Geospatial Modeling of Vegetation Cover Changes on a Small Island (Case Study: Tanakeke Island, Takalar District, South Sulawesi)

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GEOSPATIAL MODELING OF VEGETATION COVER CHANGES

ON A SMALL ISLAND

Case Study: Tanakeke Island, Takalar District, South Sulawesi

M. AKBAR AS

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

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STATEMENT

I, M. Akbar AS, hereby declare that this thesis entitled

Geospatial Modeling of Vegetation Cover Changes on a Small Island Case Study: Tanakeke Island, Takalar District, South Sulawesi

Is a result of my work under the supervision of 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, June 2014

M. Akbar AS

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RINGKASAN

M. AKBAR AS. Permodelan Geospasial Perubahan Tutupan Vegetasi di Pulau Kecil, Studi Kasus: Pulau Tanakeke, Kabupaten Takalar, Sulawesi Selatan. Dibawah bimbingan oleh BUCE SALEH dan IBNU SOFIAN.

Indonesia telah kehilangan lebih dari 1,2 juta hektar mangrove sejak tahun 1980 ketika tutupan hutan mangrove masih 4,2 juta hektar (FAO 2007). Luas hutan mangrove di Pulau Tanakeke, Kabupaten Takalar, Sulawesi Selatan, Indonesia pada awal tahun 1980 adalah 1.770 hektar, dan setelah dikonversi menjadi areal tambak luas lahan tinggal 500 hektar. Fenomena degradasi hutan mangrove di Pulau Tanakeke perlu mendapat perhatian khusus, mengingat pulau ini merupakan salah satu pulau di Sulawesi Selatan yang memiliki hutan mangrove yang sangat berharga di wilayah pesisir. Tujuan dari penelitian ini adalah untuk menganalisis dinamika perubahan tutupan mangrove (selama 41 tahun) dengan menggunakan citra Landsat multi-temporal dan memproyeksi perubahan tutupan mangrove pada tahun 2023.

Penelitian ini dilakukan dari bulan Agustus 2013 sampai April 2014, termasuk tahap persiapan data, pengolahan data, model simulasi, penulisan tesis dan publikasi. Lokasi penelitian terletak di Pulau Tanakeke, Kabupaten Takalar, Sulawesi Selatan. Untuk melakukan studi ini, ada empat data citra Landsat yang digunakan, yaitu Landsat MSS (28 Oktober 1972), Landsat Thematic Mapper

(TM) (26 Agustus 1993), Landsat Enhanced TM Plus (ETM+) (21 Juli 2003), dan Landsat 8 (Operational Land Imager, OLI) (27 April 2013) diperoleh dari United States Geological Survey (USGS). Peta probabilitas untuk perubahan tutupan mangrove menggunakan data Multi Criteria Evaluation (MCE), yaitu pasang surut, permukiman, dan substrat.

Metodologi penelitian ini terdiri dari tiga langkah utama, yaitu: (1) analisis citra: koreksi geometrik dari citra Landsat, analisis awal, dan klasifikasi; (2)

ground truth; dan (3) model Cellular Automata Markov (CA-Markov), sedangkan pengolahan data citra satelit terdiri dari lima langkah utama, yaitu: (1) koreksi geometrik, (2) analisis awal, (3) klasifikasi citra, (4) accuracyassessment, dan (5)

post classification. Data hasil analisis citra dari dua peta penggunaan lahan yang berasal dari waktu yang berbeda digunakan dalam membuat model Markov Chain. Peta penggunaan lahan dengan interval 21 tahun, memakai peta penggunaan lahan dari 1972-1993 digunakan untuk proyeksi tahun 2003 dan kemudian divalidasi menggunakan peta penggunaan lahan tahun 2003. Peta penggunaan lahan dengan interval 10 tahun (1993-2003) digunakan sebagai proyeksi untuk tahun 2013 dan divalidasi menggunakan peta penggunaan lahan tahun 2013. Sementara itu, peta penggunaan lahan dengan interval 31 tahun, (1972-2003) sebagai proyeksi untuk 2013 dan divalidasi menggunakan peta penggunaan lahan tahun 2013. Proyeksi mangrove tahun 2023 diperoleh berdasarkan tingkat akurasi dari ketiga interval yang dipilih. Jenis filtering 5x5 digunakan untuk melihat perubahan cell yang terjadi dalam memprediksi. Jika hasil validasi yang diperoleh adalah >75%, maka dapat digunakan untuk memprediksi mangrove untuk 10 tahun ke depan.

Penilaian kuantitatif dilakukan dengan menggunakan data ground truth

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dari akurasi klasifikasi, yaitu 87,78%. Produser dan pengguna akurasi untuk mangrove diperoleh 93%. Hasil analisis dinamika spasial pada tahun 1972-2013 menunjukkan bahwa luas mangrove di Pulau Tanakeke mengalami penurunan sebesar 1.595,55 ha (63,91%). Tingkat perubahan yang terjadi tidak seragam antara tahun 1972-1993, 1993-2003, dan 2003-2013. Laju perubahan luas tutupan mangrove selama tahun 1972-1993 merupakan yang tertinggi di Pulau Tanakeke, dimana mengalami penurunan luas tutupan mangrove sebesar 1.166,61 ha (46,73%). Pada tahun 1993 hingga 2003, mangrove di Pulau Tanakeke mengalami penurunan seluas 252,49 ha (18,98%), dan pada tahun 2003-2013 terjadi penurunan seluas 176,45 ha (16,37%).

