8/12/2019 Bathymetric Mapping Using Satellite Image
1/148
COASTAL BATHYMETRIC MAPPING
OF THE UPPER BAY OF BENGAL
USING OPTICAL SATELLITE
Chandan Roy
2003
Rajshahi University
8/12/2019 Bathymetric Mapping Using Satellite Image
2/148
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
8/12/2019 Bathymetric Mapping Using Satellite Image
3/148
...To my father and mother
with all my pride
v
8/12/2019 Bathymetric Mapping Using Satellite Image
4/148
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.
vi
8/12/2019 Bathymetric Mapping Using Satellite Image
5/148
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
vii
8/12/2019 Bathymetric Mapping Using Satellite Image
6/148
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
viii
8/12/2019 Bathymetric Mapping Using Satellite Image
7/148
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
ix
8/12/2019 Bathymetric Mapping Using Satellite Image
8/148
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
x
8/12/2019 Bathymetric Mapping Using Satellite Image
9/148
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
xi
8/12/2019 Bathymetric Mapping Using Satellite Image
10/148
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
xii
8/12/2019 Bathymetric Mapping Using Satellite Image
11/148
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 5.12 3D view of the sea bottom relief of sub frame 5.. 81
Figure 5.13 3D view of the sea bottom relief of sub frame 6.. 82
Figure 5.14 3D view of the sea bottom relief of sub frame 7.. 83
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
xiii
8/12/2019 Bathymetric Mapping Using Satellite Image
12/148
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
Figure 5.27 Simulated sea floor relief generated from
satellite image (sub frame 2).. 110
Figure 5.28 Simulated sea floor relief generated from
satellite image (sub frame 3)... 111
Figure 5.29 Simulated sea floor relief generated from
satellite image (sub frame 4)... 112
Figure 5.30 Simulated sea floor relief generated from
satellite image (sub frame 5)... 113
Figure 5.31 Simulated sea floor relief generated from
satellite image (sub frame 6)... 114
Figure 5.32 Simulated sea floor relief generated from
satellite image (sub frame 7)... 115
Figure 5.33 Simulated sea floor relief generated from
satellite image (sub frame 8)... 116
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
Figure 6.3 Pattern of slope in the BIWTA sound
generated DEM and corrected satellite image.
Profile along CD... 119
Figure 6.4 Pattern of slope in the BIWTA sound
generated DEM and corrected satellite image.
xiv
8/12/2019 Bathymetric Mapping Using Satellite Image
13/148
Profile along EF 119
Figure 6.5 Pattern of slope in the BIWTA sound
generated DEM and corrected satellite image.
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
Figure 6.8 Pattern of slope in the BIWTA sound
generated DEM and corrected satellite image.
Profile along IJ.. 123
Figure 6.9 Pattern of slope in the BIWTA sound
generated DEM and corrected satellite image.
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
Photograph 5.4 Microscopic view of the suspended sediment
at water collection location 2140/ N 9005/ E.
Magnified 900x 100
Photograph 5.5 Microscopic view of the suspended sediment
at water collection location 2124/ N 9004/E.
Magnified 900x ... 101
Photograph 5.6 Microscopic view of the suspended sediment
at water collection location 2120/ N 9004/E.
Magnified 900x 101
xv
8/12/2019 Bathymetric Mapping Using Satellite Image
14/148
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
xvi
8/12/2019 Bathymetric Mapping Using Satellite Image
15/148
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
satellite data, particularly shorter wave length of the electromagnetic spectrum,
8/12/2019 Bathymetric Mapping Using Satellite Image
16/148
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.
2
8/12/2019 Bathymetric Mapping Using Satellite Image
17/148
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
3
8/12/2019 Bathymetric Mapping Using Satellite Image
18/148
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
4
8/12/2019 Bathymetric Mapping Using Satellite Image
19/148
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.
5
8/12/2019 Bathymetric Mapping Using Satellite Image
20/148
Figure1.1:
BIWTA
echosoundchartoftheBayofB
engal
6
8/12/2019 Bathymetric Mapping Using Satellite Image
21/148
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
7
8/12/2019 Bathymetric Mapping Using Satellite Image
22/148
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
8
8/12/2019 Bathymetric Mapping Using Satellite Image
23/148
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.
