a geographic information system (gis) and multi
TRANSCRIPT
A GEOGRAPHIC INFORMATION SYSTEM (GIS) AND MULTI-CRITERIA
ANALYSIS FOR SUSTAINABLE TOURISM PLANNING
MANSIR AMINU
A project submitted in fulfillment of the
requirements for the award of the degree of
Master of Science (Planning-Information Technology)
FACULTY OF BUILT ENVIRONMENT
UNIVERSITI TEKNOLOGI MALAYSIA
April, 2007
UNIVERSITI TEKNOLOGI MALAYSIA
BORANG PENGESAHAN STATUS TESISυ
JUDUL : A Geographic Information System (GIS) and Multi-Criteria Analysis for Sustainable Tourism Planning
SESI PENGAJIAN : 2006/2007 Saya Mansir Aminu
(HURUF BESAR)
Mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah)* ini disimpan di Perpustakaan Universiti Teknologi Malaysia dengan syarat-syarat kegunaan seperti berikut :- 1. Tesis adalah hakmilik Universiti Teknologi Malaysia 2. Perpustakaan Universiti Teknologi Malaysia dibenarkan membuat salinan untuk tujuan
pengajian sahaja. 3. Perpustakaan dibenarkan membuat salinan tesisi ini sebagai bahan pertukaran antara
institusi pengajian tinggi. 4. **Sila tandakan ( 3 )
SULIT TERHAD TIDAK TERHAD Disahkan oleh
(TANDATANGAN PENULIS) (TANDATANGAN PENYELIA) Alamat Tetap : Block C Flat 7 NITEL Staff Quarters, Plot 570 Durban Street, Off Adetokumbo Prof. Dr. Ahris Bin Yaakup Ademola Crescent, Wuse II, Abuja, Nigeria. Nama Penyelia
Tarikh : 27 th April 2007 Tarikh : 27 th April 2007 CATATAN : * Potong yang tidak berkenaan ** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi
berkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai SULIT atau TERHAD
♦ Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah dan Sarjana secara penyelidikan, atau disertasi bagi pengajian secara kerja kursus dan penyelidikan, atau Laporan Projek Sarjana Muda (PSM)
(Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972)
(Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)
“We hereby declare that we have read this project report and in
our opinion this project report is sufficient in terms of scope and
quality for the award of the degree of Master of Science
(Planning-Information Technology)”
Signature : ______________________
Name of Supervisor I : Prof. Dr. Ahris Bin Yaakup
Date : ______________________
Signature : ________________________________________
Name of Supervisor II : Assoc. Prof. Dr. Ahmad Nazri B. Muhamad Ludin
Date : ________________________________________
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DECLARATION I declare that this project report entitled “A Geographic Information System (GIS)
and Multi-Criteria Analysis for Sustainable Tourism Planning”, is the result of my
own research except as cited in the references. The project report has not been accepted
for any degree and not concurrently submitted in candidature of any other degree.
Signature : ________________________ Name of Student : Mansir Aminu__________ Date : 27th April, 2007_________
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This project is dedicated to the entire members of my family
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ACKNOWLEDGEMENT
I wish to express my profound gratitude to the Almighty Allah for his blessing
and guidance throughout my master’s programme. My appreciation goes to my parents
whose support and affection can never be quantified. I would like to seize this
opportunity in thanking my brothers Abdullahi, Ibrahim and Nasiru for their financial
and moral support all through my stay here, may Allah continue to guide and bless them.
My sincere gratitude goes to my supervisors Prof. Dr. Ahris Bin Yaakup and
Assoc. Prof. Dr. Ahmad Nazri B. Muhamad Ludin for their constructive criticisms,
patience and understanding that facilitated me through all phases of my study. I am also
indebted to all my lecturers and non-teaching staff that have contributed in the course of
writing this project. Finally, I want to thank all my friends and well wishers who directly
or indirectly played a role towards the completion of my study.
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ABSTRACT
The need for a sustainable approach in tourism development is very often
addressed among the academia, the authorities and the stakeholders, as well as the
apparent need for tools which will guide the decision environment in evaluation and
planning. This project aims to identify conservation and compatible areas for tourism
development in Johor Ramsar site, using spatial modeling in Geographic Information
System (GIS). The study describes a methodological approach based on the integrated
use of Geographic Information System (GIS) and Multi Criteria Decision Model
(MCDM) to identify nature conservation and development priorities among the wetland
areas. A set of criteria were defined to evaluate wetlands biodiversity conservation and
development; the criteria include tree age class, harvesting season, size of endangered
fauna, habitat’s proximity to natural land use/ land cover, habitat area and water quality.
Having defined the criteria, the next step was selecting suitable indicators and variables
to measure the selected criteria. Subsequently the criteria were evaluated from
conservation and tourism development point of view. These criteria were then ranked
using the pair wise comparison technique of multi criteria analysis (MCA) and the
results integrated into GIS. Several conservation scenarios are generated so as to
simulate different evaluation perspectives. The scenarios are then compared to highlight
the most feasible and to propose a conservation and development strategy for the
wetlands area. The generation and comparison of conservation and development
scenarios highlighted the critical issues of the decision problem, i.e. the wetland
ecosystems whose conservation and development relevance is most sensitive to changes
in the evaluation perspective. This study represents an important contribution to
effective decision-making because it allows one to gradually narrow down a problem.
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ABSTRAK
Kepentingan pendekatan mampan di dalam pembangunan pelancongan kerap
kali di tekankan oleh golongan akademik, pihak berkuasa dan pemegang hakmilik tanah,
begitu juga keperluan yang jelas untuk kaedah bagi menentukan cara membuat
keputusan di dalam penilaian dan perancangan. Tesis ini bertujuan mengenalpasti
kawasan pemuliharaan yang sesuai sebagai kawasan pembangunan pelancongan di
kawasan RAMSAR Johor, dengan menggunakan model spatial dalam Sistem Maklumat
Geografi (GIS). Kajian ini menerangkan pendekatan metodologi berdasarkan kepada
penggunaan bersepadu GIS dan ‘Multi Criteria Decision Model’ (MCDM) untuk
mengenalpasti pemuliharaan alam semulajadi dan keutamaan pembangunan di kawasan
paya bakau. Satu set kriteria telah dikenalpasti dalam penialaian pemuliharaan
biodiversiti dan pembangunan; kriteria-kriteria adalah seperti kelas umur/kematangan
pokok, musim penebansan, saiz haiwan yang terancam, habitat berhampiran dengan
gunatanah semulajadi, kawasan habitat dan kualiti air. Melalui pengkelasan kriteria,
langkah seterusnya adalah dengan memilih pendekatan/penunjuk bersesuaian dan
kepelbagaian untuk mengukur kriteria terpilih. Seterusnya, kriteria-kriteria ini akan di
nilai melalui aspek dan pandangan pemuliharaan dan pembangunan pelancongan.
Kriteria-kriteria ini akan di susun mengikut carta menggunakan teknik perbandingan
cara berpasangan dari ‘multi criteria analysis’ (MCA) dan keputusan digabungkan di
dalam GIS. Beberapa jenis senario pemuliharaan telah di hasilkan seperti untuk
kesamaan perbezaan perspektif penilaian. Perbandingan senario dilakukan bagi
mengetengahkan strategi pemuliharaan dan pembangunan yang berpotensi untuk
dilaksanankan di kawasan paya. Penghasilan dan perbandingan bagi senario
pemuliharaan dan pembangunan menekankan isu-isu kritikal dalam masalah keputusan,
i.e. kawasan paya yang mana pemuliharaan dan pembangunan berkaitan adalah sangat
sensitif untuk sebarang perubahan di dalam perspektif penilaian. Kajian ini menjelaskan
sumbangan penting bagi penghasilan keputusan yang efektif kerana ia membantu untuk
menyelesaikan masalah dengan lebih fokus dan mudah.
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TABLE OF CONTENT Page Declaration ii Dedication iii Acknowledgement iv
Abstract v Abstrak vi Table of Content vii List of Tables xii
List of Figures xiii
CHAPTER 1 INTRODUCTION
1.1 Background 1
1.2 Statement of research problem 3
1.3 Aim of the study 5
1.4 Objectives of the study 5
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1.5 Significance of the study 5
1.6 Scope of study & methodology 7
1.7 Limitations of the study 10
CHAPTER 2 GIS and Decision Support Systems in Sustainable Tourism
2.1 Concept of sustainable tourism 11
2.2 Wetlands assessment 14
2.3 Spatial modeling environments 18
2.4 Geographic Information System (GIS) in sustainable 22 Tourism planning
2.5 Multi Criteria Decision Making and Natural resources 25 Management 2.6 Multi criteria decision making (MCDM) 27
2.6.1 Multiple criteria decision making – an overview 27
2.6.2 Multi-criteria decision making and GIS 30
2.6.2.1 Evaluation criteria 32
2.6.2.2 Criterion maps 35
2.6.2.3 Criterion standardization 36
2.6.2.4 Assigning weights 38
2.6.2.5 Decision rules 44
2.6.2.6 Error assessment 45
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CHAPTER 3 Wetlands Assessment using multi-criteria decision model
3.1 The study area 48
3.1.1 Pulau Kukup 48
3.1.2 Sungai Pulai 50
3.1.3 Tanjung Piai 51
3.2 Data collection 54
3.3 Database development for wetland assessment 54 3.3.1 Data layers for the study 56
3.3.1.1 Land use 56
3.3.1.2 Harvesting 57 3.3.1.3 Endangered Species 59 3.3.1.4 Tree age class 60 3.3.1.5 Management 61 3.3.1.6 Pulai River 62 3.3.1.7 Habitat area 64
3.4 Evaluating existing developments to the wetlands 66
3.4.1 Threat analysis 66 3.4.1.1 Port of Tanjung Pelepas (PTP) 66
3.4.1.2 Tenaga Nasional Power Transmission lines (PTL) 68 through the Sungai Pulai
3.4.2 Tourism issues 69
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3.5 Main steps of the approach 70
3.5.1 Definition of criteria 71
3.5.2 Evaluation of conservation and development criteria 72 3.5.3 Multi criteria analysis and priority ranking 79 3.5.3.1 Pairwise comparison method 79 3.5.4 Generation and analysis of conservation/ development 98 scenarios 3.5.4.1 Tourism development scenario 1 98
3.5.4.2 Tourism development scenario 2 100
3.5.4.3 Economic development scenario 100 3.5.4.4 Conservation scenarios 102
CHAPTER 4 WETLANDS ASSESSMENT AND RESULT
4.1 Introduction 105
4.2 Wetlands conservation 107
4.2.1.1 Habitat area 107
4.2.1.2 Endangered fauna 108
4.2.1.3 Wetland’s proximity to natural land cover 110
4.2.1.4 Tree age class 112
4.2.1.5 Harvesting season 114
4.2.1.6 Water quality 115
4.2.1.7 Conversion of data layers 118
4.2.1.8 Reclassification of data layers 119
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4.2.2 Conservation scenarios 119
4.2.2.1 Raster calculations of the data layers 120
4.2.2.2 Comparison of conservation scenarios 132
4.3 Wetlands Development 134
4.3.1 Tourism development 135
4.3.1.1 Habitat area 136
4.3.1.2 Threatened fauna 137
4.3.1.3 Habitat’s proximity to natural land cover 139
4.3.1.4 Water quality 141
4.3.2 Economic development 144
4.3.2.1 Tree age class 145
4.3.2.2 Harvesting season 146
4.3.2.3 Water quality 148
4.3.3 Comparison of development scenarios 150
4.4 Comparison of conservation and development scenarios 153
CHAPTER 5 CONCLUSION AND FUTURE RESEARCH
5.1 Conclusion 157
5.2 Future research 161
REFERENCES
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LIST OF TABLES
Table No Page
Table 2.1: Example of straight rank weighting procedure 39
Table 2.2: Assessing weights by ratio estimation procedure 40
Table 2.3: Illustration of pairwise comparison method 41
Table 3.1: Data inventory for the project 55
Table 3.2: Water quality parameters of Pulai River sampling stations 63
Table 3.3: Study criteria and indicators 73
Table 3.4: Illustration of pairwise comparison method 81
Table 3.5: Tourism development criteria and indicators 99
Table 3.6: Economic development criteria and indicators 101
Table 3.7: Conservation criteria and indicators 102
Table 4.1: Water quality Sub-index 116
Table 4.2: Comparison of conservation scenarios (%) 133
Table 4.3: Comparison of development scenarios (%) 151
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LIST OF FIGURES
Figure No Page
Figure 1.1 : Conceptual framework of the study 9
Figure 2.1 : A general model of MCDM (after Jankowski 1995) 29
Figure 2.2 : Spatial multicriteria evaluation 32
Figure 2.3 : Spatial multicriteria analysis in GIS after Malczewski (1999), 34 modified.
Figure 2.4 : Score range procedure in GIS 38
Figure 2.5 : The General Structure of the Super matrix 43
Figure 2.6 : Simple additive weighting method performed in GIS on raster 45 data Figure 3.1 : Study area 49
Figure 3.2 : Land use map 56
Figure 3.3 : Harvesting schedule 58
Figure 3.4 : Endangered species 59
Figure 3.5 : Tree age class 61
Figure 3.6 : Management 62
Figure 3.7 : Pulai River 63
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Figure 3.8 : Species habitat 65
Figure 3.9 : Schematic research approach 71
Figure 3.10: Steps in pairwise comparison method 82
Figure 3.11: Tourism development suitability model 99
Figure 3.12: Economic development model 101 Figure 3.13: Wetland’s conservation model 103 Figure 4.1 : Habitat area (reclassified) 108
Figure 4.2 : Endangered fauna (reclassified) 109
Figure 4.3 : Multiple ring buffer 110
Figure 4.4 : Habitat’s proximity to upland/ natural land cover 111 (reclassified) Figure 4.5 : Habitat’s proximity to upland/ natural land cover 112 (enlarged area) Figure 4.6 : Tree age class (reclassified) 113 Figure 4.7 : Harvesting (reclassified) 115 Figure 4.8 : Water quality (reclassified) 117 Figure 4.9 : Spatial analyst (Features to Raster) 118
Figure 4.10: Spatial analyst (Reclassify) 119
Figure 4.11: Raster calculations 120
Figure 4.12: Conservation model 121
Figure 4.13: scenario 1 (Conservation) 122
Figure 4.14: Scenario 2 (Conservation) 124
Figure 4.15: Scenario 3 (Conservation) 125
Figure 4.16: Scenario 4 (Conservation) 127
Figure 4.17: Scenario 5 (Conservation) 129
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Figure 4.18: Scenario 6 (Conservation) 130
Figure 4.19: Comparison of conservation scenarios 132
Figure 4.20: Tourism development model 136
Figure 4.21: Habitat area (reclassified) 137
Figure 4.22: Endangered fauna (reclassified) 138
Figure 4.23: Habitat’s proximity to upland/ natural land cover 139 (reclassified) Figure 4.24: Habitat’s proximity to upland/ natural land cover 140 (enlarged area) Figure 4.25: Water quality (reclassified) 141
Figure 4.26: Scenario 1 (Tourism development) 142
Figure 4.27: Scenario 2 (Tourism development) 143
Figure 4.28: Economic development model 145
Figure 4.29: Tree age class (reclassified) 146
Figure 4.30: Harvesting (reclassified) 147
Figure 4.31: Water quality 148
Figure 4.32: Scenario 3 (Economic development) 149
Figure 4.33: Comparison of development scenarios 151
Figure 4.34: Comparison of Conservation and development scenarios 153
Figure 4.35: Schematic description of activities 156
CHAPTER 1
INTRODUCTION
1.1 Background
The proliferation of mass tourism over the last 50 years has often occurred with
little concern for environmental and cultural protection. As outlined by Inskeep (1991)
the coastal resorts of the Mediterranean and tourism development in the Caribbean bear
witness to this uncontrolled planning and development process. Most of the tourism
destinations in developing countries, try to make the best out of this, taking everything
out of the environment and causing damage to their land that sometimes can be
permanent.
Throng tourism has been responsible for the destruction of valuable wetlands and
threatening water supplies in the Mediterranean (World Wildlife Fund, 2005). It warns
an expected boom over the next 20 years, with tourist numbers set to reach 655 million
people annually by 2025, will strain supplies further. France, Greece, Italy and Spain
have already lost half of their original wetland areas. In the case of Spain, tourism
expansion near Donana National Park can be seen to compete with the park's wetlands
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for already scarce resources. It is further stated that resorts planned on the Moulouya
estuary in Morocco could further threaten the endangered monk seal and the slender-
billed curlew, one of the rarest birds in Europe. These problems have been responsible
for pollution, shrinkage of wetlands and the tapping of non-renewable groundwater in
some regions (World Wildlife Fund, 2005).
Not only do they use up their natural resources to support the growing tourism
industry, but they also deprive local population of what is rightfully theirs. Yet, all they
do is taking without putting much back in. Unless appropriate action is taken, continued
growth of tourism will further damage such ecosystems with serious consequences in
sustaining long term development and human well being.
Most significantly, however, tourism planning processes have lacked the refined
modeling and simulation tools now available to predict potential outcomes from the
medium to long term. Similarly, the authorities in charge have lacked tools that can
provide them with value-added information that is information about remote locations
and unexploited potentials.
Geographic Information System (GIS) are valuable instruments to resource
managers in identifying "hot spots" or problem areas needing immediate work, and
allow experimentation with various management approaches to working with those
resources, without risking those resources in experimentation. Decision support systems,
ecosystem modeling, and resource assessment allow users to put GIS data bases to their
full use for individualized applications or research studies. GIS is now recognized
widely as a valuable tool for managing, analyzing, and displaying large volumes of
diverse data pertinent to many local and regional planning activities. Its use in
environmental planning is rapidly increasing. Tourism is an activity highly dependent on
environmental resources. Hence, the strength of sustainable tourism planning can be
enhanced by GIS applications.
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1.2 Statement of research problem
Wetland ecosystems are often mistakenly undervalued. Few people realize the
range of products derived from freshwater habitats such as wetlands - food such as fish,
rice and cranberries, medicinal plants, peat for fuel and gardens, poles for building
materials, and grasses and reeds for making mats and baskets and thatching houses.
These complex habitats act as giant sponges, absorbing rainfall and slowly releasing it
over time. Wetlands are like highly efficient sewage treatment works, absorbing
chemicals, filtering pollutants and sediments, breaking down suspended solids and
neutralizing harmful bacteria (World Wildlife Fund, 2005).
Yet half of the world's wetlands have already been destroyed in the past 100
years alone (World Wildlife Fund, 2005). Conversion of swamps, marshes, lakes and
floodplains for large-scale irrigated agriculture, ill-planned housing and industrial
schemes, toxic pollutants from industrial waste and agricultural run-off high in nitrogen
and phosphorous pose some of the main threats to wetlands. Among threatened species
are several river dolphins, manatees, fish, amphibians, birds and plants. In addition, alien
'invasive' species brought from ecosystems in foreign lands disrupt functions in native
ecosystems. Africa alone spends about US$60 million annually to control aquatic
invasive species (World Wildlife Fund, 2005).
Johor wetland reflects an extraordinary diversity of Malaysia: a region of lakes,
mangroves, and woodlands. Owing to a variety of habitats with fascinating landscape,
the wetlands support an incredibly high species biodiversity with a high level of
endemism. It has been a major source of attraction to visitors from all over the world.
However, tourism development is taking place rapidly in this sensitive wetlands
environment with modest concern on the environment. For example the threats faced by
the Sungai Pulai mangrove forest around the Port Tanjung Pelapas (PTP) area, it is
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alarming to note that the site is surrounded by development, which has encroached into
the locale; in addition to this is the continuous logging of its forest in an unsustainable
manner. Rapid and unsustainable development of these wetlands and the river basins
especially the construction of a new port at the river estuary represent a direct impact on
the wetland ecosystem, causing coastal erosion, water pollution and natural habitat
destruction from associated dredging and reclamation works and traffic which has led to
the disruption of natural hydrological cycles.
The degradation and loss of wetlands and their biodiversity has imposed major
economic and social losses; and costs to the human populations of these river basins.
Thus, appropriate protection and management of the wetlands is essential to enable these
ecosystems to survive and continue to provide important goods and services to the local
communities. The main threat to Pulau Kukup comes from the agricultural activities in
the straits, coupled with unplanned tourism, hunting, and water activities.
In view of these problems spatial modeling and Geographic Information System
(GIS) can be regarded as powerful tools that facilitate mapping of wetland conditions,
which is useful in varied monitoring and assessment capacities. More importantly, the
predictive capability of modeling provides a rigorous statistical framework for directing
management and conservation activities by enabling characterization of wetland
structure at any point on the landscape. Spatial (environmental) data can be used to
explore conflicts, examine impacts and assist decision-making. Impact assessment and
simulation are increasingly important to tourism development in wetland areas, and GIS
can play a role in examining the suitability of locations for proposed developments,
identifying conflicting interests and modeling relationships. Systematic evaluation of
environmental impact is often hindered by information deficiencies. GIS seems
particularly suited to this task.
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1.3 Aim of the study
The study aims to identify conservation and compatible areas for tourism
development in Johor Ramsar site, using spatial modeling of Geographic Information
System (GIS) and Multi Criteria Decision Model (MCDM).
1.4 Objectives of the study
1. To study the concept and principles to sustainable tourism/ wetland assessment,
environmental modeling and multi criteria evaluation.
2. To identify suitable areas for tourism and economic development in Ramsar site.
3. To conserve unsuitable areas for tourism development in Ramsar site.
4. To develop a GIS and multi criteria evaluation model for the conservation and
development of Ramsar site.
1.4 Significance of the study
The study area comprises of Johor wetlands that have been declared as wetlands
of international importance at the Ramsar convention, namely Sungai Pulai, Tanjung
Piai and Pulau Kukup; all in Southern Johor State not far from Singapore, particularly
rich in mangroves and inter-tidal mudflats. These coastal and estuarine sites support a
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large number of species, notably vulnerable and threatened species, and provide both
livelihoods and important functions for the local population.
These study areas are chosen because of their ecological significance, serving a
source of food and water, a place for recreation, education and science and most
importantly, a home for the many plants and animals which need wetlands to survive. As
well as providing a buffer against coastal erosion, storm surges and flooding; they also
provide breeding and roosting sites for migratory birds and local water birds. Wetland
plants shelter many animals and birds and are vital for the survival of many threatened
species. Information on the location and conservation value of existing wetlands is
valuable for anyone, particularly those who are involved in coastal activities including
management, recreation and living on the coast.
These study sites are selected among others in view of the problems they face
despite their declaration as wetlands of international significance at the Ramsar
convention. The Convention on Wetlands, signed in Ramsar, Iran, in 1971, is an
intergovernmental treaty which provides the framework for national action and
international cooperation for the conservation and wise use of wetlands and their
resources. There are presently 154 Contracting Parties to the Convention, with 1650
wetland sites, totaling 149.6 million hectares, designated for inclusion in the Ramsar List
of Wetlands of International Importance.
Study will attempt to utilize spatial modeling tools in GIS software, which can be
used for tourism development and conservation in the wetland areas. The use of GIS in
sustainable tourism development and planning demands the development of indicators
of sustainable tourism. This study will be carried out because most previous research
have only focused on identifying potentials of the area with regard to tourism, without
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looking at its environmental effects. On the other hand a significant number of preceding
researches have tended to use the conventional methods of planning and evaluation.
Therefore, Geographic Information System (GIS) application in this respect will
be of significant benefit. Since, most environmental planning problems can be shown to
have spatial or geographical characteristics and tend to be increasingly multi-
dimensional and complex, it is likely that such a project could be more accurately
managed using the techniques and tools found in a GIS environment.
The study intends to apply GIS tools and techniques to bring significant value in
tourism planning; (a) emphasis remote localities or situations where tourism
development is only at the consideration stage and (b) where issues of sustainability are
on the planning agenda because the environment remains largely unprotected. The result
of this research will aid in exploiting hidden potentials for tourism development, also it
will help in preventing conflict between environmentally sensitive areas and the areas to
be developed for tourism. Moreover the authorities will be able to monitor
developmental activities, to ensure compliance. This in the long run will ensure a
sustainable tourism development.
1.6 Scope of study & methodology
The study will focus only on the physical assessment of the wetlands i.e
biodiversity value of the study area using spatial modeling techniques and Multi Criteria
Decision Model (MCDM). It will centre on identifying potential tourism areas and areas
that needs to be conserved in the wetland area. This study is to understand how GIS can
be used to identify potential areas for tourism development; at the same time locating
environmentally sensitive areas that needs to be conserved.
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Considering the project objectives, the methodology will be looked at from two
perspectives i.e conservation and development. The data collection procedure will
mainly be based on secondary sources with partial primary investigation of the study
sites. The data collected will be processed by the use of Multi Criteria Decision Making
model (MCDM) and Geographic Information System (GIS).
In order to assess the relevance for wetlands conservation and development, a set
of evaluation criteria will be selected and suitable indicators to measure the selected
criteria. These criteria will be represented inform of data layers, representing different
needs for conservation and development. Subsequently the criteria will be evaluated by
reclassifying the data layers; they will be evaluated from conservation point of view by
considering areas of high biodiversity as most relevant for conservation and low
biodiversity areas most appropriate for development. This will be computed by using
typical functionalities of raster-based GIS; such as distance operators, conversion and
reclassification functions. The GIS package ArcGIS 9.0 will be used because it is
provided with tools for analysis and transformation of raster data.
Pair wise comparison method of Multi Criteria Evaluation will be used in order
to support solution of a decision problem by evaluating possible alternatives from
different perspectives. The pair wise comparison will be developed in Microsoft Excel
and results transferred into ArcGIS framework. Alternatives to be evaluated and ranked
will be represented by different criterion maps. As different criteria are usually
characterized by different importance levels, the subsequent step of MCE will be the
prioritization of the criteria by means of pair wise technique; which allows for the
comparison of two criteria at a time. This can be achieved through the assignment of a
weight to each criterion that indicates its importance relatively to the other criteria under
consideration. Conservation and development scenarios will be generated, with each
scenario representing the best solution to decision problem, according to the assessment
perspective adopted. Map scenarios reflecting the opinion of different experts or
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stakeholders involved will be compared using the Boolean overlay approach of GIS, in
order to highlight the robustness of the solution and support decision making (Figure
1.1)
Feed back Figure 1.1: Conceptual framework of the study
Aim and objectives
Setting-up of criteria and parameters
Database design and
development
Model development
Wetlands development
model
Wetlands conservation
model
Assessment of conservation and
development scenarios
Conservation
and development scenarios
Issues and problems
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1.7 Limitations of the study
This project will be restricted to identifying potential tourism and conservation
areas only and will not be dealing with other aspects of tourism as; travel cost,
perception, definition of wilderness and other principles inherent to sustainable tourism.