Hasil simulasi perubahan tutupan mangrove pada tahun 1993 dan 2003 (selang 10 tahun) digunakan untuk memprediksi perubahan tutupan mangrove pada tahun 2013 dan dibandingkan dengan tutupan mangrove yang sebenarnya pada tahun 2013 (peta hasil klasifikasi). Hasil perhitungan akurasi yang diperoleh dengan menggunakan iterasi 35 dan grid 30, yaitu 86,87%, dan dengan menggunakan iterasi 35 dan grid 60 diperoleh akurasi, yaitu 84,85%. Perubahan tutupan mangrove pada tahun 1972 dan 1993 (selang 21 tahun) dibuat untuk memprediksi perubahan tutupan mangrove pada tahun 2003. Hasil simulasi perubahan tutupan mangrove dibandingkan dengan tutupan mangrove yang sebenarnya pada tahun 2003 (peta hasil klasifikasi). Hasil perhitungan akurasi yang diperoleh dengan menggunakan iterasi 25 dan grid 30, yaitu 81,96%, dan dengan menggunakan iterasi 25 dan grid 60 akurasi yang diperoleh, yaitu 79,89%. Perubahan tutupan mangrove pada tahun 1972 dan 2003 (selang 31 tahun) dibuat untuk memprediksi perubahan tutupan hutan mangrove pada tahun 2013. Hasil simulasi perubahan tutupan mangrove kemudian dibandingkan dengan tutupan mangrove yang sebenarnya pada tahun 2013 (peta hasil klasifikasi). Hasil perhitungan akurasi diperoleh dengan menggunakan iterasi 10 dan grid 30, yaitu 83,70%, dan dengan menggunakan iterasi 10 dan grid 60 diperoleh akurasi, yaitu 81,87%.

Berdasarkan hasil proyeksi dengan menggunakan interval tahun yang berbeda, diperoleh data dengan akurasi yang lebih tinggi pada interval 10 tahun dengan menggunakan iterasi 35. Dalam membuat model proyeksi untuk 10 tahun ke depan (2023), digunakan interval waktu 10 tahun (2003-2013) dengan iterasi 35 dan grid 30 meter, maka diperoleh proyeksi luas tutupan lahan mangrove pada tahun 2023, yaitu 662,28 ha (mengalami penurunan sebesar 238,83 ha).

Rekomendasi berdasarkan penelitian ini bahwa dengan menggunakan Model CA-Markov dari hasil interpretasi visual dalam grid 60 m, dapat digunakan untuk memproyeksikan perubahan luas tutupan mangrove di Pulau Tanakeke karena memiliki akurasi yang baik. Pengembangan areal tambak dan penebangan mangrove untuk arang harus dibatasi di Pulau Tanakeke guna melestarikan mangrove untuk pengembangan pariwisata.

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SUMMARY

M. Akbar AS. Geospatial Modeling of Vegetation Cover Changes on a Small Island (Case Study: Tanakeke Island, Takalar District, South Sulawesi). Supervised By BUCE SALEH and IBNU SOFIAN.

mangrove forests degradation in Tanakeke Island needs to receive specific attention, considering that the island is one in South Sulawesi having precious mangrove forests in a coastal region. The objective of this study was to analyze dynamics of mangrove cover changes (41 years) using multi-temporal Landsat imagery and projection of mangrove cover changes in 2023.

This research was conducted from August 2013 to April 2014, including the stages of data preparation, data processing, simulation model, thesis writing and publication. The study area is located in Tanakeke Island, Takalar District, South Sulawesi. To conduct the study, Landsat data, including four Landsat MSS (28 October 1972), a Landsat Thematic Mapper (TM) images (26 August 1993), a Landsat Enhanced TM Plus (ETM+) image (21 July 2003), and a Landsat 8 (Operational Land Imager, OLI) image (27 April 2013) were acquired from the United States Geological Survey (USGS). The probability maps for mangrove cover changes in the study area using the multi criteria evaluation were the tidal, settlement, and substrate.

The methodology of this study consisted of three main steps: (1) image analysis: geometric correction of Landsat images, preliminary analysis, and classification; (2) ground truth; (3) Cellular Automata Markov model (CA-Markov) while the data processing was composed of five main steps: (1) geometric correction, (2) preliminary analysis, (3) image classification, (4) accuracy assesment, and (5) post classification. Data input used for Markov Chain on two land use maps derived from two different times. Land use map with interval of 21 year, using land use map of 1972 to 1993, is used for projection 2003 and then was validated using land use map on 2003. A land use map with 10-year interval, which was the map of 1993 to 2003 used for projection of 2013, and then was validated using land use map on 2013. Meanwhile, land use map with a 31-year interval, which used a land use map of 1972 to 2003, for projection 2013, was validated using land use map on 2013. The results of the third interval were selected based on the level of accuracy for projection change of mangrove in 2023. The cellular change of state was then determined in which 5x5 filter was used. If the result of validation standard agreement was >75%, it could be used to predict the future trend of mangrove for the next 10 years.

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(63.91%). The rate of change, however, was not uniform from 1972 to 1993, 1993 to 2003, and from 2003 to 2013. Deforestation rate during 1972 to 1993 was the highest in Tanakeke Island, and mangrove area decreased by 1,166.61 ha (46.73%). From 1993 to 2003, mangrove in Tanakeke Island decreased by 252.49 ha (18.98%), and from 2003 to 2013 there was a decreased of 176.45 ha (16.37%).

The simulation result of mangrove cover changes in 1993 and 2003 (interval 10 years) was made to predict mangrove cover change in 2013. The simulation result of mangrove cover change was compared with the actual mangrove cover in 2013 (map classification). Calculation results were obtained using iteration 35 and grid 30 by 86.87%, iteration 35 and grid 60 by 84.85% overall accuracy. Mangrove cover changes in 1972 and 1993 (21 years interval) were made to predict mangrove cover change in 2003. The simulation result of mangrove cover change was then compared with the actual mangrove cover in 2003 (map classification). Calculation results were obtained using iteration 25 and grid 30 by 81.96%, iteration 25 and grid 60 by 79.89% overall accuracy. Mangrove cover changes in 1972 and 2003 (interval 31 years) were made to predict mangrove cover change in 2013. The simulation result of mangrove cover change was then compared with the actual mangrove cover in 2013 (map classification). Calculation results were obtained using iteration 10 and grid 30 by 83.70%, iteration 10 and grid 60 by 81.87% overall accuracy

Based on the results of the projection by using different intervals, the data with higher accuracy were obtained during ten-year intervals with in which 35 iterations were used. In making a projection for the next 10 years (2023), an interval of 10 years (2003 to 2013) with iterations 35 was used. The simulation model trend, mangrove cover change is a major concern which continued to be broken from 2013 to 2023 as illustrated in the total area of mangrove cover is predicted to be 662.28 ha in 2023 (decreased by 238.83 ha) by using grid 30 m.

