Spatial Multi Criteria Decision Making For Coastal Land Management (A Case Study in Maros, South Sulawesi)

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SPATIAL MULTI CRITERIA DECISION MAKING

FOR COASTAL LAND MANAGEMENT

(A Case Study in Maros, South Sulawesi)

FERRARI PINEM

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

2006

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SPATIAL MULTI CRITERIA DECISION MAKING

FOR COASTAL LAND MANAGEMENT

(A Case Study in Maros, South Sulawesi)

FERRARI PINEM

This Thesis submitted for the degree of Master of Science Of Bogor Agricultural University

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY

FOR NATURAL RESOURCES MANAGEMENT

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

2006

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STATEMENT

I, Ferrari Pinem, here by stated that this thesis entitled:

Spatial Multi Criteria Decision Making For Coastal Land Management (Case Study in Maros, South Sulawesi)

Are results of my own work during the period of January until July 2006 and that it has not been published before. The content of this thesis has been examined by the advising committee and the external examiner.

Bogor, August 2006

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ACKNOWLEDGEMENTS

First of all, I would like to express my gratitude to God, the Almighty for His Mercy, Favor, blazing me to carry out this task with sound health.

I wish to express my Earnest thanks and sincere appreciation to the National Coordinating Agency for Surveys and Mapping (Bakosurtanal), Indonesia for giving me the opportunity to continue my studies at Master of Science in Information Technology for Natural Resource management (MIT). Consequently, I am greatly indebted to all responsible persons from the Bakosurtanal for providing the financial support essential for overall expenses during the whole study period in MIT study program under Bogor Agricultural University, Indonesia. It was not possible for me to complete the research work without this financial providing.

I would like to express my deepest gratitude to Dr. Tania June, who has mainly supervised my research work during the whole period. I am also thanks to my Co-supervisor Dr. Anton B. Wijanarto. He always encouraged me and kindly provided me with invaluable advice. Moreover, I would like to express my profound gratitude to the external examiner Dr. Gatot H. Pramono, Bakosurtanal, who made my thesis acceptable with a lot of valuable discussions and comments on the final presentation day.

My express, my gratitude to all of responsible person from the Marine Natural Resources Center of Bakosurtanal for not only supporting necessary spatial data and non spatial data of study area Maros, South Sulawesi, Indonesia but also for their compassionate hands throughout my research period.

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I am obliged to many persons who have given me immeasurable encouragement and beneficial inspiration in realizing the completion of this work. Among them, Mr. Yuni Hartanta, Meity, Atty Rahadiaty and Mr. Sein min are the most supporting to give suggestions during my research time.

Most of all I would like to dedicate this thesis to my lovely mom in heaven, Jesus always with u, and to my father who provided patient, understanding, and encouragement during this course. Without their sincere support I never could have completed this thesis.

My final appreciation should go to my brother and my sister. They always encouraged me and gave me psychological support. Thank you very much indeed.

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CURRICULUM VITAE

The author, Ferrari Pinem, was born on February 13th, 1978 in Kabanjahe, Karo Township, Indonesia. He is the oldest son of P.Pinem and N. Meliala. As his education background, he was studied his basic education in Markus Height School, Medan Township, Indonesia and passed his Basic Education High School in 1996. He was allowed to study in Gadjah Mada University and he received B.Sc degree in Geographic in 2002. In 2004, he was selected as a state scholar to study Master of Science in Information Technology for Natural Resource Management in Bogor, Indonesia and he received M.Sc in IT for NRM degree in 2006.

As working experience, he entered Atlas Center, National Coordinating Agency for Surveys and Mapping (Bakosurtanal), Indonesia as staff officer since 2002. In 2003, he got opportunity from Bakosurtanal and Meteorology and Geophysics Agency (BMG) to become the coordinator of the project on Rainfall Atlas. He participated the Technical Training for the study on Cartography and Geographic Information System (GIS) in the Master Plan Agency of Pangkal Pinang as a lecturer hold at Pangkal Pinang local government, Indonesia by Bakosurtanal on July 2003.

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ABSTRACT

FERRARI PINEM (2006), Spatial Multi Criteria Decision Making For Coastal Land Management (A Case Study in Maros, South Sulawesi). Under the supervision of Dr. Tania June and Dr. Antonius B.Wijanarto.

Generally, master planning is created to determine areas that are suitable with its land characteristics and capability for specific uses. The process of land suitability classification is the evaluation and grouping of specific areas of land in terms of their suitability for a particular use. This is a complex process involving multiple decisions that may relate to biophysical, socio-economic and institutional/organizational aspects. Coastal management plan is created to achieve sustainable coastal resources uses, and coastal ecosystem protection from disturbance including pollution. It is not easy to maintain sustainable coastal resources, many problems were found to arrange it. One of coastal resources problems is conflict of space/land uses among stakeholders, population, and government in appropriate coastal development activities.

Integration of remote sensing, GIS and spatial multi criteria decision making were used to support coastal master planning for a sustainable coastal management. They are used for classify coastal zoning based on coastal land characteristics and spatial data building and focus to find particularly suitable area for some uses (aqua/ marine cultivation, fishing activities, and tourism in Maros), and identify conflicting area between land suitability and existing landuse and also to develop a coastal master planning using spatial multi criteria decision making for a sustainable coastal management.

Based on the result, Coastal zones in Maros were divided into 2 parts, they are buffer and uses zones, where each area have 343,07 km2 and 572,83 km2 respectively. It means that there is no conservation area, hence all areas is allowed for any uses by still considering environmental aspects. Mostly, fishpond exists in Maros coastal area, but based on land suitability analysis, this area can be converted into tourism area, where it was classified into 2 classes (suitable = 47.988 km2 and marginally suitable = 23.767 km2), especially in marine also showed that almost areas can support tourism activities, because characteristics of marine such as depth and brightness of water are supporting factors. In the land, there is conflict of interest between fishpond and resort tourism. Meanwhile conflict of interest among seaweed, kejapung, and fishing activities were found in marine area. Based on this conflict, analysis of policy scenario was needed.

Some assessment criteria (economic, sustainable and tourism aspects) have been chosen to determine the best policies/scenarios using multi criteria analysis, and the result showed that alt2b (alternative for tourism development) is the best scenarios for land alternatives, while alt3e (alternative for seaweed development) is the best for marine alternatives.

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Research Title : Spatial Multi Criteria Decision Making For Coastal Land Management (A Case Study in Maros, South Sulawesi)

Name : Ferrari Pinem

Student ID : G.051040151

Study Program : Master of Science in Information Technology for Natural Resources Management

Approved by, Advisory Board

Dr. Ir. Tania June, M.Sc Dr. Antonius Bambang Wijanarto

Supervisor Co-supervisor

Endorsed by,

Program Coordinator Dean of the Graduate School

Dr. Ir. Tania June, M.Sc Dr. Ir. Khairil A. Notodiputro, M.S

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Pages

STATEMENT ……….. i

ACKNOWLEDGEMENT ……… ii

CURRICULUM VITAE ……….. iv

ABSTRACT ……….. v

TABLE OF CONTENTS ………. vi

LIST OF TABLES .…. ..……….. ix

LIST OF FIGURES .……….……….. …. x

LIST OF APPENDIX ……… xi

I. INTRODUCTION 1.1. Background ….. ………. 1

1.2. Problem Definition .………. 4

1.3. Objective ……….………. 5

1.4. Output ……….. 5

II. LITERATURE REVIEW 2.1. Definition and Scope of Coastal Ecosystem ………. 6

2.1.1. Mangrove Ecosystem ………. 7

2.1.2. Coral Reef Ecosystem ……… 7

2.1.3. Seagrass Ecosystem ……… 8

2.2. Coastal Region Master Zoning and Planning ………. 9

2.3. Development of Marine and Coastal Resources.……….. 11

2.3.1. Marine Cultivation (Fishpond, Seaweed and Keramba Jaring Apung) ……….. 12

2.3.2. Fishing Resources Potencies ……….……….. 14

2.3.3. Tourism Potencies ……….. 15

2.4. Remote Sensing …...…. ……….…………. 15

2.5. Geographic Information System ……….. 18

2.6. The Role of GIS and Remote Sensing for Coastal Management .…… 20

2.7. Land Suitability Analysis ..……… 22

2.8. Multi Criteria Decision Making (MCDM) ……….. 22

2.9. Framework of Decision Making and SMCDM ……… 24

III. METHODOLOGY 3.1. Time and Location ……… 26

3.2. Equipment And Data Requirement ……… 26

3.2.1. Data Collection ……….. 26

3.2.2. Hardware, Software, And Equipment Used ……… 27

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3.3. Framework of Research ……..……….. 28

3.3.1.Framework of Coastal Ecosystem Development Decision Making 28 3.3.2. Data Compilation ..……….. 28