Also the study will dependent on secondary data, with partial primary investigation of
the study sites.
Another limitation is in the technique to be used in data analysis. This technique
(pair wise comparison method) has the capacity of comparing only two criteria’s at a
time. Also the highly subjective nature of preference weights and rapid elicitation of the
method can lead to questions of validity. Moreover problems with inconsistencies in
preferences between objectives sometimes arise.
CHAPTER 2
GIS AND DECISION SUPPORT SYSTEMS IN SUSTAINABLE TOURISM
2.1 Concept of sustainable tourism
The World Summit held in Rio de Janeiro in 1992, declared that there is a need
for a more balanced approach in development planning and outlined a framework in
which economic, socio-cultural and environmental aspects are equally important for a
sustainable future. Ever since, governmental and non-governmental organizations,
international, national and regional authorities and the academic community have been
trying to interpret the term sustainable development and take action. One approach for
doing this is to examine the concept of sustainability and ascertain how it applies in the
different sectors of the economy. Tourism is an economic activity and cannot be
marginalized as its development and prosperity strongly depends on the environmental
and socio-cultural resources in each destination.
A definition of sustainable tourism is rather clear; Sustainable tourism may be
thought of as "tourism which is in a form which can maintain its viability in an area for
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an indefinite period of time" (Butler, 1993). The definition of sustainable tourism
development is quite different and more elusive; as it is a relatively recent concept
whose definitions win continue to evolve. Yet, a number of notions advanced by the
World Commission on Environment and Development (WCED) contribute to the
definition.
Inskeep (1991) thought of sustainable tourism development as "meeting the
needs of present tourism and host regions while protecting and enhancing opportunity
for the future". Sustainable tourism development involves management of all resources
in such a way that "economic, social and aesthetic needs are fulfilled while maintaining
cultural integrity, essential ecological processes, biological diversity and life support
systems". It involves the minimization of negative impacts and the maximization of
positive impacts. Yet, while sustainable tourism may therefore be regarded as a form of
sustainable development as well as vehicle for achieving the latter, there is not as direct
a relationship between the two terms as might be expected. The Brundtland Report,
curiously, makes no mention of tourism even though the latter had already attained
‘megasector’ status by the mid 1980’s. This neglect was evident several years later in the
agenda 21 strategy document that emerged from the seminal Rio Earth Summit in 1992,
which made only few incidental references to tourism as both a cause and potential
ameliorator of environmental and social problems (UNCED, 1992).
Budowski’s (1976) defines sustainable tourism as tourism that wisely uses and
conserves resources in order to maintain their long-term viability. Butler (1993) believed
that a working definition of sustainable development in the context of tourism could be
taken as tourism which remains viable over an indefinite period and does not degrade or
alter the environment (human and physical) in which it exists to such a degree that it
prohibits the successful development and well-being of other activities and processes".
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The concept of tourism sustainability points to the need for better spatial,
environmental, and economic balance of tourism development, requiring new integrative
public-private approaches and policies in the future. When the principle of sustainability
is applied to new tourism development, it would mean that coastal hotels would not
pollute their water bodies with raw sewage, that hillside resort will not incite soil
erosion, and that sites of fragile and rare vegetation or wildlife would not be used for
tourism except as scenery and interpretation. Tourist businesses can benefit by land use
decision making that offers long-range protection of resources. Only by accepting such
responsibility will tourism be assured a continuing quality future. Some of the
guidelines, approaches and principles to sustainable tourism development include;
Tourism should provide real opportunities to reduce poverty; create quality employment
to the community residents and stimulate regional development. Prospects for economic
development and employment should be enhanced while maintaining protection of the
environment. Linkage between the local businesses and tourism should be established.
This is aimed at improving the quality of life in local communities.
Tourism should also conserve the natural and cultural assets; it should guarantee
the protection of nature, local and the indigenous cultures. The relationship between
tourism and the environment, both natural and cultural, must be managed so that it is
sustainable in the long term. Tourism should enhance and complement the unique
natural and cultural features of its area. It should provide mechanisms to preserve
threatened areas that could protect wildlife; and also preserve the historic heritage,
authentic culture and traditions. In addition, tourism should ensure that the local or
regional plans contain a set of development guidelines for the sustainable use of natural
resources and land; and are consistent with overall objectives of sustainable
development. These plans should establish a code of practice for tourism at all levels;
national, regional, and local, based on internationally accepted standards. Guidelines for
tourism operations, impact assessment, monitoring of cumulative impacts, and limits to
acceptable change should be established and.
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Tourism should minimize the pollution of air, water, land and the generation of
waste by tourism enterprises and visitors. This is about outputs from the tourism sector,
minimizing pollution in the interests of both the global and the local environment. Some
key issues for tourism include promoting less polluting forms of transport as well as
minimizing and controlling discharges of sewage into sensitive environments. Integrated
management approaches should be used to carry out restoration programmes effectively
in areas that have been damaged or degraded by past activities.
2.2 Wetlands assessment
In physical geography, a wetland is an environment at the interface between truly
terrestrial ecosystems and truly aquatic systems making them different from each yet
highly dependent on both (Mitsch & Gosselink, 1986). In essence, wetlands are
ecotones. Wetlands are typically highly productive habitats, often hosting considerable
biodiversity and endemism. In many locations such as the United Kingdom and USA
they are the subject of conservation efforts and Biodiversity Action Plans. The United
States Army Corps of Engineers and the Environmental Protection Agency (1987)
jointly define wetlands as: Those areas that are inundated or saturated by surface or
ground water at a frequency and duration sufficient to support, and that under normal
circumstances do support, a prevalence of vegetation typically adapted for life in
saturated soil conditions. Wetlands generally include swamps, marshes, bogs, and
similar areas.
In the 1970s, a growing number of scientists, ecologists, and conservationists
began to articulate the values of wetlands. During the last three decades, dozens of
international, national, and state wetland related policies, agreements, and initiatives
were brought into effect. Actions like the Convention on Wetlands, signed in Ramsar,
Iran, in 1971, which is an intergovernmental treaty which provides the framework for
15
national action and international cooperation for the conservation and wise use of
wetlands and their resources. There are presently 154 Contracting Parties to the
Convention, with 1650 wetland sites, totaling 149.6 million hectares, designated for
inclusion in the Ramsar List of Wetlands of International Importance. This treaty
demonstrates a community understanding of the need to protect and rehabilitate
wetlands. However, the growing community desire to rehabilitate wetland areas is being
hampered by a general lack of objective knowledge on wetland condition at appropriate
scales, where the condition is defined as the relative ability of a wetland to support and
maintain its complexity and capacity for self-organization with respect to species
composition, physio-chemical characteristics, and functional processes as compared to
wetlands of a similar class without human alterations (Fennessy et al. 2004).
In the United States, this has produced changes in national policy, which include
increased regulation of wetlands as well as both public and private conservation efforts
to protect, acquire, enhance and restore these resources. At the same time, wetland areas
are under increasing pressure from development and urbanization within watersheds.
Both resource management concerns, as well as regulatory needs, often force choices
among the different, sometimes conflicting uses. The need to make decisions about
wetlands has thus created a need for information on the value, both from an ecological
and a societal standpoint, of these wetland resources; hence the need for wetland
assessment. Here the United States (US) is being used as a case to examine methods
available for wetland assessment, evaluates their applications and shortcomings.
The United States Congress directed the US Fish and Wildlife Service (USFWS)
in 1996 to develop a nationwide inventory of wetlands, in order to provide information
to the public and to the government on the location and types of wetlands in the US.
This National Wetlands Inventory (NWI), which is approximately 89% complete
(USFWS, 1996) has identified the location of wetlands in the US using stereoscopic
pairs of infrared photographs. Fieldwork is then performed to confirm, or ‘ground-truth’
16
photographic data and collect additional data, from which the wetlands are ultimately
mapped. The inventory further classifies wetlands by type based on substrate or soil
type, dominant hydrologic regime, vegetation community and aquatic habitat type,
among other things (USFWS, 1996). NWI maps are not intended to provide wetland
boundaries for regulatory purposes, but rather to provide information to the public about
the possible locations and types of wetlands in a given geographic area. Information
arising from the National Wetlands Inventory indicates that the United States has lost
over half of the wetlands which historically existed in the lower 48 states, most
frequently as a result of drainage for agriculture (Dahl 1990). The development of
inventory data is a type of assessment which provides information identifying the
locations, areal extent and types of wetlands existing within a landscape. The term
assessment, however, as it is most commonly used, implies a more detailed evaluation of
how a specific wetland or range of wetlands functions. Assessment may also involve an
evaluation of the condition, or ecological integrity, of the wetland system.
In discussing wetland assessment, it is often discussed in terms of wetland
functions and wetland values. Wetland functions are defined as physical, chemical, or
biological processes occurring within wetland systems. Wetland values are attributes of
wetlands which are perceived as valuable to society. Wetland functions are therefore
able to be more objectively assessed or measured, while wetland values are inherently
subjective and may be difficult to assess. Nevertheless, decision making is a valuative
process and consequently must consider wetland values in weighing decision
alternatives and consequences. Consideration of wetland value is often indirectly
imbedded in the assessment process as well, because the choice of which functions to
assess is often made based on the perception of which wetland functions are most
important.
There are a wide variety of applications for which information on wetland
function and condition may be used. The most common uses of assessment have been:
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1) The evaluation of wetlands proposed for fill development; 2) Evaluation of impacts
for planning purposes; 3) Evaluation of wetland restoration potential for conservation
programs; 4) Determining wildlife habitat potential for properties proposed for
acquisition for wildlife management purposes, or where changes in land management
are proposed to occur.
In response to the desire to achieve the goal of no net loss of wetland function,
there have been over forty different methods developed in the last decade alone which
are designed to assess wetlands (Bartoldus, 1999). They range in level of rigor from
those based on ad hoc consensus among professionals to more sophisticated peer-
reviewed mechanistic models. Consequently, these techniques differ greatly in the level
of detail, objectivity and repeatability of the results. There is also considerable
variability in the range of wetland functions that are considered by any given technique.
Some methodologies are narrowly focused and may only consider a single or a small
related group of functions such as fish habitat, bird habitat, wildlife habitat, flood
storage, etc (USFWS, 1996); others look at a broader range of wetlands functions
concurrently, such as flood storage capacity, sediment stabilization, nutrient uptake,
primary production export, fish and wildlife habitat (Adamus et al. 1987, Bartoldus,
1999). Some of these techniques have components to consider wetland values as well as
functions. Because wetlands are such complex systems, however, there is no single
technique, no matter how comprehensive, which can evaluate all functions performed by
a given wetland. Generally speaking, assessment methods fall into approximately four
general types of approaches:
1. Inventory and classification. These are objective techniques which describe the areal
extent and/or types of wetlands within a given landscape. This includes such information
as the National Wetland Inventory maps.
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2. Rapid Assessment Protocols. These are mostly low-cost techniques in which the data
necessary to perform the assessment may be gathered in a short period of time. Rapid
assessment protocols tend to focus mostly on single wetlands or small populations of
wetlands. The results are likely to be either completely qualitative, or involve a large
extent of subjective (best professional judgment) information.
3. Data-driven Assessment Methods. These are usually expensive to develop, often
model based, but provide a high degree of reproducibility. The results often have
predictive value.
4. Bio-indicators/Indices of Biotic Integrity. These techniques involve a selected set of
variables, which are measured across wetland types. The variables may be evaluated
separately, or used to develop multi-metric indices, which can be used to measure the
condition or ecological integrity of a wetland and can be used as environmental triggers
to identify long-term changes.
However, these methods have lacked the predictive capability of spatial
modeling in GIS. Spatial modeling provides a rigorous statistical framework for
directing management and conservation activities by enabling characterization of
wetland structure at any point on the landscape. Spatial (environmental) data can be used
to explore conflicts, examine impacts and assist decision-making.
2.3 Spatial modeling environments
In general, a spatial modeling environment may be thought of as an integrated set
of software tools providing the computer facilities needed to develop and execute
spatially explicit simulations and display model results. These integrated environments
have been designed to support modeling efforts of groups engaged in activities as varied
19
in scope as global climate change research, watershed management, and urban planning.
Various approaches have been undertaken to integrate spatial modeling with GISs.
These approaches have been described relative to intensity of coupling, as well as degree
of modeling flexibility Albrecht et al. (1997). A number of these efforts have resulted in
methods for modeling environmental processes such as forest dynamics and hydrologic
processes. Other developments have introduced graphical user interfaces with sliders to
modify weightings within models. While these method allows exploration of alternative
scenarios, they are domain specific and do not support generic spatial model
development.
Other approaches to spatial modeling and GIS integration have required users to
write code in a formal programming language or assisted users to specify model
structure either through guided question and answer sessions Robertson et al. (1991) or
using pseudo-English to generate code (Lowes and Walker, 1995). Albrecht et al.
(1997), in pointing out limitations of these approaches, have noted that they tend to be
domain-specific, require users to learn a specific programming language, may be
difficult to follow through model implementation, and importantly, do not support
creative conceptual model development.
Another approach to integrating spatial modeling and GIS is diagrammatic, that
is, spatial models are represented as process flow diagrams that graphically illustrate
relationships among input data, geo-processing functions, and output or derived data.
Applications of this approach range from image analysis (ERDAS IMAGINE
Professional 8.4, Spatial Modeler) to static cartographic modeling (Virtual GIS or VGIS
prototype described by Albrecht et al., (1997), and ESRI's ModelBuilder in the Spatial
Analyst 2.0 extension to ArcView GIS) to dynamic simulation modeling (Spatial
Modeling Environment, SME). This approach has a number of advantages. First, these
types of flow diagrams frequently appear in various disciplines and therefore represent a
common conceptual framework. In fact, such flow charts are a standard process-oriented
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tool in visual programming Chang et al., (1990). Process flow diagrams make
relationships among model elements apparent and model behavior easy to follow and
explain to others. This is a powerful advantage for non-GIS model developers, as well as
stakeholders and decision-makers, as they engage in exploring and solving
environmental problems.
Lately, spatial modeling and GIS have become popular as assessment tools in
many disciplines such as environmental protection, watershed management, wetland
evaluation and land use changes; which sometimes integrate the workings of the above
methods. GIS technology was initially developed as a tool for spatial data storage,
retrieval, manipulation and display, and now more and more powerful analytical
functions have been built into commercial GIS software to perform much of its general
spatial analysis as well as data management tasks. One of the most persistent and
pervasive words in the field of GIS is “integration”. Indeed, the ability of GIS to
integrate diverse information is frequently cited as its major defining attribute, and its
major source of power and flexibility in meeting user needs. The analytical module in
many of the specific areas such as, environmental modeling, wetland functional
assessment, ecological and economic impacts of agricultural policy, must be developed
and then integrated into GIS (Drayton et al. 1996). A system with this type of function
and analytical module falls into the category of Decision Support System (DSS).
Decision makers are increasingly turning to GIS to assist them with solving complex
spatial problems. Spatial Decision Support Systems (SDSS) are explicitly designed to
support a decision research process. SDSS provides a framework for integrating
database management systems with analytical models, graphical display, tabular
reporting capabilities and expert knowledge of decision makers. The concepts and
technologies of DSS and SDSS are still evolving (Densham, 1991; Power, 2003).
Many recent works raise the crucial question of decision-aid within GIS
(Malczewski 1999). Most if not all of these works have come to the conclusion that GIS
21
by itself can not be an efficient decision-aid tool and they have recommended the
combination between GIS and a form of decision aid. The long-term objective of such
integration is to develop a Spatial Decision Support System (SDSS). What really makes
the difference between a SDSS and a traditional decision support system (DSS) is the
particular nature of the geographic data considered in different spatial problems. In
addition, traditional DSS are designed primarily for solving structured and simple
problems which make them non practicable for complex spatial problems. Since the end
of the 1980s, several researchers have oriented their works towards the extension of
traditional DSS to SDSS that support spatially-related problems (Densham 1991;
Jankowski 1994; Malczewski 1999). This requires adding to conventional DSS a range
of specific techniques and functionalities used especially to manage spatial data. These
additional capacities enable the SDSS to (Densham 1991): acquire and manage the
spatial data; represent the structure of geographical objects and their spatial relations;
diffuse the results of the user queries and SDSS analysis according to different spatial
forms including maps, graphs, etc., and; perform an effective spatial analysis by the use
of specific techniques.
In spite of their power in handling the first three operations, GIS are particularly
limited tools in the fourth one. Moreover, even if the GIS can be used in spatial problem
definition, they fail to support the ultimate and most important phase of the general
decision-making process concerning the selection of an appropriate alternative. To
achieve this requirement, other evaluation techniques instead of optimization or cost-
benefit analysis ones are needed. Undoubtedly, these evaluation techniques should be
based on Multi Criteria Decision Model (MCDM) in GIS.
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2.4 Geographic Information System (GIS) in sustainable tourism planning
Although GIS is rarely discussed in the context of tourism, its wider use by
planners concerned with environmental issues and resource management is now well
established (Berry, 1991; Robinson, 1992). One of the earliest applications of GIS in
tourism planning is discussed by Berry (1991) in the US Virgin Islands. GIS was used to
define conservation and recreation areas and determine the best locations for
development. Best locations were determined according to engineering, aesthetics, and
environmental constraints. Similarly, Boyd and Butler (1993) demonstrated the
application of GIS in the identification of areas suitable for ecotourism in Northern
Ontario, Canada. At first, a resource inventory and a list of ecotourism criteria were
developed. At a next stage GIS techniques were used to measure the ranking of different
sites according to the set criteria and therefore identify those with the ‘best’ potential.
Minagawa & Tanaka (1998) used GIS to locate areas suitable for tourism development
at Lombok Island in Indonesia. The main objective was to propose a methodology for
GIS based tourism planning. Using map overlay and multi-criteria evaluation a number
of potential sites for tourism development was identified. Beedasy and Whyatt (1999)
developed a GIS based decision support system for sound spatial planning for tourism in
Mauritius. Given the space limitation of Mauritius, the increasing tourist demand and the
need to consider alternative sites in order to avoid further deterioration of existing tourist
zones, a spatial decision support system was developed to support tourism planning. GIS
technology was considered as the appropriate platform for such a system because it can
integrate both qualitative and quantitative information, it can provide a visual display of
results thus permitting an easy and efficient appraisal of results, and can communicate
information to all interested parties becoming thus a participatory and exploratory tool.
Williams et al., (1996) also used GIS to record and analyze tourism resource
inventory information in British Columbia, Canada. He developed a tourism capability
map which indicates areas of high, moderate, and low capability for specific tourism
23
activities. Ribiero de Costa (1996) used GIS to create a map of tourism potential in the
Mediterranean area of Europe. Carver (1995) used GIS to describe the development of a
‘wilderness continuum map’ showing areas designated as wilderness in the UK and its
use to identify areas of potential risk from recreational development. Bahaire and Elliott-
White (1999) provided a brief description of various applications of GIS in tourism
planning in the United Kingdom. These applications included data integration and
management (for example data on tourism destination types and accommodation),
landscape resource inventory, designation of tourist areas in terms of use levels, tourism
suitability analysis, and pre and post-tourism visual impact analysis. The overall
conclusion is that GIS is an efficient and effective means of helping the various
stakeholders examine the implications of land-use decisions in tourism development.
GIS has also been used to analyze tourism related issues such as the perception
and definition of wilderness (Kliskey & Kearsley, 1993; Carver, 1997), countryside
management (Haines- Young et al., 1994) and travel costs (Bateman et al. 1996).
Another early example of the use of GIS in tourism is provided by Binz & Wildi (cited
in Heywood et al. 1994 who modeled the effect of increased tourist development in the
Davos Valley in Switzerland; based on scenario analysis. However, more recent
publications (Elliott-White & Finn, 1998) suggest a growing interest in GIS applications
in tourism. GIS applications are now common place in the utilities, land information and
planning. Tourism growth is intensifying an often stretched and overloaded tourism
infrastructure and is itself threatened by local and environmental pressure groups. GIS
can be an effective tool in the design and monitoring of sustainable.
GIS can be used to identify areas or zones which should be undisturbed by
tourism or any kind of development. Gribb (1991) describes the planning effort that took
place at the Grayrocks Reservoir in Wyoming, US. The aim was to come up with a
recreation development plan that would contribute at the same time to environmental
conservation of the Reservoir. McAdam (1994) reported the case of a GIS prototype
24
application developed for monitoring the impacts resulting from the increasing number
of trekking and special interest tourists in a remote region in Nepal. Shackley (1997)
within her involvement in regional and site tourism management issues newly opened to
visitors, Himalayan Kingdom of Lo (Mustang), Nepal, suggested the development of a
GIS based spatially-referenced multimedia cultural archive. This archive, with data
collected at an early stage of tourism development, would serve to monitor possible
change through time.
Dietvorst (1995) used a survey based time-space analysis at a theme park in the
Netherlands, to better understand visitors’ preferences for the various attractions of the
park. A GIS was used for the analysis of the coherence between the various attractions
and other elements of the park. Findings were then used for a more balanced diffusion of
visitor streams and a better routing system. Van der Knaap (1999) used GIS to
understand the use of the physical environment by tourists in order to promote
sustainable tourism development. Bishop and Gimblett (2000) presented the use of
spatial information systems, spatial modeling and virtual reality in recreation planning.
Using rule-driven autonomous agents moving in a GIS-based landscape, the movement
patterns of the visitors can be simulated. In this way it is argued that better management
of the recreational area is achieved through the effective management of recreationists’
behavior; a case study was conducted at Broken Arrow Canyon, Arizona.
Tourism destinations are usually characterized by three different landscape
features: points, lines, and polygons. Point features are individual tourist attractions, for
example, a campground in a park, or a historic site along the highway. Streams and
coastal beaches often follow a linear pattern, while habitat location or natural parks are
characteristics of a polygon feature. These locational attributes are essential to a
Geographic Information System. It is apparent that GIS has tremendous potential for
application in sustainable tourism.
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However, due to the general lack of databases and inconsistencies in data, its
applications are limited. For example, there is very little site-specific information about
suitability of sites for conservation or tourism development, sources of visitor’s origin
and destination, travel motivation, spatial patterns of recreation and tourism use, visitor
expenditure patterns and levels of use and impacts- all of which are suitable application
areas of GIS. So far, applications of GIS in tourism has been limited to recreational
facility inventory, tourism-based land management, visitor impact assessment,
recreation-wildlife conflicts, mapping wilderness perceptions and tourism information
management system.
2.5 Multi Criteria Decision Making and Natural resources Management
Rapid socioeconomic improvements driven by increased income and wealth have
increased the demand for ecosystem services, such as aesthetic enjoyment and
recreation. Nature-based tourism is an important income source in many countries and
having a pristine environment is paramount for its success. Planning and management of
natural areas are inherently difficult because of the multiple attributes of nature-based
tourism, and conflicts between use and conservation of those areas. Management of
nature-based tourism and natural areas should control use patterns and implement
resource protection practices that maintain the quality of visitor experiences without
denigrating ecological, cultural, and social values (Figgis 1993). The emergence of the
concept of sustainable development in the 1980s was a reflection of the failure to
safeguard ecosystem values from population and economic growth. Sustainable resource
management requires maintaining environmental quality and ecological integrity for
future generations.
The management of wetlands needs to be changed in order to improve their
quality and ensure that economic development does not degrade their health. Wetlands
26
perform a variety of critical functions in maintaining healthy river systems, and have
ecological, hydrologic, and economic value (Herath 2004). They improve water quality,
replenish groundwater, retain floodwater, provide habitat for a diversity of plants and
animals, trap sediment, reduce nutrients, and remove contaminants. Such critical
ecosystem services of wetlands are lost when wetlands are converted to other uses
and/or degraded. Stakeholder perceptions of river ecosystems and wetlands need to be
changed through education and intervention strategies.
Improving decision making for human and natural resource management requires
consideration of a multitude of non-economic objectives, such as biodiversity,
ecological integrity, and recreation potential. When ecosystems become degraded, the
provision of ecosystem services is impaired. There are limits to the changes that
ecosystems can undergo and still remain productive. Decision making related to the
sustainable use of natural resources involves important tradeoffs because increasing one
benefit typically decreases other benefits. For example, converting a natural forest to a
plantation forest increases timber output, but reduces wildlife habitat in the remaining
forest compared to the untouched forest. Furthermore, the values of environmental
attributes, such as biodiversity, cannot be properly measured using monetary criteria;
appropriate non-monetary criteria need to be developed.
Methods that facilitate better management and policy decisions must account for
the variation in stakeholders’ preferences for attributes, and conflicting stakeholder
interests and values. As the complexity of decisions increases, it becomes more difficult
for decision makers to identify a management alternative that maximizes all decision
criteria. This difficulty has increased the demand for more sophisticated analytical
methods that consider the myriad of attributes of decision outcomes and differences in
stakeholders’ preferences for those attributes. The neoclassical economic approach
based on maximization of a single objective (i.e., utility for consumers and profit for
businesses) has limited applicability in multi-attribute decision problems in natural
27
resource management (Joubert et al. 1997). Over the past two decades, considerable
attention has been focused on developing and using multi-criteria decision making
(MCDA) techniques to identify optimal alternatives for managing natural resources.
The foregoing discussion highlights the difficulties of natural resource planning
and management when there are a multitude of heterogeneous stakeholders, objectives,
goals, and expectations, and stakeholder conflicts. Planning requires a multi-objective
approach that leads to well conceived and acceptable management alternatives and
expands the ability to make decisions in complex natural resource management settings.
It also requires analytical methods that examine tradeoffs, consider multiple political,
economic, environmental, and social dimensions, reduce conflicts, and incorporate these
realities in an optimizing framework.