The recommendation based on the study is that by using visual interpretation results in a grid of 60 m, CA-Markov model can be used in projecting change of mangrove in Tanakeke Island because it has good accuracy. Development of aquaculture and charcoal should be restricted in Tanakeke Island to conserve mangrove for tourism development.

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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 is 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.

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

Management Program Study

GEOSPATIAL MODELING OF VEGETATION COVER CHANGES

ON A SMALL ISLAND

Case Study: Tanakeke Island, Takalar District, South Sulawesi

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

2014

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Research Title : Geospatial Modeling of Vegetation Cover Changes on a Small Island (Case Study: Tanakeke Island, Takalar District, South Sulawesi)

Name : M. Akbar AS Student ID : G051110071

Approved by,

Advisory Board

Dr Ir Buce Saleh, MS Supervisor

Dr Ibnu Sofian, MEng Co-Supervisor

Endorsed by,

Program Coordinator of MSc in IT for Natural Resources

Management

Dr Ir Hartrisari Hardjomidjojo, DEA

Dean of Graduate School

Dr Ir Dahrul Syah, MScAgr

Date of examination:

June 13, 2014

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ACKNOWLEDGEMENT

In the name of Allah SWT, the Most Gracious and the Most Merciful. Alhamdulillah, all praises to Allah for the strengths and His blessing in completing this thesis, I would like to express my highly appreciation to the following:

1. Dr Ir Buce Saleh, MS as my supervisor for his supervision support. His invaluable help in the forms of constructive comments and suggestions throughout the experimental and thesis works have contributed to the success of this research and Dr Ibnu Sofian, MEng as the co-supervisor for his ideas and knowledge regarding this topic during my research.

2. Dr Antonius Bambang Wijanarto as the external examiner for his positive inputs and ideas.

3. Dr Ir Hartrisari Hardjomidjojo, DEA, as the program coordinator and all my lecturers for giving knowledge and experience.

4. Dr Nurjannah Nurdin, ST for helping provide data and suggestions during my research.Your kindness means a lot to me. Thank you very much.

5. All my friends at MIT, IPB, for their help and support in finishing my assignment and study.

6. Staff of Center for Regional Development and Spatial Information, Hasanuddin University for all the support and help.

7. MIT secretariate and all of its staff members for helping me to arrange the administration, technical procedures, and facilities.

8. All of my family. Thank you for always supporting me.

Bogor, July 2014

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LIST OF CONTENTS

Remote Sensing of Mangrove Vegetation 4

Dynamics of Mangrove Forest 4

Multi Criteria Evaluation (MCE) 12

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REFERENCES 32

CURRICULUM VITAE 34

LIST OF TABLES

1 Factors influencing loss of mangrove in Indonesia. 3

2 Satelite data. 8

3 Confusion matrix. 10

4 Criteria and categorization of factors for mangrove. 12 5 Areal estimates of major land use/cover types. 14 6 Areal and percentages of land use/cover changes. 14 7 Areal of land use/cover changes from the 1972 to 1993. 17 8 Percentages of land use/cover changes from the 1972 to 1993. 17 9 Areal of land use/cover changes from the 1993 to 2003. 17 10 Percentages of land use/cover changes from the 1993 to 2003. 18 11 Areal of land use/cover changes from the 2003 to 2013. 18 12 Percentages of land use/cover changes from the 2003 to 2013. 19 13 Areal of land use/cover changes from the 1972 to 2013. 19 14 Percentages of land use/cover changes from the 1972 to 2013. 20 15 Matrix kappa Index of Agreement (KIA). 30 16 Mangrove cover change projection using a grid 30 m. 30

LIST OF FIGURES

1 Temporal and spatial hierarchical organization of key ecosystem components in mangrove forests including leaves, trees, forests

and watershed regions. 5

2 Map of the study area, Tanakeke Island. 7 3 Flow chart of methodology. 8 4 Flow chart of image processing. 9 5 Flow chart of Markov Chain model. 11 6 Flow chart of Cellular Automata-Markov (CA-Markov) model 13 7 Land use/cover maps of Tanakeke Island (a) in 1972, (b) 1993,

Tanakeke Island. 16

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10 Tidal data of Tanakeke Island in 2014. 23 11 Probability maps of (a) Substrat, (b) Tidal, (c), Settlement, (d)

Result from Boolean approach. 23 12 Interval of 10 years: (a) iteration for grid 30 m, (b) iteration for

grid 60 m. 24

13 Interval of 10 years: (a) mangrove cover in 2013 (Grid 30 m), (b) mangrove cover simulation in 2013 (Grid 30 m), (c) mangrove cover in 2013 (Grid 60 m), (d) mangrove cover

simulation in 2013 (Grid 60 meter). 25 14 Interval of 21 years: (a) iteration for grid 30 m, (b) iteration for

grid 60 m. 26

simulation in 2003 (Grid 60 meter). 27 16 Interval of 31 years: (a) iteration for grid 30 m, (b) iteration for

grid 60 m. 28

simulation in 2003 (Grid 60 meter). 29 18 Condition of mangrove cover in Tanakeke Island: (a) Mangrove

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1 INTRODUCTION

Background

Mangrove forests found in the inter-tidal zone in the tropics and subtropics, play an important role in stabilizing shorelines and in helping reduce devastating impacts of natural disasters such as tsunamis and hurricanes. They also provide important ecological and social goods and services including breeding and nursing grounds for marine and pelagic species, food, medicine, fuel, and building materials for local communities. These forests, however, are declining at an alarming rate, perhaps even more rapidly than inland tropical forests, and much of what remains is in degraded condition (Wilkie and Fortune 2003).

As a complex and unique ecosystem, mangrove has important functions and benefits for coastal environments (Bengen 1999), some of which are a muffler of waves and storms, a protector of abrasion, a trapper of mudguard, and sediment, a producer of some detritus from the leaves and branches of mangrove trees, a nursery, feeding, and spawning ground of various types of fish, shrimp and other marine biota, a producer of wood for constructions materials, firewood, charcoal materials, paper materials, and furniture-making materials, and a supplier of fish larvae, shrimp and other marine biota.

