Bathymetric Mapping Using Satellite Image

8/12/2019 Bathymetric Mapping Using Satellite Image

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COASTAL BATHYMETRIC MAPPING

OF THE UPPER BAY OF BENGAL

USING OPTICAL SATELLITE

Chandan Roy

2003

Rajshahi University


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MAPPING COASTAL BATHYMETRY OF THE

UPPER BAY OF BENGAL USING SATELLITES

OPTICAL RADIANCE

by

Chandan Roy

Thesis submitted in partial fulfillment of the

requirements for the degree of Master of

Science in Geography and Environmental Studies

Approved by :Professor Dr. Raquib Ahmed

Date :

Rajshahi University


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...To my father and mother

with all my pride

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ABSTRACT

Understanding coastal bathymetry is important for monitoring the emergence of

new land, navigational channel maintenance as well as for fish resources tracking

purposes. Manual sounding system based on off shore vessel is highly time and

resource dependent method that significantly limits frequent repetition. Recent

introduction of satellite survey has opened up the possibility of the use of optical

channels for water depth detection as an alternative method. The unique character

of the shorter weave length visible channel, such as blue has the ability to penetrate

water to a significant depth and generates radiance that reflects submarine albedo.

Calibration by the information of energy attenuation due to water column depth

and back scattering due to suspended loads in the bay water helped to create a

relief map of submarine shelf areas up to about 150 km from coast of

Bangladesh. The result shows a close conformation with the sound prepared

bathymetric chart except where the presence of suspended sediment is too high and

varied, such as in the upper estuary. In addition to cheaper and quicker mapping,

the study is also important to track the rapid development of near-coastal offshore

lands in the shelf region due to deposition of fluvial sediments that unpredictably

generates a bump in the water surge and devastate resources. The study

interpolated sound data of selected points, generated the 3D surface and identified

its relation with the reflectance of blue channel of Landsat data that helped

develop a model for image-based surface generation. The collected sample of sea

waters from several locations determined the impact of sediments in energy

scattering and was able to rectify the image-based 3D generation algorithm.

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TABLE OF CONTENTS

Declaimer............................................................................................................................. iii

Acknowledgement ............................................................................................................. iv

Dedication ............................................................................................................................ v

Abstract................................................................................................................................vi

Table of Contents..............................................................................................................vii

List of Figures .....................................................................................................................xi

List of Photographs ..........................................................................................................xvList of Tables.....................................................................................................................xvi

1. INTRODUCTION.....................................................................................................11.1. Research objectives ............................................................................................. 21.2. Hypotheses to be tested ..................................................................................... 31.3. Data and materials...............................................................................................31.4. Method used.........................................................................................................7

1.4.1.Introduction................................................................................................71.4.2.Research stages...........................................................................................8

1.4.2.1. Preparation.....................................................................................8

1.4.2.2. Processing and description........................................................10

1.4.2.3. Mapping and analysis .................................................................10

1.4.2.4. Evaluation and reporting...........................................................10

2. STUDY AREA...........................................................................................................12

2.1. Study Area: Upper Bay of Bengal....................................................................12

2.1.1. Geographical location and settings ......................................................12

2.1.1.1. Hydrological conditions............................................................16

2.1.1.2. Temperature................................................................................16

2.1.1.3. Salinity .......................................................................................... 18

2.1.1.4. Tides..............................................................................................21

2.1.1.5. Color and water transparency .................................................. 22

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2.1.1.6. Sea level ........................................................................................ 23

2.1.1.7. Ocean current ............................................................................. 23

2.1.2. Bottom topography.........................................................................................24

2.1.2.1. Continental shelf.........................................................................26

2.1.2.2. Swatch of no ground .................................................................27

2.1.2.3. Ninety east ridge.........................................................................28

2.1.2.4. Eighty five ridge..........................................................................29

2.1.2.5. Bengal deep sea fan....................................................................29

3. REVIEW OF LITERATURE AND

CONCEPTUAL BACKGROUND.......................................................................31

3.1. Coastal water parameters .................................................................................. 31

3.1.1. Suspended matter.....................................................................................31

3.1.2. Estimating suspended sediment concentration..................................33

3.1.2.1. Introduction ................................................................................ 33

3.1.2.2. Empirical approach....................................................................34

3.1.2.3. Semi-empirical approach...........................................................35

3.1.2.4. Analytical approach....................................................................36

3.2 Bathymetric mapping using satellite data........................................................38

4. REMOTE SENSING AND ITS MARINE USE............................................ 42

4.1. Introduction.........................................................................................................42

4.2. The electromagnetic spectrum.........................................................................43

4.3. Energy interactions with the earth surface features.....................................45

4.3.1. Interaction With the Water Bodies.......................................................46

4.4. Observing the Earths Surface Through Satellite.........................................51

4.4.1. Land Observation Satellites....................................................................51

4.4.1.1. Landsat ......................................................................................... 51

4.4.1.2. SPOT............................................................................................54

4.4.2. Remote sensing of the sea......................................................................56

4.4.2.1. Sensor calibration .......................................................................57

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4.4.2.2. Atmospheric correction ............................................................57

4.4.2.3. Positional registration................................................................58

4.4.2.4. Oceanographic sampling for "sea truth" ............................... 58

4.4.2.5. Image processing........................................................................60

4.4.2.6. Oceanographic applications of satellite

remote sensing............................................................................60

4.4.2.6.1. Visible wavelength ocean color

sensor ..........................................................................60

4.4.2.6.2. Sea surface temperature from

infrared scanning radiometers ................................ 61

4.4.2.6.3. Passive microwave radiometers ............................. 61

4.4.2.6.4. Satellite altimetry of sea surface

topography.................................................................62

4.4.2.6.5. Active microwave sensing of

sea-surface roughness................................................62

4.4.3. Marine observing satellites......................................................................63

4.4.3.1. CZCS............................................................................................ 63

4.4.3.2. MOS..............................................................................................65

4.4.3.3. SeaWiFS ....................................................................................... 67

5. DATA ANALYSIS AND SURFACE MODELING ...................................... 69

5.1. 3D map generation from BIWTA sound chart............................................70

5.2. Satellite data processing.....................................................................................85

5.3. Water column correction ..................................................................................86

5.3.1. Light attenuation in water.......................................................................88

5.3.1.1. Absorption...................................................................................88

5.3.1.2. Scattering......................................................................................89

5.3.2. Classification of water bodies ................................................................89

5.3.3. Compensating for the influence of variable

depth on spectral data .............................................................................90

5.3.3.1. Removal of scattering................................................................90

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5.3.3.2. Lineariseing the relationship.....................................................91

5.3.3.3. Calculating the ratio ...................................................................92

5.3.3.4. Generation of depth

invariant indices.........................................................................93

5.3.4. Implementation ........................................................................................ 96

5.4. Data correction ................................................................................................... 97

5.5. Satellite data and 3D model............................................................................107

6. CONCLUSION......................................................................................................117

6.1 Causes of error in the result ...........................................................................117

6.1.1. Turbidity .................................................................................................120

6.1.2. Tide..........................................................................................................121

6.1.3. Seasonal variation of water level ........................................................121

6.1.4. Wave........................................................................................................121

6.1.5. Depth of water sample collection......................................................122

6.1.6. Depth of water ......................................................................................123

REFERENCES...............................................................................................................125

