Multispectral image analysis of suspended sediment concentration along the Southern coast of Kanyakumari, Tamil Nadu, India

J O U R N A L O F C O A S T A L S C I E N C E S

63

O R I G I N A L A R T I C L E

1. Introduction

The suspended sediments are derived from river discharge, shore

erosion and weathering of rocky shore that can control the formation

of coastal head landforms and provide source materials to the

physical, chemical and biological inputs in the offshore (Whitelock et

al. 1981; Baban 1995). The coastal region is generally accumulated

with larger suspended sediment concentration which dominated by

silt and clay particles (Warrick et al. 2004; Wang et al. 2009). The

transportation and deposition of suspended sediments triggering the

changes of coastal morphology and an accumulation of excess

nutrients can change the aquatic ecosystem (James 2002). Moreover,

the sediments transportation process influences various coastal

geomorphological changes such as constructing or destructing the

landforms of the coastal environment at different scale (Mertes et al.

1993; Kaliraj et al. 2013a).

Major physical processes of marine system include littoral current,

tidal, wave height and wind direction have influence transportation

and dispersion of suspended material along the nearshore (Sarkar

2011). The sediment movement along the offshore is driven by tides,

waves, river discharges, wind, and currents; temporal capacity of

remotely sensed image provides the feasibility to monitoring the

changes in suspended sediment concentration (Chen et al. 1992;

Tassan 1997). Forget and Ouillon (1998) and Doxaran et al. (2002)

have pointed that the sediments transportation process along the

offshore is directed by wave breaking and coastal configuration and

it tends radiation stress decay based on the depth and distance to the

shoreline.

The advanced space technology namely remote sensing provides

synoptic coverage of the earth surface image by continuous

observation for mapping and monitoring the coastal changes and

help us to understand how the changes happened in various parts of

the environment including coastal waters (Xia 1993). Landsat

satellite multispectral images of MSS, TM and ETM+ have an ability to

detect and map the amount of suspended sediment entering,

residing, and moving in the nearshore region (Oestlund et al. 2001).

The advantage of satellite images with high spectral properties

provide better information on movement and concentration of

S. Kaliraj*, N. Chandrasekar, N.S. Magesh Centre for Geotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu 627 012, India

Multispectral image analysis of suspended sediment concentration

along the Southern coast of Kanyakumari, Tamil Nadu, India

The suspended sediment concentration in the coastal water is an indicator of erosion and deposition of coastal

landforms. This study is attempted to estimate the suspended sediment concentration along the Southern coast of

Kanyakumari, Tamil Nadu, India using Landsat ETM+ image acquired on 10th April, 2013. The different bands of image

available in geotiff format were applied for data fusion, gab-fill analysis and atmospheric correction to remove the

noise and other errors using the ENVI 4.8 software. The empirical multivariate regression algorithm is used to

estimate the suspended sediment from the image at various concentration and spatial distribution. The correlation

between sediment and reflectance shows that high concentration of sediments produces more reflection in green and

red bands than the other, whereas near IR has noticed with significant reflectance due to the presence of organic

matter in suspended sediments and this phenomenon is attributed to estimate sediment from the surface water.

Quantitative estimation of suspended sediments in the surface water is used to understand their contribution to the

coastal landform formation and magnitudes of littoral drift. The result reveals that high sediment concentration is

accumulated in the shallow depth region (less than 5m) of the offshore and it is estimated as 276.3 mg/L. However, it

is decreased into is 152.1mg/L at the water surface with the depth of 5-10 m and the distance from shoreline

approximately between 1 and 2.5km. This variation indicates the suspended sediment concentration is gradually

decreased with the increase of distance and depth of offshore. Changes in suspended sediment concentration in

different parts may be due to shoaling action induced by wind and waves, littoral currents and depth to seabed. It is

observed that the shallow depth area near to shore is estimated with high sediment concentration than other places

and offshore with steep slope prevail high energy wave action frequently diverting movement of sediment towards

low energy zone leads accretion processes. This study proofs an efficacy of multispectral image to estimate sediment

and provide information for sediment transportation and dynamic studies in order to manage and conserve the

coastal environment.