3.3.3. Image Preprocessing ……….. 28

3.4. Field Checking ..………. 31

3.5. Data Analysis ……… 31

3.5.1. Physical and Chemical Variables ……….……… 31

3.5.2. Social, Economic and Ecology ……… 32

3.6. Determining Coastal Ecosystem Zones ……… 32

3.7. Coastal Ecosystem Development ..………. 34

3.7.1. Resort Tourism Area ……… 34

3.7.2. Marine or Aqua Cultivation ……… 34

3.7.3. Fishing Activities ………. 37

3.8. Land Suitability Analysis ……….. 37

3.9. Weighting and Scoring /Land Suitability Classes ………. 38

3.10. Framework of Coastal Development Decision Making ……….. 39

3.10.1. Landuse Priority Analysis ………. 39

3.10.2. Areas of Conflict ……….. 40

3.10.3. Formulation of Policy Alternatives .……… 40

3.10.4. Comparison of Alternatives ……….. 42

3.10.5. Selection of the Evaluation Criteria ………. 43

IV. RESULT AND DISCUSSION 4.1. Digital Image Processing ……….…………. 45

4.1.1. Radiometric Correction ……….. 45

4.1.2. Geometric Correction ………. 45

4.2. Characteristic Of Coastal Region ……….. 47

4.2.1. Land Characteristic ………. 47

4.2.1.1. Topography ……… 47

4.2.1.2. Geology ……… 47

4.2.1.3. Landform ……….. 51

4.2.1.4. Soil ……… 51

2.2.1.7. Landuse ……… 52

4.2.2. Hydro-Oceanography Characteristic ……… 53

4.2.2.1. Physical Hydro-Oceanography ……… 53

4.2.2.2. Chemical Hydro-Oceanography ……….. 63

4.3. Coastal Ecosystem .………. 67

4.4. Potencies of Domestic Resources ……… 68

4.5. Coastal Ecosystem Zoning ……… 70

4.6. Land Suitability Analysis .……….. 72

4.6.1. Land Suitability for Tourism .………. 72

4.6.2. Land Suitability for Fishpond …….……….. 73

4.6.3. Land Suitability for Seaweed ……… 76

4.6.4. Land Suitability for Kejapung ……… 77

4.6.5. Land Suitability for Fishing Activities ……….. 80

4.7. Priority of Coastal Development …….……… 81

4.7.1. Land Conflict ………. 81

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SPATIAL MULTI CRITERIA DECISION MAKING

FOR COASTAL LAND MANAGEMENT

(A Case Study in Maros, South Sulawesi)

FERRARI PINEM

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

2006

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SPATIAL MULTI CRITERIA DECISION MAKING

FOR COASTAL LAND MANAGEMENT

(A Case Study in Maros, South Sulawesi)

FERRARI PINEM

This Thesis submitted for the degree of Master of Science Of Bogor Agricultural University

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY

FOR NATURAL RESOURCES MANAGEMENT

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

2006

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STATEMENT

I, Ferrari Pinem, here by stated that this thesis entitled:

Spatial Multi Criteria Decision Making For Coastal Land Management (Case Study in Maros, South Sulawesi)

Bogor, August 2006

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ACKNOWLEDGEMENTS

First of all, I would like to express my gratitude to God, the Almighty for His Mercy, Favor, blazing me to carry out this task with sound health.

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My final appreciation should go to my brother and my sister. They always encouraged me and gave me psychological support. Thank you very much indeed.

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CURRICULUM VITAE

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ABSTRACT

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Research Title : Spatial Multi Criteria Decision Making For Coastal Land Management (A Case Study in Maros, South Sulawesi)

Name : Ferrari Pinem

Student ID : G.051040151

Study Program : Master of Science in Information Technology for Natural Resources Management

Approved by, Advisory Board

Dr. Ir. Tania June, M.Sc Dr. Antonius Bambang Wijanarto

Supervisor Co-supervisor

Endorsed by,

Program Coordinator Dean of the Graduate School

Dr. Ir. Tania June, M.Sc Dr. Ir. Khairil A. Notodiputro, M.S

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Pages

STATEMENT ……….. i

ACKNOWLEDGEMENT ……… ii

CURRICULUM VITAE ……….. iv

ABSTRACT ……….. v

TABLE OF CONTENTS ………. vi

LIST OF TABLES .…. ..……….. ix

LIST OF FIGURES .……….……….. …. x

LIST OF APPENDIX ……… xi

I. INTRODUCTION 1.1. Background ….. ………. 1

1.2. Problem Definition .………. 4

1.3. Objective ……….………. 5

1.4. Output ……….. 5

II. LITERATURE REVIEW 2.1. Definition and Scope of Coastal Ecosystem ………. 6

2.1.1. Mangrove Ecosystem ………. 7

2.1.2. Coral Reef Ecosystem ……… 7

2.1.3. Seagrass Ecosystem ……… 8

2.2. Coastal Region Master Zoning and Planning ………. 9

2.3. Development of Marine and Coastal Resources.……….. 11

2.3.1. Marine Cultivation (Fishpond, Seaweed and Keramba Jaring Apung) ……….. 12

2.3.2. Fishing Resources Potencies ……….……….. 14

2.3.3. Tourism Potencies ……….. 15

2.4. Remote Sensing …...…. ……….…………. 15

2.5. Geographic Information System ……….. 18

2.6. The Role of GIS and Remote Sensing for Coastal Management .…… 20

2.7. Land Suitability Analysis ..……… 22

2.8. Multi Criteria Decision Making (MCDM) ……….. 22

2.9. Framework of Decision Making and SMCDM ……… 24

III. METHODOLOGY 3.1. Time and Location ……… 26

3.2. Equipment And Data Requirement ……… 26

3.2.1. Data Collection ……….. 26

3.2.2. Hardware, Software, And Equipment Used ……… 27

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3.3. Framework of Research ……..……….. 28

3.3.1.Framework of Coastal Ecosystem Development Decision Making 28 3.3.2. Data Compilation ..……….. 28

3.3.3. Image Preprocessing ……….. 28

3.4. Field Checking ..………. 31

3.5. Data Analysis ……… 31

3.5.1. Physical and Chemical Variables ……….……… 31

3.5.2. Social, Economic and Ecology ……… 32

3.6. Determining Coastal Ecosystem Zones ……… 32

3.7. Coastal Ecosystem Development ..………. 34

3.7.1. Resort Tourism Area ……… 34

3.7.2. Marine or Aqua Cultivation ……… 34

3.7.3. Fishing Activities ………. 37

3.8. Land Suitability Analysis ……….. 37

3.9. Weighting and Scoring /Land Suitability Classes ………. 38

3.10. Framework of Coastal Development Decision Making ……….. 39

3.10.1. Landuse Priority Analysis ………. 39

3.10.2. Areas of Conflict ……….. 40

3.10.3. Formulation of Policy Alternatives .……… 40

3.10.4. Comparison of Alternatives ……….. 42

3.10.5. Selection of the Evaluation Criteria ………. 43

IV. RESULT AND DISCUSSION 4.1. Digital Image Processing ……….…………. 45

4.1.1. Radiometric Correction ……….. 45

4.1.2. Geometric Correction ………. 45

4.2. Characteristic Of Coastal Region ……….. 47

4.2.1. Land Characteristic ………. 47

4.2.1.1. Topography ……… 47

4.2.1.2. Geology ……… 47

4.2.1.3. Landform ……….. 51

4.2.1.4. Soil ……… 51

2.2.1.7. Landuse ……… 52

4.2.2. Hydro-Oceanography Characteristic ……… 53

4.2.2.1. Physical Hydro-Oceanography ……… 53

4.2.2.2. Chemical Hydro-Oceanography ……….. 63

4.3. Coastal Ecosystem .………. 67

4.4. Potencies of Domestic Resources ……… 68

4.5. Coastal Ecosystem Zoning ……… 70

4.6. Land Suitability Analysis .……….. 72

4.6.1. Land Suitability for Tourism .………. 72

4.6.2. Land Suitability for Fishpond …….……….. 73

4.6.3. Land Suitability for Seaweed ……… 76

4.6.4. Land Suitability for Kejapung ……… 77

4.6.5. Land Suitability for Fishing Activities ……….. 80

4.7. Priority of Coastal Development …….……… 81

4.7.1. Land Conflict ………. 81

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4.7.3. Formulation of Policy Alternatives ……….. 83 4.7.4. Comparison of Alternatives Using MCA ……….. 89 4.7.5. Recommended Coastal Landuse Planning ……….. 95

V. CONCLUSION AND RECOMMENDATION

5.1. Conclusion ……… 97 5.2. Recommendation……… 98

REFERENCES ……… 99-103

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LIST OF TABLES

2.1. Matrix of suitability for fishpond cultivation …..……….. 13 2.2. Matrix of suitability for seaweed cultivation. ………. 13 2.3. Matrix of suitability for keramba jaring apung cultivation ……… 14 2.4. Landsat TM spectral electromagnetic ………. 16 2.5. Landsat TM electromagnetic for specific application ……… 17 3.1. List of hardware, software and equipment ………. 27 3.2. Parameter, mapping unit and scoring variables .……… 32 3.3. Weighting and scoring of zone classification ……… 34 3.4. Matrix of suitability for resort tourism activities ……….. 35 3.5. Matrix of suitability for keramba jaring apung ……….. 35 3.6. Matrix of suitability for fishpond cultivation .……….. 36 3.7. Matrix of suitability for seaweed cultivation ………. 36 3.8. Matrix of suitability for fishing activities ……….. 37

