Ecosistem Change

8/3/2019 Ecosistem Change

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Operational use of Geo-information forrapid identification and evaluation of

feasible areas for marine habitatconservation.

Application to Banten Bay on Java's

Northwest Coast, Indonesia

Michael Anthony Cusi

March, 2002


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Operational use of Geo-information for rapid identificationand evaluation of feasible areas for marine habitat

conservation.

Application to Banten Bay on Java's Northwest coast,Indonesia

by

Michael Anthony Cusi

Thesis submitted to the International Institute for Aerospace Survey and Earth Sciences in partialfulfillment of the requirements for the degree of Master of Science in (fill in the name of the

course)

Degree Assessment Board

Name Professor

Name Examiners

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS


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Disclaimer

This document describes work undertaken as part of a programme of study at the International

Institute for Geo-Information Science and Earth Observation. All views and opinions expressedtherein remain the sole responsibility of the author, and do not necessarily represent those of

the institute.


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Operational use of Geo-information for rapid identification and evaluation of feasible areas for marinehabitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia

i

ABSTRACTWithin the framework of an MSc research project in Geo-information for Coastal Zone Studies at ITC,

methods for the operational use of geo-information for rapid identification and evaluation of areas for

marine habitat conservation is being developed. The work aims to develop specific procedures and

protocols with the use of multi-criteria evaluation and knowledge-based expert systems that will allow

the semi-automatic / automatic identification of candidate areas that could be used for conservation or

nature reserve purposes. The traditional method of delineating such areas involves a tedious system of

expensive and time-consuming field exploration activities that are focused entirely on the bio-

chemical-physical characteristics of the areas concerned. This method being developed uses not only

such data but includes to a nominal extent the use of socio-economic and demographic data for the

identification of sites suitable for use as marine reserves that could be managed at the local

government or community level. Selected sites will also have as an added value its own level of

potential sustainability as may be deduced from this additional input. Likelihood of success will be

implicit in the selection. The need for time-consuming and expensive field surveys will also be

minimized by this method which pre-selects the prospective areas before any field surveys are done

contrary to the classical method which always begins with an intensive and therefore expensive, site

visit of the entire area being considered before the selection of prospective sites for more detailed

evaluation can even begin. This method is expected to aid planners and government environmental

agencies in the rapid delineation of reserves thereby focusing their valuable manpower and financial

resources on only those areas that are feasible for adoption and has the most likely chance of success

as a reserve.


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Acknowledgements


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Table of Contents

page

Abstract i

Acknowledgement ii

Table of Contents iii

List of Tables v

List of Figures vi

Chapter 1 Introduction to the topic 1

1.1 Introduction 1

1.2 Problem Definition 2

1.3 Aim of the Research 2

1.4 Research Objectives 2

1.5 Research Questions 2

Chapter 2 Conceptual Framework and Background 4

2.1 Conceptual Framework 4

2.2 Background of the Study 5

2.3 Attributes, Criteria and Values 5

2.4 The Study Site 6

Chapter 3 Methodology & Practical Approach 9

3.1 Review of Related Literature 9

3.1.1 Criteria for Nature Reserve Site Selection 9

3.1.2 Computer Systems for Site Selection 11

3.2 Selection of the Criteria for the Study 12

3.3 Criteria Valuation / Value Functions 15

3.3.1 Biotope / Benthic Habitat 16

3.3.2 Bathymetry / Depth 17

3.3.3 Total Suspended Materials (TSM) 17

3.3.4 Hydrology River Mouth 19

3.3.5 Sand Mining Activities 19

3.3.6 Industries / Factories 20

3.3.7 Other Existing Reserves 20

3.3.8 Fishing Activities 20

3.3.9 Population Coastline Index 21

3.4 Data Sources 21

3.5 Detailed procedure for each criteria 21


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3.5.1 Biotope / Benthic Habitat 23

3.5.2 Bathymetry / Depth 26

3.5.3 Total Suspended Materials (TSM) 27

3.5.4 Hydrology River Mouth 27

3.5.5 Sand Mining Activities 27

3.5.6 Industries / Factories 28

3.5.7 Other Existing Reserves 28

3.5.8 Fishing Activities 28

3.5.9 Population Coastline Index 30

3.6 The map overlays 32

3.6.1 The Ilwis

Overlays 32

3.6.2 The ArcView Overlays 33

Chapter 4 Results and Discussion 35

4.1 The Criteria Maps 35

4.2 The Selected Sites 39

4.3 Comparison between the Ilwis and ArcView procedures. 47

4.4 Minimum Geo-Information Requirements 48

Chapter 5 Conclusions and Recommendations 49

Appendices:

Appendix A Listing of Ilwis Script for the calculations of the bathymetriccriteria map

51

Appendix B Listing of Ilwis script for the calculations of the fishing activities

criteria map

53

References 54


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List of Tables

page

Table 3.1. The guiding rules used in the generation of the values in the criteria

maps. The best area for a marine reserve is one which has the following

set of characteristics.

15

Table 3.2. Projection parameters used for the customized map projection for all the

various maps.

22

Table 3.3. The properties of the map extents defined for the raster maps used in the

study.

23

Table 3.4. The description of the fishing methods (Nuraini, 2001) included in this

study together with the weights assigned them in the computations.

29

Table 4.1. Area in hectares of the suitability classes for marine reserves. Criteria

name indicates those criteria that was omitted from the calculations. The

column difference shows the area of the maps that deviate from the ideal

which is taken as the calculations for scenario 5. Negative values in

parentheses.

39

Table 4.2. The scores and their respective weights used for the five different

scenarios created for the analysis of the site selection for marine reserves.

44

Table 4.3. The calculated area in hectares of the selected sites for the three scenarios

that appeared to have a more realistic output. The values in the maps

ranged from 0 to 1. (Upper boundary for unsuitable is 0.5)

45


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List of Figures

Figure 2.1 The study area. Red dot in map A, indicates the location of the study site

while the red box (in Map B) shows the limits of the area used in the

analysis. Note that the extent of data available was much larger than the

study area considered.

7

Figure 3.1. A schematic diagram showing the general steps followed in the

identification, selection and preparation of the criteria maps.

13

Figure 3.2. The preliminary criteria tree created for the comprehensive examination

of all possible criteria that could be included in the analysis.

14

Figure 3.3. The pruned criteria tree showing the final criteria selected for the

analysis. Numbers indicate hierarchy.

15

Figure 3.4. Graphical representations of the various value functions applied to the

different criteria used in the study.

18

Figure 3.5 The mosaiced map sheets used for the examination of the spatial

accuracy of the input maps taken from various sources. Only one sheet

was missing, Serang (1109-634) from the series. The map sheet Pontang

(1109-643) was not scanned since it did not include areas that were

within the study site.

23

Figure 3.6. The different types of common stretching parameters available in ENVI

which allows on-the-fly visualization of the most favorable stretching

method that can be based either on the entire image, the selected view or

only on the zoomed part.

24

Figure 3.7. The partial cross table generated from distance classes of the various

biotope types with the assigned weights for each class. Highlighted row

A shows the score given to areas which are far from coral reefs but have

mangrove and seagrass near each other. Highlighted row B is the highest

class with all three biotope types occurring near each other.

25

Figure. 3.8. The domain table used for converting the total suspended materials to

criteria values.

27

Figure 3.9. The procedure followed for the creation of the coastline population index

criteria map.

