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Operational use of Geo-information for rapid identification and evaluation of
feasible areas for marine habitat conservation.
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 identification and 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 partial fulfillment 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
3
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 expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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ABSTRACT Within 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.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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Acknowledgements
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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 bathymetric criteria map
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Appendix B Listing of Ilwis script for the calculations of the fishing activities criteria map
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References 54
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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.
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Table 3.2. Projection parameters used for the customized map projection for all the various maps.
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Table 3.3. The properties of the map extents defined for the raster maps used in the study.
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Table 3.4. The description of the fishing methods (Nuraini, 2001) included in this study together with the weights assigned them in the computations.
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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.
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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.
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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)
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Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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.
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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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Figure 3.2. The preliminary criteria tree created for the comprehensive examination of all possible criteria that could be included in the analysis.
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Figure 3.3. The pruned criteria tree showing the final criteria selected for the analysis. Numbers indicate hierarchy.
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Figure 3.4. Graphical representations of the various value functions applied to the different criteria used in the study.
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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.
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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.
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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.
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Figure. 3.8. The domain table used for converting the total suspended materials to criteria values.
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Figure 3.9. The procedure followed for the creation of the coastline population index criteria map.
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Figure 3.10. The analysis types provided in the ArcView extension for multicriteria decision making.
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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.
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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.
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Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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.
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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.
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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.
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Figure 4.5. Zoomed in portion of coral reefs showing the suitability selections for the three scenarios considered.
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Figure 4.6. The areas within the study site that was selected as highly suitable for a marine reserve.
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Figure 4.7 The six main sites that were at least four hectares in size. Numbers indicate the area in hectares.
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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.
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Figure 4.9. The same area output for scenario 5 showing the similarity in the classifications done using Ilwis (A) and ArcView (B).
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Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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?
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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 and Background 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 introducing
and 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.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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 study’s 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
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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 ΣpiLogpi (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 a
criteria, 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 the
northeastern 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.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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A B.7
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.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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 a
relatively 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 studies
represent 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
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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 on
species 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 reserve
species 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 couldbe included in the analysis.
Biological
Corals
Seagrasses
Mangroves
Biotope / Benthic Habitat
Chlorophyll concentration Primary productivity
Anthropogenic
Population employment
Domestic discharges
Other reserves
Economic activities
Fishing activities
Industries / factories
Industrial discharges
Seaweed culture
Ports and harbors
Resource extraction Sand mining
coral mining
Physical -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
Mar
ine
Res
erve
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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 ILWIS®2.23,
Arcview®3.2, ENVI®3.4, and Surfer®7.
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 indicatehierarchy.
Threat Factor Human Activities 9 Fishing activities
7 Industries / factories
8 Sand mining
6 Population
Bio-physico-chemical
Water Quality 3 Total suspended
Hydrology 2 Water depth
4 Rivers / canals
Biotope / Benthic Habitat
1.1 Corals
1.2 Seagrasses
1.3 Mangroves
5 Other reserves M
arin
e R
eser
ve
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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 criteria’s 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 and
management 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 everyone’s 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 one’s 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 cd’s 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 Cilegon
1110-311 Bojonegara
1110-312 Pasirputih
1109-643 Pontang
1109-634 Serang
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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 pixels
currently 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 the most favorable stretching method that can be based either on the entire image, the selected view or only on the zoomed part.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
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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 map’s
domain as its domain, a
weights 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 but have 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 redistribute the values between 0 and 1.
3.5.5. Sand Mining Activities
The distance function was used to generate a distance map showing the increasing distance from the
areas where this resource extraction activity takes place. No reclassification was done as the absolute
distance was sufficient enough to represent the value function defined for the criteria. There was
however the standardization of the distance map to values between 0 to 1 which was done by dividing
each pixel with the maximum distance value present.
Figure. 3.8. The domain table used for converting the total suspended materials to criteria values.
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3.5.6 Industries / Factories
Similar to the previous criteria, the distance function was used to generate a distance map. There was
also no reclassification done on the map as the absolute distance was sufficient enough to represent the
value function defined for the criteria. There was however the standardization of the distance map to
values between 0 to 1 which was done by dividing each pixel with the maximum distance value
present.
3.5.7. Other Existing Reserves
Although the value function for the criteria was different from the previous, the distance function was
also used to generate a distance map. The only difference is that areas that were considered as further
from the reserve sites should get lower values, whereas the distance function generates values that
increases as one moves further away. To compensate for this and to reverse the trend, the maximum
distance value present in the map was subtracted from each pixel and then multiplied by negative one
to make the value positive. The resulting map was then divided by the maximum value to generate a
standardized criteria map with values from 0 to 1. Although the study area had two separate locations
where other reserves could be found, only one input criteria map was generated for this criteria. There
was no distinction made between the two previously declared reserve areas.
3.5.8. Fishing Activities
Since each fishing method by its very nature, has a different effect on the environment, it is quite
obvious that each method should be given a different weight in its contribution to the criteria. This was
done by determining the nature of the fishing method and its manner of operation together with its
target species. The following Table 3.4 lists the general characteristics of each method as explained by
an expert (Nuraini, 2001). To account for these differences in impacts between fishing gears /methods
used, more weight was accorded to active fishing methods than to passive methods. Obviously, since
active methods tend to catch more fish, its operations are more detrimental. The inherently invasive
nature of active fishing methods, which is usually accomplished by driving a net either by motorized
boat or by manual methods, can also destroy some parts of the substrate, particularly if done
indiscriminately. The greater the weights assigned to a method, the more destructive it is perceived to
be. Since the maps that are available only indicates the presence or absence of a particular type of
fishing method, this was converted immediately to the map score by assigning the weight to all pixels
that were within the locations where a particular method was practiced. An aggregate score of all maps
was then made by summing up all the maps and then dividing it by the summation of all weights (see
operational Ilwis® 2.23 script listing, Appendix B). A criteria map with values between 0 and 1 was
then generated.
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Table 3.4. The description of the fishing methods (Nuraini, 2001) included in this study together with the weights assigned them in the computations.
Fishing Method Description Assigned Weight
Standing - Bagan (stationary lift net)
It is the most important gear used to catch anchovies in many parts of the shallow areas in Indonesia. There is a platform of bamboo stick functioning as rollers to lift the net and operating during the night. Kerosene pressure lamps is used to attract the schooling fish. Fishing is done during a dark moon. Mostly two fishers operate one bagan. a square gear.