3.9. Policy alternatives ……… 41

3.10. Assessment criteria ……… 43 4.1. Radiometric correction on digital imagery data ………. 45 4.2. Ground control point for geometric correction ……….. 46 4.3. Coastal ecosystem zone ………. 72 4.4. Land suitability analysis for tourism ………. 72 4.5. Land suitability analysis for fishpond ……….. 73 4.6. Land suitability analysis for seaweed ……… 76 4.7. Land suitability analysis for kejapung ……….. 77 4.8. Conflict matrix for fishpond and tourism suitability ………. 82 4.9. Conflict matrix for seaweed, kejapung and fishing activities suitability ….. 83 4.10. Present landuse in diferent suitability units for resort tourism and fishpond 84 4.11. Area of each land alternatives map ………. 89 4.12. Area of each marine alternatives map ……… 89

4.13. Assessment Criteria ……… 90

4.14. Effect table for land alternatives ……… 90 4.15. Effect table for marine alternatives ………. 91 4.16. Priority ranking of effect of policy alternatives ……….. 92 4.17. Ranking of land alternatives per policy schemes ……….... 92 4.18. Ranking of marine alternatives per policy schemes ………. 92

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LIST OF FIGURES

2.1. Coastal Zone Boundary ………. 6 2.2. System Diagram for GIS Illustration ……….. 19 2.3. Integration of MCDM and GIS into Spatial MCDM ………. 25 3.1. Maros Location . ………. 26 3.2. Flowchart of Research ……… 29 3.3. Image Preprocessing ………. 30 3.4. Land Suitability Processing ………. 38 3.5. Determining Policy Alternative Maps ………. 42 3.6. MCA Flow ………... 44 4.1. Landsat TM of Maros before and after Correction ………. 46 4.2. Maros Topographic Map ……… 47

4.3. Maros Slope Map ……….. 48

4.4. Maros Geology Map ………. 49

4.5. Landform Map ………. 50

4.6. Soil Map ……… 52

4.7. Landuse Map ……… 55

4.8. Sample Point Map ……… 56

4.9. Sechi Disk Depth Map ………. 57

4.10. Sechi Disk Depth Map (%) ………... 58 4.11. Sea Surface Temperature Map ……… 60

4.12. Current Velocity map ………. 61

4.13. Bathymetric Map ……….. 62

4.14. Turbidity Map ………. 64

4.15. Dissolved Oxygen Map ……… 65 4.16. Potential of Hydrogen Map ……….. 66 4.17. Fishing Productivity in South Sulawesi 1998 – 2002 ……….. 70

4.18. Zonation Map ………. 71

4.19. Land Suitability for Tourism Map ……….. 74 4.20. Land Suitability for Fishpond Map ………. 75 4.21. Land Suitability for Seaweed Cultivation Map ………... 78 4.22. Land Suitability for Kejapung Map ……….. 79 4.23. Conflict Tourism Vs Fishpond Suitability Map ………. 82 4.24. Conflict Seaweed Vs Kejapung Suitability Map ……….. 83 4.25. Alternatives Map ………...… 85 4.26. Land Alternatives Ranking ………. 93 4.27. Marine Alternatives Ranking ………. 93 4.28. Coastal Landuse Planning Map ……… 96

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LIST OF APPENDIX

Appendix-1: Sample Points ……… 104

Appendix-2: Effect Table for Each Alternatives ……… 105 Appendix-3: Product of Seaweed and “Kejapung” Cultivations Analysis ….. 108

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I. INTRODUCTION

1.1. Background

Indonesia is the largest archipelagic country in the world, in which two third of its area is covered by water, and the length of its coastline is approximately 81,000 km that makes Indonesia becoming one of the longest coastline countries in the world. Furthermore, coastal region is the center of biodiversity, which it has resource potencies to be developed. Development of coastal resources is supported by ecosystems with high biological productivity such as: coral reef, seagrass, seaweed, mangrove, etc. At least, about 30 % of total world mangrove forest and 18 % of total world coral reef exist in Indonesia.

The existing coastal resources are very important for Indonesian, because more than 60 % or about 140 million of them live within 50 km from coastline. The effect of excessive utilization could disturb the integrity of coastal ecosystem. Therefore, a good coastal management is needed by considering its carrying capacity to maintain the sustainability of the existing biological conditions.

The Act (UU) no. 24/1992 of Republic of Indonesian about master planning, states that master planning should consider synchronization, harmonization, and balancing of cultivation and protection, time, technology, socio-culture, and of its defense and security functions. A good coastal management will benefit both the socio-economic and ecological aspects.

Maros covers an area of approximately 1,613.11 km2 that consists of 14 districts. Referring to administrative boundary, in northern site is Pangkajene region, in southern side is Makassar region, in eastern side is Bone region and western side is Makasar ocean.

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Based on the master planning that has been compiled by local government, the southern part of Maros will become a conservation area with mangrove as priority landuse. While, the northern part (research area) will become tourism area, which is supported by marine and socio conditions.

The plan of coastal management needs data and information about coastal natural resources. The information needs to display as numeric or spatial data, hence it can be used for planning and decision making quickly and accurately.

There are coastal management and master planning that have been produced by the Ministry of Marine and Fisheries Affairs to fulfill the requirement of coastal region with protection aspects. This management is expected to support life system and sustainable biodiversity, which will give some advantages, such as: (1) sustainable biodiversity productivity, and (2) increasing job opportunity, economic and region development.

Data inventory for coastal ecosystem master planning may be time consuming and expensive. One alternative is by using remote sensing (RS) techniques. This technique offers possibilities to collect information over larger areas but requiring only few numbers of sample points.

Many kinds of remote sensing imagery can be used for inventory of coastal resources. One of them is Landsat TM imagery, which has several spectral combinations that make easier for objects identification. The use of remote sensing technology is also possible for monitoring coastal ecosystem conditions. It means that remote sensing imagery (Landsat TM) can be used to detect coastal ecosystem condition at different time, due to its capability to record a data of same place in every 16 days.

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Geographic Information System (GIS) can support spatial analysis, which is often combined with remote sensing technique. The integration of remote sensing and GIS application has long been used in spatial analysis and proven effective and efficient. GIS has the capability to gather and analyze data that are derived from remote sensing results with other data including field data and secondary data as input into GIS spatial analysis, as well as developing coastal master planning.

Some of spatial analyses have often been used for coastal master planning such as for determining coastal/marine zones and landuse planning. Coastal/marine zoning is locating regions that have similar characteristics and requirements into area suitability zones for specific purpose, which can be used for controlling and protecting coastal/marine regions. Meanwhile, coastal landuse planning considers land capability for specific purposes to maintain sustainable environment.

Generally, master planning is created to determine areas that are suitable with its land characteristics and capability for specific uses. The process of land suitability classification is the evaluation and grouping of specific areas of land in terms of their suitability for a particular use. This is a complex process involving multiple decisions that may relate to biophysical, socio-economic and institutional/organizational aspects. Therefore, multi criteria decision-making is needed to help coastal master planning development. Integration of spatial analysis and multi-criteria decision-making is expected to provide better coastal master planning.

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1.2. Problem Definition

Coastal management plan is created to achieve sustainable coastal resources uses, and coastal ecosystem protection from disturbance including pollution. It is not easy to maintain sustainable coastal resources, due to many conflicting interests among stakeholders, population, and government in appropriate coastal development activities. Hence, recommendation of a master planning has to consider local government policy, population needs, and aspects of economic, ecology, and socio-culture. It is a complex process involving multiple decisions that may relate to biophysical, socio-economic and institutional/organizational aspects.

Remote sensing and Geographical Information System (GIS) have often been used in coastal master planning that have related to zoning and land suitability analysis. Usage of these technologies is one of solutions to answer the challenge of coastal data base uses.

Integration of remote sensing, GIS and spatial multi criteria decision making were used to support coastal master planning for a sustainable coastal management. This thesis will answer the following questions:

a. How to acquire physical and non-physical land characteristic to determine zonation of coastal ecosystem using remote sensing techniques,

b. How to develop coastal master planning based on coastal ecosystem zonation and land evaluation (its suitability) using GIS analysis, and

c. How to give the best solution alternatives to coastal master planning development using spatial multi criteria decision-making for a sustainable coastal management.