APPENDIX . ...................................................................................................129

Appendix A. Summery of image geo registration.129

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LIST OF FIGURES

Number Page

Figure 1.1 BIWTA echo sound chart of the Bay of Bengal... 6

Figure 1.2 Satellite digital data of Landsat ETM+ (20 jan 2001)

of the Bay of Bengal 7

Figure 1.3 Flowchart of the Research Methodology 9

Figure 1.4 Flowchart of the Landsat ETM image and BIWTA

sound chart processing 10

Figure 2.1 Bangladesh, Bay of Bengal and part of the Indian

Ocean. 13

Figure 2.2 Study area 14

Figure 2.3 Upper coastal regions of the Bay of Bengal and major

rivers of Bangladesh... 15

Figure 2.4 Vertical distribution of temperature in the Bay of

Bengal 17Figure 2.5 Distribution of the surface salinity of the Bay in

Summer.. 19

Figure 2.6 Distribution of the surface salinity of the Bay in

Winter. 19

Figure 2.7 Vertical distribution of salinity in the Bay of Bengal. 20

Figure 2.8 Bottom relief of the Bay of Bengal... 25

Figure 2.9 Hypsographic/hypsometric curves... 26

Figure 2.10 Depth zones and the Swatch of no ground of the

Bay of Bengal.. 28

Figure 2.11 Location of the Ninety east ridge. 30

Figure 3.1 Volume reflectance spectra for various suspended

matter concentrations in a water column.. 33

Figure 4.1 Electro magnetic Remote Sensing of earth resources... 42

Figure 4.2 The electromagnetic spectrum 44

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Figure 4.3 Atmospheric attenuation of electromagnetic

energy and transmission windows 45

Figure 4.4 Basic interactions between electromagnetic energy

and an earth surface feature 46

Figure 4.5 Major factors influencing spectral characteristics of

a water body... 47

Figure 4.6 Energy loss in water column depth/attenuation

of light with different wavelengths .. 49

Figure 4.7 Interaction of water with the spectrum 50

Figure 4.8 Typical spectral reflectance curves for vegetation, soil,

concrete, asphalt and water... 50

Figure 4.9 Atmospheric pathways of electromagnetic radiation

between the sea and the satellite sensor 59

Figure 4.10 Advanced Very High Resolution Radiometer

(AVHRR) image of sea surface temperature. 61

Figure 4.11 Spectral reflectance of different remote sensing objects 66

Figure 4.12 Pigment and sediment concentration in the Ganges

estuary region of the Bay of Bengal, viewed with

MOS sensor 66

Figure 5.1 Work flow chart.. 69

Figure 5.2 Some Reference Points of Sonic Bathymetric Survey... 71

Figure 5.3a Point coordinates of BIWTA sound chart 72

Figure 5.3b Point coordinates of BIWTA sound chart... 73

Figure 5.4 Relief generated through the interpolation of point data... 74

Figure 5.5 3D surface generated by the interpolated data.. 75

Figure 5.6a Location of profile in the study area. 75

Figure 5.6b Pattern of slope in the BIWTA sound

generated DEM before (upper) and after (lower)

contraction (row and column reduction). Profile

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along 8915E.. 75

Figure 5.7 Study area divided into 8 sub frames 76

Figure 5.8 3D view of the sea bottom relief of sub frame 1... 77

Figure 5.9 3D view of the sea bottom relief of sub frame 2 . 78

Figure 5.10 3D view of the sea bottom relief of sub frame 3... 79

Figure 5.11 3D view of the sea bottom relief of sub frame 4.. 80




Figure 5.15 3D view of the sea bottom relief of sub frame 8 84

Figure 5.16a Location of profile in the study area. 85

Figure 5.16b Pattern of slope in the blue band image

before (upper) and after (lower) contraction

(row and column reduction). Profile drawn

along 9020E. 86

Figure 5.17 Differential attenuation of the four wavebands

in the water column. 87

Figure 5.18 Processes of water column correction, showing the

steps involved in creating depth-variant indices

of bottom type for sand and sea grass .92

Figure 5.19 Bi-plot of log-transformed CASI bands 3 and 4.

Data obtained from 348 pixels of sand with variable

depth from 2-15 meter 95

Figure 5.20 Distribution pattern of the suspended sediments

in the study area. Water column collection sample

locations are also shown in the image using dots.. 99

Figure 5.21a Pattern of the distribution of the amount of

suspended load in sea water... 102

Figure 5.21b Pattern of the distribution of the suspended

load size in sea water.. 103

Figure 5.22 Contour lines of total signal decay. The corresponding

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blur figures are representing the total signal decay

(in DN) which has been used in generating continuous

surface of total signal decay ... 105

Figure 5.23 Relation between image and the actual depth. 106

Figure 5.24 3D image of the whole study area.. 108

Figure 5.25 Corrected image divided into 8 sub frames. 109

Figure 5.26 Simulated sea floor relief generated from

satellite image (sub frame 1)... 109


satellite image (sub frame 2).. 110













Figure 6.1 Location of profile in the study area... 118

Figure 6.2 Pattern of slope in the BIWTA sound

generated DEM and corrected satellite image.

Profile along AB 118



Profile along CD... 119



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Profile along EF 119



Profile along GH... 119

Figure 6.6 Effect of turbidity upon spectral properties of water... 120

Figure 6.7 Spectra of calm and wind-roughed water surfaces... 122



Profile along IJ.. 123



Profile along KL 124

LIST OF PHOTOGRAPHS

Number PagePhotograph 5.1 Collection of sea water.. 98

Photograph 5.2 The vessel used for water collection... 99

Photograph 5.3 Microscopic view of the suspended sediment

at water collection location 2144/ N 9005/ E.

Magnified 900x 100


at water collection location 2140/ N 9005/ E.

Magnified 900x 100


at water collection location 2124/ N 9004/E.

Magnified 900x ... 101


at water collection location 2120/ N 9004/E.

Magnified 900x 101

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LIST OF TABLES

Number Page

Table 1.1 Characteristics of different sensors for visible regions 5

Table 2.1 Tidal levels at the coastal tide gauging stations 21

Table 2.2 Tidal levels at the coastal tide gauging stations

on 20 january 2001...22

Table 4.1 Landsat MSS bands. 53

Table 4.2 Landsat TM bands..54

Table 4.3 HRV mode spectral ranges.. 55

Table 4.4 CZCS spectral bands.. 64

Table 4.5 MOS visible and infrared bands... 67

Table 4.6 SeaWiFS spectral bands... 68

Table 5.1 Water sample data and radiance calibration 104

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C h a p t e r 1

INTRODICTION

Mapping coastal bathymetry has been an important point in geographical

application work concerning two points, one is to gather information of the sea

bottom condition for academic interest and the other is to gather information

for management purposes. The original bathymetric survey has been evolved

from using simple chain or stick suspended from boat to the bottom to

currently used sonic bathymetric system. Although bathymetric measurement

conducted from vessels using sonic system does not use any direct contact to

the ground, yet it is quite physical involving. General echo-sound bathymetric

system is done using a device that collect sound echoed from the bottom. The

sound is basically gunned from the vessel using a sound generator. The time

difference between sound generated and echo receiving is the base of the sonic

bathymetric system. To get the depth of that point the time is multiplied with

the velocity of sound in water. The result obtained through multiplication is

divided by two (2) because the time difference here is the total time required by

the sound wave to reach the bottom and after reflection from the bottom to be

recorded by the sound receiver. This system is found to be very accurate and

dependable. The major two limitations of the system may be lake of frequent

visit and wider coverage, which is mainly due to the huge involvement of shipand constraint of time. Even covering an area of about hundred square

kilometers, it takes several weeks. The other limitation is that the survey can not

be done on a continuous basis. So the obtained result is technically interpolated

and extrapolated. Considering these limitations there has always been a search

for an alternative method. Several alternatives have been tried but the use of

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such as blue has been found to be effective as an alternative method of

bathymetric survey. Bay of Bengal is a part of the Indian ocean which has been

significantly less surveyed. This is mainly due to less navigational traffic.

Another phenomenon is the frequent change of the near shore sea bottom by

siltration. This is one reason that sonic bathymetric survey conducted once in

several years becomes partially ineffective. The application of optical data

collected from satellite has been tested in several parts of the world but two

technical limitations are yet to be settled to make the application a global one.

One is the suspended particles of different nature in the water which creates anerror in the reflected signal. The second is the unique character of the condition

everywhere over the surface of the earth. So the major point in front of using

the satellite data lies in place of the condition in the world and the behavior of

the reflected radiance. This is where the research is specifically targeted.