7
Bondet
(beach seine)
A beach seine that operated using small boat with 7 to 9 crews targeted for estuarine small fishes such as mysids, and ponyfishes. This gear has small mesh sizes range from 0.5 to 1 cm. Therefore, in non pelagic season, this gear in the shallow water caught young fishes in segrass such as rabbitfish, groupers, apogon etc in rainy season.
9
Sero A stationary trap that is set up near the mouth of the river. Catches of the fish based on diel tidal. The fish will enter the trap when the water is high. Catches estuarine fish, mullet, catfish, sardine etc.
5
Payang
(danish seine)
Gear targeted for pelagic species, anchovies or sardine a few miles from the coastal area. It is operated from an outboard or inboard engine boat. The net consists of several parts with its own mesh size ranging from the smallest (3/8inch) at the bag part to the widest 8 inches at the wings part. The length of the net varies from 120 to 300m.
6
jaring-udang
(shrimp trammel net)
A three-layer bottom gillnet which consists of different mesh sizes. It is targeted for prawn, namely udang jerbung (Penaeus merguensis), udang dogol (P. indicus), rajungan (Portunus pelagicus). It ranged from 120-150m in length and 3m wide.
4
Pancing
(hand line)
A line with a hook on the end placed with bait. 1
jaring rampus
(gill net /nylon gill net)
targeted for all kind of fish, mullet, pelagic fish. The mesh size usually small 4
Bubu
(fish trap)
made of bamboo or wire. There are many types of traps but mostly having a hexagonal shaped mesh, others are cubic or rounded. The length is 80cm and with 30-40cm height. Its mesh size is about 1 inch in diameter. The entrance mouth is round in shape and 15 - 20cm diameter. Setting and hauling are conducted during the day at the reef flat or reef slopes and having a soak time of 2-3 days. It targets live groupers and reef fish.
2
Arad
(Trawl)
Without wings and otterboard, only a bag with the diameter of the bag of about 1.5m. The mesh size is small, 0.5inch. Targeted mostly for shrimp or demersal fishes. Usually fished at muddy area or seagrass beds when the catch is low.
7
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3.5.9. Population – Coastline Index
This criteria map was created specifically with a modeling difficulty in mind that can be defined by
addressing its main problem. This is, stated simply, how to project the effects of an attribute that is
usually existing in the form of a land-based polygon file over the water part of the study area, the part
we are concerned about. The population data for example, is available as one of the attribute columns
within the "desa" or village polygon file, which intrinsically means that the population itself is
distributed equally within the extents of the individual village polygon (which is of course also not
entirely true). To put it in another way, how can population data be represented on the map area so as
to allow for variable effects or weights (based on the attribute itself like population) on the aquatic part
of the bay when the stored location available is defined only by the position of the village polygons.
Some literature (Bryant et al., 1998) suggests using derived indices like population density instead of
actual counts but the problem remains even when using a derived index since the location pointers and
therefore its extent of influence remains generally the same.
For this purpose, the procedure followed for the criteria is shown in Figure 3.9. This basically involves
a series of spatial data and tabular data manipulations beginning with extracting the coastline from the
polygon files and determining the length of the coastline. The population attribute that is attached to
the same polygon as the extracted coastline is then divided by the total calculated length of coastline
providing an index of population based on the coastline rather than on the area. This procedure
assumes that communities with larger populations (more mouths to feed) but with a smaller coastline
(less resources available) would tend to adversely influence the environment more due to their
increased exploitation pressure potential. For purposes of simplifying the calculations it was decided
to use the level of the “Kecamatan” for this purpose although the data available was for the much finer
level of the “desa” or village. Each extracted coastline was then disaggregated by kecamatan and then
for each one along the coastal area, the coastline index of population for 1998 was calculated by
dividing the population for 1998 with the length of the coastline. For any given population, the longer
the coastline, the lower this index becomes. For this index, it is assumed that the greater the value of
this index, the greater the negative impact of the population on the environment.
Distance maps were then created for each individual kecamatan coastline followed subsequently by
the generation of a weighted distance map calculated by dividing the distance map created by the
coastal population index. Since the distance values generated by the distance function are divided by
the index, a lower weighted value indicates a greater influence of the population to that pixel since it
will make the absolute value smaller. Finally, the weighted distance maps for each kecamatan were
combined by adding together all the individual maps to produce an overall criteria map. The final map
was then standardized by dividing it with the maximum value present in all the combined map.
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1. Use polygon of administrative boundaries using the “Kecamatan” as index.
2. Extract polygons that have a side adjacent to the coastline.
3. Extract the coastline by manual clipping and determine length.
4. Join table of extracted coastline to the original administrative boundaries via the “Desa” key.
5. Calculate the coastline population density index for 1998 based on the length of coastline and the 1998 population.
6. Create distance maps for each “kecamatan” coastline.
7. Create weighted distance map by dividing output of Step 6 with the coastline population density index calculated in Step 5.
8. Combine all kecamatan weighted distance maps by adding them together.
Figure 3.9. The procedure followed for the creation of the coastline population index criteria map.
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3.6. The Map Overlays
To attain the objective of selecting suitable sites for marine reserves, a series of overlay procedures
were pursued. In general this involved the process of applying appropriate weights to the different
criteria determined previously and then combining the maps to reflect the effects of each criteria to the
overall score that will eventually be calculated. Since the combination of the maps will be done at the
pixel level, it was very important to determine whether all the maps used were properly geo-referenced
to each other and that one feature on one map properly overlays on the other maps. This was done
previously and is described in Chapter 3.5.
The original intention was to use exclusively, ArcView® for all the overlay operations. However, due
to the inherent design limitations of the software, it was decided that the actual process of doing the
overlaying of the maps was to be done following two different procedures. One involved the use of a
modified index overlay with multiple attribute maps employing Ilwis® 2.23 and described in (Van
Westen, 1997), and the other involved the use of a customized extension for multi-criteria decision
making with ArcView® 3.2 and Spatial Analyst® that was also employing an index overlay algorithm
for its map combinations. This customized extension was downloaded previously from the ESRI®
website (but is not available anymore) and was created specifically for use in an ArcView® 3.2
environment with the calculations consisting primarily of Spatial Analyst® procedures (Pouliot, 2000)
and written in Avenue®.
3.6.1. The Ilwis® Overlays
Prior to the overlays in Ilwis®, weights were assigned to the different criteria maps as shown in Table
3.6. The original test run weights were arbitrarily assigned by the researcher based on expert
knowledge and was adjustable in almost any manner to attain varying degrees of selection resolution
or for providing more importance to any another criteria based on the natural preference of the current
analysis persona. There appears however to be numerous problems with the use of arbitrary
assignment of weights as it was difficult to take into consideration the weight being given in relation
to the other criteria. For this purpose, the same criteria weights (map scores) that were assigned in the
overlays done using the ArcView® extension were the same weights used for the overlays done with
the use of Ilwis®.
