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Indian Journal of Geo Marine Sciences
Vol. 47 (03), March 2018, pp. 549-557
Mapping the distribution of coral reef extent and its temporal variation in Gulf of Mannar – Comparison of pixel and object based approach
S.Rebekah1* & A.B.Inamdar
2*
1
Department of Geography, University of Madras, Chennai, 600005, India. 2Center for Studies in Resource Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India.
[E-Mail: [email protected]]
Received 29 February 2016 ; revised 17 November 2016
In spectral based image analysis supervised classification was done using minimum distance through ERDAS Imagine
2013. On the other hand object based analysis was performed using nearest neighbourhood classifier through eCognition
software. The results of classified images shown that Object based approach gave more accurate results than spectral based
classification algorithm.
[Keywords: Coral reef; temporal variation; remote sensing; Landsat].
Introduction
Coral reefs are one of the most important
ecosystems usually found in Tropical Ocean which
lives in shallow and near shore environment under
the condition where the nutrient concentration and
sediments are low. The coral reef requires certain
condition to occur and it can flourish only in
shallow water, with optimum temperature of 23-
25°C. They serve as the natural protection against
shoreline erosion and also act as the protective
barrier to the living organism. The livelihood of
many people is dependent on this unique ecosystem
as the considerable proportion for their food and
earning from the productivity of coral reef1, 2
.
In India with its coastal line extending over
two 7,500 kilometre and sub-tropical climatic
condition has very few coral reef areas. The
disincentive of reef growth in Bay of Bengal are
the heavy monsoon rain fall and high human
presence on the coastal line1,3
. This paper focuses
on Gulf of Mannar, which consist of major coral
reef extent along 21 islands. The islands have
fringing reef around them. The narrow fringing reef
are located mostly at the distance of 10-100 m from
islands4.
Remote sensing is the fundamental tool for
mapping, monitoring and management of coral
reef2. In this study mapping of coral reef
distribution was done using Landsat data for the
year 1990, 2000 & 2014. The spectral and spatial
information of 30 m of Landsat data are used for
reef cover mapping. Generally, remote sensing
based mapping of coral reef in Tropical Ocean
faces major difficulty of water column attenuation,
so it can be corrected by water column
correction5,6
.The factors influencing the coral reef
growth are climatological and anthropogenic
stress. Increase in anthropogenic activities such as
over fishing, pollution, improper tourism and
mining7. The climatological factors includes the
changes in Sea Surface Temperature, Sea Surface
Salinity and rain fall.
INDIAN J. MAR. SCI., VOL. 47, NO. 03, MARCH 2018
In this paper, the analysis on coral reef mapping
includes spectral and object based approaches. A
spectral based analysis utilises spectral pattern
value combination associated with different
feature assigned as unique DN value. A pixel
classes are determined for image specified overall
DN value. Both super and unsupervised spectral
based approaches are routinely applied to remotely
sensed data for classification. The object based
image analysis combines spectral and spatial
information, so with object oriented image
analysis is the combination of spectral and spatial
information. For classification information, not
only spectral information is considered but also
the texture and context information is considered
in to classification as well7.
Material and Methods
The study area, Gulf of Mannar, extends 140
km2
in the SW-NE direction between 78°5’ and
79°0n’E longitudes and 8°47’ and 9°15’ N shown
in Fig. 1. There are 21 islands situated in the
average distance of 8 km from the coast and
running parallel to the coastline. The Gulf of
Mannar endowed with the combination of
ecosystem including mangroves, sea grass, sea
weed, algal communities and coral reef8.
The climate of Gulf of Mannar is that of
Tropical zone, consisting of relatively high
temperature from Jan to May, and heavy rainfall
due to north east monsoon. In 1989 Gulf of Mannar
was declared as a Marine Bio reserve jointly by
State Government of Tamil Nadu and Central
Government of India8.
