Modeling Spatial Dynamic of Coral Reef on The Small Island, Spermonde Archipelago (Case Study : Barrang Lompo Island, Makassar District, Indonesia)

1

MODELING SPATIAL DYNAMIC OF CORAL REEF ON THE
SMALL ISLAND, SPERMONDE ARCHIPELAGO
Case Study: Barrang Lompo Island, Makassar District, Indonesia

AGUS

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014

STATEMENT
I, Agus, hereby declare that this thesis entitled
Modeling Spatial Dynamic of Coral Reef on the Small Island,
Spermonde Archipelago (Case Study: Barrang Lompo Island, Makassar
District, Indonesia)
Is a result of my work under the supervision advisory board and that it has not
been published before. The content of this thesis has been examined by the
advisory board and external examiner.

Bogor,

April 2014

Agus
G051110011

3

RINGKASAN
AGUS. Permodelan Dinamika Spasial Terumbu Karang di Pulau Pulau Kecil,
Kepulauan Spermonde (Studi Kasus: Pulau Barrang Lompo, Kota Makassar,
Indonesia). Dibawah bimbingan oleh VINCENTIUS P SIREGAR dan IBNU
SOFIAN.
Terumbu karang di Coral Triangle Asia sampai sekarang terancam oleh
aktivitas manusia dan ancaman alam. Kerusakan terumbu karang juga terjadi di
kepulauan Spermonde khususnya pulau Barrang Lompo (COREMAP II 2010).
Pulau Barrang Lompo memberikan kontribusi tinggi terhadap masyarakat,
sebagian besar mata pencahariannya bergantung pada perairan dangkalnya. Oleh

karena itu, pemetaan dinamika spasial dan prediksi tren masa depan terumbu
karang diperlukan untuk membuat perencanaan tata ruang untuk daerah pesisir
dengan menggunakan data penginderaan jauh dan model M-CA. Adapun tujuan
dari penelitian ini adalah menganalisis dinamika spasial terumbu karang dengan
menggunakan data penginderaan jauh dan prediksi tren masa depan perubahan
terumbu karang.
Penelitian ini dilakukan dari bulan November 2012 sampai Desember 2013
di perairan Pulau Barrang Lompo. Ada beberapa data yang digunakan di
penelitian ini. Landsat TM / ETM + tahun 1993 , 1997 , 2002 , 2007, dan 2012
telah di proses menggunakan klasifikasi untuk membuat sebuah peta terumbu
karang. Peta probabilitas diperoleh dari perkalian peta salinitas, SST, DO, pH,
TSS, kecerahan, batimetri, dan peta constraint yang merupakan daratan dan
perairan laut dalam di Pulau Barrang lompo yang diasumsikan tidak akan berubah
menjadi terumbu karang untuk jangka waktu 10 tahun.
Penelitian ini telah dilakukan dalam beberapa langkah; a) survei lapangan,
b) pengolahan citra, c) analisis rantai Markov, d) analisis MCE, e) analisis CA, f)
validasi dan, g) prediksi perubahan terumbu karang. Data dari lapangan telah
digunakan untuk pengolahan citra. Hal ini diterapkan pada jenis habitat yang
diperlukan dari seluruh wilayah studi dengan menggunakan GPS. Pengolahan
citra digunakan untuk klasifikasi peta terumbu karang pada berbeda waktu. Hasil

klasifikasi digunakan sebagai masukan analisis rantai Markov untuk mendapatkan
transition area matrix. Analisis MCE digunakan membuat peta probabilitas
dengan menggunakan beberapa parameter. Hasil pengolahan citra, hasil analisis
rantai Markov dan hasil MCE digunakan sebagai masukan untuk melakukan
model CA. Hasil simulasi CA divalidasi dengan peta sebenarnya yang diperoleh
dari citra Landsat untuk mengevaluasi hasil prediksi. Jika hasil validasi >75%,
kemudian memprediksi tren masa depan terumbu karang 10 tahun ke depan.
Berdasarkan matriks kesalahan, menemukan bahwa Overall Kappa pada
hasil klasifikasi citra tahun 2012 sebesar 80,24 %. Hasil analisis dinamika yang
terjadi dari 1993 sampai 2012 menunjukkan karang hidup dan lamun menurun
sekitar 11,21 ha (12,45%) dan 3,57 ha (8,02%), sementara karang mati meningkat
sampai 5.05 ha (86,18 %), pecahan karang meningkat 5,14 ha (68,72 %), dan
pasir 4,59 ha (35.23 %). Karang hidup dan padang lamun menurun dari tahun
1993 sampai 2012, dan penurunan terbesar terjadi dari tahun 2002 sampai 2007
masing- masing sebesar 4,51 ha dan 1,47 ha. Penurunan terbesar kedua untuk
karang dan padang lamun masing-masing sebesar 4,15 ha dan 1,46 ha pada tahun

2007 sampai 2012, sementara karang mati, pecahan karang, dan pasir meningkat
dari tahun 1993 sampai 2012. Berdasarkan pengamatan lapangan, kerusakan
habitat terumbu karang di Pulau Barrang Lompo sebagian besar disebabkan oleh