31

Figure 3.10. The analysis types provided in the ArcView extension for multicriteriadecision making.

33

Figure 3.11. A decided advantage for the use of the MultiCriteria Decision Making

Tool extension is the provision of an interactive slider for the assignment

of criteria weights (map scores) which shows not only the actual assigned

score but also its relative weight as a percentage of the total.

34

Figure 4.1. The various source and the resulting criteria maps as used in the analysis.

All the criteria maps share the same legend and have been standardized to

values between 0 and 1.

35 -38


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Figure 4.2 The output maps generated using the index overlay with multi-class maps

in Ilwis 2.23, using different criteria weights. Although the legend is

the same for all the maps, the underlying class boundaries differ between

all the maps. The common class boundary for all the maps is only for the

not suitable class that was approximately at a value of 0.50.

40 - 41

Figure 4.3 The output maps generated using the multi-criteria decision making

extension (MCDM.AVX) for ArcView 3.2 , using different criteria

weights. Although the legend is the same for all the maps, the underlying

class boundaries differ between all the maps. The common class

boundary for all the maps is only for the not suitable class that was

approximately at an upper boundary value of 50.

42 -43

Figure 4.4. The weights table generated using the pairwise comparison in Definite

2.0. The final weight values were multiplied by ten and used for the

scenario 5 run of the overlay procedure.

44

Figure 4.5. Zoomed in portion of coral reefs showing the suitability selections for the

three scenarios considered.

45

Figure 4.6. The areas within the study site that was selected as highly suitable for a

marine reserve.

46

Figure 4.7 The six main sites that were at least four hectares in size. Numbers

indicate the area in hectares.

46

Figure 4.8. The red polygons indicate the areas where the heaviest fishing takes

place being the intersection of all the fishing methods surveyed. Areas

selected as reserves that lie within them cannot be used as a reserve

without changing fishing rules or policies.

47

Figure 4.9. The same area output for scenario 5 showing the similarity in theclassifications done using Ilwis (A) and ArcView (B). 48


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1. Introduction to the topic

1.1 Introduction

From the days our ancestors first walked this planet to the dawn of the technological age, man's

relationship with his environment is specifically characterized as maximum exploitation for the

maximal survival of the species. It was only in the later part, (the very recent last decades) of the

second millenium, after the persistent lobbying by several growing and increasingly vocal and

persistent environmental groups when particular attention was given by governments to such factors as

environmental protection and conservation of biodiversity. It is in this context that planners and

government agencies that are tasked to protect the environment, are usually drawn to make decisions

regarding the establishment and maintenance of nature / biodiversity reserves and sanctuaries. Initial

efforts to establish natural reserves were based on the classical notion of bigger is better and more

diverse is more desirable. This attitude has relaxed considerably such that more recently, there are

more efforts to establish such reserves / sanctuaries at the much smaller and more manageable local

government level. Such efforts have been enhanced by the concerted decision of most of the

developing worlds governments (the ones that are most in need of such nature reserves) to

progressively decentralize and devolve the actual operations of their environmental agencies (de

Fontaubert et al., 1996) to the lowest possible levels of government, having the focused efforts and

therefore the effects and benefits reaching up to the very grassroots of society.

These have led to the efforts by small communities and their leaders to additionally inculcate

conservation attitudes by the establishment of "community" managed nature reserves. The logic being,

that having the people themselves involved will make the exercise more meaningful and consequently

provide it with a much better chance for success. Not surprisingly, the major choke point for these

efforts have been in the identification of and eventual selection of suitable sites for use as a marine or

nature reserve. More often, the communities decide on the location of their reserves using their or their

leaders personal preferences such as proximity and existing non-use of the area, which may or may not

coincide with the inherent suitability of the bio-physical characteristics for a reserve. On the other

hand, some government agencies which assist the communities often use solely bio-physical

characteristics to decide where a reserve should be located, also mostly completely disregarding the

practical aspects of maintaining the reserve and the social costs involved in the setting up of the

reserve. This study aims to provide a desirable mix of objective and subjective information for

determining the suitability of coastal sites as marine reserves and therefore also implicitly includes the

identification of the likelihood of success for each identified site.


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1.2 Problem definition

Current classical method of identifying areas for marine reserves involves numerous physical and

financial requirements. It usually begins with an identified need for highly trained /expert individuals

who can perform the usually costly and time consuming field verification of numerous prospective

sites. Due to the natural constraints posed by the marine environment, selection procedures for reserve

site establishment always involves a considerable amount of financial capital, particularly since field

surveys done in an aquatic medium is always much more expensive than one done on land.

This study aims to develop operational procedures for creating an automatic (rapid) system for marine

reserve site identification / selection using a desirable mixture of both objective and subjective geo-

information utilizing the available tools for remotes sensing and a GIS. The main purpose of the research

is to develop an acceptable operational procedure to carry out the objective using a minimum mix of geo-

information with the least need for pre-site selection field visits.

1.3 Aim of the Research

The aim of this research is to develop:

A procedure /method or model for the automatic identification of prospective sites with a high

potential as a marine reserve using geo-information of various types without a need for

previous site visits or an intimate knowledge of the area

A checklist of minimum number and types of geo-information needed to accomplish the

objective.

1.4 Research Objectives

Illustrate the use of geo-information for the automatic selection of candidate sites for marine

reserves.

Determine the minimum number and quality of the geo-information needed to accomplish the

objectives.

Demonstrate how a GIS maybe used for planning purposes to assist environmental planners in

making informed decisions regarding marine reserve site selection.

1.5 Research Questions

Can GIS be used to determine where a marine sanctuary or reserve may be located?


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What are the minimum number and quality of geo-information needed to determine in a GIS

where a marine sanctuary or reserve should be located?

What can improve the process of site selection for marine sanctuaries /reserves?


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2. Conceptual Framework andBackground

2.1 Conceptual Framework

A very important concept that serves as a backbone for this work is the notion of conservation in

general and nature reserves in specific. To a large extent, the practice of environmental conservation

has been going on a worldwide scale with the establishment of nature reserves / sanctuaries in almost

every corner of the earth. These activities have brought to light the importance of such reserves not

only for its originally anticipated function of protection and preservation of plant and animal species

and its fragile ecosystem habitats, but for its added benefit of serving as a rallying tool for introducingand embedding the conservation ideas and values of a safeguarding minority to the majority of

occupants of the planet.

Stringently following the widely accepted norms and procedures for the establishment of reserves, it is

probably the case that the rate of reserve creation can never sufficiently cover its intended goals.

Rainforests continue to be destroyed. Corals reefs continue to be blasted. And even areas as remote as

the frozen Southern Continent become increasingly palatable targets for human exploitation. What is

needed then is a means of not only creating more reserves or series of reserves but a means of

inculcating the values of conservation, protection and preservation to those members of society that

have made it their business to exploit to the maximum nature and its resources. For indeed, direct

conservation measures are the most effective method of biodiversity conservation (Ferraro and

Simpson, 2001).

The system proposed in this study should allow for a more lenient although fairly accurate and

convenient system for nature reserve establishment particularly at the local government or community

level, the grassroots level. Since the output provides for a set of possible sites where a marine reserve

should be situated, there is still a need to eventually carry out some amount of field surveys to verify

and justify the selections (most probably to officially satisfy national or international funding

requirements). Convenience and fast fairly accurate output would in the long run save more of the

meager government funds allocated for this purpose. That can accomplish not only the original

rationale behind the concept of conservation itself but have the added benefit of providing the

participant peoples themselves the feeling of being part of the conservation efforts and therefore give

them the impetus to, in their own way conserve and preserve and protect nature and ultimately

themselves.