MCDA techniques have emerged as a major approach for solving natural
resource management problems and integrating the environmental, social, and economic
values and preferences of stakeholders while overcoming the difficulties in monetizing
intrinsically non-monetary attributes. Quantifying the value of ecosystem services in a
non-monetary manner is a key element in MCDA (Martinez-Alier et al. 1999; Munda,
2000).
2.6 Multi criteria decision making (MCDM)
2.6.1 Multiple criteria decision making – an overview
Multicriteria decision making (MCDM) is a term including multiple attribute
decision making (MADM) and multiple objective decision making (MODM). MADM is
applied when a choice out of a set of discrete actions is to be made. In MODM, it is
28
assumed that the best solution can be found anywhere in the feasible alternatives space,
and therefore is perceived as continuous decision problem. MADM is often referred as
multicriteria analysis (MCA) or multicriteria evaluation (MCE). Instead, MODM is
more close to Pareto optimum searching with use of mathematical programming
techniques (Jankowski 1995, Malczewski 1999). Here, the term multicriteria decision
making is used in reference to multiple attribute decision-making and the other
expressions are used as equivalents. The main objective of MCDM is “to assist the
decision-maker in selecting the ‘best’ alternative from the number of feasible choice-
alternatives under the presence of multiple [decision] criteria and diverse criterion
priorities”. Every MCDM technique has common procedure steps, which are called a
general model (after Jankowski 1995). This procedure includes the following actions
(Figure 2.1):
1. Deriving a set of alternatives
2. Deriving a set of criteria
3. Estimating impact of each alternative on every criterion to get criterion scores
4. Formulating the decision table with use of the discrete alternatives, criteria and
criterion scores.
5. Specifying decision-maker’s (DM) preferences in the form of criterion weights
6. Aggregating the data from the decision table in order to rank the alternatives (simple
and multiple aggregation functions)
7. Performing sensitivity analysis in order to deal with imprecision, uncertainty, and
inaccuracy of the results
8. Making the final recommendation in the form of either one alternative, reduced
number of several ‘good alternatives’, or a ranking of alternatives from best to worst.
All the MCDM techniques are based on the above presented general model.
However, division can be made for compensatory and non-compensatory methods. The
compensatory methods can be further subdivided into additive and ideal point
29
techniques, where the first includes e.g. weighted summation, concordance analysis and
Analytical Hierarchy Process and the latter, Technique for Order Preference by
Similarity to Ideal Point (TOPSIS), Aspiration-level Interactive Method (AIM) and
Multi-Dimensional Scaling (MDS). Non-compensatory techniques are for example
dominance, conjunctive, disjunctive and lexicographic techniques. Two of the most
popular techniques will be discussed here. Good summary of the MCDM techniques and
its choice strategy is given by Jankowski (1995); Voogd (1983) provides a
comprehensive theoretical background.
Figure 2.1: A general model of MCDM (after Jankowski 1995)
All additive methods, being compensatory techniques, are based on the
standardized criterion scores, which can be then compared and added. Standardization
allows comparison of criterion scores within one alternative, to come into some kind of
30
trade-off when poor performance of the alternative under one criterion can be
compensated by a high performance under another criterion. Total score for each
alternative is achieved by multiplying criterion score with its appropriate weight and
adding all weighted scores. Weighted summation technique, being a basic form of
additive methods, can be written down in the matrix algebra as follows:
Where: Si is a total score for alternative i, Cji is a criterion score for alternative i and criterion j Wj is criterion weight.
The weighted summation allows for evaluation and ordering of all alternatives
based on the criteria preferences by decision-makers. However, there are techniques
which allow setting preferences to both criteria and criterion scores. Second technique,
Analytical Hierarchy Process (AHP) “uses a hierarchical structure of criteria and both
additive transformation function and pairwise comparison of criteria to establish
criterion weights” Jankowski (1995).
2.6.2 Multi-criteria decision making and GIS
GIS has good capabilities of handling spatial problems, and as such can be used
to support spatial decision-making. Solving a complex multiple criteria problem without
spatial analytical and visualization tools would be computationally difficult, if not
impossible Jones (1997). Multicriteria decision making techniques, as stand alone tools,
have been computerized and nowadays there is much software to use. However, it is not
31
common that such software is capable to handle spatial problem in the form of maps.
There exist two strategies: loose and tight, for coupling of GIS with MCDM techniques
Jankowski (1995). The loose coupling relies on a file exchange mechanism which
enables communication with the two types of software. Separate tasks are performed in
either of software. GIS is used for performing land suitability analysis, selecting a set of
criteria and their scores in order to export the decision table into MCDM program. The
MCDM module is used for executing multicriteria evaluation and the result is
transferred again into the GIS for display. The tight coupling strategy instead, is realized
by a common interface and common database for GIS and MCDM. This in fact means
that the multicriteria evaluation functions are embedded into the GIS software. The
advantage is that all necessary functions are on place and troublesome data exchange is
avoided. However, not every proprietary GIS have developed such a facility in its basic
version. There is example of IDRISI, which employs pairwise comparison and Analytic
Hierarchy Process to evaluate weight scores (Clark Labs). Another software Spans, by
Tydac Technologies, has inbuilt weighted overlay functions, which are similar to
weighted summation MCE technique Carver (1991). The ESRI software provides a
cartographic modeling tool called Model Builder, which is capable to handle similar
decision problems, hence requires some initial input of work. Generally speaking,
multicriteria evaluation with use of GIS can be done in two stages, (i) survey and (ii)
preliminary site identification. In the first step, the area is screened for feasible
alternatives using deterministic decision criteria. Here, all the sites, which meet all the
exclusion criteria (constraints) simultaneously, are identified and taken away from the
analysis. This stage is sometimes referred as suitability analysis, traditionally performed
by manual map overlay, further revolutionized by GIS digital maps.
The second stage, called preliminary site identification, is operationalized by
MCE techniques. First, secondary siting factors are elaborated and then weighted
according to their importance. The second stage allows handling multiple objective
problems Carver (1991); Jankowski (1995). Multiple criteria overlay was proposed by
McHarg (1969) who suggested identifying physical, economic and environmental
32
criteria in order to assure social and economic feasibility of the project. The complexity
of the decision problem determines whether binary or multiple values overlay technique
is used (Figure 2.2a and b.). In geographic analysis, most commonly used operations are
AND and OR (Boolean), which correspond to spatial ‘intersection’ and ‘union’. If the
decision factors have different levels of importance, weighted overlay should be used
(Figure 2.2). However, special scores aggregation procedure is required to achieve
meaningful results Jones (1997).
Source: McHarg (1969)
Figure 2.2: Spatial multicriteria evaluation: a) binary overlaying; b) multiple
values overlaying; c) multiple values weighted overlay
2.6.2.1 Evaluation criteria
An evaluation criterion is a term used to encompass both objectives and
attributes of multicriteria decision problem Malczewski (1999). Other authors refer them
as decision criteria or factors and scores respectively Voogd (1983); Carver (1991). The
33
objectives describe the desirable state of a geographical space. They formulate the
criteria that need to be fulfilled in order to make the right decision by “minimizing” or
“maximizing” some variables. The attributes, on the other hand, contain measures used
to assess the level of achievement of the criterion by each alternative. Evaluation criteria
are presented in GIS as thematic maps or data layers. It is required that decision
attributes fulfill several requirements. Firstly, they need to be measurable, which implies
that it should be easy to assign numerical values that correctly asses the references to or
the level of achievement of the objective. Secondly, an attribute should clearly indicate
to what degree the objective is achieved, which is unambiguous and understandable for
decision maker. This is called comprehensiveness of an attribute. Furthermore a set of
attributes should be operational. If the attribute is understandable for the decision maker,
he/she can correctly describe relation between the attribute and a level of achievement of
the overall objective than it can be used meaningfully in the decision-making process. A
set of attributes should also be complete, which means that it covers all aspects of a
decision problem. The set of attributes should be minimal, which form the smallest
possible set that completely describes the decision problem. No redundancy means that
consequences of valuation of decision influence only one attribute. The test of
coefficient of correlation can be used for every pair of attributes to test for no
redundancy. Lastly the set of attributes should be decomposable. It is true if evaluation
of the attributes in the decision process can be simplified into few smaller decisions.
Usually evaluation criteria form a hierarchical structure Malczewski (1999).
Selecting a proper set of evaluation criteria can be done by means of literature
study, analytical studies or survey of opinions. Literature can be found with some
authors providing literature review of criteria evaluation to a specific spatial decision
problem. Governmental agencies and governmental publications can provide guidelines
for selection of evaluation criteria. Another method is to recognize objectives from
governmental or other documents and review relevant literature to identify attributes
associated with every objective. Analytical studies can be performed for example by
34
system modeling. Opinions’ survey is aimed at people affected by decision or a group of
experts, where several formalized techniques exist Malczewski (1999).
Figure 2.3: Spatial multicriteria analysis in GIS after Malczewski (1999), modified
A set of objectives and attributes used for a specific decision is affected by data
availability. It may not be feasible to obtain required information for the ideal set of
attributes designed for a specific objective, or data may not exist. The choice of
attributes is also limited by cost and time of gathering the data. It must be a trade-off
between the accuracy of prediction and cost and time required. An example is taken
from the case study considering location of a water transmission line, where six pipeline
35
corridor alternatives are evaluated. The criteria were, among others: total cost of route,
amount of public right-of-way, area of wetlands and length of streams falling inside each
corridor. All of the cited criteria have natural measured scale, dollars, acres and meters
respectively. The decision table would have rows representing the alternatives, columns
representing the criteria and fields for criterion scores. The field values are derived from
spatial analysis. Another table is constructed to weight every criterion and then the total
score for each alternative calculated (Jankowski and Richard 1994). Another example of
criteria could be geology, land use type, land acquisition cost, buildings, conservation,
etc. certain type of behavior is assigned to each of them.
2.6.2.2 Criterion maps
Criterion maps form an output of evaluation criteria identification phase. This
follows after input of data into GIS (acquisition, reformatting, georeferencing, compiling
and documenting relevant data) stored in graphical and tabular form, manipulated and
analyzed to obtain desired information. Usually, with help of various GIS techniques a
base map over the study area is created and used to produce several criterion maps. Each
criterion is represented at a map as a layer in GIS environment. Every map represents
one criterion and can be called a thematic layer or data layer. They represent in what
way the attributes are distributed in space and how they fulfill the achieving of the
objective. In other words, a layer represents a set of alternative locations for a decision.
The alternatives are divided into several classes or are assigned values to represent the
level of preference of the alternative upon given criterion. This is a kind if internal
relation within a layer between alternative locations in respect to the attribute. In this
way one visualizes more and less desirable alternatives. The attributes need to be
measured in certain scale, which reflects its variability. The scale can be classified as
qualitative or quantitative. For example, soil types and vegetation types are expressed in
qualitative scale, while precipitation level in a quantitative measure. Scales can be
natural or constructed. The natural scale is a scale expressed in objective units, for
36
example in km or in quantity per square km. The constructed scale is a subject of
personal judgment e.g. landscape aesthetic, ranked witch numbers or assigned linguistic
scale. Another issue is raised for direct and proxy scales. The direct scale measures
directly the level of achievement of an objective. If the objective is a cost of building a
road, the direct scale would map sites with respect to cost associated with building a
road there. The proxy scale is used when the attribute for specific criterion is not
obvious and should be measured indirectly. Different techniques are used to generate
various types of criterion maps scales.
2.6.2.3 Criterion standardization
As far as criteria and the criterion maps have different scales of measurement,
they can not be compared by their raw scores. In order to allow comparability, which is
essential to multicriteria evaluation, the criterion maps should be standardized.
Basically, linear and nonlinear standardization procedures exist. If it concerns
deterministic maps, where each alternative is related to a single value, linear scale
transformation methods are most frequently used. Two linear methods will be described
below: maximum score procedure and score range procedure. Other standardization
methods, including probabilistic and fuzzy relationships, are described thoroughly by
Malczewski (1999). Maximum score procedure is one of the linear scale transformation
methods. It uses a simple formula, which divides each raw score by the maximum value
of a given criterion Malczewski (1999):
x’ij = xij / xmax j
where x’ij is the standardized score for the ith object (feasible alternative / location) and
the jth attribute, xij is the raw score of this object and xmax j is the maximum score of
the jth attribute. The standardized scores range from 0 to 1. A benefit criterion is a
37
criterion which should be maximized. For example, the larger the raw score the better
the performance. However, if the criterion should be minimized formula
x’ij = 1 – xij / xmaxj
Should be used; such criterion is referred as cost criterion.
The advantage of the straight transformation is that it is proportional and relative
order of magnitude remains the same. For example 23/45 = 0.511/1 = 0.511 and 5/23 =
0.111/0.511 = 0.217. The disadvantage is that, when the scores are larger than zero the
standardized minimal score will not equal zero. This may make interpretation of least
attractive alternative difficult Malczewski (1999). The best alternative is always scored
1. The alternative method is score range procedure which is calculated by formula:
x’ij = xij – xj min / xj max– xjmin
For benefit criteria, and
x’ij = xj max – xij / xj max – xj min
for cost criteria. Factor xj min is the minimum score of the jth attribute, xj max is the
maximum score for the jth attribute, and xj max – xj min is the range of given criterion.
The range of scores is from 0 to 1, the worst standardized score is always equal 0 and the
best equals 1. Unlike the maximum score procedures, the score range procedure does not
preserve proportional changes in the outcome. Linear scale transformation can be used
for example to standardize the proximity map Malczewski (1999). Such defined
standardization procedures can be easily transformed to fit raster-based GIS data model.
Figure 2.4 shows the example of score range procedure.
38
Source: Malczewski (1999) Figure 2.4: Score range procedure in GIS 2.5.2.4 Assigning weights
Criterion weights are usually determined in the consultation process with
decision makers (DM) which results in ratio value assigned to each criterion map. They
reflect the relative preference of one criterion over another. In such a case, they can be
expressed in a cardinal vector of normalized criterion preferences:
w = (w1, w2, …, wj) and 0 <= wj <= 1
Normalization implies that the numbers sum up to 100 or to 1, depending on
whether they are presented in percentage or ratio. Another way to express preferences is
in regard to criterion scores. Then, they have a form of cut-off values (minimum and
39
maximum threshold) or desired aspiration levels Jankowski (1995). The second
approach is more preferable in formulating location constraints. The task of assigning
weights (deciding the importance of each factor) is usually performed outside GIS
software; unless such a module is specially programmed or embedded in the proprietary
GIS (compare Carver 1991, Jankowski 1995, Rapaport and Snickars 1998, Grossardt et
al. 2001). The values of weights are then incorporated into the GIS-model. There are
several techniques for assigning criterion weights. Some of the most popular include:
ranking methods, rating methods, and pairwise comparison method. A common
characteristic of them is that they imply subjective judgment of the decision maker about
relative importance of the decision factors. The basic idea of rating methods is to
arrange the criteria in order according to its relative importance. In straight ranking
criteria are ordered from most important to least important, in inverse ranking it is done
the other way round. After the ranks are established, several procedures for calculating
numerical weights can be used.
One of the simplest methods is rank sum, in the following formula.
wj = (n - rj + 1) / SUM(n - rk + 1)
where wj is the normalized weight for jth factor, n is number of factors under
consideration and rj is the rank position of the factor. The example of how the weighted
ranked values are calculated is shown in Table 2.1 below.
Table 2.1: Example of straight rank weighting procedure
Source: Carver, 1991; Jankowski, 1995; Rapaport et al., 1998; Grossardt et al., 2001
40
Ranking method is the simplest criterion weighting methods. It is though
criticized for its lack of theoretical foundations in interpreting the level of importance of
a criterion Malczewski (1999). Second group of weighting methods are rating methods.
There are two most commonly used approaches: point allocation and ratio estimation
procedure. The common characteristic is that the decision maker has a total amount of
points, usually 100 that he or she needs to distribute among the decision criteria
depending on their importance. More important factors get higher scores and factors that
are of no importance to the decision would be assigned zero value. These methods are
compared to budget allocation. In the point allocation approach one assigns points
among criteria according to its importance. Commonly used scale is 0 to 100 or 0 to 10.
The points are then transformed into weights summing sum up to 1. The ratio estimation
procedure is a modification of point allocation method. Here, the most important
criterion is assigned value of 100 and rest of the attributes is given smaller values,
proportionally to their importance. The smallest ration is used as an anchor point for
calculating the ratio. Every criterion value is divided by the smallest value and then the
weights are normalized by dividing each weight by total. Similarly to ranking methods,
rating methods lack theoretical and formal foundations, thus the meaning of weights is
difficult to justify Malczewski (1999)
Table 2.2: Assessing weights by ratio estimation procedure
Source: Malczewski (1999)
Last but not the least is the Analytical Hierarchy Process (AHP) which was
proposed by Saaty in 1980 uses pairwise comparison method for criterion weighting.
The method is carried out in three steps. Firstly, pairwise comparison of criteria is
performed and results are put into a comparison matrix. The matrix is populated with
41
values from 1 to 9 and fractions from 1/9 to ½ representing importance of one factor
against another in the pair. The values in the matrix need to be consistent, which means
that if x is compared to y receives a score of 5 (strong importance), y to x should score
1/5 (little unimportant). Something compared to itself gets the score of 1 (equal
importance). The linguistic explanation of scores is attached to the table. The next step is
to calculate criterion weights. Firstly, values from each column are summed and every
element in the matrix is divided by the sum of the respective column. The new matrix is
called normalized pairwise comparison matrix. Finally, an average from the elements
from each row of the normalized matrix is calculated. The consistency ratio is calculated
in order to make sure whether the comparison of criteria made by decision maker is
consistent. Weights received by this method are interpreted as average of all possible
weights. The pairwise comparison method is illustrated in a Table 2.3.
Table 2.3: Illustration of pairwise comparison method
Source: Saaty (1980)
This method is much more sophisticated than the previous ones. Nevertheless it
is criticized by the way of receiving the ratios of importance. The questionnaire asks
about the relative importance of a criterion without respect to the scale it is measured.
Moreover, the more criteria are required the more labor-intensive it becomes. However,
the advantage is that only two criteria need to be compared at a time Malczewski (1999).
While selecting any specific method one should take into account level of understanding
of the problem by decision makers and their proficiency in the field. Expected accuracy
of outcome versus simplicity of the procedure is also a factor. Malczewski (1999) states
that pairwise comparison is more appropriate if accuracy and theoretical foundations are
the main concern. Ranking and rating methods are used when ease-of-use, time and cost
42
in generating weights is in concern. It is also recognized that the more sophisticated the
technique the less transparent become the process for the general public.
Another decision theory similar to Analytical Hierarchical Process (AHP) is
Analytical Network Process (ANP). Over time, Thomas Saaty, the creator of the AHP,
developed a more advanced framework for setting priorities known as the Analytic
Network Process (ANP) method of decision making. The ANP differs from the AHP in
that it generalizes the pairwise comparison process so that decision models can be built
as complex networks of decision objectives, criteria, stakeholders, alternatives, scenarios
and other environmental factors that all influence one another's priorities. The key
concept of the ANP is that influence does not necessarily have to flow only downwards
as is the case with the hierarchy in the AHP. Influence can flow between any factors in
the network causing non-linear results of priorities of alternative choices.
For example, as a user increases the weight of a criterion, the result is that an
alternative starts to get a higher priority, but as the criterion continues to be increased,
feedback effects of the network actually cause the alternative to start to get a lower
priority. This concept is similar to the concept in economics of decreasing marginal
returns which states that each additional unit of anything at some point becomes
relatively less valuable than previous units to a decision-maker.
Conversely both the AHP and the ANP derive ratio scale priorities for elements
and clusters of elements by making paired comparisons of elements on a common
property or criterion. Although many decision problems are best studied through the
ANP, one may wish to compare the results obtained with it to those obtained using the
AHP or any other decision approach with respect to the time it took to obtain the results,
43
the effort involved in making the judgments, and the relevance and accuracy of the
results.
The ANP is extremely useful for predictive modeling and broader environmental
influences can be factored into decisions. The best applications of the ANP are in
decisions where risks and threats are major factors in the decision process and
organizational success is highly dependent on a thorough understanding of the entire
environment rather than just business goals and objectives.
The general form of the analytical network process (ANP) super matrix can be described
in Figure 2.5.
Source: Saaty (1980) Figure 2.5: The General Structure of the Super matrix
44
Where CN denotes the Nth cluster, eNn denotes the nth element in the Nth
cluster, and Wij block matrix consists of the collection of the priority weight vectors (w)
of the influence of the elements in the ith cluster with respect to the jth cluster. If the ith
cluster has no influence to the jth cluster then Wij = 0. The matrix obtained in this step is
called the initial supermatrix.
2.6.2.5 Decision rules
The next step aims in ordering all the alternatives gathered in the decision table
according to their performance. A method of aggregating alternative’s scores is called a
decision rule. The decision table is composed of evaluation criteria and their attributed
scores for every feasible alternative. The decision table can be written down into a
matrix
where: i = alternatives, and j = criteria. It is further multiplied by weights’ vector
according to weighted summation method. Now, the weighted scores matrix needs to be
aggregated in respect to each alternative. One of the most often used techniques is
simple additive weighting (SAW) method. It is based on the concept of weighted
average of all the decision criteria. The weighted alternatives are simply summed in
order to provide a total performance score for each alternative. SAW ranks alternatives
from the highest to the lowest score (e.g. highest score =1) whereas inverse additive
weighting method assigns best rank for the lowest score. In GIS, this technique results in
the overall score map and final rank map (Figure 2.6).
45
Source: Malczewski (1999) Figure 2.6: Simple additive weighting method performed in GIS on raster data 2.6.2.6 Error assessment
Sensitivity analysis in multicriteria evaluation is a procedure that aims in
detection of possible errors associated with the criterion maps inaccuracy and DM’s
uncertainty of assessing the decision’s effects on every alternative. Some authors also
raise the problem of the choice of MCE technique, which is supposed to influence the
results Carver (1991). The sensitivity analysis of the obtained ranking of alternatives
should be performed in order to assess the robustness of the results. If the results are not
much affected by the input data and preferences of DM the final recommendation can be
displayed on the map. Geographical errors result from inaccuracy and imprecision of
spatial data. This is because a map presents a simplified model of reality, which was
obtained in the process of generalization and discretization. The GIS database errors can
be then classified into positional (location) and attribute errors. They can also be
measurement or conceptual errors (Malczewski 1999 after Chrisman 1987). Propagation
techniques decide a course of action when the error is detected. Positional accuracy of
data is one of the components responsible for overall data quality. When the mapped
features are close in location to their true position, they are accurate. All of the spatial
data are of limited accuracy.
46
Malczewski (1999) points out problem of location and scale dependency of
spatial criteria. A set of objectives may vary from one area to another and from one scale
to another. Therefore when data is collected it should be prepared at as small
aggregation as possible. The aggregation of the spatial data is not a great problem
anymore thanks to technological and computational advancements. Attribute errors may
result from attribute inaccuracy. This means that attribute values are not the same or
close to the true values. They may change over time. Attribute accuracy must be
checked in different ways depending on the nature of the data. For the categorical
attributes, the errors may result from e.g. method of measurements used to classify real-
world objects into classes, from delimiting borders between classes, and from number of
categories to completely describe the heterogeneity of the phenomenon. In the former,
the method of measurement for gathering the source data might have been inadequate to
ascribe correctly the real-word object to the designed category. In the second case, the
borders between categorized objects had been mapped in such a way that e.g. part of the
class A falls into B. It is even more difficult with fuzzy classifications, like e.g. soil
classes (ontological problems). The measurement error is based on the distance between
two points on the map and its truth distance. It can be characterized by root mean square
(RMS). For the n known error values, RMS errors are determined as follows:
RMS = [ SUMi (xi - xit)2/n-1]0.5
where xi and xit are a measurement and the true value, respectively, and n is the number
of measurements. The RMS is often used to assess error associated with digital elevation
models. If the data are categorical data not numerical data the error can be described by
classification error matrix which assesses the observed and mapped attribute values for
sample locations Malczewski (1999). Additionally, data should be logically consistent,
which implies topological consistency (closed polygons, nodes at arcs cross), and
complete. Linage, which records data source for digitized data, date of its collection,
person who collected the data and process steps to obtain the final product is a useful
indicator of data accuracy. A group of errors in data is produced while processing. They
may result from misuse of logical operations, interpretation problems, generalization,
47
mathematical errors, low precision computations, or rasterization of vector data.
Conversion of data from vector to raster produces unnatural picture where the boundary
cells contain e.g. parts of all adjacent cells (NCGIA 1990). If it concerns decision
maker’s preferences, it is recognized that in some cases the decision makers are not able
to provide precise judgments due to limited or not adequate information or knowledge
about the decision criteria. While assigning weights, it is important how the alternatives
and criteria are represented on the criterion maps.
This should be presented to the decision maker in such a way that he understands
properly the information conveyed by the criterion maps Malczewski (1999). The choice
of MCDM technique is to some extent imposed by GIS data model. In a raster, the
whole decision space is divided into discrete regular-shape sites, usually in the form of a
square grid. Every such created cell is regarded as a potential alternative, and a
candidate for evaluation. If this is the case, Jankowski (1995) proposes the weighted
summation MCDM technique, motivating this by a large number of alternatives. In fact,
while having a study area of 360 km2 and grid of 10 m, the amount of cells equals about
36,000 alternatives. Therefore, it is impractical to perform pairwise comparison of all
alternatives. Suitability analysis performed beforehand in vector by a general overlaying
technique often results in much smaller amount of alternative sites. Thus, in such a case
other than weighted summation techniques can be used more freely. The sensitivity
analysis can be performed in two ways, either by considering two alternatives at a time
and checking how should the weight values and criterion scores change if they would
have the same ranking, or by considering all alternatives in the same time and checking
how their ranking positions change together with change of criterion scores and criterion
weights. As far as raster approach is considered, it is computationally very demanding to
use pairwise comparison method.
CHAPTER 3
WETLANDS ASSESSMENT USING MULTI-CRITERIA DECISION MODEL
3.1 The study area
Malaysia has recently designated three new Wetlands of International
Importance namely Sungai Pulai, Tanjung Piai and Pulau Kukup; all in southern Johor
State not far from Singapore (Figure 3.1), particularly rich in mangroves and inter-tidal
mudflats. These coastal and estuarine sites support a large number of species, notably
vulnerable and threatened species, and provide both livelihoods and important functions
for the local population.