Indonesia has lost more than 1.2 Mha of its mangrove since 1980 when mangrove forest cover was still 4.2 Mha (FAO 2007). Among the immediate impacts of mangrove loss has been the decline in fish production since mangrove are vital fish nurseries and rapid erosion in the absence of buffer against high waves. 60% of this loss has been attributed to conversion of mangrove into

brackish water aquaculture ponds, which peaked in the 1980‟s and 1990‟s during

a period of aquaculture expansion known as the blue revolution.

There is no agreement for the extent of mangrove vegetation in Indonesia, but various forums usually use the number of 4.25 million ha for a measure. The occurence of mangrove forest reduction is generally caused by the exploitation and transferring of appropriation of theland. Approximately 9 years ago, the extensive vast of mangrove forest in Indonesia was about 4.13 million ha, but now only 2.49 million ha (60%) remains.

South Sulawesi is facing massive mangrove forest destruction, for over the past 30 years deforestation and pollution have taken their toll and damaged almost 90 percent of the total original areas. Before 1980s, the mangrove forest in South Sulawesi was over 214,000 hectares. The conversion of the mangrove forest into fishpond reduced the area into 23,000 hectares in the early 1991, or a decrease of 61 percent. The conversion of mangrove forests into fish farms was a result of a government policy in the early 1980s that instructed the offices of local fisheries and maritime affairs to carry out intensification of shrimp cultivation, which was then a major export commodity and earned the state a huge amount of money.

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Tanakeke Island is one small island in South Sulawesi with precious mangrove forests in its coastal region.

Remote sensing could play an important and effective role in the assessment and monitoring of mangrove forest cover dynamics. While remote sensing data analysis does not replace field inventory, it provides supplementary information quickly and efficiently. The use of remotely sensed data offers many advantages including synoptic coverage, availability of low-cost or free satellite data, availability of historical satellite data, and repeated coverage. In addition, recent advances in the hardware and software used for processing a large volume of satellite data have helped increase the usefulness of remotely sensed data. Moreover, it is extremely difficult to get into vast swamps of mangrove forests, and conducting a field inventory is time consuming and costly. A number of studies conducted in small islands have begun to develop and apply remote-sensing techniques mainly for mapping purposes (Nayak et al. 2001).

The present work used the Cellular Automata-Markov (CA-Markov) model to project the likely temporal distribution of the mangrove landscape in the pre-specified coastal region driven by some well-known local socioeconomic factors. The quantitative information achieved from such a projection might be useful for forest managers to devise better plans for long-term management of mangrove ecosystems in Tanakeke Island.

Objectives

The aim of this study was to analyze dynamics of mangrove cover changes (41 years) using multi-temporal Landsat imagery and projection of mangrove cover changes in 2023.

Problem Identification

As a result of socio-economic development and population growth, some parts of the mangrove forests were cleared for other land uses. Since the early 1997, a number of aquaculture fields, especially large-scale shrimp farms, have been constructed by clearing the mangrove forests. The destruction of mangrove forests for shrimp farming has continuously degraded ecological and socio-economic services of mangrove forests with associated environmental impacts.

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2 LITERATURE REVIEW

Mangrove Ecosystem

Deforestation led by increasing demand for land and climate change events such as the rise of sea level and reduction in freshwater flow are considered as major players behind the continuous annihilation of mangrove; however, climate change events may have an impact of only 10-15% reduction of mangrove habitats in the distant future whereas the immediate threat comes from uncontrolled exploitation and deforestation (Alongi 2008). This has resulted in serious concerns among conservationists and has exposed the coastal communities to a further increasing threat of climate change and hydro meteorological disasters.

It is easier to understand the benefits and important values of the mangrove ecosystem by classifying it into three main functions namely:

1. Physical-chemical functions: mangrove ecosystem will physically maintain and stabilize the shoreline and the banks of the river, protect it from waves and currents, and accelerate the formation of new land. Mangrove ecosystem is also a source of nutrient elements which consist of nitrogen, magnesium, sodium, calcium, sulfur and phosphor.

2. Biological functions: mangrove ecosystem functions as nursery ground, feeding ground, and spawning ground of a few shrimp, fish, birds, monitor lizards, snakes, as well as a living place of epiphytes and parasitic plants such as orchids, ferns nails, and other various lives.

3. Economic functions: mangrove ecosystem can be a place for recreational fish and shrimp ponds, salt ponds, and a food manufacturer.

Influencing Factors for Mangrove

Erosion is severe in the coast of Java islands and other provinces such as Lampung, Northeast Sumatra, Kalimantan, West Sumatra (Padang), Nusa Tenggara, Papua, South Sulawesi, and Bali. On the contrary, Irrawaddy delta mangrove in Myanmar are presently affected by increased sedimentation and coastal accretion. It is interesting that deposition in coastal areas may cause a decrease in the tidal prism in rivers running through the mangrove, resulting in the closing of tidal creeks and the degradation of the forest (Brown 2007). Sedimentation rate of the Irrawaddy is the fifth highest in the world.

Table 1 Factors influencing loss of mangrove in Indonesia

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4

Factors Influencing Categories

Wars and conflicts No Significant Impact

Urbanization Severe Impact

Environmental factors

Rising Sea level Moderate Impact Increase in salinity No Information Erosion and sedimentation Moderate Impact High tide and tsunami Severe Impact

Cyclones Low Impact

Erosion of shoreline Severe Impact

Source: Brown, 2007

Remote Sensing of Mangrove Vegetation

Remote sensing is an indispensable tool for assessing and monitoring mangrove forests primarily because many mangrove swamps are inaccessible or difficult to reach for a field survey. Remote sensing data providing synoptic coverage and those of historical satellites dating back to the 1960s are available. Global mapping initiatives have failed to map the extent and rate of deforestation with sufficient details. For example, only extensive mangrove areas were mapped as part of the Global Land Cover of 2000 survey(Stibig et al. 2007).

Remote sensing of mangrove vegetation is based on two important properties namely mangrove growing in coastal areas and the chlorophyll of their leaves (chlorophyll). The optical properties absorb the red light and very strongly reflect the infrared spectrum (Susilo 2000). Based on the results of the reflection properties of electromagnetic waves on the object, the use of satellite observations of vegetation with bands work on the electromagnetic wavelength reflectance produced high values. Remote sensing is becoming an important tool for monitoring rapid changes in the biosphere, including changes in vegetation cover.