aktivitas manusia, hal ini terbukti dari banyaknya fragmen batuan (pecahan
karang), dan kegiatan penangkapan ikan menggunakan bahan peledak/bom dan
bahan kimia oleh nelayan (Coremap II 2010). Menurut Yusuf dan Jompa (2012),
fenomena pemutihan karang juga terjadi pada akhir tahun 2009 sampai
pertengahan tahun 2010 yang disebabkan dari fenomena La Nina yang
meningkatkan SST oleh gerakan warmpool ke arah barat dari Pasifik Tengah ke
Laut Indonesia, karena penguatan angin pasat yang secara signifikan menurunkan
kualitas terumbu karang di kepulauan Spermonde.
Hasil simulasi perubahan terumbu karang yang menggunakan nilai 5
sebagai total iterasi, dan jenis filtering 5x5 digunakan untuk memprediksi tahun
2002, 2007 dan 2012. Sementara untuk memprediksi 2022 digunakan nilai 10
sebagai iterasi. Sebelum memprediksi tahun 2022, hasil simulasi perubahan
terumbu karang tahun 2002, 2007, dan 2012 dibandingkan antara hasil klasifikasi
terumbu karang yang diperoleh dari citra. Penelitian ini menggunakan KIA dan
secara distribusi spasial digunakan Post-Classification untuk melihat akurasi
model. Hasil perhitungan overall kappa diperoleh adalah 2002 (89,41 %), 2007
(88,86%), 2012 (87,71%). Meskipun hasil model M-CA di setiap tahun terdapat
over-estimasi, namun nilai validasi cukup baik untuk memprediksi tren masa
depan terumbu karang untuk tahun 2022 karena prestasi yang lebih tinggi dari
nilai standar 75 % (Montserud et al. 1992 in Wassahua 2010). Berdasarkan hasil

simulasi tren perubahan terumbu karang dari tahun 2012 sampai 2022, total
luasan terumbu karang di prediksi menurun dari 78,86 ha menjadi 63,55 ha.
Dalam hal ini, terumbu karang memiliki tingkat perubahan yang lebih tinggi dan
jika hal ini selalu terjadi maka kawasan terumbu karang akan hilang. Di sisi lain,
karang mati dan pecahan karang di prediksi meningkat masing-masing 5,86 ha
menjadi 15,83 ha dan 7,48 ha menjadi 13,62 ha.
Dari hasil dan pembahasan, kesimpulan dapat digambarkan sebagai berikut.
Kondisi karang hidup menurun dari tahun 1993 sampai 2012. Penurunan terbesar
terjadi pada tahun 2002 sampai 2007 tutupan sebesar 4.51 ha dan penurunan
terbesar kedua terjadi pada tahun tahun 2007 sampai 2012 sebesar 4.15 ha. Relatif
luas karang hidup cenderung menjadi karang mati dan pecahan karang. Hasil
model M-CA menunjukkan kondisi karang hidup dari tahun 2012 sampai tahun
2022 diprediksi mengalami penurunan sekitar 15,31 ha (19,41%), dan karang mati
dan pecahan karang di prediksi bertambah dari 5.86 ha menjadi 15.83 ha dan 7.48
ha menjadi 13.62 ha, Selain itu, rekomendasi dari penelitian ini parameter SST
digunakan sebagai skenario, hasil dari model spasial M-CA dapat digunakan
untuk prediksi masa depan perubahan terumbu karang. Namun model ini tidak
belum menggunakan aktifitas manusia sebagai masukan. Jadi penelitian
selanjutnya untuk prediksi masa depan terumbu karang sebaiknya menggunakan
model yang mempertimbangkan aktivitas manusia sebagai masukan.

Kata Kunci: Terumbu Karang, Penginderaan jauh, Dinamika Spasial, Markov
Cellular Automata

SUMMARY
AGUS. Modeling Spatial Dynamic of Coral Reef on the Small Island, Spermonde
Archipelago (Case Study: Barrang Lompo Island, Makassar District Indonesia).
Under the supervisor of VINCENTIUS P SIREGAR and co-supervisor of IBNU
SOFIAN.
The coral reef in the Coral Triangle Asia has been directly threatened by
human activities and natural threats. The coral reef destructions have also
happened in Spermonde Archipelago especially Barrang Lompo Island
(COREMAP II 2010). Barrang Lompo Island contributes highly to the society, the
most livelihoods of which depends on its shallow water. Therefore, spatial
dynamic mapping and spatial prediction model for future trend of coral reef are
needed to create good spatial planning for coastal area using remote sensing data
and M-CA Model. The objective of this research was to analyze dynamics of coral
reef by using remote sensing data and also to predict the future trend of coral reef
change.
The research was conducted from November 2012 to December 2013 at the
shallow water of Barrang Lompo Island. There are several data used in this study.

Landsat TM / ETM+ of 1993, 1997, 2002, 2007, and 2012 were processed using
image classification for coral reef map. Probability maps were derived from
multiplication maps of salinity, sea surface temperature (SST), Dissolve Oxygen
(DO), pH, total suspended sediment (TSS), water clearness, bathymetry, and the
constraint map used were land area and deep water in Barrang lompo with the
assumption that the area will not change into coral reef for 10 years.
This research has been conducted in several steps: a) ground truth, b) image
processing, c) Markov chain analysis, d) Multi criteria evaluation (MCE) analysis,
e) Cellular Automata (CA) analysis, f) validation and, g) coral reef change
prediction. The main measurement data was derived from ground truth. Ground
truth data from the field were used for image processing. They were applied on
the required habitat types of the whole study area by using Global Position
System (GPS). Image processing was used to classify coral reef at different times.
The classified images were used as input for Markov chain analysis in order to get
transition area matrix. MCE analysis was used to create a probabilities map using
several parameters. The results of image processing, Markov chain analysis and
MCE were used as inputs to perform the CA model. The final result of this model
was the coral reef change prediction. The result of simulation from CA process
was validated using the actual coral reef map obtained from Landsat image to
evaluate the prediction result. If the result validation standard agreement is >75%,

then it can be used to predict the future trend coral reef for the next 10 years.
Based on the confusion matrix, the result classification in 2012 showed the
overall accuracy was about 80.24%. The result of analysis dynamic that occurred
during 1993 to 2012 showed live coral and seagrass area decreased approximately
by 11.21 ha (12.45%) and 3.57 ha (8.02%) while dead coral increased up to 5.05
ha (86.18%), rubble increased 5.14 ha (68.72%), and sand increased 4.59 ha
(35.23%). Live coral and seagrass areas decreased from 1993 to 2012, and the
greatest decrease occurred from 2002 to 2007 covering the area of 4.51 ha and
1.47 ha, respectively. The second greatest decreases for coral and seagrass was

