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Tujuan penelitian ini adalah untuk mengetahuin tingkat sensitivitas garis pantai di Kepulauan Nusa Penida yang sensitif terhadap kerusakan berdasarkan.
THESIS

STUDY OF SHORELINE VULNERABILITY IN NUSA PENIDA ISLANDS

NI MADE NIA BUNGA SURYA DEWI NIM 0991261015

MASTER DEGREE PROGRAM STUDY PROGRAM OF ENVIRONMENTAL SCIENCE POSTGRADUATE PROGRAM UDAYANA UNIVERSITY DENPASAR 2011 i

THESIS

STUDY OF SHORELINE VULNERABILITY IN NUSA PENIDA ISLANDS

Thesis to get Master Degree At Master Degree Program on Environment Science Postgraduate Program Udayana University

NI MADE NIA BUNGA SURYA DEWI NIM 0991261015

MASTER DEGREE PROGRAM STUDY PROGRAM OF ENVIRONMENTAL SCIENCE POSTGRADUATE PROGRAM UDAYANA UNIVERSITY DENPASAR 2011 ii

Agreement Sheet

THIS THESIS HAS BEEN AGREED ON JANUARY 18th, 2012 AT ________________

First Supervisor

Second Supervisor

Ass. Prof. Dr. Takahiro Osawa

Prof. Ir. M. Sudiana Mahendra, MAppSc, PhD. NIP 19561102 198303 1 001

Endorsement,

Head of Graduate Study on Environmental Science Postgraduate Program Udayana University

Director of Postgraduate Program Udayana University

Prof. Ir. M. Sudiana Mahendra, MAppSc, PhD. NIP 19561102 198303 1 001

Prof. Dr. dr. A.A. Raka Sudewi, Sp.S(K). NIP 19590215 198510 2 001

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This Thesis Has Been Examined and Assessed by the Examiner Committees of Postgraduate Program Udayana University On December 29th, 2011 on _____On_____________

Based on the Letter of Agreement from Rector of Udayana University No.

: 2107/UN.14.4/HK/2011

Date

: December 28th, 2011

The Examiner committees are : Chairman

: Ass. Prof. Dr. Takahiro Osawa

Secretary

: Prof. Ir. M. Sudiana Mahendra, MAppSc, PhD.

Members

: 1. Prof. Dr. Ir. I Wayan Kasa, M.Rur. Sc 2. Dr. Ir. Ida Ayu Astarini, M.Sc

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STATEMENT LETTER

The undersigned below : Name

: Ni Made Nia Bunga Surya Dewi

NIM

: 0991261015

Date of Born : Denpasar, June 30th 1986 Address

: Jl. Kebo Iwa II No. 2, Denpasar Barat – Bali

Sincerely declare that I am not plagiarized either partly or fully the content of the thesis from others.

I created this statement honestly to be used accordingly. If later on it was not true, I am willing to be prosecuted in accordance with the existing regulation in the Republic of Indonesia.

Denpasar,______________ Regards,

Ni Made Nia Bunga Surya Dewi

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ACKNOWLEDGEMENT

First of all, the author would like to express sincere gratitude to Almighty God, “Ida Sang Hyang Widhi Wasa” for His gratefull, kindness, and blessing on me in finishing this thesis. With the all humbleness, I am most grateful to : 1. Ass. Prof. Dr. Takahiro Osawa as the first supervisor and the Vice Director of Center of Remote Sensing and Ocean Science (CReSOS) Udayana University for his timely supervision, guiding me in the planning, supporting and excusing in my study. 2. Prof. Tasuku Tanaka as Director of Center of Remote Sensing and Ocean Science (CReSOS) Udayana University, who also give many hardest supporting, suggestions to this thesis, and opportunity to visit Yamaguchi University and JAXA to follow joint education program between Udayana University and Yamaguchi University. 3. Prof. Ir. M. Sudiana Mahendra, MAppSc, PhD., as second supervisor and Head of Graduate Study on Environmental Science Postgraduate Program Udayana University, who helped corrected this thesis, suggestions, guidance during the writing of this thesis. 4. Prof. Dr. Ir. I Wayan Kasa, M.Rur.Sc as discusser, Dr. Ir. Ida Ayu Astarini, M.Sc. as examiners for spending time to criticisms, supporting, suggestions and some improvement for the thesis. 5. Prof. Masahiko Sekine and Ariyo Kanno, Ph.D, for the criticisms, discussion and suggestion and improvement for the thesis. 6. Center of Remote Sensing and Ocean Science (CReSOS) Udayana University, who has given support through excellent scholarship program until final duty of this thesis on budget year 2009 until 2011. 7. Coral Triangle Center (CTC) which has helped for the information of research location in Nusa Penida Island. Kak Marthen Welly as a project leader Nusa Penida, Bli Wira Sanjaya as a outreach officer, Mas Andreas Muljadi as a conservation coordinator for support and suggestions for the thesis. vi

8. Special thank and respect to my father my guardian Ir. I Ketut Sudarma, my mother my angel Ni Luh Nyoman Padmini, S.Pd, M.Psi, my beloved sister Ni Putu Iin Bunga Surya Dewi, ST., my beloved brother Komang Yoga Surya Dharma, my brother in law Gede Agustiawan, ST, my big family and friends (Bi Yuli, Ninik dagang, dr.Kt. Pasek C.B., Ibu, Memeq, Mba Eka, Nila, d’Ayu, d’Oga, d’Mirah, d’Intan, pri’sisil’lia (for the printer), Mbok ImaCan, Ita’s Family, D.I.N.D.A., KF’square, Bli Wirawan, Bli Gede Sastrawangsa, Pak M. Rusli, Bu Evi Triandini, BWS’ers) for their love, supported, encouragement and understanding. 9. Special thank to my husband Made Pasek Cindra Bayu, SE, Ak., for unlimited love, understanding, supporting during the process of this thesis. 10. Kak Derta ‘ReefCheck’, Pak Yunaldi Yahya ‘LINI’, Bli Komang82, Bli Gepeng Yellow Fin, Bli Karta and Family, Pak Ketut Sepel, for supporting me, share the knowledge and help collect in situ data of this research. 11. Finally, the author also wants to express my thankfulness to all of the colleagues in coastal and remote sensing year 2009, 2010, 2011 and 2012, PMIL staffs (Mbok Tu, Bli Made Karsika, Bli Wayan Nampa), Pak Helmi (UNDIP), Dr. Arthana, and especially to My beloved ‘tante’Mba Yani, Mas Aan, Pak Ketut, Bli Gede Hendrawan, Bli Weda, Bli Wayan Karang, Mba Eka Yanti, Indira, Madam Manessa, Tiwi, Yudhi, Irma, Cakra, Bli Eka, for their supports, helps and give many information and literatures for this thesis. Human work will never be perfect because humans have the advantages and shortcomings of each. With this work has endeavored made with all the existing capacity. Hopefully this final project report can be useful in the development of science and technology, and extensive knowledge of all parties.

Denpasar, Januari 2012

Author vii

ABSTRAK STUDI TINGKAT KERENTANAN GARIS PANTAI DI KEPULAUAN NUSA PENIDA Hubungan antara pesisir dan garis pantai sangatlah penting. Garis pantai merupakan garis pertahanan terakhir untuk menahan kekuatan yang datang dari laut yang akan menghancurkan keutuhan wilayah pesisir. Masyarakat yang hidup di Kepulauan Nusa Penida sebagian besar bergantung pada daerah pesisir. Jika daerah kawasan pesisir mengalami kerusakan maka, akan berdampak ke laut dan hal ini dapat menyebabkan kunjungan wisatawan menurun. Ini memiliki arti bahwa sangatlah penting untuk menjaga agar daerah wilayah pesisir tetap lestari. Dalam penelitian ini, digunakan metode Environmental Sensitivity Index (ESI) untuk mengetahui tingkat kerentanan garis pantai wilayah pesisir. Tujuan penelitian ini adalah untuk mengetahuin tingkat sensitivitas garis pantai di Kepulauan Nusa Penida yang sensitif terhadap kerusakan berdasarkan Environmental Sensitivity Index (ESI). Penelitian ini menggunakan analisis citra ALOS AVNIR-2 digabung dengan data lereng. Hasil yang didapat menunjukkan bahwa tingkat kepekaan area garis pantai dari peringkat kesatu sampai dengan peringkat kesepuluh. Peringkat kesatu adalah area kurang peka yang berlokasi di barat daya Pulau Nusa Lembongan, timur dan tenggara Pulau Nusa Ceningan. Desa Sampalan, Desa Batununggal, Desa Suana, Desa Semaya, Desa Pendem, Desa Sekartaji, Desa Batukandik, Desa Batumadeg dan Desa Bungamekar Pulau Nusa Penida. Sedangkan peringkat kesepuluh adalah daerah yang memiliki tingkat kepekaan tertinggi yang berada di Desa Lembongan Pulau Nusa Lembongan, timur laut Pulau Nusa Ceningan dan di Desa Sakti, Desa Toyapakeh, Desa Nyuh serta Desa Ped Pulau Nusa Penida. Kata kunci : Kerentanan garis pantai, Environmental Sensitivity Index (ESI), citra ALOS AVNIR-2