A modification of the procedure described in (Van Westen, 1997) and this study is that all the criteria
maps are derived from input maps whose class scores are all assigned based on the value functions
applied to the criteria as mentioned in Chapter 3.3 and are already containing the class scores in each
pixel that are already standardized to the increasing values between 0 and 1. Except for the criteria
maps for the biotope type and bathymetry that had crisp and well defined boundaries, all the other
criteria maps (which were all made up mostly of derived distance maps) had ratio type measurement
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scales rather than an interval type and therefore had some type of a threshold boundary. Their
suitability was therefore also considered as following a ratio scale.
3.6.2. The ArcView® Overlays
The main purpose for including this
procedure in the research was for finding out
whether a site selection procedure for nature
reserves could be carried out with a
minimum of technical knowledge in
ArcView®, a software package that offers
ease of use and extendibility using its
relatively powerful built-in scripting
functionalities present in the form of
Avenue®. Because the author (Christopher
Pouliot) of the program extension
(MCDM.avx) provided only rudimentary
information and was unresponsive to the
researcher’s queries regarding the actual
operations carried out in the extension, the
researcher was forced by the circumstances
to extract some parts of the extension. This
would enable an examination of the
procedures used by the extension author in
the map calculations. Furthermore, the built-
in settings of the extension prohibited its use
in the researcher’s workstation without some
revisions as the extension assumes that it is installed in a stand-alone system and not in the security
protected ArcView® extensions directory that resides in the Institute’s applications server. Fortunately,
the program extension author did not encrypt the scripting procedures so that a direct examination of
the extension script procedures yielded the actual basis for the overlays and allowed a slight
modification that supported its proper operation within the workspace environment of the researcher’s
workstation security setting limitations.
Examination of the procedures followed in the extension reveal that it also performs a combination of
maps via a modified index overlay method similar to that mentioned earlier. Before the overlay is
conducted however, the input maps are all converted to grid maps if it is in the vector format, followed
Figure 3.10. The analysis types provided in the ArcView extension for multicriteria decision making.
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by the reclassification of the maps into a maximum of three user-defined classes (four upper
boundaries) based on the analysis type that was chosen. The available analysis types and therefore
criteria valuation or suitability rating methods available differed depending on the format (raster or
vector) of the source criteria map as described in the help dialog box shown in Figure 3.10. This is one
of the limitations of the extension as the original input maps are reclassified to only a maximum of
three possible classes with criteria valuation / suitability rating also consequently limited in the same
manner that is as restrictive. If the monotonically increasing function was selected for example, values
less than the first class boundary get the minimum suitability score equal to naught, and values greater
than the second class boundary get the maximum score of one hundred. Between the first and second
class boundaries, the suitability increases linearly from one to ninety-nine. The class boundaries in this
case serve more as cutoff points and determine either zero suitability or maximum suitability.
A major advantage of the extension however is in the provision of an interactive weights assignment
procedure (see Fig. 3.11) which is far superior to the manual assignment of weights implemented (and
the only choice) for the Ilwis® overlay. Even to an untrained person the use of interactively adjustable
and dynamic sliders for assigning the weights to the individual criteria maps provides an added benefit
in that the process of assigning weights can be done spontaneously. The presence of the dynamic
visual cues was very helpful in that it allowed for a visually intuitive compensatory weight assignment
since one can see the overall difference of the assigned weight in a criteria being rated and at the same
time have the simultaneous overall picture of how much is its significance in relation to the whole.
The result being that a more realistic criteria weights assignment can be accomplished.
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.
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4. Results and Discussion 4.1 The Criteria Maps
The source maps and the resulting criteria maps for all the criteria used in the study are presented in
Figure 4.1. After the initial overlay operations were carried out, it was apparent that of all the criteria,
it was the criteria on fishing methods that was not giving a meaningful contribution to the selection of
the reserve site. With exception, excluding it from the overlay operations resulted in site selections
that appeared very similar to when it was included. This criterion was found to be superfluous for the
overlay operation and was not anymore considered in the overlay computations. Considered alone
however, the fishing method map was very insightful. It showed particularly those areas that are not
subjected to any kind of fishing pressure and therefore was probably a very good place for a sanctuary
or reserve. Consequently and probably more interestingly, heavily fished areas can also be identified
and avoided. It is possible that for the final choice of the site, one can use this information as a non-
formalized criteria which can temper the choice considering all other criteria are equal or it may be
used as a definitive exclusion criteria immediately after site selection as explained in Chapter 4.2.
Depending on the intensity of the weights applied, some of the criteria, particularly for the biotope
type and bathymetry, appeared to strongly influence the selection such that the other criteria
apparently did not contribute much to the selection. This may be a direct result of the nature of the
measurement scales used for the input maps as both the biotope type and bathymetry had essentially
the same type of measurement scale, the interval type and all the others had a ratio scale. The crisp
boundaries of both the biotope type and bathymetry were fairly evident in the output maps where the
weights assigned them were substantially larger than the rest of the criteria.
Source Maps Criteria Maps
A. Biotope Type
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Source Maps Criteria Maps
B. Distance from Other Reserves
C. Bathymetry
D. Distance form River Mouths
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Source Maps Criteria Maps
E. Total Suspended Materials
F. Coastline Population Index
G. Fishing Methods
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Source Maps Criteria Maps
H. Distance from Industries
I. Distance from Sand Mining Operations
Figure 4.1. The various source and the resulting criteria maps as used in the analysis. All the criteria maps share thesame legend and have been standardized to values between 0 and 1.
To determine how a certain criteria can affect the calculations, some comparisons were carried out
between some output maps generated without the criteria in consideration and the output map that was
taken as the basis for comparison, that of scenario 5 with all other weight settings taken as a constant.
Table 4.1 shows the differences between the outputs maps generated without the specific criteria as
compared to the scenario 5 output. Negative values mean that the selection identified more areas than
was selected in scenario 5 and therefore indirectly indicates the influence of that criteria to the
selection process. Consider for example, the column on “-river mouth” which shows a highly negative
value for the difference in the highly suitable class. This would suggest that without this criteria
included in the calculations, there is a greater tendency to classify an area as highly suitable even if it
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was not entirely true. Highly positive values indicate the reverse, which is that there is a greater
tendency not to classify an area as highly suitable even if it was in reality highly suitable.
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.