3.1.1 Pulau Kukup
Pulau Kukup is a state park (Johor), located at 01°19'N, 103°25'E. It is an
uninhabited Mangrove Island situated 1 km from the southwestern tip of the Malaysian
peninsula with a land area of 647 hectares; this is one of the few intact sites of this type
49
left in Southeast Asia. The wetland supports such species as the Flying Fox Pteropus
vampyrus, Smooth Otter Lutra perspicillata, Bearded Pig Sus barbatus, Long-tailed
Macaque Macaca fascicularis, all listed as threatened, vulnerable or near-threatened
under the International Union for the Conservation of Nature (IUCN) Red Book. Pulau
Kukup has been identified as one of the Important Bird Areas (IBA) for Malaysia.
Globally vulnerable Lesser Adjutant Leptoptilos javanicus chooses this as a stop-over
and breeding ground. Pulau Kukup is important for flood control, physical protection
(e.g. as a wind-breaker), and shoreline stabilization as it shelters the mainland town from
severe storm events.
Figure 3.1: Study area.
50
The coastal straits between Pulau Kukup and the mainland are a thriving industry
for marine cage culture. The mudflats are rich with shellfish and provide food and
income to local people. Tourism is another use of the island and the government has
further plans to promote ecotourism. Pulau Kukup is Ramsar site number 1287. The
island experienced extensive harvesting for mangrove wood back in the 80's, however,
wood extracting operations from this island had ceased since August 1993. Regeneration
of mangrove tree species has indeed taken place since then.
3.1.2 Sungai Pulai
Pulai River is located at 01°23'N, 103°32'E; it’s a forest reserve and the largest
riverine mangrove system in Johor State, situated at the estuary of the Pulai River having
a land area of 9,126 hectares. With its associated seagrass beds, inter-tidal mudflats and
inland freshwater riverine forest the site represents one of the best examples of a
lowland tropical river basin, supporting a rich biodiversity dependent on mangrove. The
Sungai Pulai forms the district boundary between the mangrove forests located in
Pontian and Johor Bahru. The mangrove itself is of major ecological importance because
of its continuous input of freshwater into the upper reaches of Sungai Pulai estuary.
Sungai Pulai is home for the rare and endemic small tree Avicennia lanata,
animals such as near-threatened and vulnerable Long-tailed Macaque, Smooth Otter and
rare Flat-headed Cat and threatened birds species as Mangrove Pitta and Mangrove Blue
Flycatcher, all included in the IUCN Red List. Relatively undisturbed parts including the
Nipah swamps may be nesting sites of the Estuarine Crocodile.
The site fringes play a significant role in shoreline stabilization and severe flood
prevention in the adjacent 38 villages. The local population depends on the estuary as its
51
mudflats, an ideal feeding, spawning and fattening ground; support a significant
proportion of fish species. Other mangrove uses include wood cutting, charcoal
production, aquaculture activities and eco-tourism. The current construction of a new
port at the river estuary may represent a direct impact on the mangrove ecosystem,
causing coastal erosion and water pollution from associated dredging and reclamation
works and traffic. The site is managed in line with Integrated Management Plan for the
sustainable use of mangroves in Johor state. Sungai Pulai is Ramsar site number 1288.
The Sungai Pulai Mangrove Forest Reserve (MFR) is managed primarily for
commercial wood production using the silvicultural system that requires clear felling of
trees under a 20-year rotation. About 80% of the Sungai Pulai MFR consists of
mangrove stands of less than 20 years of age. The current sustainable forestry practiced
by the State Forestry Department at the mangrove reserve is well-documented. With
some form of mangrove management in operation since 1928, it appears that forest
management practices in the Sungai Pulai MFR comply very well with the Ramsar
Convention guidelines for the implementation of the wise-use concept of wetland
resources.
3.1.3 Tanjung Piai
This is a state park (Johor) located at 01°16'N 103°31'E, with a land area of 526
hectares. The site consists of coastal mangroves and inter-tidal mudflats located at the
southernmost tip of continental Asia, especially important for protection from sea-water
intrusion and coastal erosion. Tanjung Piai supports many threatened and vulnerable
wetland-dependent species such as Pig-tailed Macaque and Long-tailed Macaque, birds
like Mangrove Pitta, Mangrove Blue Flycatcher, Mangrove Whistler. Globally
vulnerable Lesser Adjutant may be observed in the vicinity of the site. The Scaly
Anteater, Common Porcupine, Smooth Otter and Bearded Pig are classified as
52
vulnerable or near threatened listed in the IUCN Red Book 2000. Waters of the four
main rivers traversing Tanjung Piai are abundant with commercially valuable species.
Tanjung Piai forms the only mangrove corridor that connects Pulau Kukup and
the Sungai Pulai wetlands. Five rivers dissect the Tanjung Piai State Park. The mangrove
in this State Park is a typical example of a Rhizophora apiculata-Bruguiera cylindrica
dominated coastal forest. Five species of large waterbirds and 7 species of shorebirds
can be seen feeding on mudflats. These include migratory species such as the Grey
Plower, Whimbrel, Common Redshank and Greenshank, Terek Sandpiper and Common
Sandpiper.
Bunds were created along the west and east coasts of the mangrove to protect
farmlands from being inundated by salt waters. Tidal currents heavily erode Tanjung
Piai with the coastal mangrove fringes being reduced to 50m at certain stretches. The
Tanjung Piai State Park is home to about 20 'true' mangrove plant species as well as 9
more mangrove-associated species, which demonstrates high species diversity in such a
small area. This mangrove area is also rich in fauna: birds (41 species), mammals (7
species), reptiles (7 species) and amphibians (1 species). Species of conservation value
include the following; the threatened resident stork Lesser Adjutant; the rare or
uncommon species of waders (shorebirds) such as the Malaysian Plover, Spotted
Greenshank, Asian Dowitcher, Spoon-billed Sandpiper and Chinese Crested Tern; and
mammals such as the Dusky Leaf Monkey, Smooth Otter, Long-tailed and Pig-tailed
Macaques, Wild Pig and the Flying Fox.
Due to increased sea traffic, the western side of Tanjung Piai has been affected
by oil spills which caused natural erosion processes in nearly 70 ha of the mangrove
forest. In addition, the new port being established in the estuary of Sungai Pulai will
53
likely lead to increased wave energy reaching the east shore of Tanjung Piai, thus
accelerating coastal erosion. Tanjung Piai is Ramsar site number 1289. The site enjoys
the status of a State Park for eco-tourism; a visitor centre with boardwalks near the
southern tip of the park provides interpretive materials, guided walks, and overnight
facilities, with a World Wetlands Day programme beginning in 2003.
The study sites were considered in view of their environmental significance. By
absorbing the force of strong winds and tides, they are able to protect terrestrial areas
adjoining them from storms, floods, and tidal damage. They provide food, water, and
shelter for, mammals, fish, shellfish, migratory and local water birds; and also serve as a
breeding ground and nursery for numerous species. Many endangered plant and animal
species are dependent on these wetlands habitat for their survival. Furthermore is their
hydrological function, which relates to the quantity of water that enters, stored in, or
leaves the wetlands. These functions include such factors as the reduction of flow
velocity, their role as ground-water recharge and influence on atmospheric processes.
Water-quality functions include the trapping of sediment, pollution control, and the
biochemical processes that take place as water enters, stored in, or leaves the wetlands.
In addition to this is their role as a source of food and water, a place for recreation,
education and science.
The three wetlands i.e Sungai Pulai, Tanjung Piai and Pulau Kukup; are
preferred among other wetlands because of the problems they face despite their
declaration as wetland of international importance; such tribulations includes, unplanned
logging and agricultural activities, rapid and unsustainable development which has
resulted in coastal erosion, water pollution and natural habitat degradation. The Ramsar
Convention was developed and adopted by participating nations at a meeting in Ramsar,
Iran on February 2, 1971 and came into force on December 21, 1975. It is an
international treaty for the conservation and sustainable utilization of wetlands, i.e. to
stem the progressive encroachment on and loss of wetlands now and in the future,
54
recognizing the fundamental ecological functions of wetlands and their economic,
cultural, scientific, and recreational value.
3.2 Data collection
The data collection procedure was based on secondary and primary sources.
Secondary sources include extensive literature study on published and unpublished
books, journals, government documents and base map for the study area. These data
were collected from various government departments such as; Johor Forestry
Department (Pejabat Hutan Daerah-Johor selatan), Johor National Parks Corporation
(Perbadanan Taman Negara), Mapping and Survey Department (JUPEM). Primary
sources of data comprise; reconnaissance survey of the study area in order to know the
general physical characteristics of the study area with regard to scenic and
environmentally sensitive areas, also oral interview was administered to the park
officials. The data gathered was used to update the existing one; it was digitized into the
computer compatible format using ArcGIS software of Geographic Information System
(GIS).
3.3 Database development for wetland assessment
Database development for the project will be looked at from conservation and
tourism development point of view (Figure 3.1). The development of the database here
will be supported by ArcGIS 9.0 software.
55
Table 3.1: Data inventory for the project ELEMENT COVERAGE
LAYER LAYER NAME
ATTRIBUTE DATA SHAPE
DATA TYPE
WIDTH
Water bodies Objectid OID 5 Forest Shape Geometry 0 Industrial Activity String 9 Infrastructure_ and_utility
Activity_2 String 8
Institutional Shape_length Double 12 Housing Shape_area Double 13 Transportation Lot String 20 Retail_and_ services
Existing String 10
Agriculture Name String 15 Vacant_land Activity
Land use
Land use
Opens_space_and_recreation
Polygon
String
14
Objectid OID 4 Shape Geometry 0 Age_class_t Double 6 Shape_area Double 11
Vegetation Tree age class Age_class
Shape_length
Polygon
Double 10 Objectid OID 5 Shape Geometry 0 Harvesting Double 7 Shape_length Double 14 Shape_area Double 15
Harvesting season
Harvesting
Shape_length
Polygon
Double 15
Objectid OID 4 Shape Geometry 0 Area Double 13 Shape_length Double 13 Shape_area Double 14 Specific_ management
String 20
Environment and resources
Management Management_s
Management_body
String 25
Objectid OID 4 Shape Geometry 0
Physical and biophysical
Threatened fauna
Endangered_ fauna
Endangered_ fauna
Polygon Double 12
Shape Geometry 0 Objectid OID 4 Station String 24 PH Double 4 BOD_MG_L Integer 3 COD_MG_L Integer 5 TSS_MG_L Integer 4
Hydrology Water
Water_qualty
DO_MG_L
Point
Double 4 Objectid OID 4 Shape_length Double 16
Sungai Pulai Pulai_River
Shape_area
Polygon
Double 16
56
3.3.1 Data layers for the study
Data layers to be used include; land use, tree age class, harvesting season,
threatened species, water quality and management. They will be applied in order to
determine conservation and development areas in the Ramsar site.
3.3.1.1 Land use:
Land uses around the Ramsar site includes; water bodies, industrial, forest,
institutional, infrastructure/utility, residential/housing, transportation, retail/services,
agriculture, vacant land, open space and recreation.
Source: MPMJ, (1999); PTN, (2007); PHD, (2007); JUPEM, (2007) Figure 3.2: Land use map
57
The objective of land use coverage in the study is to identify agricultural or
natural areas close to habitat areas. The closest agricultural or natural area to species
habitat will be termed as the most suitable for conservation. Conversely, the most distant
area from species habitat will be classified as suitable for development.
3.3.1.2 Harvesting:
The sustainable forestry practiced by the State (Johor) Forestry Department at the
wetland area is well-documented whereby 191 compartments have been scheduled for
timber harvesting within a given time period (MPMJ 1999), which specifies the
maximum area that can be cultivated to be 20-25 hectares per year.
The overall management goal of Johor wetlands has always been sustained yield
of fairly few commercial species based on clear felling and regeneration, which at times
is complemented by natural regeneration. As the resource has become more threatened
there is a clear requirement for management plans which consider all mangrove areas
and relate them to biodiversity conservation objectives; forest habitat in general; land
use planning; complementary inter-agency; mangrove fisheries and so forth.
The current logging practice in the Ramsar Site does not make use of directional
felling which should be promoted to provide an efficient and safer approach to clear
felling area. During de-branching the slash is cut to allow for collection and stacking in
rows perpendicular to the waterways, which promote tidal flushing and reduce tidal
induced movement of slash that may cause damage to established seedlings and more
advanced growth.
58
Source: MPMJ, (1999); PTN, (2007); PHD, (2007); JUPEM, (2007) Figure 3.3: Harvesting schedule
The purpose of harvesting season coverage in the study is to ascertain within
which periods certain forest compartments are allowed for cultivation. This data layer
will be used to determine conservation areas by identifying trees that have recently been
logged as suitable, because these trees needs to be nurtured so that they are fully ripe by
the time it’s their cultivation period.
This coverage will also be used to identify economic development areas, by
identifying forest compartments that fall within the present year harvesting schedule as
most suitable. Also tree compartments that fall in the most distant year will be classed as
suitable development areas, this is because these trees must have reached or are about to
reach their harvesting period.
59
3.3.1.3 Endangered Species
Four of the mammal species recorded in the Johor mangroves are internationally
classified as ‘vulnerable’, whereas five others are ‘near-threatened’. Two birds species
are internationally classified as ‘vulnerable’ and three ‘near-threatened’, most of them
are located at Pulau Kukup however they come to Tanjung Piai during low tide. It is
recorded that over 50 Lesser Adjutant Leptoptilos janvanicus (threatened specie) are
located along the west coast of Johor (DANCED Project Document No. 4, 1998). Lopez
(1998) also observed these species on the mudflats of Pulau Kukup Island off the west
coast of Johor and also on large mangrove trees further inland on Pulau Kukup.
Source: MPMJ, (1999); PTN, (2007); PHD, (2007); JUPEM, (2007) Figure 3.4: Endangered species
60
Other threatened bird species includes; Milky Stork Mycreteria Cinerea, Straw-
headed Bulbul Pycnonotus Zeylanicus, Mangrove Pitta Pitta Megarhynca, Mangrove
Blue Flycatcher Cyornis Rufisgastra, Mangrove Whistler Pachycephala Cinerea. It
should be stressed that this is an international classification and that additional species
may be threatened locally.
The rationale for endangered fauna data layer in the study is to determine the
location and population of species that are vulnerable to extinction in the near future.
This coverage will be used to determine conservation areas, by categorizing areas with a
relatively high population of this species as suitable for conservation. Tourism
development on the other hand will be determined, by classifying locations with a
relatively low population of endangered species as suitable.
3.3.1.4 Tree age class
It is recommended to conserve young tree compartments i.e trees that are
recently replanted. Because these trees need to be nurtured so that they are fully ripe by
the time they reach their cultivation age. The trees are cultivated when they have reached
a maturity age of 20 years, while some are cultivated at the age of 15 years (MPMJ,
1999). It is recorded that 96% of the total reserve is below 35 years of age. In addition,
22 compartments and measuring almost a year of logging area is not registered.
Coverage layer on tree age class will reveal the various ages of the tree
compartments of the wetland area. This data layer will be utilized in order to ascertain
conservation areas, by identifying relatively young trees as suitable for conservation
efforts, because these trees need to be cared for before they reach the maturity age of
cultivation. Development areas will be determined, by classifying trees from the age 15
and above as suitable for economic development.
61
Source: MPMJ, (1999); PTN, (2007); PHD, (2007); JUPEM, (2007) Figure 3.5: Tree age class 3.3.1.5 Management
Sungai Pulai and Tanjung Piai comprises 500 forestry compartments established
in the year 1928 and are managed primarily for commercial wood production using
silvicultural system that requires clear-felling of trees under a 20-year rotation (MPMJ,
1999). Out of the 500, 16 forest compartments were allocated primarily for shoreline
protection under the Mangrove Forest Reserve (MFR) specific management categories
(MPMJ, 1999). Another 16 compartments were allocated for forest research while the
rest are production forest compartments. Data layer on management will help in
uncovering the different uses allowed by the authority in the wetland area, which will be
considered in the conservation and development of the wetlands.
62
Source: MPMJ, (1999); PTN, (2007); PHD, (2007); JUPEM, (2007) Figure 3.6: Management
3.3.1.6 Pulai River
Pulai River Estuary runs from mount Pulai until Tanjung Pelapas, it is 22.6 km
long and 2.83 in width. Pulai River is one of the largest mangrove forests in Malaysia,
which is originally an ancient wetland. Because it is relatively pristine, its water supports
an abundant flora and fauna. The tropical eelgrass, Enhalus acoroides, which is also the
largest seagrass species in the world extending up to 2 ft in length, can be found in the
Pulai River Estuary.
Water quality parameters layer will be used in calculating the water quality of
various sections of the river (Figure 3.2). This in the long run will reveal the different
63
quality levels of the river. The water quality will then be used to determine conservation
areas, by classifying locations with higher level of water quality as suitable conservation
areas. Development areas will be ascertained by categorizing sections of the river that
depict a lower water quality.
Table 3.2: Water quality parameters of Pulai River sampling stations
PH BOD COD SS DO NH3N
Tanjung Bin 8.2 3 127 38 6.5 <0.1
Sungai Pulai 6.7 5 134 11 6.4 <0.1
Tanjung Piai 6.6 5 141 82 6.5 <0.1
Source: MPMJ, (1999); PTN, (2007); PHD, (2007); JUPEM, (2007) Figure 3.7: Pulai River
64
3.3.1.7 Habitat area
An enormous variety of wildlife is found in Johor Ramsar Sites. Some of the
organisms live attached to the trunks and lower branches of the mangroves. Others live
up in the top branches and others live within or above the muddy sediment between the
trees. Animals from both the marine and terrestrial environments can be found in these
wetlands.
The plants here have adapted to muddy, shifting, saline conditions. They produce
stilt roots which project above the mud and water in order to absorb oxygen. Awash in
saltwater and up to their knees in mud, the plants in a Mangrove Swamp have clever
ways of coping with their environment. The plants form communities which help to
stabilize banks and coastlines, and become home to many types of animals.
Shorebirds in the wetlands are found in two areas i.e Parit Penghulu mudflats and
Sungai Nibong Bay. Some of the species found in these wetlands like Brahminy Kites
(bird) nest in medium size to tall trees in the mangroves, forest edges and open country
(Balen et al. 1993). Also observed is the Silvered Langur (mammal) around Tanjung
Pelapas area (EIA Report, 1996). Flying Fox Pteropus sp. Used to roost in Pulau Kukup
off the west coast of Johor and fly over to the mainland to feed on the fruits from the
trees planted by the villagers.
Wild pig can be considered the most common species of large mammal
occurring in the mangroves. Tracks of this species can be observed in the mangroves
along the coast and also along the banks of Sungai Nibong. Bowring's Supple Skink
Lygosoma bowringii (reptile) is found amongst the mangal roots on the landward side
and the Water Monitor Varanus salvator in the water and among the mangrove
vegetation. It is also reported in that the Ramsar Sites to be important sites for several
65
threatened species of shorebirds (EIA Report, 1996) e.g Spoon billed Sandpiper
Eurynorhynchus pygmaeus, Asian Dowitcher Limnodromus semipalmatus and Spotted
Greenshank Tringa guttifer. It should be noted that a number of the species mentioned
above are not totally restricted to mangroves, however they depend on the wetlands for
some stage of their life cycle.
Source: MPMJ, (1999); PTN, (2007); PHD, (2007); JUPEM, (2007) Figure 3.8: Species habitat
The purpose of species habitat coverage is to determine the different sizes of
species habitat and habitat area proximity to natural land use/ land cover. Habitat area
data layer will be utilized in order to determine conservation areas, by identifying higher
and connected habitat patches as most suitable for conservation effort; this ensures that
the greatest amount of suitable habitat is being conserved. On the other hand tourism
66
development areas will be identified, by classifying smaller and isolated habitat patches
as most suitable for development.
Another function of Species habitat coverage in the study is to determine habitat
area’s proximity to natural land use/ land cover. Conservation areas will be ascertained
by identifying closest natural areas to species habitat as the most suitable; because some
species require different habitat types at various stages of their life cycles. For example,
amphibians require both wetland and upland habitats for their complete life cycle.
Conversely, tourism development areas will be determined by identifying the most
distant areas from species habitat as the most suitable; this is to ensure that natural areas
next to species habitat are protected.
3.4 Evaluating existing wetlands
3.4.1 Threat analysis
Some developments have had affects on the wetlands which includes; Tanjung
Pelepas port development and Tenaga Nasional Power Transmission lines (PTL)
through the wetlands.
3.4.1.1 Port of Tanjung Pelepas (PTP)
Is situated on the eastern side of the mouth of Sungai Pulai in southwest Johor;
deemed to occupy 783 hectares of land area when fully developed by 2020. PTP is
naturally sheltered deep water port and is near the Malaysia-Singapore second crossing.
However, the development of this port is having some ecological effects on the integrity
67
of Sungai Pulai estuarine area and the shoreline. Some of the effects are discussed
below:
Degazettement of the wetlands: Some 250 hectares of mangroves will make way
for the construction of this port, out of which 40 ha was excised from within the Sungai
Pulai MFR (from eastern shores) while the rest are state land mangroves fringing the
shores Tanjung Adanag and Tanjung Kupang (EIA Report, 1996). The port boundary
begins at Sungai Perpat (lower east shore of Sungai Pulai) and ends at Parit Ghani
Dredging activities: Large dredging activities (in the amount of 12 million cubic
meters) were carried out to provide berths, turning basins and approaches for the port
development (MPMJ, 1999). Dredging activities had the following environmental
impacts: removal of part of the sea bed marine life, damage and changes in the benthic
ecosystem, sea grass in the area impacted; reduction of sea water quality and increased
turbidity causing smothering of immobile marine life forms; siltation/ erosion due to
sediment transport and water flow changes; release of adsorbed heavy metals and toxic
organics into the water phase due to re-suspension of seabed sediment during dredging
operations impacted on the water quality and the substances introduced into the food
web; dredging activities has also impacted on fisheries and marine life and interference
of dredging equipment with marine migration.
Reclamation: Reclamation work in the near shore areas of mangroves and
mudflats were required to provide wharves and terminal port, suitable and protected land
for port related activities and water front structures. The mudflats colonized by sea grass
bed in the eastern shores of the Sungai Pulai estuary will diminish in due time.
Seaward base port structures: The structures created in the seaward areas of the
Sungai Pulai estuary will likely lead to increased wave energy reaching the eastern shore
68
of Tanjung Piai, thus accelerating coastal erosion and eventually threatening the bunds
behind the mangroves. Some of these are now less than 50 meters wide (MPMJ, 1999).
Sedimentation: Due to large scale development in the seaward areas of Sungai
Pulai estuary, greater sedimentation along the port area was predicted. Sedimentation
volumes in the dredged channel were estimated to be 600,000 cubic meters/ year (EIA
Report, 1996). Therefore, the estuary is prone to dredging activities at intervals.
Tidal water movement: The Sungai Pulai estuary experiences tidal input that
brings saline waters to the upper reaches of the river. The large development at the
estuary has impeded natural flow of tidal waters that goes in and out of this estuary.
Fisheries: The port has covered the Sungai Pulai estuary and served as a barrier
to the migration of fishes in and out of the mangroves. Mangroves are natural spawning
and nursery grounds for many commercial fish and prawn species. Green turtles and
dugongs have lost their feeding ground in the form of the sea grass beds. Sea grass beds
resources for these species during their migration period.
3.4.1.2 Tenaga Nasional Power Transmission lines (PTL) through the Sungai Pulai:
The economic growth corridor envisaged for the south Johor prompted the
government to construct PTL that transverses across the Sungai Pulai in several areas as
well as the Sungai Pulai main river. The PTL route runs from Sungai Pulai east across
the main channel and cut across Sungai Pulai to the west, enroute Kg. Tanjung Karang
to Pontian. Any construction of PTL will require a strip with a minimum working width
of 60 meters (Khan et al., 1991). Trees are removed from the entire length of this strip
69
and a laterite road is constructed to enable construction and maintenance. After
construction is completed, Tenaga Nasional Berhad (TNB) will periodically cut
emerging vegetation below the transmission route.
3.4.2 Tourism issues
Though, Sungai Pulai wetland was never given attention as an eco-tourism
destination by the Johor State Government. Therefore, they lack tourism infrastructure.
However, as recommended by the DANCED Project Document No. 12 (1999), there is
considerable potential for developing limited scale mangrove tourism in this wetland as
the area is obviously rich in natural resources. Only one site-specific mangrove related
eco-tourism venture exists near the fringe of Sungai Pulai, located at Kg. Belokok. It
houses 2 chalets, a restaurant, a 40m fishing jetty, a floating raft and 280 meter long
boardwalk which transverses into the Sungai Peradin section of Sungai Pulai. This resort
is currently sitting on a former jetty point which uses to serve as a transit to Singapore
and Johor Bahru during the colonial days. The remnants of the abandoned harbor deck
still remains. Fishing trips and boat rides can be solicited from this resort.
Pulau Kukup is located at the quaint little fishing village of Kampung Air Masin
in Kukup, Pontian. Visitors who plan to visit this mangrove island are advised to first
register at the Pulau Kukup Johor National Park office located in the town centre in
Kukup, before proceeding to the nearby jetty. The park was gazetted as a national park
in March 1997 and was declared as a 'Wetland of International Importance' or Ramsar
Site, by the Geneva-based Ramsar Convention Bureau. Officially opened to the public in
August 2003, the park is home to 30 true mangroves and mangrove associated plant
species while many other plant species have yet to be discovered here. Pulau Kukup is
also an important stop over point for the migratory bird species along the East-Asian
flyway, and the forests are also thought to be a breeding ground of the threatened Lesser
70
Adjutant Stork (Leptoptilus javanicus). The park has basic facilities such as Observation
Towers, a 50 foot long suspension bridge, a boardwalk and jetty.