Mangrove habitat maps have been used for three general management applications: resource inventory, change detection and the selection and inventory of aquaculture sites. The mangrove distribution maps can be made from investigation in situ or obtained from remote sensing images and GIS techniques (Kairo et al. 2002).

Dynamics of Mangrove Forest

The modeling approach is suitable for simultaneously evaluating the effects of environmental changes and disturbances on ecological processes such as tree recruitment, establishment, growth, productivity, and mortality. Such estimates on the sustainability of mangrove resources may contribute to the evaluation of impacts of mangrove degradation on socio-economic systems (Davis et al. 2005).

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5

the climatic and landform characteristics of coastal regions which result in local and often gradual environmental gradients that represent top-down constraints of mangrove forest development. At the same time, tree performance, growth response, and interactions among trees affect bottom-up patterns of forest development (Smith 1992).

Figure 1 Temporal and spatial hierarchical organization of key ecosystem components in mangrove forests including leaves, trees, forests and watershed regions.

Markov Chain

In the Markov Chain method, transition probability is used as the basis for the transitional provisions to the possible changes of each future cell and suitable for detecting dynamic change in land. This change is determined by current and past conditions. In a probability statistics theory, which was analyzed in the Markov process, it was a time-varying phenomena of randomly assigned to a particular state (Baja 2012). As noted by Houet (2006) the Markov Chain process controls temporal dynamics among the cover types through the use of transition probability. In general, change detection to land cover phenomena can be built by probability information analysis based on Markov Chain where its process allows the giving of output like transition probability and transition matrix of different images. This output can be used in modelling and detection through a cellular automata model.

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6 used to determine when and what type of land use or land cover will change.

Markov chain model has been combined into GIS (Geographic Information System) through the integration of the remote sensing technology and GIS data. The integration of the GIS based on Markov model and cellular automata 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 the previous research using Markov chain model to model the land use or land cover change, however, this research focused on the coral reef as the spatial dimension as well as the Markov model.

Cellular Automata (CA)

Cellular automata model is an environmental simulation model based on a tool consisting of a regular grid of cells based on defined neighborhood interacting with surrounding cells only. Thus, cellular automata will be most appropriate in a process where immediate surroundings of the cell have been affected in the cell. Cellular automata models consist of a simulation environment represented by a gridded space (raster). Cellular automata system is a raster-based tool and consists of a regular grid of cells. A model is a simplification of reality. This means some assumptions are used regarding its components namely cells space (the space on which the automaton exists), 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. The formalism of CA consists of cell space, cell state, neighborhoods, transition rule, and temporal space.

The characteristics of the CA model are time, space, and discrete states. The variable only has a finite number of states, and the rules of state changes are manifested as local characteristics. The modeling of M-CA process was with and without the integration of the landscape features: riparian wetlands and hedgerows. Modelling the transition rules at the field scale through suitability maps which integrate both local spatial dependencies and driving factor at larger scale appears as a necessary step to restitute the landscape pattern with a CA based on continuity relations to model changes (Houet and Hubert 2006)

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3 METHODOLOGY

Time and Location

This research was conducted from August 2013 to April 2014, including: data preparation, data processing, simulation model, thesis writing and publication. The study area is located in Tanakeke Island, Takalar District, South Sulawesi, Indonesia. Tanakeke Island has a land area of 4,312 ha. The geographical boundary of the study area was between 119016‟55”East and 05029‟34” South (Figure 2).

Figure 2 Map of the study area, Tanakeke Island

Data Collection

A set of Landsat data, including four landsat MSS (October 28, 1972), a Landsat Thematic Mapper (TM) images ( August 26 1993), a Landsat Enhanced TM Plus (ETM+) image (July 21, 2003), and a Landsat 8 (Operational Land Imager, OLI) image (April 27, 2013) acquired from the US Geological Survey (USGS), was used in this study (Table 2). The Landsat MSS data have four spectral bands, with a spatial resolution of 60 m. landsat TM data have seven spectral bands, with a spatial resolution of 30 m for bands 1-5 and 7.

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8

Landsat 8 data have nine spectral bands with a spatial resolution of 30 m for bands 1-7 and 9. The ETM+ and OLI band 8 (panchromatic band) have a spatial resolution of 15 m. The spectral bands were generally between the optical and short-wavelength-infrared regions, except for band 9 of Landsat 8 data, which had

a cirrus wavelength between 1.36 and 1.38 μm.

Table 2 Satelite data

Satelite Resolution Path and Row Acquisition

Landsat MSS 60 122/064 28 October 1972 Landsat TM 30 114/064 26 August 1993 Landsat ETM (+) 30 114/064 21 July 2003 Landsat 8 30 114/064 27 April 2013

Method

The methodology of this study consisted of three main steps (Figure 3) consisting of: (1) image analysis: geometric correction of Landsat images, preliminary analysis, and classification; (2) ground truth; (3) Cellular Automata Markov model (CA-Markov).

.

Figure 3 Flow chart of methodology Geometric correction

Preliminary analysis

Classification

Markov analysis

CA-Markov analysis

Projection of mangrove cover changes

Ground truth Multitemporal image

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9

Image Processing Analysis

The Data Processing was composed of five main steps (Figure 4) including: (1) geometric correction, (2) preliminary analysis, (3) image classification, (4) accuracy assesment, and (5) post classification.

Figure 4 Flow chart of image processing

Geometric Correction

The varying pixel sizes of the different images were changed into a common map grid based on a reference image/map. Evenly-distributed GCPs (Ground Control Points) were selected in different images and registered with the reference images/maps. A RMS (Root Mean Square) error of less than 0.5 pixels was

Yes

Land Use/Cover Map (1972,1993,2003,2013)

Geometric Correction GCP

Preliminary Analysis

Image Classification Ground Truth

Accuracy Assesment No

Landsat MSS,TM ,ETM+,8 (1972,1993,2003,2013)

Post Classification

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accepted for the transformation. Resampling was performed by converting different pixel sizes into the same final image pixel sizes.

