GIS Application for Ecosystem Approach to Fisheries Management in Indonesia

GIS Application for Ecosystem Approach to

Fisheries Management (EAFM) in Indonesia

By

Gilar Prakoso

A MAJOR PAPER SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF MASTER OF ENVIRONMENTAL SCIENCE AND MANAGEMENT

UNIVERSITY OF RHODE ISLAND

MAY 2016

MAJOR PAPER ADVISOR: Dr. David Bengtson

MESM TRACK: Environmental Policy and Management

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ABSTRACT

A GIS application for Ecosystem Approach to Fisheries Management (EAFM) was developed

to understand the performance of EAFM in Indonesia. The objects of this application are eleven

Indonesian Fisheries Management Areas and Anambas Marine Protected Area. Methods

used in this project were divided into three sections: (1) Build the EAFM measurement tool

based on EAFM model in Indonesia; (2) Integrate the performance of EAFM into ArcGIS

software; and (3) Develop the Indonesia EAFM geodatabase, in order to analyze the performance

of EAFM along with other data that are available. The results of this project can help

stakeholders to understand the performance of EAFM and provide information for decision

making.

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ACKNOWLEDGMENTS

I would like to express the deepest gratitude to my advisor Dr. David Bengtson for all his

full support, understanding, and motivation throughout this project. In addition, I express my

appreciation to Dr. Peter V. August for his help and kindness as my Environmental Policy and

Management Track Chair in MESM program. Also, thank you to Dr. Y.Q. Wang for his patience

in teaching me about GIS and Remote Sensing.

I would like to thank Dr. Kathleen M. Castro, Barbara Somers and Laura Skrobe for all

their assistance during my study at the University of Rhode Island. Thanks also go to my fellow

graduate students at MESM. Special thanks go to my Indonesian friends for all the laugh and

invaluable experience during our time in the USA.

Finally, I would like to thank my wife, kids, and parents for their unconditional love and

support during the last two years. I would not have been able to complete my master degree without

their endless love and encouragement.

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TABLE OF CONTENTS

ABSTRACT ..................................................................................................................................... i

ACKNOWLEDGMENTS .............................................................................................................. ii

LIST OF TABLES ........................................................................................................................ iv

LIST OF FIGURES .........................................................................................................................v

INTRODUCTION ...........................................................................................................................1

OBJECTIVES ..................................................................................................................................7

METHODOLOGY ..........................................................................................................................7

Study Area .................................................................................................................................7

Data ..........................................................................................................................................10

EAFM Model ......................................................................................................................10

EAFM Indicators Performance..........................................................................................11

Geospatial Data .................................................................................................................12

Data Processing and Analysis ..................................................................................................12

EAFM Measurement Tool ..................................................................................................12

EAFM Integration into ArcGIS 10.3 ..................................................................................14

EAFM Geodatabase ...........................................................................................................16

RESULT AND DISCUSSION ......................................................................................................16

GIS Application for EAFM .....................................................................................................16

Geodatabase Application .........................................................................................................20

CONCLUSION ..............................................................................................................................20

REFERENCES ..............................................................................................................................21

APPENDICES

Appendix 1 – EAFM Assessment on Social Indicators ...........................................................23

Appendix 2 – List of Feature Class inside Geodatabase File ..................................................24

Appendix 3 – The use of Geodatabase in Eastern Indonesia ..................................................25

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LIST OF TABLES

Table 1. EAFM Indicators in Indonesia ...........................................................................................4

Table 2. Flag Model of EAFM in Indonesia ..................................................................................11

Table 3. EAFM Performance in Anambas MPA ...........................................................................18

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LIST OF FIGURES

Figure 1. Map of Indonesia’s Fisheries Management Areas ...........................................................8

Figure 2. Marine Protected Area distribution ..................................................................................9

Figure 3. Location of Marine Protected Area in Anambas Island Region .......................................9

Figure 4. Example of EAFM assessment on fish resource domain ...............................................13

Figure 5. The spreadsheet of cumulative index .............................................................................14

Figure 6. The interface of MyGeoData ..........................................................................................15

Figure 7. The attribute table of FMA shapefile/feature class after modification ...........................15

Figure 8. The attribute table of FMA shapefile/feature class with the value of EAFM composite

index ................................................................................................................................16