Not Suitable Least Suitable Moderately Suitable Highly suitable
area (ha.) Difference area (ha.) Difference area (ha.) Difference area (ha.) Difference
Scenario 5 55869.88 194.80 216.04 59.72
- tsm 55918.64 (48.76) 178.16 16.64 190.80 25.24 52.84 6.88
- sandmining 55861.32 8.56 186.76 8.04 222.28 (6.24) 70.08 (10.36)
- river mouth 55797.44 72.44 196.92 (2.12) 204.72 11.32 141.36 (81.64)
- population 55897.84 (27.96) 180.44 14.36 221.20 (5.16) 81.32 (21.60)
- industries 55859.56 10.32 185.52 9.28 223.72 (7.68) 71.64 (11.92)
- other reserves 55881.68 (11.80) 194.48 0.32 207.32 8.72 56.96 2.76
4.2 The Selected Sites
The different output maps showing the selected marine reserve sites are shown in Figure 4.2a – 4.2e
(Ilwis®) and 4.3a – 4.3e (ArcView®). Each map represents the overlay outputs using four different sets
of weighting schema that were considered as listed in Table 4.2. For the actual site selection done, to
ensure consistency in the overall analysis since there are numerous possible permutations in the
weights assignment, a general rule on the hierarchy of the different criteria was observed while
assigning the weights. This follows the hierarchic listing given in Figure 3.3. Except for the
exploratory analysis indicated, where several possibilities were being tested, this hierarchy of criteria
was strictly imposed particularly for the first and most important criteria. And although the absolute
values of the weights may change relative to each other, the sequence of the hierarchy essentially
remains permanent and unchanging. In addition, since the scenario being pursued in this study is such
that we intend to establish a marine reserve based on some specific biotope type, one cannot establish
a marine reserve where the specific biotope does not exist so that in reality, only in those areas where
any of the three preferred biotope types exists can one have a marine reserve. However, if the purpose
or intention is merely to find out where it would be best to establish a reserve without any specific
qualified consideration for the presence of any of the preferred biotopes (there are of course also other
biotope types), then the biotope type becomes only as important as the other criteria and is therefore
given as much weight as the others. This is however not the case in this study. Nonetheless, these
variable conditions were also explored.
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The rationale for the use of these varying weighing schemes was to simulate actual conditions a
planner may take when conducting the selection. One of the biggest problems was in maintaining the
consistency and logic of the weighting. And although random weighing may work for some other type
of problem, in this case a random or illogical weighing scheme will result in a meaningless output.
The different weighing schemes used represent only some of many possibilities and as the results
themselves show, the manner and choice of a weighing scheme has a direct effect on the selection
process. For example, even while maintaining the general rule of hierarchy, if the differences between
criteria were kept to a minimum, i.e., barely enforced hierarchy, the result show a tendency to select
more suitable sites than any of the other weighing schemes.
Of particular importance to the actual classification of whether a pixel value is suitable or unsuitable
for a reserve, is the manner in which the final overlay maps are sliced according to their class
boundaries. Indeed, whether a certain value falls within “highly suitable” or “moderately suitable”
depends to a large extent on the manner in which the class boundaries are actually defined. To ensure
consistency and to allow meaningful comparisons between the schemes, arbitrary values were used
after examining all the maps. And except for scenario 4, all the others used the same class boundaries
for the density slicing / reclassification / legend classes, as shown in Table 4.3.
Figure 4.2a. Scenario 1 (Assigned weights by direct assessment with a strongly enforced hierarchic rule.)
Figure 4.2b. Scenario 2 (Assigned weights by direct assessment without a strongly enforced hierarchic rule.)
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Figure 4.2c. Scenario 3 (Weights assigned using the software Definite 2.0, weighted summation with expected value method.)
Figure 4.2d. Scenario 4 (Weights assigned by direct assessment while maintaining the hierarchic scores although barely. The differences between criteria are very small.)
Figure 4.2e. (Weights assigned using the software Definite 2.0, weighted summation with pair-wise comparison)
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.
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Figure 4.3a. Scenario 1 (Assigned weights by direct assessment with a strongly enforced hierarchic rule.)
Figure 4.3b. Scenario 2 (Assigned weights by direct assessment without a strongly enforced hierarchic rule.)
Figure 4.3c. Scenario 3 (Weights assigned using the software Definite 2.0, weighted summation with expected value method.)
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Figure 4.3d. Scenario 4 (Weights assigned by direct assessment while maintaining the hierarchic scores although barely. The differences between criteria are very small.)
Figure 4.3e. (Weights assigned using the software Definite 2.0, weighted summation with pair-wise comparison)
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.
After the overlay maps were generated in both procedures, the resulting values were examined
carefully and compared to the actual locations of the biotope types to determine whether there was any
truth to the selection. The location of the coral reefs were taken as a basis for the comparison as the
greatest value was given to this biotope type and the reserve intended for establishment is a coral reef
reserve. Examination of areas other than the reefs followed, after which a general pattern emerged. In
all the overlays, the value of 0.50 (range 0 to 1) for the Ilwis® procedure equivalent to 50 (range 0 –
100) in the Arcview® procedure, appeared to be the cut-off point for those area that are not in any way
suitable for the establishment of a reserve. This is fairly evident from all the output maps and across
all the weighing schemes.
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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.
biotope type
bathymetry tsm river other reserves
population industries sandmining
Score 60 40 30 25 20 18 18 18 Scenario 1
Weight 26.20% 17.47% 13.10% 10.92% 8.73% 7.86% 7.86% 7.86%
Weighting rationale Assigned weights by direct assessment with a strongly enforced hierarchic rule.
Score 50 50 40 30 25 25 25 25 Scenario 2
Weight 18.52% 18.52% 14.81% 11.11% 9.26% 9.26% 9.26% 9.26%
Weighting rationale Assigned weights by direct assessment without a strongly enforced hierarchic rule.
Score 74.8 47.3 33.44 24.42 17.38 11.88 7.26 3.52 Scenario 3
Weight 34.00% 21.50% 15.20% 11.10% 7.90% 5.40% 3.30% 1.60%
Weighting rationale Using the software Definite® 2.0, weighted summation with expected value method.
Score 50 47 44 41 38 35 32 27 Scenario 4
Weight 15.92% 14.97% 14.01% 13.06% 12.10% 11.15% 10.19% 8.60%
Weighting rationale Maintains the hierarchic scores although barely. The differences between the criteria is very small.