The 21 islands of Gulf of Mannar are divided
into four groups namely Mandapam, Keezhakarai,
Vembar and Tuticorin due to the proximity of
islands to these locations9,10
. The geographical
locations of Gulf of Mannar islands are given in
Fig. 2.Of these 21 islands, Musal Island in the
Mandapam group is the biggest island i.e.,129
hectares and the second largest island is
Nallathanni Island in the Vembar group. In the
Gulf of Mannar, Manoliputti Island in the
Mandapam group is the smallest island (0.34
hectares). There is no human settlement in these
islands. The importance of the Gulf of Mannar as a
study area lies in the fact that the islands have
fringing coral reefs and patch reefs rising from
shallow seas10-13
.
Landsat 5 TM and Landsat 7 ETM+
were used for
the study. Landsat 5 TM data with the spatial
resolution of 30 m, having spectral range of 0.5-0.6
µm (Green), 0.6-0.7 µm (Red), 0.7-0.8 µm (NIR).
Was used for estimating the coral reef distribution
for the year 1990. Landsat 7 ETM+
data with the
spatial resolution of 30 m having the spectral range
of 0.2-0.6 µm (Green), 0.63-0.69 µm (Red), 0.77-
0.90 µm (NIR) was used for estimating the coral
distribution for the year 2000 and 2014. After the
acquisition of Landsat 5 TM and Landsat 7 ETM+
images for the year 1990, 2000 and 2014 from
Fig.1- Map of the study area
550
REBEKAH & INAMDAR: DISTRIBUTION OF CORAL REEF EXTENT AND ITS TEMPORAL VARIATION
USGS website, the pre-processing of the image is
done to remove the geometric error and water
column attenuation. Geometric correction is
performed by image to image registration method
using ENVI 4.6 software. SOI Topo sheet is used to
carry out this method and the image was
geometrically corrected with the RMS error < 1.
The optical multi spectral image is frequently
affected by the atmospheric and radiance from the
direct reflectance due to the water surface. Major
challenge of performing atmospheric correcting TM
and ETM+ images on coastal water is to obtain the
perfect radiance to surface reflectance for images in
the visible portion of electromagnetic spectrum. All
the bands of TM and ETM+ was analysed
individually to remove the atmospheric attenuation
using ATCOR2 module belongs to the tools
available in ERDAS Imagine. The module was
performed by setting up the calibration file as
sensor specific obtain from the Meta data.
For Landsat5 TM 5 and Landsat 7 ETM+
DN of the Landsat data were converted to Radiance
value by equation (NASA, 2012)
𝐿𝑖 = 𝐿𝑚𝑎𝑥 𝑖
−𝐿𝑚𝑖𝑛 𝑖
𝑄𝑐𝑎𝑙 𝑚𝑎𝑥 −𝑄𝑐𝑎𝑙 𝑚𝑖𝑛
∗
𝑄𝑐𝑎𝑙 − 𝑄𝑐𝑎𝑙𝑚𝑖𝑛 + 𝐿𝑚𝑖𝑛 𝑖
Where Li is the radiance value of band I, 𝐿𝑚𝑎𝑥 𝑖 is
the radiance value of maximum band i, 𝐿𝑚𝑖𝑛 𝑖is the
radiance value of minimum band i , 𝑄𝑐𝑎𝑙 is the
input band which to be converted to Radiance,
𝑄𝑐𝑎𝑙𝑚𝑎𝑥 maximum pixel value in DN.
For Landsat 8,
𝐿𝜆 = 𝑀𝐿 ∗ 𝑄𝑐𝑎𝑙 + 𝐴𝐿
Where 𝐿𝜆 TOA Spectral radiance (watt/ (m2
*srad*µm), 𝑀𝐿 is the Radiance multiband_x where
x is the band number, 𝐴𝐿 is the Radiance Addition
band_x, 𝑄𝑐𝑎𝑙 is the pixel value of the band (DN).
According to Nakoda (2004), the water
column above the reef will absorb energy with
wave length more than reef surface will absorb
energy with wave length more than 650 nm, based
upon the wave length of electromagnetic
spectrum. In the water column, the energy of the
penetrated sunlight will be absorbed and scattered
by the water molecules, decreasing in intensity by
increasing of depth. This effect is known as
attenuation. So correction is adequate before
processing.