1.1 Research objectivesThe objectives of the present research have been set as follows:

To generate a three dimensional surface of the sea bottom usingBIWTA sampled point data and test its validity to confirm existing the

knowledge about the Bay of Bengal.

To check the usability of satellite data and identify appropriate channelto be used as an alternative method in bathymetric mapping.

Development of the processing algorithm for the raw satellite data. Developing a model to calibrate the error generated in the satellite

data.

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To develop a method to use the satellite data to generate a threedimensional model of the sea bottom and check its validity.

1.2 Hypothesis to be testedThe general hypotheses which will be tested in this research are the following:

In the continental shelf region of the Bay of Bengal the slope is gentleand it is a huge submarine fan

Swatch of no ground is located at the south west part of the study area High sediment discharge from the estuaries of many rivers in the

coastal sea water

The amount and size of the suspended sediment declines from thecoast towards the deep sea

There may be a relation between the depth and the reflectance pattern Decay of signal and scattering due to the presence of suspended

sediment in the coastal water.

1.3 Data and materials

Two important and relevant data which have been used in this research are theBIWTA echo sound chart of the Bay of Bengal (Figure 1.1) and the raw satellite

digital data of Landsat ETM+ (20 Jan 2001)of the Bay of Bengal (Figure 1.2).

The first BIWTA echo sound survey of the Bay of Bengal was carried out in

1980. Of course Bangladesh Navy maintains a similar program of bathymetric

survey of their own since recently but it is not available for general use.

However, it takes a long time to cover such a wider coverage of the bay - from

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about 150 N up to the coast. The bay is also considered very unfriendly and

hostile during about 9 months from March to November due to high weave.

The big waves are principally because of the funnel shape of the bays northern

part. The bay is also known for frequent visit of tropical cyclones originated

from the Indian Ocean.

The potential usable satellite data are collected by various satellites such as

Landsat series, IRS series, SPOT series etc. But what is most important is the

spectral coverage of the satellites as well as the temporal resolution. As the area

coverage is significantly wide. The lower spatial resolution (even up to 1 km)impacts little to view the features. Whereas, higher spectral resolution may be

better to separate different features more correctly. Temporal resolution will

give better situation in examining time series analysis which is particularly

important for the present research. The table below gives comparative

characteristics of some satellite sensors for visible parts. It is important to note

that blue spectrum region of Landsat 7 occupies most upper part of the visible

area in compared to other satellite sensors (Table 1.1) TM channel blue having

spectrum width of 0.45 m to 0.52 m was found to be the most suitable.

Among other visible spectrums the blue has the maximum water penetration

capacity of up to 20m (Lillesand and Kiefer, 2002) due to its shorter wave

length but susceptible to back scattering (Rayleighs effect) due to the presence

of smaller suspended particles. Also, availability of Landsat data is easier and

cheaper than all others. There is of course a better option- the MODIS data. Its

bandwidth is shorter and much better. Band 10 of MODIS satellite having a

bandwidth between 0.483 and 0.493 m can provide much better bathymetric

maps (described in detail in chapter 4). Major problem incorporating MODIS in

present research was its radiometric resolution of 12 bit, which was unable to

be processed due to software limitation. However for main bathymetric data

generation for the present research the data of Landsat ETM+ blue channel

(single) of Dec 25th 2001 was used. The raw data was atmospherically and

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radiometrically corrected from its source. For calibration purposes of the

backscattering in the water column 2 liters of water samples were colleted for

pre-selected 10 locations located every 10 km from the Bangladesh coast at 900

05' east longitude. To reach the pre-selected locations hand GPS (Magellan

2000XL) was used. The error rate of 30 m of the GPS was acceptable because

of such a very big area.

Table 1.1: Characteristics of different sensors for visible regions

SCANNER SPATIALRESOLUTIONIN METERS

TEMPORALRESOLUTIONIN DAYS ATEQUATOR

RADIOMETRICRESOLUTIONIN BIT

ETM (1) 0.45 0.515 30 16 8

ETM (2) 0.525 0.605 30 16 8ETM (3) 0.63 0.69 30 16 8

IRS (1) 0.52 0.59 36.25 24 8IRS (2) 0.62 0.68 36.25 24 8

IRS (3) 0.77 0.86 36.25 24 8SPOT (1) 0.5 0.59 20 26 8

SPOT (2) 0.61 0.68 20 26 8SPOT (3) 0.79 0.89 20 26 8

AVHRR (1) 0.58 0.68 1100 2 10

AVHRR (2) 0.725 1.10 1100 2 10

AVHRR (3) 3.55 3.93 1100 2 10

MODIS (8) 0.405 0.42 1000 2 12

MODIS (9) 0.438 0.448 1000 2 12

MODIS (10) 0.483 0.493 1000 2 12

SPECTRALRESOLUTIONIN m

Reference: Lillesand and Kiefer, 2002.

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Figure1.1:

BIWTA

echosoundchartoftheBayofB

engal

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Figure 1.2:Satellite digital data of Landsat ETM+ (20 jan 2001)of the Bay of Bengal

1.4 Method used1.4.1 Introduction

There were several attempts to measure the depth of shallow sea water at

various locations in the world with the aid of remotely sensed data but none of

the works were widely acceptable and were focused on specific areas to match

with particular local characteristics. The attempts were concentrated around

some particular problems like, a) signal attenuation effect, b) effect of

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background variation and back scattering, c) amount of suspended materials in

the sea water etc. A remarkable matter here is that all the relevant researches

were more or less first of its kind because this is a newly flourishing field of

application of remotely sensed data. As the work is first in Bangladesh of its

kind so the previous works were used as main reference.

1.4.2 Research stages

The core of the research deals with extraction of the submarine relief from the

BIWTA bathymetric chart, extraction of the submarine relief from the image

through water column correction and calibration. And to do this total

suspended sediment has been measured from the collected water samples also.

In general, the methodology consists of 4 stages, namely: (1) preparation stage,

(2) processing and description stage, (3) mapping and analysis stage, and (4)

evaluation and reporting stage. Figure 1.3 presents the flow cart of the

methodology implemented to achieve the objectives of the research.

1.4.2.1 Preparation

This stage composed of activities such as literature review, proposal finalization,

collection of satellite images and BIWTA bathymetric chart, and locating the

probable points on the bathymetric chart from where water samples can be

collected. This stage was done at the laboratory of the Department of

Geography and Environmental Studies, Rajshahi University.

Literature review

This activity was done transversally throughout the entire research process. It

includes the bibliographic studies from journals and books concerning the

relevant research topic. Literature review has been carried out in order to

develop the knowledge on scientific and technical aspects. Methodology

development for bathymetric mapping from the satellite image has been the

main subject of this stage. After a systematic review of different literature

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source, some methods for mapping coastal bathymetry were found.

Collection of the image and BIWTA bathymetric chart

These two elements can be considered as the raw materials of the study so these

are collected from the relevant authorities.

Locating the water sample collection points

Before collecting the water sample from the sea some points have been selected

on the map to get greater advantages at the time of collecting the samples.

Preparation Stage

Literaturereview

Collection of imageand bath metric chart

Locating the watersam le collection oints

Processing and Descriptive Stage

Satellite Imageprocessing

BIWTA bathymetricchart processing

Measuring the sizeand amount of

suspended sediment

Mapping and Analysis Stage

Mapping of the bathymetryfrom BIWTA sound chart

Mapping of the bathymetryfrom Landsat ETM

Evaluation and Reporting Stage

Identification of problems

Figure1.3:Flowchart of the research methodology.

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1.4.2.2 Processing and description stage

This stage includes all the stages of processing the image and the BIWTA sound

chart (Figure 1.4). Later sediment size and amount has been measured for

calibration purpose.