*Corresponding author, E-mail address: [email protected] Phone: +91 9791402607

2014 Journal of Coastal Sciences. All rights reserved

Received

05 January 2014

Accepted

14 March 2014

Available online

19 March 2014

Keywords

Suspended sediment

concentration

Multispectral image

MVR algorithm

Remote sensing

GIS

Southern coast

Tamil Nadu, India

A B S T R A C T A R T I C L E I N F O

JOURNAL OF COASTAL SCIENCES

Journal homepage: www.jcsonline.co.nr

ISSN: 2348 6740 Volume 1 Issue No. 1 - 2014 Pages 63-71


64


suspended sediments compared to point data collected by on-site

instruments during in-situ survey (Stumpf et al. 1992; Tassan 1994).

The coastal water surface of an optical image has high spectral

reflectance in blue bands and it increases in green and red bands

with high suspended sediment concentrations and low reflectance

with increased salinity and depth (Sridhar et al. 2008).

The calibration of satellite sensor with its spatial, spectral and

radiometric resolutions are used to distinguish suspended sediment

concentration from the coastal water based on digital number (DN

value) and it can be mapped through suitable image processing

techniques (Kaliraj and Chandrasekar 2012). The preprocessing of

the image is performed by geometric and radiometric correction

methods makes corrections for atmospheric errors, noise, spectral

irradiance, solar elevation, atmospheric scattering and absorption to

produce perfect reflectance of suspended sediments in the water

surface (Chauhan et al. 1996). The variations in spectral reflectance

depend on the illumination geometry, atmospheric conditions and

water surface roughness and these variations determine the

relationship between water contents and sediment concentration in

the image (Schiebe et al. 1992).

The multispectral image with a wide variety of spectral, spatial,

and temporal resolutions have been used to evaluate the suspended

sediments, chemical pollutants, and chlorophyll contents of the

shallow water surface (Ritchie et al. 1990; Godin et al. 1993; Gould

and Arnone 1997; Chen et al. 2007). Computer based image

processing or signal processing methods are merely involved to

enhance the image, but the algorithm integrated with the geographic

information systems can be used to extract the suspended sediments

from the image (Baban 1997). The concentration of suspended

sediments can be affecting the optical transparency of the coastal

surface water; this produces high reflectance than the clear water in

the image (Baban 1993; Froidefond et al. 1999; Ritchie et al. 2003).

The spatial distribution and concentration of suspended sediments in

the water bodies have been successfully mapped using Landsat TM

images (Tassan 1987; Dekker et al. 2001).

The spectral signature in visible and NIR bands of Landsat image

has been used to estimate water quality characteristics such as

turbidity, suspended solids, chlorophyll and salinity (Lillesand et al.

1983; Curran and Novo 1988; Keiner and Yan 1998). The spectral

response curve of all seven bands in Landsat image was used to

estimate variability of suspended sediment concentrations in the

coastal water and the output has been placed very close to the

laboratory observation (Baban 1993; Tassan 1998; Ruhl et al. 2001;

Ma and Dai 2005; Nechad et al. 2010). The reflectance variation of

individual features in the image is an indicative factor to delineate

suspended sediments eventually to quantify their concentrations

(Jensen 2007; Pavelsky and Smith 2009).

Moreover, the two bands of Landsat image are characterized by

reflection (Red) and absorption (NIR) of maximum radiation from

the water surface, this phenomenon are attributed to distinguish

suspended sediments and water content using the empirical

algorithm (Guzmn and Santaella 2009). Many researchers have used

remotely sensed images for mapping the water quality parameters

including suspended sediment concentrations, chlorophyll-a, and

salinity in worldwide (Verdin 1985; Tassan and Strum 1986; Stumpf

and Pennock 1989; Stumpf and Goldschmidt 1992; Tassan 1993;

Baban 1995; Woodruff et al. 2001; Oestlund et al. 2001; Yanjiao et al.

2007; Wang et al. 2009). Therefore, historical studies on suspended

sediments estimation using images provide an efficacy of remote

sensing in coastal applications.

In this study, the Landsat ETM+ image analysis is performed to

estimate the suspended sediment concentration using the empirical

multivariate regression algorithm. The spectral reflectance values

corresponding to sediments and water content have distinguished by

the algorithm for estimating sediment concentration and their spatial

distribution. This study can be provides vital information for

monitoring and management of the coastal environment.