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1.3. Objectives

Based on the questions above, research objectives are the following:

a. to classify coastal zone as a first step to create coastal master planning based on coastal land characteristics,

b. to implement remote sensing and GIS techniques for spatial data building and to find particularly suitable area for particular uses (aqua/ marine cultivation, fishing activities, and tourism in Maros) by identifying coastal resources capacities and its land suitability,

c. to identify conflicting area between its suitability and existing landuse, d. to develop a coastal master planning using spatial multi criteria decision

making for a sustainable coastal management.

1.4. Output

The expected outputs for this thesis are:

a. Thematic maps of coastal resources such as coral reef distribution, sea surface temperature, land-use, salinity, water depth, current velocity, slope, turbidity, and potential of hydrogen.

b. Coastal ecosystem zonation map.

c. Land suitability maps for tourism, marine/aqua culture and fishing activities by using spatial analysis.

d. Recommended maps that will be used for coastal master planning in Maros.

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II. LITERATUR REVIEW

2.1. Definition and Scope of Coastal Ecosystem

“The term coastal zone means the coastal waters (including the land therein and thereunder) and the adjacent shoreland (including the waters therein and thereunder), strongly influenced by each and in proximity to the shorelines of the several coastal states, and includes islands, transitional and inter tidal areas, salt marshes, wetlands, and beaches” (Doydee, 2002). Coastal zone boundary is presented in Fig 2.1.

Fig 2.1. Coatal Zone Boundary (Puvadol Doyde, 2002)

The definition of coastal is based on 3 approaches, they are: ecology, administrative and planning approaches. Based on ecological aspect, coastal region is area that it is still influenced by marine and land processes. While, based on administrative aspect, coastal as government administrative where area toward marine is 12 mile from coastal line for province, and 1/3 from ½ mile from region/ city. Based on the planning aspect, coastal management is planning and arranging area and it is focused on handling issue as responsibility (Haris, 2003).

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Coastal ecosystem can be divided into 3 main ecosystems, namely: mangrove, coral reef and seagrass.

2.1.1. Mangrove Ecosystem

Mangrove ecosystem is a peculiar habitat because it is found on the boundary of land and sea. This ecosystem is important coastal natural resources because it supports other animal life. Change of mangrove ecosystem will bring impact on other ecosystem such as coral and seagrass ecosystems. Based on existing potencies and its protection function, mangrove ecosystem is very supporting sustainable coastal resources ecosystem.

The complexity of the mangrove ecosystem in Indonesia is different from one area to another place, depending on the coastal physiography and the tidal on which along with the straight coastal area, the mangrove growth in relatively narrow, 25 to 50 m, while in the deltas where the river flows bring some materials, such as mud and sands, the mangrove can growth very well and spread out widely throughout the coast. Basically, the zonation in mangrove forest is grouped naturally based topographical condition, tidal frequency, stability, sedimentation, water/soil salinity, etc (Moosa et al, 1996)

2.1.2. Coral Reef Ecosystem

Coral reef is specific habitat that provides shelter, food and breeding sites for numerous plants and animals. Coral reef development occurs only in areas with specific environmental characteristic: a solid structure for the base; warm and predictable water temperatures; oceanic salinities, clear, transparent waters low in

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phosphate and nitrogen nutrients, and moderate wave action to disperse wastes and bring oxygen and plankton to the reef (Halili, 2001).

Physically, coral reef is important material for protecting an island. In some conditions, coral reef can protect coastal such as erosion hazard, current and wave. For biological resources, coral reef ecosystem can generate many kinds of species such as fish, alga, mollusks, pearl, etc. Meanwhile, the role of coral reef for aesthetics value, it is can offer beautiful view (Nontji, 1987).

2.1.3. Seagrass Ecosystem

Seagrass is an ecosystem that grows by marine flowering plants species (called seagrass). This ecosystem is very important component of coastal ecosystem along tropical, temperate and subartic coasts. It has already been found on lowest seawater level until light sun penetrate depth. Seagrass ecosystem has a very important role in coastal ecosystem because of its location between mangrove and coral reef ecosystems, which has function to export important nutrients to surrounding ecosystems.

The most important area for seagrass is the lower intertidal and upper subtidal zone, where the complex vegetation may occur in which 7 – 8 species grow together (Moosa et al, 1996). Furthermore, seagrass can interact with other ecosystems through several mechanisms. Seagrass beds are important to finfish communities worldwide on which in the tropics, many nearshore and offshore fisheries are closely linked to seagrass and to adjacent mangrove and coral reef as well. And also provide major habitat and food sources for a large variety of finfish, crustaceans, mollusks, marine reptiles and mammals.

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2.2. Coastal Region Master Zoning and Planning

Zone is a region that has similar characteristics of physic, biology, ecology, and economic, that are determined by criteria selected (Haris, 2003). Coastal and marine zoning are allocating some of coastal and marine regions that have similar characteristics and requirements into suitability zones for its purpose, so protection or uses of coastal and marine regions can be controlled. This zoning is created to make harmonious space, meaning that coastal and marine regions should not be for development area only, but also should provide zone for preservation and conservation.

Coastal and marine zones must focus and consider some aspects;

suitability area to support productivity or behavior of life; government policy

related with land authority and development priority; current land-use pattern,

that means allocating legal land-use or existing of traditional pattern; and local culture. Therefore coastal and marine zoning needs to identify regions that have similar characteristics and requirements of its uses to determine sustainable natural resources management planning.

The making of coastal and marine zones is one of alternatives to achieve sustainable coastal and marine management by divided coastal land-uses in to some zones, such as: preservation zone, there are no exploration activities except research, but still possible to developed by observing sustainable development requirements (Odum, 1989). Buffer/ cultivation zone, is a zone decided as main function for cultivation of natural resources, human, and artificial resources potencies. That region part has cultivation function (dominated by settlement and working activities) considering the environment caring capacity (Sugandhy,

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1993). Uses zone, every activities can be performed by considering of the spatial rules to avoid overlaying between activities and coastal and marine development. Arranging and management of coastal and marine space that followed by government rules.

Based on coastal zone classification that has already been done by Soedharma et al (1992), this research will divide coastal and marine zones into 4 parts, they are: preservation zone, conservation, buffer/ cultivation zone, and uses zone (related with suitability zone), which distribution of zone based on output map scale (1 : 50.000).

Master plan is physical environment in one region that has functional relationship with some space elements that include natural resources, artificial resources, human with their activities such as: politic, economic, socio-culture and safety and defense aspects (Kartasasmita, 1996).

Coastal master plan can be performed by classifying land uses into homogenous units considering similarity of physic, biotic, socio, culture, economic, defense and safety ascpects (Dahuri et al, 1996). Coastal master planning have been attempted optimally and efficiently for space arrangement for human life, such as; local sector development, and society to achieve prosperous collectively (Martopo, 1987).

Master plan on coastal ecosystem is more complex than master plan on the land, because (a) it should consider all aspects that interact each other both in land and marine ecosystems, (b) marine and land aspects are unable to be separated physically, because both ecosystems interact each other, (c) Landform (geomorphology) of coastal ecosystem is easier to change rather than landform of

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land ecosystem, as it is a dynamic interaction between land and marine (Dahuri, 1997).

2.3. Developmentof Marine and Coastal Resources

Generally, existing coastal natural resources are divided into renewable

resources, un-renewable resources, and environmental resources. Renewable

resources consist of fish, plankton, seaweed, seagrass, mangrove, coral reef, etc. While, unrenewable resources consist of oil, mining material (iron, sand, tin, and other mining materials). Environmental resources consist of marine transportation and tourism.

Darmawan (2000) argued that generally, coastal management is focused to achieve 2 goals. Firstly, development of coastal natural resources optimally gives contributions for national economic and prosperous marine developer. Secondly, it is to maintain coastal and marine natural resources and sustainable environment. Both goals can be achieved by a good coastal development planning as well as imposing regulation and monitoring.

Integration of coastal planning and management is caused by existing of (1) some conflicts of land uses, (2) importance conflicts among some government and private institutions. These conflicts are caused by some factors, such as (a) competition at coastal spaces, (b) effect from some developing activities toward another activities (oil waste toward fishing activities), and (c) impact of developing activities toward ecosystem (Cicin Sains and Knecht, 1998).

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2.3.1. Marine Cultivation (Fishpond, Seaweed and Keramba Jaring Apung)

Coastal area can be divided in two main sub area based on its physical characteristics. They are tidal area, which is usually dominated by mangrove and fishpond, and beach land, which is located on higher elevation than tidal area. Beach land is usually used for agriculture field, such as coconut plantation and rice field (Purnomo 1998). This area has often been used for many kinds of coastal activities such as marine cultivation, tourism and fishing activities.

One of national incomes comes from fishing subsectors. Recently, the government has been developing some efforts to increase fish productivity through fishpond cultivations. In Indonesia, fishpond cultivation have been known long time ago in Indonesia, and more popular in 1980 – 1990 as marine cultivation, which can be performed in traditional, semi intensive, and intensive ways (Buwono, 1993).

There are some aspects that have to be considered in developing an area for fishpond cultivation, such as its location from coastline as well as from river, the slope, soil type and landform. Bakosurtanal (National Coordinating Agency for Surveys and Mapping) has developed some requirement for suitable area for fishpond cultivation as can be seen in Table 2.1.