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2.2 Background of the Study

The title of this paper provides the best launching structure for explaining some important concepts

directly used in the study. Operational use of geo-information involves the development of procedures

that are equipped with the proper methods for a specified purpose, that of identifying and evaluating

areas for marine habitat conservation. These procedures and methods include from the onset, the

identification of relevant information that should be used, and including the procedures employed to

prepare and formulate these information into a form which can be handled in a geographical

information system (GIS), employing GIS tools for the manipulation and creation of these resulting

significant information, and the use of spatial models for the purpose of deriving the intended output,

identifying sites suitable for marine habitat conservation. Since the studys areas of concern are

geographically fixed, all information used in the evaluation should be in the form of spatially

referenced data. Required information that is normally not available in a geographic context would

have to be generated.

This process of identification and evaluation also implies that an intensive assessment and comparison

of available information are conducted to eventually come up with a value judgment. And the presence

of value judgments mean that certain factors included in the evaluation may or may not be entirely

objective. Indeed, in any pursuit where one is made to choose something over another, the activity

approaches subjectivity and the information used in the evaluation is then used to objectively justify

the choices one makes.

2.3 Attributes, Criteria and Values

Some important elements in the evaluation procedure described in this study involve attributes,

criteria and values. As these elements will be used intensively in the course of this study, its definition

will be explained keeping in mind that the evaluation procedure being described is related to the

selection of possible marine conservation sites with a purpose other than the usual rationale.

(Colson and Bruyn, 1989) in (Sharifi, 2001), defines attributes as a characteristic of an option / object

which can be evaluated objectively or subjectively by one or several persons according to a

measurement scale. They are those characteristics or properties of a spatial object that can be used to

emulate the conservation interest in that object. They can be measured or recorded either by surveying

a site or by deriving them from topographic maps, satellite images or field reports. By itself, an

attribute cannot be directly used in an evaluation since it gives only the properties of an object. It

needs to be converted into a criterion, which is the way of expressing an attribute in a form that can be

used in an evaluation. To illustrate, we use as a good example, the study of a small patch of reef our

spatial object - so that a possible attribute which we can measure by surveying the reef will be the


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listing of species of corals occurring in that patch of reef. On its own, the species listing provides just

that, a list of the coral species. But as a consequence of listing down the species of occurring corals,

without having to exert any additional effort, the number of coral species can also be derived. Such

that when converting this attribute into a criteria that can be used for evaluation, it can be translated

into either species richness which is simply calculated by counting the total number of species present

(Krebs, 1978), or by calculating any of a number of indices of diversity such as the Shannon-Weiner

index piLogpi (Pielou, 1977) which is simply the summation of all occurrences of all species

multiplied by the logarithm of the occurrences. This implies that both attributes and criteria are

quantifiable and are therefore also differentiable into any number of arbitrary classes.

Values on the other hand, reflects the significance and meaning given to the attribute / criteria being

considered. It demonstrates how much weight is given to a criteria and how important it is. Within acriteria, a range of values may also be applied. This range of values only has to be consistent to be

effective. Going back to our example, we can regard a reef with fifteen coral species, as having a

higher value than one which has only ten, and one with only five species as having lesser value than

one with ten. It should also be emphasized that the value one places on a criteria is reflective of ones

basic principles and standards, which may or may not be influenced by the social order in which the

valuation is being applied.

2.4 The Study Site

The study area is situated on the northwestern coast of Java, Indonesia approximately 60 kilometers

west of Jakarta (see Figure 2.1). The Banten Bay (Teluk Banten) area is a north-facing bay with

approximately 150 km2

(Hoekstra et al., 2000) area characterized by shallow and highly turbid waters.

The main ecological communities are aptly represented in that coral reefs, mangroves, seagrass,

softbottom communities are present. In addition, a large population of breeding egrets are located all

over the bay but more particularly in specific breeding grounds that occur in the mainland, along the

central part of the southern margin of the bay, Pulau Dua and in an island approximately on thenortheastern section of the bay, Pulau Pamujan Besar.

Along the eastern border of the bay, an inactive delta of the Ciujung (ci, meaning river) can be found.

This inactive delta was present from the beginning of the 1920's, so that the main flow of the Ciujung

no longer discharged directly into Banten Bay, but was carried by a short-cut canal eastwards away

from the bay. From then on, the abandoned, unprotected delta has been subjected to erosion while, a

new river-dominated delta was forming to the coastal areas east of the bay. The mangrove areas,

which were in earlier times more extensive and therefore more suitably equipped for its traditional role

as an erosion buffer and wave damper, have already been reduced to a thin belt, where traditional

coastal defenses are applied to prevent further erosion due to wave attack.


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On the southern boundary of the bay, a low-lying coastal mud plain is present where the continuous

process of sediment deposition has moved the shoreline seaward several hundreds of meters over the

last centuries. This continuous accretion has resulted in the formation of two tombolos, which are links

of sediment between islands and the mainland. The resulting peninsula is named Pulau Dua, which

became a Waterbird Nature Reserve of national importance. The last of the Pulau Dua islands merged

with the mainland coast around 1980, to the detriment of nearby coral reefs and their associated biota.The process of sediment accumulation is continuing, which is apparent from recent deposition rates of

Figure 2.1 The study area. Red dot in map A, indicates the location of the study site while the red box (in Map B)shows the limits of the area used in the analysis. Note that the extent of data available was much larger than the

study area considered.

A.

B.


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up to 2.6 g cm-2 yr-1. Moreover, isobaths near the islands Kubur, Lima and Pisang suggest that the

islets are in a process of attaching to the coast, probably following the recent history of the Pulau Dua

islands (Douven, 2001).

Further west of the bay is the Sunda Strait. The coastal areas on this side of the bay are lined by

numerous industrial sites, including a steel mill which can greatly impact the shore in this area. In

addition, large-scale land reclamation and jetty construction have taken place at the north and north-

western tip of the peninsula. Further southward, shorelines are characterized by an array of cross-shore

bamboo structures anticipating possible future land reclamations. Close to the western coast, a 100-

hectare wide, dense seagrass bed can be found (Douven, 2001).

Owing to its geographical location, being very close to the equator, Banten Bay is predominated by a

typical monsoonal climate with mostly gentle winds and only very few storms. The typically wet

Northwest Monsoon lasts from December to March, whereas the normally dry Southeast Monsoon

occurs from April to October. Fortunately, during the Northwest Monsoon, or wet season, wind driven

circulation promote drift flows in the bay that are generally directed eastward, protecting Banten Bay

from the rain-induced increased flow of sediment laden Ciujung waters. The Southeast Monsoon bring

on westerly flows, which sometimes transport waters that originate from the new Ciujung towards the

bay. Turbidity (suspended sediment concentrations) rarely exceeds 15 mg/l at depths larger than 4

meters. Close to the coral reef fringes and in the shallow nearshore area however, local hydrodynamic

processes frequently raise incidental turbidity levels over 100 mg/l (Douven, 2001).