Figure 9. The performance of EAFM domains in Indonesia’s Fisheries Management Areas ......17

Figure 10. EAFM performance of Anambas MPA and Jemaja Island land cover ........................19

Figure 11. Example of the use of geodatabase files.......................................................................20

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INTRODUCTION Economic development and population growth have increased the utilization of the

coastal area in Indonesia without proper consideration of environmental sustainability. The

rapid growth and dense population in the coastal zone has caused major environmental

damage such as deforestation of mangrove ecosystems, reduction in the coral reef, seagrass

bed and estuary ecosystem, increase in pollution from land activities, and over-fishing as a

major problem (MMAF, 2006). Moreover, low awareness of coastal communities on coral

reef conservation, inadequate institutional capacity to contain land and marine-based

pollution, insufficient institutional frameworks, and persistent poverty in coastal areas have

resulted in degradation of about 70% of Indonesia’s coral reef ecosystem (Indonesia CTI,

2012).

The Research Centre for Oceanography of the Indonesian Institute of Sciences

(2014) revealed that only 5.32% of coral reefs in Indonesia is classified as very good,

whereas 27.20% of them were classified in good condition, 37.42% were sufficient, and

30.07% were in bad condition. Burke et al. (2012) mentioned that in the last half century,

degradation of coral reefs in Indonesia increased from 10% to 50%. In addition, Indonesia

has also lost most of its mangrove. From 1982 to 2000, Indonesia has lost more than half

of the mangrove forest, from 4.2 million hectares to 2 million hectares (NASA, 2010).

Hughes et al.’s (2012) comparison of Indonesian fisheries with those of 27 other

countries, indicated that they are the most vulnerable to collapse based on management

indicators of coral reef, fisheries status, and food security. Moreover, some Fisheries

Management Areas (FMAs) in Indonesia have faced the symptoms of overfishing for some

groups of essential commodities, such as large pelagics, small pelagics, shrimp and

demersal fish (MMAF, 2011). Four of the eleven FMAs have exceeded potential

production. For example, only three FMAs do not show symptoms of overfished shrimp,

whereas for demersal fish, two FMAs are already overfishing and five are categorized as

fully exploited. These problems highlights the urgent need for all parties to work together

on improving fisheries management and its linkage to the ecosystem.

Effective fishery management must include the three equal and interconnected

aspects of the natural, human and fishery management systems. Conventional approaches

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to fisheries management only partially consider these dimensions. The Ecosystem

Approach to Fisheries Management (EAFM) introduces a series of modifications to

conventional fisheries management to improve performance and contribute to sustainable

development. The shift to an EAFM from traditional fisheries management requires

thoughtful consideration of some important issues and challenges. One of them is the

availability and analysis of integrated geospatial data regarding the identification process,

verification, decision-making, and monitoring-evaluation.

Accurate assessment of EAFM performance requires reliable data at a range of

scales. Enhancement and strengthening of data collection systems will assist EAFM

implementation in Indonesia. This is the primary role of GIS application, which is

becoming a tool that can combine, analyze and present the results of the integrated mapping

for all required data. The problems are how to create a linkage system between EAFM

indicators and GIS application, and how the GIS will contribute to fisheries management

in Indonesia. Therefore, this project aims to develop GIS application based on the

analytical model of an ecosystem approach assessment.

Ecosystem Approach to Fisheries Management (EAFM)

The UN Food and Agriculture Organization (2003) defines EAFM as “an approach

to fisheries management and development that strives to balance diverse societal objectives

by taking into account the knowledge and uncertainties about biotic, abiotic, and human

components of ecosystems and their interactions and applying an integrated approach to

fisheries within ecologically meaningful boundaries.” Referring to that definition, EAFM

can be understood as a concept of how to balance socio-economic objectives in fisheries

management (welfare of fishermen, judicuous utilization of fish resources, etc) while still

considering the knowledge, information and uncertainties about biotic components, abiotic

and human interaction in aquatic ecosystems through an integrated and sustainable

fisheries management.