9
8/12/2019 Bathymetric Mapping Using Satellite Image
24/148
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
10
8/12/2019 Bathymetric Mapping Using Satellite Image
25/148
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.
11
8/12/2019 Bathymetric Mapping Using Satellite Image
26/148
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).
12
8/12/2019 Bathymetric Mapping Using Satellite Image
27/148
8/12/2019 Bathymetric Mapping Using Satellite Image
28/148
Figure2.2:Studyarea
14
8/12/2019 Bathymetric Mapping Using Satellite Image
29/148
Figure 2.3:Upper coastal regions of the Bay of Bengal and major rivers of Bangladesh
15
8/12/2019 Bathymetric Mapping Using Satellite Image
30/148
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
16
8/12/2019 Bathymetric Mapping Using Satellite Image
31/148
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.
17
8/12/2019 Bathymetric Mapping Using Satellite Image
32/148
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)
18
8/12/2019 Bathymetric Mapping Using Satellite Image
33/148
Figure 2.5:Distribution of the surface salinity of the Bay in summer
Source: Das, S.C., 2002.
Salinity in
Bay of Bengal
Figure 2.6:Distribution of the surface salinity of the Bay in winter
Source: Das, S.C., 2002.
Salinity in
Bay of Bengal
19
8/12/2019 Bathymetric Mapping Using Satellite Image
34/148
Salinity ()
Salinity
Depth
inmeters
INDEX
Summer salinity
Winter salinit
Figure 2.7:Vertical distribution of salinity in the Bay of Bengal
Source: Das, S.C., 2002.
20
8/12/2019 Bathymetric Mapping Using Satellite Image
35/148
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
21
8/12/2019 Bathymetric Mapping Using Satellite Image
36/148
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.
22
8/12/2019 Bathymetric Mapping Using Satellite Image
37/148
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.
23
8/12/2019 Bathymetric Mapping Using Satellite Image
38/148
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).
24
8/12/2019 Bathymetric Mapping Using Satellite Image
39/148
Figure2.8:Bottomr
eliefoftheBayofBengal
Source:UnitedStatesGe
ologicalSurvey
25
8/12/2019 Bathymetric Mapping Using Satellite Image
40/148
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
26
8/12/2019 Bathymetric Mapping Using Satellite Image
41/148
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.
27
8/12/2019 Bathymetric Mapping Using Satellite Image
42/148
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
28
8/12/2019 Bathymetric Mapping Using Satellite Image
43/148
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.
29
8/12/2019 Bathymetric Mapping Using Satellite Image
44/148
Figure 2.11:Location of the Ninety east ridge
Source: Geological Survey of India
30
8/12/2019 Bathymetric Mapping Using Satellite Image
45/148
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,
31
8/12/2019 Bathymetric Mapping Using Satellite Image
46/148
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
32
8/12/2019 Bathymetric Mapping Using Satellite Image
47/148
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
33
8/12/2019 Bathymetric Mapping Using Satellite Image
48/148
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
34
8/12/2019 Bathymetric Mapping Using Satellite Image
49/148
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
35
8/12/2019 Bathymetric Mapping Using Satellite Image
50/148
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;
36
8/12/2019 Bathymetric Mapping Using Satellite Image
51/148
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
37
8/12/2019 Bathymetric Mapping Using Satellite Image
52/148
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
38
8/12/2019 Bathymetric Mapping Using Satellite Image
53/148
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
39
8/12/2019 Bathymetric Mapping Using Satellite Image
54/148
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
40
8/12/2019 Bathymetric Mapping Using Satellite Image
55/148
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.
41
8/12/2019 Bathymetric Mapping Using Satellite Image
56/148
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.
42
8/12/2019 Bathymetric Mapping Using Satellite Image
57/148
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.
43
8/12/2019 Bathymetric Mapping Using Satellite Image
58/148
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
44
8/12/2019 Bathymetric Mapping Using Satellite Image
59/148
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
45
8/12/2019 Bathymetric Mapping Using Satellite Image
60/148
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
46
8/12/2019 Bathymetric Mapping Using Satellite Image
61/148
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.
47
8/12/2019 Bathymetric Mapping Using Satellite Image
62/148
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.
48
8/12/2019 Bathymetric Mapping Using Satellite Image
63/148
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
Top Related