The increasing presence of large industrial activities and its proximity to the highly navigable Sunda

Strait places the area under very high environmental threat not only from industrial and shipping

related pollution but also from increasing domestic discharges and incompatible landuse induced

erosion. The main issues facing the Banten Bay coastal zone may be traced to an increased threat of

environmental degradation brought about by intensified development activities coming from all

sectors of society. Already, a good portion of the coastline, roughly 70%, has been converted into

aquaculture facilities tasked with producing monospecific although commercially important export

products.


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3. Methodology & Practical Approach

3.1. Review of Related Literature

3.1.1 Criteria for Nature Reserve Site Selection

The selection of sites for conservation including the procedures and criteria used are discussed

exhaustively by (Margules and Usher, 1981), (McKenzie et al., 1989), (Gehlbach, 1975), (Wright,

1977). A detailed methodology for the assessment of priorities and values in nature conservation was

also prepared by (Helliwell, 1971). A central and persistent concept expounded in all these discussions

is focused on the main purposes or the justification for the establishment, and the characteristics of a

reserve, that is for the express intention of preserving and conserving biodiversity, covering arelatively large area, while keeping in mind a very broad range of conservation goals. These include

preservation of rare and endangered species, maintenance and upkeep of fragile environments,

preservation of biodiversity and environmental stability. The economic value and financial rewards of

a healthy environment (Pearce and Moran, 1994) particularly to the tourism and recreation industries

(Baldwin, 1989; Zins and Jacques, 1999), has also received some attention. Even such mundane

grounds as preservation of an areas natural beauty (Dower, 1976) and aesthetics has been used as a

rationale. Whatever the justification or purpose that is used for the establishment of a nature reserve, it

is precisely due to this variety of reasons that these confusing and overlapping assortment of criteria

has become evident.

In the studies dealing with the selection of relevant criteria for the establishment of reserves, there

appears to be an overall similarity in the types of factors/ criteria chosen and the manner of

quantifying them. Most if not all of the criteria are a direct result of actual measurements and

observations made in the field. Other qualitative information may be acquired by simple site visits.

Field surveys are therefore a necessity in order to acquire enough facts to sufficiently express or

represent criteria. On the other hand, the collection of secondary data in the form of case studiesrepresent another method of acquiring information that can also be used in the formulation of criteria

used for site selections of natural reserves. The main purpose of collecting such data is to develop a

system of selection that is structured with objectivity in mind, even when the final evaluation method

results in a working situation that has a mix of both subjective and empirical data. Depending on the

type of reserve in mind, site selection for natural reserves can choose to consider only a limited

specific set of criteria that may also be irrelevant for other types of reserves with a different purpose.

The evaluation may for example include the natural beauty of an area, its accessibility and availability

of infrastructure support (Tans, 1974). These criteria are a mixture of both subjective and objective


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types that may not be warranted if the reserve is established primarily as a seed source, gene bank or

natural stock replenishment site for commercially important food species.

Criteria that may be relevant for one type of reserve may not necessarily be relevant for another.

(Howard et al., 2000) used a combination of biological and economic factor to choose between

previously identified potential nature area reserves. Initially they selected the sites using purely

biological criteria but later modified their procedure to ensure that both opportunity costs and potential

land-use conflicts were minimized by considering other non-biological criteria. A study conducted by

(Polasky et al., 2001) on the reserve site selection for terrestrial vertebrates in Oregon used data onspecies ranges (biological) and land values (economic) and was able to find a variety of cost-effective

strategies that can represent a maximum number of species for a given conservation budget. By

varying the budget, he was able to demonstrate the cost of obtaining various levels of nature reservespecies representation. In general, effective conservation decision-making requires integrated analysis

of both biological and economic data.

In general, criteria for reserve site selection may be grouped into those that are essentially scientific

and those that are political (Margules and Usher, 1981). Scientific criteria are commonly those criteria

that can be measured directly from the environment and may require a field survey or site visit to be

sufficiently described. The assessment of the conservation value of an area may be based entirely on

its scientific characteristics. Indeed, most of the larger nature reserves that are recognized both at the

national and international levels (and are therefore funded accordingly) are all fully supported and

justified by a detailed comprehensive scientific study that is focused almost entirely on its ecological

merits. However, when the assessment provides equal indices so that the certain areas in consideration

appear to be equally desirable as a natural reserve, some other deciding criteria needs to be introduced

to allow decisions to be made as to which ones among the potentially possible sites should be

established as the reserve. This brings us to the realm of the unempirical yet inevitable political basis

for reserve site evaluation.

Political based criteria are those that are not founded on any biological, ecological or physical

characteristic of the area and are therefore not used for the primary assessment of the potential of an

area as a reserve. It is however specially considered when final decisions on the establishment of a site

has to be made whenever there are choices to be made with all other criteria being considered as equal.

The merits of a scientific evaluation may not always bring about a decisive selection and a site

evaluation may be in need of a tie-breaker. The danger however is that in some cases, the location of a

reserve is fully determined from the choice of a powerful political entity whose criteria is based

entirely on their own rules and not on any other. In such cases the reserve established operate more as

a political trophy more than anything else. On the extreme side, some politically motivated

establishment of marine reserves act mainly as a personal playground in the guise of a nature reserve


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for some politicians where the ordinary fisherman is lawfully prevented from fishing but where a

politician and their cohorts are free to do whatever they please.

3.1.2. Computer Systems for Site Selection

Studies on site selection with the use of computers were extensively reviewed by (Cleveland et al.,

1979). Similarly, there are also numerous specific papers written on the use of computer models for

the selection of various types of localization projects such as land fill sites (Basagaoglu et al., 1997;

Chalkias and Stournaras, 1997; Chang and Wang, 1995; Frantzis, 1993; Hussey et al., 1996; Juang et

al., 1995; Kao and Kao, 1996; Karthikeyan et al., 1993; Lin and Kao, 1999; Lindquist, 1987; Lolos et

al., 1997; Muttiah et al., 1996; Rouhani and Kangari, 1990; Siddiqui et al., 1996; Van-Zee and Lee,

1989), aquaculture facilities (Aguilar-Manjarrez and Ross, 1995; Arnold et al., 2000; De Silva et al.,

2001; Ross et al., 1993; Stagnitti and Austin, 1998), land treatment systems (Sun et al., 1998), waste

water treatment facilities (Wenbo, 2001), oil storage tank farms and pipelines (Kuna, 2001),

communication facilities (Piyasiri, 2001), forest management (Church et al., 2000; Stohlgren et al.,

1997), housing (Dawson, 1996; Helmy, 2001; Mahmoud Humeida, 2000)and tourism (Chilufya, 2001;

Falan, 1996; Reichel et al., 1998). A common factor in most of the studies has been the development

and implementation of an expert system that is specifically tailored to the objective in consideration.

In addition all these studies employ some kind of multi-criteria evaluation that then eventually

determines, selects or suggests the specific target areas where the objective is best put into practice or

best not to be implemented. Typically, these systems identify a broad set of suitable sites that are

assigned varying levels of suitability. The most common method employed for these systems include

the typical overlaying of attribute / criteria maps using a number of commonly accepted aggregating

functions. Moreover, the attribute / criteria maps used maybe utilized in its current form, derived from

base source maps or may be a combination of two or more criteria.