4.15 ha and 1.46 ha from 2007 to 2012, respectively. While dead coral, rubble,
and sand has been from 1993 to 2012. Based on observations in the field, the coral
reef habitat destruction in Barrang Lompo Island was largely caused by human
activities. It was proven by many fragments of rock (rubble), and the activities by
explosives fishing such as bombs and chemicals in shallow waters Barrang
Lompo (COREMAP II 2010). According to Yusuf and Jompa (2012), the
phenomenon of coral bleaching that occurred at the end of 2009 until middle of
2010 was caused by La Nina phenomena increasing the SST by the westward
warmpool movement from the Central Pacific to the Indonesian Seas, due to the
strengthening of trade winds that significantly decreased the quality of coral reefs

in the Spermonde archipelago.
The simulation result of coral reef change used the number 5 times as the
total iterations, and filtering type 5x5 cell contiguity filtering was used to predict
in 2002, 2007 and 2012, while to predict 2022 the number 10 iterations was used.
Before simulation model to predict in 2022, the simulation result of coral reef
change of 2002, 2007, and 2012 then compared with classification result of coral
reef obtained from Landsat images. This study used Kappa Index Agreement
(KIA) and spatial distribution was indicated by Post-Classification to see the
accuracy of model. Calculation results obtained were 2002 (89.41%), 2007
(88.86%), 2012 (87.71%) overall kappa. Although the results of the model M-CA
every year was over-estimated, the value of validation was good enough to predict
the future trend of coral reef of 2022 because the achievement was higher than the
standard value of 75% (Montserud et al. 1992 in Wassahua 2010). Based on the
simulation model trend of coral reef change from 2012 to 2022, the total area of
coral reef was predicted to decrease from 78.86 ha to 63.55 ha. In this case, coral
reef had a higher change rate and if it always occurs the coral reef area will
disappear or be broken in Barrang Lompo Island. On the other hand, dead coral
and rubble were predicted to increase from 5.86 ha to 15.83 ha and 7.48 ha to
13.62 ha, respectively.
From the fore going discussion, the conclusion can be described as follows.

The live coral condition decreased from 1993 to 2012. The greatest decreases
occurred from 2002 to 2007 covering about 4.51 ha and second greatest decreases
was from 2007 to 2012 about 4.15 ha. Relatively live coral areas tend to become
dead coral and rubber. The result model M-CA showed the condition of live coral
area from 2012 to 2022 was predicted to decrease about 15.31 ha (19.41%), and
dead coral and rubble predicted to increase from 5.86 ha to 15.83 ha and 7.48 ha
to 13.62 ha, respectively. Furthermore, the recommendation from this research is
that SST parameters was used as a scenario, the result of this spatial model M-CA
can be used to predict the future trend of coral reef change. However, this model
has not used human activity parameter as input. Hence, future research on
predicting the future trend of coral reef should use a model that considers the
human activities as input.
Keyword: Coral Reef, Markov Cellular Automata, Remote Sensing, Spatial
Dynamic

Copyright © 2014, Bogor Agricultural University
Copyright are protected by law
It is prohibited to cite all or part of this thesis without referring to and mentioning
the source; Citation only permitted for the sake of education, research, scientific
writing, critical writing or reviewing scientific problems.

Citation does not inflict the name and honor of Bogor Agricultural University.
It is prohibited to republish and reproduces all part of this thesis without any
written permission from Bogor Agricultural University.

MODELING SPATIAL DYNAMIC OF CORAL REEF ON THE
SMALL ISLAND, SPERMONDE ARCHIPELAGO
Case Study: Barrang Lompo Island, Makassar District, Indonesia

AGUS

A Thesis submitted for the Degree of Master of Science in
Information Technology for Natural Resources
Management Program Study

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014

External Examiner:

Dr Ir Suria Darma Tarigan, MSc

Research Title : Modeling Spatial Dynamic of Coral Reef on The Small Island,
Spermonde Archipelago (Case Study : Barrang Lompo Island,
Makassar District, Indonesia)
Name
: Agus
Student ID
: G051110011

Approved by,
Advisory Board

Dr Ir Vincentius P. Siregar, DEA
Supervisor

Dr Ibnu Sofian, MEng
Co-Supervisor

Endorsed by,

Program Coordinator of
MSc in IT for Natural Resources
Management

Dr Ir Hartrisari Hardjomidjojo, DEA

Date of examination:
January 28, 2014

Dean of Graduate School

Dr Ir Dahrul Syah, MScAgr

Date of Graduation :

-

Research Title

Name
Student 10

--

Modeling Spatial Dynamic for Coral Reef on The Small
Island, Spennonde Archipelago (Case Study : Barrang
Lompo Island, Makassar District, Indonesia)
Agus
G051110011

