Stand-alone GIS Application for Wildlife Distribution and Habitat Suitability (Case Study: Javan Gibbon, Gunung Salak, West Java)

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STAND-ALONE GIS APPLICATION FOR WILDLIFE

DISTRIBUTION AND HABITAT SUITABILITY

(Case Study: Javan Gibbon, Gunung Salak, West Java)

WIM IKBAL NURSAL

GRADUATE PROGRAM

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STAND-ALONE GIS APPLICATION FOR

WILDLIFE DISTRIBUTION AND HABITAT SUITABILITY

(Case Study: Javan Gibbon, Gunung Salak, West Java)

WIM IKBAL NURSAL

A Thesis submitted for the degree of Master of Sciences of Bogor Agricultural University

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY

FOR NATURAL RESOUCES MANAGEMENT

GRADUATE SCHOOL

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STATEMENT

I, Wim Ikbal Nursal, here by stated that this thesis entitled:

Stand-alone GIS Application for Wildlife Distribution and Habitat Suitability (Case Study: Javan Gibbon, Gunung Salak, West Java)

Is completed based on my original work during the period of June 2005 to December 2006 and that has not been published before. The content of the thesis has been examined by the advising committee and external examiner.

Bogor, February 2007

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ACKNOWLEDGEMENT

I would like to express my grateful to Allah the Merciful for His blessing and being the Closest Companion, so far that I could reach the final phase of thesis completion. My great gratitude is also addressed to:

• Dr. Lilik Budi Prasetyo and Ir. Idung Risdiyanto, M.Sc, as my supervisor and co-supervisor who have been supporting and giving me advice and insight either for the current time, that is thesis completion, and the future time.

• Dr Gatot Haryo Pramono as external examiner for giving excellent inputs.

• Dr Tania June as Program Coordinator of Master of Science in Information Technology (MIT) for Natural Resources Management – Programme who has been supporting my education progress and thesis completion.

• MIT’s crews including Uma, Devi and Bambang who have been helping me much in preparing the academic administration.

• Rufford Small Grant committee that provided fund so that the field survey could possibly be conducted.

• National park rangers, Perhutani Staff in Cangkuang and Unit III Jawa Barat for authorization in conducting survey in park.

• My mother and father, brothers and sister, and the special one who always encourage, give trust, and keep patient waiting for me.

• Yudi and Tri in Environmental Spatial Analysis PPLH-IPB for ancillary data.

• Brili, Toto for ancillary distribution data, Wen-wen, and the whole of my classmates who directly and indirectly encouraged me, and all current MIT’s students who always care for the others, and for whom that I could not mention one by one here. Thank you very much.

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CURRICULUM VITAE

Wim Ikbal Nursal was born in Medan, 18th August 1976. In 1995 he finished studying in SMA Negeri 1 Medan, and then entered Bogor Agricultural Department, Faculty of Forestry, majoring on Forest Resources Conservation Department. More than three years since graduated from bachelor degree, he had been actived and worked in local NGOs (currently established in Bogor) which have interest in forest conservation research and environmental education, such as Yayasan Mitra Rhino, Lestari Hutan Indonesia, Biodiversity Conservation Indonesia, and Institute of Mangrove Research and Development.

In 2003, he started pursuing master degree in Master of Science in Information Technology for Natural Resources Management, Faculty of Mathematic and Natural Sciences, Bogor Agricultural University. The curriculum content has taken much his current interests in GIS and Remote Sensing, Modeling, and Information Technology especially in spatial based programming and Artificial Intelligence and their application for environment and conservation.

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ABSTRACT

Wim Ikbal Nursal (2007). Stand-alone GIS Application for Wildlife Distribution and Habitat Suitability (Case Study: Javan Gibbon, Gunung Salak, West Java). Under the supervision of Dr Lilik Budi Prasetyo, M.Sc and Ir. Idung Risdiyanto, M.Sc.

The synthesis of wildlife-habitat studies, multivariate habitat analysis, and wildlife mapping techniques is very promising to provide a basis for impact assessment and mitigation, conservation and monitoring of wildlife (Morrison et al., 1992). In contrast, case studies of the synthesis still have been rarely done in Indonesia. The need of it is obvious considering that Indonesia has a lot of protected areas. In this context, developing such GIS application which carrying the synthesis could be useful to support designing management plan in spatial basis. The case study in order to test the system performance is javan gibbon (Hylobates moloch) in Gunung Salak considering to its status as a critically endangered and keystone species for Gunung Halimun-Salak National Park.

The GIS Application is developed by using iterative and incremental approach. The development has two phases, that is initial phase or requirement analysis and construction phase. In the initial phase, the intended system and its needed functionalities were determined, whereas in the construction phase the functionalities were further analyzed, designed, and finally implemented. The system was tested using the spatial data which represents habitat factor of javan gibbon and the performance of the system was examined.

The concerned system, namely SUITSTAT, was succesfully built. It is equipped with data preparation and habitat suitability model functionalities. Including in data preparation is basic geoprocessing, ecogeographical variable generation, and species distribution mapping. The habitat suitability model follows decision rules based method, specifically Simple Additive Weighting, which comprises of weight calculation based on presence data, standardization, and score calculation. The suitability habitat information is available in spatial and chart format. Based on system assessment, most of processes were performed well. Some drawbacks are existed which addressed for future development, such as refinement of dissolve algorithm and data preparation functionalities.

The suitability model was constructed by ten ecogeographical variables, which are the area of primary and secondary forest, the area that containing 0 – 15% slope, the area that containing 15-45% slope, the area that containing more than 45% slope, the area that containing lowland and submontane forest, the distance to river, road, and non-habitat area. These ecogeographical variables were extracted from landcover, elevation-based forest ecosystem, rivers and roads data, and slope data. The model outcome shows that Mt. Salak area consists of 13.20% (17.53 km2), 26.25% (34.86 km2), 19.40% (25.77 km2), 4.16% (5.53 km2), and 20.17% (26.78 km2) for high-suitable, suitable, moderate suitable, less

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STATEMENT ... ii

ACKNOWLEDGEMENT ... iii

CURRICULUM VITAE ... iv

ABSTRACT... v

TABLE OF CONTENTS... vi

LIST OF TABLES ... viii

LIST OF FIGURES ... ix

LIST OF APPENDICES ... xii

I. INTRODUCTION ... 1

1.1. Background ... 1

1.1.1. Wildlife Conservation and Information ... 1

1.1.2. Javan Gibbon and Mount Salak ... 2

1.2. Objective ... 2

II. LITERATURE REVIEW ... 3

2.1. Wildlife-Habitat Relationship ... 3

2.1.1. Wildlife Habitat Definition ... 3

2.1.2. Wildlife Habitat Selection... 3

2.1.3. Wildlife Habitat Suitability Model... 5

2.2. Wildlife-Habitat Mapping ... 6

2.3. Spatial Multi Criteria Decision Analysis ... 7

2.3.1. Criteria... 7

2.3.2. Constraints... 7

2.3.3. Criteria Weighting... 8

2.3.4. Decision Rules ... 8

2.4. The Use of Principal Component Analysis in Ecological Studies ... 8

2.5. Javan Gibbon... 10

2.5.1. Taxonomy and Morphology... 10

2.5.2. Social System ... 11

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2.5.4. Dietary... 11

2.5.5. The Pattern of Spatial and Temporal Utilization ... 12

III. METHODOLOGY ... 13

3.1. System Development Methodology ... 13

3.1.1. System Development ... 13

3.1.2. Software ... 14

3.1.3. Hardware ... 14

3.2. Javan Gibbon Habitat Suitability Model... 15

3.2.1. Model Formulation... 15

3.2.2. Required Data... 17

3.2.3. Field Data Collection Method ... 18

3.2.4. Data Input Preparation ... 18

3.3. Time and Location ... 19

IV. RESULTS AND DISCUSSION... 20

4.1. System Development Result ... 20

4.1.1. Requirement Analysis ... 20

4.1.2. System Analysis and Design ... 26

4.1.3. System Testing ... 55

4.2. Javan Gibbon Distribution and Habitat Suitability Analysis in Mount Salak ... 63

4.2.1. Javan Gibbon Distribution Survey ... 64

4.2.2. Habitat Suitability Model ... 65

4.3. Discussion ... 68

4.3.1. System Performance... 68

4.3.2. Javan Gibbon Distribution and Habitat Suitability ... 75

V. CONCLUSION AND RECOMMENDATION ... 80

5.1. Conclusion... 80

5.2. Recommendation... 80

REFERENCES... 82

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LIST OF TABLES

Table 1. Used Software for Application Development... 14

Table 2. System Component Matrix ... 23

Table 3. Spatial Features and Spatial Properties Relationship... 24

Table 4. Several Types of Analysis to Spatial Features... 28

Table 5. SUITSTAT’s User Interface ... 44

Table 6. The Nature of Geographic Distribution of Javan Gibbon in Mt. Salak Based on 29 Observation Points ... 64

