image because it is seen in different

shades of red colour in FCC’s as the red colour is assigned to the third i.e. the near

infrared band because of the fact that the vegetation highly reflects the near infrared

radiations back. As it is surrounded on all the sides by the river channels its easily gets

separated from the floodplains and because of its much larger size, it can be distinguished

form the channel bars with vegetation (Fig 4.27). Thus it can be deducted from the other

fluvial landforms on a satellite image and classified as the Islands.

Figure 4.26: IRS P6 (Resourcesat-I) post monsoon image (FCC) showing

anabranching/anastomosing pattern and the river island


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Figure 4.27: Field photograph showing the river island

4.5.6: Floodplains:

Floodplain is the flat region of a valley floor located on either side of a river channel. It

is built of sediments deposited by the river that flows through it and is covered by water

during floods when the river overflows its banks. During most floods, just a portion of

the floodplain is covered with water and only during infrequent, very large floods is the

whole floodplain covered. Floodplains tend to develop on the lower and less steep

sections of rivers. As with most large floodplains, however, the active floodplain lies

within a much broader floodplain formed by deposits laid down by the river earlier in its

history.


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The Ganges has very large floodplains that support a dense population engaged in

agriculture on the fertile floodplain soils. Floods play a central role in creating and

shaping floodplains. During a flood, the flow of a river is both larger and faster, allowing

it to carry more sediment. Some of this material comes from upstream of the floodplain,

but some of it is also eroded from the floodplain itself. As the flood recedes, the volume

and speed of the river diminishes and the river deposits some of its load of sediment.

Since floodplains are constructed of the material being carried by the river, they are

composed of relatively fine sediment. Most floodplains are composed of sand, silt, and

clay, but floodplains of gravel occur where the water flows are especially fast. The

sediments in a floodplain are constantly being eroded and re-deposited as the river

channel shifts position within the floodplain. For example, when a river makes a bend,

the water on the outside of the bend speeds up while the water on the inside of the bend

slows down. Consequently, material is eroded from the outside of bends where the flow

is a little faster and deposited along the inside of bends where the flow is slower. These

low, arc-shaped deposits on the inside of bends are called point bars. During floods, the

water level rises until it overflows the banks of the channel. The water flowing over the

floodplain is much shallower than the water flowing in the channel. As the water that has

overflowed the banks slows, it deposits some of its sediments. This leads to a build-up of

sediment, which produces ridges running parallel to the channel. Along large rivers, these

ridges, known as levees, can rise 5 to 10 m (15 to 30 ft) above the floodplain. Once

formed, levees have the effect of confining flood waters close to the channel, but during

large floods, these levee barriers are breached and the flood waters pour on to the

floodplain area behind. These high floodwaters carry with them sediment that is then

deposited on the floodplain beyond the levee. In this way, the floodplain deposits build

up vertically. Fluvial features levees were not identified on the images used in present

study.

Satellite sensors in the visible and infrared bands have long been used to provide

estimates of flood extent and flood hazard areas (Rango and Anderson, 1974). The


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characteristic to detect and identify floodplain on a satellite image is extensive growth of

vegetation reflecting mainly the infrared radiation and hence displaying shades of red on

FCC (Fig 4.28, 4.29). Numbers of fluvial features formed because of the river shift with

in the floodplain are useful to delimit the extension of the floodplain and it can also be

used to deduct this landform from other fluvial landforms. Mertes (1994) used Landsat

TM data to study Amazon river and found that the water surface on the flood plain was

frequently masked by vegetation. But the most important property for distinguishing an

active floodplain from the former floodplain is the moisture content, as it retains a large

amount of moisture after the floods recedes, which lowers its reflectance relatively (Fig

4.30). Thus considering all the above mentioned spatial and spectral characteristics this

landform can be classified as flood plain.

Figure 4.28: IRS P6 (Resourcesat-I) post monsoon image (FCC) showing Flood

plains on both the side of the Ganga river course


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Figure 4.29: field photograph showing Flood plains with cultivation

Figure 4.30: field photograph showing Flood plains with water logging


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4.5.7: Abandoned channel:

A drainage channel along which runoff no longer occurs or is cut off from the main

stream is known as the abandoned channel. River bed can be abandoned because of

many natural factors like the fluvial processes, which brings changes in the dynamics of

the river, change in the discharge, etc. Avulsion is one of the well know phenomenon

giving rise to abandoned channels.