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ABSTRACT STUDY OF SHORELINE VULNERABILITY ON NUSA PENIDA ISLAND The interrelationship between coastal and its shoreline is important. The shoreline zone is the last line of defense against forces that may otherwise destroy a healthy coastal. People who live on the island is depend on coastal area and the coastal need to protect. If the coastal area broken, the impact will influence the ocean. It is important to manage the coastal area to keep environments. In this research, Environmental Sensitivity Index (ESI) was employed to estimate the shoreline vulnerability on Nusa Penida Island. The purpose of this research is to estimate the sensitive shoreline area of Nusa Penida Island based on Environmental Sensitivity Index. This research used using ALOS AVNIR-2 satellite data compared with slope data. The results express the sensitivity area ranked from rank 1 to 10. Rank 1 is less sensitive area where the location is in the southwest of Nusa Lembongan Island, southeast and east of Nusa Ceningan Island, Sampalan, Batununggal, Suana, Semaya, Pendem, Sekartaji, Batukandik, Batumadeg and Bungamekar villages of Nusa Penida Island. Rank 10 is very sensitive area where is the location is in the Lembongan village of Nusa Lembongan Island, north of Nusa Ceningan Island, and in the Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island. Key word

: Shoreline vulnerability, Environmental Sensitivity Index (ESI), ALOS AVNIR -2 satellite data

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SUMMARY Ni Made Nia Bunga Surya Dewi, Study of Shoreline Vulnerability In Nusa Penida Island. First supervisor: Ass. Prof. Dr. Takahiro Osawa and second supervisor: Prof. Ir. M. Sudiana Mahendra, MAppSc. PhD. The interrelationship between coastal and its shoreline is important. The shoreline zone is the last line of defence against forces that may otherwise destroy a healthy coastal. Nusa Penida Islands is an island with total coastline length is 104 km², including the two other small island which are Nusa Lembongan Island, Ceningan Island and The Islands are belong to Klungkung Regency, located in the southern part of the Coral Triangle. Bali Environment Agency (BLH, 2011) predict Nusa Penida Island will be the first island in Bali disappear cause by sea level rise in 2050. Environmental Sensitivity Index (ESI) mapping guidelines have been originally prepared by Office of Response and Restoration, National Oceanic and Atmospheric Administration (U.S. OR&R of NOAA) and they have already finished preparing Geographic Information System (GIS) based ESI maps for environmental sensitivity covering whole extent of their shoreline. Utilization of ALOS with AVNIR-2 which has 10 meters resolution provide more accurate data from the substrate type of shoreline around Nusa Penida Island and support the arrangement of the ESI index. ALOS AVNIR-2 image satellite data and field survey data were combined to produce shoreline vulnerability maps of Environmental Sensitivity Index in Nusa Penida Island. This map can be used to support regional planning strategy in research area based on environmental susceptibility. Research locations are: Nusa Penida Island (8o38' ~ 8o49' S and 115o 25'~ o 115 37' E). ALOS AVNIR-2 Satellite Data were used on April 3rd , 2009. Field data survey was done on July 14th ~ July 15th and July 17th ~ July 20th, 2011. Collected 57 points for training data and 95 points for ground truth data. The results of the accuracy of satellite image was adequate. The possibility caused by 4 factors such as classification error according to complex interaction of spatial structure of topography, error definition information from the spectral class, ground truth data and error on the satellite image itself. The accuracy of the satellite image interpretation is 66.32% with the Kappa coefficient is 0.59. The spectral analysis shown that the bedrock, seawall and stone have very similar wavelength spectral patterns (0.65µm). The conclusion were: the less sensitivity area is Rank 1 located in the southwest of Nusa Lembongan Island, southeast and east of Nusa Ceningan Island. Sampalan, Batununggal, Suana, Semaya, Pendem, Sekartaji, Batukandik, Batumadeg and Bungamekar villages of Nusa Penida Island. The very sensitivity area is Rank 10 located in the Lembongan village of Nusa Lembongan Island, north of Nusa Ceningan Island, and in the Sakti, Toyapakeh, Nyuh and Ped x

villages of Nusa Penida Island. The analyzed shown Nusa Penida Island is composed from stone (cobble, pebble, boulder, granule) and bedrock. It is very difficult to separate the substrate type of seawall, stone and bedrock because the spectral of the wavelength from each substrate type is have similar patterns and the object is very small using 10m spatial resolution of satellite image Suggestion of this research are: (1) to continue the observation annually including wind speed data and wind direction to get exposure of shoreline. Bathymetry and tidal data also consider to predict the conditions of the island next view years (2) to obtain complete ESI results have to consider to human resources and biological data (3) to using satellite data with high spatial resolution to identify the object more clearly (4) to using satellite data with no cloud, shadow of cloud and cloud cover (5) to make as much as training area to get good classification results (6) to the government is to making shore protection projects design to retain and rebuild natural systems such as bluffs, dunes, wetlands, and beaches and to protect structures and infrastructure landward of the shoreline.

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LIST OF CONTETS Page INSIDE COVER ............................................................................................ i PREREQUISITE PAGE ............................................................................... ii SUPERVISOR AGREEMENT SHEET ...................................................... iii APPROVAL PAGE OF EXAMINER COMMITTEE LETTER .............. iv STATEMENT LETTER ............................................................................... v ACKNOWLEDGEMENT .............................................................................. vi ABSTRAK ...................................................................................................... viii ABSTRACT .................................................................................................... ix SUMMARY .................................................................................................... x LIST OF CONTENTS ................................................................................... xii LIST OF FIGURES ....................................................................................... xiv LIST OF TABLES ......................................................................................... xvi LIST OF ABBREVIATIONS ....................................................................... xvii CHAPTER I INTRODUCTION 1.1 Background ......................................................................................... 1.2 Problems Formula............................................................................... 1.3 Aim and Objectives ............................................................................ 1.4 Benefit of The Research ..................................................................... CHAPTER II LITERATURE REVIEW 2.1 Shoreline ............................................................................................ 2.1.1 Definition of The Shoreline .................................................... 2.1.2 Shoreline Detection ................................................................. 2.1.3 Shoreline Indicators ................................................................ 2.1.4 Factors Affecting Sensitivity of Shoreline .............................. 2.2 Vector Data ........................................................................................ 2.3 Raster Data ......................................................................................... 2.4 Remote Sensing.................................................................................. 2.5 AVNIR-2 (The Advanced Visible and Near Infrared Radiometer type-2) ......... 2.6 The Geology Component of Nusa Penida Island ............................... 2.7 Environmental Sensitivity Index (ESI) of Shoreline ......................... 2.8 Previous Study of Environmental Sensitivity Index (ESI) ................

1 3 3 3

5 5 7 9 11 13 14 15 16 16 17 18

CHAPTER III FRAMEWORK OF RESEARCH ...................................... 20 CHAPTER IV RESEARCH METHOD 4.1 Research Location .............................................................................. 4.2 Research Instrument ........................................................................... 4.2.1 Instrument Data Process ......................................................... 4.2.2 Field Instruments .................................................................... xii

22 24 24 24

4.3 Research Procedures .......................................................................... 4.3.1 Data Collection ........................................................................ 4.3.2 Digital Image Processing ......................................................... 4.3.2.1 Image Cropping........................................................... 4.3.2.2 Geometric Correction .................................................. 4.3.2.3 Radiometric Correction ............................................... 4.3.2.4 Atmospheric Correction .............................................. 4.3.2.5 Land and Sea Separation (Image Masking) ................ 4.4 Environmental Sensitivity Index (ESI) .............................................. 4.5 Ground Data ....................................................................................... 4.6 Ground Positioning Sytem (GPS) Data.............................................. 4.7 Supervised Classification ................................................................... 4.8 Maximum Likelihood Classifier ........................................................ 4.9 Accuracy Test of Image Interpretation Result ...................................

25 25 25 25 25 26 27 28 28 43 45 46 47 48

CHAPTER V RESULT 5.1 Digital Image Processing ................................................................... 5.1.1 Atmospheric Correction ........................................................... 5.1.2 Image Masking ......................................................................... 5.2 Shoreline Habitats Classification ....................................................... 5.2.1 Training Areas .......................................................................... 5.2.2 Supervised Classification (Maximum Likelihood Method) ..... 5.2.3 Substrate Map........................................................................... 5.3 Accuracy Test of Image Interpretation Result ................................... 5.4 Shoreline Slope Classification ........................................................... 5.5 Environmental Sensitivity Index Map ...............................................