Approved by,
Advisory Board

,

Dr Ir Vincentius P. Siregar, DEA
Supervisor

ｄｲｾｍｅｮｧ

Co-S upervisor

Endorsed by,

Program Coordinator of
MSc in IT for Natural Resources
Management

Dr Ir Hartrisari Hardjomidjojo, DEA

Date of examination:
January 28 , 2014

Dr Ir Dahrul Syah, MScAgr

Date of Graduation : i

I 6 APR ?014

ACKNOWLEDGEMENT
Alhamdulillahi Robbil Alamiin, Praise be to Allah SWT The Greatest, Lord
of the World, The All-Wise. Grateful for all of Your grace, I could finish my
thesis successfully. The success of this study would not have been possible
without various contribution and support from many people, and I will not be able
to mention them one by one. Of course, I would like to express my highly
appreciation to the following:
1. My family for giving the unwavering faith and supporting me to finish my
master degree
2. Dr Ir Vincentius P. Siregar, DEA as my supervisor and Dr Ibnu Sofian,
MEng as the co-supervisor for their ideas, comments and constructive
criticism during my research.
3. Dr Ir Suria Darma Tarigan, MSc and Dr Bib Paruhum Silalahi as the external
examiner for his positive inputs and ideas.
4. Dr Ir Hartrisari Hardjomidjojo, DEA, as the program coordinator and all my
teachers, my lectures for giving knowledge and experience.
5. Dr Nurjannah Nurdin, ST, MSi for helping provide data and suggestions
during my research.
6. All my friends at MIT IPB class of 2011 for helping, supporting, and
togetherness in finishing our assignment and study during our time in
pursuing our master degree.
7. All my friends at Graduate Bachelor in Marine Science class 2006 for
supporting and helping.
8. MIT secretariat and its staff for helping me to arrange the administration,
technical procedures and facilities.
9. Bapak Purwanto as Head Observation Division and Marine Meteorological
information Makassar for providing information oceanography data in my
research.
Hopefully, this thesis could give positive contribution to anyone who reads it.

Bogor, April 2014
Agus

LIST OF CONTENTS
LIST OF TABLES

vii

LIST OF FIGURES

vii

LIST OF APPENDICES

viii

1. INTRODUCTION
Background
Objectives
Problem Formulation

1
2
3

2. LITERATURE REVIEW
Coral Reef
Remote Sensing Technique for Shallow Water
Total Suspended Sediment (TSS)
Sea Surface Temperature (SST)
Markov Chain
Cellular Automata (CA)

3
4
6
6
7
8

3. METHODOLOGY
Time and Location of the Research
Material
Method

10
11
11

4. RESULT AND DISCUSSION
Image Processing
Geometric Correction
Image Processing
Accuracy Assessment

22
22
22
25

Coral Reef Change Predictions
Markov Chain Analyses
Multi Criteria Evaluation (MCE)
Cellular Automata Model

25
25
28
30

5. CONCLUSION AND RECOMMENDATION
Conclusion
Recommendation

33
33

REFERENCES

33

APPENDICES

37

CURRICULUM VITAE

40

LIST OF TABLES
Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7
Table 8
Table 9
Table 10
Table 11
Table 12
Table 13
Table 14
Table 15
Table 16
Table 17
Table 18
Table 19

Remote sensing technique for coral reef mapping
Types of data and sources
The criteria of coral cover
Confusion matrix
Transition area matrix of coral reef area
Transition probability matrix of coral reef area
Criteria and categorization of factor coral reef change
Area calculation for coral reef map 1993 to 2012
Changes in coral reef areas between in 1993 to 2012
Confusion matrix for coral reef classification in 2012
Transition probability to predict coral reef condition in 2002
Transition area to predict coral reef condition in 2002
Transition probability to predict coral reef condition in 2007
Transition area to predict coral reef condition in 2007
Transition probability to predict coral reef condition in 2012
Transition area to predict coral reef condition in 2012
Transition probability to predict coral reef condition in 2022
Transition area to predict coral reef condition in 2022
Area Statistics for coral reef change from classification in 2012
and simulation of the future trend coral reef change 2022

5
11
12
14
17
18
19
22
23
25
25
26
26
26
27
27
27
28
32

LIST OF FIGURES
Figure 1 Reflectance spectral of coral reef benthic organism (coral and
algae) and observation bands for Landsat TM. Spectral feature
of coral is indicate d by an arrow
Figure 2 The neighbored from cell (i,j) is formed from Cells (i,j) itself
and eight (8) surrounding cells
Figure 3 Study area in Barrang Lompo Island
Figure 4 General flow chart of the research
Figure 5 Flow chart of image processing
Figure 6 Flow diagram of Markov chain model
Figure 7 Flow chart cellular automata
Figure 8 Geometric correction process
Figure 9 Coral reef classification maps with different time series from
1993 to 2012
Figure 10 Coral reef changes condition 1993 to 2012
Figure 11 Driving factor maps drives by (a) DO, (b) water clearness, (c)
bathymetry, (d) TSS, (e) pH, (f) salinity, (g) SST in 2002, (h)
SST in 2007, (i) SST in 2012, (j) existing land and deep water as
constraint

5
9
10
12
14
17
21
22
23
24

29

Figure 12 Probability map from weighting linear combination approach of
MCE (a) 2002, (b), 2007, (c) 2012
Figure 13 Coral reef map classification and Coral reef prediction
using M-CA model
Figure 14 Simulation of coral reef 2012 validation using postclassification method with coral reef map 2012
Figure 15 Graphic of coral reef change area from 1993 to 2022

29
30
31
32

LIST OF APPENDICES
Appendix 1 Validation KIA of 2002 Simulation with coral reef map of
2002
Appendix 2 Validation KIA of 2007 Simulation with coral reef map of
2007
Appendix 3 Validation KIA of 2012 Simulation with coral reef map of
2012