2. Study area

The study area covers the offshore region along the southern coast of

Kanyakumari district, Tamil Nadu, India. The geographical location of

this area extends from 77 14' 35. 235" E to 77 41' 17. 772" E

longitude and the latitude extend from 8 0' 8. 976" N to 8 10'

21.666" N. The coast constitutes shallow depth in the near shore

within the ranges from 1 to 10m and it increases in depth (30 m)

with increases of distance (6 km) to the shoreline.

This coast is characterized by narrow, elongated stretches

comprises of pocket sandy beaches, beach plains, beach bar,

shoreline terraces, sand dunes, rocky shore and estuaries. The

coastal slope tends to seaward and this can be identified in thick

laterite rocky uplands, estuaries, dune vegetation, and shallow water

bodies. In the eastern part, the lateritic uplands are found between

Kanyakumari and Kovalam coast Offshore of the Kanyakumari,

Kovalam and Muttam coasts are placed with rocky outcrops.

However, western parts comprise sand dune beaches which are

spread roughly parallel to the shoreline.

The major drainage networks of this area are namely R.

Thamirabarani, R. Valliyar, R. Pazhayar and R. Hanuman Nathi and

their tributaries flow towards southeast, south and south westerly

direction respectively from the Western Ghats. These drainage

systems can be discharge large quantity of runoff materials during

northeast and southwest monsoons. Seasonal changes in wave

direction, littoral currents and wind speed influence sediments

transportation and distribution along the study area. It is observed

that the wave energy prevailing over the study area ranges from 0.5

to 8.5 kJ/km2.

In which, the tip of Cape Comorin is noticed with very high wave

energy (6.5 8.5 kJ/km2) than the other parts due to hydrodynamic

forces acting on steep slope of offshore from various direction leads

decreasing the movement of littoral sediments. The eastern and

western parts of the coast experience low wave energy condition

(0.52.5 kJ/km2) and current velocity and leads deposition of littoral

sediments eventual formation of young beach landforms. Littoral

current system influences more in accretions and deposition

processes and it is seasonally varying in directions and velocity at the

different places.

The average current velocity is measured as 0.14ms-1, in which the

fastest flow of velocity is noticed in the Kanyakumari and Kovalam

coasts with the ranges from 0.32 to 0.28 ms-1. Moreover, the littoral

current moves towards the southeast to north-west during the NE

monsoon and this phenomenon reverses during the SW monsoon

and summer depending on wave direction and wind speed. These

hydrodynamic conditions have direct influences on shoaling action

and propagate the suspended sediments matter from one place to

another.

Climatologically, the study area comprises sub-tropical climatic

conditions with the annual rainfall ranges from 826 to 1456 mm and

the optimum temperature ranges of 23.78 and 33.95 C. Recently, it

has been observed that the coastal structures like groins, revetments

and seawalls have produced impacts on sediments transportation

and movement by intervening the natural hydrodynamic processes

causes erosion and accretion in the coastal area.

65

3. Materials and Method

In this study, suspended sediment concentration and its spatial

distribution was estimated along the southern coast of Kanyakumari

in Tamil Nadu, India using Landsat ETM+ image acquired on 10

April, 2013. The image with 30m spatial resolution is composed of

seven bands, namely blue, green, red, near IR, mid IR, SWIR and

thermal IR comprises the spectral wavelength ranges from 0.450 to

2.35m. Among them, the spatial resolution (60m) of the the

band was resized into 30m using data fusion techniques in order to

bring common pixel size into all bands (Table 1). According to the

remote sensing principles, the observation of the physical

phenomena can be targeted by their spectral properties

in electromagnetic radiation (Zhang et al. 2003

property of the sea surface water is varying due to reflection of

heterogeneous composition include suspended sediment and

chlorophyll contents (Quibell 1991). The spectral reflecta

values) of the image was analyzed using the multivariate regression

algorithm to extract the suspended sediment concentration from the

surface water. The backscattering radiation from each pixel has been

calibrated using the empirical algorithm to estimate the

sediment concentration. Moreover, the total area of suspended

sediment concentration in the different range of bathymetry was

calculated by multiplying the number of pixels in each group at a

particular location with pixel size.