51 51 52 52 53 53 54 57 60 64

CHAPTER VI DISCUSSION 6.1 Accuracy Test of Image Multispectral Classification Result ............. 74 6.2 The Sensitive Shoreline Area in Nusa Penida Island Based on Environmental Sensitivity Index (ESI) .............................................. 75 CHAPTER VII CONCLUSIONS AND SUGGESTIONS 7.1 Conclusions ........................................................................................ 81 7.2 Suggestions ........................................................................................ 81 REFERENCES ............................................................................................... 83

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LIST OF FIGURES

Page Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4 Figure 2.5 Figure 3.1 Figure 3.2 Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4 Figure 4.5 Figure 4.6 Figure 4.7 Figure 4.8 Figure 4.9 Figure 4.10 Figure 4.11 Figure 4.12 Figure 4.13 Figure 4.14 Figure 4.15 Figure 4.16 Figure 4.17 Figure 4.18 Figure 5.1 Figure 5.2 Figure 5.3 Figure 5.4 Figure 5.5 Figure 5.6 Figure 5.7 Figure 5.8 Figure 5.9 Figure 5.10 Figure 5.11 Figure 5.12 Figure 5.13 Figure 5.14

High-water Shoreline at High-tide and a Low-water Shoreline at Low-tide ....................................................................................... Spatial Relationship of The Commonly Used Shoreline Indicators ...................................................................................... A Previous High-tide Line or the Wet/Dry Boundary .................. Geometry Building Elements In Vector GIS ................................ Raster Representation Of Reality ................................................. Framework of Research ................................................................ Field Survey Data Flowchart ........................................................ Research Location ........................................................................ Human Activity in Nusa Penida Island ........................................ Concept of ESI.............................................................................. 10 Substrate Type ......................................................................... SubstrateType Deformation .......................................................... Rank 1 ........................................................................................... Rank 2 ........................................................................................... Rank 3 ........................................................................................... Rank 4 ........................................................................................... Rank 5 ........................................................................................... Rank 6 ........................................................................................... Rank 7 ........................................................................................... Rank 8 ........................................................................................... Rank 9 ........................................................................................... Rank 10 ......................................................................................... Purpose of Using Ground Truth Data ........................................... Basic Steps in Supervised Classification ...................................... Probability Density Functions ...................................................... Image after Atmospheric Correction ............................................ After Masking the Research Area................................................. Training Area Using Training Data .............................................. Maximum Likelihood Method Using Ground Truth Data ........... Shoreline Habitat Classification in Nusa Penida Island ............... Shoreline Habitat Classification in Nusa Ceningan Island ........... Shoreline Habitat Classification in Nusa Lembongan Island ....... Spectral of Each Substrate ............................................................ Digital Elevation Model (DEM) of Nusa Penida Island ............... Slope of Nusa Penida Island ......................................................... Geology Map of Nusa Penida Island ............................................ ESI Map of Nusa Lembongan Island and Nusa Ceningan Island ESI Map of Nusa Penida Island .................................................... True Condition of Rank 1 ............................................................. xiv

6 10 11 14 15 20 21 22 23 31 34 35 36 37 37 38 39 40 40 41 42 43 44 46 47 51 52 53 54 55 55 56 60 61 62 63 64 64 65

Figure 5.15 Figure 5.16 Figure 5.17 Figure 5.18 Figure 5.19 Figure 5.20 Figure 5.21 Figure 5.22

True Condition of Rank 2 ............................................................. True Condition of Rank 3 ............................................................. True Condition of Rank 6 ............................................................. True Condition of Rank 7 ............................................................. True Condition of Rank 8 ............................................................. Erosion Destruction ...................................................................... True Condition of Rank 9 ............................................................. True Condition of Rank 10 ...........................................................

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66 67 68 69 70 71 72 73

LIST OF TABLES

Table 2.1 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5

Page AVNIR-2 Characteristic .................................................................. 16 Gain Value for Each Band ............................................................... 27 Environmental Sensitivity Index ...................................................... 29 Color Scheme for Representing The Shoreline Habitat Rankings On Maps ........................................................................... 30 Parameters, Indexes and Source of ESI .......................................... 31 Confusion Matrix for Accuracy Test ............................................... 49 Statistic Values after Atmospheric Correction................................. 51 Error Matrix Resulting from Classifying Training Set Pixels ......... 57 Classification Error of Training Set Data ........................................ 58 Error Matrix Resulting from Classifying Ground Truth Pixels ....... 59 Classification Error of Ground Truth Data ...................................... 59

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LIST OF ABBREVIATIONS

ADEOS ALOS AVNIR-2 BAKOSURTANAL

CTC DEM DN ESI GCP GIS GPS HWL IFOV JAXA MHSL MSL NASA NOAA RMS TNC U.S. OR&R

Advanced Earth Observing Satellite Advanced Land Observing Satellite The Advanced Visible and Near Infrared Radiometer type-2 Badan Koordinasi Survei dan Pemetaan Nasional (National Coordinating Agency for Surveys and Mapping) Coral Triangle Center Digital Elevation Model Digital Number Environmental Sensitivity Index Ground Control Point Geographic Information System Geo Positioning System High Water Level Instantneous Field-Of-View Japan Aerospace Exploration Agency Mean High Sea Level Mean Sea Level National Aeronautics and Space Administration (U.S) National Oceanic and Atmospheric Administration Root Mean Squares The Nature Conservancy United States Office of Response and Restoration

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CHAPTER I INTRODUCTION

1.1 Background Shoreline is important to define seawater administration borders of a province, a district, and a city related to decentralization. The shoreline can be extracted from remote sensing data. The sea water level position for shoreline used in the hydrographic mapping is mean high sea level (MHSL), while the seawater level for shore line used in geodetic mapping is mean sea level (MSL). The interrelationship between coastal and its shoreline is important. Because of the shoreline zone is the last line of defense against forces that may otherwise destroy a healthy coastal. A naturally-vegetated shoreline filters run off generated by surrounding land uses, removing harmful chemicals and nutrients. At the same time, shoreline vegetation protects coastal edges from the onslaught of waves and climate. The shoreline zone also provides critical habitat for aquatic insects, microorganism, fish, and other animals, thereby helping to maintain a balance in sensitive aquatic ecosystems. Due to coastal landscapes are developed, natural shorelines often are damaged or destroyed. Beneficial natural vegetation is cut, mowed, or replaced. In urban and rural environments alike, this often leads to eroded shorelines, degraded water quality and aquatic habitat, impaired aesthetics, and a reduction in property values. Badan Lingkungan Hidup (BLH, 2011) predict Nusa Penida Island will be

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the first island in Bali Province disappear in 2050 caused by sea level rise. Sea water level in Nusa Penida Island has increased 4 ~ 7 cm in the past 10 months in the year 2011 and it cause shoreline abrasion. Identifying sections of shoreline susceptible to sea-level rise is necessary for more effective coastal zone management, in order to increase resilience, and to help reduce the impacts of climate change on both infrastructure and human beings. Environmental Sensitivity Index (ESI) mapping guidelines have been originally prepared by Office of Response and Restoration, National Oceanic and Atmospheric Administration (U.S. OR&R of NOAA) and they have already finished preparing Geographic Information System (GIS) based ESI maps for environmental sensitivity covering whole extent of their shoreline. The natures of ESI guidelines are principally reflecting their own culture, social and economic condition. Correspond to the condition, Environmental Sensitivity Index (ESI) arrangement in Nusa Penida Island is needed to support sustainable and an integrated planning region strategy based on environmental shoreline condition and have been several approaches to vulnerability analysis that have used physical substrate characteristics to classify the sensitive shoreline, producing a ranking sections of shoreline. Tools and technology are used to support the concept making of Environmental Sensitivity Index. In this research, the arrangement of the index were supported by ALOS AVNIR-2 satellite data from Japan Aerospace Exploration Agent (JAXA) and ground truth data were integrated using

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geographic information system. The integration between ALOS AVNIR-2 satellite and field survey data were combined to produce shoreline vulnerability maps of Environmental Sensitivity Index in Nusa Penida. This map can be used to support regional planning strategy in research area based on environmental susceptibility. This research give information about the most sensitive area and less sensitive area based on Environmental Sensitivity Index for the area and the information can be decided some future steps of coastal management strategy in Nusa Penida area. Furthermore an appropriate coastal management regulation can also protect this area from destructions.

1.2 Problems Formula The question that should be answered is which shoreline area in Nusa Penida Island is sensitive with destruction based on Environmental Sensitivity Index?

1.3 Aim of Research The aim of this research is to estimate the sensitivity of shoreline area on Nusa Penida Island based on Environmental Sensitivity Index (ESI).

1.4 Benefit of The Research Environmental Sensitivity Index (ESI) are given as following : 1. To understand the sensitive area on Nusa Penida Island and give attentions to sensitive area.

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2. To determine the most susceptible area in order to take preventive action and support environment regional planning strategy in Nusa Penida Island. 3. ESI information can give recommendation to the decission makers including the stakeholders to decide the best coastal area and also considering possible impact to the region if someday destruction actually happens. 4. The methodology will be applied to the Bali Island and Indonesia area in the future.

CHAPTER II LITERATUR REVIEW

2.1 Shoreline 2.1.1

Definition of the Shoreline The shoreline is a line that demarks the precise boundary between the

shore and the water. Since the position of the water shifts with the elevation of the tide, then the position of the line at high tide is different from its position at low tide. Oertel et al. (1989) shows it is apparent that there is a high-water shoreline at high tide and a low-water shoreline at low tide. Sea level is generally defined as the elevation halfway between the high-and low-water elevations, and the generic shoreline is meant to describe the line associated with sea level rather with high or low tide. However, during the rise and fall of the tide, there are an infinite number of shorelines between high tide and low tide. An idealized definition of shoreline is that it coincides with the physical interface of land and water (Dolan et al., 1980). Despite its apparent simplicity, this definition is in practice a challenge to apply. In reality, the shoreline position changes continually through time, because of cross-shore and alongshore sediment movement in the littoral zone and especially because of the dynamic nature of water levels at the coastal boundary (waves, tides, groundwater and storm surge). The shoreline must therefore be considered in a temporal sense, and the time scale chosen will depend on the context of the investigation. For example, 5

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a swash zone study may require sampling of the shoreline position at arate of 10 samples per second, whereas for the purpose of investigating long-term shoreline change, sampling every 10 ~ 20 years may be adequate.