37
38
39

1

1 INTRODUCTION

Background
Coral reef is one of important ecosystems in marine and coastal area. Coral
reef ecosystem is also important for fish community and various marine biotas as
feeding, nursery, and spawning ground. Ecologically, coral reef has a function to
protect other components of marine and coastal ecosystem from pressure of wave
and storm. Despitefully, coral reef function as an interesting tourism place. If
compared with the other ecosystems, coral reef can be easily destroyed.
The coral reef in the Coral Triangle Asia has been directly threatened by
human activities and natural threats. The coral reef destructions also happened in
Barrang Lompo Island from human activity and by rising SST (COREMAP II
2010). Barrang Lompo Island is one of small islands in Makassar district, which
has high potential ecosystem especially coral reef distribution. Barrang Lompo
Island provides high contribution to the society, the most livelihoods of whom
depends on its shallow water. However, the condition of coral reef will worsen if
the illegal fishing and SST in Barrang Lompo Island increases every year.
Therefore, spatial dynamic mapping and spatial prediction model of coral reef are
needed to create good spatial planning for coastal area. Coral reef ecosystem areas
should become protected. Through this spatial prediction model, the spatial
problem of coral reef can be solved.
Thematic map can be used as a reference for spatial dynamics and habitat
distribution of coral reef. This map is important for planners and scientists
because it can be used to simplify the management planning and change detection.
There are many approaches for mapping habitats in reef waters, such as remote
sensing techniques and surveying. By using remote sensing technique the
dynamics of shallow water cover can be evaluated (Selamat 2012). Remote
sensing data can produce coral reef change and derive several oceanographic
parameters such as SST, salinity, current, tides and TSS. Visible spectrum,
infrared and microwave satellite data can be used to create map of some objects
on earth surface (Siregar et al. 2010). Satellite remote sensing data is the best tool
to create map of shallow water. Shallow water ecosystem detections such as coral
reef, seagrass and health of coral can be created by satellite remote sensing with
different spatial resolutions such us Ikonos Multispectral, Quickbird, Alos and
Landsat (multispectral) (Evanthia et al. in Siregar 2010).
According to previous research, Landsat 7 ETM+ Image has feasibility to
produce shallow water map. It includes an analysis based on the application of
Iterative Self Organizing Data Analysis (ISODATA) as a classifier for generating
classes of benthic ecosystems present in a coral reef system (Contreras-Silva et al.
2012). Landsat-7 ETM has a wide range of electromagnetic wavelength band,
including visible, infrared and thermal bands. Thermal bands can detect thermal
radiation released from objects on the earth surface. According to LEMIGAS
(2011) in Arief (2012) visible spectrum and infrared spectrum can estimate the
TSS and water quality. Lim et al. (2008) in Arief (2012) state that Landsat TM
data processing is very promising in derive TSS.

2

The next stage of this research is simulation of coral reef change in Barrang
Lompo used Markov Cellular Automata (M-CA) and Geographic Information
System (GIS). By change detection study, the differences of coral reef can be
identified in different times. Markov Chain has a simple concept of transition
probability for Land Use and Land Cover Change. In this study the future coral
reef was determined by the past and present conditions. Markov Chain can
indicate changes and predict the distribution of the future coral reef through
transition probability matrix and transition area matrix. Besides advantages, there
are weaknesses that cannot explain the interaction among the causes of changes
and do not represent the spatial aspects. Based on these limitations, integration
between the principle of Markov Chain and Cellular Automata (CA) were used to
analyze and predict coral reef change regarding to the rate of change in spatial
dimension. Recently, Wijanarto (2006) has applied Markov chain detection
technique to detect land cover change from Landsat ETM. Markov chain is a good
technique that has great capability in generating information related to changes of
specific themes.
The CA can represent spatial dimension from a dynamic process. It is
simple, transparent and strong in capacities for dynamic spatial simulation model
where it is discrete at time dimension, place, and condition consists of a regular
grid of cells (Messina and Walsh 2000). CA has been utilized as prediction
technique to study the impressive wide range of dynamic phenomena and also
exudes superior performance in simulating land changes compared to
conventional models (Hegde et al. 2008 in Wassahua 2010). According to Weng
(2002), it has been successful in analyzing the direction, rate and spatial pattern
from land use change by integrating the Markov Chain and CA. Integration of
Markov chain and CA is known as an approach that considers the principles of
coral reef change in a cell as being affected by surrounding cells (CA Principle),
while changes in the future coral reef are determined by current and past
conditions (Markov Chain). The Markov transition probabilities are used as basis
for transitional provisions to the possible changes of each cell. Another parameter
is the probability map that defines the direction of changes in surrounding cells.
Therefore, this research used application of M-CA Model to predict coral
reef change in Barrang Lompo Island and several parameters as driving factors
that influence coral reef change. The results of simulation were evaluated through
Kappa Index Agreement (KIA). Validation was conducted by comparing
simulation result with coral reef classification of Landsat image, and PostClassification method was also used to show spatial distribution.
Objectives
The objective of this research was to analyze dynamic of coral reef by using
remote sensing data and also to predict the future trend of coral reef change.

3

Problem Formulation
The coral reef destruction has been directly threatened by human activities
and by the rise of SST. Recently, with the occurrence of coral reef destruction on
the Barrang Lompo Island, it is necessary to analyze the dynamic of coral reef and
predict the future trend coral reef change. Therefore, to manage the damage of
coral reef are needed to predict the future trend of coral reef change. Landsat
image is remote sensing data providing middle spatial resolution and high
coverage that have been qualified for multi-temporal coral reef analysis. By using
Landsat image the analysis can be done easily and quickly by changing detection
such as distribution and condition. Besides image interpretation, this research will
use several oceanography parameters that affect coral reef change.
M-CA is integration of Markov chain and CA. This method can be used to
detect and predict the future trend coral reef change. The result of transition
probabilities from Markov chain are used as the basis for transitional provisions to
the possible changes of each cell and probability map from oceanography
parameters that defines the direction of changes in surrounding cells. The result
prediction of M-CA can be used to create a coastal planning map in a coastal area.

2 LITERATURE REVIEW
Coral Reef

Coral reef is animals (called polyps) that live in colonies and form reefs.
Coral reef is one of natural resources that have very important value and meaning
in terms of physical, biological and socio-economic (Westmacott et al. 2000).
Burke et al. (2002) explained that due to the increasing needs of life, most people
have to intervene the ecosystem. Coral reef damages are caused by overexploitation, overfishing, destructive fishing practices, sedimentation, and
pollution coming from the mainland. Coral reef has been long considered as
ecosystems that are confined by a relatively narrow range on the environmental
conditions. Reefs are broadly recognized as being limited to warm, clear, shallow,
and fully saline waters (Achituv and Dubinsky 1990). According to Kleypas et al.
(1999), the environmental limits on coral reefs such as light, temperature, salinity,
sedimentation, "hydromechanics" factors, and ocean circulation, with most of
these limits have been determined from measurements and laboratory experiments.
Threats of coral reef can be divided into human-induced (antropogenik) and
natural threats. Many of the threats to coral reefs are extensively discussed in
Salvat (1987). Threats can be divided into local and global threats. The main
threats at the local level are: destructive and non-sustainable fishery practices,
such as poison fishing, blast fishing, muroami fishing among others,
sedimentation, pollution, and waste, mining, and non-sustainable tourism
practices.
Currently, the main global threat is coral bleaching (Wilkinson et al. 1999).