Some systems were also designed to provide planners, and people who make locational decisions, the

use of knowledge based systems in which their domain specific expert knowledge is combined with

some problem solving strategies, techniques and mathematical models of locational analysts

(Armstrong et al., 1990). The problem for example, of selecting nature reserves has been recognized

and has become prominent in recent times such that a variety of approaches have been promoted for

selecting those sites that should be included in a reserve network. Some of the techniques developed

employ heuristic algorithms. Others use a set of models and accompanying algorithms with an integer

programming formulation of the problem (Arthur et al., 1997). In order to provide complete

information to decision makers, the ones who decide where the reserves should be, the determination

of all alternate optimal solutions is necessary. And although sophisticated computational methods have

been developed to help us to identify the optimal sets of potential nature reserves, because of

unresolved problems on data quality and an identified lack of communication between scientists and


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managers, the impact of computational site-selection tools in applied conservation planning in general

has been minimal (Cabeza and Moilanen, 2001).

3.2. Selection of the Criteria for the Study

The most comprehensive review of the subject of selecting criteria for the establishment of reserves

and conservation sites available in literature is the paper done by (Margules and Usher, 1981). The

specific criteria selected for this study were taken primarily from the list provided, although some

were actually a derivative of another criteria or may belong to the same class of criteria mentioned.

Since this study intends to be as comprehensive as possible, both bio-physical and socio-economic /

demographic criteria were considered. There were however some guidelines that were considered in

making the selections particularly when selecting the specific criteria.

The first step to any multicriteria evaluation problem is the proper identification and definition of all

the criteria that is relevant to the purpose of the evaluation. As criteria are determined, their individual

and collective relevance and therefore their role in the multicriteria evaluation is also given

consideration. Although the initial emphasis was on the comprehensive identification of all possibly

relevant criteria, special attention was also given on the availability of the data that may be used to

represent the criteria. Quality of available data was also given enough consideration and where it

merits and accuracy was clearly dubious, they were disregarded. It goes without saying that although

efforts to include all possible criteria were done, it is almost impossible to have all identified criteria

included in the multicriteria evaluation. In reality most of the criteria suggested in literature would not

be practical for the purposes in mind in this study as their rationale in the suggestion of those criteria

were somewhat different from this one. Keeping in mind that one of the main objectives of the study

was also to determine the minimum amount of geo-information needed to accomplish the site

selection, the next step after identification of all problem specific and relevant criteria was the

evaluation of its importance or significance. Figure 3.1 shows the general steps followed in the

identification, selection and preparation of the criteria maps.

In the initial stages of the research, a comprehensive criteria tree was developed as shown in Figure

3.2. This was eventually pruned to a much lesser tree after the preliminary map overlays were

conducted and initial site selections were compared (see Fig. 3.3). Some criteria maps that were found

not to be contributing sufficiently to the selection process were removed from the analysis. Some other

important criteria such as species diversity, etc., that were highly suggested in literature could not be

included due to the lack of required data that could be used as a source map. On the other hand, most

of these non-included criteria would need a more detailed study of the area before the data could be

made available in a form that could be used in the evaluation so that it defeats the main purpose of the

study which is to facilitate the rapid evaluation of sites for marine reserves without the need for the


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costly and time consuming field surveys. Other identified criteria such as representativeness and size

were not given much significance as they become critically important only if one regards strictly the

purpose of establishment of a nature reserve in its classical sense, which is first and foremost, the

preservation of biodiversity. As mentioned earlier (Chapter 1.1), the supposed purpose of marine

reserve establishment taken in this study is not all throughout the same as the purpose of a classical

reserve and therefore it is not expected to perform exactly as one and subsequently need not be

subjected to the same strict selection process. Their exclusion in this study however, does not in any

way intend to diminish their importance, only that in this particular case, their use may serve to

impede more than smooth the progress of the process of selection that is intended.

It would be important to emphasize that the final criteria used in the analysis could be divided into two

major groups (see Fig. 3.3), the bio-physico-chemical factor and a threat factor. The first principally

considers all the biological and physico-chemical parameters that contribute to an area being suitable

for the establishment of a marine reserve. The inherent ecological characteristics present in the area

define the limits or boundaries of these criteria. As such, the main emphasis is given on those factors

that clearly contribute to defining where a marine reserve should be placed. Taken alone, it picks out

the areas where a marine reserve should be best located assuming a continuing state or natural

progression of the environmental factors, without the addition of any external unnatural extenuating

Figure 3.1. A schematic diagram showing the general steps followed in the identification, selection and preparation of

the criteria maps.


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factors, which is what the second factor intends to cover. The threat factor comes from a combination

of factors that are mostly contributed by man, the main source of the unnatural extenuating factors.

The logic behind using this factor is for tempering the selection made by the previous one since it

gives a general indication of the restricting factors that will tend to emphasize those areas with

characteristics that are not very suitable for a marine reserve due to the presence of unnatural

conditions.

Figure 3.2. The preliminary criteria tree created for the comprehensive examination of all possible criteria that could

be included in the analysis.

Biolo ical

Corals

Seagrasses

Mangroves

Biotope / Benthic Habitat

Chlorophyll concentrationPrimary productivity

Anthro o enic

Population employment

Domestic discharges

Other reserves

Economic activities

Fishing activities

Industries / factories

Industrial discharges

Seaweed culture

Ports and harbors

Resource extractionSand mining

coral mining

Ph sical -Chemical Water Quality

Total suspended materials

chlorides

Dissolved oxygen

pH

nutrients

Total coliform

temperature

conductivity

landcover Land use

Hydrology

Water depth

currents

Rivers / canals

catchment

MarineReserve


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3.3. Criteria Valuation / Value Functions

Assigning values to the different identified criteria was done keeping in mind a set of general guiding

rules as shown in Table 3.1. The available source or input maps were then converted into criteria

maps using a variety of GIS processing manipulations using the software packages ILWIS2.23,

Arcview3.2, ENVI3.4, and Surfer7.

The valuation procedure for each

criteria selected in the evaluation is

described in detail in the following

paragraphs. It would be important to

note that as the valuation progressed in

the course of the work, several

possibilities and methods of valuation

became evident for each criteria. The

actual method followed for each criteria

map was made with due consideration

of the nature of the source maps. There

are for example only so much methods

allowable for a distance map. In general

the value functions used were mostly

Table 3.1. The guiding rules used in the generation of the

values in the criteria maps. The best area for a marine reserve

is one which has the following set of characteristics.

The presence of one or more marine benthic habitat (coral

reef, seagrass beds, mangroves) either singly or in

combination with the others;

depth between 0.5 15 meters;

good water quality (clear water);

at least 4 hectares of contiguous area;

furthest distance from river mouths discharging large

amounts of sediments;

least fishing pressure; controlled fishing practices;

proximity to other previously defined nature reserves;

furthest distance from aquaculture centers;

furthest distance from resource exploitative activities;

furthest distance from harmful industrial activities;

furthest distance from population centers / coastal

development;

Figure 3.3. The pruned criteria tree showing the final criteria selected for the analysis. Numbers indicate

hierarchy.

Threat Factor Human Activities9 Fishing activities

7 Industries / factories

8 Sand mining

6 Population

Bio-physico-chemical

Water Quality 3 Total suspended

Hydrology2 Water depth

4 Rivers / canals

Biotope / Benthic Habitat

1.1 Corals

1.2 Seagrasses

1.3 Mangroves

5 Other reserves

MarineReserve


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linear with exceptions noted in the detailed description of the valuation procedure for each map that

follows.