Figure 2.1 HW-Shoreline A High Tide and LW- Shoreline At Low Tide (A picture : Water position on steeply sloping beach) (B picture : Water position of shoreline after swash run up) (Oertel et al., 1989)

From the Figure 2.1 described A. Cross-sectional sketch of a steepy and gently sloping shore. Given a constant vertical range, the horizontal spreads of water across a gently sloping surface is considerably greater than across a steepy sloping surface; and B. After a waver breaks, the rush of water up the beachface (swash) shifts the location of an observed shoreline landward of the projected still-water-shoreline.

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The shoreline is the position of the land–water interface at one instant in time. As has been noted by several authors (List and Farris, 1999; Morton, 1991; Smith and Zarillo, 1990), the most significant and potentially incorrect assumption in many shoreline investigations is that the instantaneous shoreline represents “normal” or “average” conditions. A shoreline may also be considered over a slightly longer time scale, such as a tidal cycle, where the horizontal/vertical position of the shoreline could vary anywhere between centimeters and tens of meters (or more), depending on the beach slope, tidal range, and prevailing wave/ weather conditions. Over a longer, engineering time scale, such as 100 years, the position of the shoreline has the potential to vary by hundreds of meters or more (Komar, 1998). The shoreline is a time-dependent phenomenon that may exhibit substantial short-term variability (Morton, 1991) and this needs to be carefully considered when determining a single shoreline position.

2.1.2

Shoreline Detection The shoreline identifications involves two stages. First requires selection

and definition of a shoreline indicator that will act as a proxy for the land-water interface. The range of indicator features that have been used by coastal investigations and an overview of their associated advantages and limitation. The second stage of shoreline identification involves the detection of the chosen shoreline indicator within the available data source. Both the technique for identifying the shoreline position (shoreline detection) and the assumptions made

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regarding the definition of the shoreline (selection of the shoreline indicator) have the potential error when estimating a shoreline position (Stockdon et al., 2002). It has been suggested that the detection of a chosen visible shoreline indicator feature may be more subjective and less accurate when determined from aerial photographs compared with in situ detection in the field (Crowell, Leatherman, and Buckley, 1991; Pajak and Leatherman, 2002). Bali government regulation No. 16 in year 2009 article 44 paragraph (1) letter d explain the criteria to established shoreline boundary is 30 meters ahead towards from the ground to the beach (Peraturan Daerah Propinsi Bali, 2009). Unfortunately, many of the features indicating the position of the shoreline indicator, such as High Water Level (HWL), may be remnants of previous high-water events and may not represent the true position of the most recent maximum run up limit. An individual HWL has no reference to a tidal datum or a fixed elevation; instead, it may represent a combination of a number of factors, including preexisting beach face morphology, atmospheric (weather) conditions, and the prevailing hydrodynamic conditions. No matter which visually detected shoreline indicator is selected, by definition there can be no means of objective, quantitative control on the repeatability of this inherently subjective detection method. Despite the significant and valuable insights that have been gained at a great many coastal locations around the world, it is a necessary criticism that the prevailing visual shoreline detection techniques are overly reliant upon opportunistic data collection and subjective interpretation. There is a recognized need by coastal investigators to improve the accuracy of shoreline mapping

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(Morton, 1991). This can be achieved by the development of more objective, robust, and repeatable detection techniques. The objective detection techniques just described, along with other comparable digital image-processing methodologies, can be used to identify a robust and repeatable shoreline indicator feature for shoreline investigation. However, a fundamental shortcoming of these new objective methods is that they still do not resolve the basic question of the relation of the specific shoreline indicator feature to the land–water interface. For example, a recent comparative study (Plant et al., in press) has shown that the four digitally processed indicator features described earlier all occur between the elevations of the shorebreak and the maximum runup limit. The four methods described were independently tested for consistency (compared with each other) and accuracy (compared with survey data) and were found to be well correlated. However, the shoreline indicators were offset from each other and, to a constant but differing degree, from the timeaveraged intersection of the land and water surfaces. It is concluded that objective detection techniques are now available to coastal researchers to map a range of objective shoreline indicator features, using either a digital terrain model combined with local tidal datum information, or a supervised/unsupervised digital image-processing classification methodology.

2.1.3

Shoreline Indicators Because of the dynamic nature of the idealized shoreline boundary,

practical purposes coastal investigators have typically adopted the use of shoreline indicators. A shoreline indicator feature that used is a proxy to represent the “true”

10

shoreline position (Aarninkhof et al., 2000). Figure 2.2 illustrates the spatial relationship between many of the commonly used shoreline indicators. Individual shoreline indicators generally fall into one of two categories.

Figure 2.2 Spatial Relationship of The Commonly Used Shoreline Indicators (Aarninkhof et al., 2000)

Classifications in the first group are based on a visually discernible coastal feature, whereas classifications in the second group are based on a specific tidal datum. A visually discernible indicator is a feature that can be physically seen, for example, a previous high-tide line or the wet/ dry boundary in Figure 2.3.

11

Figure 2.3 A Previous High-Tide Line On The Wet/ Dry Boundary (Aarninkhof et al., 2000)

2.1.4

Factors Affecting Sensitivity of Shoreline Factors affecting shoreline sensitivity occur across a broad range of

spatial and temporal scales. They involve a complex combination of interactions between geologic, oceanographic, and to a lesser extent biological processes. Deposition and tectonic deformation. In this regard, a body of recent evidence indicates that a cycle of tectonic activity occurs along the convergent plate boundary. During one part of the tectonic cycle, an extended period of gradual aseismic uplift of the coastal margin occurs in response to the accumulation of strain within the subduction zone. Gradual variations in mean water level, and hence shoreline position, accompany this part of the tectonic

12

cycle. In contrast, the other part of the tectonic cycle is characterized by a major seismic event which occurs as the strain that has accumulated within the subduction zone is suddenly and dramatically released. Rapid variations in mean water level due to subsidence of the margin are associated with this part of the tectonic cycle. Superimposed upon these tectonically-induced variations in shoreline position are variations in global eustatic sea level due to the alternating growth and melting of glaciers. These repeated marine transgressions and regressions have also shaped regional coastal morphology (Komar et al.,1991). Along shorelines processes of wave attack are the primary control on shoreline vulnerability. From the standpoint of ocean water rise it is primarily the magnitude of an extreme runup event that is of particular interest. In this regard, tides, storm surges, barometric pressure effects, temperature effects, and baroclinic currents all affect mean water level. Superimposed upon these longer term elevations in mean water level are short-term variations associated with the passage of waves. Extreme water surface elevations achieved during storms, and expressed at the shoreline as wave runup, result from the simultaneous occurrence of individual maxima within this range of forcing events (Komar et al.,1991). The magnitude of wave run up is influenced not only by water levels and wave heights, but also by beach morphology. On wide, gently-sloping, dissipative beaches runup is weak because much of the incoming wave energy is expended in breaking before it reaches the shoreline. On narrow, steeply-sloping, reflective beaches runup is strong because incoming waves break right at the shoreline with little prior loss of energy (Komar et al.,1991).

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Human activities affect the sensitivity of shorelines. At longer time and larger space scales jetty construction and maintenance dredging are factors that can affect shoreline vulnerability. There are a variety of human activities that can affect shoreline vulnerability over shorter time and smaller space scales. Examples of activities typically associated with residential and commercial development include grading and excavation, surface and subsurface drainage alterations, vegetation removal, and vegetative as well as structural shoreline stabilization. These activities tend to be a particular concern along shorelines where they affect slope vulnerability. Human activities associated with recreational use and that are particularly relevant along shorelines include not only pedestrian and vehicular traffic, but also graffiti carving. These activities may result in the loss of fragile vegetation cover (Komar et al.,1991).

2.2 Vector Data Vector data is objects are represented by two main components: a set of thematic attributes that linked to a specific object class through a unique identification code, normally referred to as “object ID” and geometry building elements. Geometry building elements are points, lines, polygons, nodes and chains (Burrough, 1986). Depending on the GIS software one works with, there are two alternative vector models for representing the objects geometry. The first one is the ring model or spaghetti model, in which all point, line and polygon features are represented by separate geometric elements (only point, line and polygon) without explicit definition of topology (properties such as adjacency, inclusion) (Burrough, 1986; Bonhamcarter, 1994).

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Figure 2.4 Geometry Building Elements In Vector GIS (David et al., 1990). The second vector model is the topological model, in which there is a definition of topology through the use of chains (also called arcs) and nodes (Burrough, 1986; Bonham-carter, 1994).

2.3 Raster Data Raster data is spatial reality is represented by means of regular grid that covers the whole study area. Each cell of the raster grid receives only one value that describes the thematic content of that cell. Normally defined by: its origin (xy coordinates), its resolution (the pixel size) and its dimensions (number of rows and number of columns). Since each raster cell can only have one attribute value, combining different rasters leads to the typical layer structure that characterizes the GIS. Raster model makes it computationally easy to combine different themes using various operations (overlay analysis) and this is the main concept that will be used in the present research (Burrough, 1986; Tomlin, 1990).