Table 7. Principal Component Loadings and Weight for Each Spatial Variable ... 65

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LIST OF FIGURES

Figure 1. Iterative and Incremental Approach (modified after Barroca et al.,

2000 in Zhao, 2002) ... 13

Figure 2. Theoretical Framework... 15

Figure 3. Data Preparation Work Flow ... 19

Figure 4. Organization Structure of GHSNP Based on the Decree of MoF No. 355/Menhut-II/2004... 22

Figure 5. Conceptual System ... 22

Figure 6. Illustration of Spatial Properties (Ecogeographical Variable) that Influencing Wildlife Response. Each Grid has Its Own Spatial Characteristic, such like Proximity (Distance to Road [represented by black line], River [represented by blue line]) and Content Arrangement (the Area of Landcover Contained In, Marked by x and y Letter) ... 24

Figure 7. Major Functionalities of the SUITSTAT... 26

Figure 8. Flowchart of Content Analysis ... 29

Figure 9. Flowchart of Proximity Analysis... 30

Figure 10. The Illustration of Neighboring Grid of a Point ... 31

Figure 11. Aggregation Analysis Method (Notes: Further Description is Available in the Text) ... 33

Figure 12. Flowchart of Ecogeographical Data Generation... 34

Figure 13. The Illustration of Visual Method (a) and Triangle Method (b). Visual Method Uses Observer Position (x, y), Compass Bearing (α) and Observer Distance to Species (d) to Determine Species Position. In Constrast, Triangle Method Only Needs Two Observers Positions and Compass Bearing of each Position to the Sign of Invisible Species (S). ... 35

Figure 14. Algorithm for Determining or Mapping Species Position... 36

Figure 15. The Illustration of Point Buffering Algorithm... 37

Figure 16. The Illustration of Dissolve Algorithm ... 38

Figure 17. Vector-based Gridding Algorithm... 39

Figure 18. Process Flow of Habitat Suitability Module... 40

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STAND-ALONE GIS APPLICATION FOR WILDLIFE

DISTRIBUTION AND HABITAT SUITABILITY

(Case Study: Javan Gibbon, Gunung Salak, West Java)

WIM IKBAL NURSAL

GRADUATE PROGRAM

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STAND-ALONE GIS APPLICATION FOR

WILDLIFE DISTRIBUTION AND HABITAT SUITABILITY

(Case Study: Javan Gibbon, Gunung Salak, West Java)

WIM IKBAL NURSAL

A Thesis submitted for the degree of Master of Sciences of Bogor Agricultural University

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY

FOR NATURAL RESOUCES MANAGEMENT

GRADUATE SCHOOL

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STATEMENT

I, Wim Ikbal Nursal, here by stated that this thesis entitled:

Stand-alone GIS Application for Wildlife Distribution and Habitat Suitability (Case Study: Javan Gibbon, Gunung Salak, West Java)

Bogor, February 2007

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ACKNOWLEDGEMENT

• Dr Gatot Haryo Pramono as external examiner for giving excellent inputs.

• MIT’s crews including Uma, Devi and Bambang who have been helping me much in preparing the academic administration.

• Rufford Small Grant committee that provided fund so that the field survey could possibly be conducted.

• National park rangers, Perhutani Staff in Cangkuang and Unit III Jawa Barat for authorization in conducting survey in park.

• My mother and father, brothers and sister, and the special one who always encourage, give trust, and keep patient waiting for me.

• Yudi and Tri in Environmental Spatial Analysis PPLH-IPB for ancillary data.

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CURRICULUM VITAE

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ABSTRACT

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STATEMENT ... ii

ACKNOWLEDGEMENT ... iii

CURRICULUM VITAE ... iv

ABSTRACT... v

TABLE OF CONTENTS... vi

LIST OF TABLES ... viii

LIST OF FIGURES ... ix

LIST OF APPENDICES ... xii

I. INTRODUCTION ... 1

1.1. Background ... 1

1.1.1. Wildlife Conservation and Information ... 1

1.1.2. Javan Gibbon and Mount Salak ... 2

1.2. Objective ... 2

II. LITERATURE REVIEW ... 3

2.1. Wildlife-Habitat Relationship ... 3

2.1.1. Wildlife Habitat Definition ... 3

2.1.2. Wildlife Habitat Selection... 3

2.1.3. Wildlife Habitat Suitability Model... 5

2.2. Wildlife-Habitat Mapping ... 6

2.3. Spatial Multi Criteria Decision Analysis ... 7

2.3.1. Criteria... 7

2.3.2. Constraints... 7

2.3.3. Criteria Weighting... 8

2.3.4. Decision Rules ... 8

2.4. The Use of Principal Component Analysis in Ecological Studies ... 8

2.5. Javan Gibbon... 10

2.5.1. Taxonomy and Morphology... 10

2.5.2. Social System ... 11

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2.5.4. Dietary... 11

2.5.5. The Pattern of Spatial and Temporal Utilization ... 12

III. METHODOLOGY ... 13

3.1. System Development Methodology ... 13

3.1.1. System Development ... 13

3.1.2. Software ... 14

3.1.3. Hardware ... 14

3.2. Javan Gibbon Habitat Suitability Model... 15

3.2.1. Model Formulation... 15

3.2.2. Required Data... 17

3.2.3. Field Data Collection Method ... 18

3.2.4. Data Input Preparation ... 18

3.3. Time and Location ... 19

IV. RESULTS AND DISCUSSION... 20

4.1. System Development Result ... 20

4.1.1. Requirement Analysis ... 20

4.1.2. System Analysis and Design ... 26

4.1.3. System Testing ... 55

4.2. Javan Gibbon Distribution and Habitat Suitability Analysis in Mount Salak ... 63

4.2.1. Javan Gibbon Distribution Survey ... 64

4.2.2. Habitat Suitability Model ... 65

4.3. Discussion ... 68

4.3.1. System Performance... 68

4.3.2. Javan Gibbon Distribution and Habitat Suitability ... 75

V. CONCLUSION AND RECOMMENDATION ... 80

5.1. Conclusion... 80

5.2. Recommendation... 80

REFERENCES... 82

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LIST OF TABLES

Table 1. Used Software for Application Development... 14

Table 2. System Component Matrix ... 23

Table 3. Spatial Features and Spatial Properties Relationship... 24

Table 4. Several Types of Analysis to Spatial Features... 28

Table 5. SUITSTAT’s User Interface ... 44

Table 6. The Nature of Geographic Distribution of Javan Gibbon in Mt. Salak Based on 29 Observation Points ... 64

Table 7. Principal Component Loadings and Weight for Each Spatial Variable ... 65

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LIST OF FIGURES

Figure 1. Iterative and Incremental Approach (modified after Barroca et al.,

2000 in Zhao, 2002) ... 13

Figure 2. Theoretical Framework... 15

Figure 3. Data Preparation Work Flow ... 19

Figure 4. Organization Structure of GHSNP Based on the Decree of MoF No. 355/Menhut-II/2004... 22

Figure 5. Conceptual System ... 22

Figure 7. Major Functionalities of the SUITSTAT... 26

Figure 8. Flowchart of Content Analysis ... 29

Figure 9. Flowchart of Proximity Analysis... 30

Figure 10. The Illustration of Neighboring Grid of a Point ... 31

Figure 11. Aggregation Analysis Method (Notes: Further Description is Available in the Text) ... 33

Figure 12. Flowchart of Ecogeographical Data Generation... 34

Figure 14. Algorithm for Determining or Mapping Species Position... 36

Figure 15. The Illustration of Point Buffering Algorithm... 37

Figure 16. The Illustration of Dissolve Algorithm ... 38

Figure 17. Vector-based Gridding Algorithm... 39

Figure 18. Process Flow of Habitat Suitability Module... 40

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Figure 22. The Flowchart of Point Buffer Utility ... 47

Figure 23. Flowchart of Dissolve Menu ... 48

Figure 24. Flowchart of Vector-based Gridding ... 49

Figure 25. Flowchart of Distribution Mapping Menu... 51

Figure 26. The Flowchart of Habitat Suitability Menu Execution... 53

Figure 27. Flowchart of Classification Menu ... 54

Figure 28. Species Positioning from Geographical Position Data... 55

Figure 29. Buffering Point Result ... 56

Figure 30. Vector Based Grid Conversion... 56

Figure 31. Dissolve Utility Result... 57

Figure 32. Successful extracting original data (a) with three subclasses into separated classes (a), (b), (c) subsequently using contain analysis and (e) and (f) using neighbor (proximity) analysis... 58