3.3.1. Biotope / Benthic Habitat

Valid types are areas that are containing any of three possible major biotope types; coral reefs,

seagrass beds or mangroves. The most important habitat or biotope was assigned to coral reefs

followed by seagrass beds and then to mangroves. Needless to say, this order of importance is purely

incidental and is based solely on the authors preference (a coral reef biologist by training) and may be

rearranged by anyone who has a special predilection for another biotope type. It would be interesting

however to find out how the order of importance for the major biotope types can influence or affect

the outcome of the analysis since their value and therefore their effects are taken not just individually

but in combination with the other types. Any variations should however, not be expected to

significantly alter the major candidate sites that will be selected, as the final map that will be used to

represent this criteria is an aggregate of all three. This aspect is nonetheless investigated further and is

discussed in more detail in a later chapter.Areas with valid habitat types that are in proximity to another habitat (with a combination of two or

more of the biotopes) get the highest values. Therefore, the value of any valid area increases if another

habitat type is near it. As an example, an area with a coral reef is valid and has a high value, but a

coral reef area that is occurring very near a seagrass bed and a mangrove forest would be assigned the

highest value. Consequently, areas where seagrass beds are found would have better values if they

occur close to mangrove forests or even much higher if situated close to a coral reef. Likewise, areas

with mangroves become more valuable if they are found close to either seagrass beds or coral reefs.

This leads us then to our definition of proximity. From experience, six arbitrary distance boundaries

were specified. These include distances of 50, 100, 200, 500, 1000 and 40000 meters. Since the pixel

resolution used for the analysis was 20 meters, a minimum distance attribute of fifty covers at least

those pixels that are almost contiguous with the pixel in question and at the same time is distant

enough so that it can be differentiated in the satellite image. It was also not very difficult to choose a

20-meter pixel size for the analysis since it is also the resolution of the satellite image (SPOT-XS)

used in the delineation of the underwater habitats (coral reefs and seagrass beds). In reality, a distance

of fifty meters as a transition zone between different biotope types is not uncommon and is usually the

case particularly in shallow tropical reefs. Except for the last exceedingly large class, which is more a

required computational boundary constant more than any, the succeeding distance classes chosen are

all almost a representation of a subsequent two-fold increase ending with 1000 meters or 1 kilometer,

a distance which is certainly, already too far for the biotope types to effectively influence another and

therefore change its valuation.


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3.3.2 Bathymetry / Depth

Figure 3.4a. shows a graphical representation of the symmetrical value function applied to the depth.

Since we are concerned with areas to be designated as nature reserves, an innate limitation for any

reserve containing such biotopes as corals and seagrasses would have to consider the inherent limits of

growth and development of such biotopes in their natural environments. (This is of course not true for

mangroves.) Due to the natural requirements of corals and seagrasses for relatively shallow waters,

only the relatively shallow depths would have any value. A depth of 30 meters is considered as the

maximum depth that will be included in the analysis. This depth is considered as the lowermost

boundary of the optimal depth in which corals and seagrasses would thrive. To compensate for the

unavoidable although predictable variations in water depth due to tidal influences, the range of the

tides in the study area (1 meter) was considered such that those areas whose depth is less than half a

meter, and therefore completely exposed or above water during low tides, were considered as having

no value in the analysis.

To closely mimic the real world situation, it is also important to note that the best growth rates for

coral and seagrasses occur at depths between five to fifteen meters due to the maximal availability of

sunlight, an important biotic necessity. Areas therefore, that are found between these depths will have

the highest values and those areas with depths between the minimal and maximal limits to these

optimal depths will have a progressively increasing value although their highest value will never

approach the maximum and their lowest will never be naught. On the maximum boundary, all areas

with depths of twenty meters or greater will have no value.

3.3.3 Total Suspended Materials (TSM)

Figure 3.4b. shows a graphical representation of the value function applied to the criteria of total

suspended materials. This is a monotonically decreasing function that has a cut-off value at the

observed measurement of total suspended materials standard of 6 mg/L. The class boundaries defined

(and indeed the input map) in this criteria is derived, without alteration from the work of

(Ambarwulan, 2002) and will not be rationalized further in this paper. The logic however, of deciding

on the cut-off class is explained. And the explanation can again be found in considering the essential

biotic requirements of the major organisms present in the study site.

Corals thrive in clear water with low amounts of suspended sediments which tend to clog their

digestive and respiratory tracts, and which at the same time lowers the amount of sunlight that their

symbiotic zooxanthellae (provides the corals with their food and nutrients) needs for survival. The

higher the amount of suspended materials the more detrimental to coral growth and development.

Such areas will be given the lowest values. Although the increased sediment load, if it settles to the

bottom, will tend to provide some form of benefit for a seagrass community that can use it as a growth


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substrate, its initial effect is patently unconstructive since its suspension in water dissipates the greatly

needed sunlight and lowers its penetration depth and therefore diminishes the seagrasses

photosynthetic activities. For a plant, that can only be a drawback.

A. The value function applied to the depth criteria B. The value function applied to the criteria on total

suspended materials.

C. The value function for the distance to river nouths. D. The value function for distance from sand mining

activities

E. The value function as applied to the distance to

industrial facilities.

F. The value function as applied to the presence of other

existing reserves.

G. The value function for the presence of different types

of fishing activities.

H. The value function as applied to the criteria of distance

from populated areas.

Figure 3.4. Graphical representations of the various value functions applied to the different criteria used in the study.


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3.3.4 Hydrology River Mouth

The influence of rivers to the marine environment may be simplified by considering its main

contribution sediment laden fresh water. Although not immediately obvious, the presence of

sediment in the river discharge would tend to relate this criterias valuation to the previous. The main

difference is that the previous criteria refers to in situ sediment, that which is already in the water

column and constitutes a clear and present danger, completely different from the kind of detriment as

the future sediment that are still coming forth from the rivers.

Additional volumes of fresh water input from the river are also not very favorable for coral growth and

development, as the organisms prefer marine saline environments in contrast to estuarine saline

environments with low salinity. Seagrasses on the other hand are more tolerant of lower salinities and

may in some instances even thrive at slightly less than pure marine waters. In general however, the

cons outnumber the pros so that the greater the possibility of an increase in future fresh water and

sediment from the rivers, the lesser the benefits expected for the target biotopes and therefore the

greater its impact. It should be noted however that the presence of a strong dispersing factor such as

wind driven or tidal currents would tend to mitigate this influence making its effects less intense.

However, it is assumed that the water currents occurring in the study area is on the average fairly

constant and relatively not so intense as is expected in areas characterized by shallow water

embayments.

The characteristics of the watershed / catchment that are found upstream of the river mouths is also

expected to contribute largely to varying levels of impact. These are all dependent among others, on

the size of the watershed, the actual volume of water that is discharged, the general slope of the area,

its internal relief and the prevailing land use practices that may retard or promote soil erosion rates. A

more comprehensive characterization / representation for this criteria was not possible because enough

information that will allow even a rough computation of an index (for example, total discharge

multiplied by the average slope then divided by the total area of the catchment) for the impact

potential for each river was unavailable for the entire area so that it was assumed that for this situation,

the impact potential and consequently the effects of each river were equal (which makes everything

simpler). The value function created for this criteria, therefore is monotonically increasing (see Fig.