3.1. Pre-processing methodology for Landsat ETM+ image

3.1.1. Gap fill analysis

The Landsat 7 ETM+ image acquired on 10th April, 2013 is collected

from the Global Land Cover Facility (GLCF), USA in geotiff format

with UTM-WGS 84 projection and coordinate system.

Fig. 1 Study area location


In this study, suspended sediment concentration and its spatial

distribution was estimated along the southern coast of Kanyakumari

in Tamil Nadu, India using Landsat ETM+ image acquired on 10th

April, 2013. The image with 30m spatial resolution is composed of

seven bands, namely blue, green, red, near IR, mid IR, SWIR and

thermal IR comprises the spectral wavelength ranges from 0.450 to

2.35m. Among them, the spatial resolution (60m) of the thermal IR

band was resized into 30m using data fusion techniques in order to

bring common pixel size into all bands (Table 1). According to the

remote sensing principles, the observation of the physical

phenomena can be targeted by their spectral properties and changes

Zhang et al. 2003). The spectral

property of the sea surface water is varying due to reflection of

heterogeneous composition include suspended sediment and

The spectral reflectance (DN

values) of the image was analyzed using the multivariate regression

algorithm to extract the suspended sediment concentration from the

from each pixel has been

estimate the suspended

Moreover, the total area of suspended

sediment concentration in the different range of bathymetry was

calculated by multiplying the number of pixels in each group at a

rocessing methodology for Landsat ETM+ image

April, 2013 is collected

from the Global Land Cover Facility (GLCF), USA in geotiff format

WGS 84 projection and coordinate system. The raw image

-composes line dropout error since May, 2003 onwards due to the

failure of Scan Line Corrector (SLC) instrument in the sensor. This

produces approximately 22% of the missing data in a single scene. So

that, the pixels in a line dropout zone in the image were replaced by

valid pixels of gap mask file using

method in the ENVI 4.8 environment to produce complete scene.

analysis is performed using the moving window (3x3)

the pixels statistically to fill the gap with valid pixels.

Consequently, the moving window calculates the mean values of

neighbouring pixels fall within a window

into the position of the central pixel of the matrix

1992). At the final stage of computations performed on the original

image, it produces an output image according to

pixels in a commonly scanned image by the satellite sensor.

3.1.2. Atmospheric corrections and image enhancement

The optical multi-spectral image is frequently affected by the

atmosphere and radiation from the direct reflectance due to the

water surface. Moreover, the digital numbers (DN values) in the raw

image are not only dependent on the reflectance characteristi

the earth objects, but also contain noise and errors due to viewing

geometry of the satellite, the angle of the sun radiation and

atmospheric effects like haze and water particles. The major

challenge of performing the atmospheric correction of ETM+

on coastal water is to obtain the perfect radiances to surface

reflectance for images in the visible portion of the electromagnetic

spectrum. In order to produce images with actual reflectance values,

all bands of ETM+ image have been analyzed indi

the atmospheric error using (Fast Line

Analysis of Spectral Hypercubes) FLASSH model in ENVI 4.8

software. FLAASH is an atmospheric correction

retrieving spectral reflectance for ETM+ image that in



composes line dropout error since May, 2003 onwards due to the

C) instrument in the sensor. This

produces approximately 22% of the missing data in a single scene. So

that, the pixels in a line dropout zone in the image were replaced by

valid pixels of gap mask file using single file gap triangulation

I 4.8 environment to produce complete scene. This

the moving window (3x3) that executes

the pixels statistically to fill the gap with valid pixels.

he moving window calculates the mean values of

neighbouring pixels fall within a window (3x3) and replace the value

into the position of the central pixel of the matrix (Pan and Chang

At the final stage of computations performed on the original

according to the distribution of

pixels in a commonly scanned image by the satellite sensor.