An avulsion will occur every time the bed of a river channel aggrades enough that the

river channel is super-elevated above the floodplain by one channel-depth. In this

situation, enough hydraulic head is available that any breach of the natural levees will

result in an avulsion (Bryant et. al., 1995 and Mohrig et. al., 2000). As the slope of the

river channel decreases, it becomes unstable for two reasons. First, water under the force

of gravity will tend to flow in the most direct course downslope. If the river could breach

its natural levees (i.e., during a flood), it would spill out onto a new course, thereby

obtaining a more stable steeper slope (Slingerland and Smith, 1998). Second, as its slope

gets lower, the amount of shear stress on the bed will decrease, which will result in

deposition of sediment within the channel and for the channel bed to rise relative to the

floodplain. This will make it easier to breach its levees and cut a new channel,

abandonning the older channel.

Rivers can also avulse due to the erosion of a new channel that creates a straighter path

through the landscape. This can happen during large floods in situations in which the

slope of the new channel is significantly greater than that of the old channel, which

eventually becomes abandoned channel . Where the new channel's slope is about the

same as the old channel's slope, a partial avulsion will occur in which both channels are

occupied by flow (Slingerland et. al., 2004).

An example of a minor avulsion is known as a meander cutoff, where the high-sinuosity

meander bend is abandoned in favor of the high-slope. Slingerland and Smith show that

this occurs when the ratio between the channel slope and the potential slope after an


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avulsion is less than 1/6 (Slingerland and Smith, 1998). Avulsion typically occurs during

large floods which carry the power necessary to rapidly change the landscape. Avulsions

usually occur as a downstream to upstream process via head cutting erosion. If a bank of

a current stream is breached a new trench will be cut into the existing floodplain. It either

cuts through floodplain deposits or reoccupies an old channel (Nanson and Knighton,

1996).

When a river channel gets abandoned, the sand/silt deposits deposited along the river bed

gets exposed to the surface. On the standard FCC image the sandy bed almost shows the

spectral signature like the sandbars/channelbars. It can be detected/identified as there is

no runoff, the sandy riverbed is exposed, and exhibiting the bright tone on an image as

the sand is highly reflective. But it is distinguished from sandbar/channel bar deposits by

its drainage pattern, shape, size and its association with the active river channels (Fig

4.31). Thus this class is deducted from the other fluvial features and classified as the

abandoned channels.

Figure 4.31: IRS FCC/field photograph showing the abandoned channel


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4.5.8: Palaeochannel:

A remnant of a stream channel cut the older rock and filled by the sediments of younger

overlying rock is called palaeochannel. It is also referred as a buried stream channel.

Palaeochannels are deposits of unconsolidated or semi-consolidated sedimentary rocks

deposited in ancient, currently inactive river and stream channel systems. The word

palaeochannel is formed from the words "palaeo" or 'old', and channel; ie; a

palaeochannel is an old channel. A crescent/concave mark on the face of bluff or valley

wall, produced by the lateral planation of meandering stream is termed as palaeo meander

scar. The meandering stream undercut the bluff and indicates the abandoned route of the

stream. An abandoned meander often filled in by deposits and vegetation but still

discernible is also called palaeo meander scar.

Palaeochannels are formed because of the source of river flows being removed, either

via a river changing course, climate change affecting the inflows into the catchment, or

perhaps faulting or tectonic movements altering the dynamics of a river system and/or its

flow direction. Palaeochannels are not necessarily permanent; it is possible for them to

become eroded via reactivation of erosional activity or reactivation of the original river

system. Palaeochannels are important for understanding movements of faults, which may

redirect river systems and thus form stranded channels which are in essence

palaeochannels.

It preserves Tertiary, Eocene and Recent sediments which are useful for understanding

climatic conditions which are used in understanding climate change and global warming.

It also preservs the evidence of older erosional surfaces and levels, useful for estimating

the net erosional budget of older regolith.

Remote sensing can be used for locating palaeo rivers successfully. Visible images, when

enhanced by contrast streatching, reveales palaeofeatures such as abandoned channels,

meander scars and oxbow lakes (Schultz and Engman, 2000). Landsat MSS and TM

were used by Philip and Gupta (1993) to lacate palaeo rivers and map three distict stages

in the migration of the Burhi-Gandak river in north-eastern India. It is detectable/