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Figure 2.5 Raster Representation of Reality (David et al.,1990)

2.4 Remote Sensing Remote sensing is defined as the science and technology by which the characterestics of objects of interest can be defined, measured or analyzed the characteristics without direct contact (Japan Association on Remote Sensing, 1993). According to Sugayana et al., (2000), the major advantages of using remote sensing are as follows: 1) Repetitive coverage for monitoring of coastal and marine processes and ocean dynamics and also for monitoring the coastal environment, 2) Synoptic coverage of regional areas, 3) Comparatively cheaper than the conventional data or studies, 4) The microwave satellite and aircraft provide data in cloudy and rainy season, 5) It is also possible to provide real time picture for natural hazard evaluations like flood damages and cyclone.

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The aim and benefit of remote sensing are highly varies depend on user’s science discipline. According to Lo (1995), usually the remote sensing resulting in some form of image, then processed and interpreted to get the useful image. Special target of remote sensing is to collect the environmental and bioresource data, where that information sent to observer via energy of electromagnetic wave as carrier of information and communications link.

2.5 AVNIR-2 (The Advanced Visible and Near Infrared Radiometer type-2) The AVNIR-2 (Advanced Visible and Near Infrared Radiometer - type 2) is a visible and near infrared radiometer for observing land and coastal zones. It provides better spatial land-coverage maps and land-use classification maps for monitoring regional environments. The AVNIR-2 characteristic : Table 2.1 AVNIR-2 Characteristic (JAXA, 2006) Number of Bands

4 Band 1 : 0.42 to 0.50 micrometers

Wavelength

Band 2 : 0.52 to 0.60 micrometers Band 3 : 0.61 to 0.69 micrometers Band 4 : 0.76 to 0.89 micrometers

Spatial Resolution

10m

2.6 The Geology Basic Component of Nusa Penida Island Nusa Penida Island dominantly composed from “Sentolo Formation (Tmps)” and “Alluvium (Qa)”. Alluvium is unconsolidated (not cemented together into a solid rock) soil or sediments, which is then eroded, deposited, and reshaped by water in some form in a non-marine setting, typically made up of a

17

variety of materials, including fine particles of silt and clay and larger particles of sand and gravel. Sentolo formation is consists of limestones (gray-white-brown colored), sandstone, medium-coarse grained, composed of tuff and rock fragments (Chisholm and Hugh, 1911).

2.7 Environmental Sensitivity Index (ESI) Definitions Prepared by NOAA ESI maps have been developed digitally using GIS software, and contain three categories of information― shoreline classification, biological resources and human-use resources. Each category is significant to determine the sensitivity of an area and describing the species, habitats and economic factors that will be affected in the case of an oil spill (NOAA, 2002). The first category is shoreline classification, which is ranked according to its physical and biological character, including its relative exposure to wave and tidal energy; shoreline slope; substrate type and biological productivity and sensitivity. A shoreline’s natural persistence to the oil and potential ease of clean up also will be considered (NOAA, 2002). NOAA’s shoreline classification descriptions are found in Table 4.4. The second category in ESI mapping is biological resources, encompassing animal species and habitats potentially at risk to an oil spill. This category is segmented into seven elements: marine mammals, terrestrial mammals, reptiles and amphibians, invertebrates, habitats and plants, birds, and fish. These elements are further divided into sub-categories. For example, the following are sub-categories for habitats and plants: algae, kelp, wetlands, coral

18

reefs, etc. Attribute information about these biological resources are collected and input into a database associated with the ESI map. Such information includes the scientific and ordinary names, concentration and species number (NOAA, 2002). The final category is human-use resources, which is divided into four components: high-use recreational access locations, management areas, resource extraction locations, and archaeological/historical resource locations. Recreational access locations can include boat ramps, ports and marinas, and beaches. Wildlife protection areas, national parks, and marine sanctuaries are represented under management areas. Resource extraction locations include such things as water intakes and fishnets, while archaeological/historical resources include locations that are deemed a cultural significance (NOAA, 2002).

2.8 Previous Study of ESI Environment Sensitivity Analysis For Near Shore Region Using GIS Based ESI Map (Shintaro et al.,2004) : Recently, hazard maps relating to various types of natural disasters like flood, landslide, volcanic activity and earthquake have become common in Japan. Along with these, ESI (Environmental Sensitivity Index) maps for oil spill have also been prepared. However, practical use of ESI has not always fully examined. On the other hands, ESI mapping guidelines have been originally prepared by U.S. OR&R of NOAA (Office of Response and Restoration, National Oceanic and Atmospheric Administration) and they have already finished preparing GIS based ESI maps for environmental sensitivity covering whole extent of their shoreline. The natures of ESI guidelines are

19

principally reflecting their own culture, social and economic condition. In this study, fishery damage and its economic loss is defined as one of the major component of “environmental sensitivity” because the Sea of Okhotsk is known as one the best fishing grounds in the world and fishery industries have been underpinning Hokkaido local economy. Damage risk will be increased by Sakhalin oil and gas developing projects in the Sea of Okhotsk. This study proposes an example of ESI guideline containing fishery data around Abashiri city facing the Sea of Okhotsk. ESI maps containing information on fish catch and precise fishing grounds are thought to be able to solve conflictions between stakeholders for managing spill incident and compensation. Environmental Sensitivity Index Shoreline Classification Of The Alaskan Beaufort Sea and Chukchi Sea (Research Planning Inc, 2002) : The Beaufort Sea was mapped between the Colville River and Point Barrow in the west and between the Canning River and the Canadian border to the east. The middle section of the Beaufort Sea had recently (1994-1996) been mapped and the shoreline data were incorporated into hardcopy ESI maps and digital data produced by the National Oceanic and Atmospheric Administration (NOAA) in 1999. The Chukchi Sea was mapped from Point Barrow to Point Hope. This research shows the shoreline mapped for each area and the shoreline classification methods and final products are described in this research.

CHAPTER III FRAMEWORK OF RESEARCH

The conceptual framework to estimate the shoreline vulnerability is shown in Figure 3.1. The yellow line box, shows the data process by ArcGIS 9.3 software and the green line box shows the data process by ENVI 4.7 software.

Nusa Penida Topographic Map

ALOS AVNIR-2

DEM

Image Cropping

(Digital Elevation Model)

Slope

Substrate

Vector Conversion

Vector Conversion

Slope grid

Substrate grid

Re-class

Re-class

Slope Reclass

SubstrateReclass

Geometric Correction Radiometric Correction Atmospheric Correction (Dark Pixel Substraction) Masking

Training Area

Reference Data

Field Survey Supervised Classification (Maximum likelihood) Classification Result

Arithmetic Overlay (Multiply)

SHORELINE – ESI MAP OF NUSA PENIDA

Accuracy of Image Analysis Map of Shoreline Substrate in Nusa Penida Island

Figure 3.1 Framework of Research

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Ground Truth Data

21

Figure 3.2 shows the field surveys data flowchart. The field survey data is divided into two parts, training data (reference data) and ground truth data. The training data is employed for supervised classification. The ground truth data is to measure and give information about the actual conditions on the ground in order to determine the relationship between remote sensing data and the object to be observed. Field Survey Data ( 152 Points)

80%

20%

Training Data (57 Points)

Ground Truth Data (95 Points)

Accuracy 82.46%

Accuracy 66.32%

Figure 3.2 Field Survey Data Flowchart

CHAPTER IV RESEARCH METHOD

4.1 Research Location Figure 4.1 show the research location of Nusa Penida Island. This islands is an island with total area of coastline is 104 km², including the two other small island, Nusa Penida Island, Lembongan Island, Ceningan Island where the geographical position of Nusa Penida Island is 8°38' ~ 8°49' S and 115°37'E.

115° 25' ~

Around 146,000 tourists visit the island every year to experience

scenery and marine life unrivalled by other venues in Bali (Welly et al., 2010).

Figure 4.1 Research Location 22

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The sub-regency has in total of 20,000 hectare of land mass, and populated by approximately 46,745 people living in 16 villages (Welly et al., 2010). The main source of livelihood for people of Nusa Penida is seaweed farming and marine tourism (Figure 4.2). It make the people in Nusa Penida depend on coastal area. If the coastal area broken, the impact will influence the ocean and the tourism will decrease. It important to manage the coastal area to mantain the environment with rich natural resources.

Figure 4.2 Human Activities in Nusa Penida Island

Nusa Penida Island have 230.07 hectares of mangrove and majority can be found in Nusa Lembongan and Nusa Ceningan. The other world famous are Manta Ray (Manta birostrisof), Green Turtle (Chelonia mydas), Hawksbill Turtle

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(Eretmochelys imbricate), Dugong (Sea manatee), Sperm Whale (Physeter catodon), several species of dolphins and the endangered ocean sunfish also known as Mola mola is an icon of Nusa Penida Island, is a deep sea fish that inhabits the shallows around Nusa Penida Island between July to September and attracts divers from around the world. According to recently conducted Rapid Ecological Assessment in 2008 with Gerry Allen (fish expert) and Mark Erdmann (coral expert), Nusa Penida waters has at least 296 species of coral reefs and 576 species of reef fishes, including five recently discovered species (Welly et al., 2010).