Next is Tanjung Piai, or the 'Southernmost Tip of Mainland Asia'. Located in the
district of Serkat, a name derived from the Malay word 'sekat' or blocked off. This
indicated that the district is indeed located at 'Land's End'. The Tanjung Piai Johor
National Park 8km shoreline borders the Straits of Malacca. It has a 325 meters long
boardwalk leading to southern-most point of South East Asia. The boardwalk help
visitors walk through the most strategic location that signifies the southern-most tip of
the Asian continent
The park is somewhat different from Pulau Kukup because Tanjung Piai is
located on land and unlike its counterpart; one can opt to camp out under the stars here
by paying a reasonable fee. A challenging obstacle course is also available for those who
wish to test their endurance or plan to have a friendly match with friends. As waters
subside, watch as crabs, lizards and mudskippers of various sizes scavenge for food.
3.5 Main steps of the approach
1. Definition of criteria to evaluate wetlands biodiversity conservation and development.
2. Evaluation of conservation and development criteria.
3. Multi criteria analysis and priority ranking of the wetlands biodiversity.
4. Generation and analysis of conservation/development scenarios and decision making.
71
3.5.1 Definition of criteria:
In order to assess the relevance for nature conservation and tourism development
of the different wetland areas, a set of evaluation criteria was selected; having defined
the criteria (i.e standard of Judgment according to which the relevance for nature
conservation and development is to be assessed), the next step was selecting suitable
indicators and variables (Figure 3.9) (i.e the parameters to be used in practice to measure
the selected criteria), (Table 3.3).
The criteria signified different needs for conservation and development, they
were represented inform of criterion maps/ data layers. The study criteria is selected
based on extensive literature study and includes tree age class, harvesting season, habitat
area, water quality, threatened fauna and wetlands close to natural land use/ land cover.
Figure 3.9: Schematic research approach
Definition of criteria
Evaluation of conservation and
development criteria
Pairwise comparison of the criterions
Generation of conservation and
development scenarios
Assessment of conservation and
development scenarios
72
3.5.2 Evaluation of conservation and development criteria
The study criteria were evaluated from conservation and tourism development
point of view. In order to determine conservation areas, Ramsar Site coverage was
considered as the habitat area due to non-availability of this data layer, since the whole
of Ramsar Site is known to be a habitat area for wildlife. The river and its tributaries
served as the boundary for the habitat patches. To be able to identify bigger habitat area
which is said to be more suitable for conservation (Alderson, 2005); habitat area
coverage was converted to raster and classed from the biggest to the smallest habitat
area. Threatened species coverage was used to ascertain species that are vulnerable to
extinction in the near future; which are said to be important for conservation efforts so
that their population can continue to persist (U.S. Fish and Wildlife services, 1996).
Here conservation relevance was based on the number of threatened fauna in each
cluster; as such threatened fauna coverage was converted to raster and classed according
to the size of these species in each huddle.
To find out Wetlands that are surrounded by similar or complementary natural
areas, which have much potential for conservation (Long Island Sound Study, 2003);
Ramsar Site data layer was again utilized, however in this case excluding Pulau Kukup
since it is not surrounded by any upland area. Natural land use/ land cover close to
wetlands in the other two Ramsar Sites i.e Pulai River and Tanjung Piai was determined
by using multiple ring buffer of 20, 30 and 40 meters around the periphery of the
wetland area, with the closest ring being the most suitable. This is converted to raster
and classified according to the proximity of the surrounding natural land use/ land cover
to wetland area.
73
Table 3.3: Study criteria and indicators
Objective Criterion Indicators Variables Classes Reference To ascertain conservation/ Preservation area.
Tree age class The lower the age class of trees in the wetland area, the higher the need for biodiversity preservation.
It is recommended to conserve
young tree compartments i.e
trees that are recently replanted.
Because these trees needs to be
nurtured so that they are fully
ripe by the time they reach their
cultivation age. The trees are
cultivated when they have
reached a maturity age of 20
years, while some are cultivated
at the age of 15 years.
Age class of trees will be
categorized into most
suitable, suitable, less suitable
and not suitable.
MPMJ (1999), management plan for the mangroves of Johor 2000-2009. Forestry department peninsular Malaysia, Johor state and DANCED. Biodiversity audit and conservation plan for the mangroves of Johor (1999), project document No 6.
Water quality Level of water quality The higher the water quality of a river the greater its conservation value.
River will be categorized into
suitable and not suitable
water portions to be
conserved.
Biodiversity audit and conservation plan for the mangroves of Johor (1999), project document No 6.
Critical ecosystems
Size of endangered species in a cluster
These regionally and nationally
significant populations are
especially vulnerable to human
disturbances and habitat
degradation (U.S. FWS 1996). It
Here conservation needs will
be categorized according to
the sizes of endangered
species in various clusters; in
the order of most suitable,
U.S. Fish and Wildlife
Service (1996), Significant
Habitats and Habitat
Complexes of the New York
Bight Watershed.
74
is important that these critical
species habitats are protected or
restored to ensure that viable
populations of key species can
continue to persist.
suitable, less suitable and not
suitable.
Charlestown, R.I.: US Fish
and Wildlife Service.
Habitat area Size of habitat area The largest habitat patch in a
wetland is considered most
suitable for conservation efforts.
Favoring habitat patches with
the largest area when prioritizing
and selecting sites for
conservation ensures that the
greatest amount of suitable
habitat is being conserved.
It will be classified in the
range of most suitable,
suitable, less suitable and not
suitable, based on different
sizes of habitat patches.
Alderson, Carl. 26 January 2005. NOAA Restoration Center Personal Communication.
Wetlands close to natural land use/ land cover
Wetlands that are
surrounded by similar
or complementary
natural areas have
greater potential to be
conserved.
Some species require different
habitat types at various stages of
their life cycles. For example,
amphibians require both wetland
and upland habitats for their
complete life cycle. If a
population becomes isolated in
only one of its required habitats,
then the population cannot
survive (LISS 2003). For these
reasons, land use that is in close
This will be classed according
to the proximity of wetland
area to natural land use/ land
cover. The most important
being the closest wetland area
to natural land use/ land cover
and the farthest wetland area
carrying the least importance.
It will be categorized from
the less suitable to the most
suitable.
Long Island Sound Study, (2003). Long Island Sound Habitat Restoration Initiative: Technical Support for Coastal Habitat Restoration. Stamford, CT: United States Environmental Protection Agency Long Island Sound Office.
75
proximity to natural habitat areas
is most suitable for conservation/
restoration.
Harvesting
season. Permissible
compartments for
distinct seasons.
The most distant compartment
from the harvesting season will
be the most suitable for
conservation, because the trees
need to be nurtured so that they
are fully ripe by the time it’s
their harvesting period.
This will be classed according
to the ranges allowed for
cultivation. In the order of
most suitable, suitable, less
suitable and not suitable.
MPMJ (1999), management plan for the mangroves of Johor 2000-2009. Forestry department peninsular Malaysia, Johor state and DANCED.
To determine development area suitability
Beyond high biodiversity and environmentally sensitive areas
The developable areas
includes low water
quality sections, a
relatively smaller
habitat area, lower
clusters of endangered
species, a higher tree
age class, permissible
area for cultivation at
certain periods and
lastly land use/ land
cover farther from
habitat area.
The protection zone should
allow for low impact tourism
activities such as boardwalks,
lookout areas, boating, camping
grounds and low rise & low
density chalets (SJER). It is also
stated that economic
development should be
diversified by using existing and
natural resources in a sustainable
manner (SJER).
The developable areas will be
ranked according to the level
of biodiversity and the
distance away from them in
the sequence of most suitable,
suitable, less suitable and not
suitable.
Comprehensive Development Plan for South Johor Economic Region (SJER) 2006-2025
76
To fulfill the objective of a conservation criterion which carries that; the lower
the age class of trees the higher its need for biodiversity preservation (MPMJ, 1999);
because these recently replanted trees needs to nurtured, so that they are fully ripe by the
time its their cultivation period. Their cultivation period is usually 20 years and
sometimes 15 years. Tree age class coverage was used; it was converted to raster and
reclassified according to the age class of each forest compartment. Similarly harvesting
season’s data layer was used to uncover within which periods certain forest
compartments can be cultivated (MPMJ, 1999); the most distant compartment from the
harvesting season will be the most suitable for conservation, because the trees needs to
be nurtured so that they are fully ripe by the time its their harvesting period. This
coverage was converted to raster and reclassified according to the forest compartments
that fall in distant years from the harvesting season to those that fall within the present
season of harvest. In order to determine the water quality of the river, parameters as PH,
BOD, COD, SS, AN and DO of the sampling stations were used to calculate the sub-
indices (SI). This was achieved with the following formula of water quality index of
Malaysia (WQI).
WQI = 0.22 x SIDO + 0.19 x SIBOD + 0.16 x SICOD + 0.15 x SIAN + 0.16 x SISS + 0.12 x SIpH Where; Subindex for DO (in % saturation): SIDO = 0 for x<= 8 = 100 or x >= 92 SIDO = -0.395 + 0.030x2 - 0.00020x3 for 8 < x < 92 Subindex for BOD SIBOD = 100.4 - 4.23x for x <=5 SIBOD = 108* exp (-0.055x) - 0.1x for x > 5 Subindex for COD SICOD = -1.33x + 99.1 for x <=20 SICOD = 103*exp (-0.0157x) - 0.04x for x >= 20 Subindex for AN SIAN = 100.5 - 105x for x <= 0.3
77
SIAN = 94*exp (-0.573x) - 5 * I x - 2 I for 0.3 < x < 4 SIAN = 0 for x >= 4 Subindex for SS: SISS = 97.5*exp (-0.00676x) + 0.05x for x<= 100 SISS = 71*exp (-0.0061x) - 0.015x for 100 < x < 1000 SISS = 0 for x >=1000 Subindex for pH SIpH = 17.2 - 17.2x + 5.02x2 for x < 5.5 SIpH = -242 + 95.5x - 6.67x2 for 5.5 <= x < 7 SIpH = -181 + 82.4x -6.05x2 for 7 <= x 8.75 SIpH = 536 - 77.0x + 2.76x2 for x >= 8.75
After getting the result from the formula above, the river was dissected into
different classes, by considering a point to represent its up stream. The classes were
based on Interim National Water Quality Standards of Malaysia. This was done
manually using digitization function of GIS; afterwards it was converted to raster format
and reclassified, having the higher quality sections to be more suitable for conservation
efforts.
To determine tourism development areas in such a protected area, in order to
comply with South Johor Economic Region (SJER) objective which states that; the
protection zone should allow for low impact tourism activities such as boardwalks,
lookout areas, boating, camping grounds and low rise/ low density chalets
(Comprehensive Development Plan for SJER, 2006-2025). Habitat area coverage was
used to identify smaller habitat patches and categorized as suitable areas for
development. As all the data layers have been converted to raster in the conservation
evaluation above; habitat area coverage was classified from the smallest to the largest
78
habitat patch. Threatened species data layer was used to identify clusters with less
population of such species thus will be identified as a suitable development area.
Endangered species layer was reclassified from the smallest to largest number of
threatened fauna found in each cluster. Similarly the farther a natural land use/ land
cover is to wetland area, the more it is considered suitable for development. Therefore,
multiple ring buffer of 20, 30 and 40 meters were performed on the habitat area
coverage. It was classified from most distant to the closest area from the wetlands. Using
water quality coverage, its lower quality portions were categorized as development area.
Afterwards it was classified from low to high quality sections.
To determine development area from the economic yield point of view based on
South Johor Economic Region (SJER) objective which asserts that; economic
development should be diversified by using existing and natural economic resources in a
sustainable manner. Higher tree age class was considered as developable areas, using
tree age class coverage as input. Subsequently it was classified from forest
compartments with the highest age class to those with the lowest. Similarly harvesting
season’s coverage was employed in order to identify compartments that fall within the
present harvesting season, thus was categorized suitable for development. This data
layer was classified from forest compartments that fall within the present year harvest to
the most distant ones. Also lower river section of the wetlands was considered as
developable area i.e it can yield huge revenue and provide employment from the fishing
activities, using water quality data layer as input. This data layer was reclassified from
lower to higher quality portions.
Data layers reclassification and conversion above were performed using the
conversion tools and spatial analyst function of GIS. Then the processed data layers
were compared using the Boolean overlay approach, with pair wise comparison result as
input.
79
3.5.3 Multi criteria analysis and priority ranking
Multi criteria evaluation techniques were used in order to support the solution of
a decision problem by evaluating the possible alternatives from different perspectives.
Alternatives to be evaluated and ranked were represented by different criterion maps. As
different criteria are usually characterized by different importance levels, the subsequent
step of MCA was the prioritization of the criteria. This was achieved through the
assignment of a weight to each criterion that indicates its importance relatively to the
other criteria under consideration, by using information from literatures, decision
makers/ expert's views, focused group meeting and surveys. There are several techniques
for assigning criterion weights. Some of the most popular includes; ranking methods,
rating methods and pair wise comparison method. However this study utilized pair wise
comparison method, due to the nature of the problem at hand. Here, the conservation
criteria need to be compared with each other. As such pair wise comparison method is
particularly suited for this task, as it allows for the comparison of two criteria at a time.
Similarly pair wise comparison method is more appropriate than the other methods if
accuracy and theoretical foundations are the main concern (Malczewski, 1999). Also
ranking and rating methods have been criticized for their lack of theoretical and formal
foundations in interpreting the level of importance of a criterion (Malczewski, 1999).
3.5.3.1 Pair wise Comparison Method.
Analytical Hierarchy Process (AHP) was proposed by Saaty in 1980 and uses
pairwise comparison method for criterion weighting. The method was carried out in a
few steps; the criterion weights were used to generate cell values in a square matrix;
where 'i' is a row and 'j' is a column. Since each factor is of equal importance to itself,
the diagonal matrix was filled with 1's. Where Ci (row element) and Cj (column
element) are of equal importance, then aij (the value in the matrix at the intersection of
row i and column j) equals 1; and where Cj is more important than Ci, then aij is set
80
equal to the importance score and was >1. The entries aij in the matrix are based on the
1-9 interval scale with the following scale value meaning:
1- Same importance
2- Slightly more important
3- Weakly more important
4- Weakly to moderately more important
5- Moderately more important
6- Moderately to strongly more important
7- Strongly more important
8- Greatly more important
9- Absolutely more important
Judgments were synthesized by summing the columns of the matrix, and the
matrix normalized by dividing each column entry by the columns sum. Then the
arithmetic average of each row in the normalized matrix was computed. Because
individual’s judgment will never agree perfectly, the degree of consistency achieved in
the ratings was measured by a consistency ratio (CR) indicating the probability the
matrix ratings were randomly generated. The rule is that a CR less than or equal to 0.10
indicates an acceptable reciprocal matrix. To compute consistency ratio (CR); weighted
sum vector was determined by multiplying the matrix by the vector of criterion weights
i.e each column was multiplied by the corresponding criterion weights and the products
summed over the rows; then the consistency vector was determined by dividing the
weighted sum vector by the criterion weights; afterwards the average value of the
consistency vector was computed; then the consistency index (CI) computed (λ-n/ n-1) ,
its calculation is based on the observation that is always greater or equal to the number
of criteria. Finally the consistency ratio (CR) was calculated (CR= CI/RI) in order to
make sure whether the comparison of criteria made by decision maker is consistent
(Figure 3.10), where RI is the random index representing the consistency of a randomly
81
generated pair wise comparison. The pairwise comparison method is illustrated in Table
3.4; it was developed in Microsoft Excel and the results transferred into Raster
Calculator of ArcGIS framework.
Table 3.4: Illustration of pairwise comparison method
This method is much more sophisticated than ranking and rating methods.
Nevertheless it is criticized by the way of receiving the ratios of importance. The
questionnaire asks about the relative importance of a criterion without respect to the
scale it is measured. Moreover, the more criteria are required the more labor-intensive it
becomes. While selecting any specific method one should take into account level of
understanding of the problem by decision makers and their proficiency in the field.
Expected accuracy of outcome versus simplicity of the procedure is also a factor.
Malczewski (1999) states that pairwise comparison is more appropriate if accuracy and
theoretical foundations are the main concern. Ranking and rating methods are used when
ease-of-use, time and cost in generating weights is in concern. It is also recognized that
the more sophisticated the technique the less transparent become the process for the
general public.
82
Figure 3.10: Steps in pairwise comparison method
The following criteria were used in wetlands conservation decision making; C1:
Tree age class, C2: harvesting season, C3: endangered fauna, C4: habitat’s proximity to
natural land use/ land cover, C5: habitat area and C6: water quality. The derivation of
weights for the criteria follows the sequence of steps, which are detailed below.
C1: Tree age class
Step 1: The following square pair wise comparison matrix was formed; and judgments
synthesized by summing the columns of the matrix.
Identify Criteria (Factors)
Assign Standardized Criteria Scores
Create Decision Hierarchy
Weighting of Criteria
Check Consistency
Integrate with GIS
83
Criteria C1 C2 C3 C4 C5 C6 C1 1 4 4 4 4 4 C2 0.25 1 2 2 2 2 C3 0.25 0.5 1 2 2 2 C4 0.25 0.5 0.5 1 2 2 C5 0.25 0.5 0.5 0.5 1 2 C6 0.25 0.5 0.5 0.5 0.5 1 2.3 7.0 8.5 10.0 11.5 13.0
The interpretation of the above matrix is that Tree age class (C1) is the most
important criterion; it is weakly to moderately more important than the other criteria.
Step 2: Matrix was normalized by dividing each column entry by the column’s sum; and
the arithmetic average of each row in the normalized matrix was computed.
Criteria C1 C2 C3 C4 C5 C6 C1 0.44 0.57 0.47 0.40 0.35 0.31 0.42 C2 0.11 0.14 0.24 0.20 0.17 0.15 0.17 C3 0.11 0.07 0.12 0.20 0.17 0.15 0.14 C4 0.11 0.07 0.06 0.10 0.17 0.15 0.11 C5 0.11 0.07 0.06 0.05 0.09 0.15 0.09 C6 0.11 0.07 0.06 0.05 0.04 0.08 0.07
Step 3a: Because individual judgments will never agree perfectly, the degree of
consistency achieved in the ratings is measured by a Consistency Ratio (CR) indicating
the probability the matrix ratings were randomly generated. The rule-of-thumb is that a
CR less than or equal to 0.10 indicates an acceptable reciprocal matrix, and ration over
0.10 indicates the matrix should be revised. The computation of Consistency Ratio was
carried out in a few steps as follows; the weighted sum vector was determined by
multiplying the matrix by the vector of the criterion weights (each column was multiplied
by the corresponding criterion weights and the products were summed over the rows).
84
1 4 4 4 4 4 0.25 1 2 2 2 2 0.25 0.5 1 2 2 2 0.25 0.5 0.5 1 2 2 0.25 0.5 0.5 0.5 1 2 0.25 0.5 0.5 0.5 0.5 1
* 0.42 0.17 0.14 0.11 0.09 0.07
0.42 0.68 0.56 0.44 0.36 0.28 2.74 0.11 0.17 0.28 0.22 0.18 0.14 1.10 0.11 0.09 0.14 0.22 0.18 0.14 0.87 0.11 0.09 0.07 0.11 0.18 0.14 0.69 0.11 0.09 0.07 0.06 0.09 0.14 0.55 0.11 0.09 0.07 0.06 0.05 0.07 0.43
Step 3b: The consistency vector was determined by dividing the weighted sum vector by
the criterion weights; and the average value of consistency vector was computed.
2.74 0.42 6.47 1.10 0.17 6.46 0.87 / 0.14 = 6.30 0.69 0.11 6.19 0.55 0.09 6.14 0.43 0.07 6.27 Sum/criteria no. 37.83/6 6.30
Step 3c: In this step the Consistency Index (CI) was determined. The calculation of CI is
based on the observation that is always greater or equal to the number of criteria. If the
pair wise comparison matrix is a consistent matrix, accordingly the number of criteria can
be considered as a measure of the degree of inconsistency. This measure was normalized
as follows;
Consistency Index (CI) = (λ-n)/(n-1) = 6.30-6/6-1= 0.06
85
Step 3d: To compute the Consistency Ratio (CR);
Consistency Ratio (CR) = CI/RI =0.06/1.24= 0.05 Where RI is the random index representing the consistency of a randomly generated pair
wise comparison matrix. The value of RI depends on the number of criteria being
compared.
n 3 4 5 6 7 8 RI 0.58 0.9 1.12 1.24 1.32 1.41
The value of CR = 0.05 falls much below the threshold value = 0.1 and it indicates a high
level of consistency. Hence the weights can be accepted.
C2: Harvesting Season Step 1: The following square pair wise comparison matrix was formed; and judgments
synthesized by summing the columns of the matrix.
Criteria C1 C2 C3 C4 C5 C6 C1 1 0.25 2 2 2 2 C2 4 1 4 4 4 4 C3 0.5 0.25 1 2 2 2 C4 0.5 0.25 0.5 1 2 2 C5 0.5 0.25 0.5 0.5 1 2 C6 0.5 0.25 0.5 0.5 0.5 1 7.0 2.3 8.5 10.0 11.5 13.0
The interpretation of the above matrix is that Harvesting season (C2) is the most
important criterion; it is weakly to moderately more important than the other criteria.
86
Step 2: Matrix was normalized by dividing each column entry by the column’s sum; and
the arithmetic average of each row in the normalized matrix was computed.
Criteria C1 C2 C3 C4 C5 C6 C1 0.14 0.11 0.24 0.20 0.17 0.15 0.17 C2 0.57 0.44 0.47 0.40 0.35 0.31 0.42 C3 0.07 0.11 0.12 0.20 0.17 0.15 0.14 C4 0.07 0.11 0.06 0.10 0.17 0.15 0.11 C5 0.07 0.11 0.06 0.05 0.09 0.15 0.09 C6 0.07 0.11 0.06 0.05 0.04 0.08 0.07
Step 3a: The computation of Consistency Ratio was carried out in a few steps as follows;
the weighted sum vector was determined by multiplying the matrix by the vector of the
criterion weights (each column was multiplied by the corresponding criterion weights and
the products were summed over the rows).
1 0.25 2 2 2 2 4 1 4 4 4 4 0.5 0.25 1 2 2 2 0.5 0.25 0.5 1 2 2 0.5 0.25 0.5 0.5 1 2 0.5 0.25 0.5 0.5 0.5 1
* 0.17 0.42 0.14 0.11 0.09 0.07
0.17 0.11 0.28 0.22 0.18 0.14 1.10 0.68 0.42 0.56 0.44 0.36 0.28 2.74 0.09 0.11 0.14 0.22 0.18 0.14 0.87 0.09 0.11 0.07 0.11 0.18 0.14 0.69 0.09 0.11 0.07 0.06 0.09 0.14 0.55 0.09 0.11 0.07 0.06 0.05 0.07 0.43
Step 3b: The consistency vector was determined by dividing the weighted sum vector by
the criterion weights; and the average value of consistency vector was computed.
87
1.10 0.17 6.44 2.74 0.42 6.52 0.87 / 0.14 = 6.21 0.69 0.11 6.27 0.55 0.09 6.06 0.43 0.07 6.14 Sum/criteria no. 37.65/6 6.28
Step 3c: In this step the Consistency Index (CI) was determined. The calculation of CI is
based on the observation that is always greater or equal to the number of criteria. If the
pair wise comparison matrix is a consistent matrix, accordingly the number of criteria can
be considered as a measure of the degree of inconsistency. This measure was normalized
as follows;
Consistency Index (CI) = (λ-n)/(n-1) =6.28-6/6-1= 0.06 Step 3d: To compute the Consistency Ratio (CR);
Consistency Ratio (CR) = CI/RI =0.06/1.24= 0.04 Where RI is the random index representing the consistency of a randomly generated pair
wise comparison matrix. The value of RI depends on the number of criteria being
compared.
n 3 4 5 6 7 8 RI 0.58 0.9 1.12 1.24 1.32 1.41
The value of CR = 0.04 falls much below the threshold value = 0.1 and it indicates a
high level of consistency. Hence the weights can be accepted.
88
C3: Endangered fauna Step 1: The following square pair wise comparison matrix was formed; and judgments
synthesized by summing the columns of the matrix.
Criteria C1 C2 C3 C4 C5 C6 C1 1 2 0.25 2 2 2 C2 0.5 1 0.25 2 2 2 C3 4 4 1 4 4 4 C4 0.5 0.5 0.25 1 2 2 C5 0.5 0.5 0.25 0.5 1 2 C6 0.5 0.5 0.25 0.5 0.5 1 7.0 8.5 2.3 10.0 11.5 13.0
The interpretation of the above matrix is that Endangered fauna (C3) is the most
important criterion; it is weakly to moderately more important than the other criteria.
Step 2: Matrix was normalized by dividing each column entry by the column’s sum; and
the arithmetic average of each row in the normalized matrix was computed.
Criteria C1 C2 C3 C4 C5 C6 C1 0.14 0.24 0.11 0.20 0.17 0.15 0.17 C2 0.07 0.12 0.11 0.20 0.17 0.15 0.14 C3 0.57 0.47 0.44 0.40 0.35 0.31 0.42 C4 0.07 0.06 0.11 0.10 0.17 0.15 0.11 C5 0.07 0.06 0.11 0.05 0.09 0.15 0.09 C6 0.07 0.06 0.11 0.05 0.04 0.08 0.07
Step 3a: The computation of Consistency Ratio was carried out in a few steps as follows;
the weighted sum vector was determined by multiplying the matrix by the vector of the
criterion weights (each column was multiplied by the corresponding criterion weights and
the products were summed over the rows).
89
1 2 0 2 2 2 0.5 1 0.25 2 2 2 4 4 1 4 4 4 0.5 0.5 0.25 1 2 2 0.5 0.5 0.25 0.5 1 2 0.5 0.5 0.25 0.5 0.5 1
* 0.17 0.14 0.42 0.11 0.09 0.07
0.17 0.28 0.11 0.22 0.18 0.14 1.10 0.09 0.14 0.11 0.22 0.18 0.14 0.87 0.68 0.56 0.42 0.44 0.36 0.28 2.74 0.09 0.07 0.11 0.11 0.18 0.14 0.69 0.09 0.07 0.11 0.06 0.09 0.14 0.55 0.09 0.07 0.11 0.06 0.05 0.07 0.43
Step 3b: The consistency vector was determined by dividing the weighted sum vector
by the criterion weights; and the average value of consistency vector was computed.