Score 42.5 24.7 10.2 8.7 5.4 3.8 2.7 2.1 Scenario 5
Weight 42.46% 24.68% 10.19% 8.69% 5.39% 3.80% 2.70% 2.10%
Weighting rationale Using the software Definite® 2.0, weighted summation with pair-wise comparison of the different criteria were done taking care that the inconsistency was below 0.1 as suggested. The inconsistency was pegged at 0.06 (see Fig. 4.4)
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.
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Of the five scenarios analyzed in the
study, three (1, 3, 5) were found to
produce an output map that appeared to
be most realistic for the situation. This
was true for both procedures followed.
And among the three, it was the fifth
scenario, the one with a pairwise
comparison weighing scheme, that
appeared to be the most logical and
most appealing to the researcher.
Particularly since it selected as highly
suitable those areas that were near the centroids of most of the identified coral reefs. An objective
comparison of the three scenarios is shown in the Table 4.3 that shows the area of the selections of the
three scenarios as derived from their histograms. Pairwise comparisons weighing between criteria
appears to be the best method as it produces the more logically defined selections. In addition, it also
yielded the best in terms of identifying the prime areas highly suitable for a marine reserve.
In all the three weighing schemes, the highest values in the overlay occurs in basically the same
general area and as shown in Figure 4.5, the resolution of the problem was far superior in scenario 5 as
it not only selects the biggest areas for the highly suitable areas, it also properly selects the lowest
acceptable areas for a reserve which is in scenarios 1 and 3 a bit more liberal.
Table 4.3. The calculated area in hectares of the selected sites for thethree scenarios that appeared to have a more realistic output. Thevalues in the maps ranged from 0 to 1. (Upper boundary forunsuitable is 0.5)
upper boundary scenario 1 scenario 3 scenario 5
Least Suitable 0.6 3409.32 1983.6 194.8
Moderately Suitable 0.7 102.44 216 216.04
Highly suitable 1.0 3.08 34.04 59.72
Scenario 1 Scenario 3 Scenario 5
Figure 4.5. Zoomed in portion of coral reefs showing the suitability selections for the three scenarios considered.
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A final criterion that is considered only
after selection of all the possible candidate
sites, is that of the minimum area of the
reserve which should at least be four
hectares of contiguous areas. Sites that are
identified as suitable but are smaller than
this threshold size should be discarded and
only those that are of sufficient size
should be considered and if needed,
ranked. Figure 4.6 shows all the areas
within the study site that was selected as
suitable in scenario 5. Of these areas, the
final six areas within the study area that
are at least four hectares in size are shown
in Figure 4.7.
At this point depending upon a planner, a choice may
be made as to which one among these sites should be
declared as a reserve. The choice becomes easier when
the options are visually inspected. A visual comparison
can be made easily and choices made based on other
non-formal criteria mentioned earlier. Additional
information in the form of the specific properties of
each area is also helpful in deciding which one among
the selected suitable sites should be the choice. Table
4.4 gives a summary of the properties of each of the
selected suitable areas. One can, at this time, also
consider the actual contribution of fishing pressure by
consulting the fishing method criteria maps which were
not included in the calculations for the analysis, but can
just as well be used to produce a map showing not only
where there is less fishing pressure but more
importantly where the heaviest fishing actually takes place (see Fig. 4.8). If this map were compared
with the final six selected sites, then it is quite evident that the two southernmost sites are definitely
out of the running as candidate sites for reserves since they fall within the area where the greatest
fishing pressure can be found. Without changing the fishing regulations in these areas (which is
Figure 4.6. The areas within the study site that was selected as highly suitable for a marine reserve.
Figure 4.7 The six main sites that were at least four hectares in size. Numbers indicate the area in hectares.
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usually easier said than done),
these sites cannot be used for a
reserve regardless of whether
they fit very nicely into the
highly suitable range. The use of
the fishing method criteria as a
constraining factor in this manner
is probably better than if it was
used as part of the analysis or
calculations since it can clearly
show that if the fishing
regulations were changed to
accommodate these sites, they can become nature reserves because they have all the characteristics of
a good nature reserve. If it was used as part of the index overlay calculations and then given a fairly
heavy weight, it would have precluded the two southern sites, regardless of their inherently reserve
friendly and favorable characteristics. Furthermore, if it were not given the proper weight then it
would have still erroneously selected those areas as moderately suitable even if because of the actual
heavy fishing practices taking place in the areas, they were not.
4.3 Comparison between the Ilwis® and ArcView® procedures.
The overall site selections between the two
procedures were generally the same. Figure
4.9 shows the same area under both
procedures. The differences between the two
depend upon the generalizations that the
ArcView® extension does to the input data
before the actual overlay operations are
carried out. The weights are also only
accepting integer inputs that can also
contribute to the generalizations due to the
rounding errors of the values. However,
except for the more detailed output maps
produced in Ilwis® and the convenience of the
command line when implementing the
Table 4.4. The specific properties of the areas found suitable for the establishment of a marine reserve.
Name Area (ha.)
Mean score
Total score
Min Score
Max Score
npix
Area_1 6.32 0.73 115.69 0.71 0.75 158
Area_4 4.92 0.74 91.39 0.71 0.81 123
Area_10 10.52 0.75 197.60 0.71 0.82 263
Area_28 7.64 0.77 147.48 0.72 0.86 191
Area_29 10.40 0.74 191.83 0.71 0.79 260
Area_34 8.64 0.72 156.14 0.71 0.74 216
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.
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overlays, everything could also be
done very well in ArcView®. The
limitations in fact, of the ArcView®
extension are not insurmountable and
may be easily remedied by an astute
Avenue programmer. In terms of
technical capability one can actually
implement the multi-criteria analysis
entirely in ArcView® with at least
the Spatial Analyst extension available. ArcView® alone is insufficient to accomplish the task. The
advantage of carrying out the multi-criteria analysis in an environment like ArcView® is the
availability of customizable tools that can be used to facilitate the analysis even for a planner with
minimal hands-on experience with GIS software. This advantage is of course lost to the advanced user
who can with one hand type in an Ilwis® command on the readily available command line. Therefore,
implementing an ArcView® based multi-criteria analysis for reserve site selection is probably more
desirable particularly for implementation in a regional or municipal planning office. The ease of using,
and therefore of teaching the use of, a customized tool that is implementing a multi-criteria analysis
like “MCDM.AVX” in Arcview® appears to be very promising.
4.4 Minimum Geo-Information Requirements
The minimum geo-information requirements to accomplish a site selection for marine reserves are
listed in Figure 3.3. There may be cases however where some of the information may not be available
so that the reader is referred to Table 4.1 for guidance. It would be important to note that excluding
some of the criteria in the list may not severely affect the accuracy of the selection. The most
important thing to remember though is that omitting one or more criteria may tend to classify an area
as highly suitable when in fact it is not, a Type II error, which is in this case not very desirable.