Ecosystem‐based fishery management is a new direction for fishery management,

essentially reversing the order of management priorities so that management starts with the

ecosystem rather than a target species (Pikitch et al., 2004). EAFM differs from

conventional fisheries management in that, at its core, EAFM seeks to manage fisheries

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within the context of the ecological and social systems in which they exist. Recognizing

the need for an ecosystem approach stems from the increased understanding of fisheries

systems holistically: the interactions within and among fish species; the habitat and wider

ecosystem; the fish and fishermen; fishing communities; and broader social, economic, and

governance systems that support and influence them.

EAFM gives a more extensive structure to manage marine assets and promote

improvement through enhanced ecological welfare (e.g. territory assurance and rebuilding,

contamination reduction and waste management, sustainable fisheries resources) and

human prosperity (e.g. food security, sustainable livelihoods, equitably distributed wealth).

Zhang et al. (2009) predicted that, when fishery managers understand the complex

ecological and socio-economic environments in which fish and fisheries exist, they will be

better able to anticipate the effects that fishery management will have on the ecosystem, as

well as the effects that ecosystem change will have on fisheries.

Moreover, EAFM can be executed crosswise over distinctive spatial and

governance scales and can be customized to suit prioritization of real issues and objectives.

EAFM requires a comprehensive analysis and decision-making method based on spatial

data and metadata information that covers multi-layer aspects like physical, ecological,

social and economic. Therefore, the use of GIS has a significant role in achieving that

requirement.

EAFM Indicators

Zhang et al. (2009) indicated that four principles should apply for the integration of

ecosystem considerations into decisions: (1) the approach should be evolutionary rather

than revolutionary; (2) it should be capable of being applied with available information;

(3) it should be precautionary and environmentally sound; and (4) it should be simple and

pragmatic. In the context of fisheries management, the desirable properties of indicators

are: (1) directional; (2) sensitive to change; (3) range spans natural variability; (4) precision

and variance estimable and reasonable; (5) unambiguous; (6) not duplicative nor

repetitious; and (6) expressive or representative of key processes (Link, 2010). From these

principles and properties, stakeholders need to set indicators and benchmarks to measure

management performance to determine whether management is meeting the objectives.

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“Indicators and benchmarks are developed only after an objective has been agreed

upon. An indicator tracks the key outcome identified in the objective and, when compared

with an agreed-upon benchmark (often a target or a limit value or trend), provides a

measure of how well management is performing (performance measure). A performance

measure is simply the difference between the indicator value and its benchmark (often

referred to as reference points) at any time of assessment. The group of objectives,

indicators, benchmarks, and performance measures provide a means of communication

with decision makers and their ability to make appropriate changes in management.”

(Staples & Funge, 2009).

The main purpose of developing a set of indicators is to assist in assessing the

performance of EAFM and to stimulate action for better sustainability objectives. They can

also enhance communication, transparency, effectiveness and accountability in fisheries

management. In order to implement EAFM in Indonesia, a set of indicators is required to

monitor and evaluate progress. A suite of indicators was developed in consultation with

the primary stakeholders in fishery management. The results are 33 indicators spanning six

fishery domains: fish resource, habitat, fishing technique, social, economic and

institutional (Table 1).

Table 1. EAFM Indicators in Indonesia Domain Indicator Weight

Fish Resource

Raw CPUE 40 Fish size trend 20 Proportion Juvenile caught 15 "Range Collapse" 10 Fish catch composition 10 Species of ETP (endangered, threatened, and protected species)

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Habitat

Quality of waters 25 Status of seagrass 20 Status of mangrove 15 Status of coral reefs ecosystem 15 Unique habitat 15 Climate change on habitat and waters condition 10

Fishing Technique Destructive fishing technique 30 Modification of fishing tools and FAD 25 Fishing capacity and effort 15

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Domain Indicator Weight Fishing selectivity 15

The compliance function and the size of vessel with legal documents

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Certification of fishing crew 5

Social Stakeholder participation 40 Fisheries conflict 35 Local knowledge utilization 25

Economic Asset ownership 45 Household fishery 30 Saving ratio 25

Institutional

Pursuance of responsible fisheries principle in formal or non-formal fisheries management plan

25

Completeness rules in fisheries management 26 Decision rules mechanism 18 Fisheries management plan 15

Fisheries management policy and synergetic institutional level

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Stakeholder capacity 5 Note. Adapted from Assessment of Ecosystem Approach to Fisheries Management Indicators by MMAF, 2014a