Figure 33. The Suitability Information: (a) Form of Information Summary and (b) Habitat Suitability Map... 59

Figure 34. Map Viewer of SUITSTAT ... 59

Figure 35. GUI for Categorization of Dataset, Chosing Spatial Properties Analysis Types, Presence Dataset Types, and Output Dataset of the Model Result ... 60

Figure 36. GUI for Giving and Matching and Abbreviation of Ecogeographical Variables... 61

Figure 37. GUI for Selecting and Modifying Constraint and Criteria, and Calculating Eeight ... 62

Figure 38. PCA Calculation Result... 62

Figure 39. GUI of Standardization... 63

Figure 40. GUI of Model Outcome and Histogram ... 63

Figure 41. Area of each Javan Gibbon Habitat Suitability in Mt. Salak (in km2)... 66

Figure 42. Map of Javan Gibbon Habitat Suitability in Mt. Salak ... 67

Figure 43. Attribute Information of Species Distribution Dataset... 68

Figure 44. Error Message in Point Creation ... 68

Figure 45. The Partial Grid Created in the Edge of Concerned Polygon... 69

Figure 46. Attribute Table of the Output File from Embedding Process... 70

Figure 47. The Vertices of Circular Polygon Produced from Buffering Utility .. 71

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Figure 49. Polygon Object Structure... 72 Figure 50. The Imperfect Solution of SUITSTAT’s Dissolve Utility ... 73 Figure 51. Degraded Forest in Kawah Ratu... 75 Figure 52. Primary Forest at Mt. Salak (Cangkuang) ... 76 Figure 53. A Male Gibbon Which Living in the Low-Suitable Habitat ... 78

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LIST OF APPENDICES

Appendix 1. Javan Gibbon Distribution Data in Mt. Salak Used in the

Analysis... 88 Appendix 2. Habitat Suitability Algorithm... 89 Appendix 3. MapObjects SearchByDistance Method of Shape Object (ESRI,

2001)... 90 Appendix 4. MapObjects SearchShape Method of Shape and Layer Object

(ESRI, 2001)... 91 Appendix 5. MapObjects SearchShape Constant ... 92 Appendix 6. Javan Gibbon Distribution Survey Map... 93 Appendix 7. Accessibility (road and track) in Mt. Salak... 94 Appendix 8. Forest Ecosystem (Elevation Based) in Mt. Salak ... 95 Appendix 9. Land Cover of Mt. Salak ... 96 Appendix 10. Slope in Mt. Salak ... 97 Appendix 11. Rivers in Mt. Salak Map ... 98

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I. INTRODUCTION

1.1. Background

Information technology is applied in many fields nowadays. In each field, the utilization context would be different or unique. The relationship between wildlife conservation and information is described henceforward, together with rationale of strategic value of the case study.

1.1.1. Wildlife Conservation and Information

The importance and values of biodiversity has driven some peoples to find out the way to conserve the remaining land in the earth, especially of that containing high biodiversity. One well-known approach is through the establishment of conservation area (Primack et al., 1998).

Wildlife information, constitutes of habitat and population aspects, is often used as standard criteria to select the certain land to be assigned as conservation areas. Some area is established as Wildlife Sanctuary (Suaka Margasatwa) based on the uniqueness wildlife community within and/or its capability to support the survival of this wildlife (Republik Indonesia, 1998). In an established park, such as Gunung Halimun National Park (recently expanded and renamed as Gunung Halimun-Salak National Park), it has been used to formulate action plan (LIPI et al., 2003).

In the park management which primarily focused on keystone species management approach, the accumulated information on wildlife-habitat relationship probably the most important wildlife information to be considered into management practices. Wildlife-habitat relationship is alleged by many ecologists in providing scientific basis and framework for conservation area management, formulating management plan and making decision (De La Ville et al., 1998; Morrison et al., 1992). Although it has not been applied yet, every park in Indonesia has to complementarily acquire this framework for determining management zonation (Republik Indonesia, 1998).

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A synthesis on wildlife-habitat relationship knowledge, multivariate habitat analysis, with wildlife mapping techniques (which primarily done with certain GIS software) is very promising method to produce efficient wildlife information for management practices (Miller, 1994). These wildlife-habitat interactions provide a consistent basis for impact assessment, mitigation, baseline, conservation and monitoring studies (Morrison et al., 1992). Their utilization can be seen in a loose or series of resources, such as in Capen (1981), Miller (1994), De Leeuw et al. (2002), just to mentioned a few.

In contrast, case studies concerning to this synthesis still have been rarely done in Indonesia. The need of it is obvious considering that Indonesia has a lot of protected areas. In this context, developing such GIS application which carrying this wildlife-habitat relationship could be useful to support designing management plan in spatial basis.

1.1.2. Javan Gibbon and Mount Salak

This research took javan gibbon (Hylobates moloch) in Mount (Mt.) Salak as a case study or an assessment for the developed GIS application. The conservation status of javan gibbon is critically endangered (Eudey and MPSG2000, 2004). It means that this species will extinct in the immediate times and require urgent actions to inhibit extinction process and promote its survival. Reintroduction was arising for one option and hence need assessment to the relatively large habitat, where Mt. Salak is included in the list (Supriatna et al., 1994; LIPI et al., 2003). Besides having large natural areas, this area still has been connected by a corridor to mountainous landscape of Mt Halimun and commend as the extended area for Gunung Halimun National Park. Therefore, the development of new park zonation is required upon Mt Salak.

1.2. Objective

The study covers two objectives. The first objective is to develop standalone GIS application to support the users in generating predictive information on habitat suitability map and the second objective is to produce habitat suitability map of javan gibbon in Mt. Salak.

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II. LITERATURE REVIEW

2.1. Wildlife-Habitat Relationship

Wildlife habitat relationship is one subject under ecological studies that being investigated since long time ago. It was pioneered by the naturalist in the early 1900 and generates numerous concepts. Even the purpose and concepts behind this study still are growing, the existing concepts are likely significant to formulate solution over environmental (wildlife) crisis today (Morrison et al., 1992). The most important concept is habitat selection. Before getting into this topic and the others, some important terms will be briefed first.

2.1.1. Wildlife Habitat Definition

Habitat is simply defined as where the species lives (Odum, 1971). It provides water, coverage, and food to supports their lives and activities, which fashioned by the dynamic interaction between physical and biological factors (Bailey, 1984). Morrison et al. (1992) give a clear-cut definition which sounds more spatially based, that is an area with the combination of resources (like food, cover, and water) and environmental conditions (temperature, precipitation, presence or absence of predators and competitors) that promotes occupancy by individuals of a given species (or population) and allows those individuals to survive and reproduce. Additionally, they defined the high quality habitat is areas that afford conditions necessary for relatively successful survival and reproduction over relatively long periods when compared to other similar environments. In short, high quality habitat is related to the rates of survival and reproduction, vitality of the offspring, length of time the site remains suitable for occupancy.

2.1.2. Wildlife Habitat Selection

The knowledge of biotic and abiotic factors roles to the wildlife survival in general are widely known from analytical biology studies. It is applied to manipulate and manage wildlife and their habitat (see Bailey, 1984). In the

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scientific side, the questions to wildlife habitat relationship are not stopped but continue to grow.

Habitat selection concept seems taking so much ecologists interest. The concept which established over the theory of evolution and its evidences was proposed in order to answer why and how the species or the population occupies a certain space as their habitat which suitable for their survival (Morrison et al., 1992). Several ideas emerged from habitat selection studies are briefly described in the following:

• Animals somehow perceive the correct configuration of habitat required for their survival (Morrison et al., 1992).

• Different with ultimate factors among environmental gradients (such as food, water, nutrients, etc.), proximate factors (physical appearance which give a clue to ultimate factor) caused the release innate behavior that resulted in a certain “settling pattern” (Hilden, 1965 in Morrison et al., 1992). This idea formed a foundation to the subsequent analysis of habitat selection. Similar to this idea, Bailey (1984) thought that some vertebrates respond to the life form or physiognomy of their habitat, rather than to the presence of particular plant species.

• Experimental study on deer mice by Wecker (1964) in Morrison et al. (1992) showed that patterns of habitat selection are genetically determined and learning at an early age will enforce these patterns (preadaptation).