4.2 Research Instrument The instruments used in this study are as follows: 4.2.1. Instrument Data Process a. ENVI 4.7 (remote sensing software) b. ILWIS 3.6 (DEM analyze) c. ArcGIS 9.3 (GIS software) d. Map Source 6.13.7 (GPS software) e. Microsoft Excel 2007 for analysis attribute data 4.2.2. Field Instruments a. Garmin hand GPS (Global Positioning System) type GPS 60. Specifications: 500 waypoints with name/ graphic symbol, current speed, average speed, resettable max. speed, trip timer and trip distance, built-in celestial tables for best times to fish and hunt, sun

25

and moon rise, set and location, More than 100 plus user datum, position format (Lat/ Lon, UTM). b. Stationery (pencil, coloring pencil, paper, nonius of caliper). c. Digital Camera (SonyDSC-W170, 10.1 Mega Pixels).

4.3 Research Procedures 4.3.1

Data Collection The research materials collected are as follows:

1. AVNIR-2 satellite data recorded on April 3rd 2009 (ALAV2169943770). 2. Topography map of Bali 1 : 25,000 in 2000 from National Coordinating Agency for Surveys and Mapping (BAKOSURTANAL).

4.3.2

Digital Image Processing

4.3.2.1 Image Cropping In remote sensing, satellite sweep large areas depending on the spatial resolution of satellite sensors on the spacecraft. To limit the image according to the research sites, cropping was done. Cropping the image is useful to clarify the research sites that facilitate the process of interpretation. 4.3.2.2 Geometric Correction Geometric correction was done because geometric distortion caused there were moving up of pixel position from real position. This condition occur because there were incompletely of scan deflection system work and instability of sensor and satellite. There were two steps in this process, as follows:

26

a. Coordinate Transformation (Geometric Transformation) The step has been done by Ground Control Point (GCP) which was obtained from direct field using GPS or to know it in the simultaneous image and the guide daGrota (e.g. satellite image, air photograph, topography map). In this research, GCP was determined by topography map which scale 1 : 25,000, and images are assigned the same coordinate system with parameters: 1). Projected Coordinate System: UTM , 2). Units: Meters, 3). Zone: 50S, 4). Datum: WGS_1984. Then the result of GCP placed to image with accuracy level is one pixel. Placing the true GCP produced transformation matrix of relation between image point and selected projection system (Lillesand et al., 2008). b. Re-sampling The resampling technique of nearest neighbor (assigns the digital number (DN) of the closest input pixel (in terms of coordinate location) to the corresponding output pixel) was employed. The minimum five of GCP was employed for analysis level accuracy and the individual error transformation value with Root Mean Squares (RMS) near or under 0.5. If RMS above 0.5, it means the classification results or other analytical precision is reduced and position of objects in the image does not exactly same in the field (Lillesand et al., 2008). 4.3.2.3 Radiometric Correction, used for modification of DN of each pixel in bands of image so influence of noise could be eliminate. Nevertheless, target of this process is to reconstruct the digital number each pixel of bands of image so

27

that calibrated in physical (from raw data of sensor). The spectral radiance for particular band was calculated using following equation (As-syakur et al.,2010) ........... (1) Where: =

at-sensor spectral radiance (mWcm-2sr-1μm-1)

DN

=

digital number (the pixel values in the original image data files)

a

=

gain value

b

=

offset value

That was different for each image band, and also depends on the gain setting that was used to acquire the image. Gain value for AVNIR-2 is presented in Table 4.1 Table 4.1 Gain Value for Each Band (JAXA, 2006) Band Number

Gain Value

1 2 3 4

0.5800 0.5730 0.5020 0.5570

4.3.2.4 Atmospheric Correction The ‘dark pixel substraction’ method was applied to remove atmospheric effects. These atmospheric characteristics are used to invert the image radiance to scaled surface reflectance asuming that dark pixel have 0 DN or nearly 0 (Lillesand et al., 2008). -

Locate area in image with dark pixel

-

Record DNdark for 0 reflectance feature in each spectral band

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-

Substract DNdark-band_n from all pixel in band n

The Equation: ……… (3) 4.3.2.5 Land and Sea Separation (Image Masking) Land object is not necessary, therefore land and sea object must be separated by masking process (Image Masking). The basic of image masking is to determine limited value between land and sea pixel using band 4 of the image. First step is sampling the limit point of land and sea. After that, the value that more than limit value of land must be given value 0 (zero) for masking (Lillesand et al., 2008).

4.4 Environmental Sensitivity Index (ESI) In coastal environments the shoreline habitats have a high likelihood of being directly oiled when the oil spill impact the shoreline (NOAA, 2002). It has been recognized that the oil fate and effects are shoretype-dependant, and most of the clean up methods are, therefore, shoreline-specific (Hayes and Michel, 1997; Gundlach and Hayes, 1978). The Vulnerability Index proposed by Gundlach and Hayes (1978) has been modified and refined and the present ESI system is the widely used one. The present ranking system for shoreline habitats is mostly developed for Sub-Arctic, Temperate and Tropical zones. The system has also been modified to include shoreline types (NOAA, 1995). Environmental Sensitivity Index (known also as 10 points procedure) that produced by NOAA (2002) is probably the best-known procedure for ESI mapping.

29

This procedure aims to produce one map contains information about shoreline habitats classified according to a scale relating to sensitivity, natural persistence.

Table 4.2 Environmental Sensitivity Index (NOAA, 2002)

VERY SENSITIVITY

LESS SENSITIVITY

RANK

1

Impermeable Vertical Substrate

2

Impermeable Non-Vertical Substrate

3

Semi-Permeable Substrate

4

Medium Permeability

5

Medium-to-High Permeability

6

High Permeability

7

Flat, Permeable Substrate

8

Impermeable Substrate

9

Flat, SemiPermeable Substrate

10

Vegetated Emergent Wetlands

PHYSICAL FACTOR

Composed of steepy dipping vertical bedrock. and a slope of 30° or greater is included into this ranking. This shoreline is similar to that in Rank 1, except the slope is less than 30°, composed with bedrock This shoreline is composed of low-sloping profile, the substrate is semi-permeable (fine-to medium-grain sand) and the slope is very low less then three degrees. The substrate is permeable (coarse – grained sand). The grain of this shoreline is much coarser than that in Rank 3. Its slope is between 5 and 15°. This shoreline composed with Medium-to-high permeability of the substrate (mixed sand and gravel) with slope is intermediate between 8 and 15°. This shoreline is intermediate to steep, between 10 and 20°. Composed of coarse grained sands, gravel of varying sizes and possibly shell fragments The highly permeable substrate is dominated by sand and gravel to boulder-sized components. The beach usually flat, less than three degrees. This shore line is similar to that in Rank 2. The substrate is compacted and hard, composed of bedrock, man-made materials (seawall), or stiff clay, and the slope is greater than 15°. Usually found along bay. These are dominated by very soft mud or muddy sand, in the wetland areas with a flat slope, less than three degrees These shoreline elements include mud to sand, marshes, mangroves and other vegetated wet lands with flat slope, less than three degrees.

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The classification scheme (Table 4.2), is based on an understanding of the physical character of the shoreline environment. The sensitivity ranking is controlled by the following factors: Shoreline slope and Substrate type (grain size, mobility, penetration and/or burial, and trafficability). Shorelines are mapped with different colors to indicate their sensitivity. On ESI maps, warm colors like orange and red are used to indicate the shorelines that are most sensitive to erosion. Cool colors like blue and purple denote the least sensitive shorelines, such as rocky headlands and sand and gravel beaches. Shades of green denote shorelines of moderate sensitivity (NOAA, 2002). Table 4.3 Color Scheme for Representing the Shoreline Habitat Rankings on Maps (NOAA, 2002) ESI RANK

COLOR

RGB

1

Dark Purple

119/38/105

2

Light Purple

174/153/191

3

Blue

0/151/212

4

Light Blue

146/209/241

5

Light Blue Green

152/206/201

6

Green

0/149/32

7

Olive

214/186/0

8

Yellow

255/232/0

9

Orange

248/163/0

10

Red

214/0/24

This research only used shoreline habitats classification because if used three main components, it need Ecologist (degree in marine biology experience,

31

plus familiarity with the local affected habitats and organisms) and Archaeologist (main responsibilities are identifying, updating archaeological and historical sites). The concept about ESI parameters use, indexes, and source to get the shoreline vulnerability based on ESI map shows in the Table 4.4 and Figure 4.3 shows the ESI concept is to make easier to understanding how to get ESI map.