Geographic Information Systems (GIS)

Geospatial technology describes the use of a number of different high-tech systems

and tools that acquire, analyze, manage, store, or visualize various types of location-based

data. GIS is defined as a computer-based mapping, analysis, and retrieval of location-based

data (Shellito, 2012). “In short, GIS adds value to spatial data. By allowing data to be

organized and viewed efficiently, by integrating them with other data, by analysis and by

the creation of new data that can be operated on, GIS creates useful information to help

decision-making” (Heywood et al. 2006). From this brief definition, it may be clear that

GIS can be divided into a number of essential components:

• “People. GIS cannot operate in isolation from the organizational context, and there

must be people to plan, implement and operate the system, as well as to make

decisions based on the output.

• Data. For most GIS operatives, data have now become the most important element

to GIS, a fact largely based on their high costs relative to other operating costs.

Today, a vast array of data is available from varied and diffuse sources. The

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requisite data for any specific project must be carefully identified and acquired, and

the quality of these data will determine the usefulness of the final GIS output.

• Hardware. A range of hardware exists for transforming data into digital formats,

which must be stored, manipulated and processed by computers before output can

be obtained via plotters, printers and screens.

• Software. GIS has the potential to utilize a range of software for carrying out a

variety of tasks, most of which provide the essential instructions and other linkages

between the data and the hardware.

• Procedures. Analysis requires well-defined, consistent methods to produce

accurate, reproducible results” (FAO, 2013).

Meaden (2007) states that GIS has become virtually ubiquitous in the world of

science and research, if not yet in the world of fisheries or its management. It is also worth

noting that almost all uses of GIS in the fisheries area, have been related to more-or-less

static mapping, e.g. creation of nautical charts, coastal zone management, optimum

locations for aquaculture, etc., and it was only in an examination of the potential uses for

GIS that mention was made of anything more complex. Nowadays, there is almost no facet

of the use of GIS for spatial analys that cannot be attempted. The many benefits of GIS to

assist in fisheries management include, habitat mapping, fish distribution, fishing fleet

disposition, resource analyses, vessel monitoring system, and marine reserve allocation.

Moreover, spatial analysis using GIS is recognized as an essential tool to integrate

ecosystem information from various sources. An experienced GIS user can run many

analyzes using off-the-shelf software and readily available data sets – but customized tools

can streamline the workflow of these analyzes, and make them feasible for someone with

less expertise. One successful example of how GIS application contributes to EAFM is the

EcoGIS project by NOAA. The EcoGIS project was launched in September 2004 to

investigate how GIS, marine data, and custom analysis tools can better enable fisheries

scientists and managers to adopt EAFM (NOAA, 2009). Furthermore, the project has

focused on four priority areas: Fishing Catch and Effort Analysis, Area Characterization,

Bycatch Analysis, and Habitat Interactions. The main result is a working prototype for

catch and effort analysis, the Fishery Mapper Tool.

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Finally, GIS plays a central role in information management, including natural

resource management like fisheries. A growing problem confronting natural resource

managers is the management of several interdependent resources, each of which has

multiple uses and multiple users with multiple value systems (McCormick 1999). The

challenges of natural resource management are to improve our understanding of complex

ecosystem dynamics, better assess natural resources and protect or restore natural systems.

Thus, GIS can directly benefit the assessment of natural resources, which is to visualize a

complex ecosystem at different scales, as well as spatially distributed environmental

impacts.

OBJECTIVES The objectives of this project are to:

1. Develop a simple tool in Microsoft Excel to the EAFM model in Indonesia.

2. Develop a GIS application based on an analytical model of EAFM.

3. Develop a geodatabase file to support EAFM in Indonesia.

Project location is Indonesia, and the primary client is Ministry of Marine Affairs

and Fisheries (MMAF), Republic of Indonesia. The GIS application will cover the scope

of Indonesia Fisheries Management Areas (FMAs) or Marine Protected Areas (MPAs).