• Hutchinson (1957) proposed a new theory on species niche that caused a substantial revolution of the niche and thus habitat selection concept. He viewed niche in geometrical way and defined it as multidimensional hyper volume defined by the sum of all the interactions of an organism and its (abiotic and biotic) environment. This perspective caused the extensive use of multivariate statistics in analyzing wildlife habitat.

• The first study using Hutchinson’s theory and multivariate analysis to avian habitat came from James (1971 in Morrison et al. 1992). She thought that each species has a characteristic perceptual world. The species responds to its perceptual world as an organized whole, and that it has a predetermined set of

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specific search images. A lot of studies with similar methodology were emerged after this.

• Hutto (1985) in Morrison et al. (1992) thought that habitat selection could best be viewed as a spatially hierarchical decision making process. At broadest level of habitat selection, i.e. geographic scale, evolutionary process seems plays role in species distribution, restricts the species to certain geographic boundaries. At this scale, habitat selection is primarily innate. Within that restricted range, the animal can explore alternatives and make choices based on the cost and benefits associated with the use of each habitat, such as food availability. At much lower range, the animal must chooses specific site within certain part of habitat (which is called microhabitat). Inside this range, the choices could be affected by mate availability, predators, and/or competitors.

It is obvious that the structure of habitat will influence the habitat selection of the species. Based on habitat selection concept, if the habitat variables and their magnitude can be recognized of certain species (where they live at), their distribution in space could be predicted, as long as the logical relationship between variables and species survival are exist.

2.1.3. Wildlife Habitat Suitability Model

Shamberger and O’Neil (1986) in De La Ville et al. (1998) defined habitat suitability model as a model that shows the ability of a habitat to provide life requisites for certain species. The definition of habitat suitability model could be defined semantically. A model is an abstraction or simplification of some part of the real world system (Shenk and Franklin, 2001; Morrison et al., 1992). It is actually a translation of real system into simpler representation of interest.

It is already mentioned earlier that suitable habitat is areas that afford conditions necessary for relatively successful survival and reproduction over relatively long periods when compared to other environments. Hence, habitat suitability model can defined as a model that shows the affordability of habitat unit for species survival and reproduction through certain representation.

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The general purpose of a model is used as a guide of management decisions (Shenk and Franklin, 2001). In case of suitable habitat modeling, it could be used to set up conservation priorities (Margules and Austin, 1994 in Guisan and Zimmermann, 2000) and may yield biological insight to explain species distribution and evaluate land activities.

At present, many algorithms have been developed to formulate habitat suitability model. Any of these algorithms is used to determine the response variable and estimating model coefficient (Guisan and Zimmermann, 2000). Most of the available algorithm families in the present used statistical approach, and some of them used mathematical and artificial intelligence (machine learning) approach. The complete algorithm families can be seen in the Appendix 2.

2.2. Wildlife-Habitat Mapping

Wildlife-habitat mapping as a process to produce spatially represented wildlife information. Eventually, it has been being a complementary method in wildlife research and management. There are several reasons why spatial information becomes a major, in contrast to the other type of information. First, spatial perspective provides simple, but useful, framework for handling large amounts of data (Fischer et al., 1996). For example, in a topographic map, we could extract information on elevation, land cover, settlement distribution, primary infrastructures, and so forth. Therefore, it is suitable for manager and policy makers that often do not have time to digest all information, communicate and focus to the important subjects (Miller, 1994). Secondly, most of wildlife information holds geographical dimension. It is already a geographic phenomenon (since it has been lying on geographic space) and interconnected with the other geographic elements. And the third, by the capability of GIS, it is possible to have almost complete picture of situation in the concerned place (by linking all relevant geographic features in various types into one single frame, known as overlay or superimposition). Hence, GIS is very useful tools for studying wildlife that inherently influenced by numerous ecological elements in space and time.

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2.3. Spatial Multi Criteria Decision Analysis

Mostly decision is made under many criterions (criteria), rarely depend on single criterion. The term multi criteria decision making (analysis) is used to explain the decision methodology which involve incommensurate criteria.

Based on Worral (1991) in Malczewski (1999), 80% of data used by managers and decision makers is related geographically. Furthermore, spatial decision is multi criteria in nature. Consequently, there is a need for utilizing spatial multi criteria decision analysis that dealt with geographical data, which previously used for aspatial problem.

Malczewski (1999) thought spatial multicriteria decision analysis as a process that combines and transforms geographical data into resultant decision through decision rules (multi criteria decision procedures). At least three components should be considered to utilize multi criteria decision making, i.e.: decision criteria, criteria’s weight, and decision procedures/rules. The following subsection describes these components.

2.3.1. Criteria

Criteria are also referred as attributes. In spatially based decision making, the attribute is represented by concerned map. This map is called as an evaluation criteria map, attribute map, thematic map or data layer, which defined as a unique geographical attribute of the alternative decisions that can be used to evaluate the performance of the alternatives (Malczewski, 1999).

2.3.2. Constraints

Constraint is limitation imposed by nature or by human being that do not permit certain actions to be taken (Keeney, 1980 in Malczewski, 1999). It is important in determining the feasibility of the alternatives of decision. If decision is made which failed to take account the constraints, then the decision will be called infeasible or unacceptable decision.

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2.3.3. Criteria Weighting

The purpose of criterion weighting is to express the importance of each criterion relative to other criteria (Malczewski, 1999). In decision making, it is a common that each criterion is incommensurate or each criterion has influence level to decision result. There are some procedures to estimate weight, for example: rating, ranking, pair wise comparison method, trade-off analysis method, and so forth (Malczewski, 1999).

2.3.4. Decision Rules

A decision rule is a procedure that allows for ordering alternatives. It integrates the data and information on alternatives and decision maker’s preferences into an overall assessment of the alternatives (Malczewski, 1999).

There are numerous decision rules that can be used for solving the multi criteria decision making problem. Simple additive weighting (SAW) methods are the most often used techniques for tackling spatial attribute decision making. The techniques are also referred to as weighted linear combination (WLC) or scoring methods (Malczewski, 1999).

The following formula is the formal model of SAW method (Malczewski, 1999):

∑

i ij i

i w x

A (2.1)

where xij, is the score of the ithalternative with respect to the jth attribute, and w is

the normalized weight. The most preferred alternative is selected by identifying the maximum value of Ai(i = 1, 2, 3… m number of attributes/criteria).

2.4. The Use of Principal Component Analysis in Ecological Studies

It is a nature that process and interaction of ecosystem involve many factors (biotic and abiotic). These interactions sometimes synergic, which can be strengthening or weakening, balancing or reinforcing, dynamically affect the behavior, distribution, and abundance of an organism. Hence, understanding the species response solely on the single variable or factor could be misleading. In fact, many scientists already knew the complexity of ecosystem processes.

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However, it is remarkable of which after the niche theory was reformulated by Hutchinson (1957), ecological research (especially in wildlife-habitat relationship studies) concerns many variables (Morrison et al., 1992). One approach to consider many variables simultaneously in explaining organism responses is multivariate statistics.

According to McGarigal et al. (2000), Principal Component Analysis (PCA) is the most well-understood and widely used ordination technique. They stated that ordination essentially seeks to uncover a more fundamental set of factors that account for the major patterns across all of the original variables. They realized the principle behind using ordination in ecology is that much of the variability in a multivariate ecological data set often is concentrated on relatively few dimension, and that these major gradients are usually highly related to certain ecological or environmental factor. The important characteristic of PCA in the following is summarized after McGarigal et al. (2000):

• PCA assesses relationship within a single set of interdependent variables, regardless any relationship they may have to variables outside the set.

• PCA does not attempt to define the relationship between a set of independent variables and one or more dependent variables.

• Its main purpose is to condense information contained in a large number of original variables into smaller set of new composite dimensions, with a minimum loss of information, where each dimension is defined by weighted linear combination of the original variables (called principal components). This linear combination represents gradients of maximum variation within the data set.

PCA usually begins with standardize the raw data set (mean centering). From this matrix, the correlation matrix (or variance-covariance matrix) is calculated. After that, eigenvalues and eigenvector, the most important quantity in PCA, are obtained through decomposing matrix and QR Algorithm technique (Wikipedia, 2005).

According to Morrison et al. (1992), eigenvectors are best combinations of correlated predictor variables that account for most of the variation in the response

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component is measured in eigenvalue. The weights of linear equation of each principal component (PC) are actually these eigenvectors.

2.5. Javan Gibbon

A short review of javan gibbon is given below. It comprises of taxonomy and morphology, social system, distribution, dietary and the pattern of spatial and time utilization. This information is important for formulating the model.