Generated DEM from theBIWTA sound chart

Corrected Satellite ima e

Regression analysis between image and DEM

pplying the Algorithms into entire image

3D image generation

Bathymetric map

Figure 1.4:Flowchart of the Landsat ETM image and BIWTAsound chart processing.

1.4.2.3 Mapping and analysis stage

Later with the calibrated image bathymetry of the coastal region has beenmapped. In this case regression model between BIWTA sound data and image

has been used.

1.4.2.4 Evaluation and reporting stage

This is the last stage of the research. This stage includes the evaluation of the

methods applied in this research and also the evaluation of the used remote

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sensing images (Landsat ETM) for studying bathymetry. Except this a

comparative study has been done between the image generated 3D model and

the BIWTA sound chart generated DEM. The present report, including maps is

the final result of this thesis work.

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C h a p t e r 2

STUDY AREA

2.1 Study Area: upper Bay of Bengal

This chapter deals with the description of the area where this research was

conducted. The description includes the geographical location and setting and

bottom topography of the study area.

2.1.1 Geographical location and settings

The study area covers the upper part of the Bay of Bengal. Basically Bay of

Bengala northern extended arm of the Indian Ocean and is located between

latitudes 5N and 22N and longitudes 80E and 100E(Figure2.1). As remotely

sensed data is only suitable for shallow coastal waters only the upper part of the

Bay of Bengal has been selected as the study area. The study area is locatedbetween 20N and 22N latitudes and 897E and 9120E longitudes

(Figure:2.2) covering an area of about 32400 square kilometers. The Bay of

Bengal is bounded in the west by the east coasts of Sri Lanka and India, on the

north by the deltaic region of the Ganges-Brahmaputra-Meghna river system,

and on the east by the Myanmar peninsula extended up to the Andaman-

Nicobar ridges. The southern boundary of the Bay is approximately along the

line drawn from Dondra Head in the south of Sri Lanka to the north tip of

Sumatra. The Bay occupies an area of about 2.2 million sq km and the average

depth is 2,600m with a maximum depth of 5,258m. Bangladesh is situated at the

head of the Bay of Bengal (Figure: 2.3).

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Figure2.2:Studyarea

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Figure 2.3:Upper coastal regions of the Bay of Bengal and major rivers of Bangladesh

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2.1.1.1 Hydrological conditions

Surface hydrology of the Bay of Bengal is basically determined by the monsoon

winds and to some extent by the hydrological characteristics of the open part of

the Indian Ocean. Fresh water from the rivers largely influences the coastal

northern part of the Bay. The rivers of Bangladesh discharge the vast amount of

1,222 million cubic meters of fresh water (excluding evaporation, deep

percolation losses and evapotranspiration) into the Bay. The temperature,

salinity and density of the water of the southern part of the Bay of Bengal is,

almost the same as in the open part of the ocean. In the coastal region of the

Bay and in the northeastern part of the Andaman Sea where a significant

influence of river water is present, the temperature and salinity are seen to be

different from the open part of the Bay. The waves and ripples entering from

the southern part of the Bay provide the energy for mixing the water and

consequently bring uniformity in its chemical and physical properties. Tidal

action is also very great in the shallow coastal zones.

2.1.1.2 Temperature

As the bay is surrounded by land mass from three sides so the land mass has a

great impact upon the water temperature of the bay. The temperature of about

two third water of northern portion of the bay remains between 25C and 28C

from December to March. From April the temperature of the bay starts to

increase. The maximum temperature is observed in May (30C). But in July the

temperature is reduced and remains same till September. In October the

temperature reduces again and in January the lowest temperature is seen and the

minimum temperature is 25C. The mean annual temperature of the surface

water is about 28C. But the annual variation in temperature is not large, about

2C in the south and 5C in the north. In the bay water has a inverse

relationship with depth of water, that is if the depth increases the temperature

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decreases. Average vertical distribution of temperature of the bay in given in

Figure 2.4.

Temperature (C)

Temperature

Depth

inmeters

INDEXSummer temperature

Winter temperature

Figure 2.4:Vertical distribution of temperature in the Bay of Bengal

Source: Das, S.C., 2002.

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2.1.1.3 Salinity

Bay of Bengal is unique in the world in terms of salinity. As some large rivers of

the world have fallen into the bay so the salinity of the surface water of the bay

is less saline than other seas of the world. Except this seasonal variation in

salinity is seen in the bay, this causes mainly due to the variation in rainfall

seasonally. In rainy season when rainfall is highest then the water discharge

from the river increases also and due to this huge amount of discharge in the

monsoon sometimes at the estuary the salinity becomes 0. The surface salinity

in the open part of the Bay oscillates from 32 to 34.5 (parts per thousand,

i.e. grams per kilogram of sea water) and in the coastal region varies from 10

to 25. But at the river mouths, the surface salinity decreases to 5 or even

less. The coastal water is significantly diluted throughout the year, although the

river water is greatly reduced during winter. Along the coast of the Ganges-

Brahmaputra Delta, salinity decreases to 1 during summer (Figure 2.5) and

increases up to 15 to 20 in winter (Figure 2.6). Salinity gradually increases

from the coast towards the open part of the Bay and near the coast the seasonal

variation in salinity is greatest when in the deep sea this variation is very less.

The surface salinity at the mouths of some large rivers like the Ganges,

Brahmaputra, Irrawaddy and some Indian rivers like the Krishna, Godavari,

Cauvery and Mahanadi varies widely from one day to another, especially in

summer. Salinity of water also changes vertically (Figure 2.7). The influence of

the fresh water is experienced up to depths of 200-300m. From the surface, thesalinity gradually increases downward and at about 200-300m it reaches 35

and at about 500m the salinity is more than 35.10, but at 1,000m it decreases

slightly and attains 34.95. With further increase of depth salinity decreases

and at 4,500m it is close to 34.7 (Banglapedia CD-ROM edition, Version-1)

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Figure 2.5:Distribution of the surface salinity of the Bay in summer


Salinity in

Bay of Bengal

Figure 2.6:Distribution of the surface salinity of the Bay in winter


Salinity in

Bay of Bengal

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Salinity ()

Salinity

Depth

inmeters

INDEX

Summer salinity

Winter salinit

Figure 2.7:Vertical distribution of salinity in the Bay of Bengal


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2.1.1.4 Tides

In the Bay the tide is semi-diurnal in nature, i.e. two high and two low tides

during the period of 24 hours and 52 minutes. The highest tide is seen where

the influence of bottom relief and the configuration of the coast are prominent,

i.e. in shallow water and in the Bay and estuary. The average height of tidal

waves at the coast of Sri Lanka is 0.7m and in the deltaic coast of the Ganges it

is 4.71m (due to funnel effect). In the Bay of Bengal tidal currents specially

develop in the mouths of the rivers, like the Hooghly and the Meghna . Tidal

levels at the coastal tide gauging stations are given in table 2.1 and tidal levels at

those stations on 20 january 2001 are given in table 2.2.