Some criteria, such as fishing methods, may not be used in the selection process per se, although as
mentioned earlier in Chapter 4.2, having a general impression about the intensity of fishing in an area
can also be used as a management tool such that it can be used to justify the enforcement of a no
fishing zone in certain areas where the suitability as a reserve is so high that it merits such extra
attention. Using this criteria in the index overlay calculations will probably have a lesser impact than
when it is used as a post-selection exclusion criterion.
A B
Figure 4.9. The same area output for scenario 5 showing the similarity
in the classifications done using Ilwis® (A) and ArcView® (B).
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5. Conclusions and Recommendations 5.1 Conclusions
GIS can be used to employ a multi-criteria analysis for marine reserve site selection. Allowing planners to conduct the reserve site selection without costly field verification.
Analog navigation maps, when scanned properly and with detailed geo-referencing may be used as a source for bathymetric information.
ArcView® with Spatial Analyst® extension and an improved version of the multi-criteria decision making extension tool can be used as a starting platform for a local government based decision support system for rapid marine reserve selection.
The ability of the customized decision making tool to allow the planner to visually assign weights to the various criteria is an added advantage of this tool.
Ilwis® provides a more detailed analysis for the index overlays so that it may be used when a more detailed examination is required, i.e., for justification in aid of legislation for the legal declarations of the selected sites.
5.2 Recommendations
Update and improve the ArcView extension “MCDM.AVX” to accommodate valuation methods other than linear; to accept real number inputs instead of just integers; to allow pre-analysis reclassification to more than three classes to avoid excessive generalization; to provide customized map query features that can determine total area by suitability class; to use external tables as a data source for the evaluation settings; to provide a pairwise comparison module for criteria weighting
Implement the method using another dataset of input maps to test the performance of the selection.
Conduct an interview with the pertinent and site-experienced source people regarding the validity of the site selections made. This will allow for a sort of test for the accuracy of the procedure / method.
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Appendix A. Listing of the ILWIS® script for the calculation of the bathymetric criteria map.
rem convert all negative values to positive bathypos=bathymap*-1 calc bathypos.mpr rem extract first class boundary areas with depths <0.5m convert rem it to a score and make sure it gets a maximum score of no rem more than 0.75 and make all other pixels undefined bathy1=iff((bathypos<0.5),(bathypos/0.5)-0.25,?) calc bathy1.mpr rem extract second class boundary areas with depths <5m and >0.5m rem convert it to a score and make sure it gets a maximum score of 1.45 rem and make all other pixels undefined bathy2=iff(((bathypos<5) and (bathypos >0.5)),(bathypos/5)+0.45,?) calc bathy2.mpr rem extract third class boundary areas with depths <10m and >5m rem convert it to a score and make sure it gets the maximum score of 1.5 rem and make all other pixels undefined bathy3=iff(((bathypos<10) and (bathypos >5)),1.5,?) calc bathy3.mpr rem extract 4th class boundary areas with depths <15m and >10m rem and make all other pixels undefined bathy4=iff(((bathypos<15) and (bathypos >10)),bathypos,?) calc bathy4.mpr rem convert the 4th class values between values of 0 to 5 rem undefined remains undefined bathy4a=Bathy4-10 calc bathy4a.mpr rem this will convert all values to 10 bathy4b=(bathy4-bathy4a) calc bathy4b.mpr rem this will convert all values between 5-10 bathy4c=bathy4b-bathy4a calc bathy4c.mpr rem this will convert the values to a score by dividing all values by rem the maximum value of 9.9999 and add 0.25 to make rem the maximum value = 1.25 bathy4d=(bathy4c/9.9999)+0.25 calc bathy4d.mpr rem extract 5th class boundary areas with depths <20m and >15m rem and make all other pixels undefined bathy5=iff(((bathypos<20) and (bathypos >15)),bathypos,?) calc bathy5.mpr rem convert the 5th class values between values of 0 to 5 rem undefined remains undefined bathy5a=bathy5-15
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calc bathy5a.mpr rem this will convert all values to 15 bathy5b=(bathy5-bathy5a) calc bathy5b.mpr rem this will convert all values between 10-15 bathy5c=bathy5b-bathy5a calc bathy5c.mpr rem this will convert the values to a score by dividing all values by rem the maximum value of 14.9999 and subtracting 0.25 to make rem the maximum value = 0.75 bathy5d=(bathy5c/14.9999)-0.25 calc bathy5d.mpr rem extract 6th class boundary areas with depths >20m rem and give it the value 0 bathy6=iff((bathypos>20),0.0,?) calc bathy6.mpr rem merge 1st and 2nd classes b12=ifnotundef(bathy1,bathy1,(ifnotundef(bathy2,bathy2))) calc b12.mpr rem merge 3rd class with previous b123=ifnotundef(b12,b12,(ifnotundef(bathy3,bathy3))) calc b123.mpr rem merge 4th class with previous b1234=ifnotundef(b123,b123,(ifnotundef(bathy4d,bathy4d))) calc b1234.mpr rem merge 5th class with previous b12345=ifnotundef(b1234,b1234,(ifnotundef(bathy5d,bathy5d))) calc b12345.mpr rem merge 6th class with previous b123456=ifnotundef(b12345,b12345,(ifnotundef(bathy6,bathy6))) calc b123456.mpr rem create standardized criteria map by dividing b123456 with rem maximum possible score of 1.5 to create values between 0-1 bathyc=b123456/1.5 calc bathyc.mpr rem negative values are on areas on land so extracting this out rem and converting it to undefined bathy_c=iff((bathyc<0),?,bathyc) calc bathy_c.mpr
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Appendix B. Listing of the ILWIS script to calculate the fishing activities criteria map.