Figure 4.3 Concept of ESI

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Table 4.4 Parameters, Indexes and Source of ESI NO. PARAMETER 1 Shoreline slope

SOURCE Digital Elevation Model (DEM) and Slope

2

Ground truth survey with GPS

Substrate type

NO. INDEXES 1 Impermeable Vertical Substrate

SOURCE Field surveys data, combine with : ALOS AVNIR-2

2

Impermeable Non-Vertical Substrate

Field surveys data, combine with : ALOS AVNIR-2

3

Semi-Permeable Substrate

Field surveys data, combine with : ALOS AVNIR-2

4

Medium Permeability

Field surveys data, combine with : ALOS AVNIR-2

5

Medium-to-High Permeability

Field surveys data, combine with : ALOS AVNIR-2

6

High Permeability

Field surveys data, combine with : ALOS AVNIR-2

7

Flat, Permeable Substrate

Field surveys data, combine with : ALOS AVNIR-2

8

Impermeable Substrate

Field surveys data, combine with : ALOS AVNIR-2

9

Flat, Semi-Permeable Substrate

Field surveys data, combine with : ALOS AVNIR-2

10

Vegetated Emergent Wetlands

Field surveys data, combine with : ALOS AVNIR-2

In practice, the sensitivity of a particular shoreline habitat is the integration of the following factors (Hayes and Gundlach, 1975; Michel et al., 1978; RPI, 1996; NOAA, 2002): 1. Shoreline slope Shoreline slope is a measure of the steepness of the intertidal zone between maximum high and low tides. It can be characterized as steep (greater than 30 degrees), moderate (between 30 and 5 degrees), or flat (less than 5 degrees). One of the most important factors that determine the shoreline sensitivity is the slope of the intertidal zone. Shoreline slope has a pronounced effect on wave reflection and breaking. Waves usually broken or even reflected in place in steep intertidal areas. While flat intertidal areas promote dissipation of wave

33

energy further offshore, and in which natural persistence remain longer (NOAA, 2002). 2. Substrate type In ESI mapping substrate types (Figure 4.4) are classified as following (NOAA) : 

Bedrock, which can be further divided into impermeable and permeable;



Sediments, which are divided by grain size (measure using sliding-term) as :



-

Mud, consisting of silt and clay

less than 0.06mm

-

Fine- to medium-grained sand

from 0.06-1 mm

-

Coarse-grained sand

from 1-2 mm

-

Granule

from 2-4 mm

-

Pebble

from 4-64 mm

-

Cobble

from 64-256 mm

-

Boulder

greater than 256 mm

Man-made materials, such as : -

Riprap, or broken rock of various sizes , usually cobble or larger, that are permeable to natural persistence penetration.

-

Seawalls that are composed of solid material, such as concrete or steel which are impermeable to natural persistence penetration.

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Figure 4.4 10 Substrate Type (NOAA, 2002) It is very difficult to separate the 10 substrate type of wavelength spectral. The 10 substrate type classification, deformation was divided into 6 categories (Figure 4.5) and it gave impact to the ESI Rank (missing one rank or more).

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Bedrock

Bedrock

(Natural)

(Natural)

Med-Grained Sand Sand Coarse Sand Boulder Cobble Stone Pebble Granule Seawall

Seawall

(Artificial)

(Artificial)

Mud

Mud

Mangrove

Mangrove

Figure 4.5 Substrate Type Deformation However the most important distinction is between the bedrock and unconsolidated sediments. Some properties such as permeability, trafficability, grain size, sorting and grading are most important in describing the substrate type. For example, deepest penetration of natural persistence is expected in well-sorted poorly grade coarse gravels. On the other hand, saturated muddy sediments have much lower permeability and penetration and/or burial of natural persistence is limited. Hence, gravely shorelines are usually given higher ESI values than finegrained ones. Thinking about the use of heavy machinery in clean up method, trafficability of the substrate is an important aspect that should be taken into account in assigning ESI rankings (Hayes, 1996; NOAA, 2002).

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3. ESI Rank Condition Rank of 1: Impermeable Vertical Substrate The essential elements are:  Substrate is impermeable (usually bedrock, cliffs).  Slope of the intertidal zone is 30 degrees or greater.  No clean up is generally required or recommended if destruction happen. The example picture of real condition of Rank 1 is in Figure 4.6.

Figure 4.6 Rank 1 (NOAA, 2002)

Rank of 2: Impermeable Non-Vertical Substrate The essential elements are:  Substrate is impermeable (bedrock)  Slope of the intertidal zone is usually less than 30 degrees.  Clean up is not necessary except for removing destruction in areas of high recreational use, or to protect a nearshore resource. The example picture of real condition of Rank 1 is in Figure 4.7.

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Figure 4.7 Rank 2 (NOAA, 2002)

Rank of 3: Semi-Permeable Substrate The essential elements are:  The substrate is semi-permeable (fine-to medium-grained sand  The slope is very low, less than 5 degrees. The example picture of real condition of Rank 1 is in Figure 4.8

Figure 4.8 Rank 3 (NOAA, 2002) Rank of 4: Medium Permeability The essential elements are:

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 The substrate is permeable (coarse-grained sand).  The slope is intermediate, between 5 and 15 degrees.  Coarse-grained sand beaches are ranked separately and higher than fine- to medium- grained sand beaches because of the potential for destruction. The example picture of real condition of Rank 1 is in Figure 4.9.

Figure 4.9 Rank 4 (NOAA, 2002) Rank of 5 : Medium-to-High Permeability The essential elements are:  The slope is intermediate, between eight and 15 degrees.  Medium-to-high permeability of the substrate (mixed sand and gravel, pebble, composed of bedrock, shell fragments, or coral rubble).  Spatial variations in the distribution of grain sizes are significant, with finer-grained (sand to pebbles) and (cobbles to boulders). The example picture of real condition of Rank 1 is in Figure 4.10.

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Figure 4.10 Rank 5 (NOAA, 2002)

Rank of 6 : High Permeability The essential elements are:  The substrate is highly permeable with fine-grained gravel beaches are composed primarily of pebbles and cobbles (from 4 to 256 mm), with boulders as a minor fraction. Little sand is evident on the surface, and there is less than 20 percent sand in the subsurface. There can be zones of pure pebbles orcobbles, with the pebbles forming berms at the high-tide line and the cobbles and boulders dominating the lower beachface.  The slope is intermediate to steep, between ten and 20 degrees. The example picture of real condition of Rank 1 is in Figure 4.11.

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Figure 4.11 Rank 6 (NOAA, 2002)

Rank of 7 : Flat, Permeable Substrate The essential elements are:  It is flat (less than three degrees).  The highly permeable substrate is dominated by sand, although there may be gravel components. The example picture of real condition of Rank 1 is in Figure 4.12.

Figure 4.12 Rank 7 (NOAA, 2002)

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Rank of 8 : Impermeable Substrate The essential elements are:  The type of bedrock can be highly variable, from smooth, vertical bedrock, to rubble slopes  Substrate is hard, composed of bedrock, man-made materials, or stiff clay.  Slope is generally steep (greater than 15 degrees).  Clean up is often difficult and intrusive. Seawall and riprap are the manmade equivalents. With added this component it is shown this placed at the high-tide line where the highest destruction are found and the riprap boulders are sized so that they are not reworked by storm waves. The example picture of real condition of Rank 1 is in Figure 4.13.

Figure 4.13 Rank 8 (NOAA, 2002)

Rank of 9 : Flat, Semi-Permeable Substrate The essential elements are:  The substrate is flat (less than three degrees) and dominated by mud.  Width can vary from a few meters to nearly one kilometer.

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The example picture of real condition of Rank 1 is in Figure 4.14.

Figure 4.14 Rank 9 (NOAA, 2002) Rank of 10 : Vegetated Emergent Wetlands The essential elements are:  The slope is flat and can vary from mud to sand, though high organic, muddy soils are most common.  Various types of wetland vegetation, swamps, scrub-shrub wetlands, mangroves.  Marshes, mangroves, and other vegetated wetlands are the most sensitive habitats because of their high biological use and value, difficulty to clean up, and potential for long-term impacts to many organisms. When present, mangroves are considered a specific habitat type and are not grouped with scrub-shrub vegetation. The example picture of real condition of Rank 1 is in Figure 4.15.

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Figure 4.15 Rank 10 (NOAA, 2002)

4.5 Ground Data Ground data, in some cases called ground "truth" is defined as the observation, measurement and collection of information about the actual conditions on the ground in order to determine the relationship between remote sensing data and the object to be observed. Generally ground data should be collected at the same time as data acquisition by the remote sensor, or at least within the time that the environmental condition does not change. It should not be inferred that the use of the word "truth" implies that ground truth data is not without error. Ground data is used as for sensor design, calibration and validation, and supplemental use, as shown in Figure 4.16. For this research, collected the training sample of gound data has just been done for classification minimum 5 point for each object (Lillesand et al., 2008).

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Figure 4.16 Purpose of Using Ground Truth Data (Lillesand et al., 2008).

For the sensor design, spectral characteristics are measured by a fotospectrometer to determine the optimum wavelength range and the band width. For supplemental purposes, there are two applications; analysis and data correction. The former case, for example, is ground investigation, at a test area, to collect training sample data for classification. The latter case, for example, is a survey of ground control points for geometric correction (Lillesand et al., 2008). The items to be investigated by ground data are as follows : a. Information about the environment, the sun azimuth and elevation, irradiance of the sun, air temperature, humidity, wind direction, wind velocity, ground surface condition, dew, precipitation. Information about such as the object type, status, spectral characteristics, circumstances, surface temperature. b. Ground data will mainly include identification of the object to be observed, and measurement by a spectrometer, as well as visual interpretation of aerial photographs and survey by existing maps, and a review of existing literature and statistics.