1.10 0.17 6.46 0.87 0.14 6.30 2.74 / 0.42 = 6.47 0.69 0.11 6.19 0.55 0.09 6.14 0.43 0.07 6.27 Sum/criteria no. 37.83/6 6.30
Step 3c: In this step the Consistency Index (CI) was determined. The calculation of CI is
based on the observation that is always greater or equal to the number of criteria. If the
pair wise comparison matrix is a consistent matrix, accordingly the number of criteria
can be considered as a measure of the degree of inconsistency. This measure was
normalized as follows;
Consistency Index (CI) = (λ-n)/(n-1) = 6.30-6/6-1= 0.06
90
Step 3d: To compute the Consistency Ratio (CR);
Consistency Ratio (CR) = CI/RI =0.06/1.24= 0.05 Where RI is the random index representing the consistency of a randomly generated pair
wise comparison matrix. The value of RI depends on the number of criteria being
compared.
n 3 4 5 6 7 8 RI 0.58 0.9 1.12 1.24 1.32 1.41
The value of CR = 0.05 falls much below the threshold value = 0.1 and it indicates a
high level of consistency. Hence the weights can be accepted.
C4: Habitat’s proximity to natural land use/ land cover Step 1: The following square pair wise comparison matrix was formed; and
judgments synthesized by summing the columns of the matrix.
Criteria C1 C2 C3 C4 C5 C6 C1 1 2 2 0.25 2 2 C2 0.5 1 2 0.25 2 2 C3 0.5 0.5 1 0.25 2 2 C4 4 4 4 1 4 4 C5 0.5 0.5 0.5 0.25 1 2 C6 0.5 0.5 0.5 0.25 0.5 1 7.0 8.5 10.0 2.3 11.5 13.0
The interpretation of the above matrix is that Habitat’s proximity to natural land
use/ land cover (C4) is the most important criterion; it is weakly to moderately more
important than the other criteria.
91
Step 2: Matrix was normalized by dividing each column entry by the column’s sum; and
the arithmetic average of each row in the normalized matrix was computed.
Criteria C1 C2 C3 C4 C5 C6 C1 0.14 0.24 0.20 0.11 0.17 0.15 0.17 C2 0.07 0.12 0.20 0.11 0.17 0.15 0.14 C3 0.07 0.06 0.10 0.11 0.17 0.15 0.11 C4 0.57 0.47 0.40 0.44 0.35 0.31 0.42 C5 0.07 0.06 0.05 0.11 0.09 0.15 0.09 C6 0.07 0.06 0.05 0.11 0.04 0.08 0.07
Step 3a: The computation of Consistency Ratio was carried out in a few steps as
follows; the weighted sum vector was determined by multiplying the matrix by the
vector of the criterion weights (each column was multiplied by the corresponding
criterion weights and the products were summed over the rows).
1 2 2 0 2 2 0.5 1 2 0.25 2 2 0.5 0.5 1 0.25 2 2 4 4 4 1 4 4 0.5 0.5 0.5 0.25 1 2 0.5 0.5 0.5 0.25 0.5 1
* 0.17 0.14 0.11 0.42 0.09 0.07
0.17 0.28 0.22 0.11 0.18 0.14 1.10 0.09 0.14 0.22 0.11 0.18 0.14 0.87 0.09 0.07 0.11 0.11 0.18 0.14 0.69 0.68 0.56 0.44 0.42 0.36 0.28 2.74 0.09 0.07 0.06 0.11 0.09 0.14 0.55 0.09 0.07 0.06 0.11 0.05 0.07 0.43
Step 3b: The consistency vector was determined by dividing the weighted sum vector
by the criterion weights; and the average value of consistency vector was computed
92
1.10 0.17 6.46 0.87 0.14 6.30 0.69 / 0.11 = 6.19 2.74 0.42 6.47 0.55 0.09 6.14 0.43 0.07 6.27 Sum/criteria no. 37.83/6 6.30
Step 3c: In this step the Consistency Index (CI) was determined. The calculation of CI is
based on the observation that is always greater or equal to the number of criteria. If the
pair wise comparison matrix is a consistent matrix, accordingly the number of criteria
can be considered as a measure of the degree of inconsistency. This measure was
normalized as follows;
Consistency Index (CI) = (λ-n)/(n-1) = 6.30-6/6-1= 0.06 Step 3d: To compute the Consistency Ratio (CR);
Consistency Ratio (CR) = CI/RI =0.06/1.24= 0.05 Where RI is the random index representing the consistency of a randomly generated pair
wise comparison matrix. The value of RI depends on the number of criteria being
compared.
n 3 4 5 6 7 8 RI 0.58 0.9 1.12 1.24 1.32 1.41
The value of CR = 0.05 falls much below the threshold value = 0.1 and it indicates a high
level of consistency. Hence the weights can be accepted.
93
C5: Habitat area Step 1: The following square pair wise comparison matrix was formed; and judgments
synthesized by summing the columns of the matrix.
Criteria C1 C2 C3 C4 C5 C6 C1 1 2 2 2 0.25 2 C2 0.5 1 2 2 0.25 2 C3 0.5 0.5 1 2 0.25 2 C4 0.5 0.5 0.5 1 0.25 2 C5 4 4 4 4 1 4 C6 0.5 0.5 0.5 0.5 0.25 1 7.0 8.5 10.0 11.5 2.3 13.0
The interpretation of the above matrix is that Habitat area (C5) is the most
important criterion; it is weakly to moderately more important than the other criteria.
Step 2: Matrix was normalized by dividing each column entry by the column’s sum; and
the arithmetic average of each row in the normalized matrix was computed.
Criteria C1 C2 C3 C4 C5 C6 C1 0.14 0.24 0.20 0.17 0.11 0.15 0.17 C2 0.07 0.12 0.20 0.17 0.11 0.15 0.14 C3 0.07 0.06 0.10 0.17 0.11 0.15 0.11 C4 0.07 0.06 0.05 0.09 0.11 0.15 0.09 C5 0.57 0.47 0.40 0.35 0.44 0.31 0.42 C6 0.07 0.06 0.05 0.04 0.11 0.08 0.07
Step 3a: The computation of Consistency Ratio was carried out in a few steps as follows;
the weighted sum vector was determined by multiplying the matrix by the vector of the
criterion weights (each column was multiplied by the corresponding criterion weights and
the products were summed over the rows).
94
1 2 2 2 0.25 2 0.50 1 2 2 0.25 2 0.50 0.50 1 2 0.25 2 0.50 0.50 0.5 1 0.25 2 4 4 4 4 1 4 0.50 0.50 0.50 0.50 0.25 1
* 0.17 0.14 0.11 0.09 0.42 0.07
0.17 0.28 0.22 0.18 0.11 0.14 1.10 0.09 0.14 0.22 0.18 0.11 0.14 0.87 0.09 0.07 0.11 0.18 0.11 0.14 0.69 0.09 0.07 0.06 0.09 0.11 0.14 0.55 0.68 0.56 0.44 0.36 0.42 0.28 2.74 0.09 0.07 0.06 0.05 0.11 0.07 0.43
Step 3b: The consistency vector was determined by dividing the weighted sum vector by
the criterion weights; and the average value of consistency vector was computed.
1.10 0.17 6.46 0.87 0.14 6.30 0.69 / 0.11 = 6.19 0.55 0.09 6.14 2.74 0.42 6.47 0.43 0.07 6.27 Sum/criteria no. 37.83/6 6.30
Step 3c: In this step the Consistency Index (CI) was determined. The calculation of CI is
based on the observation that is always greater or equal to the number of criteria. If the
pair wise comparison matrix is a consistent matrix, accordingly the number of criteria can
be considered as a measure of the degree of inconsistency. This measure was normalized
as follows;
Consistency Index (CI) = (λ-n)/(n-1) =6.30-6/6-1= 0.06
95
Step 3d: To compute the Consistency Ratio (CR);
Consistency Ratio (CR) = CI/RI =0.06/1.24= 0.04 Where RI is the random index representing the consistency of a randomly generated pair
wise comparison matrix. The value of RI depends on the number of criteria being
compared.
n 3 4 5 6 7 8 RI 0.58 0.9 1.12 1.24 1.32 1.41
The value of CR = 0.04 falls much below the threshold value = 0.1 and it indicates a high
level of consistency. Hence the weights can be accepted.
C6: Water quality Step 1: The following square pair wise comparison matrix was formed; and judgments
synthesized by summing the columns of the matrix.
Criteria C1 C2 C3 C4 C5 C6 C1 1 2 2 2 2 0.25 C2 0.5 1 2 2 2 0.25 C3 0.5 0.5 1 2 2 0.25 C4 0.5 0.5 0.5 1 2 0.25 C5 0.5 0.5 0.5 0.5 1 0.25 C6 4 4 4 4 4 1 7.0 8.5 10.0 11.5 13.0 2.3
The interpretation of the above matrix is that Water quality (C6) is the most
important criterion; it is weakly to moderately more important than the other criteria.
96
Step 2: Matrix was normalized by dividing each column entry by the column’s sum; and
the arithmetic average of each row in the normalized matrix was computed.
Criteria C1 C2 C3 C4 C5 C6 C1 0.14 0.24 0.20 0.17 0.15 0.11 0.17 C2 0.07 0.12 0.20 0.17 0.15 0.11 0.14 C3 0.07 0.06 0.10 0.17 0.15 0.11 0.11 C4 0.07 0.06 0.05 0.09 0.15 0.11 0.09 C5 0.07 0.06 0.05 0.04 0.08 0.11 0.07 C6 0.57 0.47 0.40 0.35 0.31 0.44 0.42
Step 3a: The computation of Consistency Ratio was carried out in a few steps as follows;
the weighted sum vector was determined by multiplying the matrix by the vector of the
criterion weights (each column was multiplied by the corresponding criterion weights and
the products were summed over the rows).
1 2 2 2 2 0.25 0.5 1 2 2 2 0.25 0.5 0.5 1 2 2 0.25 0.5 0.5 0.5 1 2 0.25 0.5 0.5 0.5 0.5 1 0.25 4 4 4 4 4 1
* 0.17 0.14 0.11 0.09 0.07 0.42
0.17 0.28 0.22 0.18 0.14 0.11 1.10 0.09 0.14 0.22 0.18 0.14 0.11 0.87 0.09 0.07 0.11 0.18 0.14 0.11 0.69 0.09 0.07 0.06 0.09 0.14 0.11 0.55 0.09 0.07 0.06 0.05 0.07 0.11 0.43 0.68 0.56 0.44 0.36 0.28 0.42 2.74
Step 3b: The consistency vector was determined by dividing the weighted sum vector by
the criterion weights; and the average value of consistency vector was computed.
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1.10 0.17 6.46 0.87 0.14 6.30 0.69 / 0.11 = 6.19 0.55 0.09 6.14 0.43 0.07 6.27 2.74 0.42 6.47 Sum/criteria no. 37.83/6 6.30
Step 3c: In this step the Consistency Index (CI) was determined. The calculation of CI is
based on the observation that is always greater or equal to the number of criteria. If the
pair wise comparison matrix is a consistent matrix, accordingly the number of criteria
can be considered as a measure of the degree of inconsistency. This measure was
normalized as follows;
Consistency Index (CI) = (λ-n)/(n-1) =6.30-6/6-1= 0.06 Step 3d: To compute the Consistency Ratio (CR);
Consistency Ratio (CR) = CI/RI =0.06/1.24= 0.04 Where RI is the random index representing the consistency of a randomly generated pair
wise comparison matrix. The value of RI depends on the number of criteria being
compared.
n 3 4 5 6 7 8 RI 0.58 0.9 1.12 1.24 1.32 1.41
The value of CR = 0.04 falls much below the threshold value = 0.1 and it indicates a
high level of consistency. Hence the weights can be accepted.
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3.5.4 Generation and analysis of conservation/ development scenarios and decision
making
Conservation and development scenarios were generated, with each scenario
representing the best solution to decision problem, according to the assessment
perspective adopted. Map scenarios reflecting the opinion of different experts or
stakeholders involved were compared in order to highlight the robustness of the solution
and support decision making. Scenarios were generated using the weights derived from
pair wise comparison method, which was compared with the Boolean Overlay
Approach. This is done with the aid of raster calculator; the raster calculator which is a
Spatial Analyst function that provides a tool for performing multiple tasks: one can
perform mathematical calculations using operators and functions, set up selection
queries, or type in Map Algebra syntax. GIS should act as the interface between
technology and the decision maker with integrating MCE methods into the GIS
(Heywood et al. 1993) Development scenarios were viewed from tourism and economic
development point of view:
3.5.4.1 Tourism development scenario 1
This includes development in the low spot of natural resources/ lower
sensitive areas. This is to conform with South Johor Economic Region (SJER) objective
which carries that; the protection zone should allow for low impact tourism activities
such as boardwalks, lookout areas, boating, camping grounds and low rise/ low density
chalets, (Comprehensive Development Plan for SJER, 2006-2025) (Figure 3.11).
99
Figure 3.11: Tourism development suitability model
Table 3.5: Tourism development criteria and indicators Criterion Indicators Classes
Water quality Relatively lower quality sections
of the river.
It was classed from the lowest
quality part of the river being
most suitable, to the highest
quality portions being not
suitable.
Endangered fauna Relatively smaller clusters of
endangered species.
Smallest cluster of threatened
fauna was ranked most suitable
and largest not suitable.
Habitat area Smaller habitat patches. The smallest habitat area was
considered most suitable for
development and the biggest not
suitable.
Proximity of natural land use/
land cover to habitat area.
Farther land use/ land cover from
wetland areas.
The farthest natural land use/
land cover to habitat area was
categorized as the most suitable
Endngerd fauna
Water quality
Habitat area
Boundary
Boundary
Feature to raster
Boundary
Union
Union
Feature to raster
Reclasify
Reclasify
Union
Feature to raster
Reclasify
Reclasify
Boundary
Habitat area
Union MRbuffer Feature to raster
Raster calc.
Tourism Development
100
for development and the closest
not suitable.
3.5.4.2 Tourism development scenario 2
The only difference with this scenario and the above is that the river will be
completely restricted from any kind of development. This is in view of its gazettement
(Ecological Assessment of Sungai Pulai MFR, 2001), due to its major ecological
importance of continuous input of freshwater into the upper reaches of Sungai Pulai
estuary, home to a variety of wetland plant species, as well as habitat of fauna and birds.
In addition to this is its function of sedimentation retention, nutrient retention and
toxicant removal. Therefore the whole river will be classified as unsuitable under this
scenario.
3.5.4.3 Economic development scenario
This entails development that will yield the economic development of the
people and the authorities in general, at the same time minimizing adverse
environmental impact on the environment. This is to act in accordance with South Johor
Economic Region (SJER) and Draft Johor Structure Plan 2006-2020 objective which
assert that; economic development should be diversified by using existing and natural
economic resources in a sustainable manner, (Figure 3.12).
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Figure 3.12: Economic development model
Table 3.6: Economic development criteria and indicators Criterion Indicators Classes
Tree age class The higher the age classes of trees the
greater their chances of being cultivated.
The most suitable was the
highest age class trees and the
lowest age class not suitable.
Harvesting season The closer the trees compartment to
harvesting, the higher their suitability for
economic yield.
Compartments allowed for
cultivation within the present
year were termed the most
suitable and the most distant
year not suitable.
Water quality The lower the water quality, the greater its
chances being used for low impact fishing
activities.
Lower water quality section
was categorized most suitable
and higher quality sections not
suitable.
Age class
Harvestg season
Boundary
Boundary
Union
Union
Feature to raster
Feature to raster
Reclasify
Reclasify Raster calc
Resource Development
Boundary
Water quality
Union Feature to raster
Reclasify
102
3.5.4.4 Conservation scenarios
The study’s conservation scenarios were produced using the same factors, with
however variation in the criterion weights in each of the scenario. Each criterion was
given a higher weight over others based on its function in the conservation of wetlands
i.e each criterion was considered of more importance than others in 6 different scenarios,
(Figure 3.13). The purpose of the criterion weighting is to express the importance of
each criterion relative to other criteria.
Table 3.7: Conservation criteria and indicators
Criterion Indicators Classes Tree age class The lower the age class of trees
in an area, the higher the need for biodiversity preservation.
Lowest age class was termed as
the most suitable and the highest
not suitable. River The higher the water quality of a
river the greater its conservation value.
The highest water quality section of the river was considered most suitable and the least part not suitable.
Endangered fauna The higher the clusters of
endangered fauna, the greater the
need for its conservation
Largest clusters of endangered
fauna was categorized most
suitable and the smallest not suitable.
Habitat area The larger the habitat area, the
greater its conservation need.
Biggest habitat patch was classed
as the most suitable and the
smallest habitat area not suitable.
Wetlands close to natural land
use/ land cover The closer a natural land use/
land cover to wetland’s area the
more it conservation value.
The closest natural land use/ land
cover to habitat area was
categorized as the most suitable
and the most distant not suitable.
Harvesting season The farther a tree compartment is
to harvesting season, the greater
its conservation relevance
The most distant tree
compartment from harvesting
season will be considered as the
most suitable and compartments
that fall in present year
harvesting was categorized as not
suitable.
Figure 3.13: Wetland’s conservation model
Tree age class
Harvestg
Water quality
Endangrd Species
Habitat area
Habitat area
MRbuffer
Feature toraster
Reclasify
Feature toraster
Reclasify
Boundary
Union
Boundary
Union
Boundary
Union Feature toraster
Reclasify
Boundary
Union Feature toraster
Reclasify
Boundary
Union Feature toraster
Reclasify
Boundary
Union Feature toraster
Reclasify
Union
Union Raster calc.
Conservation
104
CHAPTER 4
WETLANDS ASSESSMENT AND RESULTS
4.1 Introduction
The basic concern of this study is to identify conservation and compatible areas
for tourism development in Johor Ramsar Sites, using spatial modeling in Geographic
Information System (GIS). In other words the study intends to address the conservation
principle of sustainable tourism planning. Conservation in this case refers to the
preservation, management and care of flora/ fauna, their habitat and the whole wetlands
area. Conversely, sustainable tourism planning may be regarded as a form of tourism
which involves management of all resources in such a way that economic, social and
aesthetic needs are fulfilled while maintaining cultural integrity, essential ecological
processes, biological diversity and life support systems; it involves the minimization of
negative impacts and the maximization of positive impacts of the environment it occurs.
The main objectives of the study have been to identify areas that need to be
conserved in the wetlands area; these area areas of high biodiversity that are highly
sensitive to human interference. Another objective is to identify relatively low
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biodiversity areas that can be used for low impact tourism and economic development.
These areas can be allowed for tourism activities such as boardwalks, lookout areas,
boating, camping grounds and low rise/ low density chalets; these vicinities are
characterized by a relatively low biodiversity of natural resources. Areas that can be
used for economic development includes; mangrove trees that have attained a high age
period as decided by the management body, river locations that depict a lower water
quality, trees that fall within the present and subsequent harvesting periods. Economic
development here will help in improving the living conditions of the local people by
providing employment opportunities, thus improving their income. It will also help in
generating revenue to the government. The study sites include Johor wetlands that have
been declared as wetlands of international importance at the Ramsar convention. They
include; Tanjung Piai, Sungai Pulai and Pulau Kukup.
Ideally, the approach to addressing these objectives has been to develop a GIS
and multi criteria evaluation model for wetland assessment. This is by applying the tools
of spatial analyst integrated with the workings of Multi Criteria Evaluation. In order to
achieve this, a set of evaluation criteria were defined by using information from
literatures, decision makers/ expert's views and surveys. These criteria includes; tree age
class, harvesting season, size of endangered fauna, habitat proximity to natural land
cover, habitat area and water quality. Having defined the criteria, suitable indicators and
variables were selected to measure the chosen criteria. Subsequently, the criteria were
evaluated by using typical functionalities of raster-based GIS; such as distance
operators, conversion and reclassification functions embedded in ArcGIS 9.0.
Afterwards, Pair wise comparison method of Multi Criteria Evaluation was used
to evaluate possible alternatives from different perspectives. The pair wise comparison
was developed in Microsoft Excel and results transferred into ArcGIS framework.
Conservation and development scenarios were generated, with each scenario
representing the best solution to a decision problem, according to the assessment
107
perspective adopted. Map scenarios reflecting the opinion of different experts or
stakeholders involved were compared using the Boolean overlay approach of GIS with
the aid of Raster Calculator, in order to highlight the robustness of the solution and
support decision making.
4.2 Wetlands conservation
As highlighted in the preceding section conservation refers to the preservation,
management and care of flora/ fauna, their habitat and the whole wetlands area. It is one
of the main principles of sustainable tourism planning. Conservation of the high
biodiversity areas of the wetlands will ensure that tourism does not serve to degrade
these internationally important sites. This is achieved by categorizing locations that
portray a relatively high abundance of natural resources as areas to be controlled from
tourism activities. This will ensure that tourism maintains the viability of the area for an
indefinite period of time.
4.2.1.1 Habitat area
Habitat area or environment can be defined as a place where an organism or
ecological community normally lives or occurs. The largest habitat patch in a wetland is
considered most suitable for conservation efforts. Favoring habitat patches with the
largest area when prioritizing and selecting sites for conservation ensures that the
greatest amount of suitable habitat is being conserved (Alderson, 2005). To be able to
identify bigger habitat areas, habitat area coverage was employed; this data layer was
converted to raster (feature to raster) and classed in such a manner that bigger habitat
patches are favored (Figure 4.1). Habitat’s area coverage conversion and reclassification
were performed using the spatial analyst function of GIS (ArcGIS 9.0).
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Figure 4.1: Habitat area (reclassified)
As can be seen from the figure above, the ‘low’ value depicts areas with
relatively smaller size of habitat patches, which are less suitable for conservation. These
areas accommodate a lesser number of flora and fauna. The ‘high’ value on the other
hand represents locations with comparatively bigger patches of species, which are said
to be more suitable for conservation efforts.
4.2.1.2 Endangered fauna
These regionally and nationally significant populations are especially vulnerable
to human disturbances and habitat degradation (USFWS, 1996). It is important that these
critical species habitats are protected or restored to ensure that viable populations of key
species can continue to persist. Endangered fauna coverage was used to ascertain species
109
that are vulnerable to extinction in the near future. Here conservation relevance was
based on the population of threatened fauna in each cluster; as such threatened fauna
coverage was converted to raster and classed according to the population size of these
species in each huddle (Figure 4.2). Conversion and reclassification of endangered fauna
coverage were performed using the spatial analyst function of GIS (ArcGIS 9.0).
Figure 4.2: Endangered fauna (reclassified)
As revealed from the diagram above the ‘low’ value portrays areas with
relatively small population of the endangered fauna, which are less suitable for
conservation when compared to areas with higher population of such species. The ‘high’
value on the other hand, depicts locations with a large number of species that are
vulnerable to extinction in the near future. Such localities are more suitable for
conservation as they host a relatively larger number of such species.
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4.2.1.3 Wetland’s proximity to natural land cover
Some species require different habitat types at various stages of their life cycles.
For example, amphibians require both wetland and upland habitats for their complete
life cycle. If a population becomes isolated in only one of its required habitats, then the
population cannot survive (LISS, 2003). For these reasons, land use that is in close
proximity to natural habitat areas is most suitable for conservation/ restoration.
To find out Wetlands that is surrounded by similar or complementary natural
area. Habitat area data layer was again utilized, however in this case excluding Pulau
Kukup since it is not surrounded by any upland area.
Figure 4.3: Multiple ring buffer
Natural land use/ land cover close to wetlands in the other two Ramsar Sites i.e
Pulai River and Tanjung Piai was determined by using multiple ring buffer of 20, 30 and
111
40 meters around the periphery of the wetland area (Figure 4.3), with the closest ring
being the most suitable, (Figure 4.4). This is converted to raster and classified according
to the proximity of the surrounding natural land use/ land cover to wetland area. This
coverage’s conversion and reclassification were performed using the spatial analyst
function of GIS (ArcGIS 9.0).
Figure 4.4: Habitat’s proximity to natural land cover (reclassified)
A section of the map above (Figure 4.4) is enlarged in order to have a clearer
picture of natural land uses surrounding the wetland area (Figure 4.5). This is in order to
identify those areas that are most suitable for conservation i.e relatively closer natural
upland areas to the wetlands and areas that are far away from the wetland area, which
are less suitable for conservation.
Enlarged area
112
Figure 4.5: Habitat’s proximity to natural land cover (enlarged area)
As can be seen from the diagram above, the ‘low’ value depict areas that are
farther from the habitat/ wetlands area which are less suitable for conservation. These
are locations rarely used by the wetland species because of their distance away from the
habitat area. The ‘high’ value on the other hand represents natural land use/ land cover
that are next to habitat/ wetlands area, which are said to be most suitable for
conservation. As highlighted in the above section, some species require both wetlands
and upland area for their complete life cycle. This signifies natural areas in closer
proximity to the wetlands as most suitable for conservation, because these areas are
more patronized by the some of the wetland fauna for their survival.
5.2.1.4 Tree age class
It is recommended to conserve young tree compartments i.e trees that are
recently replanted. Because these trees needs to be nurtured so that they are fully
113
matured by the time they reach their cultivation age. The trees are cultivated when they
have reached a maturity age of 20 years, while some are cultivated at the age of 15 years
(MPMJ, 1999). The rationale behind the cultivation of these internationally important
wetlands is that; the management authority/ government need to benefit from this natural
endowment as will yield huge revenue and provide employment opportunities. This is
coupled with problems as polluting water ways, which occurs when trees are
decomposed as they approach the limit of their life span.
Tree age class coverage was used; it was converted to raster and reclassified
according to the age class of each forest compartment, (Figure 4.6). The conversion and
reclassification of tree age class layer were done using spatial analyst function of GIS
(ArcGIS 9.0).
Figure 4.6: Tree age class (reclassified)
114
As seen from the above map, the ‘low’ value represents tree compartments with
high ages, which fall within the age class that are ripe for cultivation as outlined by the
management authority. Therefore, these categories of trees class are less suitable for
conservation. The ‘high’ value on the other hand, is tree compartments falling in the low
age classes, which need to be conserved so that they are fully matured by the time it’s
their cultivation period. These areas also include untouchable areas i.e areas that have
been reserved and managed as state parks. Therefore, they are considered suitable for
conservation efforts.