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identifiable and deductible from flood plain on an image mainly because of its drainage

pattern (Fig 4.32). It has high moisture content and a vegetation cover (Fig 4.34, 4.35),

which shows dull tone with different shades of red in standard FCC image. Its presence in

the flood plain/interfluves, in proximity with the active stream also helps in its

identification (Fig 4.33). A palaeochannel is distinct from the overbank deposits of

currently active river channels, including ephemeral water courses which do not regularly

flow, because the river bed of palaeochannel is filled with sedimentary deposits which are

unrelated to the normal bed load of the current drainage pattern. Palaeochannels can be

most easily identified as broad erosional channels into a basement which underlies a

system of depositional sequences which may contain several episodes of deposition and

represent meandering peneplains. Thereafter, a palaeochannel may form part of the

regolith of a region and, although it is unconsolidated or partly consolidated, is currently

part of the erosional surface (Anand and Paine, 2002). Thus the landform exhibiting all

the above listed characteristics on a standard FCC image, can be classified as the

palaeochannels

Figure 4.32: IRS P6 (Resourcesat-I) post monsoon image (FCC) showing Palaeo

meander scar of the Ganga river


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Figure 4.33: IRS P6 (Resourcesat-I) post monsoon image (FCC) showing Palaeo

channels of the Ganga river

Figure 4.34: Field photograph showing traces of Palaeo channels of the Ganga river


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Figure 4.35: Field photograph showing traces of Palaeo channels of the Ganga river


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4.5.9: Oxbow lake, Water body and Dry/Silted lake:

It is crescent-shape body of standing water, situated near by the side of the river/stream in

the abandoned channel of the meander. A cutoff neck of the river/stream and the ends of

the original bends were silted up. The lake is often ephemeral in nature and located in

abandoned meandering channel present in floodplain. It is also defined as a single

meander loop, typically isolated or cutoff from the main stream by natural processes or

by human activities. Thus it can be detected/identified on a a standard FCC image by its

dark tone, dark blue colour. Its crescent shape, comparatively smaller size than a

meandering stream, drainage pattern and association with an active stream helps to

deduct this form and classify it as the oxbow lake.

A water body is a body of stagnant water and can be identified on a standard FCC image

by its dark tone, dark blue colour, its shape depending on its origin (natural or man-

made), comparatively much smaller size than a stream and absence of any kind of

drainage pattern (Fig 4.36).

A dried and silted up water body can be identified on a standard FCC image by its bright

tone, its shape depending on its origin (natural or man-made) and comparatively much

smaller size.

Figure 4.36: IRS FCC/field photograph showing the oxbowlake


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4.6: Integration and Output

For most data available in digital format from a 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 multitemporal,

multiresolution, multisensor, multispectral and/or multi-data type (i.e. spatial or non

spatial data types) in nature.

For performing the applications of multitemporal, multiresolution, multisensor,

multispectral and/or multi-data type (i.e. spatial or non spatial data types) data integration

the data was geometrically registered, either to each other or to a common geographic

coordinate system or map base. Having the entire database in a common projections and

geographic coordinates also allows other ancillary (supplementary) data sources to be

integrated with the remote sensing data.

Multitemporal data integration has already been alluded to in previous section, when we

discussed image subtraction. Imagery collected at different times is integrated to identify

areas of change. Multitemporal change detection can be achieved through simple

methods such as these. Multiresolution data merging is useful for a variety of

applications. Integration of multispectral serves to retain good spectral resolution and can

give information captured in different regions of electromagnetic spectrum. Data from

different sensors may also be merged, bringing in the concept of multisensor data fusion.

The results from a Interpretation of a remote sensing data set in raster and/or vector

format, could be used to integrate with the other datasets (ancillary data in spatial or non

spatial formats) in a GIS.

Combining data of different types and from different sources, such as we have described

above, is the pinnacle of data integration and analysis. In a digital environment where all

the data sources are geometrically registered to a common geographic base, the potential


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for information extraction is extremely wide. This is the concept for analysis within a

digital Geographical Information System (GIS) database. Any data source which can be

referenced spatially can be used in this type of environment.

In essence, by analyzing diverse data sets together, it is possible to extract better and

more accurate information in a synergistic manner than by using a single data source

alone. There are a myriad of potential applications and analyses possible for many

applications.

Following information or the new themes andr the quantities generated by integration of

the available database and the database generated from the present exercise (which

includes the spatial and non spatial data sets) has been generated:

Change in the bankline

Change in the main and the secondary river channels

Change in the area/surfacial extent of the sandbars and the channel bars

Change in the area/surfacial extent of the islands

Change in the area/surfacial extent of the aggradation and the erosion of land

along both the banks

Change in the area/surfacial extent of the floodplains

Pattern of the palaeofeatures including the palaeochannels, palaeomeander scars,

recently abandoned channels and oxbow lakes within the floodplains

Formulation of the oscillation zone of the Ganga river on the basis of the behavior

of the river in past five decades

Inference regarding the factors including the natural and anthropogenic, affecting

the behavior of the Ganga river

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Chapter 4: Materials and Methods -...

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