2.5.1. Taxonomy and Morphology

According to Simpson (1945) in Napier (1972), the taxonomy of javan gibbon is:

Species : Hylobates moloch Audebert 1798 Genus : Hylobates

Family : Hylobatidae Super-family : Hominoidea Sub-ordo : Anthropoidea Ordo : Primata

Class : Mammalia Sub phyllum : Vertebrata Phyllum : Chordata Kingdom : Animalia

Hylobatidae is a sub-group of primate which doesn’t have physical tail which is common to other primates. They usually live in the top of forest layer. They have unique morphological hinds, which the front hinds are longer than back hinds. They fully depend on their front hinds for moving or ranging from tree to tree, without reducing their rapidity in swinging and moving. This could indicate that they already adapted to canopy and branches structure of trees.

Javan gibbon can be identified by its silvery-grey hairs. The top of the head is black as well as its face. The eyebrow is similar to the other hair.

In Indonesia, the family Hylobatidae can be found in major islands, such as Sumatera (H. syndactylus or siamang, H. agilis, and H. lar), Kalimantan (H. muelleri and H. agilis), Java (H. moloch), and in Mentawai Island (H. klossii).

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2.5.2. Social System

Javan gibbon has a monogamous and family group system. In one group, usually 1-2 infants can be found. Sometimes sub-adult can be found in a group, but it couldn’t stay longer but later excluded and establish new self-family group. Pregnancy period of javan gibbon is about 197 – 210 days. The elapsed time after one birth to the second is about 3-4 years. Generally, they could live for 35 years.

2.5.3. Distribution

The latest information stated there are only 400 – 2000 javan gibbons left in Java Island; therefore, it has been classified as critically endangered species in 1996 IUCN Red List and listed in Appendix I CITES (Eudey and MPSG2000, 2004). It is dispersed and only found in Java, especially in remnant and relatively undisturbed mountain forest. As a top-arboreal, complete brachiated and frugivorous monkey, it needs an evergreen primary forest to live (Napier and Napier 1985, Napier and Napier 1967, Kappeler 1981, Kappeler 1984). There are only eight sites that still serve as Javan gibbon’s effective habitat - with large enough population and inter population genetic flow - out of 30 sites that previously identified as their habitats in Java. Five of those sites located in West Java, including Gunung Salak protection forest, Gunung Gede-Pangrango National Park, Gunung Halimun National Park, Ujung Kulon National Park, and Gunung Simpang Nature Reserve (Primack et al. 1998).

2.5.4. Dietary

Like the other hylobatids, they eat ripe fruits, bud or young leaves, bud-flower or complete bud-flower and small creatures. Usually these supplies are collected 10 m above the ground (Ellefson, 1967; Chivers, 1974; Gittins, 1979 in

Kappeler, 1984).

According to Prastyono (1999), about 43 species of gibbon’s diet plants is identified from the total of 143 species in Cikaniki, Ciawitali, and Pasir Bivak resort, Gunung Halimun National Park. The life forms (habitus) of these dietary

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2.5.5. The Pattern of Spatial and Temporal Utilization

Javan gibbon is living arboreal in trees and rarely come down to the ground. According to Tobing (1999), javan gibbon frequently uses the height of about 20 -25 m above the ground, either in disturbed or undisturbed forest. The utilization of space in 0 – 5 m of height was happened when there was a forest gap which could not trespass through forest canopy.

Daily ranging can reach 1500 m. Tobing (1999) reffered to Chivers (1984) and Kappeler (1984) stated that spatial usage type for daily activities is territorial. The size of this territorial area is about 16 ha. They speculated if the destruction happened over their area, small possibility for them to migrate into a new area.

The home range or territory area is believed to be defended through calling mechanisms. Alarming bout is conducted by any individual when the intruder (could be gibbon from other group, other primates, human, or predators such as leopard, raptors, and so forth) came approaching their area.

They used about 70.4 % of their home range for their territory. The daily range patterns every day was influenced by habitat and floristic season. The family used the middle strata in the forest, a height of between 15-30 m. The top layer height is about (> 30 m) and the lower (0-15 m) was rarely used (Ladjar, 1996).

Elevation is related to the gibbon distribution. Javan gibbon is found below 1600 m asl (above sea level) (Kappeler, 1984). They are rarely found in elevation above 1500 m asl. The ecosystem on that elevation contains a little number food tree species. Moss is very fine available that could obstruct javan gibbon to move.

Human pressure is believed to be a barrier or source of disturbance to javan gibbon movement and habitat. Based on Tobing (1999), javan gibbon could detect the human existence in 20 m and then produce an alert behavior.

The study of daily activity conducted in Cikaniki, Ciawitali and Pasir Bivak TN Gunung Halimun revealed that the percentages of daily activity were resting 39.1 %, feeding 30.3%, moving 24.1% and other social activity 6.5% in average (Prastyono, 1999).

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III. METHODOLOGY

3.1. System Development Methodology

The description of system development method is illustrated in thi section, including the brief explanation and information on software and hardware used to develop the system.

3.1.1. System Development

The system, namely SUITSTAT, is developed by using modified iterative-incremental approach. This approach focuses on the developing the functionality of system (process-based), not only the activities or management plan to accomplish the system development, such as in the system development life-cycle (SDLC) or waterfall process (Fowler, 2005).

The development process has two phases, i.e. initial and construction phase. The scheme of the development method can be seen in the figure below:

Increment #1 Basic Functionality Iteration 1

Iteration 2 Iteration ...

Increment #1 Core Functionality

Iteration 1 Iteration 2 Iteration ... Increment #2 Add More Functionality

Iteration 1 Iteration 2

Iteration ...

Increment #3 Complete Functionality

Iteration 1 Iteration 2

Iteration ...

testing analysis design coding

• Requirement Analysis • Habitat Suitability Concept & Methodology

System Requirement

in it ia l Ph a se

con st r u ct ion ph a se

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Initial phase is distinguished by identifying the objectives and requirements of the intended system. In this phase need analysis (describing current state and identifying the necessity of the system), information and user analysis in conducted. The requirement analysis is mostly formulated based on literature (habitat suitability concepts) and national park document. From this analysis, the rough description of the system is given as the solution.

The second phase is contruction phase. In this phase, the system is viewed as a block of functionalities. In each functionality development, a mini SDLC (analysis, design, coding, and testing) is conducted until it is finished or working. This phase will produce the proposed conceptual system. It is expected when the construction reach the last functionality, the application is completely developed.

3.1.2. Software

Microsoft Visual Basic Professional (VB6) is chosen as software development (implementation) considers that VB6 is popular software for application development over Microsoft Windows Operating System environment by its simple computer language syntax, almost object-oriented and user-friendliness. Additionally, many components and libraries such like MapObjects built for VB platform. MapObjects is the main component used to develop SUITSTAT. Software that is shown in the Table 1 is used to develop this application.

3.1.3. Hardware

The hardware used to develop the application is desktop PC with specification: Processor Intel Pentium IV, 512MB RAM, and 64MB Graphic Card.

Table 1. Used Software for Application Development

No Software Utility

1 Microsoft Visual Basic 6.0 Rapid Application Development tool, programming language which all codes will be written and executed with.

2 MapObjects 2.1 Commercial object component from ESRI to make map interfaces. Provides certain already-used functionalities that enable developers to make customized GIS application.

3 ESRI ArcView 3.x Commercial GIS application map viewer from ESRI. Used to prepare data.

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3.2. Javan Gibbon Habitat Suitability Model

The formulation of javan gibbon habitat suitability model is described even the aim is to test SUITSTAT performance. Additionally, the required data, field data collection, and the preparation of the data before processed by the system are informed in the following subsection.

3.2.1. Model Formulation

In the geographic scale, the habitat can be analyzed simply based on its major shaping-factor, which are physical and biological factors, and also human factors due to their activities which are often causing the rapid changing on the environment. Those factors almost can be represented into spatial data. By means of spatial and statistical (numerical) analysis, the pattern of driving factors of habitat selection can be identified. This theoretical framework is drawn in the Figure 2.

Suitability model is estimated using GIS-based decision rules, i.e.: Simple Additive Weighting (SAW) method. It considers habitat factors, such as biotic, abiotic and human factor as decision criteria. Habitat factors are represented by available spatial data, such as land cover, river/water body, roads, slopes, elevation-based forest ecosystem. The model outcome (decision) is represented by the score of correspond feature which reflects the suitability level (the higher score, the higher suitability level).