3.4c). The further the distance from the river mouths (where the sediment laden freshwater pours out)

the greater the value.

3.3.5 Sand Mining Activities

Since sand mining tends to increase the release of additional potentially erodable sediment to the

environment, it is very similar in form as in manner of impact with the previous criteria. Albeit

probably more consequential since the process of sand mining would also tend to release potential


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pollutants (a very definite deficiency for nature reserves) that would otherwise remain trapped and

inaccessible among the terrigenous deposits, a monotonically increasing value function (see Figure

3.4d) was also applied to this criteria. The further is the distance from the sand mining activities the

greater the values applied.

3.3.6 Industries / Factories

The presence of any type of industry or factory is almost always considered as a negative factor for

any type of nature reserve. They are immiscible in disposition, and are from any point of view,

contraindicated. Nature reserves need to be located as far away as possible from industrial areas with

their pollutants, by-products and disturbances. By default, a cut-off distance should be imposed on this

criteria for indeed, only up to a certain extent, can factories and industries affect the natural

environment. But considering that the area is characterized as a shallow water embayment, and that

the prescribed cut-off distance (Bryant et al., 1998) is more than the longest fetch in the study area, the

cut-off distance was not applied. A monotonically increasing value function (see Fig. 3.4e) was

applied to this criteria.

3.3.7 Other Existing Reserves

Ideally, a series or nature reserves forming a coherent network with segments that are neither too far

away nor too near to be considered as one is the preferred configuration from both a biological andmanagement point of view. With this in mind, the valuation for this criteria is described as a

monotonically decreasing function (see Fig. 3.4f), with increasingly reduced values the farther away

one moves from the two defined and already managed mangrove egret breeding reserves.

3.3.8 Fishing Activities

The practice of any type of fishing activity represents a certain kind of disturbance that is not very

compatible with nature reserves. In fact, one of the main reasons for the establishment of a reserve is

to provide areas where no fishing activities or for that matter any kind of exploitative activity can take

place thereby setting aside a definite part of the reef area for reproductive and growth purposes of the

economically important target species of fish. Fishing as an exploitative endeavor represents a defined

disruption of the natural state of the environment and the greater the diversity of methods employed in

fishing, the more efficient the activity becomes and therefore the more damaging its effect. It is also

true that some fishing methods have more negative effects or are more damaging than the others.

Taken singly, if only one fishing method is employed in the area, it can be considered as behaving in a

Boolean manner, either it exists or not and should impact an area equally all throughout. However

when diverse types of fishing methods are employed covering areas of concern that are also all

overlapping, the impacts to specific individual areas become additive, so that in general we applied to


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this criteria, a monotonically decreasing value function (see Fig. 3.4g). The more fishing methods

employed in an area the lesser the value. Locations within the bay where only one type of fishing

method is operational or allowed gets the highest values.

3.3.9 Population Coastline Index

The effect of human populations on natural environments can only be described as being detrimental if

not outright destructive. In general with very few exceptions, the larger the population is, the greater

the stress that it puts on to the environment. This is mainly due to the inherently finite nature of

resources and space available for everyones use. And with increased population come increased

resource use and the evenly balanced distribution and replenishment of such resources also become

more difficult. Areas that exhibit high population densities, if left unrestrained, generally demonstrate

an increased rate of environmental deterioration. Particularly for coastal areas where the main source

of protein is usually right outside ones front yard, the impulsion to extract as much from the resource

becomes more difficult to curb. For this criteria, a monotonically increasing value function was

applied.

3.4. Data Sources

The major source of the data in this study was taken from the Teluk Banten GIS CD, and the Banten

Bay MIS version 3.0 CD. All the vector data were available in Arcview shapefile, AutoCAD (*.dxf)

or in ArcInfo

format. Although the major topographic layers were in the same coordinate system,

some were in another unkown coordinate system that was significantly different. This was evident in

the presence of a consistent shift to the east of about 135m in some of the vector files. The raster

layers were the most problematic when it comes to the georeferencing. Since the studies were

conducted by different people at different times, each data set were essentially using their own

georeferences and most of the preparatory work for raster maps constituted the re-georeferencing to

the same georeference as the vector files.

3.5. Detailed procedure for each criteria

It is essential to remember that each criteria considered for use in this model is fundamentally

represented by one map although as explained in Chapter 2.2, some criteria may in reality be an

aggregate of two or more related criteria. The purpose of aggregating the maps are varied and may be

considered as essentially one way of simplifying the model procedures carried out. It is always easier

to consider fewer elements in the analysis and aggregating some criteria into one is one way of making

it simpler.


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All the maps available for the study

was examined to determine spatial

and attribute quality. Spatial quality /

accuracy was verified by direct

comparison with the latest version

(Edisi 1-1999) of analog topographic

maps of the area which was scanned

and georeferenced (see Table 3.2 for

the projection parameters) to within

an RMS error of 1 pixel. The maps

were scanned to a resolution of 300

pixels per inch (dpi) and with 32K colors. Since the size of the scanner precluded the single pass

scanning, the analog maps were scanned in four sections with more than sufficient overlaps and with

the edges firmly in place along the fixed sides of the scanner. The section scans for each map (four

each per analog map) were then individually georeferenced in ENVI

3.4 using as tiepoints/ ground

control points, the visible corners / grid intersections of the map. A minimum of six points were

chosen for each scanned section. Each section was then individually resampled to within a pixel

resolution of 5 meters. The output map from this operation was then mosaiced together, again with a

pixel size of 5 meters to form the final map sheet. Each map sheet was then mosaiced with other map

sheets to cover almost the entire study area. A total of 5 whole map sheets (see Fig. 3.5) were treated

this way with one more map missing to make up the complete study area.

Since map overlays will be done for the analysis and with map sources being quite varied, it was very

important that from the very beginning, all maps were sure to be within the same map extents and

having the same pixel size. For this purpose, a fixed geo-reference (hereinafter referred to as

banten.grf) was created with the properties listed in Table 3.3. All raster maps and vector maps that

were later rasterized were then resampled to this same geo-reference. Vector maps were also examined

in the manner in which they would be overlaid. Since the study requires that map overlays will be

done, it is a given requirement that all maps should be properly overlaying each other. Consistency

checks for topology and for cleaning and building were also done using ArcInfo

8. This was done to

ensure the validity of the raster files that will eventually be generated since some vector files that had

spurious topologies could not be correctly rasterized.

There is no conclusive means of assessing absolute attribute quality so that this was done primarily

only on the level of how well the attribute conforms to the tabular requirements of the data. The

attribute population 1990 by kecamatan for example cannot be checked for its absolute accuracy,

but within the database it can be cross - checked with the population 1990 by desa to determine

Table 3.2. Projection parameters used for the customized map

projection for all the various maps.

Projection Type : Transverse Mercator

Projection Datum : WGS-84

False Easting : 500000.00

False Northing : 10000000.00

Latitude of projection origin : 0 0 0.00

Longitude of central meridian : 105 0 0.00

Scale Factor : 0.999600


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whether the data is consistent. Consistent data were considered as acceptable. Where no other

information can be used to check consistence, it is assumed that the information is correct.

Criteria Standardization

All of the final criteria maps used for the calculations were subjected to standardization. Primarily for

purposes of comparisons, we need to convert all the criteria to the same scale allowing for the

maintenance of the relative importance for each criteria. All the standardizations were done to a final

map value range of 0 to 1.