Table 2.1: Tidal levels at the coastal tide gauging stations

STATION LAT MLWS MLWN ML MHWN MHWS HATHiron point -0.256 0.225 0.905 1.700 2.495 3.175 3.656

Sundarkota -0.553 0.036 0.636 1.829 3.022 3.694 4.211

Khepupara -0.323 0.195 1.025 2.060 3.096 3.925 4.445Galachipa -0.159 0.283 0.937 1.764 2.592 3.245 3.689

CharChanga

-0.375 0.256 1.060 2.037 3.014 3.818 4.449

Sandwip -0.583 0.238 1.634 3.243 4.851 6.248 7.070

Sadarghat(CTG)

-0.423 0.239 1.100 2.481 3.861 4.722 5.385

Khal No 10 -0.444 0.261 1.231 2.664 4.097 5.067 5.772

Coxs Bazar -0.339 0.205 1.023 1.995 2.967 3.785 4.329

Shahpuri -0.348 0.191 1.045 1.874 2.703 3.557 4.096

LAT = Lowest astronomical tideMLWS = Mean low water springMLWN = Mean low water neepML = Mean levelMHWN = Mean high water neepMHWS = Mean high water springHAT = Highest astronomical tide

Source: Tide tables, 2001, BIWTA

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Table 2.2: Tidal levels at the coastal tide gauging stations on

20 January 2001

TIDEGAUGINGSTATION

DATE OFGAUGING

TIME OFGAUGING

HEIGHT(IN

METER)Hiron point 20 january 2001 8 : 42 am 0.55

Sundarkota 20 january 2001 10 : 28 am 0.23

Khepupara 20 january 2001 9 : 31 am 0.43

Galachipa 20 january 2001 10 : 51 am 0.58

Char changa 20 january 2001 12 : 25 pm 0.61

Sandwip 20 january 2001 12 : 14 pm 0.86

Sadarghat (CTG) 20 january 2001 11 : 56 am 0.59

Khal No 10 20 january 2001 11 : 15 am 0.46

Coxs Bazar 20 january 2001 8 : 37 am 0.52

Shahpuri 20 january 2001 7 : 14 am 0.62

Source: Tide tables, 2001, BIWTA

2.1.1.5 Color and water transparency

The color of the water in the open part of the Bay is dark blue which gradually

changes to light blue to greenish towards the coast. Transparency is high, 40-

50m in some places. In the central part of the Bay of Bengal, the anticyclone

circulation is generated and the zone of convergence lies in the center of this.

This region is characterized by high water transparency. Regions of low

transparency and turbid water are available in the limited area of the pre-deltaic

part of the rivers Ganges and Brahmaputra. The absorption and scattering of

the light by the water depends upon the suspended and dissolved materials in

the water. These elements may be organic or inorganic in nature.

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On the basis of transparency of water Bay of Bengal can be divided in the

following three regions:

i. Region of transparent oceanic waterii. Zone of normal oceanic transparency, andiii. Region of low transparency

2.1.1.6 Sea level

Due to the influence of water density and wind the seasonal changes of the sea

level in the Bay are remarkable and one of the highest in the world. The range

of sea level change at Khidirpur is 166 cm, at Kolkata 130 cm and at Chittagong

118 cm. But towards the southwestern coast at Madras and Vishakhapatnam

[Vishakhapatnam] the range is small compared to the northern and northeastern

coasts of the Bay. The lowest variation of sea level at the southeastern coast of

India is due to its geographical location at the edge of a comparatively deep sea.

2.1.1.7 Ocean current

Surface circulation is found to be generally clockwise during January to July and

counter-clockwise during August to December, in accordance with the

reversible monsoon wind systems. The flow is not constant and depends on the

strength and duration of the winds. The effects of a strong wind blowing for a

few consecutive days are reflected in the rate of flow. Currents to the northeast

generally persist longer and flow at greater speed because of the stronger

southwest monsoons. An important vertical circulation in the Bay of Bengal is

up-welling. In this process, sub-surface water is brought toward the surface

which causes enormous mixing of sediments with the water in the coastal areas,

and conversely a downward displacement is called down-welling or sinking.

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Up-welling and down-welling are seasonal, being created by monsoon winds

that blow from the southwest during the summer, then reverse direction and

come from the northeast during the winter. The persistence of the monsoon,

especially from the southwest and the orientation of the coasts cause up-welling

to occur along most of the east coast of India. That is why in the east coast of

India the up-welling takes place in summer and down welling in winter, and in

the eastern part of the Bay of Bengal and in the Myanmar coast, up-welling

occurs in winter and the down-welling in summer. However, the duration and

intensity of vertical movement of water on both sides of the Bay of Bengal isnot as great as on the Somalia or North and South American coasts. But it does

have a profound effect on the food economy of the sea through its influence on

chemical properties and biological populations.

2.1.2 Bottom topography

Bottom topography of the bay is characterized by a broad U-shaped basin with

its south opening to the Indian Ocean. A thick uniform abyssal plain occupies

almost the entire Bay of Bengal gently sloping southward at an angle of 8-10.

In many places underwater valleys dissect this plain mass. As we are working

with the coastal bathymetry of the Bay so the bottom topography of the Bay

basically the shelf region is more important to us (Figure 2.8).

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Figure2.8:Bottomr

eliefoftheBayofBengal

Source:UnitedStatesGe

ologicalSurvey

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Most of the features of the bottom topography of the Bay are similar to other

bays and seas of the world. The overall topography of the bay can be discussed

under the following three headlines:

i. Continental shelfii. Continental slope, andiii. Deep sea plains (Figure:2.9)

Figure 2.9:Hypsographic/hypsometric curve

Source: Singh Savindra, 2003.

As the study area covers only the upper part of the Bay that is the continental

shelf region so description of the continental self region has been given here

only.

2.1.2.1 Continental shelf

The width of the continental shelf off the coast of Bangladesh varies

considerably. It is less than 100 km off the south coast between Hiron Point

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and the swatch of no ground and more than 250 km off the coast of Coxs

Bazar. Sediments are fine seaward and westward with the thickest accumulation

of mud near the submarine canyon, the Swatch of no Ground. The shallow part

(less than 20m) of the continental shelf off the coast of Chittagong and Taknaf

is covered by sand and the intertidal areas show well-developed sandy beaches.

The shallower part of southern continental shelf off the coast of the

Sundarbans, Patuakhali and Noakhali is covered by silt and clay; and extensive

muddy tidal flats are developed along the shorelines. It is mainly due to the high

sediment yield from the rivers in this region. Some of the shoals and sand ridgespresent on this part of the continental shelf show an elongation pattern pointed

towards the Swatch of no Ground. The over all depth of the continental shelf

region is not more than 30 meters and this character of this shelf has supported

us to map the coastal bathymetry with satellites optical radiance.

Except these common features it has some unique features also, those are:

2.1.2.2 Swatch of no groundIt is the most unique feature of the Bay and also known as Ganges Trough.

Swatch of no Ground has a comparatively flat floor 5 to 7 km wide and walls of

about 12 inclination. At the edge of the shelf, depths in the trough are about

1,200m. The Swatch of no Ground has a seaward continuation for almost 2,000

km down the Bay of Bengal in the form of fan valleys with levees (Figure 2.10).

The sandbars and ridges near the mouth of the Ganges-Brahmaputra delta

pointing toward the Swatch of no Ground showing sediments are tunneled

through this trough into the deeper part of the Bay of Bengal. The Swatch of no

Ground is feeding the Bengal Deep Sea Fan by turbidity currents.

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2.1.2.3 Sunda Trench

It is also known asJava Trench. Running parallel along the west side of the arcof the Nicobar and Andaman islands it is extended northward up to 10N into

the Bay and joins the eastern limit of the Himalayan range. It originated

tectonically at the junction of the Indian and Myanmar plates.

Figure 2.10:Depth zones and the Swatch of no ground of the Bay of Bengal

Source: Banglapedia CD-ROM edition (version 1)

2.1.2.3 Ninety east ridge

Major feature of the Indian Ocean which runs in a north-south direction

approximately along the longitude 90E. It lies at the immediate outboard of the

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Sunda Trench between the Bengal Fan and the Nicobar Fan (Figure 2.11). The

Ninety East Ridge has existed since early in the formation of the Bay of Bengal.

The ridge represents the trace of a hot spot formed during the northward flight

of India and its associated oceanic lithosphere of the Bay of Bengal.

2.1.2.4 Eighty-five ridge

It is a ridge along 85E longitude. More than 5 km thick sediments have been

deposited on either sides of the ridge. The main turbidity current channel of the

sub aerial drainage pattern lies immediately east of the buried ridge.

2.1.2.5 Bengal deep sea fan

The world's largest submarine fan, also known as Bengal Fan. It is 2,800 to

3,000 km long, 830 to 1,430 km wide and more than 16 km thick beneath the

northern Bay of Bengal (Figure 2.10). Sediments are tunnelled to the fan via a

delta-front trough, the Swatch of no Ground. It can be divided into three parts:

upper fan, middle fan and lower fan. Rapid terrigenous sedimentation on an

incipient Bengal fan began in the Eocene age (58 to 37 million years ago) as a

response to the first intraplate collision and continued to the present, building

the world's largest submarine fan.