corrections and image enhancement

spectral image is frequently affected by the

atmosphere and radiation from the direct reflectance due to the

water surface. Moreover, the digital numbers (DN values) in the raw

image are not only dependent on the reflectance characteristics of

the earth objects, but also contain noise and errors due to viewing

geometry of the satellite, the angle of the sun radiation and

atmospheric effects like haze and water particles. The major

challenge of performing the atmospheric correction of ETM+ images

on coastal water is to obtain the perfect radiances to surface

reflectance for images in the visible portion of the electromagnetic

spectrum. In order to produce images with actual reflectance values,

all bands of ETM+ image have been analyzed individually to remove

the atmospheric error using (Fast Line-of-sight Atmospheric

Analysis of Spectral Hypercubes) FLASSH model in ENVI 4.8

software. FLAASH is an atmospheric correction-modeling tool for

retrieving spectral reflectance for ETM+ image that incorporates the

66

-most accurate correction for visible wavelengths (MODTRAN)

radiative transfer code and produces atmospherically corrected

reflectance values of coastal water surface (Chavez 1996). Moreover,

FLAASH model uses the atmospheric settings include date, time, sun

angle and temperature to represent local atmospheric condition

during image acquisition. Therefore, the image free from

atmospheric error can be used to estimate the concentration of

suspended sediments from the coastal water surface. F

spectral and spatial properties of image pixel values (DN value) were

enhanced using histogram equalization technique.

stretch that redistributes pixel values within the range. The result

the image is increased the contrast gray tone at both head and tail of

the histogram to improve the shape of the objects

Chandrasekar 2012). In this analysis, the narrow spectral ranges of

DN values (18-156) of individual bands in unprocessed

expanded in 1-255. The result of this image has a wide range of DN

values produce distinct groups of water contents and sediment matters (Gower 2006).

3.4. Multivariate regression algorithm

The variation in spectral reflectance of coastal water was used to

distinguish water contents and suspended sediment matter in the

image (Gordon and Clark 1981). Similarly, the colour index is an

indicator to a quantitative measure of ocean water colour, can be

defined as the nadir radiance in the water at different wavelength of

bands (Gordon et al. 1988). Therefore, on the basis of this definition,

the suspended sediment load in the coastal water can be estimated

from Landsat ETM+ image with relatively high accuracy using the

multivariate regression algorithm.

The suspended sediment retrieval algorithm deals with the ratios

of spectral reflectance received by sensors in the form of an image

(Tassan 1993). Empirical algorithm in this study comprises


most accurate correction for visible wavelengths (MODTRAN)

radiative transfer code and produces atmospherically corrected

reflectance values of coastal water surface (Chavez 1996). Moreover,

ude date, time, sun

angle and temperature to represent local atmospheric condition

Therefore, the image free from

estimate the concentration of

suspended sediments from the coastal water surface. Further, the

values (DN value) were

histogram equalization technique. It is a nonlinear

stretch that redistributes pixel values within the range. The result of

gray tone at both head and tail of

of the objects (Kaliraj and

In this analysis, the narrow spectral ranges of

processed image were

result of this image has a wide range of DN

distinct groups of water contents and sediment

The variation in spectral reflectance of coastal water was used to

contents and suspended sediment matter in the

). Similarly, the colour index is an

indicator to a quantitative measure of ocean water colour, can be

defined as the nadir radiance in the water at different wavelength of

). Therefore, on the basis of this definition,

the suspended sediment load in the coastal water can be estimated

from Landsat ETM+ image with relatively high accuracy using the

ieval algorithm deals with the ratios

of spectral reflectance received by sensors in the form of an image

). Empirical algorithm in this study comprises -

multivariate regression analysis to estimate total suspended matter

from the image. In the ETM+ image, the surface reflectance of coastal

water is affected by volume scattering in the visible and infrared

(near IR, mid IR and SWIR) bands.

regression analysis uses all seven bands of this image separately to

derive the exact spectral reflectance suspended sediment matter and

it is expressed as, (Zhang et al. 2003)

Where, SSC refers suspended sediment concentration (mg/L);

Bandi refers the pixel value (DN value) of the visible and infrared

bands of the ETM+ image; k is the band (channel) number, and

and Ai are the empirical regression coefficient constants.

sediment concentration was estimated by using various independent

variables in the regression algorithms. The empirical algorithm

substitutes with all seven bands of ETM+ image and regression

coefficient constants is expressed as,

SSC = [8.6880 0.0221(1)] [0.0202(

[0.2822(TM4)] + [0.3639(TM5)] [0.0405(TM6)

The algorithm executes all bands by using a model builder module

in ERDAS Imagine 9.2 software. In this, the algorithm can be

correlating the spectral reflectance variability of surface water to

quantify the suspended sediments with the coefficient of

determination (R2) as 0.572 and by the root mean square error

(RMSE) is 0.98 for mg/L in a unit of area.