4

The suitability of artificial reefs has been considered as factors that affect
the growth of coral reefs namely environmental, biological, and physical factors.
According to Nybakken (1998), there are several factors supporting the growth of
coral as following:
 Bathymetry. Coral reef can grow at depths less than 25 meters and cannot
live in water more than 50-70 meters.
 Light. Light is a limiting factor for coral reefs; this is related to the process
of photosynthesis from zooxanthellae that needs the sunlight.
 Temperature. Optimal temperature for coral reefs is about 23 ° to 25 ° C and
is still be able to tolerate temperatures up to 36 ° to 40 ° C (Nybakken,
1998).
 Salinity. Normal salinity for coral reef is between 32 to 35 ppt (Nybakken
1998). Sukarno (1986) in Nybakken (1998) suggested that coral reef can
still live within the salinity range of 25 to 40 ppt.
 Sedimentation. Coral reef cannot live in areas of high sedimentation;
sediment will cover the coral polyps, so it will be that difficult to get food
and sunlight needed for life.
Remote Sensing Technique for Shallow Water
In remote sensing, classification of coral reef ecosystem is determined by
geomorphology and combination with ecology. Ecology classification based on
habitat is determined by limiting habitat species of plants, animals and substrates,
for examples, corals, algae dominance, dominance substrate and the dominance of
seagrass (Mumby 1998 in Asmadin 2011); combination of classification
geomorphology and ecology, the class hierarchy exemplified on the basis of
shallow water in lagoon with seagrass (ecology class specified in more detail into
the density of species) (Mumby et al. 2000 in Asmadin 2011). By using Remote
sensing data and combine Reef check classification, the image classification can
be shown as classes as the following: sand, live coral, rock, rubble, and algae. The
other classes (dead coral, soft coral, sponge, and other) do not appear in the image
classification because they did not occur in proportions large enough to comprise
the majority of substrate at the scale of a Landsat 7 ETM+ pixel (Joyce et al.
2003).
Landsat TM/ETM+ and SPOT HRV have been mainly used for
classification images of coral reef (Kato et al. 1992; Hasegawa 1993; Miyazaki et
al. 1995, Nadaoka et al. 1997, 1998 in Nadaoka et al. 2002). Recently, high
resolution satellite sensor Ikonos was used for classification coral reef that have
an OA of 81% and was achieved in Shiraho, while the 64% OA was obtained by
Landsat ETM+ (Andréfouët et al. 2003 in Nadaoka et al. 2002). Furthermore, the
use of hyperspectral satellite EO-1 hyperion was also used for classification of
coral reef benthic habitats situated at the eastern coast of Ishigaki island. They
acquired and examined hyperion data for diagnosing spectral features from
shallow coral reef area and benthic classification. It was also shown that spectral
derivative analysis might pose potential for classifying sea bottom coverage
(Matsunaga et al. 2001a in Nadaoka et al. 2002). According to Hedley and
Mumby (2002) in Asmadin (2011), remote sensing has provided capabilities to
maximize class of corals, namely: discrimination of basic ecology class, spectral

5

separability, attenuation depth for determination of separability capability,
extraction of separability information with sensor, and discrimination of benthic
class through the resultant analysis of data.

Figure 1 Reflectance spectral of coral reef benthic organism (coral and
algae) and observation bands for Landsat TM. Spectral feature of
coral is indicate d by an arrow (Nadaoka et al. 2002)
Method of classification on some satellite imageries that was developed for
coral reef habitat mapping with accuracy variations indicated in Table 1 (De
Mazieres 2008 in Asmadin 2011)
Table 1 Remote sensing techniques for coral reef mapping
References

Andréfouët
et al. 2003

Subject
Mapping
3 to 15 benthic
class

Classification
Method

Accuracy

IKONOS,
Landsat ETM

Unsupervised
and Supervised

77 % for 4 to 5
class, 71% for
7 to 8 class,
For
Landsat
56% 5 to 11
class
Qualitative
assessment

Landsat TM Visual
and ETM
Interpretation,
Supervised
classification
and contextual
editing
Five benthic
Landsat ETM
Unsupervised
72%
Joyce et
classes
classification
al. 2004
10
Landsat TM
Supervised
62%
Neil et al.
geomorphological
classification
2000
classes
Sources: De Mazieres (2008)
Andréfouët
& Guzman
2005