In Indonesia, seaweed cultivation has been introduced by the European. Seaweed is a commercial name of algae species that is harvested from marine, (Nontji, 1993). There are many uses of this cultivation, such as: organic fertilizer, standard material of food and cosmetic, and medicine. Seaweed that has high economic value is often developed, for example, Eucheuma sp, Gracilliria sp, Gelidium sp, and Sargasum Sp.

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Table 2.1. Matrix of suitability for Fishpond Cultivation

Categorize and scoring No Variable weighting

S1 (Highly Suitable)

Score S2 (Suitable)

Score S3 (marginally

Suitable)

Score N (Not Suitable)

Score

1. Distance from coastline (km)

4 0 – 5 4 > 5 – 6 3 > 6 – 7 2 > 7 1

2. Distance from river (m)

3 0 – 500 4 > 500 – 800 3 > 800 – 1100 2 > 1100 1

3. Slope (%) 4 0 – 2 4 > 2 – 3 3 - 2 > 3% 1 4. Litho logy 4 Qac 4 Temt 3 Tme 2 Tmet, Tmcv 1 5. Soil type 4 Entisol,

inceptisol 4 Entisol, inceptisol 3 Entisol, inceptisol, Ultisol

2 Oxisol 1

6. Landform 3 F7, F11, M4, M10, M14

4 F5, F12, F13 3 D4, K3 2 D1, D3, D2, D15, S1, S2,

V15 1

7. Land use 3 A 4 B 3 2 C 1

Source: Bakosurtanal, 2004

To obtain optimal seaweed productivity location should be selected that is suitable with its eco-biology (grown requirements). More detail about seaweed growth requirements can be seen in Table 2.2.

Fish cultivation can be also performed by traditional method, namely keramba jaring apung, which has been developed long time ago in Indonesia. It is one of the cultivation techniques being developed to avoid coral reef disturbance (Subandar, 2003).

Table 2.2. Matrix of suitability for Seaweed Cultivation

Categorize and scoring No Variable weighting

S1 (Highly Suitable)

Score S2 (Suitable)

Score S3 (marginally

Suitable)

Score N (Not Suitable)

Score

1. Current Velocity (m/sec)

4 10 - 14 4 5 - < 10 3 3 – 5 2 < 3 and > 14

2. Depth water (m)

4 8 – 12 4 > 12 – 16 3 4 - < 8 > 16 – 20

2 < 4 > 20

3. Sechi Disk Dept (%)

4 > 75 4 50 – 75 3 25 - < 50 2 < 25 1

4. PH 3 7,6 – 8,3 4 7 - < 7,6 > 8,3 – 8,5

3 6,5 - < 7 > 8,5 – 9

2 < 6,5 > 9

5. Oxygen Demand

4 > 6 4 > 5 – 6 3 > 4 – 5 2 < 4 1

6. Salinity 4 31 – 33 4 28 - < 31 > 33 – 35

3 25 - < 28 > 35 – 36

2 < 25 > 36

7. Surface temperature

4 25 – 31 4 23 – 25 > 31 – 33

3 20 - < 23 > 33 – 36

2 < 20 > 36

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One example for determining appropriate location of “keramba jaring apung” have been done by MCMA (Marine Coastal of Management Area) surveys team in South Sulawesi (1995) in which “keramba jarring apung” can be found both in southern and western of South Sulawesi.

Selected location for “keramba jaring apung” must follow environment criteria for cultivation, which will determine cultivation successfulness. Some requirements such as current velocity, depth water, PH, oxygen demand, salinity and temperature should fulfill the requirements for “keramba jaring apung”. More details about the requirement for “keramba jaring apung” developed by Bakosurtanal are shown in Table 2.3.

Table 2.3. Matrix for suitability of Keramba Jaring Apung (kajapung)

Categorize and scoring

Variable weighting

S1 (Highly Suitable)

Score S2 (Suitable)

Score S3 (marginally

Suitable)

Score N (Not Suitable)

Score

1. Current Velocity (m/sec)

4 10 - 14 4 5 - < 10 3 3 – 5 2 < 3 and > 14

2. Depth water (m)

4 8 – 12 4 > 12 – 16 3 4 - < 8 > 16 – 20

2 < 4 > 20

3. Sechi Disk Dept (%)

4 > 75 4 50 – 75 3 25 - < 50 2 < 25 1

4. PH 3 7,6 – 8,3 4 7 - < 7,6 > 8,3 – 8,5

3 6,5 - < 7 > 8,5 – 9

2 < 6,5 > 9

5. Oxygen Demand

4 > 6 4 > 5 – 6 3 > 4 – 5 2 < 4 1

6. Salinity 4 31 – 33 4 28 - < 31 > 33 – 35

3 25 - < 28 > 35 – 36

2 < 25 > 36

7. Surface temperature

4 25 – 31 4 23 – 25 > 31 – 33

3 20 - < 23 > 33 – 36

2 < 20 > 36

Source : Bakosurtanal, 2004

2.3.2. Fishing Resources Potencies

The health of fish is affected by variations and extremes in the characteristics of the water, in which they live, such as PH, salinity, tidal wave, sea surface temperature, oxygen demand, etc.

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According to Nybaken (1992), about 90 % of the world’s fisheries products come from the sea area (2 – 3 %), most of them identified as upwelling areas. Fishing activities do not require specifically the criteria of determining land suitability area. In fact, most marine areas can be used for fishing activities area. This is the last alternative to be done.

2.3.3. Tourism Potencies

Basically, marine tourism development is aimed at using marine objects such as coastal natural resources, flora and fauna diversity to increase local advantages. Some of areas in Maros has good coastal landform and high natural resources potencies. Therefore, this area is able to be a product of marine tourism development. Kusumastanto (2002) states some of marine tourism products can be developed in coastal areas such as; beach, business, resort, sport and culture tourisms.

Tourism development will bring direct effect to local people and government. The goal of tourism development is to promote an environmental friendly business and to generate local income. Such development must also protect cultural values and the rights of local people.

2.4. Remote Sensing

Remote sensing is the science and art to obtain information about object, area or phenomenon through the analysis of data acquired by a device without contact with the object, area, or phenomenon under investigation (Lilesand and Kiefer 1994). Satellite data are remotely obtained based on concept of the

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interaction between electromagnetic radiation and the objects within various ranges of spectrum or bands depending on the sensors (Dowreand and Disbunchong1992).

The reasons for the increase of the use of remote sensing are that remote sensing image represent objects, phenomenon in surface of the earth with (1) shape and location of objects similar with earth surface, (2) completely, (3) large area coverage, (4) permanent (Sutanto, 1986). These reasons may be implemented in some sectors, such as hydrology, oceanography, geography, biology, forestry and agriculture, and also for coastal and marine management.

Landsat TM is one type of remote sensing imagery that is often used for natural resources inventory. Landsat TM have many spectral that makes easier to identify objects in earth surface, hence this type of images has often been used for natural resources inventory. More details about Landsat TM spectral electromagnetic can be seen in Table 2.4

Table 2.4. Landsat TM Spectral electromagnetic

Channel Wave Length (μm) Spatial Resolution (m) Spectrum Name

1 0,45 – 0,52 30 Blue

2 0,52 – 0,60 30 Green

3 0,63 – 0,690 30 Red

4 0,76 – 0,90 30 Near Infrared

5 1,55 – 1,75 30 Middle Infrared I

6 10,40 – 12,50 120 Thermal Infrared

7 2,08 – 2,35 30 Middle Infrared II

For the application of Landsat TM remote sensing algorithms to coastal zone, it needs some understanding of spectral properties, both of targeted

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parameters and surrounding surface materials. An understanding of the properties of marine and coastal materials and their interaction to produce a complex signal on the satellite sensor is required to properly analyze the processed imagery.

Every channel on Landsat TM has particular functions, hence it can be implemented to some aspects (Mika, 1997). Application of channels is shown in Table 2.5.

Table 2.5. Landast TM electromagnetic for specific application

Channel Application

1 Vegetation and soil differentiation, clean water bathymetry

2 Indication of vegetation fertility, sedimentation, dirty water bathymetry 3 Vegetation classification, snow and ice mapping

4 Biomass, and water body boundary

5. Vegetation water content, snow and cloud differentiation 6 Thermal, settlement, and non settlement mapping 7 Hydrothermal, geology and soil type differentiation

One way of easier identification process is making composite image to derive new image. From composite image, object view may produce more contrast and be easier to be identified. Basically, image- processing principal is arranging picture on digital image including spatial and spectral aspects. Therefore, this image is divided into 2 spaces; they are spatial and spectral aspects. Spatial is three (3) dimentional space that are represented by coordinate (x, y, and z), while spectral shows spectral value distribution in every channel inside coordinate (Danoedoro, 1996).