Fig.2- Islands in the Gulf of Mannar
(1)
(2)
551
INDIAN J. MAR. SCI., VOL. 47, NO. 03, MARCH 2018
Water column correction is done in accordance
with Lyzenga method (1981). Lyzenga algorithm
are widely used in basic cover mapping shallow
water. The radiance measurements were performed
on same type bottom substrate with different depth,
the radiance value of the bands will be corrected
linearly. The linear correction equation was the
approximation of attenuation coefficient ratio
between band i and j. the equation is written as
𝐷𝑒𝑝𝑡ℎ 𝑉𝑎𝑟𝑖𝑎𝑛𝑡 𝐼𝑛𝑑𝑒𝑥 = 𝑙𝑛(𝑏𝑖) +𝑘𝑖
𝑘𝑗𝑙𝑛(𝑏𝑗 )
𝑘𝑖𝑘𝑗
= 𝑎 + √(𝑎2) + 1
𝑎 = (𝜎𝑏𝑖2 − 𝜎𝑏𝑗
2 )/2𝜎𝑏𝑖 .𝑏𝑗
Where bi is the radiance of channel I, bj is the
radiance of channel j, a is the variance covariance
variable, σi σj is the variance and σij is the covariance
between the bands.
The attenuation coefficient is calculated by
collecting the samples from band combination.
From the collected samples variance and covariance
are calculated. The water column correction is done
through ER Mapper (ERDAS 2013).
Image processing includes Layer stacking the
corrected image and precedes the mosaicking
process involves in combining the adjacent scenes,
for extracting Gulf of Mannar two scene were
mosaicked and subset to the study area. The whole
process were done using ERDAS Imagine (2013).
Pixel based image analysis means that the
classic image classification method that classifies
remote sensing images according to the spectral
information in the image and the classification
manner is pixel by pixel and one pixel only belongs
to one class14,15
. In the pixel-based approach, the
classifier is the minimum distance classifier. In this
method for the spectral value of a pixel to be
classified the distance towards the class means are
calculated, if the shortest (Euclidian) distance to
class mean is smaller than the user-defined
threshold, then this class name is assigned to the
output pixel, else the undefined value is assigned. In
in process seven classes are assigned based upon its
properties they are coral reef, Reef flat, dead corals,
Dead corals with algae and reef vegetation.
Classified image shows distribution of reef cover
according this algorithm.
Results and Discussion
Pixel and object based classification has been
performed by classifying the remote sensing image
of Landsat ETM for the year 1990, 2000 & 2014.
The comparison of the results shows that object
oriented image analysis attains higher overall
accuracy and higher coral reef cover class. This
classification results also persists the drastic
changes temporal variation of the coral reef extent.
Fig. 3-7 shows the temporal variation and
comparison of pixel and object based analysis.
Through the analysis it was clearly observed
about the areal change of reef distribution was
drastic. In 1990 the total reef area was about 87.650
km2. In 2000 the total reef area was decreased to
56.234 km2. . In 2014 the total reef area was
drastically decreased to 37.3754 km2. This drastic
changes might be due to changes in environmental
conditions and mainly due to anthropological
stress. The area is calculated based on the object
based classification results because of the accuracy.
The accuracy of spectral based classification is
better than the object based classification24
. Fig.8
shows the reef area changes over years. Accuracy
assessment is a general term for comparing the
classification result reference data in order to
determine the accuracy of the classification
process. For pixel based classification, the accuracy
assessment is done through reference of training
samples. It is done through ERDAS (Imagine
2013) accuracy assessment tool. The overall
accuracy for the pixel based classification results
for the year 1990, 2000 & 2014 are 78.29%,
80.15% and 82.43%. For Object based
classification, the accuracy assessment is done
through reference of samples. It is done through
ecognition developer accuracy assessment tool.