Geomorphology
and benthic
diversity

Remote
Sensing
Data

6

Total Suspended Sediment (TSS)
TSS is defined as solids or particles with a larger size of 1 μm that are
suspended in water resulting in decreased quality of water making it difficult for
the water to be used as intended. Penetration of sunlight to the surface and deep
water is not perfect and thus photosynthesis does not take place as it should. In
general, the suspended material can be formed in the watershed, ground material,
and pollutants; from the atmosphere in the form of dust or ash that drifts; and
from the sea in the form of inorganic sediment that formed at sea (Arief 2012).
The presence of organic and inorganic materials suspended can affect the
value of the spectral reflectance in water bodies. TSS is one of factors that
influence the spectral properties of the water body, where the turbid water has
high spectral reflectance values than clear water. According to Meaden and
Kapetsky in Ansory (2000) TSS can absorb and reflect radians of visible spectrum
that can penetrate into the water surface; however, the effect is a lot more as back
scattering that shows turbid water.
There are various methods that have been made in TSS mapping based on
remote sensing satellite data using low and high resolution. This paper describes
TSS algorithm directly applied to the digital number value of Landsat image.
Remotely sensed images provide information for quantifying sedimentation rates
and different factors that cause it such as erosion, river discharge or contaminants.
Budiman (2004) in Ansory (2000) states that using several satellite data including
Landsat TM and ETM, Aster and SeaWiFS algorithm the model in the
determination of TSS in the waters of the Mahakam Delta can be obtained.
According to Maryanto (2001) in Ansory (2000) the appearance of the
distribution of TSS using Landsat TM satellite with false color composite in
Segara Anakan waters show that the high visibility of TSS was found in the image
of bright color and low TSS was in the image of dark color.
Sea Surface Temperature (SST)
The earliest measurements of SST were from sailing vessels the common
practice of which was to collect a bucket of water while the ship was underway
and then measure the temperature of this bucket of water with mercury in glass
thermometer. This then because a sample from the few upper tens of centimeters
of the water. Modern powered ships made this bucket collection impractical and it
became a common practice to measure SST as the temperature of the seawater
entering to cool the ship’s engines. The depth of the inlet pipe varies with ship
from about one meter to five meters. Called “injection temperature” (a thermistor
is “injected” into the pipe carrying cooling water) this measurement is an analog
reading of a round gauge recorded by hand and radioed in as part of the regular
weather observations from merchant ships. Located in the warm engine room, this
SST measurement has been shown to have a warm bias (Saur 1963) and is
generally much nosier than buoy measurements of bulk SST (Emery et al. 2001).
According to Meadows and Campbell (1978) in Shenoi et al. (2009) the
average of seawater temperature ranged from 20 to 300C. This range depends on
factors such as depth, pressure, and light intensity. In general sea temperature,
based on its depth is divided into three layers, namely the thermocline, mixed, and

7

depth layer. In the homogeneous layer with a depth of 0 to 7m, mixing water is
found having a temperature resulting in homogeneous layer and below
homogeneous layer there is thermocline, where temperatures plummets
drastically. The thermocline layer also changes in a higher density due to the
drastic drop on temperature, while the drop on temperature causes an increase in
water density.
One of oceanographic parameter that can be instantly measured or extracted
by satellite data is water SST. The computation of SST from infrared satellite data
started in the mid-1970’s using the primary instrument called the Scanning
Radiometer (SR) on NOAA’s polar orbiting weather satellites. On the same
spacecraft the Very High Resolution Radiometer (VHRR), however, had a 1 km
spatial resolution, which was much better than the 8 km spatial resolution of the
SR (Emery et al. 2001).
Previous reseach used the thermal band from Landsat Imagery to estiamsi
SST. According to Trisakti et al. (2002), the compare is on of Landsat and NOAA
Image to estimate SST, showed the distribution pattern on the SST from Band
thermal in Lansat image and was similar to NOAA image. Landsat thermal
imagery produces a better image because the resolution is 60m, so it is useful to
look at the pattern of spread of SST locally, such as the bay area.
Markov Chain
According to Cho (2000) in Wen (2008), change detection is the process of
identifying differences in the state of an object or phenomenon by observing it at
different times. Essentially, it involves the ability to quantify temporal effects
using multi-temporal data sets. There are many methods available today for
detection of change example Markov Change, Monotemporal Change Delineation,
Multidimensional Temporal Feature Space Analysis, Composite Analysis, Change
Vector Analysis, and Image Regression.
In the Markov Chain method, transition probability has been used as the
basis for transitional provisions to the possible changes of each future cell and is
suitable for detecting dynamic change in land. This change is determined by
current and past conditions. As noted by Houet et al. (2006) the Markov Chain
process controls temporal dynamics among the land cover types through the
transition probability. In general change detection to land cover phenomena can
be built by probability information analysis based on Markov chain, where the
process gives output like transition probability and transition matrix of different
image. This output can be used in modeling and detection through a CA model.
Markov method is one of applications to detect and predict future changes
based on probability that a given piece of land will change from one state to
another state. Markov analyzed two qualities of land cover of different times that
can produce a transition area matrix and transition probability matrix. Baker
(1989) noted that the probability of information in the land cover change
modeling is often based on Markov chains. Markov chain models have been used
to model landscape changes in understanding and predicting the behavior of
complex systems (Baker 1989, Weng 2002, Fortin et al. 2003 in Tang et al.
2007). However, Markov chain model has the limitation to explain the interaction
between the changes that occur. Another limitation is that the model cannot

8

answer the question why the changes occur. Markov model can only be used to
determine when and what type of land use or land cover will change.
Markov chain model has been combined into GIS (Brown et al. 2000;
Lopez et al. 2001; Hathout 2002; Weng 2002 in Wen 2008) through the
integration of the remote sensing technology and GIS data. The integration of the
GIS based on Markov model and CA can represent spatial interaction for land
cover and land use change and can also be used to determine or predict the land
use change in the spatial dimension. Based on previous research using Markov
chain model to model the land use or land cover change, however, this research
will focus on the coral reef as the spatial dimension as well as the Markov model.
Cellular Automata (CA)
A model is a simplification of reality. This means some assumption is used
regarding its components namely cells space (the space on which the automaton
exist), cell state (in which the automaton resides and thus constrained its state),
neighborhood (the cells surrounding the automaton), transition rules (the behavior
of the automaton), and temporal space (discrete time steps in which the automaton
evolutes) to assure as realistic representation of reality as possible. As noted by
Hegde et al. (2008) the formalism of CA consists of cell space, cell state,
neighborhoods, transition rule, and temporal space.
CA model is an environmental simulation model based on a tool consisting
of regular grid of cells based on defined neighborhood interacting with
surrounding cell only. Thus, CA will be the most appropriate in a process where
immediate surroundings of the cell have been affected in the cell. As noted by
Almeida et al. (2005) CA models consist of a simulation environment represented
by a gridded space (raster). The Hedge et al. (2008) in Wassahua (2010) noted CA
system is a raster-based tool and consists of regular grid of cells.
The usage of CA model is not only in assessment of statistical shape but
also in the dynamics systems: discrete space and time systems. It can be utilized
to predict a wide range of dynamics phenomena. As noted by Hedge et al. (2008)
in Wassahua (2010) CA can be utilized as prediction technique in the study of an
impressively wide range of dynamics phenomena.
Typically, entity of CA varies independently, where the current conditions
are determined by past conditions independently. Here, there are similarities
between Markov Chain and CA principles. The difference is the provisions of
change transitions, namely the CA transition which changes not only based on
previous conditions but also based on the conditions in the surrounding cells. In
this case the CA has a spatial aspect, while the transition changes in Markov do
not represent the spatial aspect.
The characteristics of CA model are described as having six characters
(Sirokoulis et al. 2000):
1. The number of the spatial dimension (n)
2. Width/distance for each side from a cell composition (w). Wj is width from
side to j from a cell composition, where j = 1,2,3,...,n (total of the cell)j.
3. Width from the closest neighbors cell (d). dj is the closest neighbors distance
alongside from composition
4. Each cell condition of CA