rem take the arad map and if not an undefined value give it the value of 7 rem this assigns a value of 7 to all pixels where the arad fishing method is practiced zfish_1=ifnotundef(arad,7,0) calc zfish_1.mpr rem take the bubu map and if not an undefined value give it the value of 2 rem this assigns a value of 2 to all pixels where the bubu fishing method is practiced zfish_2=ifnotundef(bubu,2,0) calc zfish_2.mpr rem take the jarampus map and if not an undefined value give it the value of 4 rem this assigns a value of 4 to all pixels where the jaring rampus fishing method is practiced zfish_3=ifnotundef(jarampus,4,0) calc zfish_3.mpr rem take the pancing map and if not an undefined value give it the value of 1 rem this assigns a value of 1 to all pixels where the pancing fishing method is practiced zfish_4=ifnotundef(pancing,1,0) calc zfish_4.mpr rem take the jarudang map and if not an undefined value give it the value of 4 rem this assigns a value of 4 to all pixels where the jarring udang fishing method is practiced zfish_5a=ifnotundef(jarudang,4,0) calc zfish_5a.mpr rem take the payang map and if not an undefined value give it the value of 6 rem this assigns a value of 6 to all pixels where the payang fishing method is practiced zfish_5b=ifnotundef(payang,6,0) calc zfish_5b.mpr rem take the sero map and if not an undefined value give it the value of 5 rem this assigns a value of 5 to all pixels where the sero fishing method is practiced zfish_6a=ifnotundef(sero,5,0) calc zfish_6a.mpr rem take the bondet map and if not an undefined value give it the value of 9 rem this assigns a value of 9 to all pixels where the bondet fishing method is practiced zfish_6b=ifnotundef(bondet,9,0) calc zfish_6b.mpr rem take the stdbagan map and if not an undefined value give it the value of 7 rem this assigns a value of 7 to all pixels where the standing bagan fishing method is practiced zfish_7=ifnotundef(stdbagan,7,0) calc zfish_7.mpr rem calculate the criteria map by adding all maps rem then dividing by the sum of all map scores rem the additional operation of subtracting 0.98 and multiplying –1 is for converting all values so that rem the highest values represent the best condition zfish_c=(((zfish_1+zfish_2+zfish_3+zfish_4+zfish_5a+zfish_5b+zfish_6a+zfish_6b+zfish_7)/45)-0.98)*-1 calc zfish_c.mpr
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References:
Aguilar-Manjarrez, J. and Ross, L.G., 1995. Geographical information system (GIS) environmental models for aquaculture development in Sinaloa State, Mexico. Aquaculture International, 3(2): 103-115.
Ambarwulan, W., 2002. Mapping of TSM concentration from SPOT and Landsat TM satellite images for Integrated Coastal Zone Management in Teluk Banten, Indonesia. MSc. Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 90 pp.
Armstrong, M.P. et al., 1990. A knowledge - based approach for supporting locational decisionmaking. Environment and Planning B: Planning and Design, 17(3): 341-364.
Arnold, W.S., White, M.W., Norris, H.A. and Berrigan, M.E., 2000. Hard clam (Mercenaria spp.) aquaculture in Florida, USA: geographic information system applications to lease site selection. Aquacultural Engineering, 23: 1-3.
Arthur, J.L., Hachey, M., Sahr, K., Huso, M. and Kiester, A.R., 1997. Finding all optimal solutions to the reserve site selection problem: formulation and computational analysis. Environmental and Ecological Statistics, 4(2): 153-165.
Baldwin, C.L., 1989. Water quality and management in the Great Barrier Reef Marine Park. Water Science and Technology, 21(2): 267-272.
Basagaoglu, H., Celenk, E., Marino, M.A. and Usul, N., 1997. Selection of waste disposal sites using GIS. Journal of the American Water Resources Association, 33(2): 455-464.
Bryant, D., Burke, L., McManus, J. and Spalding, M., 1998. Reefs at Risk. World Resources Institute, Washington D.C., 54 pp.
Cabeza, M. and Moilanen, A., 2001. Design of reserve networks and the persistence of biodiversity. Trends in Ecology and Evolution, 16(5): 242-248.
Chalkias, C.N. and Stournaras, G., 1997. GIS application for the selection of sanitary waste disposal landfills and quarries sites in major Sparti area, Greece. Engineering geology and the environment Proc symposium Athens: 1675-1680.
Chang, N.B. and Wang, S.F., 1995. A locational model for the site selection of solid waste management facilities with traffic congestion constraints. Civil Engineering Systems, 11(4): 287-306.
Chavez, P.S., Berlin, G.L. and Mitchell, W.B., 1977. Computer enhancement techniques of Landsat MSS digital images for landuse/landcover assessments. Remote Sensing of Earth Resources, 6: 259.
Chilufya, K.A., 2001. Using GIS and MCE to select a suitable trail route to eco - tourism sites in Bataan natural park, Bagac, Philippines. Individual Final Assignment, Professional
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
54
Masters Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 35 pp.
Church, R. et al., 2000. Understanding the tradeoffs between site quality and species presence in reserve site selection. Special section: Spatial modeling. Forest Science, 46(2): 157-167.
Cleveland, J.-A., Grover, R.-B., Petrillo, J.-L. and Ladd, E., 1979. Using computers for site selection. Environmental Science and Technology, 13(7): 792-797, 4 figs, table, 5 refs.
Colson, G. and Bruyn, C.d., 1989. Models and methods in multiple objectives decision making. Mathematical Computer Modelling, 12(10-11): 1201-1211.
Dawson, N.A., 1996. Site selection for the relocation of squatters : a case of the Msimbazi river valley Dar Es Salaam. MSc. Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 156 pp.
de Fontaubert, A.C., Downes, D. and Agardy, T., 1996. Biodiversity in the Seas: Implementing the convention on Biological Diversity in Marine and Coastal Habitats., World Conservation Union, Gland, Switzerland.
De Silva, S.S., Amarasinghe, U.S., Nissanka, C., Wijesooriya, W.A.D.D. and Fernando, M.J.J., 2001. Use of geographical information systems as a tool for predicting fish yield in tropical reservoirs: Case study on Sri Lankan reservoirs. Fisheries Management and Ecology, 8(1): 47-60.
Douven, W.J.A.M., 2001. Banten Bay MIS Version 3.0. IHE, International institute for Infrastructure, Hydraulic and Environmental Engineering.
Dower, M., 1976. National parks, three paces forward, march! Town and Country Planning: 152-155.
Falan, G., 1996. Tourism potential assessment for the city of Jingsha. MSc. Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 100 pp.
Ferraro, P.J. and Simpson, R.D., 2001. Cost-effective conservation: a review of what works to preserve biodiversity. Resources Washington(143): 17-20.
Frantzis, I., 1993. Methodology for municipal landfill sites selection. Waste Management and Research, 11(5): 441-451.
Gehlbach, F.R., 1975. Investigation, evaluation, and priority ranking of natural areas. Biological Conservation, 8: 79-88.
Green, E.P., Mumby, P.J., Edwards, A.J. and Clark, C.D., 2000. Remote Sensing Handbook for Tropical Coastal Management. Coastal Managements Sourcebooks 3. UNESCO, Paris, 316 pp.