METHODOLOGY

Study Area

GIS development in this paper is based on fishery region. The objects used to test

the application are Indonesian FMAs and one MPA location in Indonesia. Fisheries

Management Areas of the Republic of Indonesia are management areas for fishing, fish

farming, conservation, research, and development of fisheries which include internal

waters, archipelagic waters, territorial sea, contiguous zone and the Exclusive Economic

Zone of Indonesia (MMAF, 2011). The Indonesian marine waters are divided into 11

(eleven) FMAs, which are (Fig. 1): FMA-RI 571 (Malacca Strait and Andaman Sea), FMA-

RI 572 (Indian Ocean of Western Sumatera and Sunda Strait), FMA-RI 573 (Indian Ocean

of Southern Java, Southern Nusa Tenggara, Sawu Sea, and Western Timor Sea), FMA-RI

711 (Karimata Strait, Natuna Sea and South China Sea), FMA-RI 712 (Java Sea), FMA-

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RI 713 (Makassar Sea, Bone Bay, Flores Sea and Bali Sea), FMA-RI 714 (Tolo Bay and

Banda Sea), FMA-RI 715 (Tomini Bay, Maluku Sea, Halmahera Sea, Seram Sea and Berau

Bay), FMA-RI 716 (Sulawesi Sea and Northern of Halmahera Island), FMA-RI 717

(Cendrawasih Bay and Pacific Ocean), and FMA-RI 718 (Aru Bay, Arafura Sea and

Eastern Timor Sea).

Figure 1. Map of Indonesia’s Fisheries Management Areas (Source: MMAF, 2014b)

Marine Protected Area is “a clearly defined geographical space, recognized,

dedicated and managed, through legal or other effective means, to achieve the long-term

conservation of nature with associated ecosystem services and cultural values” (IUCN,

2008). MMAF (2014c) defined MPA as a protected area, which is managed on zoning

system, to realize the sustainable fisheries resources and environment management. Based

on data from Directorate of Conservation of Areas and Fish Species, MMAF, in May 2016,

there were 17,302,747.78 hectares of MPAs Indonesia-wide. These comprise 154 MPAs,

of which 32 managed by Ministry of Forestry (MoF) and 122 by local governments and

the MMAF.

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Figure 2. Marine Protected Area Distribution (MMAF 2016, adapted from www.kkji.kp3k.kkp.go.id)

For the purpose of this project, demonstration of GIS application for EAFM focuses

on Anambas Islands, Riau Islands Province. Anambas Islands is a small archipelago of

Indonesia, located 150 nautical miles northeast of Batam Island in the South China Sea

between the Malaysian mainland to the west and the island of Borneo to the east. Marine

Protected Area of Anambas is a conservation area under MMAF and local governance with

potential of ecotourism and abundant fish resources. The total area of Anambas MPA is

1,262,686.2 hectares (MMAF, 2014c).

Figure 3. Location of Marine Protected Area in Anambas Island Region

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Data

Data used in this project are categorized as secondary data. The data includes

EAFM models, EAFM performance indicators, and geospatial data.

EAFM Model

An EAFM model was used as a primary reference in building the logic of the

application of GIS. The EAFM Model that was applied by the Government of Indonesia

was created using a Multi-Criteria Analysis (MCA) approach. MCA is a decision-making

tool developed for complex multi-criteria problems that include qualitative and/or

quantitative aspects of the problem in the decision-making process (Prabhu et al., 1999).

Moreover, “MCA techniques can be used to identify a single most preferred option, to rank

options, to short-list a limited number of options for subsequent detailed appraisal, or

simply to distinguish acceptable from unacceptable possibilities” (DCLG, 2009).

Technical flag modeling was done by a MCA in which a set of criteria was built as

a base for the performance analysis of the fishery management area seen from the

ecosystem approach through the development of a composite index, with the following

stages (Adrianto et al., 2005; MMAF, 2011; Pregiwati et al., 2015):

1. Determine the criteria for each indicator in each domain of EAFM (fish resource,

habitat, fishing technique, social, economic and institutional);

2. Assess the performance of each object (based on location or species) for each

indicator tested;

3. Give a score for each of the performance indicators in each object (Likert score

based ordinal 1,2,3);

4. Determine the weighting for each indicator;

5. Develop a composite index of each domain for each object with a model function:

CAi = f (CAni….n=1,2,3…..m)

6. Develop a composite index for the entire EAFM on each object with the model

functions as follows:

C-object-i = f (CAiy 1,2,3...... y = 1,2,3….. z; z = number of object).