4. Results and discussion

The reflectance variation of the surface water is clearly visible

satellite image and this undoubtedly indicates the presence of

1 = ( ) - - - (1)

k

o i ii

SSC A A B and=

+



estimate total suspended matter

from the image. In the ETM+ image, the surface reflectance of coastal

water is affected by volume scattering in the visible and infrared

(near IR, mid IR and SWIR) bands. Therefore, the multivariate

all seven bands of this image separately to

derive the exact spectral reflectance suspended sediment matter and

refers suspended sediment concentration (mg/L);

refers the pixel value (DN value) of the visible and infrared

is the band (channel) number, and Ao

are the empirical regression coefficient constants. Suspended

concentration was estimated by using various independent

variables in the regression algorithms. The empirical algorithm

of ETM+ image and regression

[0.0202(TM2)] + [0.2831(TM3)]

0.0405(TM6)] [0.2579(TM7)] ---(2)

all bands by using a model builder module

in ERDAS Imagine 9.2 software. In this, the algorithm can be

correlating the spectral reflectance variability of surface water to

quantify the suspended sediments with the coefficient of

) as 0.572 and by the root mean square error

(RMSE) is 0.98 for mg/L in a unit of area.

The reflectance variation of the surface water is clearly visible in


= ( ) - - - (1)o i iSSC A A B and

Fig. 2 Spectral reflectance of

SSC in different wavelengths

of bands in ETM+ image


67


suspended sediments in large quantity along the offshore. In the

Landsat ETM+ image, the total suspended sediment matter in the

coastal water can be characterized by absorption (NIR) and

reflection (Red) of maximum radiation (Bhargava and Mariam 1991).

On the basis of this concept, the empirical algorithm executes the DN

values of all bands of the ETM+ image to produce the total suspended

sediment concentration in pixel scale wise.

4.1. Multi-spectral image reflectance response to SSC

The suspended sediment matter in the coastal water consists of both

organic and inorganic materials derived from river discharge, littoral

drift and beach erosion process. The reflectance of image pixels

depends upon SSC variations and that can be altering the optical

properties of the water column (Curran and Novo 1988; Mertes et al.

1993; Kunte 2008; Katlane et al. 2013). In general, the reflectance

tends to increase with the increase in SSC in the visible spectrum

(blue, green and red bands) and decrease in the wavelength of near

IR and thermal IR bands (Yanjiao et al. 2007; Chen et al. 2010). In

this study, the total suspended sediments ranges were divided into

five classes based on the depth and distance to shoreline to estimate

the spectral properties in different wavelengths include both visible

and infrared portion. Figure 2 shows the relationship between

spectral reflectance and sediment concentration in different

wavelength of the image. The suspended sediments with various

concentrations have produced relatively nearest reflection ranges

from 0.02 to 0.036 % within the wavelength range of 0.450 - 0.515

m (blue band). So that, the separation of spectral curves within this

wavelength is more difficult to distinguish sediment particles from

water content (Ramakrishnan et al. 2013).

This is due to low amount of radiation backscattering by suspending

materials present in the surface water. At the highest concentration

of SSC (276.3 mg/L) and above, it is observed that the range of

reflectance increases from 0.12 to 0.13% in the wavelength of green

(0.525-0.625 m) and red (0.630 - 0.690 m) bands. Comparative

analysis of reflection and wavelength at different sediment

concentration produce a non-linear spectral profile that indicates the

distribution of suspended sediments in the surface water. Whereas,

the reflection level is decreased into 0.08 0.05 % in the green band

and 0.084 0.062 % in red band with the SSC ranges of 152.1 and

69.4mg/L respectively.

4.2. Estimation of suspended sediment concentration

The reflectance variation of the surface water is clearly visible in


suspended sediments in large quantity along the offshore. In the

Landsat ETM+ image, the total suspended sediment matter in the

coastal water can be characterized by absorption (near IR) and

reflection (Red) of maximum radiation (Warrick et al. 2004). On the

basis of this concept, the empirical algorithm executes the DN values

of all bands of the ETM+ image to produce the total suspended

sediment concentration in pixel scale wise. The mixture of suspended

particles increases turbidity of coastal water that produces more

reflectance and therefore this often determines DN values in the

image with respect to availability of sediments (Ritchie et al. 2003).