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Figure 2.11:Location of the Ninety east ridge

Source: Geological Survey of India

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C h a p t e r 3

REVIEW OF LITERATURE AND CONCEPTUAL

BACKGROUND

The aim of this chapter is to give a theoretical background related to this

research. In order to illustrate the possibility of mapping the coastal bathymetry

by using remotely sensed images, this chapter starts with the coastal water

parameters (Section:1) and Section:2 contains the descriptions regarding the

application of remote sensing in mapping coastal bathymetry.

3.1 Coastal water parametersWater quality is a general term used to describe the physical, chemical, and /or

biological properties of water. Water quality has no parameters that can be

defined easily or which can be standardized to meet all uses and user needs.

Ritchie and Schiebe (1998) mentioned that the major factors affecting water

quality in fresh water estuaries and coastal regions are suspended matters;

chlorophylls (algae); chemicals substances; dissolve organic matter; nutrients;

pesticides; thermal releases; and oils. Among these the suspended sediments

(turbidity), affect the surface water in their spectral properties most. Such

changes in spectral signals from surface waters are measurable by remote

sensing techniques from many platforms and causes noise in the image in the

case of bathymetric mapping. The relationship between spectral signature of the

water and the amount of the substances in that water is still an active field of

research.

3.1.1 Suspended matter

All natural water bodies contain a suspended matter component that comprises

organic and inorganic material. It is generally measured in (in mg/l). In general,

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all of the non-chlorophyllous matter, phytoplankton and detritus are referred to

total suspended matter (TSM). The inorganic fraction of TSM can be formed

from biological sources (e.g. coccolihs), benthic (re-suspension of bottom

sediment) or fluvial origin from river discharge. It is measured by optical

methods that are often difficult to be quantified accurately in terms of weight or

volume. Some researchers have discussed the relationship between suspended

sediment and reflectance. Ritchie et al. (1996) mentioned that the suspended

sediment increases the radiance from surface water in visible and near infrared

ranges of the electromagnetic spectrum. Laboratory measurements have shownthat the surface water radiance is affected by sediment type, texture, color,

sensor view and sun angles, as well as water depth (Ritchie and Schiebe, 1998).

Since the mid 1970s, remote sensing studies of suspended matter have been

using the data from satellite platforms such as Landsat, SPOT, IRS, Coastal

Zone Color Scanner (CZCS) and SeaWiFS (Sea-viewing Wide Field of View

Sensor). Those studies have shown a significant relationship between suspended

matter and radiance or reflectance from single band or combination of somebands in satellite or airborne platforms. Ritchie et al. (1976), concluded that the

wavelength between .7m and .8m were the most useful range for determining

suspended matter in surface water. Dekker (1993) described that the remote

sensing of water bodies is restricted to a relatively narrow range of optical

wavelength compared to remote sensing of terrestrial object. This is caused by

low solar irradiance at wavelengths shorter than approximately .4m and by a

combination of lower solar energy and the sharply increasing absorption of light

beyond approximately .85m. Therefore, the range of .4m to .85m is often

used for research aimed at estimation of water quality parameters. Figure: 3.1,

illustrates the impact of suspended matter on volume reflectance spectra, just

beneath the air water interface (Bukata et al., 1995). The impact of suspended

matter on volume reflectance spectra is clearly evident. Even at small

concentrations, suspended matter can substantially increase the volume

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reflectance in a manner that becomes more pronounced as the wavelength

becomes longer. The absorption of radiance by suspended sediment is generally

much smaller than that of chlorophyll, but the scattering is much higher. An

increase of sediment concentration results in an increase of the backscattering

and hence, an increase in the emergent radiance leaving the water.

Figure 3.1: Volume reflectance spectra for various suspendedmatter concentrations in a water column (Bukata et al., 1995).

3.1.2 Estimating suspended sediment concentrations by remote sensing

3.1.2.1 Introduction

Coastal water often requires site-specific algorithms to take into account the

differences in the constituents and their optical properties at different location

and times (Pennock and Sharp, 1986; Stumpf and Pennock, 1989; Tassan, 1993,

in Keiner and Xiao-Hai Yan, 1998). These differences are caused by several

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factors such as fluctuation of river flow, sediment load and phytoplankton. As a

result, data must be acquired at the same time as the overpass of the satellite.

The most common techniques used for analysis of remote sensing data to

determine water quality concentration are based on the brightness of

reflectance. To obtain the water quality concentration from the water leaving

radiance that is detected by the optical sensor, the retrieval algorithms can be

used. Morel and Gordon (1980) pointed out three different approaches: a)

empirical approach, b) semi-empirical approach and c) analytical approach.

3.1.2.2 Empirical approach

It is also known as statistical approach. This approach is based on calculation

of statistical relation between the constituent concentration and water leaving

radiance or reflectance. Spurious results may occur while using this method,

because a causal relationship does not necessarily exist between the parameters

studied. Empirical models always need in-situ data because the following

parameters may change between different remote sensing missions (Dekker etal., 1999):

a) Above the air-water surface:

The total down welling irradiance (solar elevation)The fraction of diffuse to direct solar irradianceThe amount of specular reflection at the air-water interfaceThe roughness of the water surfaceThe height and the composition of the atmosphere column between the

sensor and the water surface leading to differences in path radiance.

b) Below the air-water interface:

The radiance to irradiance conversion of the subsurface upwelling lightsignal

The relation between R (0-) and the specific inherent optical properties

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The relation between inherent optical properties and the optical waterquality parameters.

There are simple and multiple regression equations. These are the subjects of

research done by Ritchie and Cooper (1988), Baban (1993) and Shimoda et al.

(1986). Linear and multiple regressions were proved useful for the study of the

suspended sediment. They yielded sufficiently accurate concentration

estimations. They gave better accuracy if the measurement is at the same time as

the acquisition date of remotely sensed imagery.

3.1.2.3 Semi-empirical approach

In this type of algorithms, the spectral characteristics of the water constituents

are well known and this knowledge is used to improve the algorithms developed

by statistical approach. Reasonable algorithms can be found by common sense

and improved by experience. Quantitatively, the coefficients could be applied

just to the data set at hand, so each application must be individually calibrated.

The semi-empirical approach is commonly used. Semi-empirical algorithms

based on R(0-) are significantly better than the empirical algorithms. This is

because the only parameters that may change between different times are the

relation between R(0-) and the inherent optical properties, and the relation

between inherent optical properties and the optical water quality parameter

(Dekker et al., 1999). In many remote-sensing applications, semi-empirical water

quality algorithms are used for estimating water quality parameters from the

reflectance. The reason of wide application of this algorithm is that they are

straightforward and easy to use in several image processing software (Dekker et

al., 1995; Hilton, 1984; Kirk, 1999). Shimell and Hesselmens (1999) have

developed a semi-empirical algorithm for coastal waters. They applied multiple

regressions and band ratio algorithm by using simulated channels of a new

ocean color sensor such as SeaWiFS and MERIS (Medium Resolution Imaging

Spectrometer). This approach is quick and constitutes a simple method of

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obtaining sediment map in coastal regions. Spectral mixture analysis, as a data

analysis tool, is done using a fixed reference (end-members). The end-members

are represented by spectral data from either the purest pixel of a specific

material on an image or the purest material in the laboratory (Metres et al.,

1991). He proved that spectral mixture analysis is a powerful tool for estimating

suspended sediment concentration in the surface waters. The neural network

can be applied to define the transfer function between the chlorophyll or

sediment concentration and the satellite receiver radiance (Keiner and Xiao-Hai

Yan, 1997). It was found that a neural network using three visible bands ofLandsat TM as input has been successful in modeling the water quality

parameters.

