mapping the distribution of coral reef extent and its …...indian journal of geo marine sciences...

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


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


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


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


classification of coral reef distribution in

KeezhakaraiGroup of Islands –Gulf of Mannar

553



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


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


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.

References 1. Ali Kourosh Niya , Ali Asghar Alesheikh, Mohsen

Soltanpor Mir Masoud Kheirkhahzarkesh. 2013.

“Shoreline Change Mapping Using Remote Sensing and

GIS.” International Journal of Remote Sensing

Applications 3 (3): 102–7.

2. Arthur R. (1996) "A survey of the Coral Reefs of the

Mahatma Gandhi Marine National Park" A report

submitted to ANET.

3. Alesheikh, a. a., Ghorbanali, a. & Nouri, N. Coastline

change detection using remote sensing. Int. J. Environ.

Sci. Technol. 4, 61–66 (2007).

4. A. Goodman, James & Purkis, Sam & R. Phinn, Stuart.

(2013). Coral Reef Remote Sensing: A Guide for Mapping,

Monitoring and Management. 10.1007/978-90-481-9292-

2.

5. Benfield, S. L., H. M. Guzman, J. M. Mair, and J. a. T.

Young. 2007. “Mapping the Distribution of Coral Reefs

and Associated Sublittoral Habitats in Pacific Panama: A

Comparison of Optical Satellite Sensors and Classification

Methodologies.” International Journal of Remote Sensing

28 (22): 5047–70. doi:10.1080/01431160701258062.

6. Contreras-Siilva, Ameris Ixchel, Alejandra a López-caloca,

Universidad Autónoma Metropolitana, and Unidad

Iztapalapa. 2012. “Satellite Remote Sensing of Coral Reef

Habitats Mapping in Shallow Waters at Banco Chinchorro

Reefs , México : A Classification Approach.” Remote

Sensing-Applications. doi:10.5772/2670.

7. Hoegh-Guldberg, Ove, Serge Andrefouet, Katharina E

Fabricius, Guillermo Diaz-Pulido, Janice M Lough, Paul a

Marshall, Morgan S Pratchett, and Serge Andréfouët. 2011.

“Vulnerability of Coral Reefs in the Tropical Pacific to

Climate Change.” Vulnerability of Tropical Pacific Fisheries

and Aquaculture to Climate Change, 251–96.

http://www.spc.int/climate-change/fisheries/assessment.

8. Thanikachalam, M. 2010. “Digital and Visual Analyses of

IRS Satellite Data for Zonation and Change Deduction of

Coral Reefs in Gulf of Mannar Marine Biosphere Reserve” 1

(3): 308–26.

9. Kakuta, S, T Hiramatsu, T. Mitani, Y Numata, H Yamano,

and M Aramaki. 2010. “Satellite-Based Mapping of Coral

Reefs in East Asia, Micronesia and Melanesia Regions.”

International Archives of Photogrammetry, Remote Sensing

and Spatial Information Science XXXVIII (8): 1–2.

10. Kalimantan, East, and Ana C Fonseca Escalante. 2004.

“Spectral Discrimination and Mapping Using Landsat Etm in

the Berau Reef System .” East, no. March.

11. Joyce, K. E., S. R. Phinn, C. M. Roelfsema, D. T. Neil, and

W. C. Dennison. 2004. “Combining Landsat ETM+ and Reef

Check Classifications for Mapping Coral Reefs: A Critical

Assessment from the Southern Great Barrier Reef,

Australia.” Coral Reefs 23 (1): 21–25.

doi:10.1007/s00338-003-0357-7.

12. Zoffoli, Maria, Robert Frouin, and Milton Kampel. 2014.

Water Column Correction for Coral Reef Studies by Remote

Sensing. Sensors. Vol. 14. doi:10.3390/s140916881.

13. Sukumaran, S., R. M. George, and C. Kasinathan. 2007.

“Biodiversity and Community Structure of Coral Reefs

around Krusadai Island, Gulf of Mannar, India.” Indian

Journal of Fisheries 54 (3): 275–82.

http://epubs.icar.org.in/ejournal/index.php/IJF/article/view/6

940.

14. Matinfar, H. R., Sarmadian, F., Panah, S. K. A. & Heck, R.

J. Comparisons of Object-Oriented and Pixel-Based

Classification of Land Use/Land Cover Types Based on

Lansadsat7, Etm+ Spectral Bands (Case Study : Arid Region

of Iran). Am. J. Agric. Environ. Sci. 2, 448–456 (2007).

15. Repaka, S. R., Truax, D. D., State, M., Kolstad, E. &

Manager, C. Comparing spectral and object based

approaches for classification and transportation feature

extraction from high resolution multispectral imagery.

ASPRS Annu. Conf. Proc. (2004). doi:10.1.1.84.3803.

16. Roelfsema, C.M., S Phinn, S Jupiter, J Comley, M Beger,

and E Patterson. 2010. “The Application of Object Based

Analysis of High Spatial Resolution Imagery for Mapping

Large Coral Reef Systems in the West Pacific at

Geomorphic and Benthic Community Spatial Scales.” IEEE

International Ultrasonics Symposium (IUS) San Diego,

California, 4346–49.doi:10.1109/IGARSS.2010.56.

17. Krishnan, P., Dam, R.S, George, G., Srivastava, R.C, Anand,

A., Murugesan, S., Kaliyamoorthy, M., Vikas, N. &

Soundararajan. R., Elevated sea surface temperature during

May 2010 induces mass bleaching of corals in Andaman,

Curr. Sci., 100(2010) 111- 117.

18. Thinesh T, Mathews G, Edward J K P, Coral disease

prevalence in Mandapam group of islands, Gulf of Mannar,

Southeastern India, Indian J. Mar. Sci., 38(4)(2009) 444-

450.Nobi, E.P., Dilipan, E., Sivakumar, K. & Thangaradjou,

T., Coral reefs of Lakshadweep – a glimpse, Sheshaiyana

16(4)(2009) 1-4.

556


19. Loya Y, Plotless and transect methods, in:

Monographs on Oceanic Methodology. Coral Reefs:

Research Methods, edited by Stoddart, D.R.&

Johannes, R.E, (UNESCO Press) 1978, pp. 197- 218.

20. Prerna, R., Naidu, V. & Soniya, S. Identification of

mangroves and coral reefs in the southern Gulf of

Kachchh using principal component analysis and

geo-spatial techniques: A case study. 1–10 (2012).

doi:10.1007/s11852-015-0385-9.

21. Downs, S. & Centre, T. E. Bathymetry of Near-Shore

Zones in Rongelap Atoll , Marshall Islands Using

Satellite Imagery. 1–4

22. Ituen, U. J., Johnson, I. U. & Njoku, J. C. Shoreline Change

Detection in the Niger Delta: A Case Study of Ibeno

Shoreline in Akwa Ibom State, Nigeria. 14, (2014).

23. Eakin, C. M. et al. Monitoring Coral Reefs from Space.

Oceanography 23, 118–133 (2010).

24. Lotliker, A. Utilization of remote sensing data for

operational services at INCOIS By. (2013).

25. Us, About, Software Uw, Oceanography Web, Arcgis

Arcview, Envi Erdas, Sonya Remington, Open Erdas

Imagine, et al. 2015. “ERDAS IMAGINE Exercise 1.,” 1–16.

557

mapping the distribution of coral reef extent and its …...indian journal of geo marine sciences...

Documents