Preliminary Analysis

After satellite images were geometrically corrected, preliminary analysis methods could be applied for image enhancement, composite RGB, and cropping. The land use/cover classes consisted of mangrove, aquaculture, dry land vegetation, settlement, and shallow water.

Image Classification

Image classification is a process of grouping all of the pixels into an image consisting of classes. Time series data Landsat imagery were used in this research to obtain information about actual land use from each time series. A classification process in this research was to determine the number of classes as representatives of the entities. Visual classification is a semi-automatic method using a screen digitizing technique. Detection performed on objects by making delineation of the outer limits of the group had the same color which differentiates objects from the others.

Accuracy Assessment

Accuracy test was used to make any calculation matrix for each error (confusion matrix) on any kind of habitat shallow water cover resulting from the analysis satellite imagery. The following is a table of confusion matrix form (Table 3).

Table 3Confusion matrix

Classified to class Reference (sample point) Row Total

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Table 3 helped make the transition of equation and mathematical notation easy to understand.

Post Classification

From these classification maps, changes in the extent of mangrove forests during the periods of 1972 to 1993, 1993 to 2003, 2003 to 2013, and 1972 to 2013 were examined to gain geographic understanding of the spatiotemporal evolution of land use in the region.

Mangrove Cover Projection

Markov Chain analysis

Marcov Chain. A markovian process is one in which the state of a system at time (t2) can be predicted by the state of the system at time (t1). In this research, where the result of image processing is land use/cover map in 1972, 1993, 2003, and 2013, Markov model would generate transition probability matrix and transition area matrix (Figure 5).

Data inputs used for Markov chain were two land use/cover maps derived from different times. Land use/cover map with an interval of 21 years, where land use/cover map of 1972 to 1993 was projected for 2003, was validated using land use/cover map in 2003. Land use/cover map with a 10-year interval, where land use/cover map of 1993 to 2003 was projected for 2013, was validated using land use/cover map of 2013. Land use/cover map with a 31-year interval, where land use map of 1972 to 2003 was projected for 2013, was validated using land use/cover map in 2013. The results of the third interval would be selected based on the level of accuracy for projection change of mangrove in 2023. The raster data with pixel 30x30 meter and 60x60 meter were applied for all variabels that were involved in the model.

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Multi Criteria Evaluation (MCE)

A suitability map shows the degree of suitability for a particular purpose at any location. It is most often produced from multiple images since most suitability problems incorporate multiple criteria. This type of Boolean suitability evaluation is often referred to as constraint mapping since each criterion is defined by a Boolean image indicating areas that are either suitable for use (value 1) or constrained from use (value 0).

Table 4 Criteria and categorization of factors for mangrove

Factor Value Category factors future state. The specific realization processes are as follow: (1) determination of conversion rules. By converting interpreted data from vector to raster, via a GIS overlay analysis, the transition probability matrix as well as transition and conditional probability image of mangrove types can be determined. (2) construction of CA filters. The cellular change of state is then determined using a 5x5 filter which means a rectangular space that is composed with one cellular section. This filter has a remarkable influence on the change in the cellular state. (3) determination of start time and iteration number.

According to the characteristics of the individual CA and Markov models, they can be combined with each other. The study area will be divided into several sections and each section is a cellular one. Each cellular section has a corresponding state and neighbors.

There are three data inputs for CA-Markov model (Figure 6): (1) basis land cover image that is land use map. (2) markov transition matrix areas that is loaded from transition area matrix. (3) transition suitability image collection that is collection of land suitability images. CA-Markov is nonlinear formula with the computarization was iterated and stop end kappa accuracy value.

Kappa accuracy

Kappa „ mathematical accuracy is :

∑ ∑

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Figure 6 Flow chart of CA-Markov model

3 RESULT AND DISCUSSION

Mangrove Cover Dynamics

Image Classification

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use/cover classes, i.e., mangrove, aquaculture, dry land vegetation, settlement, and shallow water. The classification result for the 1972, 1993, 2003, and 2013 can be seen in Figure 7.

Table 5 Areal estimates of major land use/cover types

Land Use/Cover Area (Ha) initial area). Areal estimates of major land use/cover for the four time periods are presented in Table 5. Clearing forests for farms on a large scale by investors from the mainland of Takalar district in cooperation with the local community in Tanakeke Island resulted in loss of mangrove. Tanakeke Island community has imposed a system of ownership convention on mangrove areas for trees used as firewood or charcoal. The result of the opening of the mangrove areas on a large scale is feared to threaten the sustainability of mangrove. Factors influencing loss of mangrove in Tanakeke Island from the anthropogenic are conversion of mangrove for aquaculture, clearing of mangrove trees for charcoal production on a large scale, and utilization of mangrove trees for domestic use.

From 1972 to 2013, mangrove in the Tanakeke Island decreased by 1,595.55 ha (63.91%). The rate of change, however, was not uniform from the 1972 to 1993, 1993 to 2003 and from 2003 to 2013. Deforestation rate during 1972 to 1993 was highest in Tanakeke Island; mangrove area decreased by 1.166.61 ha (46.73%), in 1993 to 2003; mangrove in Tanakeke Island decreased by 252.49 ha (18.98%), and from 2003 to 2013, mangrove was decreased by 176.45 ha (16.37%). Areal and percentage of land use/cover changes in Tanakeke Island are presented in Table 6.

Table 6 Areal and percentages of land use/cover changes

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Accuracy Assessment

The ground data may, however, represent another classification by the interpreter which may contain errors. Also such ground data do not correspond with the date of satellite data classified. The results of land use/cover classification were checked with an accuracy assessment method in order to obtain the level of map accuracy. As mentioned previously, the standard map accuracy was more than 70%. Quantitative assessment was performed using ground truth data stratified random sampling approach. An error matrix was generated with an overall classification accuracy of 87.78%. Producer‟s anduser‟s accuracy for mangrove was 93%. Generally, the difference between the classification results and reality in the field is caused by spatial resolution of satellite-derived data.

(a) (b)

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Figure 8 Actual condition adjusted to Landsat 8 images of 2013 in Tanakeke Island.