3.1.2.4 Analytical approach

The inherent and apparent optical properties modulate the reflectance and vice

versa. The water constituents can be characterized by their specific (per unit

measure) absorption and backscatter coefficients. Subsequently, if theseproperties are known, analytical methods can be used optimally to retrieve the

concentration of water constituents from the remotely sensed up welling

radiance or radiance reflectance signal. In many coastal and inland waters, the

combination effects of backscattering and absorption introduce non-linear

relationship between the water constituents and spectral reflectance. As has

been mentioned by Dekker et al. (1999), the processing from light measurement

at a remotely sensor into concentration map of water quality parameter is

complex. By modeling, it becomes possible to derive an accurate remote sensing

algorithm for the estimation of suspended sediment for the water bodies. The

main advantages of the analytical approach are:

Consistency of retrieved constituents concentrations is secured; It is transparent, which makes it easy to review and understand how each

component works;

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The air water system can be divided into subsystems, for which theseparate model and inversion procedures can be developed and

improved more easily;

It allows the analysis of error propagation, which enables us to predicterrors in retrieved concentration;

It can be adapted to other spectral bands; Only initial measurement is needed to establish optical properties of the

relevant waters in an area, require little measurement; this approach is

cost effective and optimizes the use of archives images

Estimation the concentration of total suspended solids using Thematic Mapper

(TM) data was carried out in the coastal waters of Penang by K. Abdullah, Z. B.

Din, Y. Mahamod, R. Rainis, and M. Z. MatJafri. The algorithm used is based

on the reflectance model which is a function of the inherent optical properties

of water which can be related to its constituents concentrations. A multiple

regression algorithm was derived using multiband data for retrieval of the water

constituent. The digital numbers coinciding with the sea truth locations were

extracted and converted to radiance and exoatmospheric reflectance units. Solar

angle and atmospheric corrections were performed on the data sets. These data

were combined for multi-date regression analysis. The efficiency of the present

algorithm versus other forms of algorithms was also investigated. Based on the

observations of correlation coefficient and root mean-square deviations with

the sea-truth data, the results indicated the superiority of the proposed

algorithm. The solar corrected data gave good results, and comparable accuracy

was obtained with the atmospherically corrected data. The calibrated total

suspended solid algorithm was employed to generate water quality maps. The

relationship between TM signals versus total suspended solid concentration

shows that as the concentration increases, the response from each TM band

also increases. Other investigators using remote sensing data in the visible

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channels for suspended sediment studies showed similar characteristics (Schiebe

et al. 1992, Choubey and Subramaniam 1992). The trend suggests that the non-

linear relation is preferred by the data set. The single band method was found to

be less accurate. Generally the accuracy increased when more spectral bands

and higher order series were included in the regression analysis.

3.2 Bathymetric mapping with satellite data

Bathymetric mapping with the satellite data is a very recent field of application

of the satellite data. As it is a quietly new field of application so literatures

regarding this are not so available. A few literatures which are available are not

suitable for all the coastal waters of the world, which have been proved by the

adoption of different calibration techniques for different regions. That is why

any literature could not be followed uniquely.

A valuable work on bathymetric charting was done at the Penang Strait in

Malaysia where the signal reflectance data were corrected (and compared too)

using sound signals by K. Abdullah, M.Z. MatJafri and Z.B.Din. They

conducted a survey to measure the new sounding points using a boat equipped

with an echo sounder and the sounding locations were determined with a GPS

system. Landsat TM and SPOT data acquired between January 1997 and

February 1997 were used by them for the study. Image locations were related to

the map GCP coordinates through the second degree polynomial

transformation equations. The pixel values of the same locations were extracted

and were used as independent variables and the measured sounding points as

dependent variables. In the study multiband water depth algorithm was used in

the calibration analysis. Regression techniques were used for calibration of the

satellite signals for water depth measurement. From the regression equation

they examined the correlation coefficient and root-mean-square deviations for

each data set. Later the accuracy of each calibration algorithm was further

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verified using other known points. At last the calibration algorithm was applied

to the corresponding image to generate water depth map.

A notable work could be referred on mapping benthic habitats and bathymetry

near the Lee Stocking Island of the Bahamas (Louchard, E.M., Pamela Reid, R.

and Carol Stephens, F., 2003). The depth was not more than 10 meters and they

used multispectral data as it included to identify sea grass where bathymetry was

an influencing factor. To correct error due to low light availability was

compensated by using a portable hyperspectral imager for low light

spectroscopy. For rapid identification of benthic features in coastal

environments they used a spectral library of remote sensing reflectance

generated through radiative transfer computations, to classify image pixels

according to bottom type and water depth. Later they tested the library

classification method on hyperspectral data collected using a portable

hyperspectral imager for low light spectroscopy airborne sensor near Lee

stocking island, Bahamas. In their paper they have illustrated a comparative

technique that is used to estimate bathymetry from remotely sensed data.

According to them an individual band is not suitable to extract bathymetry, that

is as multispectral data typically do not contain enough spectral information to

differentiate between complex bottom types, so in this case hyperspectral data

will give good result. The detailed spectral information available in hyperspectral

images provides an opportunity to develop new approach for an analyzing and

modeling of benthic reflectance.

Philpot tried to develop a spectral analysis tool with the hyperspectral image

data that can be used to detect ocean color and water quality, extract

bathymetry, and bottom type information. Their main objectives were to

develop specific algorithm and procedures to classify water type, differentiate

among different bottom types and extract bathymetry from passive

hyperspectral image data. They indicated that, when the water type and bottom

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reflectance are uniform over the study area, bathymetric mapping with passive

remote sensing data is a relatively straight-forward, one variable problem and

requires a minimum ground information data. But the physical properties of

water is not same everywhere, it differs from region to region. In that case the

depth can not be determined without simultaneously resolving the bottom

reflectance and basic optical water properties. That is why he suggested to use

more than one band to extract bathymetry from the satellites optical radiance

and in this case the hyperspectral images are more effective (opl.ucsb).

Satellite remote sensing techniques can be used together with limited water

depth measurements from conventional methods to chart the coastal areas in a

cost-effective manner (Dr. Seeni Mohd, M.I., Ahmad, S, Yem, M.). This paper

reports on a study to obtain water depths in the coasts! Waters of Pulau

Tioman, Malaysia using the Landsat-5, Thematic Mapper data that were

acquired on 1 April 1990. Band 1 (0.45 0.52 m ) of the data was used since it

has the best depth penetration capability in the relatively clear waters of PulauTioman. They corrected the satellite data for atmospheric effects prior to

computation of water depths with a computer program. An algorithm which

expresses the exponential relationship between water depth and pixel intensities

were used together with a few in-situ calibration depths that were taken at the

time of satellite pass. Comparisons of calculated depths with measured depths

at some check points indicate an error of 0.5 2.0 m in depths of up to about

50 m of water. The depth accuracy requirement are 30 cm for depths up to 30

m , 1 m for depths from 30 m to 100 m and 1% of the depth for deeper than

100 m according to the accuracy standards recommended for hydrographic

surveys by the International Hydrographic Organization. The results obtained in

this study and other studies (Ibrahim 1989) indicate that these accuracy

requirements are difficult to achieve by remote sensing techniques. However,

the hydrographical chart derived from the Landsat-5, TM satellite data show

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many similarities with the corresponding hydrographic chart derived from the

Admiralty hydrographic charts despite the large difference in the dates of field

survey and satellite data acquisition (1960 and 1990). This shows that in areas

where the water clarity is good, satellite data can be used to obtain some general

idea on the depth contours.