9

5. CA rule, as the arbitrary function F
6. Cell X condition, at time t = 1, is calculated based on F. F is the function
from cell X condition at time (t) and the condition of surrounding cells at time
(t) is known with a rule as the change transition. The simple description from
the two dimension of CA (n=2), with the nearest neighbored distance d1=3
and d2=3 is shown in, (Figure 2)

Figure 2 The neighbored from cell (i,j) is formed from Cells
(i,j) itself and eight (8) surrounding cells
(Sirokoulis et al. 2000)
Another parameter is the map of land probability. This land probability is a
factor that determines the direction of changes in surrounding cells. Hedge et al.
(2008) in Wassahua (2010) defines transition rules based on multi criteria
evaluation (MCE) methods. Relevant input requirement of CA is important in
relation to what Land Use and Land Cover type will be predicted to achieve good
results of prediction. They enhance the model where relations among spatial
elements govern spatial changes. Hence, the requirements consist of area
transition probabilities or suitability based on MCE method. Several CA studies
have been done namely (Messina and Walsh 2000; Houet et al. 2006).
GIS serves the MCE function of suitability assessment well, providing the
attribute values for each location and both the arithmetic and logical operators for
combining attributes (Eastman 2003). Furthermore MCE may be used to develop
and evaluate alternative plans that may facilitate compromise among interested
parties (Malczewski 2000). In general, the GIS-based land suitability analysis
assumes that a given study area is subdivided into a set of basic unit of
observations such as polygons or raster (MCE). According to Soe and Le (2006)
multi criteria technique can be used to drive factor for predicting of future
scenario in which decision of land allocation was done by considering the
different criteria. Driving factor is a common method to assess and aggregate
many criteria. It is used as a procedure that multiplies each of the factors. Driving
factor will be used for probability map and resource allocation decision using GIS
(Wassahua 2010).

10

3 METHODOLOGY

Time and Location of the Research
This research was conducted from November 2012 to December 2013 in
Laboratory of Information and Technology for Natural Resources Management
(SEAMEO-BIOTROP Bogor), and in the shallow water of Barrang Lompo island.
The research location can be seen in Figure 3. Barrang Lompo island is one of the
islands in Spermode Archipelago, which is located in Makassar District.
Administratively, the Barrang Lompo island belong to in the Sub-district Ujung
Tanah Makassar, as well as some neighboring islands such as the Bonetambung,
Kayangan, Samalona, and Barrang Caddi island. Barrang Lompo island has a land
area of 0.49 km2. Geographically, Barrang Lompo island is located at longitude
119019’48” East and latitude 05002’48” South, which is located 7 Km from
Makassar. Based on administrative data obtained from the island local village,
Barrang Lompo island has population of about 4,046 people.

Figure 3 Study area in Barrang Lompo Island

11

Material
This research used several different data. Those data were satellite image
and ground truth. The following table shows data types and the sources (Table 2):
Table 2 Types of data and sources
Data Type
Acquisition
Landsat TM
 June 23. 1993
 June 02. 1997
Landsat ETM (+)  July 10. 2002
 May 21. 2007
 July 08. 2007
 October 09. 2012
 September 01. 2012
ALOS AVNIR
2010
Water Quality
2012
(Salinity, pH,
SST, Water
Clearness, DO)
Bathymetry

Sources
United State
Survey (USGS)
United State
Survey (USGS)

Geology
Geology

Ground Truth

Some hardware and software required were used for data collection,
processing, and analyzing i.e computer, printer, GPS, echosounder, SCUBA
equipment, underwater digital camera, Remote Sensing and GIS software (ArcGis
9.3, Er Mapper 7.1, Surfer 10, Idrisi Kilimanjaro).
Method
Modeling spatial dynamic for coral reef has been conducted in several steps:
a) ground truth, b) image processing, c) Markov chain analysis, d) MCE analysis,
e) CA analysis, f) validation and, g) coral reef change prediction (Figure 4). The
main measurement data were derived from ground truth. Ground truth data from
the field were used in assessing image processing. It was applied on the required
habitat types of the whole study area by using GPS. Other data were required in
this study especially for spatial data like raster and vector as mentioned. Image
processing was used to classify coral reef at different times. The classified images
were used as input for Markov chain analysis to get a transition area matrix. MCE
analysis was used to create a probability map using several parameters. The result
of image processing, Markov chain analysis and MCE were used as inputs to
perform the CA model.
The final result of this model was the coral reef change prediction. The
result of simulation from CA process was validated using the actual coral reef
map obtained from Landsat image to evaluate the prediction result. If the result
validation standard agreement is >75% then it can be used to predict the future
trend coral reef for the next 10 years.

12

Problem
Identification &
Objectives

Image Processing
(coral reef map from
1993 to 2012)
Markov Chain
(T