4.2.1.5 Harvesting season
This includes permissible compartments for distinct seasons. The most distant
compartment from the harvesting season and recently cultivated compartments will be
the most suitable for conservation, because the trees need to be nurtured so that they are
fully ripe by the time it’s their harvesting period (MPMJ, 1999). Harvesting season data
layer was used to uncover within which periods certain forest compartments can be
cultivated. This coverage was converted to raster and reclassified according to the forest
compartments in distinct years of harvesting season (Figure 4.7). The conversion and
reclassification were performed using the spatial analyst function of GIS (ArcGIS 9.0).
115
Figure 4.7: Harvesting (reclassified)
As revealed from the above figure, the ‘low’ value symbolizes trees that fall
within the present year harvesting schedule and subsequent year of harvesting. These
tree compartments are the least suitable for conservation efforts. Conversely, the ‘high’
value signifies those tree compartments that are recently replanted. In other words, they
are tree compartments that need to be taken care of before it’s their harvesting period.
The ‘high’ value also includes untouchable areas i.e areas that have been reserved and
managed as state parks. Thus, they are the most suitable for conservation.
4.2.1.6 Water quality
The higher the water quality of a river the greater its conservation value (MPMJ,
1999). In order to determine the water quality of the river, parameters as PH, BOD,
116
COD, SS, AN and DO of the sampling stations were used to calculate the sub-indices
(SI). This was achieved with the following formula of water quality index of Malaysia
(WQI).
Table 4.1: Water quality Sub-index
PH BOD COD SS DO NH3N SI
Tanjung
Bin
8.2 3 127 38 6.5 <0.1 54.50
Sungai
Pulai
6.7 5 134 11 6.4 <0.1 56.09
Calculations for Tanjung Bin;
SIPH = -181+82.4(8.2)-6.05(8.2)2= 87.87
SIBOD=100.4-4.23(3) = 87.71
SICOD=103*e(-0.0157*127)-0.04*(127)=8.94
SISS=97.5*e(-0.00676*38)+0.05(38)=77.31
SIDO=0
SIAN=100.5-105(0.1) =90
Answers above will be substituted in the following equation;
WQI=0.22*(0)+0.19*(87.71)+0.16*(8.94)+0.15*(90)+0.16*(77.31)+0.12*(87.87)=
54.50
Calculations for Sungai Pulai;
SIPH=-242+95.5*(6.7)-6.67*(6.7)2=98.43
SIBOD=100.4-4.23*(5) =79.25
SICOD=103*e(-0.0157*134)-0.04*(134)=7.20
SISS=97.5*e(-0.00676*11)+0.05*(11)=91.06
SIDO=0
SIAN=100.5-105(0.1) =90
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Answers above will be substituted in the following equation;
WQI=0.22*0+0.19*79.25+0.16*7.20+0.15*90+0.16*91.06+0.12*98.43= 56.09
According to the Interim National River Water Quality Standards of Malaysia,
the results for Tanjung Bin and Sungai Pulai fall in class III (51.9-76.5) of the Water
Quality Standards (WQI); which has its interpretation as “Extensive treatment required,
Fishery III-common, of economic value and tolerant species livestock drinking”. It can
be deduced from this classification that the water quality of the two sections of the river
is not so good; however there will be an attempt at conserving it, by giving priority to
the higher quality section (Figure 4.8). This data layer was converted to raster format
and reclassified by identifying the high quality portions of the river as most suitable for
conservation and the lower quality sections were identified to be less suitable. The
conversion and reclassification were performed using the spatial analyst function of GIS
(ArcGIS 9.0).
Figure 4.8: Water quality (reclassified)
118
As can be seen from the above map, the ‘low’ value depicts a lower water quality
section of the river, having a Water Quality Index (WQI) value of 54.50. This could be
attributed to its location close to Port of Tanjung Pelepas (PTP), whose development is
said to have some ecological effects on the integrity of Sungai Pulai estuarine area and
the shoreline (MPMJ, 1999). Therefore this part of the river has lesser conservation
value. The ‘high’ value section on the other hand, portrays a higher quality when
compared with the area around Port of Tanjung Pelepas (PTP). This section of the river
has a WQI value of 56.09, which is more suitable for conservation.
4.1.1.7 Conversion of data layers
Any shapefile, coverage, or geodatabase feature class containing point, line, or
polygon features can be converted to a raster dataset. Here all the data layers were
converted to raster format (features to raster). The output cell size was determined by the
size of each pixel in the output raster dataset. This tool always uses the cell center to
decide the value of a raster pixel (Figure 4.9).
Figure 4.9: Spatial analyst (Features to Raster)
119
4.2.1.8 Reclassification of data layers
The reclassification function was used to change cell values to alternative values.
This function is designed to allow one to easily change many values on an input raster to
desired, specified, or alternative values. All reclassification methods are applied to each
cell within a zone. That is, when applying an alternative value to an existing value, all
the reclassification methods apply the alternative value to each cell of the original zone.
No reclassification method applies alternative values to only a portion of an input zone.
This function was applied to all the data layers with the support of spatial analyst of
ArcGIS 9.0 (Figure 4.10).
Figure 4.10: Spatial analyst (Reclassify)
4.2.2 Conservation scenarios: various conservation maps were produced; these maps
are generated by altering the criterion weights, such that each map represents the best
solution to a decision problem. Each conservation criteria was considered to be the most
important in each of the scenarios. The idea behind this is to see how each conservation
120
criteria plays a role in the conservation process (Figure 4.12). Data layers were
compared using the Boolean overlay approach. This was performed using raster
calculator, with pair wise comparison result as input.
4.2.2.1 Raster calculations of the data layers
The Boolean overlay comparison was done with the aid of Raster Calculator.
Raster calculator is a Spatial Analyst function that provides a tool for performing
multiple tasks. It allows one to perform mathematical calculations using operators and
functions, set up selection queries, or type in Map Algebra syntax. The calculator is
located on the Spatial Analyst toolbar drop down menu. It uses both "operators" and
"functions" to perform tasks. Map algebra operators work with one or more inputs to
develop new values, they are generally the same operators found on scientific
calculators. The operators used most often are arithmetic, relational, Boolean, and
logical. Below is an illustration of Raster calculations for the first scenario. In this
scenario priority was given to tree age class (Figure 4.11).
Figure 4.11: Raster calculations
121
Same calculation was applied to all other criteria; by giving priority to each of
the criterions in different scenarios, just like it’s given to tree age class in the calculation
above. For clearer understanding of the criterions in the Raster calculator, below their
complete denotations;
Crclageclass – Tree age class
Crclharv – Harvesting schedule
Crclendf – Endangered fauna
Crclhabmrb – Habitat’s proximity to natural land cover
Crclhabitat – Size of habitat area
Crclwq – Water quality
Figure 4.12: Conservation model
122
Below are the conservation scenarios, which include six scenarios. They are generated
by giving more importance to each of the scenarios in six different evaluations in the
following order; Tree age class, harvesting season, size of endangered fauna, habitat’s
proximity to natural land cover, size of habitat area and water quality. The purpose of the
criterion weighting is to express the importance of each criterion relative to other
criteria.
Figure 4.13: scenario 1 (Conservation)
In this scenario tree age class was
given priority, thus it carried a
higher weight than other criteria.
The ranking was given in the
following order; tree age class
(0.42), harvesting season (0.17),
endangered fauna (0.14), habitat’s
proximity to natural land use (0.11),
habitat area (0.09) and water
quality (0.07). The ‘not suitable’ category are areas with the least biodiversity of natural
resources. Therefore, these areas can accommodate tourism infrastructures such as
lowrise/ low density chalets, boardwalks, camping grounds, public convinience, look
out areas and bird watching. Even though, this category represents the least sensitive
areas; however, this activities should still be carried out with caution.
C1: TREE AGE CLASS
11476.4% 4420
24.6%
526129.2%
717539.9%
Not suitable Less suitable Suitable Most suitable
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The ‘less suitable’ category on the other hand, signifies areas with low extent of
biodiversity, in other words they are low sensitive areas. These category can therefore
be allowed for tourism activities such as boating, boardwalks, look out areas and bird
watching. However, with a more strict control than the ‘not suitable’ category. The ‘not
suitable’ and ‘less suitable’ category can be used for some form of resource
development, such as cultivation of trees and fishing activites; though, it has to comply
with the management plan of the authority in charge.
Conversely, the ‘suitable’ category depicts high sensitivity areas, in other words
these are high biodiversity areas. These areas need to be conserved to ensure that
valuable wetland species continue to persist for an indefinite period of time. However,
this area will be allowed for research and educational activities. It will be provided with
look out areas for tourists, though with strictest control measures on the time of access
and limited number of admittance. Access to this areas will be subjected to certain
guidelines, this is to ensure that this highly sensitive locality is not impacted in any way.
The ‘most suitable’ category are areas that depict the highest value of wetland
resources, in other words they are locations with the highest level of biodiversity in the
wetland area. This areas will therefore be restricted from any form of tourism
development, so as to ensure certain amount of the wetlands endowment are completely
protected from tourism activities. Though, this area will be allowed access for research
and educational purposes. But there should be limit on the time of access, number of
people and days of access. This should further be reinforced by more guidelines, to
ensure that the research and educational activities do not cause any harm to this
extremely sensitive environment.
124
Figure 4.14: Scenario 2 (Conservation)
This scenario gave priority to
harvesting season, thus accorded a
higher weight. It was ranked in the
following manner; tree age class
(0.17), harvesting season (0.42),
endangered fauna (0.14), habitat’s
proximity to natural land use (0.11),
habitat area (0.09) and water quality
(0.07). The ‘not suitable’ category
are areas with the least biodiversity
of natural resources. Therefore, these areas can accommodate tourism infrastructures
such as lowrise/ low density chalets, boardwalks, camping grounds, public convinience,
look out areas and bird watching. Even though, this category represents the least sensitive
areas; however, this activities should still be carried out with caution. The ‘less suitable’
category on the other hand, signifies areas with low extent of biodiversity, in other words
they are low sensitive areas. These category can therefore be allowed for tourism
activities such as boating, boardwalks, look out areas and bird watching. However, with a
more strict control than the ‘not suitable’ category. The ‘not suitable’ and ‘less suitable’
category can be used for some form of resource development, such as cultivation of trees
and fishing activites; though, it has to comply with the management plan of the authority
in charge. Conversely, the ‘suitable’ category depicts high sensitivity areas, in other
words these are high biodiversity areas. These areas need to be conserved to ensure that
valuable wetland species continue to persist for an indefinite period of time. However,
this area will be allowed for research and educational activities. It will be provided with
C2: HARVESTING SEASON
1480.8%
16969.4%
532029.6%10839
60.2%
Not suitable Less suitable Suitable Most suitable
125
look out areas for tourists, though with strictest control measures on the time of access
and limited number of admittance. Access to this areas will be subjected to certain
guidelines, this is to ensure that this highly sensitive locality is not impacted in any way.
The ‘most suitable’ category are areas that depict the highest value of wetland
resources, in other words they are locations with the highest level of biodiversity in the
wetland area. This areas will therefore be restricted from any form of tourism
development, so as to ensure certain amount of the wetlands endowment are completely
protected from tourism activities. Though, this area will be allowed access for research
and educational purposes. But there should be limit on the time of access, number of
people and days of access. This should further be reinforced by more guidelines, to
ensure that the research and educational activities do not cause any harm to this
extremely sensitive environment.
Figure 4.15: Scenario 3 (Conservation)
In this scenario endangered fauna
was given priority, thus it carried a
higher weight than the other criteria.
The ranking was given in the
following order; tree age class (0.17),
harvesting season (0.14), endangered
fauna (0.42), habitat’s proximity to
natural land use (0.11), habitat area
(0.09) and water quality (0.07). The
‘not suitable’ category are areas with
C3: ENDANGERED FAUNA
290.2%
387921.5%
618434.3%
791143.9%
Not suitable Less suitable Suitable Most suitable
126
the least biodiversity of natural resources. Therefore, these areas can accommodate
tourism infrastructures such as lowrise/ low density chalets, boardwalks, camping
grounds, public convinience, look out areas and bird watching. Even though, this
category represents the least sensitive areas; however, this activities should still be
carried out with caution.
The ‘less suitable’ category on the other hand, signifies areas with low extent of
biodiversity, in other words they are low sensitive areas. These category can therefore be
allowed for tourism activities such as boating, boardwalks, look out areas and bird
watching. However, with a more strict control than the ‘not suitable’ category. The ‘not
suitable’ and ‘less suitable’ category can be used for some form of resource development,
such as cultivation of trees and fishing activites; though, it has to comply with the
management plan of the authority in charge.
Conversely, the ‘suitable’ category depicts high sensitivity areas, in other words
these are high biodiversity areas. These areas need to be conserved to ensure that
valuable wetland species continue to persist for an indefinite period of time. However,
this area will be allowed for research and educational activities. It will be provided with
look out areas for tourists, though with strictest control measures on the time of access
and limited number of admittance. Access to this areas will be subjected to certain
guidelines, this is to ensure that this highly sensitive locality is not impacted in any way.
The ‘most suitable’ category are areas that depict the highest value of wetland
resources, in other words they are locations with the highest level of biodiversity in the
wetland area. This areas will therefore be restricted from any form of tourism
development, so as to ensure certain amount of the wetlands endowment are completely
protected from tourism activities. Though, this area will be allowed access for research
and educational purposes. But there should be limit on the time of access, number of
127
people and days of access. This should further be reinforced by more guidelines, to
ensure that the research and educational activities do not cause any harm to this
extremely sensitive environment.
Figure 4.16: Scenario 4 (Conservation)
Here habitat close to natural land
use/ land cover was prioritized. It
was given higher weight than other
criteria and ranked as follows; tree
age class (0.17), harvesting season
(0.14), endangered fauna (0.11),
habitat’s proximity to natural land
use (0.42), habitat area (0.09) and
water quality (0.07).
The ‘not suitable’ category are areas with the least biodiversity of natural resources.
Therefore, these areas can accommodate tourism infrastructures such as lowrise/ low
density chalets, boardwalks, camping grounds, public convinience, look out areas and
bird watching. Even though, this category represents the least sensitive areas; however,
this activities should still be carried out with caution.
C4: HABITAT'S PROXIMITY TO NATURAL LAND COVER
290.2%
260714.5%
469726.1%
1067059.3%
Not suitable Less suitable Suitable Most suitable
128
The ‘less suitable’ category on the other hand, signifies areas with low extent of
biodiversity, in other words they are low sensitive areas. These category can therefore
be allowed for tourism activities such as boating, boardwalks, look out areas and bird
watching. However, with a more strict control than the ‘not suitable’ category. The ‘not
suitable’ and ‘less suitable’ category can be used for some form of resource
development, such as cultivation of trees and fishing activites; though, it has to comply
with the management plan of the authority in charge.
Conversely, the ‘suitable’ category depicts high sensitivity areas, in other words
these are high biodiversity areas. These areas need to be conserved to ensure that
valuable wetland species continue to persist for an indefinite period of time. However,
this area will be allowed for research and educational activities. It will be provided with
look out areas for tourists, though with strictest control measures on the time of access
and limited number of admittance. Access to this areas will be subjected to certain
guidelines, this is to ensure that this highly sensitive locality is not impacted in any way.
The ‘most suitable’ category are areas that depict the highest value of wetland
resources, in other words they are locations with the highest level of biodiversity in the
wetland area. This areas will therefore be restricted from any form of tourism
development, so as to ensure certain amount of the wetlands endowment are completely
protected from tourism activities. Though, this area will be allowed access for research
and educational purposes. But there should be limit on the time of access, number of
people and days of access. This should further be reinforced by more guidelines, to
ensure that the research and educational activities do not cause any harm to this
extremely sensitive environment
129
Figure 4.17: Scenario 5 (Conservation)
This scenario gave priority to habitat
area, thus accorded a higher weight.
It was ranked in the following
manner; tree age class (0.17),
harvesting season (0.14), endangered
fauna (0.11), habitat’s proximity to
natural land use (0.09), habitat area
(0.42) and water quality (0.07). The
‘not suitable’ category are areas with
the least biodiversity of natural
resources. Therefore, these areas can accommodate tourism infrastructures such as
lowrise/ low density chalets, boardwalks, camping grounds, public convinience, look out
areas and bird watching. Even though, this category represents the least sensitive areas;
however, this activities should still be carried out with caution. The ‘less suitable’
category on the other hand, signifies areas with low extent of biodiversity, in other
words they are low sensitive areas. These category can therefore be allowed for tourism
activities such as boating, boardwalks, look out areas and bird watching. However, with
a more strict control than the ‘not suitable’ category. The ‘not suitable’ and ‘less
suitable’ category can be used for some form of resource development, such as
cultivation of trees and fishing activites; though, it has to comply with the management
plan of the authority in charge.
Conversely, the ‘suitable’ category depicts high sensitivity areas, in other words
these are high biodiversity areas. These areas need to be conserved to ensure that valuable
C5: HABITAT AREA
209411.6%
734740.8%
519528.9%
336718.7% Not suitable
Less suitable Suitable Most suitable
130
wetland species continue to persist for an indefinite period of time. However, this area
will be allowed for research and educational activities. It will be provided with look out
areas for tourists, though with strictest control measures on the time of access and limited
number of admittance. Access to this areas will be subjected to certain guidelines, this is
to ensure that this highly sensitive locality is not impacted in any way.
The ‘most suitable’ category are areas that depict the highest value of wetland
resources, in other words they are locations with the highest level of biodiversity in the
wetland area. This areas will therefore be restricted from any form of tourism
development, so as to ensure certain amount of the wetlands endowment are completely
protected from tourism activities. Though, this area will be allowed access for research
and educational purposes. But there should be limit on the time of access, number of
people and days of access. This should further be reinforced by more guidelines, to ensure
that the research and educational activities do not cause any harm to this extremely
sensitive environment.
Figure 4.18: Scenario 6 (Conservation)
Priority was given to water quality
in this case. Therefore it was ranked
higher; the weights are given as
follows; tree age class (0.17),
harvesting season (0.14),
endangered fauna (0.11), habitat’s
proximity to natural land use (0.09),
C6: WATER QUALITY
17079.5%
527529.3%
1044358.0%
5783.2% Not suitable
Less suitable Suitable Most suitable
131
habitat area (0.07) and water quality (0.42). The ‘not suitable’ category are areas with the
least biodiversity of natural resources. Therefore, these areas can accommodate tourism
infrastructures such as lowrise/ low density chalets, boardwalks, camping grounds,
public convinience, look out areas and bird watching. Even though, this category
represents the least sensitive areas; however, this activities should still be carried out
with caution. The ‘less suitable’ category on the other hand, signifies areas with low
extent of biodiversity, in other words they are low sensitive areas. These category can
therefore be allowed for tourism activities such as boardwalks, look out areas and bird
watching. However, with a more strict control than the ‘not suitable’ category. The ‘not
suitable’ and ‘less suitable’ category can be used for some form of resource
development, such as cultivation of trees; though, it has to comply with the management
plan of the authority in charge.
Conversely, the ‘suitable’ category depicts high sensitivity areas, in other words
these are high biodiversity areas. These areas need to be conserved to ensure that
valuable wetland species continue to persist for an indefinite period of time. However,
this area will be allowed for research and educational activities. It will be provided with
look out areas for tourists, though with strictest control measures on the time of access
and limited number of admittance. Access to this areas will be subjected to certain
guidelines, this is to ensure that this highly sensitive locality is not impacted in any way.
The ‘most suitable’ category are areas that depict the highest value of wetland
resources, in other words they are locations with the highest level of biodiversity in the
wetland area. This areas will therefore be restricted from any form of tourism
development, so as to ensure certain amount of the wetlands endowment are completely
protected from tourism activities. Though, this area will be allowed access for research
and educational purposes. But there should be limit on the time of access, number of
people and days of access. This should further be reinforced by more guidelines, to
132
ensure that the research and educational activities do not cause any harm to this
extremely sensitive environment.
4.2.2.2 Comparison of conservation scenarios
Conservation scenarios were generated above, with each scenario representing the
best solution to decision problem, according to the assessment perspective adopted. Map
scenarios reflecting the opinion of different experts or stakeholders involved were then
compared in the following section in order to highlight the robustness of the solution and
support decision making (Figure 4.19).
Figure 4.19: Comparison of conservation scenarios
133
Table 4.2: Comparison of conservation scenarios (%)
Suitability categories
Criteria
Not suitable Less suitable Suitable Most
suitable
C1:Tree age class 6.4 24.6 29.2 39.9
C2:Harvesting season 0.8 19.4 34.3 45.5
C3:Endangered fauna 0.2 21.5 34.3 43.9
C4:Habitat’s proximity to natural land cover
0.2 24.4 30.8 44.6
C5:Habitat area 11.6 40.8 28.9 18.7
6:Water quality 9.5 29.3 58.0 3.2
It is revealed from the results above that the ‘not suitable’ category carries the
least percentage in most of the scenarios except in ‘C6’, which portrays the wetlands as
a protected area. Beside this, a lot more variations and parallels exist amongst the
scenarios under study. It can be seen that the ‘less suitable’ category of ‘C1’ and ‘C4’
carries similar percentage value, which is more safeguarded than ‘C3’ and ‘C2’ and less
protected than ‘C5’ and ‘C6’ in this category. On the other hand the ‘not suitable’
category of ‘C3’ and ‘C4’ portrays the same percentage values, which are more
protected than the rest of the scenarios in this category. On the contrary, the ‘most
suitable’ category of ‘C2’ and ‘C4’ carry a matching percentage value, which describes
them as the most rigid in that category. Conversely, ‘suitable’ category of ‘C2’ and ‘C3’
exhibit the same value. This depicts them as highly protected areas, thought not as
protected as ‘C6’ in the same category.
Though, ‘C2’ and ‘C4’ appear to have a relatively higher percentage in the ‘most
suitable’ category. However, considering the ‘not suitable’ category of these two
scenarios (C2 and C4), ‘C4’ has a relatively lower value compared to ‘C2’ and the rest
of the scenarios, except for ‘C3’ in this category. This explains that the 2 scenarios (C3
and C4) have the highest percentage land area falling in the category that should be
conserved i.e less suitable, suitable and most suitable than the entire scenarios. As such,
134
these scenarios will ensure more land area is protected. However, looking at the ‘most
suitable’ category, ‘C4’ exhibits a relatively higher value than ‘C3’; therefore it will be
adopted.
In the preferred conservation scenario (C4), the ‘most suitable’ category can be
spotted in Pulau Kukup, Tanjung Bin area, northeastern part of Sungai Pulai (Southwest
of Kampung Ulu Pulai) and several other small compartments all over the study site.
The ‘suitable’ category can be observed next to Kampung Sungai Dinor, Kampung
Senai, Kampung Belokok and Kampung Peradin to the east; this category is also
observed next to Kampung Sungai Muleh and Kampung Jeram Batu to the south. More
so, ‘suitable’ category can be noticed next to Kampung Sungai Belukang to the east,
higher quality river section (upper part) and a host of other locations in the wetlands
area. The ‘less suitable’ category however, can be observed at the tip of Tanjung Piai
(southernmost tip of mainland Asia), a relatively lower cluster of endangered fauna in
Tanjung Bin area, a few small compartments in Sungai Pulai area and lower quality
section of the river (lower part). The reason for the lower quality of the river in this
section can be attributed to its location close to Port Tanjung Pelapas (PTP), whose
development is said to have some ecological effects on the integrity of Sungai Pulai
estuarine area and the shoreline (MPMJ, 1999). The ‘not suitable’ category on the other
hand can be seen in minuscule locations in the wetlands area.
4.3 Wetlands Development
As mentioned in the introductory part of this chapter; one of the main tasks of
this study is to identify relatively low biodiversity areas that can be used for low impact
tourism and economic development. These are the areas that can be allowed for tourism
activities such as boardwalks, lookout areas, boating, camping grounds and low rise/ low
135
density chalets; these localities are characterized by a relatively low biodiversity of
natural resources.
Areas that can be used for resource development includes; mangrove trees that
have attained a high age period as decided by the management body, river locations that
depict a lower water quality, trees that fall within the present and subsequent harvesting
periods. Economic development here will help in improving the living conditions of the
local people by providing employment opportunities, thus improving their income. It
will also help in generating revenue to the government.
Two scenarios were generated from tourism development perspective and one
scenario from economic development perspective.
4.3.1 Tourism development
To determine tourism development areas in such a protected area, in order to
comply with South Johor Economic Region (SJER) objective which states that; the
protection zone should allow for low impact tourism activities such as boardwalks,
lookout areas, boating, camping grounds and low rise/ low density chalets
(Comprehensive Development Plan for SJER, 2006-2025). These areas are characterized
by a relatively low biodiversity of natural resources (Figure 4.20); thus, fulfilling
sustainable tourism planning definition which regard tourism as an activity which
involves management of all resources in such a way that economic, social and aesthetic
needs are fulfilled while maintaining cultural integrity, essential ecological processes,
biological diversity and life support systems; it involves the minimization of negative
impacts and the maximization of positive impacts of the environment it occurs.
136
Figure 4.20: Tourism development model
4.3.1.1 Habitat area
Habitat area coverage was used to identify smaller habitat patches and
categorized them as suitable areas for development. As all the data layers have been
converted to raster (feature to raster) in evaluating conservation areas above; habitat area
coverage was reclassified by categorizing relatively smaller habitat patches as the most
suitable for tourism development; as it will ensure less impact due to the small number
of species in such locations (Figure 4.21). The reclassification of habitat area coverage
was performed using the spatial analyst function of GIS (ArcGIS 9.0).
137
Figure 4.21: Habitat area (reclassified)
As revealed from the above figure, the ‘low’ value signifies larger size of habitat
patches, which will be protected from tourism development. This is to ensure that larger
habitat areas are rescued from human impact, thus these areas will be categorized as not
suitable for tourism development. The ‘high’ value on the other hand, portrays relatively
smaller habitat areas, which will be used for tourism development as it will ensure
minimum impact due to the smaller number of species in the those localities.