Post Classification

The mangrove cover change dynamics was studied over more than four decades, during the years 1972 to 2013. For change detection post classification techniques were used. This approach may have three sources of uncertainty: (1) semantic differences in class definitions between maps, (2) positional errors, and (3) classification errors. To minimize the semantic differences in class definitions the same number of classes for distribution change of land use/cover in 1972 to 1993, 1993 to 2003, 2003 to 2013 and 1972 to 2013 was used.

 Period of 1972 to 1993

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Table 7 Areal of land use/cover changes from the 1972 to 1993

Land Use/Cover in 1972

Land Use/Cover area in 1993 (Ha)

Mangrove Aquaculture Dry land vegetation Settlement Shallow water

Mangrove 1,042.95 1,386.74 - 2.86 64.11

Aquaculture - 35.77 - - -

Dry land vegetation - 74.37 769.10 - -

Settlement - - - 13.20 -

Shallow water 287.10 193.93 - 1.79 2,907.59

Major change areas were concentrated either in the outer periphery or near the shoreline caused by anthropogenic. Percentage of land use/cover changes from the 1972 to 1993 can be seen in Table 8 and spatial dynamics of mangrove cover from 1972 to 1993 can be seen in Figure 9.

Table 8 Percentage of land use/cover changes from the 1972 to 1993

Land Use/Cover in 1993 (%)

Mangrove 41.77 55.54 0.00 0.11 2.57 increased by 54.82 ha (1.84%) from shallow water (Table 9).

Mangrove 1,015.99 269.42 - 0.25 44.39

Aquaculture 6.75 1,684.05 - - -

Shallow water 54.82 4.60 - 0.29 2,911.99

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farming failed in some areas or shrimp ponds were used only in the short period. After few years, land has been degraded and farmers have continued to cut down mangrove forests and to make new shrimp ponds. Shrimp farmers cut down the mangrove forest to make new shrimp ponds.

Mangrove changing into shallow water occured near the shoreline in west of Tanakeke Island. Before 1995, shrimp farming was conducted in Tanakke Island, but after an outbreak of shrimp disease in the period from 1995 to 1997, land has been degraded and shrimp ponds have become bare-land and/or dry. There was mangrove in aquaculture area of 6.75 (0.40%) ha in the period 1993 to 2003 and an increase of mangrove by 54.82 ha from shallow water were caused by environmental factors. In the periods 1993 to 2003 there was no planting program of mangrove in Tanakeke Island.

Percent of land use/cover changes from the 1993 to 2003 can be seen in Table 10 and the spatial dynamics of mangrove cover from 1993 to 2003 can be seen Figure 9.

Table 10 Percentages of land use/cover changes from the 1993 to 2003

Mangrove 76.39 14.19 0.00 1.37 1.48

Between 2003 to 2013, mangrove loss by 61.43 ha (5.70%) to aquaculture, decreased by 122.91 ha (11.41%) to shallow water, and increased by 7.89 ha (0.27%) from shallow water (Table 11). Increased mangrove in 2003 to 2013 by 7.89 ha from shallow water was caused by environmental factors. With the some coverage of mangrove that has been lost, making the increase in the last 10 years of mangrove reduced compared with 1972 to 1993 and 1993 to 2003. There was a planting program of mangrove by Mangrove Action Project (MAP) NGO in 2010 but it was not successful.

Table 11 Areal of land use/cover changes from 2003 to 2013

Land Use/ Cover in 2003

Mangrove 893.22 61.42 - - 122.91

Aquaculture - 1,948.72 - - 14.89

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Percent of land use/cover changes from the 2003 to 2013 can be seen in Table 12 and spatial dynamics of mangrove cover from 2003 to 2013 can be seen in Figure 9.

Table 12 Percentages of land use/cover changes from the 2003 to 2013

Mangrove 82.89 5.70 0.00 0.00 11.41 aquaculture, decreased by 2.95 ha (0.12%) to settlement, decreased by 162.69 ha (6.52%) to shallow water, and increased by 246.44 ha (7.27%) from shallow water (Table 13).

Mangrove 654.67 1,676.35 - 2.95 162.69

Aquaculture - 35.77 - - -

Dry land vegetation - 137.95 705.51 - - Settlement - - - 13.20 -

Shallow water 246.44 218.11 - 2.57 2,923.28

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Table 14 Percentage of land use/cover changes from the 1972 to 2013

Mangrove 26.22 67.14 0.00 0.12 6.52

Aquaculture 0.00 100.00 0.00 0.00 0.00

Dry land vegetation 0.00 16.36 83.64 0.00 0.00

Settlement 0.00 0.00 0.00 100.00 0.00

Shallow water 7.27 6.43 0.00 0.08 86.22

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Mangrove Cover Projection

Probability map is a factor that sets the direction of change in surrounding cells as a function of cell conditions themselves and adjacent cells. Simulation of mangrove cover change using CA-Markov has been done by making the three components as inputs. These components are the basis of land cover image, Markov transition area, and transition suitability image collection. There were three time intervals used in this study comprising an interval of 30 years (1972 to 2003), an interval of 20 years (1972 to 1993), and an interval of 10 years (1993 to 2003). The third interval was used to determine the results of good accuracy to make projection in 2023. The cellular change of state is then determined 5x5 filter is used for three time intervals.The raster data with pixel 30x30 meter and 60x60 meter have been applied for all variabels that were involved in the model.

The calculation of transition probability and transition area matrix during 1972 to 1993, 1993 to 2003, 1972 to 2003, and 2003 to 2013 was done to determine the characteristics of the past and current land use. The transition area matrix has the number of cell values in columns by the number of cells. The matrix was produced by multiplication of each column in the transition probability matrix by the number of cells of corresponding land use in the next time period. Projection of mangrove cover change was carried out for 2003 (1972 to 1993), 2013 (1993 to 2003), 2013 (1972 to 2013), and 2023 (2003 to 2013).

Probability maps for mangrove cover change were one of the parameters aside from temporal data needed in this simulation (Figure 11). The transition rules resulting from biophysical factors contributed to mangrove cover change. Probability map was created based on evaluation of probability criteria for mangrove cover change in the study area using the Multi Criteria Evaluation. The criteria used were the tidal, settlement, and substrate. The criteria mentioned were developed and standardized as simple Boolean aggregation values (0 and 1) to find the most probable area. The result was the best location possible for mangrove cover change as interrelation among factors. This probability map is used for projection of mangrove cover change in the future.