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Indicators are assessed and then analyzed using a simple composite based on the

arithmetic average to display in the form of the flag model (MMAF, 2011) as shown in the

following table.

Table 2. Flag Model of EAFM in Indonesia Score Value Flag Model Description

100-125 Bad 126-150 Not good 151-200 Medium 201-250 Good 251-300 Best

EAFM Indicators Performance

To test the GIS application, this project used the results of EAFM indicators

assessment that was done previously. Two EAFM performance data sets were employed

in this project: (1) EAFM assessment of 11 FMAs in Indonesia carried out by MMAF in

2011; and (2) EAFM assessment of Anambas Marine Protected Area conducted by

Pregiwati et al. in 2015.

The assessment of 11 FMAs in Indonesia is the result of collaboration between

MMAF, Indonesia World Wildlife Fund (WWF) and the Center for Coastal and Marine

Resource Studies (CCMRS) - Bogor Agricultural University. This study is done by using

the content analysis approach, in which the study focused on the substance of the

performance of fisheries management in FMAs and then was tested with EAFM indicators.

Moreover, this study has a limitation regarding data collection because it is based solely

on secondary data and data that were sourced on the administrative scale at the provincial

level. Therefore, caution is needed to interpret the results of the assessment.

EAFM assessment of Anambas Marine Protected Area is part of a study titled

“Linking Indicators for Ecosystem Approach to Fisheries Management and Management

of Marine Protected Areas Effectiveness in Anambas Island, Indonesia”. According to

Pregiwati et al. (2015), the research was conducted in March 2015 to June 2015, and data

collected in this study consisted of primary and secondary data. The primary data were

obtained through direct observation and in-depth interview.

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Geospatial Data

Shellito (2012) defined geospatial data as information that refers to location-based

data, which is tied to a specific real-world location. The ability to assign a location to data

is what makes geospatial technology different from other systems. The main benefit of

using geospatial technology is to link non-spatial data to a location. In this project, all data

related to EAFM model and EAFM performance are categorized as non-spatial data.

Meanwhile, geospatial data collected for this project are:

1. Indonesia’s administrative boundary (shapefile);

2. Indonesia’s Fisheries Management Areas (KMZ file);

3. Bathymetry (shapefile);

4. Ocean topography (raster dataset);

5. Coral reefs and mangroves (shapefile);

6. Lakes, reservoirs, rivers and roads (shapefile);

7. Fishing port and fish landing sites (shapefile);

8. Marine Protected Areas (KMZ file)

The geospatial data above are combined with EAFM indicators performance data

and other non-spatial data inside a geodatabase file in ArcGIS 10.3, such as provincial

fisheries statistics and national fisheries statistics. The geodatabase is a collection of files

in a folder on disk that can store, query, and manage both spatial and non-spatial data.

Furthermore, the geodatabase is the core geographic information model to organize GIS

data into thematic layers and spatial representations (ESRI, 2016).

Data Processing and Analysis

In accordance with the objectives of this project, data processing was divided into

three sections: (1) Build the EAFM measurement tool with Microsoft Excel; (2) Integrate

the performance of EAFM into ArcGIS software; and (3) Develop the Indonesia EAFM

geodatabase, in order to demonstrate the performance of EAFM along with other data that

available.

EAFM Measurement Tool

Implementation of EAFM needs to be supported by a simple tool to measure its

performance in a particular fisheries management unit. This tool was developed in the

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Excel file as an assessment template, which is based on the EAFM model and its indicators.

The indicators are compiled separately by their respective fisheries domain. Moreover,

each domain is created in a different spreadsheet with eight main columns in each sheet:

indicators, definition, data collection/monitoring, criteria, reference data, score, weight,

and value. Performance value is at the bottom of the“value” column, which indicates the

overall performance of a domain. The automated flag color was constructs by the

Conditional Formatting tool in Excel with format style: 3-color scale (minimum-1-red,

midpoint-2-yellow, and maximum-3-green).

Figure 4. Example of EAFM assessment on fish resource domain

A cumulative index and histogram bar are displayed in the spreadsheet to help the

user interpret the evaluation result. The cumulative index is a compiled index that reflect

the effect of all accumulated data. The value of the cumulative index is generated

automatically based on the input value in the sheet of each domain. From this spreadsheet,

the user can decide which domain or indicator needs more attention for management

improvement.