4.3.1.2 Threatened fauna
Threatened species data layer was used to identify clusters with less population
of such species, thus was identified as a suitable development area. Endangered species
layer was reclassified by categorizing smaller clusters of endangered fauna as suitable
138
and bigger clusters as not suitable. This is to ensure larger areas of such species are
restricted from tourism development, thereby ensuring their population continues to
persist in the near future (Figure 4.22). Endangered fauna’s reclassification was
performed using the spatial analyst function of GIS (ArcGIS 9.0).
Figure 4.22: Endangered fauna (reclassified)
As seen from the above figure, the ‘low’ value depicts localities with a larger
population of species that are vulnerable to human disturbances. Therefore these
locations will be restricted from tourism development, so as to ensure viable populations
of these species continue to persist in the near future. Conversely, the ‘high’ value
signifies locations with relatively smaller number of endangered fauna. These areas will
be allowed for tourism development as they will ensure a minimum impact in the
wetland area, due to the smaller population of endangered fauna in those areas.
139
4.3.1.3 Habitat’s proximity to natural land cover
Similarly the farther a natural land use/ land cover is to wetland area, the more it
is considered suitable for development. Therefore, multiple ring buffer of 20, 30 and 40
meters were performed around the habitat area coverage. It was reclassified by
identifying the most distant natural land cover to habitat area as most suitable for
development and the closest was categorized as not suitable. Because these areas are
more patronized by the some of the wetland fauna for their survival (Figure 4.23); the
reclassification of habitat proximity to natural land cover was performed using the
spatial analyst function of GIS (ArcGIS 9.0).
Figure 4.23: Habitat’s proximity to upland/ natural land cover (reclassified)
Enlarged area
140
A section of the above map (Figure 4.23) is enlarged in order to have a clearer
picture of natural land uses surrounding the wetland area (Figure 4.24). This is in order
to identify those areas that are most suitable for tourism development i.e relatively
farther upland areas from the wetlands and areas that are in close proximity to the
wetland areas, which are less suitable for development.
Figure 4.24: Habitat’s proximity to natural land cover (enlarged area)
As can be seen from the diagram above, the ‘low’ value depicts areas that are
close to the habitat/ wetlands area which are less suitable for tourism development.
These are locations that are most patronized by the wetland species because of their
close proximity to the habitat area. The ‘high’ value on the other hand represents natural
land use/ land cover that are far away from habitat/ wetlands area, which are said to be
most suitable for tourism development. This signifies natural areas that are farther from
wetlands as most suitable for development, because these areas are rarely used by the
wetland’s fauna.
141
4.3.1.4 Water quality
The higher the water quality of a river the greater its conservation value (MPMJ,
1999). Here, water quality coverage was employed, it was reclassified by identifying the
lower quality sections as suitable for tourism development and higher water quality
sections were classed as not suitable for tourism development (Figure 4.25). Water
quality reclassification was carried out using the spatial analyst function of GIS (ArcGIS
9.0).
Figure 4.25: Water quality (reclassified)
As can be seen from the above map, the ‘low’ value depicts a lower water quality
section of the river, having a Water Quality Index (WQI) value of 54.50. Therefore this
part of the river has higher tourism development value; it will be allowed for low impact
tourism activities such as, small scale fishing and boating activities. The ‘high’ value
142
section on the other hand, portrays a higher water quality. This section of the river has a
WQI value of 56.09, which is less suitable for development.
Figure 4.26: Scenario 1 (Tourism development)
In scenario one all tourism factors/
criteria (water quality, endangered
fauna, habitat area and habitat’s
proximity to natural land use/ land
cover) were scaled and ranked
equally, such that tourism
development will only be restricted
in the high biodiversity area; and
allowed in certain section of the
water area. The ‘not suitable’ category depicts areas with the highest biodiversity
concentrations. Therefore, these areas need a high level protection from tourism and
other activities, this can be achieved by putting in place severe guidelines and ensure
compliance. This will ensure a significant population of the wetlands species are not in
any way hampered by these activities. However these areas will be allowed for research
activities, but has to comply with the guidelines in place. The ‘less suitable’ category
on the other hand, are areas with high level of natural resources. These areas need to be
protected to ensure that valuable population of wetland species continues to persist.
Yet, these areas will be provided with look our area for the tourists, allow the use of
non-motorized boats, it will also be allowed for research and educational activities;
though with strictest control measures. This can be achieved by imposing guidelines
C1: TOURISM DEVELOPMENT
287516.0%
369420.5%
399122.2%
744341.3%
Not suitable Less suitable Suitable Most suitable
143
that will limit the number of people that can gain access to those areas and also set out
the time people can gain entrance; thereby ensuring the protection of such sensitive
areas.
Conversely, the ‘suitable’ category portrays relatively low areas of biodiversity.
These areas can be used for tourism activities such as boating, boardwalks, look out
areas and bird watching; however, in accordance with the code of practice. On the
contrary, the ‘most suitable’ category are areas depicting the least intensity of
biodiversity. As such, these areas can contain tourism infrastructures such as lowrise/
low density chalets, boardwalks, camping grounds, public convinience, look out areas
and bird watching. However, the placement and use of these facilities should be done
with great caution.
Figure 4.27: Scenario 2 (Tourism development)
In this case water quality was
scaled in such a manner that, it
restricts any kind of development in
its area.
Therefore this scenario restricts
development not only in the high
biodiversity, but also in the whole
water area. The ‘not suitable’
category depicts areas with the
highest biodiversity concentrations. Therefore, these areas need a high level protection
C2: TOURISM DEVELOPMENT
551930.7%
284215.8%
219912.2%
744341.3%
Not suitable Less suitable Suitable Most suitable
144
from tourism and other activities, this can be achieved by putting in place severe
guidelines and ensure compliance. This will ensure a significant population of the
wetlands species are not in any way hampered by these activities. However, these areas
will be allowed for research activities, but has to comply with the guidelines in place.
The ‘less suitable’ category on the other hand, are areas with high level of natural
resources. These areas need to be protected to ensure that valuable population of
wetland species continues to persist. Yet, these areas will be provided with look our area
for the tourists, it will also be allowed for research and educational activities; though
with strictest control measures. This can be achieved by imposing guidelines that will
limit the number of people that can gain access to those areas and also set out the time
people can gain entrance; thereby ensuring the protection of such sensitive areas.
Conversely, the ‘suitable’ category portrays relatively low areas of biodiversity.
These areas can be used for tourism activities such as boardwalks, look out areas and
bird watching; however, in accordance with the code of practice. On the contrary, the
‘most suitable’ category are areas depicting the least intensity of biodiversity. As such,
these areas can contain tourism infrastructures such as lowrise/ low density chalets,
boardwalks, camping grounds, public convinience, look out areas and bird watching.
However, the placement and use of these facilities should be done with great caution.
4.3.2 Economic development
To determine development area from the economic yield point of view based on
South Johor Economic Region (SJER) objective which asserts that; economic
development should be diversified by using existing and natural economic resources in a
sustainable manner. Based on this objective; mangrove trees that have attained a high
age period as decided by the management body, river locations that depict a lower water
quality, trees that fall within the present and subsequent harvesting periods, will be
identified as suitable areas for economic development (Figure 4.28).
145
Figure 4.28: Economic development model
4.3.2.1 Tree age class
Trees in the Johor mangroves are cultivated when they have reached a maturity
age of 20 years, while some are cultivated at the age of 15 years (MPMJ, 1999). The
rationale behind the cultivation of these internationally important wetlands is that; the
management authority/ government need to benefit from this natural endowment as will
yield huge revenue and provide employment opportunities. This is coupled with
problems as polluting water ways, which occurs when trees are decomposed as they
approach the limit of their life span.
Tree age class data layer, was reclassified by identifying higher age classes as
most suitable for development and lower tree age classes as not suitable for economic
development, as these trees need to be taken care of until they are matured (Figure 4.29).
The reclassification of tree age class coverage was performed using the spatial analyst
function of GIS (ArcGIS 9.0).
146
Figure 4.29: Tree age class (reclassified)
As seen from the above map, the ‘high’ value represents tree compartments with
relatively high ages, which fall within the age class that are ripe for cultivation as
outlined by the management authority. Therefore, these categories of tree classes are
more suitable for economic development. The ‘low’ value on the other hand, is tree
compartments falling in the low age classes, which are less suitable for development.
These trees need to be conserved so that they are fully matured by the time it’s their
cultivation period. These areas also include untouchable areas i.e areas that have been
reserved and managed as state parks. These locations are therefore classified as less
suitable for development.
4.3.2.2 Harvesting season
This includes permissible compartments for distinct seasons. The closest
compartment to harvesting season and those that fall within the present year harvesting
147
season will be the most suitable for development. Harvesting season’s coverage was
employed in order to identify compartments that fall within the present and subsequent
harvesting season. This data layer was reclassified by identifying forest compartments
that fall within the present and subsequent year harvesting season as most suitable and
those compartments that are recently cultivated were identified as not suitable for
economic development (Figure 4.30). Harvesting season’s reclassification was
performed using the spatial analyst function of GIS (ArcGIS 9.0).
Figure 4.30: Harvesting (reclassified)
As seen from the above figure, the ‘high’ value symbolizes trees that fall within
the present year harvesting schedule and subsequent year of harvesting. These tree
compartments are the most suitable for resource development. Conversely, the ‘low’
value signifies those tree compartments that are recently replanted. In other words, they
are tree compartments that need to be taken care of before it’s their harvesting period.
The ‘low’ value also includes untouchable areas i.e areas that have been reserved and
managed as state parks. Therefore, they are least suitable for economic development.
148
4.3.2.3 Water quality
The higher the water quality of a river the greater its conservation value (MPMJ,
1999). Therefore, lower river section of the wetlands was considered as developable
area; as it can yield huge revenue and provide employment from the fishing activities,
using water quality data layer as input. This data layer was reclassified by identifying
lower quality sections of the river as suitable for economic development and higher
quality sections was categorized as not suitable for economic development (Figure 4.31).
The reclassification of water quality was performed using the spatial analyst function of
GIS (ArcGIS 9.0).
Figure 4.31: Water quality
149
As revealed from the above map, the ‘low’ value depicts a lower water quality
section of the river, having a WQI value of 54.50. This could be attributed to its location
close to Port of Tanjung Pelepas (PTP), whose development is said to have some
ecological effects on the integrity of Sungai Pulai estuarine area and the shoreline (MPMJ,
1999). Therefore this part of the river will be more suitable for economic development; as
it will be allowed for small scale resource development such as fishing activities. The
‘high’ value section on the other hand, portrays a higher water quality when compared
with the area close to Port of Tanjung Pelepas (PTP). This section of the river has a WQI
value of 56.09, which will be restricted from any form of resource development.
Figure 4.32: Scenario 3 (Economic development)
This scenario allows for the
development of certain forest
compartments and water area in
the wetlands. This is geared
towards economic and
employment benefit to both the
local people and the authorities in
general. The ‘not suitable’
category includes high water
quality area and forest compartments that will not yield immediate economic benefit.
These are recently replanted trees and tree that fall in the distant year harvesting
season. These trees require adequate protection as they need to be nurtured for certain
period of time after which they can be harvested for economic gains. The higher river
C3: Economic development
1123862.0%
550030.3%
8744.8%
5252.9% Not suitable
Less suitable Suitable Most suitable
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section on the other hand is restricted from economic development (large scale fishing)
to ensure that quality of this part of the river is maintained, thus enhancing the water
quality in the long run. The ‘less suitable’ category on the other hand, includes lower
water quality section of the river; which can be used for fishing activities with however
some guidelines, so that the water quality is restored at the same time providing some
benefits. This category also includes tree compartments that are close to their
harvesting season and trees that are around their maturity age. The ‘suitable’ category
entails trees that have just reached or about to reach their maturity period and tree
compartments next to harvesting season, therefore this category can be cultivated to
some extent, but preferable they should be allowed to reach their harvesting period and
attain full prime of life.
Conversely, the ‘most suitable’ category are the tree compartments that have
reached their maturity age and tree compartments that fall within the present year
harvesting schedule. This tree compartments are therefore the most suitable for
cultivation, they should be cultivated in line with the guidelines provided by the
authority in charge; some of the guidelines includes the kind of materials to be used for
cutting the trees, care to the surrounding forest during the cutting process so as to
minimize impact, compliance with the period within which the trees should be re-
planted and many other guidelines deemed by the management authority.
4.3.3 Comparison of development scenarios
Development scenarios were generated above, with each scenario representing the
best solution to decision problem, according to the assessment perspective adopted.
These scenarios were then compared in the following section in order to highlight the
robustness of the solution and support decision making (Figure 4.33).
151
Tourism development Economic development
Figure 4.33: Comparison of development scenarios
Table 4.3: Comparison of development scenarios (%)
Not suitable Less suitable Suitable Most suitable
Scenario 1 16.0 20.5 22.2 41.3
Scenario 2 30.7 15.8 12.2 41.3
Scenario 3 62.0 30.3 4.8 2.9
Though one of the scenarios is not meant for the same kind of development, but
they are all aimed at certain form of exploitation in the wetland area. As revealed from
the table above, scenario one and two has the same value in the ‘most suitable’ category.
However, a wide margin exists in the ‘not suitable’ category of the two scenarios. This
could be explained by the restriction of water activities in scenario two. Therefore
scenario one seems more suited for tourism development as it has a greater land area that
could be developed for tourism purposes.
In the chosen scenario for tourism development (C1), the ‘most suitable’ areas
are located in Tanjung Piai area; this could be attributed to a relatively smaller habitat
152
area. A fragment of this category can also be found around the smallest cluster of
endangered fauna in Tanjung Bin area; besides they can be observed next to Kampung
Senai, Kampung Belokok Kampung Peradin and Kampung Sungai Punai to the east.
Most suitable category is also located next to Kampung Jeran Batu to the south.
‘Suitable’ category on the other hand, can be seen in Pulau Kukup, innermost part of
Tanjung Bin area, lower water quality section, low-medium cluster of endangered fauna
also in Tanjung Bin area; the suitable category can also be spotted in an area south of
Kampung Sungai Muleh and a host of minuscule patches around the wetlands.
Conversely, the ‘less suitable’ category can be spotted around the central part of Sungai
Pulai area, high-medium cluster of endangered fauna in Tanjung Bin area and higher
water quality section. The ‘not suitable’ category can be seen in tiny locations
surrounding the tributaries of the river and at the bottom of Sungai Pulai area, this could
be linked to a larger habitat area in that location.
Scenario 3 (economic development) on the other hand, has its low percentage
value in the ‘suitable’ and ‘most suitable’ category; while ‘not suitable’ category carries
the highest percentage, followed by the ‘less suitable’ category. These represent the area
as highly protected, especially concerning cultivation of the wetland environment for
economic yield. This scenario is similar with scenario one, in that it allows for the
development of low biodiversity and low quality portion of the river; however, the
development here is for economic yield.
For the economic development scenario (C3) it’s ‘most suitable’ and ‘suitable’
categories are located in Sungai Pulai area and small areas in Tanjung Piai. Conversely,
the ‘less suitable’ category can be seen in the lower quality river section, Tanjung Bin
area and a multitude of other forest compartments in Sungai Pulai area. The ‘not
suitable’ category however, can be observed in the high quality water section of the
river, Pulau Kukup and a group of other forest compartments in Tanjung Piai and
Sungai Pulai area fringing the nearby villages.
153
4.4 Comparison of conservation and development scenarios
This section aims to compare the selected, conservation, tourism and resource
development scenarios based on the achievement of the study’s objectives and location’s
advantage to the surrounding villages (Figure 4.34). The preferred scenario here will be
the one to be used for policy making by the authority concerned.
Conservation (C1) Tourism development (C2) Economic development (C3)
Figure 4.34: Comparison of Conservation and development scenarios
Considering the fact that the wetlands are fringed by a number of villages, most
of which are situated on the western side of Sungai Pulai, Tanjung Bin and Tanjung Piai;
with a few of these communities located to the north of Sungai Pulai. These villages
have high dependence on mangrove resources namely fisheries and wetlands plantation
activities. Tourism is the other income earning activity at these villages; tourism
facilities available in some of these communities includes, home stay amenities,
historical sites, seafood restaurants, boat ride and fishing activities. However, some of
these communities mainly engage in farming activity as the hinterlands of the study sites
have extensive farmlands (WIMP, 2001).
154
Looking at the three scenarios namely; conservation, tourism and economic
development scenarios, hereafter called C1, C2 and C3 respectively. ‘C3’ focused
mainly on economic development, by merely considering criterion variables that can
yield some financial gains; and also identified incompatible areas for economic
development. This scenario (C3) however did not give attention towards identifying
areas that can be used for tourism development, hence did not achieve this objective of
the study. In addition, this category has its ‘not suitable’ category bordering the
surrounding villages and its ‘suitable’ category at the inner part of the wetlands. This
scenario (C3) is therefore not favorable for the village people as they can not have
benefit from any of their next door land area.
Conversely ‘C2’ concentrated largely on tourism development, by taking into
account criterion variables that can be used for some form of tourism activities and also
identified incompatible areas for tourism development. However, this scenario (C2) did
not consider in any way locating wetland areas that can be cultivated for some form of
economic gains, thus did not achieve this objective of the study. Therefore the ‘most
suitable’ category of this scenario bordering these communities can only be used for
tourism purposes; without yielding much economic benefits from the forest resources
development of which the local people have high dependence.
On the contrary, ‘C1’ whose main aim was to produce conservation areas in the
wetlands i.e areas that should not be used for tourism and economic development
purposes. This scenario (C1) however went ahead to identify areas that can be used for
different levels of tourism activities in the wetlands; it also identified areas that can be
used for economic development, which can yield some financial gains to the local
people and the authorities in general. This scenario (C1) therefore has achieved the main
objectives of the study by identifying tourism and economic development areas, as well
as areas that should not be disturbed by these activities. In addition, this scenario (C1)
has some of its ‘suitable’ category located next to the surrounding villages which can
155
support some tourism activities, this category can be provided with lookout areas. Also,
a greater area of the ‘less suitable’ and ‘not suitable’ categories of this scenario (C1) is
located next to the surrounding communities.
The ‘less suitable’ category can be used for boat ride and fishing activities, these
areas can also be used certain form of cultivation of wetlands forest, thus boosting the
economic level of the local people and supporting tourism activities. The ‘not suitable’
category however can accommodate tourism infrastructures such as low density chalets,
boardwalks, camping grounds and lookout areas; however with the limited land area of
this category, this activity will be supplemented by the tourism facilities that are already
in place in the surrounding villages.
The ‘not suitable’ category can also be used for cultivation purposes in order to
generate income, this will be augmented by the agricultural activities in the hinterlands
due to the limited land area for this activity; as the hinterlands have extensive land for
agricultural activities of which the local people highly depend on. Areas of this scenario
under the ‘most suitable’ category i.e conservation areas, should be provided with green
belts and complete fencing in order to prevent them from intrusion by the local people
and the tourists; as well as to safeguard them from impacts of the surrounding
developments.
Looking at the benefits of this scenario (C1) and its location advantage to the
surrounding communities, it will therefore be adopted. Below is the schematic
description of the kind of activities that can be allowed in the different areas of the
Ramsar sites (Figure 4.35).
156
Figure 4.35: Schematic description of activities
Research & educational activities.
Boardwalks, lookout areas, bird watching, research & educational activities. This area could further be cultivated for certain form of economic gains.
Lookout areas, research and educational activities.
Boating, low density homestay facilitites boardwalks, lookout areas, bird watching, research and educational activities. These areas could also be cultivated for economic yields.
Boardwalks, low density chalets, lookout areas, bird watching, research and educational activities. These areas could also be cultivated for economic yields.
CHAPTER 5
CONCLUSION AND FUTURE RESEARCH
5.1 Conclusion
This paper presents a methodological approach based on a GIS and multi criteria
evaluation to perform an ecological assessment of the wetland ecosystems, and to
consequently support the identification of conservation and development areas in the
wetlands environment. The main purpose of the study was to identify conservation and
compatible areas for tourism development in Johor (State) Ramsar sites. In other words
the study aimed to address the conservation principle of sustainable tourism planning.
The idea of sustainable tourism, which evolved from the World Summit in Rio
de Janeiro in 1992 has changed people’s notion about tourism. This form of tourism has
been able to contribute to development which is economically, ecologically and socially
sustainable; because it has proved to have less impact on natural resources and the
environment than most other industries. Sustainable tourism provides an economic
incentive to conserve natural environments and habitats, which might otherwise be
allocated to more environmentally damaging land, uses, thereby, helping to maintain
bio-diversity. The development of a sustainable tourism industry in wetland areas offers
158
numerous opportunities; such as those for nature conservation which, given the
increasing interest in high quality natural and cultural experiences, can help to reverse
the decline of destructions caused to these destinations.
The strength of sustainable tourism is further enhanced by Geographic
Information System (GIS); GIS have proved beneficial for supporting decision-making
and planning for sustainable tourism; as tourism is an activity which strongly implies the
geographical dimension and GIS is a technology specifically developed for the
management and study of spatial phenomena. Moreover, tourism is a complex
phenomenon involving besides its spatial dimension, social, economic and
environmental implications. It involves tourists and locals in an interactive way; it
generates income, which in many destinations is the major source; and it depends on the
use of the natural resources and the quality of the environment. GIS has demonstrated to
be a technology capable of integrating various data sets both qualitative and quantitative
in a single system. This is even more important within the context of sustainable
development the implementation of which regards the evaluation of economic, social
and environmental parameters against pre-established targets.
Besides, the integration of environmental, social and economic parameters in a
single system, GIS has proved itself as an integrating technology capable of working
along with other systems such as Decision Support System (DSS) which further
facilitate and offer more tools to sustainable tourism planning and decision-making. The
development of a GIS based decision support system for sustainable tourism planning
and management have shown to provide a significant contribution in highlighting
implementational aspects and offer the framework and the tools for evaluating,
monitoring and planning sustainable tourism. Such a system includes criteria and
indicators for their evaluation based on established policy goals and possibly weights to
reflect relative importance of the parameters examined. With particular reference to
indicators, GIS has contributed not only to their definition but also to their measurement.
159
GIS distinctive ability, to generate new information from the existing datasets and thus
offering added value information, has lead to the identification of sustainability
indicators which otherwise would not have been possible to be defined and measured.
Areas that can be used for tourism development have been determined in the
study, which are mostly areas of low biodiversity and some high biodiversity areas.
Tourism activities in these areas will ensure a viable one, considering the variety of
regulations and guidelines imposed for carrying out these activities i.e the more the
biodiversity level of an area in the wetlands, the more strict regulation for the execution
of tourism activities. Economic development areas have also been ascertained, by
identifying forest compartments and water areas that can be used by the local people and
the authorities for economic purposes. Hence, boosting their economic level and
providing quality employment, at the same time minimizing the impact on the natural
environment. In contrast, conservation areas were established, by identifying areas that
exhibit relatively high level of natural resources. These are areas that are highly sensitive
to human interference; therefore they are only permitted for research and educational
activities. This will ensure tourism activities do not cause any harm to the sensitive
wetlands environment.
In spite of the potentials of GIS, it has shown to be limited in performing an
effective spatial analysis by the use of specific techniques. Even though, GIS has been
used in the spatial problem definition, yet it has failed to support the ultimate and most
important phase of the general decision-making process concerning prioritizing the
alternatives. To achieve this requirement, other evaluation techniques instead of
optimization or cost benefit analysis were employed; undoubtedly, this is based on Multi
criteria Decision Model (MCDM).
160
Multi Criteria Decision Model (MCDM), which is a form of Decision Support
System (DSS) have demonstrated to a wide extent to be a valuable decision tool in the
conservation and development of wetlands environment. As planning requires a multi-
objective approach that leads to well conceived and acceptable management alternatives
and expands the ability to make decisions in complex natural resource management
settings. Furthermore, natural resource planning requires analytical methods that
examine tradeoffs, consider multiple political, economic, environmental, social
dimensions and incorporate these realities in an optimizing framework. MCDM
techniques have demonstrated to be well suited for these tasks.
MCDM techniques have emerged as major approaches to solving wetlands
resource management problems and integrating the environmental, social, and economic
values and preferences of stakeholders while overcoming the difficulties in monetizing
intrinsically non-monetary attributes. Quantifying the value of ecosystem services in a
non-monetary manner is a key element in MCDM. Multi criteria evaluation techniques
have shown to support a solution of a decision problem by evaluating possible
alternatives from different perspectives. Pairwise comparison method has demonstrated
to be the most suitable MCDM technique for this study, as it allows for the comparison
of two criteria at a time. This technique has also proved to be more precise and exhibit
strong theoretical foundations than other methods of MCDM. Results obtained in this
study, indicate that the integration of GIS and MCE is useful in providing analytical
tools for wetlands assessment and planning. This methodological framework showed to
be a feasible approach by incorporating different views for the evaluation of wetlands
biodiversity.
The generation and comparison of scenarios highlighted the critical issues of the
decision problem, i.e. the wetland ecosystems whose conservation and development
relevance is most sensitive to changes in the evaluation perspective. This represents an
161
important contribution to effective decision-making because it allows one to gradually
narrow down a problem.
5.2 Future research
Even though, the study has succeeded in dealing with the conservation principle
of sustainable tourism planning in wetlands ecosystem. A lot more need to be done on
the database aspect; there is need for an extensive development of GIS database for the
wetlands biodiversity. The database should contain the precise location, identity,
boundary and state of the flora and fauna of this natural environment. Further more, the
database should be able to include an approximate population of the wetlands fauna and
detailed land uses adjoining the wetlands area.
In addition, conservation of this unique ecosystem from tourism and other form
of developments is not the only aspect of sustainable tourism; it is only one principle of
sustainable tourism planning. There are many other principles that can be addressed by
Geographic Information System (GIS). Some of these principles include; tourism should
provide real opportunities to reduce poverty and create quality employment to the
community residents and stimulate regional development, though this principle has been
has been looked at to some extent in this study, still a lot more need to be done. Another
principle is that, tourism should minimize the pollution of air, water, land and generation
of waste by tourism enterprises and visitors; furthermore tourism should ensure that the
local or regional plans contain a set of development guidelines for the sustainable use of
natural resources and are consistent with the overall objectives of sustainable
development.
162
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