Through spectral space, characteristics of an object can be known and differentiated from the other objects. Characteristic of identification of object is called image interpretation. Identification includes the following characteristics such as spectral, spatial, and temporal (Lilesand and Kiefer, 1979). Among these

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characteristics, spectral is the main identification characteristic; it means that other characteristics can be known after knowing the spectral characteristic.

2.5. Geographic Information System (GIS)

Geographic Information System (GIS) is a set of tools that are used for compiling, storing, manipulating, updating, analyze, and present is all data to be spatial information (ESRI, 1990). Related with this meaning, this system has four (4) main capabilities to handle geographic reference data, they are: data entry, data management, data manipulate and analyze, and data output (Arronoff, 1989).

GIS is one information system used to working with spatial reference data or geographic coordinate (Star and Ester 1990). In other words, GIS is a system of database with specific capability for spatial reference data with a set of working operation.

The advantage of GIS if compared with other database processing systems is its capability to display spatial and non-spatial information at the same time. For example; land-use data will be able to be presented within polygons boundary (spatial information) and attributes that contain information of polygon (non-spatial information). Information with different themes is represented into different layer. SIG tries to make simple real earth phenomenon, and it is expected to represent real condition for one particular application. (Haris, 2003).

In GIS, data storage is divided into 2 parts, they are: spatial and attribute data. For analysis need, spatial and attribute data will be stored separately, and then both will be integrated (Macguire and Goodchild, 1991). Occasionally, some data derive from remote sensing image will be combined with GIS data storage

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for implementation particularly goal.

GIS must follow some rules (Arronoff, 1989): (1) involving concept and geographic data that have relationship with spatial distribution, (2) information from data analyzed and related with decision making goal, (3) a system that include data entry, processing, and data output, (4) three components above are functioned based on high technology. More details about illustration of data management processing using GIS is shown in Fig 2.2.

Map Table Field Survey Digital Data Remote sensing Data

GIS Analysis

INPUT GIS OUTPUT

Textual Report Map Photography Statistic and Table Data for other GIS Digital Data Base

DATABASE PROCESSING Capture Code Edit Store and finding Display and report Manipulate and analysis

Fig 2.2. System Diagram for GIS illustration (Meaden and Kapetsky, 1991)

Generally, data sources requirement for GIS analysis process can be divided into 3 categories (Khairul Jamil 2005), they are (1) Field data, (2) Map data, which information has been recorded on paper or film that are converted into digital format, and (3) Remote sensing involves airborne photo and satellite imagery.

The process from data input become data output is a connecting structure that is started from real world and recorded on image and airborne photo, then by GIS facility, data are stored and processed to generate output that will be used for decision-making in the real world.

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Procedure of GIS working system is organizing hardware, software, and geographic data to optimize the system of storing, manipulating, analysis and displaying all geographic information. Attribute and spatial data have relationship with space aspect-location that is presented as database on a map. To acquire spatial analysis result overlaying techniques from some thematic maps (vector or raster) are used. New spatial information is acquired based on new digital value that constitutes an integration of old digital value.

2.6. The Role of GIS and Remote Sensing for Coastal Management

There are many data that can be used for GIS data input as well as thematic maps that are available on analog picture, recording imagery from airborne photo and satellite, field survey data, and map that have already recorded as digital data. Those data are useful for GIS data input that will be analyzed for further study such as for suitability area study, land-use changing, and natural resources distribution.

GIS has been used for land-use management, such as: agriculture, forestry, and military. Many kinds of spatial analyses have been done using GIS, including coastal and marine management. For coastal and marine management cases, GIS can be implemented for arranging coastal master plan, like: predicting tourism and fishing potencies, and determining and developing aquaculture zones in coastal ecosystem (Purwadhi, 1993).

Characteristics and potencies data of coastal region can be represented better by using GIS, it is caused that GIS can integrate some data and maps together at the same time, and easy to updating data also. Data selected will be

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used for some interesting in planning and decision-making on coastal master plan. GIS for coastal resources management can be used to display spatial databases that are related to some problems: (1) coastal physical database including bathymetry/topography, morphology, land cover, sedimentation, erosion and deposition, climate, habitat boundary, etc, and (2) human-social coverage, a database that includes administrative boundary, population distribution, transportation, and any other human-social characteristics. GIS database is used for coastal and marine resources management for (a) knowing natural resources exploitation level (b) meeting variation human needs, and (c) keep existing coastal ecosystem (Gunawan, 2000).

Generally, the advantages of using GIS for natural resources planning and management are (1) integration data from any other data format (graphic, text, analog and digital) (2) good data exchange from any knowledge (3) efficient and effective processing and analyzing (4) modeling and comparing some alternative of activities before being applied in field (5) efficient in data updating, especially graphical model, (6) accommodate large data volume (Dahuri, 1997)

GIS has great contribution for coastal ecosystem management (1) it is helpful to facility some sectors, private and local government that plan something to mapping and integration of optimal options management and alternative planning. Planning sectors are expected to be able to select what activities reasonable to perform. (2) As a tool that is used to support coastal resources management with environmental knowledge. Using GIS, it is easy and faster to perform spatial analysis and monitoring toward coastal ecosystem changing (Gunawan, 1998).

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2.7. Land Suitability Analysis

Land suitability analysis is an interdisciplinary approach by including the information from different domains like soil science, crop science, meteorology, social science, economics and management. Being interdisciplinary, land suitability analysis deals with information, which is measured in different scales like ordinal, nominal, and ratio scale.

The process of land suitability classification is the evaluation and grouping of specific areas of land in terms of their suitability for a defined use. The main objective of the land suitability is the prediction//evaluation of the inherent capacity of a land unit to support a specific land use for a long period of time without deterioration, in order to minimize the socio-economic and environmental costs (Prakash T.N 2003).

As mentioned earlier, determining suitable land for a particular use is a complex process involving multiple decisions that may relate to biophysical, socio-economic and institutional/organizational aspects. Therefore, a structured and consistent approach to Land Suitability Analysis (LSA) is essential to decide the success of coastal development planning.

2.8. Multi Criteria Decision Making (MCDM)

There are many criteria upon which land suitability depends. The suitability analysis evaluates many alternative land use types under the light of various criteria from various streams. Alternatives here are competing with one another; criteria are both qualitative and quantitative.

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Evaluation/Analysis) and GIS can be integrated (Jankowski 1995). MCE seems to be applicable in GIS-based land suitability analysis (Pereira and Duckstein 1993) for different coastal land uses.

MCDM methods deal with real world problems that are multi dimensional in nature. When it comes to environmental issue the methods have to deal with heterogeneous criteria that are both qualitative and quantitative in nature. In order to incorporate heterogeneous information with different measurement scales, one has to bring them into a common domain of measurement. This process is called

Standardization, a basic operation in MCE. Criteria should be standardized

keeping in mind the goal and alternatives that are under evaluation. Standardization can change the outputs entirely if proper attention is not paid. For environmental criteria, there is a lack of valid and reliable standardization processes.

Decision-making is a subjective process, as the perception regarding a problem can diverge from person to person. One cannot expect a decision maker or an expert to be highly consistent while dealing with such a subjective process. The real world problems are influenced by many natural factors and processes that are difficult to measure and model precisely. After the problem is evaluated for optimum conditions, sensitivity analysis assesses different conditions near the optimum values to check for the sensitivity of the criteria. Many decision-making methods lack a valid approach towards sensitivity analysis. Sensitivity analysis also aids in understanding the interaction between the criteria, dominant criterion and its effect, i.e. the variation in the final results when the weight of that criterion is varied.

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2.9. Framework of Decision-Making and SMCDM

In the early time the use of remote sensing and GIS was confined only for the process of mapping. In time progress the information technology develops tools to use these maps in the process of planning and decision-making. Sustainable management means the utilization of the available land resources in such a way that the occupation, which is conducted over a piece of land, is without or with least impact over the resources. For the sustainable use of the land, the area needs to be used for a specific purpose, which suits the local conditions best.

Spatial multi-criteria decision-making (MCDM) is a process where geographical data are combined and transformed into a decision. Multi-criteria decision-making involves input data, the decision maker’s preferences and manipulation of both information using specified decision rules. In spatial MCDM, the input data are geographical data. Spatial MCDM is more complex and difficult in contrast to conventional MCDM, as large numbers of factors need to be identified and considered, with high correlated relationships among the factors a spatial decision problem is the difference between the desired state in a geographical system and an existing state in real world (Malczewski 1999).

Spatial MCDM aims at achieving solutions for spatial decision problems, derived from multiple criteria. These criteria, also called attribute, must be identified carefully to arrive at the objectives and final goal. The performance of an objective is measured with the help of these attributes. These evaluation criteria should be comprehensive and measurable.

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suitability evaluation demands for visualization of the impact of the alternatives and criteria in the form of maps. This demands for visualization in the form of maps. This demand can be accomplished effectively by the integration of spatial analysis and conventional multi-criteria evaluation techniques, as shown in Fig 2.3. Moreover, environmental decision problems are characterized of having multiple and often conflicting objectives. When evaluating such a complex phenomenon, the spatial dimension seems to be the big hurdle. Here, the integration of GIS and MCDM techniques becomes useful (Prakash, 2003).