3.5.1. Biotope Map / Benthic Habitat

The only input / source map that was actually

generated for this study is the biological habitat or

biotope map. The existing data that was included

with the provided information that came with the

Teluk Banten and Banten MIS cds were not entirely

sufficient for the requirements of the study which

Figure 3.5 The mosaiced map sheets used for the examination of the spatial accuracy of the input maps taken

from various sources. Only one sheet was missing, Serang (1109-634) from the series. The map sheet Pontang

(1109-643) was not scanned since it did not include areas that were within the study site.

Table 3.3. The properties of the map extents

defined for the raster maps used in the study.

Minimum x 617020.00

Maximum x 638540.00

Minimum y 9329060.00

Maximum y 9355420.00

Pixel size 20 20 meters

Number of Lines 1319

Number of Columns 1077

1110-321

Lontar

1109-633

Cile on

1110-311

Bo one ara1110-312

Pasirputih

1109-643

Pontan1109-634

Seran


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needed a more comprehensive albeit a bit less accurate habitat map. The major concern was with

finding all possible sites where these habitats could be identified from the satellite images and not on

the accuracy of such identification. A general procedure therefore, for using satellite images as a

source for visual interpretation together with expert knowledge was implemented for this purpose. It is

important to emphasize at this point that no field verifications were carried out for this part of the data

interpretation. Only expert knowledge of the author was used to deduce from the images the location

of the various benthic habitats of interest. Where applicable, the already prepared landuse maps were

also consulted.

This involved a series of creating various color composites using archive SPOT

XS and Landsat

TM

images of 1997 which were taken from some of the images used as input files by (Wignyowinoto,

2001) and to a lesser extent the newer images available for free, the ASTER images. The color

composites were then examined and on-screen digitizing of the location of the benthic habitats were

carried out based on where the author can best discern the presence of such habitats. In some cases,

additional stretching of different

parts of the image provided better

contrast for some of the habitats

being distinguished such that

stretching was done interactively

with continuous manual

delineation. Some stretch

combinations were good for some

parts of the images but made other

parts of the image too bright or too

dark. Upon reaching such parts,

another stretch is applied which is

based on the values of the pixelscurrently in consideration. One of

the benefits of using the software

package ENVI

(version 3.4) is the

way one can interactively apply

various enhancing stretch

algorithms depending either on the

entire image or only the currently

loaded part of the image (see Figure

3.6). The images were not subjected

Figure 3.6. The different types of common stretching parameters

available in ENVI which allows on-the-fly visualization of themost favorable stretching method that can be based either on the

entire image, the selected view or only on the zoomed part.


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to any type of radiometric correction except for submitting it through the darkest pixel method as

described in (Chavez et al., 1977) in (Green et al., 2000).

After the delineation of the three major benthic habitats, distance maps were generated for each of

them. The output distance maps were then reclassified into the six arbitrary classes mentioned in

Chapter 3.3.1. To determine the

areas with the desired

combination of classes, these

output reclassified maps were

then crossed. Since map

crossing can only be done to

two maps at any one time, the

coral and seagrass maps were

first crossed and its output

subsequently crossed with the

mangrove distance classes. A

combined domain from all

three maps yielded 188 items.

The domain items were then

sorted into an ascending order

of importance and then

appropriate hierarchic scores

assigned using a maximum

score of 15 and minimum of

0.1 (see Figure 3.7). Utilizing

the original final cross maps

domain as its domain, aweights table was then

generated and the assigned

scores placed in one column.

Together with the final cross

map output, this table was then

used in the generation of an

attribute maps applying the

scores column as the attribute.

The resulting attribute map was

Figure 3.7. The partial cross table generated from distance classes of the

various biotope types with the assigned weights for each class. Highlighted

row A shows the score given to areas which are far from coral reefs buthave mangrove and seagrass near each other. Highlighted row B is the

highest class with all three biotope types occurring near each other.

B

A


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then divided by the maximum score of 15 to get a standardized biotope criteria map with values

ranging from 0 to 1.

3.5.2. Bathymetry / Depth

Various sources were considered for this criteria. Some of the maps were already in the form of a

DEM while others consisted of raw point files and even scanned analog navigation maps. For purposes

of identifying which maps to use, various sources were examined. The optical method of determining

depths (Wignyowinoto, 2001) were examined together with the maps generated by the SIMBA

project.

If however, some fairly accurate navigation maps are available, then such maps can also be a good

source for bathymetric data. In this study, because the area was near a fairly busy shipping lane, the

Sunda Strait, navigation maps as a source of bathymetric information was extensively examined. In

some cases, the only other source of bathymetric data may even be topographic maps with at least

five, ten, fifteen and twenty meters isobaths depicted as lines parallel to the shore. Where present, such

maps may be acceptable. If on the other hand no other maps are present, due to its convenience, the

optical method for determining depths was also examined. Particularly in areas not frequently used for

as a shipping lane (a characteristic not present in the study area), the presence of bathymetric data is at

best sparse and unreliable. In this case, the use of optical methods for determining water depths could

be a possible solution. In this study though, a more reliable source was available.

To simulate the real world situation, it was decided that the source map that will be used for this

criteria was to be a merge of the scanned navigation map and the field measurements in x, y and z

made by (Wignyowinoto, 2001). Indeed, with a cheap GPS, a boat, and a weighted line, one can easily

generate a point map of depth measurements with the minimum of time and finances. The scanned

navigation maps were geo-referenced using the visible grid markings on the map as the tiepoints. The

geo-referenced map was then resampled to the prepared geo-reference and coordinate system with

parameters mentioned in Table 3.3. On screen digitizing of depth points was then carried out followed

by the merging with data taken from the field measurements. The resulting point file was then

subjected to a kriging interpolator using Surfer

with configuration setting based on a linear model and

with an output extent and grid size similar to all the other maps (banten.grf).

The interpolated depths were then reclassified based on the symmetrical value function mentioned in

Chapter 3.3.2, which is easier said than done. To begin with, in the GIS processing, all the depth

measurements were in negative depth values so that to simplify the arithmetic computations, all the

values were first converted to positive by multiplying the entire depth map with negative one. A series


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of map calculation statements were then executed as listed in Appendix A., the script used to generate

a final criteria map that had values from 0 to 1.

3.5.3. Total Suspended Materials (TSM)

The source maps inspected for use with this

criteria were taken directly and without alteration

of the content from the output maps generated by

the procedures outlined by (Ambarwulan, 2002).

The GIS operations performed on the source maps

were limited to the adjustment of the geo-

reference and the reclassification of the classes to

facilitate the adaptation of the defined value

function that was supposed to be employed and is

discussed in Chapter 3.3.3. These involved the

conversion of the interval scale used in the source

maps to values ranging from 0 to 1 as shown in

Figure 3.8.

3.5.4. Hydrology River mouth

The location of the different rivers and canals emptying into the bay was digitized onscreen using the

digital topographic maps provided by the Teluk Banten GIS project. The created point maps were then

rasterized and distance maps generated. Since reclassifying the distance maps into distinct arbitrary

boundaries will tend to imply the presence of an artificial hierarchic class where there was none, it was

decided that the absolute distance from the river mouths best represents the value function for the

criteria. However for purposes of facilitating the standardization, the maximum distance was taken as

a divisor for all other pixels within the map to redis

Ecosistem Change

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