The overall accuracy for the classification results
for the year 1990, 2000 & 2014 are 89.22%,
92.48% and 92.85%.
The comparison of result shows that object
based method attains higher overall accuracy and
higher individual user’s and producer accuracy for
each classified reef classes. Fig. 9-14 shows the
comparison of producer and user accuracy of pixel
and object based classification for the year 1990,
2000 and 2014. In ecognition, classified image
objects are not only assigned to one class or not, but
also get a detailed list with the membership values
of each of the class contained in the class hierarchy.
In object oriented image analysis, object is not a
(3)
(4)
(5)
552
REBEKAH & INAMDAR: DISTRIBUTION OF CORAL REEF EXTENT AND ITS TEMPORAL VARIATION
single pixel takes part in the classification. Properly
performed segmentation creates good image objects
that facilities the extraction from the image. From
the classifiers that are used in two approaches, in
object oriented, the classifier is Nearest Neighbour
(NN). The NN classifier has the following
advantages; NN evaluates the correlation between
object features favourably; NN overlaps in the
feature space increase with its dimension and can be
handled much easier with NN; NN allows very fast
and easy handling of the class hierarchy for the
classification.7 classes are assigned based upon its
properties they are coral reef, Reef flat, dead corals,
Dead corals with algae and reef vegetation.
Fig.6-Comparison of Spectral and object based
classification of coral reef distribution in Vembar Group of
Islands –Gulf of Mannar
Fig.4-Comparison of Spectral and object based
classification of coral reef distribution in Keeezhakarai
group of Islands –Gulf of Mannar
Fig.3-Comparison of Spectral and object based classification
of coral reef distribution in Mandapam groupgroup of Islands
–Gulf of Mannar
Fig.5-Comparison of Spectral and object based
classification of coral reef distribution in
KeezhakaraiGroup of Islands –Gulf of Mannar
553
INDIAN J. MAR. SCI., VOL. 47, NO. 03, MARCH 2018
Fig.7-Comparison of Spectral and object based
classification of coral reef distribution in Tuticorin Group
of Islands –Gulf of Mannar 8
7.6
50
1
56
.23
4
35
.37
54
1 9 9 0 2 0 0 0 2 0 1 4
Are
a in
sq
km
Year
Fig.8- Reef area changes
Fig.9- Producer Accuracy Comparison-1990
Fig.10- User Accuracy Comparison-1990
554
REBEKAH & INAMDAR: DISTRIBUTION OF CORAL REEF EXTENT AND ITS TEMPORAL VARIATION
Fig.12-User Accuracy Comparison-2000 Fig.11- Producer Accuracy Comparison-2000
Fig.13- Producer Accuracy Comparison-2014 Fig.14- User Accuracy Comparison-2014
555
INDIAN J. MAR. SCI., VOL. 47, NO. 03, MARCH 2018
Conclusion
This study reveals that the satellite remote
sensing and GIS has unique capabilities to detect
the temporal changes in coral reef extent. It also
helps to examine the status of coral reef over time.
This study also shows the crucial pre-processing of
images in order to identify submerged aquatic
ecosystems. This is because when quantitative
information is mapped or derived from satellite
images of aquatic environments, the depth of the
water causes spectral confusion and therefore
significantly affects the measurements of
submerged habitats. Water column correction
minimizes this effect, which enables
distinguishing the classes of coral reef extent
present in the Gulf of Mannar. Thus, water column
correction is an indispensible pre-processing
method in the cartography of submerged aquatic
ecosystems. In this study we have examined
certain spectral and object based method for coral
reef feature extraction. Both classification method
gives good results but object based method is
comparatively better than pixel based method.
This is because spectral based method accounts
only the pixel characteristics but object based
method in addition accounts both the textural and
contextual knowledge.
The results from the analysis denote the
temporal degradation of coral extent due to the
external influencing threats. It also gives best
classification results of both spectral and object
based analysis. Through the accuracy assessment
it is examined that object based analysis gives
better results than pixel based analysis.
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