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Figure 5. The spreadsheet of cumulative index

EAFM Integration into ArcGIS 10.3

The cumulative index from the EAFM assessment was added as a new field in the

shapefile attribute table. Attribute tables are often joined or related to spatial data layers,

and the attribute values they contain can be used to find, query, and symbolize features or

raster cells (ESRI, 2016). Shapefiles used as reference are geospatial data that represents

the study objects, which are FMAs and Anambas MPA.

Spatial data of FMAs and Anambas MPA were sourced from MMAF (downloaded

from http://sig.kkp.go.id/). Data were obtained in the form of a KMZ file (Keyhole

Markup-language Zipped), which is a file extension for a placemark file used by Google

Earth. Furthermore, the file was converted into a shapefile, so that it can be displayed in

ArcGIS 10.3. In this project, the conversion process used the online converter named

MyGeoData (http://converter.mygeodata.eu/). This Converter has a user-friendly interface

and fast processing, the conversion process was done by just uploading the KMZ file into

the website.

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Figure 6. The interface of MyGeoData (source: http://converter.mygeodata.eu/)

The FMAs shapefile consists of eleven polygon areas that represent their location

in Indonesian water. The geographic coordinate system of this file is GCS_WGS_1984.

Then, this file was projected inside a data layer into Asia South Albers Equal Area

projection for calculation purposes. The original data consisted of fields in Indonesian

language, so editing was applied to translate all the fields and their values. After translation,

the original fields include Objectid, Shape, FMA, and Archeology_Zone. Furthermore, the

EAFM domains were added to the table, along with the “Area” field by the Calculate

Geometry command. The FMA attribute table serves as the main connector between the

result of EAFM assessments in Excel and FMAs spatial data in ArcGIS. The result of

modification of the FMA attribute table can be seen in the figure below.

Figure 7. The attribute table of FMA shapefile/feature class after modification

Almost all the steps above were also applied to build Anambas MPA. The only

difference is the step to extract the Anambas MPA from the original file that covers all of

the MPAs in Indonesia; this process was applied by the Export Selected Features command

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in ArcGIS. The fields for Anambas file after modification consist of Objectid, Shape,

Name, Area_ha, and then followed by six EAFM domains.

EAFM Geodatabase

Compilation data inside Geodatabase on this project were derived from government

agencies, which are: Centre for Analysis on International Cooperation and Inter Institution

– MMAF, Directorate of Fish and Area Conservation – MMAF, Geospatial Information

Agency, and Research Centre for Oceanography – The Indonesian Institute of Sciences. In

addition, new data were developed from the book “Statistics of Marine Capture Fisheries

by Fisheries Management Area (FMA), 2005 - 2014”, published by Directorate General of

Capture Fisheries – MMAF in 2016. These data provided as additional information to

analyze the performance of EAFM. Furthermore, all the data were converted into feature

classes in the geodatabase using ArcCatalog application.

RESULT AND DISCUSSION

GIS Application for EAFM

The application built using Excel and ArcGIS was combined with the data of

EAFM indicators assessment, both for eleven Fishery Management Area and also for

Anambas MPA. The composite index of each domain was inserted into the attribute table.

For easier interpretation, quantities symbology was applied with a manual classification

that refers to the flag model of EAFM in Indonesia. The FMAs attribute table with the

value of EAFM composite index can be seen in the following figure.

Figure 8. The attribute table of FMA shapefile/feature class with the value of EAFM composite index

From the figure above, it can be seen that the lowest domain is the economy, with

an average composite index for all FMAs of 163.64. The indicators of the economy domain

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are asset ownership, household fishery and saving ratio. This condition suggests that the

availability of fish resources with the existing management have not been able to guarantee

the welfare of the fishermen in Indonesia, especially for artisanal fisheries. Further

interpretation can be done by looking at the following GIS visualization with ArcGIS.

Figure 9. The performance of EAFM domains in Indonesia’s Fisheries Management Areas

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The biggest gap between the economy index and the resources aspects (habitat and

fish resources) occurs in FMA 711 (Karimata Strait, Natuna Sea, and South China Sea).