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III. METHODOLOGY

3.1. Time and Location

This research was conducted from January until May 2006 in Maros. The study area is located in South Sulawesi, Indonesia, approximately between 4° 45’ - 5° South and 119° 21’ - 119° 42’ 30’’ East.

Research Area

Fig3.1. Maros Location

3.2. Equipment And Data Requirement 3.2.1. Data Collection

The types of data needed for this research were: a. Primary Data

Landsat TM is to extraction of land physical characteristic data and coastal identification to determining of coastal zones.

b. Secondary Data

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Physical Social/ Economic

- Topographic Map - Salinity

- Brightness of water - Current Velocity - Depth of Sea - Population - Sechi Disk Depth - Sea Surface Temperature - DO, PH

Demography (Agriculture and fishing productivity)

c. Other Data

Necessary data as well as map related with this study is needed, such as: - Topographic map (1 : 50.000 scale, Maros region) as base map to plotting

criteria and data.

- Administrative map for determining administrative boundary.

3.2.2. Hardware, Software and Equipment Used

The following application software, hardware were used for the overall processes in this study.

Table 3.1. List of hardware, software and equipment. PC Pentium IV 1,8 GHZ, 256

For software running and reporting Hardware

Canon I - 255 For printing

Arc/View 3.3 version For spatial data processing and analyzing

PC Arc/Info 3.5 version For spatial data processing and analyzing

ER Mapper 6.4 version For raster data processing

Ilwiss For raster and tabular data processing Definite Multi criteria analyzing

Excel Tabular data analyzing Software

MS Word Report

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3.3. Framework of Research

3.3.1. Framework of Coastal Ecosystem Development Decision Making

The decision-making problem of land suitability analysis for coastal ecosystem development/ coastal land use planning is analyzed using integration of remote sensing, GIS and MCDM. Fig 3.2 depicts the conceptual flow of the research approach completely.

3.3.2. DataCompilation

Data used for this research are primary and secondary data. Primary data was derived from remote sensing imagery (Landsat TM), while secondary data was derived from field check and some institutions. Compiling data is needed to recognize research location and to build model of coastal landuse development planning.

3.3.3. Image Preprocessing

For acquiring better geometric and radiometric aspects from Landsat TM imagery, geometric and radiometric corrections should be accomplished. It is the first step prior to analysis of the remotely sensed data that is to remove errors.

Geometric correction is a replacing pixel position as truly condition. Geometric correction that is commonly used to make digital remote sensor data truly useful is geometric rectification. It is a process using Ground Control Point (GCPs), which are selected transform the geometry of the image so that each pixel corresponds to a position in a real world coordinate system.

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RMS (Roat Mean Square) is differentiating between original coordinates based on line and row of GCP coordinates that are measured with square of deviation of GCP on satellite imagery. Equation following:

RMS error = √ ((xi – x orig)2 + (yi – y orig)2)

Where: x orig and y orig are original coordinate line and row. xi and yi are GCP from Imagery corrected.

Raw Image Radiometric Correction

Geometric Correction

Corrected Image

Image Calibration

Atmospheric Correction

Ground Control Point (GCPs)

Rectification

Resampling to common grid

Fig 3.3. Image Preprocessing

Radiometric correction is one of histogram adjustment as a way to minimize bias. The effect of atmospheric scattering caused by particles is a problem in imagery and should be removed or minimized to avoid bias in each spectral band. Radiometric correction is performed to reduce bias effect of atmosphere on satellite imagery for good display, it means that pixel value on imagery is represented actual/ real value of field.

Radiometric correction on this research was done by displaying all histogram used to known actual value. The lowest spectral value must be 0 (zero) as well as bit-coding sensor. The equation used for radiometric correction is:

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BV ijout = (BV i – l, j, k + BV i+j, j,k / 2)

Where : BV ijout : Correction Result

BV i – l, j, k : Actual Spectral Value BV i+j, j,k : Actual Value

3.4. Field Checking

- Determine sample point, it was done for compiling field data, which were difficult to derived by using remote sensing imagery.

- Ground checking to determine the accuracy of interpretation result. Interpretation of remote sensing imagery was not exactly correct, hence ground truth is needed to prove correcting of interpretation result. - Compiling data from other institutions that has relationship with this

research. Some data, such as fishing sector, socio and culture is needed to support this research.

3.5. Data Analysis

Data analysis involves two (2) kinds of data: secondary and primary data. Primary data include depth of sea, sea surface temperature and water quality, and other physic variables. Meanwhile, secondary data include socio, economic and ecology data.

3.5.1. Physical and Chemical Variables

Physical and chemical data are acquired from institution and field survey that has relationship with each theme (depth of sea, sea surface temperature and water quality). Every data will be plotted in certain location as one value.

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Distribution of values (points) can be generated by linear interpolation method, so each area will have a value that indicates variable above.

3.5.2. Social, Economic and Ecology

Socio, economic and ecology data are acquired from some institution. Here, mapping analysis is needed to acquire habitat, accessibility and utility serving maps. Even the analysis is focus to physical criteria, the socio, economic and ecology is needed for additional information.

3.6. Determining Coastal Ecosystem Zones

Soedharma, et al (1992) has developed coastal zone classification, which its zone is divided into four (4) parts, namely: preservation, conservation, buffer and uses zones. More detail about coastal zone classification can be seen in Table 3.2.

Table 3.2. Parameter, mapping unit and scoring variables.

Categorize and scoring

No Variable unit weighting

Preservation Score Conservation Score Buffer Score Uses Score Land

1. Distance from

Beach

M 2 < 100 4 100 – 150 3 150 – 200 2 > 200 1

2. Vegetation % 1 Mangrove 4 Mangrove 3 - 2 - 1

3. Slope Type 1 0 – 15 4 15 – 25 3 25– 40 2 > 40 1

4. Substrate Distribution

1 Seagrass &

Coral Reef

4 Seagrass &

Coral Reef

3 - 2 - 1

5. Population pressuring

4 Very Critical 4 Critical 3 Marginal 2 Not Critical 1

Marine

6. Sea Surface temperature

oC 1 29 – 30 4 28 – 29

31 - 32

3 - 2 < 27

> 33 1

7. Salinity ppt 1 31 – 32 4 30 – 31

32 – 33

3 - 2 < 30

> 33 1

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Coastal ecosystem zones are determined using seven (7) physical variables; they are

1). distance from coastal line, 2). coastal vegetation,

3). slope,

4). sea surface temperature, 5). salinity,

6). substrate distribution 7). and population pressuring.

A score was given for each variable to determine coastal ecosystem zones.

All thematic maps will be crossed (overlaid) to obtain new information (coastal zone), in which score of mapping unit is calculated by using the formula below:

……. ZC = (2 x SD) + (2 x V) + DB + S + PP 1)

Where: ZC = Coastal Zone S = Slope

SD = Substrate Distribution PP = Population Pressuring V = Vegetation DB = Distance from beach

Referring to scoring and weighting on Table 3.2, the total of minimal and maximal scores that were calculated by multiplying the minimal and maximal scores with its weight can be seen in Table 3.3.

Only conservation, buffer and uses zones can be developed for any activities, but they still consider environment requirements to maintain sustainable coastal ecosystem. Preservation area is not allowed for any uses.

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Appendix 2

Effect Table for Each Alternatives (areas in m2)

Landuse alt1a alt2a alt2b alt2c alt3a alt3b alt3c alt5a alt5b alt5c alt5d

Forest 967928 967928 967928 0 834209.4 834198.2 834199.6 1120595 1120595 1120590 1120595

Agriculture Land 10287867.01 10287867.01 344102.346 0 7509923.2 4196363.93 4196358.6 0 4196360.88 0 4196334.18

Settlemet 2972061.944 2972061.944 2972198.144 2972061.94 2972058.544 2972061.944 2972061.944 2971792.306 2971963.944 2972061.944 2972062.506

Fishpond 53711765.54 53711765.54 19507255.69 0 56621415.28 59917610.64 59936937.64 0 59650627.64 53493613.82 59650608.02

Tourism 0 0 44148130.61 64955977.6 0 0 0 63839484.64 0 10353318 0

(areas in Ha)

Landuse alt1a alt2a alt2b alt2c alt3a alt3b alt3c alt5a alt5b alt5c alt5d

Forest 96.79 96.79 96.79 0.00 83.42 83.42 83.42 112.06 112.06 112.06 112.06

Agriculture Land 1028.79 1028.79 34.41 0.00 750.99 419.64 419.64 0.00 419.64 0.00 419.63

Settlemet 297.21 297.21 297.22 297.21 297.21 297.21 297.21 297.18 297.20 297.21 297.21

Fishpond 5371.18 5371.18 1950.73 0.00 5662.14 5991.76 5993.69 0.00 5965.06 5349.36 5965.06