Human Interaction

Biological factor

Wildlife Response

Physical factors

Spatial & statistical analysis,

decision rules

Wildlife habitat suitability Selected

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Almost of all variable are related to gibbon behavior and hence its survival. These factors were selected through rationalizing knowledge specifically on javan gibbon and generally on wildlife. Javan gibbon is a brachiated monkey which primarily depends on the forest structure (Napier and Napier, 1985; Kappeler, 1984a). In Mt Salak, such forest structure that enabling gibbons to perform their daily activity (ranging, feeding, and resting or sleeping) is satisfied by primary lowland and submontane forest. Therefore, forest succession stage or maturity (primary and secondary forest) and forest elevation based ecosystem are considered as a cue to habitat suitability.

The existing of non-habitat (nonforested area) such as settlement, paddy field, crops, bushes, and road/tracks gives influence to the health of habitat which related with the concept of edge effect and fragmentation (Morrison et. al., 1992; Primack et al., 1998). The influence to the species habitat correlates with habitat distance to non-habitat area. These factors are also related with detectability of javan gibbon to the intruders of their homerange or territory, as observed by Tobing (1999).

As a territorial species, every group of javan gibbon moves inside a relatively fixed area (homerange) to find resources. The movement is highly relied on the continuance of the canopy. The canopy surface usually follows the terrain. Even there has been no evidence revealing the difference of ranging distance on the canopy above slopy and plain topography, topographic condition commonly affects the movement of wildlife and probably javan gibbon. Generally, the effort for accomplishing a route in steeper area is bigger than plain area. Hence, slope factor is included in the model, which arbitrarily divided into three slope classes, 0 – 15% (representing plain topography), 15 – 45% (representing plain to steep terrain) and more than 45% (representing steep terrain).

The existence of rivers or water body is vital for wildlife survival. Seeing that javan gibbon rarely came down from top forest canopy, this factor seems unimportant. However, this factor was included into the model consider to the possible relationship of this factor to community structures in javan gibbon habitat as noted by Hadi (2002).

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The decision constraints are also considered in the model. The constraints are considered due to the existence of a factor in the land entity that is not livable for gibbon. Non-forested area (such as tea plantation, bushes, open land, and settlement) and area on which trespassed by the road are considered as model constraints. Since there is no information on edge-effect to javan gibbon habitat structure, this factor is approximated from javan gibbon alert behavior. As observed by Tobing (1999), javan gibbon could detect human existence in 20 m (flash distance). Therefore, the area within the distance of 20 m from roads and non-forested land are considered also as model constraints.

The occurrence of gibbon group is meant as proxy (indication) of suitable habitat. The concerned habitat variable are measured according those gibbon distribution (over 29 distinct groups) and analyze with Principal Component Analysis (PCA). Considers that principal component loading value indicates the contribution of a variable to variance explained by correspond principal component (eigenvalues); Hence, the maximum of principal component loading of the interpretable component suggest level of importance of variable in determining suitable habitat. Subsequently, it is used to calculate weight of each variable weight. Broken Stick Distribution is used to determine how many components were interpretable (McGarigal et al., 2000).

The process of GIS-Based SAW Method was performed by SUITSTAT system which adopts Malczewski’s (1999) procedure.

3.2.2. Required Data

The data that will be used as a test case of this application are:

1) Digital topographic map (topomap) of Mt Halimun Salak National Park (sheet number: 1209-132, 1209-141, and 1209-114; scale 1:25.000). This map is the special and newest version of topographical situation on Mt Halimun Salak National Park, produced by the National Coordinator Agency on Survey and Mapping (BAKOSURTANAL) consultant for Mt Halimun Salak National Park Management. Landsat ETM+ year 2003 at this park was used to describe the latest land cover condition. The other topographic features (such as road,

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These features will be further processed by this system to derive the ecological geographic (ecogeographic) factor and to determine habitat suitability.

2) Javan gibbon distribution data in Mt Salak. The data were collected during field survey and some data came from previous research (Djanubudiman et al., 2004). This data was also used by this system to determine javan gibbon habitat suitability.

3.2.3. Field Data Collection Method

Javan gibbon distribution data were collected by using triangle count and direct count along the available track in the study area. This method is appropriate to be applied on gibbon population counting and positioning (Rinaldi, 1992). This method is working based on the intersection between two (imaginary) lines, which each line was created by observer position (measured by GPS) and the measured compass bearing (azimuth) of observer to the source of sound. These two points should be in a quite distant to prevent the occurrence of parallel lines. After the species position was determined by drawing these lines upon the map, the observer went to that position to verify the species existence. Some record list in the tally sheet were marked, when the species was found.

3.2.4. Data Input Preparation

Data input preparation is meant to generate required dataset and transform the data so that they are accessible for SUITSTAT. Some processes were performed in ArcView 3.2 and some others were in SUITSTAT.

Elevation and slope classes dataset was generated from Digital Elevation Model (DEM) dataset in ESRI Grid format, which previously generated by surfacing the countur lines dataset. In turn, elevation classes together with settlement and land cover dataset was processed to produce forest type (based on succession stage) and forest ecosystem (based on elevation classes). These processes were conducted in ArcView 3.2. Subsequently, the attribute of road, slope classes, rivers, forest type and ecosystem dataset were adjusted before further processed by SUITSTAT (habitat suitability modeling). This adjustment

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is intended to provide coding system which represents the available feature class in the data.

The dataset which transformed by SUITSTAT were land cover and species distribution dataset. Land cover dataset upon Mt. Salak area was transformed into surface flat polygon and species distribution data was buffered. Surface flat polygon dataset (vector-based grid) was made to represent the area of which its suitability being investigated. Species distribution data was buffered in rectangle shape which represents the homerange of javan gibbon (about 16.7 Ha based on Kappeler, 1984). The work flow of data preparation is shown in the Figure 3.

Figure 3. Data Preparation Work Flow

3.3. Time and Location

The study was started in September 2005 to December 2006. The field survey was done in December 2005, January to February 2006 and May to June 2006 at several places in Mt. Salak, West Java. Most of time was spent to develop this application in GIS/RS Laboratorium in MIT-Biotrop.

at t r ibut e adj ust m ent

DEM count ur landcover

rivers

road forest

ext ract ion elevat ion

class

slope class

SUI TSTAT syst em set t lem ent

forest t ype

forest ecosy st em

Species dist ribut ion

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IV. RESULTS AND DISCUSSION

This chapter consists of two major sections. The first section exposes the result of SUITSTAT system development, javan gibbon field survey and habitat suitability model. The last section is the discussion on the performance of the system, javan gibbon distribution and habitat suitability analysis in Mt. Salak.

4.1. System Development Result

The development process of SUITSTAT is following iterative and incremental approach, where system functionality is developed through iterative process. The process of the development is described in the series order as it could be easier to understand by the reader, that is requirement analysis, system analysis and design, and system testing.

4.1.1. Requirement Analysis

The elements of system requirement are identified by analyzing the need, data and information, and user of the system which described in the next subsection. The synthesis of these elements is described in the last subsection followed by the proposed (conceptual) system.

4.1.1.1. Need Analysis

National park management is managed by zonation system (Republic of Indonesia, 1998). In developing park zonation, the information on wildlife distribution and their habitat condition should be considered, including habitat suitability.

In Mt. Halimun Salak National Park, the information of habitat suitability is not available or provided at once. In addition, this issue is relatively new and its study in the Indonesia is rare. In contrast with the developed country which the methods and techniques has been evaluated. Because of that, park staff is not familiar with the method and procedure to produce habitat suitability.

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Nevertheless, the park is still facing management problem in handling data with the current format, which is not so maximally useful for biodiversity analysis.

The spatial information on habitat suitability is information whose process needs spatial data of species distribution and certain habitat factors. The park collects species data is regularly. However, it is not collected through proper method and stored into the approriate format which could be further processed. In case of habitat suitability information, it has been always produced through modeling process or using certain analysis method and complex procedures. It is not practice sometimes for staff that involves with and solve the practical problem to do complex procedures.

4.1.1.2. Data and Information Analysis

At least the information related with habitat suitability must answer the questions:

• What suitability level, shows how suitable a locale has, based on considered habitat factors.

• Where, contains information of relative position of a locale.

• How extent, shows how large (area) of each suitable level.

4.1.1.3. User Analysis

According to the Decree of Mof No.355/Menhut-II/2004, park staff could be distinguished based on their task and function, as shown in the Figure 4. The Assessor of Park Data Planning and Manager of Conservation and Protection Supervision are park staff that most possibly has relation to species conservation and park management. Consider to this, these staff is chosen as potential user of this system.