Hence, the algorithm is applied to construct an empirical relationship

between the reflectance values of water and sediments to produce

the suspended sediment concentration with relatively accurate scale

(Chen et al. 1991).

Fig. 3 Suspended sediment

concentration (SSC) in

different parts of the study

area

68

Surface water with low SSC has no significant influences on

reflectance values in visible spectrum include (0.450

blue, green and red bands. However, it is noticed with high reflection

(0.06 0.08 %) in the wavelength of near IR (0.750

This is because of the availability of organic matter in the suspended

sediments reflecting more radiation in the near IR. The effect of

organic matter i.e. phytoplankton on reflectance is to decrease the

reflectance in the short wavelengths, from 400 to 515 nm, and to

increase the reflectance in the longer wavelengths like near IR and

mid IR and SWIR (Quibell 1991; Ruhl et al. 2001).

Band

No. Band Name Spectral Resolution

1 Blue 0.450 - 0.515 m

2 Green 0.525 - 0.605 m

3 Red 0.630 - 0.690 m

4 Near IR 0.750 - 0.900 m

5 Mid IR 1.55 - 1.75 m

6 Thermal IR 10.40 - 12.5 m

7 SWIR 2.08 - 2.35 m

8 Panchromatic 0.52 - 0.90 m

Table 1. Spectral and spatial characteristics of Landsat ETM+ image

Fig. 4 Major factors influence SSC

and its distribution along the study

area


has no significant influences on

0.450 - 0.690 m) in

blue, green and red bands. However, it is noticed with high reflection

0.750 - 0.900 m) band.

se of the availability of organic matter in the suspended

sediments reflecting more radiation in the near IR. The effect of

organic matter i.e. phytoplankton on reflectance is to decrease the

reflectance in the short wavelengths, from 400 to 515 nm, and to

increase the reflectance in the longer wavelengths like near IR and

This analysis reveals that the response of reflectance in the image

tends to increase with the increase of SSC in wavelength of all bands

with a few minor exceptions in the near

the sediment concentration is often reflected mor

visible portion than the infrared portion include near IR, mid IR,

SWIR and thermal IR bands. The variation in reflectance from

individual band of ETM+ image is attributed to estimation of

suspended sediments, and can be useful informati

hydrodynamic and sediment transport studies.

In this study, the ETM+ image is used to estimate suspended

sediment concentration (SSC) using a multivariate regression

algorithm.

Spatial

Resolution Spectral Characteristics

30 x 30 m Reflectance is just below peak transmittance of water to upper limit of

suspended matter or chlorophyll absorption

30 x 30 m High reflectance to green matter and corresponds to absorption of red

and blue chlorophyll, healthy vegetation

30 x 30 m High reflectance of reddish matter and corresponding to red

chlorophyll absorption region

30 x 30 m Reflective Infra Red region - corresponding absorption to reddish

matter and responsive to the amount of chlorophyll (biomass) present

in image

30 x 30 m Sensitivity to turbidity (chlorophyll and sediments

clear water contents of surface water

60 x 60 m Corresponds to emission of heat matter from the particles or objects

in Thermal Infra Red region

30 x 30 m Sensitivity to absorption of suspended materials and water contents

15 x 15 m Reflection of matter in long spectral properties with high spatial

resolution

Spectral and spatial characteristics of Landsat ETM+ image



This analysis reveals that the response of reflectance in the image

tends to increase with the increase of SSC in wavelength of all bands

with a few minor exceptions in the near-IR spectrum. This is due to

the sediment concentration is often reflected more radiation in the

visible portion than the infrared portion include near IR, mid IR,

SWIR and thermal IR bands. The variation in reflectance from

individual band of ETM+ image is attributed to estimation of

suspended sediments, and can be useful information for

hydrodynamic and sediment transport studies.