Helliwell, D.R., 1971. A methodology for the assessment of priorities and values in nature conservation., Merlewood Research and Development Paper, pp. 51.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
55
Helmy, Y.A.E.H.M., 2001. Governmental low - income housing site selection. MSc. Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 85 pp.
Hoekstra, A.Y., Douven, W.J.A.M. and Meesters, E.H., 2000. The influence of human activities on the coral reefs in Banten Bay, West Java.
Howard, P.C. et al., 2000. Protected area planning in the tropics: Uganda's national system of forest nature reserves. Conservation Biology, 14(3): 858-875.
Hussey, V., Dodd, V.A. and Dennison, G.J., 1996. Locating a landfill site for Dublin using geographic information systems. Proceedings, ICE: Municipal Engineer, 115(3): 125-133.
Juang, C.H., Lee, K. and Chen, J.W., 1995. A new approach for siting landfills using fuzzy sets. Civil Engineering Systems, 12(2): 85-103.
Kao, J. and Kao, J.J., 1996. A raster-based C program for siting a landfill with optimal compactness. Computers and Geosciences, 22(8): 837-847.
Karthikeyan, K.G., Elliott, H.A., Brandt, R.C. and Mitchell, J.K., 1993. Using a geographic information system for siting water treatment sludge monofills. Integrated resource management & landscape modification for environmental protection. Proceedings of the International Symposium Chicago, Illinois, USA.
Krebs, C.J., 1978. Ecology: the experimental analysis of distribution and abundance. Harper & Row, 678 pp.
Kuna, G.K., 2001. Site selection for construction of oil storage tank farm and pipeline in L'Ufinac area of Spain and preparation of tender document. IFA Professional Masters Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 55 pp.
Lin, H.Y. and Kao, J.J., 1999. Enhanced spatial model for landfill siting analysis. Journal of Environmental Engineering, 125(9): 845-851.
Lindquist, R.C., 1987. Applying a geographic information system to the site selection of a regional landfill, GIS '87. Proc. 2nd international conference, San Francisco, 1987. Asprs/Acsm, pp. 621-627.
Lolos, G. et al., 1997. A multicriteria decision support system for landfill site selection. Engineering geology and the environment Proc symposium Athens: 1975-1981.
Mahmoud Humeida, S., 2000. Environmental impact assessment for low - income housing site - selection using GIS and MCE : case study, Dar es Salaam, Tanzania. IFA Professional Masters Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 31 pp.
Margules, C. and Usher, M.B., 1981. Criteria used in assessing wildlife conservation potential: a review. Biological Conservation, 21(2): 79-109, 3 figs, 5 tables, c125 refs.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
56
McKenzie, N.L., Belbin, L., Margules, C.R. and Keighery, G.J., 1989. Selecting representative reserve systems in remote areas: a case study in the Nullarbor Region, Australia. Biological Conservation, 50(1-4): 239-261.
Muttiah, R.S., Engel, B.A. and Jones, D.D., 1996. Waste disposal site selection using GIS-based simulated annealing. Computers and Geosciences, 22(9): 1013-1017.
Nuraini, S., 2001. Personal Communication. Fishing gear information To: M.A. Cusi
Pearce, D. and Moran, D., 1994. The economic value of biodiversity. Earthscan ; IUCN The World Conservation Union, London; Cambridge, 172 pp.
Pielou, E.C., 1977. Mathematical Ecology. Wiley & Sons., New York, 385 pp.
Piyasiri, A.V.L., 2001. Site selection for an underground communication center at I'Aufinac area of Spain and preparation of tender document. IFA Professional Masters Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 65 pp.
Polasky, S., Camm, J.D. and Garber Yonts, B., 2001. Selecting Biological Reserves Cost-Effectively: An Application to Terrestrial Vertebrate Conservation in Oregon. Land Economics, 77(1): 68-78.
Pouliot, C., 2000. Multi-Criteria Decision Making Tool.
Reichel, A., Mehrez, A. and Altman, S., 1998. Neve-Ilan, Israel, a site selection and business feasibility case study. Tourism Management, 19(2): 161-170.
Ross, L.G., Mendoza-Q.-M, E.A. and Beveridge, M.C.M., 1993. The application of geographical information systems to site selection for coastal aquaculture: an example based on salmonid cage culture. Aquaculture, 112(2-3): 165-178.
Rouhani, S. and Kangari, R., 1990. Landfill site selection. In: T.J. Kim and et al. (Editors), Expert systems: applications to urban planning. Springer-Verlag, pp. 159-169.
Sharifi, M.A., 2001. Introduction to Decision Support Systems and Multicriteria Evaluation Techniques - Lecture Notes.
Siddiqui, M.Z., Everett, J.W. and Vieux, B.E., 1996. Landfill siting using geographic information systems: a demonstration. Journal of Environmental Engineering ASCE, 122(6): 515-523.
Stagnitti, F. and Austin, C., 1998. DESTA: a software tool for selecting sites for new aquaculture facilities. Aquacultural Engineering, 18(2): 79-93.
Stohlgren, T.J., Chong, G.W., Kalkhan, M.A. and Schell, L.D., 1997. Rapid assessment of plant diversity patterns: A methodology for landscapes. Environmental Monitoring and Assessment, 48(1): 25-43.
Sun, T. et al., 1998. Evaluation on suitability for land treatment of municipal waste water. China Environmental Science, 18(6): 506-509.
Operational use of Geo-information for rapid identification and evaluation of feasible areas for marine habitat conservation. Application to Banten Bay on Java's Northwest coast, Indonesia
57
Tans, W., 1974. Priority ranking of biotic natural areas. Michigan Botanist, 13: 31-39.
Van Westen, C.J., 1997. Tools for map analysis applied to the selection of a waste disposal site, Ilwis Applications Guide. ITC, Enschede.
Van-Zee, C. and Lee, J.E., 1989. Hazardous waste disposal site selection using interactive GIS technology, Auto carto 9. Proc. symposium, Baltimore, MD, 1989. ASPRS/ACSM, Falls Church, VA, pp. 391-396.
Wenbo, X., 2001. Site selection for new wastewater treatment plants : case study, Han Kou Town, Wuhan City, China. MSc. Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 64 pp.
Wignyowinoto, I., 2001. Remote sensing applications for bathymetric mapping of Banten bay, Indonesia, using LANDSAT TM. MSc. Thesis, International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, 95 pp.
Wright, D.F., 1977. A site evaluation scheme for use in the assessment of potential nature reserves. Biological Conservation, 11: 293-305.
Zins, M. and Jacques, J., 1999. The tourism and economic value of landscapes. Teoros, Revue de Recherche en Tourisme, 18(1): 48-51.