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KSBTU Seksi Konservasi Wilayah 1 Seksi Konservasi Wilayah 2 Seksi Konservasi Wilayah 3 Penyaji Evaluasi & Pelaporan Penata Kerjasama & Hub. Masyarakat Penata Usaha Umum Penata Usaha Perlengkapan & Rumah Tangga Fungsional Penata Usaha Keuangan Penata Usaha Kepegawaian Penelaah & Penyusun Data Perencanaan Penata Bina Cinta Alam dan Kader Konservasi

Penata Bina Konservasi dan

Perlindungan

Penata Promosi, Informasi, & Hub. Masyarakat

Penata Usaha Kepegawaian, Perlengkapan &

Rumah Tangga Penata Rencana Program & Pelaporan Penata Usaha Umum & Keuangan Penata Bina Wisata Alam dan Kader Konservasi Penata Bina Konservasi dan Perlindungan Kepala TNGHS Manajer Stasiun Penelitian dan Perkemahan Kepala Resort

Figure 4. Organization Structure of GHSNP Based on the Decree of MoF No. 355/Menhut-II/2004

4.1.1.4. System Requirement Definition

Based on the need and information analysis, the intended system could be simply described into three parts, namely input, process, and output. It is illustrated by Figure 5.

Figure 5. Conceptual System

There are two sources of system input which are input that came from the user and from storage. The input from user commonly has a purpose to choose an

•Spatial data of species distribution

•Habitat factors of concerned species •Conservation area

boundary

•Habitat suitability map with chart

•Spatial data of species distribution

•Geoprocessed data •Geometric Operation

(geoprocessing) •Decision Rules •Classification and

summarization

Storage

INPUT

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available option. The input that from storage is used for further processed. In determining habitat suitability, some factors that usually considered are species distribution, habitat factors of concerned species (e.g.: land cover, slope, rivers, etc.), and the predicted region or area (such as protected area).

The system can be viewed as matrix of resources, products and activities. It also highlights how the basic system activities of input, processing, output, storage, and control are accomplished, and how the use of people, hardware, and software resources to support system activities. Such matrix is called system component matrix is given in the table below.

Table 2. System Component Matrix

Hardware Software Brainware Dataware

Input - ArcView (optional) Technician

Vector data format (polygon, line, point), including presence data,

habitat factors, and surface flat polygon

Processing

Minimal CPU 512MB RAM, 64MB VGA.

PCA Module, Score function, basic geo-processing function,

species distribution mapping module

Technician -

Output Computer

Display MapViewer, Chart

Technician, Manager

Suitability Information Dataset

Storage Disk Drive - Technician -

Control - - Manager -

It is important to determine representation type of data which involves directly into habitat suitability model process (i.e.: ecogeographic variables data). The very well-known spatial data representation is raster (tessellation) and vector type. One of them has their own primacy over the other, so that the utilization sometimes different (depend on the analysis purpose). Another type of data model is vector-based grid. It is actually a vector data but utilized like a grid data. This data type is also known as cell-based or vector grid but actually refers to the same thing. The vector-based grid is chosen to represent ecogeographic variable based on the reasons below:

1) Habitat suitability is formulated using ecogeographic variables. Ecogeographic variable is a spatial property of a unit of area based on the arrangement of corresponding spatial features that representing habitat factors.

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the characteristic of certain area based on related habitat factors. These spatial characteristics are assumed motivates wildlife to behave appropriately in certain land. The arrangement of habitat factor gives indication to the quality of habitat (Morrison et al., 1992). It encourages the species to exploit or stay in certain area. The examples of possible spatial properties and the corresponded spatial features are given by Table 3 and illustrated by Figure 6.

Table 3. Spatial Features and Spatial Properties Relationship

Spatial Features Feature Types Spatial Properties Forest type Polygon The frecuency, area, density of forest, etc.

Forest ecosystem Polygon The frecuency, area, density of forest, etc. Soil type Polygon The frecuency of soil type

Settlement Polygon Distance to settlement Temperature Polygon The average of temperature

Nutrient Polygon Rate, amount, etc.

River / water body Line / Polygon Distance to river, water quality parameter, etc.

Road Line Distance to road

Competitor / Predator Point Number of competitor / predator Mutualist Species Point Number and / or pattern of distribution

Figure 6. Illustration of Spatial Properties (Ecogeographical Variable) that Influencing Wildlife Response. Each Grid has Its Own Spatial Characteristic, such like Proximity (Distance to Road [represented by black line], River [represented by blue line]) and Content Arrangement (the Area of Landcover Contained In, Marked by x and y Letter)

x y

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2) Habitat suitability is estimated by using decision rules method, which its criteria are ecogeographical variables. It is well-known method applied for raster data type. The spatial operation of vector-based grid is similar to (binary) raster data type.

3) It is possible to make a compact structure of environment representation (spatial database) by using vector-based grid, since this format is built under ESRI Shapefile format. ESRI Shapefile format is used database to maintain (manage) the attributes of spatial data (spatial properties).

System processes are determined based on data input characteristic, intended output and chosen modeling method. Generally, there are three functionalities required in the system, as illustrated in the Figure 7, namely data preparation, habitat suitability model and output visualization. Data preparation sub-system has three objectives:

1) It is used to prepare the predicted land in vector-based grid format (rasterizing process). Predicted area is divided into parcels (grid), which each parcel size is determined by the user. This process needs a flat surface in polygon format. 2) It is used to generate ecogeographical variable data and embed it as spatial

properties of observed or predicted area.

3) Species distribution data is available in text or tabular format, instead of spatial format. This data is important to determine the weight of the model. The system supports the creation of species distribution data in spatial format (point feature).

For these reasons, several processes that needed in data preparation modules are geoprocessing (including dissolving, point buffering, and rasterizing), species distribution mapping, and data extraction on ecogeographical variable.

Habitat suitability model follows one of decision rules method that is Simple Additive Weighting (SAW). It is chosen consider to its simplicity in processing procedure (overlay). The weight of the model is determined by applying

Principal Component Analysis (PCA) to species distribution data. The spatial properties in the area of which the existence of species is observed, are used as a cue to determine what eco-geographical variables is important to be considered.

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The weight is defined as the maximum loading factor of the variable to the corresponded principal component, which is still interpretable.

The last functionality is classification (map rendering) and summarization. They are a set of processing used in displaying the result of habitat suitability processing. The method of classification used is linear and incremental division. The summarization process is a process to summarize the suitability model result into descriptive information (text-based and pie chart format).

The functionalities of SUITSTAT are illustrated by the figure below.

Figure 7. Major Functionalities of the SUITSTAT

4.1.2. System Analysis and Design

In the following section, the design of SUITSTAT system is described. The design of the system followed process design approach. The description is divided into three sections, namely data preparation processing, habitat suitability, graphical user interface.

Some important terms used here are explained before further discussion, such as:

Distribution Mapping

Ecogeographic Data Extraction

Data Preparation Habitat Suitability Model

Weight Calculation

Score Calculation

Score Classification

Area Summarization

MAP VIEWER Dialog Form and Component

Chart

Dialog Form and Component

Process

User Inter

face

Table

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a) Habitat suitability is the affordability of an area indicated by the availability of resources and environmental conditions necessary for relatively successful species survival and reproduction.

b) Habitat factor is spatial representation of the resources and environmental condition needed by the species for its survival.

c) Estimated land is a spatial representation of an area which has suitability value to be estimated. It is actually a collection of small (arbitrary) land unit. Each unit of area has suitability score. As well as observation unit data below, it is represented by vector-based grid or similar to adjacent isometric cell (Hirzel, 2001).

d) Observation unit is an area where measurement to ecogeographic variable was conducted. It is represented by uniform polygon such as rectangle or circle. The observation and estimated land feature are categorized as evaluated feature.

e) Ecogeographic variable is spatial properties of a unit of area based on the arrangement of corresponding habitat factor.

f) Species distribution is a collection of species position in certain space related to their survival.

4.1.2.1. Data Preparation Utility

All processes built in the system are developed by exploiting basic geometric function of MapObjects considerably. Even the design of process is narrow in application, means that is specific for MapObjects application, the process is described here consider to the importance of documentation for better development in the future.

4.1.2.1.1. Ecogeographical Data Generation

According to the definition ecogeographical variable, the generation of ecogeographical variable means to measure the arrangement (structure) of correspond habitat factor. There are three basic types of spatial feature, i.e. point, line, and polygon. Therefore, the analysis of spatial features arrangement of an

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There are two type of spatial analysis developed for SUITSTAT, i.e.: content and proximity analysis. Content analysis is intended to know the structure of certain feature in the area. Proximity analysis is used to obtain the short distance to certain feature which elucidates the contiguity (relation) of an area to its surrounding. These analyses were further developed for obtaining the attribute information of feature which satisfied the analysis. A list of analysis available in SUISTAT is provided in the Table 4.