Tourism 0.00 0.00 4414.81 6495.60 0.00 0.00 0.00 6383.95 0.00 1035.33 0.00

Rehabilitation Cost (Rp jt) Higher rehabilitation cost, the worse

Landuse rehabilitation cost alt1a alt2a alt2b alt2c alt3a alt3b alt3c alt5a alt5b alt5c alt5d

Forest 0 0 0 0 0 0 0 0 0 0 0 0

Agriculture Land 0 0 0 0 0 0 0 0 0 0 0 0

Settlemet 0 0 0 0 0 0 0 0 0 0 0 0

Fishpond 0 0 0 0 0 0 0 0 0 0 0 0

Tourism 20 0.00 0.00 88296.26 129911.96 0.00 0.00 0.00 127678.97 0.00 20706.64 0.00

Aesthetic Value Higher aesthetic value, the worse

Landuse Aesthetic Value alt1a alt2a alt2b alt2c alt3a alt3b alt3c alt5a alt5b alt5c alt5d

Forest 3 290.38 290.38 290.38 0.00 250.26 250.26 250.26 336.18 336.18 336.18 336.18

Agriculture Land 2 2057.57 2057.57 68.82 0.00 1501.98 839.27 839.27 0.00 839.27 0.00 839.27

Settlemet 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Fishpond 5 26855.88 26855.88 9753.63 0.00 28310.71 29958.81 29968.47 0.00 29825.31 26746.81 29825.30

Tourism 2 0.00 0.00 8829.63 12991.20 0.00 0.00 0.00 12767.90 0.00 2070.66 0.00

area total 6496.75 29203.83 29203.83 18942.45 12991.20 30062.96 31048.34 31058.00 13104.08 31000.76 29153.65 31000.75

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Fertilizer Use (kg nitrogen/ha/yr) Higher fertilizer use, the worse

Landuse fertilizer alt1a alt2a alt2b alt2c alt3a alt3b alt3c alt5a alt5b alt5c alt5d

Forest 0 0 0 0 0 0 0 0 0 0 0 0

Agriculture Land 250 257.20 257.20 8.60 0.00 187.75 104.91 104.91 0.00 104.91 0.00 104.91

Settlemet 0 0 0 0 0 0 0 0 0 0 0 0

Fishpond 375 2014.19 2014.19 731.52 0.00 2123.30 2246.91 2247.64 0.00 2236.90 2006.01 2236.90

Tourism 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Aggreagation 2271.39 2271.39 740.12 0.00 2311.05 2351.82 2352.54 0.00 2341.81 2006.01 2341.81

Multiple Use More polygons per alternative, the higher the multiple landuse value (diversity)

Landuse Multiple Use alt1a alt2a alt2b alt2c alt3a alt3b alt3c alt5a alt5b alt5c alt5d

Forest Agriculture Land Settlemet Fishpond Tourism

Multiple Use 39 39 64 31 48 45 45 32 44 36 44

Gross Margin (Rp jt) The higher the gross margin, the better

Landuse Gross Margin alt1a alt2a alt2b alt2c alt3a alt3b alt3c alt5a alt5b alt5c alt5d

Forest 0.00702 0.68 0.68 0.68 0.00 0.59 0.59 0.59 0.79 0.79 0.79 0.79

Agriculture Land 25.74 26480.97 26480.97 885.72 0.00 19330.54 10801.44 10801.43 0.00 10801.43 0.00 10801.36

Settlemet 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Fishpond 12.87 69127.04 69127.04 25105.84 0.00 72871.76 77113.96 77138.84 0.00 76770.36 68846.28 76770.33

Tourism 40 0.00 0.00 176592.52 259823.91 0.00 0.00 0.00 255357.94 0.00 41413.27 0.00

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Area (Ha)

Landuse Alt3d

Alt3e

ALt3f`

Alt3g

fishing

45290.594

2586.0310

45290.5940

2586.030469

seaweed

953.470 31835.8179

734.6985

16835.189900

kejapung

981.871 12804.0900

1200.6391

27804.720000

Investment (Rp million/ha/yr)

Landuse Investment

Alt3d

Alt3e

ALt3f`

Alt3g

fishing

0

0.000 seaweed

3.206 3056.825

102065.632

2355.443

53973.619

kejapung

20.5 20128.352

262483.845

24613.102

569996.760

Aggregation

23185.177

364549.477

26968.546

623970.379

Benefit (Rp million/ha/yr)

Landuse Benefit

Alt3d

Alt3e

ALt3f`

Alt3g

fishing

0

0.000 seaweed

22.148 21117.456

705099.694

16272.103

372865.786

kejapung

25.654 25188.914

328476.125

30801.196

713302.287

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Appendix 3

Product of Seaweed Cultivation Analysis (1 ha)

Source: Khairu

l Jamil (2005)

Description U.E. (year) Unit Number of unit Unit Price (Rp) Total Cost Total of Cost

(1 year = 10 month)

A. Investment

1. Tool cultivation a. String no.8 b. Reach string no.3 c. Floating bottle (1,5 ltr) d. Boat

e. Wood f. Wash pole g. Waring jemuran h. Bag

i. Floating

Kg Kg Bottle Unit Stick Stick Piece Unit Unit 6 50 1500 1 85 15 3 100 10 18500 18500 200 1050000 5000 3000 50000 1000 10000 111000 925000 300000 1050000 425000 45000 150000 100000 100000 111000 925000 300000 1050000 425000 45000 150000 100000 100000

Total of Investment 3206000

B. Cost

1. Permanent Cost 1. Biang string 2. Bentangan string 3. Rapiah string 4. Floating bottle 5. Boat 6. Wood 7. Wash pole 8. Waring jemuran 9. Bag

10. Floating 2. Boat Maintenance Cost

5 3 2 7 3 3 2 1 1 Kg Kg Roll Bottle Unit Stick Stick Piece Unit Unit Unit

50 7500 375000

22200 308333,333 750000 150000 150000 141666,666 15000 75000 100000 100000 189000

Total of Permanent Cost 1851200

C. Non Permanent Cost 1. String preparation 2. Growing 3. Maintenance 4. Seed 5. Transportation

HOK HOK HOK Kg Kg 100 100 30 4000 2000 1000 1500 5000 750 100 100000 150000 1000000 1500000 1500000 150000 3000000 200000 30000000 2000000

Total of Non Permanent Cost 36000000

Total of Cost (B + C) 37851200

D. Revenue (Dry Seaweed reduce to 70%) Kg 20000 3000 60000000 60000000

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Product of “Kejapung” Cultivation Analysis

Description Number of unit Unit Unit Price (Rp) Total Cost

A. Investment

a. Boat (11 x 7 m) b. Guard Home c. Katinting Boat d. Keramba Net 8m3

1 1 1 20

Unit Unit Unit Unit

7000000 6000000 2500000 250000

Total of Investment 20500000

B. Cost

1. Non Permanent Cost

9000 2500

6750000 15000000 8640000 1216000

1. Kerapu fish Tail

Kg Personal Liter 2. Food (750X8FCRX250)

3. Employer 4. Gasoline 5. Food for employer 6. Transportation

8640000

Rp 1000000

Total of Non Permanent Cost 41246000

C. Permanent Cost 1. Reducing

1. Boat (11 x 7 m) (6 yr) 1 Unit 1166667 1166667

2. Guard Home 1 Unit 1200000 1200000

3. Boat (3 yr) 1 Unit 833333 833333

4. Keramba Net (2 yr) 20 Unit 2500000 2500000

Maintenance Cost 1025000

Production Tool 1000000

Total of Permanent Cost 7725000

Another Cost 4124600

Total of Cost 53095600

D. Revenue Kg 3000 60000000

Gross Benefit 87500000

Tax 10 % 8750000

Net Benefit – tax

Source: Khairul Jamil (2005)

78750000

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Vectoring Data

Landsat TM Imagery

Radiometric & Geometric Correction

Classification

Land and Water Separation

Mangrove Distribution Marine

Substrate

Land use Population

Pressuring Salinity Slope & Distance

from beach

Digitations & Interpolation Data:

- Topographic Map - Salinity

- Brightness of water - Velocity & Direction Wave - Depth of Sea

- Population

- Sea Surface Temperature - DO, PH

Population Pressuring Map

Mangrove Distribution Map Marine

Substrate Map

Land-use map

Salinity, Slope & Distance from beach

Criteria & Giving Score Ground Check & Re-interpretation

Overlay & Classification

Coastal Ecosystem Zone

Criteria _{for Resort tourism}Suitable Area Suitable Area for Aqua Culture Suitable Area for Fishing Activities

Scoring Analysis Administration

Map

Digitations

Plotting

Indonesia Coastal Environment

Map

Existing Land Use

Digitations

Existing Land Use Map

Conflict Map

Formulation Of Alternatives

SMCDM and Ranking Best…. worst Assessment

Criteria

Recommended Master Planning of Coastal Ecosystem