In this study, the ETM+ image is used to estimate suspended

concentration (SSC) using a multivariate regression

Reflectance is just below peak transmittance of water to upper limit of

suspended matter or chlorophyll absorption

High reflectance to green matter and corresponds to absorption of red

and blue chlorophyll, healthy vegetation

High reflectance of reddish matter and corresponding to red

corresponding absorption to reddish

matter and responsive to the amount of chlorophyll (biomass) present

Sensitivity to turbidity (chlorophyll and sediments mixture) and the

Corresponds to emission of heat matter from the particles or objects

suspended materials and water contents

Reflection of matter in long spectral properties with high spatial


69


The result reveals that the SSC along the study area is estimated with

ranges from 11.2 to 276.3mg/L (Table 2). Among them, coastal water

surface extends (6.28 km2) with the distance of 1km from the

shoreline and less than 5m depth to seabed has been estimated with

high concentration of suspended sediments as 276.3mg/L. In the

north-eastern part of Kanyakumari coast, the suspended sediments

were estimated in large quantity due to discharge of Uppar River and

Hanuman Nadi River flow from the Western Ghats. Moreover, the

geographical nature of coastal configuration of this area experiences

the shoaling effect process in the surf zone as the frequent wind

generated wave action and rip currents. This phenomenon is

attributed to movement of near-bottom SSC towards the southern

coast through littoral currents.

The middle part of southern coast was noticed deposition

landforms due to the swash of the significant amount of suspended

materials by less energy waves and low velocity of littoral currents

(Kaliraj et al. 2013a). Whereas, the SSC is decreased rapidly with the

increases of distance to the shoreline and depth to seabed and it is

estimated as 152.1mg/L between the distance from 1 to 2.5km to the

shoreline at the bathymetry level of 5 10m. As the increase of

depth, the low amount of sediments available to move towards the

shore and so that it's seen as low concentrations in the surface water.

Also, the wave produces low frequency of shoaling action in the

deeper water cause to sparely distribution sediment concentration

(Kaliraj et al. 2013b). The SSC is remained low along the surface

water with 10m depth and above from the distance approximately

2.5 to 6 km of shoreline (Figure 3). The variation of SSC reveals that

the SSC is indirectly proportional to the depth to seabed

(bathymetry) and distance to the shoreline and have positive

correlation with wave direction and littoral current prevail over the

offshore (Figure 4).

5. Conclusions

Multi-spectral image of the coastal water surface comprises spectral

properties of suspended matter along with water contents. The

variation in reflectance indicates presence of suspended sediments in

the coastal water and empirical algorithmic analysis of image

provides a quantitative estimation in pixel scale wise. SSC is

extracted from the Landsat ETM+ image based on its spectral

properties attributed to suspended sediments. Analysis of the total

range of wavelength (0.450 - 0.900 m) in both visible and near IR

bands reveals that the blue bands have no significant reflectance to

various sediment concentration levels. Whereas, high SSC produces

maximum reflectance in green and red bands, however, it is limited

to the volume of sediment concentrations. At the low SSC level, the

reflectance value is poor in blue, green and red bands. In contrast

with that, near IR band has noticed a high reflection against low SSC

-level due to availability of organic matters in the suspended

sediments cause more radiation in the near IR. This phenomenon is

attributed to understanding the relationship between spectral

reflectance and SSC at different wavelengths and it produces a non-

linear spectral profile that indicates concentration of suspended

sediments in the surface water. Moreover, high SSC is estimated

along the shallow depth in the nature area, especially in the eastern

part due to the large quantity of river discharge materials shoaling by

waves and currents. Whereas, the water surface away from the shore

has found with low SSC, it tends to increase depth and distance, the

low amount of sediments only available to spread along the vast

water surface area. Hence, the variation of SSC is decreasing with the

increase of depth and distance to the shoreline and it concluded that

the SSC is indirectly proportional to depth and distance to the

shoreline and also have a direct relationship to wave direction and

littoral current prevailing over the study area.

This study demonstrates the efficacy of multispectral optical image

to estimate the suspended sediments based on its reflectance

characteristics and provide information for understanding sediment

transportation and coastal dynamic process for researchers and

management authorities.

Acknowledgements

The corresponding author is thankful to DST-INSPIRE Division,

Department of Science & Technology (DST), Government of India, for

the award of INSPIRE Fellowship (DST/INSPIRE/2011/IF110366) as

a financial support for pursuing his Ph.D Degree Program.

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Documents

Multispectral image analysis of suspended sediment concentration along the Southern coast of Kanyakumari, Tamil Nadu, India