Mangrove can grow in silty areas, especially in areas where silt accumulates. Figure 11, show areas that have a silty substrate and those that do not in Tanakeke Island, where the red color is silty (suitable for mangrove) and black color is not silty (not suitable for mangrove). The settlement distance plays an important role in changes of mangrove by cutting mangrove for new settlement. It was observed, and dynamics of settlement indicated that the area within the 50 meter distance from settlement was more suitable for mangrove. In this study, it was one of the drivers for change in mangrove.

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Figure 10 Tidal data of Tanakeke Island in 2014

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Interval of 10 years (1993 to 2003) for the projection in 2013

The past and current conditions of mangrove cover affect the future condition. Therefore, the measuring of mangrove cover changes in 1993 and 2003 (interval of 10 years) was done to predict mangrove cover change in 2013. The simulation result of mangrove cover change was compared with the actual mangrove cover in 2013 (map classification). Calculation results was obtained using iteration 35 and grid 30 by 86.87%, iteration 35 and grid 60 by 84.85% overall accuracy (Figure 12).

Figure 12 Interval of 10 years: (a) iteration for grid 30 m, (b) iteration for grid 60 m.

The value was good because the achievement was higher than the standard value of 75%. As suggested by Monsterud et al, (1992), the value of kappa 75% or greater showed a very good to excellent performance. Interval of 10 years with mangrove cover map in 2013 (Grid 30 m) and mangrove cover simulation map in

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2013 (Grid 30 m). Mangrove cover map in 2013 (Grid 60 m) and mangrove cover simulation map in 2013 (Grid 60 meter) are presented in Figure 13.

Figure 13 Interval of 10 years: (a) mangrove cover in 2013 (Grid 30 m), (b) mangrove cover simulation in 2013 (Grid 30 m), (c) mangrove cover in 2013 (Grid 60 m), (d) mangrove cover simulation in 2013 (Grid 60 meter).

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Interval of 20 years (1972 to 1993) for the projection in 2003

Mangrove cover changes in 1972 and 1993 (21 years interval) were made to predict mangrove cover change in 2003. The simulation result of mangrove cover change was then compared with the actual mangrove cover in 2003 (map classification). Calculation results was obtained using iteration 25 and grid 30 by 81.96%, iteration 25 and grid 60 by 79.89% overall accuracy (Figure 14). The value was good because the achievement was higher than the standard value of 75%.

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2003 (Grid 60 m) and mangrove cover simulation map in 2003 (Grid 60 meter) are presented in Figure 15.

(a) (b)

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Interval 31 years (1972 to 2003) for the projection in 2013

Mangrove cover changes in 1972 and 2003 (interval 31 years) were made to predict mangrove cover change in 2013. The simulation result of mangrove cover change was then compared with the actual mangrove cover in 2013 (map classification).

Figure 16 Interval of 31 years: (a) iteration for grid 30 m, (b) iteration for grid 60 m.

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Projection for mangrove cover in 2023

Based on the results of the projection by using different intervals, the data were obtained with higher accuracy when 10-year intervals with 35 iterations were used. In making a projection for the next 10 years (2023) an interval of 10 years (2003 to 2013) with iterations 35 was used. Mismatching among land use polygons due to different interpretations of land use definitions contributed to inaccuracies in the simulation of land use change. In addition, CA-Markov exhibited difficulty in change simulation due to marginal changes in this area over time. CA-Markov models have better modelling capability than general GIS in the simulation of settlement growth and land use changes. CA-Markov based models were used for studying temporal dynamics. Transition probabilities for the typical CA-Markov model depend on the state of a cell, the state of its surrounding cells, the physical characteristics of the cell such as soil, tidal, and settlement characteristics.

Table 18 Matrix kappa Index of Agreement (KIA)

Years Projection Iteration categories and combines the CA1 and Markov chain procedures (Eastman 2003). Markov analysis does not account for the causes of land use change, and it is insensitive to space. However, CA-Markov using the CA approach relaxes strict assumptions associated with the Markov approach and explicitly considers both spatial and temporal changes (Agarwal et al. 2002). The simulation model trend, mangrove cover change, is a major concern which continues to be broken from 2013 to 2023 as illustrated in Table 16. The total area of mangrove cover is predicted in 2023 to be 662.28 ha (a decreased of 238.83 ha) with using grid 30 m. Condition of mangrove cover in Tanakeke Island with mangrove cover in 2013 and projection mangrove cover in 2023 are presented in Figure 18.

Table 16 Mangrove cover change projection using a grid 30 m

Class Actual Grid 30

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Figure 18 Condition of mangrove cover in Tanakeke Island: (a) Mangrove cover in 2013, (b) Projection mangrove cover in 2023.

5 CONCLUSION AND RECOMMENDATION

Conclusion

The combination of RS and GIS techniques can help to identify the spatial dynamics of mangrove cover in Tanakeke Island. There was a large change in the net area of Tanakeke Island mangrove (approximately 63.91% loss) in the last 41 years (1972 to 2013). Deforestation rate during the period of 1972 to 1993 was the highest in Tanakeke Island, showing a decrease in mangrove area by 46.73%. Factors influencing loss of mangrove in Tanakeke Island from the anthropogenic are conversion of mangrove for aquaculture, clearing of mangrove trees for charcoal production on a large scale, and utilization of mangrove trees for domestic use. An increase of mangrove from shallow water and aquaculture in the last 41 years (1972 to 2013) was caused by environmental factors including silty substrate, current, tidal, and wavelength. Natural growth of mangrove have been carried out some places in Tanakeke Island, but were far from compensating losses by deforestation. An increase in the coverage of mangrove areas from 1972 to 1993 was the highest accounting for 8.47%.

Based on the CA-Markov model, the use of different time intervals, iterations and grid shows different accuracies. The 10-year interval obtained by

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By using visual interpretation results in a grid of 60 m, CA-Markov model can be used in projecting changes of mangrove in Tanakeke Island because it has a good accuracy. Development of aquaculture and charcoal in Tanakeke Island should be restricted to conserve mangrove for tourism development.

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