Table 4. Several Types of Analysis to Spatial Features Features Type Analysis Type Detail analysis

The existence of point The number of point

The aggregation level of points Content Analysis

The attribute value of point The short distance value to a point Point

Proximity Analysis

The attribute value of nearest point The length of line feature

The number of segments Content Analysis

The attribute value of line feature The short distance value to a line Line

Proximity Analysis

The attribute value of nearest line The area of polygon feature The number of polygon Content Analysis

The attribute value of line feature The short distance value to a polygon Polygon

Proximity Analysis

The attribute value of nearest polygon

Basically, any type of content analysis is using the same procedure or algorithm. Specifically, content analysis is used to find the existence, number of features, size of feature dimension (such as area for polygon, length for line), and also the attribute value of considered feature inside or belonged to the evaluated feature. The algorithm of content analysis is illustrated by Figure 8.

The algorithms above were developed using the SearchShape,

SearchByDistance, and other geometric operation methods, which available (built-in methods) for shape and layer object of MapObject. Further description on these methods is available in the Appendix 3, 4, and 5.

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Figure 8. Flowchart of Content Analysis

In contrast to content analysis, neighbor or proximity analysis uses distance function, such as SearchByDistance and DistanceTo. Searching process begin with gradual distance to the searched layer. When records that containing shape was found, the process to determine the shortest distance among those shapes begins. Figure 9 shows the algorithm of proximity analysis.

Among of all types of content analysis, the exception is given to analysis of (vector-based) point aggregation which has different algorithm. The method adopts He et al. (2000) or aggregation index (AI), which applied for raster data.

The very basic of AI idea is the relation between the number shared edges of cells in i-th class (patch’s cells) to its aggregation appearance over the area. The more clump the cells, the higher the shared edges among the cells. According to He et al. (2000), the maximum aggregation level is reached when the areaclumps into one patch that has the largest ei;i (it does not have to be a square). Formally,

it could be defined as the proportion of shared edges between the patch’s cells (ei,i) with the maximum (possible) shared edges (max_ei,i). The equation of AIof

Sum the area of found shape Search shape inside Evaluated

Shapes (ES) on variable layer

Search shape crossing ES on variable layer

Search shape contained by ES on variable layer Intersect ES with

found shapes

Sum the area of found shapes

NO NO

YES YES

END START

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Figure 9. Flowchart of Proximity Analysis

Given a class i is composed by Ai cells and n is the side of largest integer

square smaller than A, then the disparity between A and n or m, is equal with A – n2_{. Afterward, the maximum shared edges in}_A

i will take one of the three forms

(He et al., 2000):

d = initial distance searched

layer

Cell / grid distance

tolerance

initial distance

d < distance tolerance ?

Search records in searched layer by ‘d’

distance upon cell

Get shape from records Records

count > 0 ?

Return dshort Get the distance from shape to cell

(dcell-shape)

Next Record

End of Records ? If dshort <

dcell-shape

Yes

dshort =

dcell-shape

Yes

Yes No

d = d * 2

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) 1 ( 2

max_e_i_,_i = n n− , when m = 0, or (4.2) 1

2 ) 1 ( 2

max_e_i_,_i = n n− + m− , when m < n, or (4.3) 2

2 ) 1 ( 2

max_ei_,i = n n− + m− , when m≥0 (4.4)

Method adjustment is needed for vector-based point aggregation measurement, since AI is originally developed for raster data. The attention is especially given to the shared edges measurement. It must be noticed that the use of shared edges in the clumping measurement method obviously needs the determination of cell size. In the vector-based, the virtual cell size is determined based on the shortest distance among points (dmin). Each point is assumed placed

on the center of imaginary squared grid (as illusrated in the Figure 10). The grid has maximum of four eligible adjacent grids, i.e. shared-edge adjacent grids with a point inside (white grids, marked by roman capital number). It does not have a shared edge with the ineligible adjacent grid (shaded grid). Hence, the main problem in measuring shared-edge is how to identify that a point is inside the eligible (white grid) and ineligible virtual grid. The solution is given by knowing the domain of inelegible and eligible grid. The definition of the domain is simple since the points are laid in the same coordinate system.

Figure 10. The Illustration of Neighboring Grid of a Point

Any point of Z(xz, yz) to the center point O(xo, yo) has horizontal and vertical

distance as defined as dx = | xo – xz | and dy = | yo – yz |, respectively. Every points

dmin

P (x0, y0)

0.5 dmin

dmin dmin

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95 Appendix 8. Forest Ecosystem (Elevation Based) in Mt. Salak

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96 Appendix 9. Land Cover of Mt. Salak

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97 Appendix 10. Slope in Mt. Salak

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98 Appendix 11. Rivers in Mt. Salak Map

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V. CONCLUSION AND RECOMMENDATION

5.1. Conclusion

1) This research shows the development a mapping system that used to produce habitat suitability information in vector-based grid format. It is supported by vector-based grid transformation, basic geo-processing tools, ecogeographical data generation, species distribution mapping, weight calculation based on presence species data, and suitability score calculation. Through SUITSTAT, the user is able to dynamically select spatial data which represents habitat factors, modify criteria and constraint, and calculate the score.

2) Based on habitat suitability model which grouped into 5 classes, Mt. Salak area is dominated by suitable class. The area with score more than the moderate suitable class covers 52.39 km2 or 39.5% from total area 132.78 km2.

3) Two javan gibbon groups are located for each low and less and suitable habitat, 13 groups in moderate suitable, 9 groups in the suitable habitat and 3 groups are living in the high suitable habitat.

5.2. Recommendation

This research promotes some idea for further development of this system: 1) Developing a modular system which could combine many algorithms in

determining habitat suitability.

2) The system that could read or integrate various spatial data format could be promising since there is no single format to represent habitat data.

3) The further development is more likely better constructed under the available open-source GIS software (as the extension) consider to the cost effectiveness, the utilization of modular framework, and availability comprehensive geoprocessing function. Some open-source GIS softwares can be used are Quantum GIS (http://qgis.org) and Jump Project.

4) The future system is that providing information through internet (web-based suitability information) could be the most challenge. At least, it should apply

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81 the most pertinent of internet utility, i.e. data retrieval from many data providers whose data is used as input for suitability model.

Related to javan gibbon conservation, some proposed recommendations are: 1) Providing spatial database for gibbon distribution data for easier to monitor

this species in Mt. Salak.

2) Management authority is suggested to begin developing monitoring plan for javan gibbon groups which living in low suitable areas.

Stand-alone GIS Application for Wildlife Distribution and Habitat Suitability (Case Study: Javan Gibbon, Gunung Salak, West Java)

STAND-ALONE GIS APPLICATION FOR WILDLIFE

DISTRIBUTION AND HABITAT SUITABILITY

(Case Study: Javan Gibbon, Gunung Salak, West Java)

WIM IKBAL NURSAL

GRADUATE PROGRAM

STAND-ALONE GIS APPLICATION FOR

WILDLIFE DISTRIBUTION AND HABITAT SUITABILITY

(Case Study: Javan Gibbon, Gunung Salak, West Java)

WIM IKBAL NURSAL

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY

FOR NATURAL RESOUCES MANAGEMENT

GRADUATE SCHOOL

STATEMENT

ACKNOWLEDGEMENT

CURRICULUM VITAE

ABSTRACT

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

STAND-ALONE GIS APPLICATION FOR WILDLIFE

DISTRIBUTION AND HABITAT SUITABILITY

(Case Study: Javan Gibbon, Gunung Salak, West Java)

WIM IKBAL NURSAL

GRADUATE PROGRAM

STAND-ALONE GIS APPLICATION FOR

WILDLIFE DISTRIBUTION AND HABITAT SUITABILITY

(Case Study: Javan Gibbon, Gunung Salak, West Java)

WIM IKBAL NURSAL

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY

FOR NATURAL RESOUCES MANAGEMENT

GRADUATE SCHOOL

STATEMENT

ACKNOWLEDGEMENT

CURRICULUM VITAE

ABSTRACT

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF APPENDICES

I.

INTRODUCTION

II.

LITERATURE REVIEW

∑

III.

METHODOLOGY

IV.

RESULTS AND DISCUSSION

Process

User Inter

face

V.

CONCLUSION AND RECOMMENDATION

Parts

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