From Figure 9, the economy index of FMA 711 is considered bad, even though its

performance on habitat and fish resources are classified as best and good respectively. Why

fishermen in this area remain relatively poor, should be studied in the context of IUU

fishing activity by other countries in this area, and/or the lack of infrastructure and

technology. Many things can be questioned and analyzed. The EAFM performance

displayed via GIS application allows the user to access the information faster, to more

focus on the priority issues for deeper evaluation, and then respond with effective action.

The use of GIS was also demonstrated for the Anambas MPA. According to

Pregiwati et al. (2015), domains of fish resources, fishing technique and economy are in

the good category with the green flag, while domains of social, habitat and institutional

are in the medium category with the yellow flag, so the total average assessment is in the

good category. The composite value of each EAFM domain can be seen in the following

table.

Table 3. EAFM Performance in Anambas MPA Domain Value Flag Model Description

Fish resource 209 Good Habitat 191 Medium Fishing technique 269 Best Social 185 Medium Economy 205 Good Institutional 186 Medium Overall 207.5 Good

From Table 3, the lowest value of EAFM domain is Social with the score 185. The

social domain contains three indicators, which are stakeholder participation, fisheries

conflict and local knowledge utilization. The question is which indicator contributes most

strongly to the adverse impact in the social domain. The answer will be displayed in the

GIS visualization of the Anambas MPA. Moreover, the EAFM performance is presented

along with additional information on one of the island in Anambas (Fig. 10). The island

selected for this display is Jemaja Island, which has a long stretch of beach and is

surrounded by abundant coral reef ecosystems. This island is one of the most beautiful

islands in Indonesia for ecological tourism.

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Figure 10. EAFM performance of Anambas MPA and Jemaja Island land cover

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Geodatabase Application Data from several agencies were compiled as feature classes inside the geodatabase

file. The file geodatabase uses an efficient data structure that is optimized for performance

and storage. According to ESRI (2016), the data structure of the geodatabase file uses about

one-third the feature geometry storage required by shapefiles. The FMAs and MPAs

shapefiles from this project are combined with other spatial/non-spatial data for

management purpose. This geodatabase is a dynamic file, which is editable and can be

updated continually.

Figure 11. Example of the use of geodatabase file

From this geodatabase file, a user can display the map on national scale to show all

locations of fishing ports, fish landing sites and MPAs in Indonesia, like the example in

Figure 11. Moreover, the user can also focus on a particular regency, province, or FMA.

Non-spatial data can be linked in this geodatabase to gain more additional information,

such as fisheries statistics, population density, land use/cover, and much more. The list of

feature classes compiled in this geodatabase file can be seen in Appendix 2.

CONCLUSION

The use of this GIS application can help stakeholders to understand the

performance of EAFM in Indonesia. The result of EAFM measurement can be visualized

through GIS application, and successfully provide information for decision making. The

next challenge is to develop this project into an online basis, so it can be accessed by anyone

anywhere.

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Appendix 1. EAFM Assessment on Social Indicators

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Appendix 2. List of Feature Class inside Geodatabase File

Feature Class Explanation SANDSHL Estuaries ROADWL National Road NATROADWL Roads LUNITA Seabed Surface Layer Sediment LANDUA Land Use/Cover OneMap_Reef Coral Reef NEIGHBA Neighbour Countries LU_Natuna Land Use Natuna Islands LAMACAM Mangrove HYDROLPDTK River HYDROAKLH Lake/Reservoirs HARBFISHLN Fish Landing Sites HARBFISH Fishing Port FMAs FMAs with EAFM Performance FISHSTATFMA FMAs Fisheries Status FISHGR Fishing Ground Location FISHSTATPROV Province Fisheries Statistics FISHCATCH FMAs Fisheries Statistics MINIAP MPA 1 CSSEA MPA 2 CSPARK MPA 3 CSNATURE MPA 4 Coral_Jemaja Coral Reef Jemaja Island BSDSTA Villages Database Anambas_MPA MPA of Anambas Islands Adm_ID Indonesia Administrative Boundary Adm_AN Anambas Islands BATHYA Bathymetry DEM_Laut Ocean Digital Elevation Model

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Appendix 3. The Use of Geodatabase in Eastern Indonesia