In most of the literatures stated in the above paragraphs, hyperspectral satellite

images have been used for coastal bathymetric mapping. Beside this Landsat

TM image has been used also. In this research only the Landsat ETM+ blue

band image has been used. One of the most important causes behind theselection of this band is its greater water penetration capacity. Blue band of

Landsat ETM+ having wavelength between 0.45 m and 0.52 m penetrates up

to 20 meters in the clear water. But as those hyperspectral satellite data are very

costly and not easily available. So the blue band of Landsat ETM+ has been

used. Except this the study area: The upper part of Bay of Bengal is a region of

active delta building. So here the amount of suspended sediment is greater in

comparison with the study areas of the above mentioned literatures. Because ofthis excessive amount of sediment concentrations in the water a different

calibration technique has been used in this research.

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C h a p t e r 4

REMOTE SENSING AND ITS MARINE USE

4.1 Introduction

Remote Sensing is the science and art of obtaining information about an object,

area, or phenomena through the analysis of data acquired by a device that is not

in contact with the object, area, or phenomena under investigation. The term

"remote sensing" is itself a relatively new addition to the technical lexicon. Itwas coined by Ms Evelyn Pruitt in the mid-1950's when she

(geographer/oceanographer) was with the U.S. Office of Naval Research

(ONR) outside Washington, D.C. In much of remote sensing, the process

involves an interaction between incident radiation and the targets of interest.

This is exemplified by the use of imaging systems where the following nine

elements are involved. Note, however that remote sensing also involves the

sensing of emitted energy and the use of non-imaging sensors.

The generalized processes and elements involved in electromagnetic remote

sensing of earth resources are represented in schematically in Figure 4.1. The

two basic processes involved here are data acquisition and data analysis.

Figure 4.1: Electro magnetic Remote Sensing of earth resources

Source:Lillesand, T.M. and Kiefer, 2002.

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The elements of the data acquisition process are:

energy sources propagation of energy through the atmosphere energy interactions with the earth surface features retransmission of the energy through the atmosphere airborne and/or space borne sensors generation of sensor data in pictorial and/or digital format

On the other hand the data analysis process involves:

examining the data using various viewing and interpretation devices toanalyze pictorial data and/or a computer to analyze digital sensor data

compilation of the information in the form of hard copy, maps andtables or as computer files that can be used for further interpretation

presentation of the information to the users so that they can use it fortheir decision making process.

4.2 The electromagnetic spectrum

Electromagnetic radiation occurs as a continuum of wavelengths and

frequencies from short wavelength, high frequency cosmic waves to long

wavelength, low frequency radio waves. And this systematic arrangement of

these different electromagnetic waves is called electromagnetic spectrum (Figure

4.2). There are several regions of the electromagnetic spectrum which are useful

for remote sensing.

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Figure 4.2:The electromagnetic spectrum

Source:Lillesand, T.M. and Kiefer, 2002.

A narrow range of EMR extending from 0.4 to 0.7 m, the interval detected by

the human eye, is known as the visible region (also referred to as light but

physicists often use that term to include radiation beyond the visible). White

light contains a mix of all wavelengths in the visible region

The light which our eyes - our "remote sensors" - can detect is part of the

visible spectrum. It is important to recognize how small the visible portion is

relative to the rest of the spectrum. There is a lot of radiation around us which

is "invisible" to our eyes, but can be detected by other remote sensing

instruments and used to our advantage. The visible wavelengths cover a range

from approximately 0.4 to 0.7 m. The longest visible wavelength is red and the

shortest is violet. Blue, green, and red are the primary colors or wavelengths of

the visible spectrum. They are defined as such because no single primary color

can be created from the other two, but all other colors can be formed by

combining blue, green, and red in various proportions. Although we see

sunlight as a uniform or homogeneous color, it is actually composed of various

wavelengths of radiation in primarily the ultraviolet, visible and infrared

portions of the spectrum. The visible portion of this radiation can be shown in

its component colors when sunlight is passed through a prism, which bends the

light in differing amounts according to wavelength. The whole portion of the

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spectrum is not suitable for remote sensing. The sun light before falling upon

the earths surface and after being reflected from the earths surface has to travel

through the atmosphere. And light while traveling through the atmosphere the

suspended particles of varying size present in the atmosphere causes scattering

effect. Except this effect when the light moves through the atmosphere certain

portion of it is absorbed by ozone, carbon dioxide, and water molecules etc.

which are present in the atmosphere. This effect is called absorption. Those

areas of the spectrum which are not severely influenced by atmospheric

absorption and thus, are useful to remote sensors, are called atmosphericwindows (Figure 4.3)

Source: Lo, C.P. and Yeung, A.K.W., 2002.

Figure 4.3 :Atmospheric attenuation of electromagnetic energy and transmission windows

4.3 Energy interactions with the earth surface features

When electromagnetic energy is incident on any given earth surface feature,

three fundamental energy interactions with the feature are possible. This is

illustrated in Figure 4.4 for a water body. Various fractions of the energy

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incident on the element are reflected, absorbed, and/or transmitted. Applying

the principle of conservation of energy, we can state the interrelationship

between these three energy interactions as:

E1() = ER() + EA() + ET()

where,

E1= Incident energy

ER= Reflected energy

EA= Absorbed energy

ET= Transmitted energy

Figure 4.4:Basic interactions between electromagnetic energy and an earthsurface feature

Source: Lillesand, T.M. and Kiefer, 2002.

E1()= Incident energy E1() = ER() + EA() + ET()

ER()= Reflected energy

ET()= Transmitted energyEA()= Absorbed energy

4.3.1 Interaction with the water bodies

Spectral qualities of water bodies are determined by the interaction of several

factors, those are:

the radiation incident to the water surface optical properties of water

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roughness of the surface angles of observation and illumination, and in some extent, reflection of light from the bottom (Figure 4.5)

Figure 4.5:Major factors influencing spectral characteristics of a water body

Source: Campbell, J.B., 1996.

As incident light strikes the water surface, some is reflected back to the

atmosphere; this reflected radiation carries little information about the water

itself. This portion of light can be used to measure the roughness of the surface,

and therefore, about wind and waves. The spectral properties (i.e., color) of a

water body are determined largely by energy that is scattered and reflected

within the water body itself, known as volume reflectionbecause it occurs over a

range of depths rather than at the surface. Some of this energy is directed back

toward the surface, where it again passes through the atmosphere, and then is

recorded by the sensor (Figure 4.5). This light sometimes known as underlight, is

the primary source of color of a water body.

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The light that enters a water body is influenced by:

absorption and scattering by pure water, and scattering, reflection, and diffraction by particles that may be suspended

in water.

For the deep water bodies, it is expected (in the absence of impurities) that

water will be blue or blue-green in color. Maximum transmittance of light by

clear water occurs in the range 0.44 to 0.54 m, with peak transmittance at 0.48

m. Because the color of water is determined by volume scattering, rather than

surface reflection, spectral properties of water bodies are determined bytransmittance rather than surface characteristics alone. In the blue region the

light penetration is not at its optimum, but at the slightly lower wavelengths, in

the blue-green region, penetration is greater and at these wavelengths the

opportunity for recording features on the bottom of the water body are greatest

Longer wavelengths, visible and near infrared radiation is absorbed more by

water than shorter visible wavelengths. Thus water typically looks blue or blue-

green due to stronger reflectance at these shorter wavelengths, and darker ifviewed at red or near infrared wavelengths. If there is suspended sediment

present in the upper layers of the water body, then this will allow better

reflectivity and a brighter appearance of the water. But if the Water body is

relatively free of suspended sediments then the light with shorter wavelengths

(like blue) can penetrate easily up to 20 meters (Figure 4.6), basically this

characteristics of the blue band has made it usable in bathymetric mapping.

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Figure 4.6:Energy loss in water column depth/attenuation oflight with different wavelengthsSource: Edwards, A.J., 1999.

Blue Green Red Near IR

Water with lessSuspended sediment

The apparent color of the water will show a slight shift to longer wavelengths.

Suspended sediment can be easily confused with shallow (but clear) water, since

these two phenomena appear very similar. Chlorophyll in algae absorbs more of

the blue wavelengths and reflects the green, making the water appear more

green in colo

Bathymetric Mapping Using Satellite Image

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