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c. Depending on the purpose, the above items and the time of ground investigation should be carefully selected.

4.6 Ground Positioning Sytem (GPS) Data In order to achieve accurate geometric correction, ground control points with known coordinates are needed. The requirements of ground control points are that the point should be identical and recognizable both on the image and on the ground or map, and its image coordinates (pixel number and line number) geographic coordinates (latitude, longitude and height), should be measurable (Lillesand et al., 2008). Use of a topographic map is the easiest way to determine the position of ground control point. However maps are not always available, especially in developing countries. In such cases, control surveys had previously been required. Today, however GPS (Global Positioning System) can provide geographic coordinates in a short time using a GPS receiver to measure time information from multiple navigation satellites (Lillesand et al., 2008). GPS is a technique used to determine the coordinates of a GPS receiver which receives radio signals from more than four navigation satellites. The received navigation message includes exact time and orbit elements which can be converted into the satellite position. GPS has 18 satellites in total, at an altitude of 20,000 km, with three satellites each in six different orbits, which enable any point on the earth to view at least four satellites. The relative positioning method determines the relative relationship between a known point and an unknown point

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to the measured. In this case, at least two GPS receivers should be located at the same time. The accuracy is 0.1~1 ppm of the base length between a known point and an unknown point. It is about 2 ~ 5 cm in planimetric accuracy and 20 ~ 30 cm in height accuracy (Lillesand et al., 2008).

4.7 Supervised Classification

Figure 4.17 Basic Steps in Supervised Classification (Lillesand et al., 2008). Figure 4.17 summarizes the three basic steps involved in a typical supervised classification procedure. In the training stage (1), the analyst identifies representative training areas and develops a numerical description of the spectral attributes of each classification type of interest in the scene. Next, in the classification stage (2), each pixel in the image data set is categorized into the classification class it most closely resembles. If the pixel is insufficiently similar to any training data set, it is usually labeled “unknown”. After the entire data set has been categorized, the results are presented in the output stage (3). Being digital in character, the results may be used in a number of different ways. Three

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typical forms of output products are thematic maps, tables of full scene or subscene area statistics for the various classification classes, and digital data files amenable to inclusion in GIS (Lillesand et al., 2008).

4.8 Maximum Likelihood Classifier The maximum likelihood classifier is one of the most popular methods of classification in remote sensing, in which a pixel with the maximum likelihood is classified into the corresponding class. The maximum likelihood quantitatively evaluates both of variance and covariance of the category spectral response patterns when classifying an unknown pixel (Lillesand et al., 2008).

Figure 4.18 Probability Density Functions (Lillesand et al., 2008).

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The probability density functions (Figure 4.18) are used to classify an unidentified pixel by computing the probability of the pixel value belonging to each category. That is, the computer would calculate the probability of the pixel value occurring in the distribution of class “corn”, then the likelihood of its occurring in class “sand”, and so on. After evaluating the probability in each category, the pixel would be assigned to the most likely class (highest probability value) or labeled “unknown” if the probability values are all below a threshold set by the analyst (Lillesand et al., 2008).

4.9 Accuracy Test of Image Interpretation Result The purpose of this step is to determine the accuracy level and truth of the image interpretation. This step can be done if there are two types of data which can be compared to the results of the grouping of data and image data in ground truth data (from the test data with ground pixel check). Determination of the number and location of samples obtained with a random sample from each class. The number of samples of right and wrong can be found from ground test data. One of the most common means of expressing classification accuracy is the preparation of a classification error matrix (sometimes called a confusion matrix or a contingency table) (Lillesand et al., 2008). An error matrix is an image analyst that has been prepared to determine how well classification has categorized a representative subset of pixels used in the training process of a supervised classification. This matrix stems from classifying the sampled training set pixels and listing the known cover types used

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fro training (columns) versus the pixels actually classified into each category by the classifier (rows) (Lillesand et al., 2008). Several characteristics about classification performance are expressed by an error matrix, the various classification errors of omission (exclusion) and commission (inclusion). Note in Table 4.5 that the training set pixels that are classified into the proper categories are located along the major diagonal of the error matrix (running from upper left to lower right). All non diagonal elements of the matrix represent errors of omission or commission. Omission errors correspond to nondiagonal column elements (e.g., the pixels that should have been classified as “B” were omitted from that category). Commission errors are represented by nondiagonal row elements (e.g., “D” pixels plus “F” pixels were improperly included in the “E” category) (Lillesand et al., 2008). Table 4.5 Confusion Matrix for Accuracy Test Ground Truth Trial Result Data Classification Result Data X Y Z Total

(Lillesand and Kiefer, 2008) Omission errors Commission errors Producer’s accuracy User’s accuracy Overall accurancy

X A D G P

Y B E H Q

Z C F I R

Total M N O T

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Several other descriptive measures can be obtained from the error matrix. The overall accuracy is computed by dividing the total number of correctly classified pixels (i.e., the sum of the elements along the major diagonal) by the total numberof reference pixels. Likewise, the accuracies of individual categories can be calculated by dividing the number of correctly classified pixels in each category by either the total number of pixels in the corresponding row or column. What are often termed producer’s accuracies result from dividing the number of correctly classified pixels in each category (on the major diagonal) by the number of training set pixels used for that category (the column total). This figure indicates how well training set pixels of the given cover type are classified. User’s accuracies are computed by dividing the number of correctly classified pixels in each category by the total number of pixels that were classified in that category (the row total). This figure is a measure of commission error and indicates the probability that a pixel classified into a given category actually represents that category on the ground (Lillesand et al., 2008).

CHAPTER V RESULTS

5.1 Digital Image Processing 5.1.1 Atmospheric Correction Resulting of reflectance values with the values of the dark pixel resulting in reflectance values at 0, as shown in Figure 5.1 and the values of images after atmospheric was correction shown in Table 5.1.

Figure 5.1 Image after Atmospheric Correction

Table 5.1 Statistic Values After Atmospheric Correction Band 1

Band 2

Band 3

Band 4

Minimum

0

0

0

0

Maximum

104.664

123.768

116.967

209.585

Mean

12.975

15.060

11.384

30.888

Std.Dev

9.098

12.474

11.483

36.945

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Table 5.1 shown the reflectance DN value of atmospheric correction. Before the atmospheric correction was done, the minimum DN value is not yet 0. It mean the satellite data is still have influence of noise.

5.1.2

Image Masking Image masking is to determine limited value between land and sea pixel

using band 4 of the image. First step is sampling the limit point of land and sea. After that, the value that more than limit value of land must be given value 0 (zero) for masking. Masking is also erase the noise from the image like cloud (Figure 5.2).

Figure 5.2 After Masking the Research Area

5.2

Shoreline Habitats Classification There are five step to define shoreline habitats classification :

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5.2.1

Training Areas To make training areas, training data give reference for software to

separate the objects based on characteristics of training data for each object. This research use 57 points (Figure 5.3) for substrate type (10 bedrock, 7 mud, 10 mangrove, 10 sand, 10 seawall and 10 stone) (Figure 5.3).

Figure 5.3 Training Area Using Training Data

5.2.2

Supervised Classification (Maximum Likelihood Method) Supervised classification (Maximum Likelihood Method) classified

reference data for object x in the y pixel to be classification x in pixel y, that’s why the accuracy of training data will get a good accuracy >75% because this is already a provision in this algorithm. In the end, the accuracy use ground truth data, not training data because the function of training data is to guiding ground

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truth data. In this research 95 points ground truth datas were used for Supervised Classification (10 points for Bedrock, 14 points for Stone, 27 points for Sand, 7 points for Mud, 12 points for Seawall and 25 points for Mangrove) (Figure 5.4) .

Figure 5.4 Maximum Likelihood Method of Ground Truth Data

5.2.3

Substrate Map Figure 5.5, 5.6 and 5.7 showed the substrate types of the study area. There

are six major classes in the coastal zone. The substrate map is converted into raster map with numerical values ranked according some considerations and turn the sensitivity of the shoreline. These considerations are, oil penetration and persistence, ease of clean up, and sensitive with the abrasion and erosion phenomenon.

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Figure 5.5 Shoreline Habitat Classification on Nusa Penida Island

Figure 5.6 Shoreline Habitat Classification on Nusa Ceningan Island

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Figure 5.7 Shoreline Habitat Classification on Nusa Lembongan Island

Figure of 5.5, 5.6 and 5.7, show Nusa Penida Island is composed from stone. Nusa Ceningan Island is composed from stone and in the west-north of Nusa Ceningan is composed from sand and little mangrove vegetation, and Nusa Lembongan Island is composed from stone in the south-west, half of island is composed from mangrove vegetation, and in the west-north is composed from sand and seawall. These figures also shown that the Nusa Lembongan at seawall position, erosion has already occurred.

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5.3

Accuracy Test of Image Interpretation Results The results of multispectral classification process shown in the Table

5.2 - 5.5. To get the real conditions in the area, the field surveys were conducted to complement additional data or the accuracy of the image classification. Error matrixes compare on a category-by-category basis based on the relationship between training set data and ground truth data. There are 57 points has been use for training data. Table 5.2 shown from the 10 points of bedrock, only 6 point compatible with bedrock. From the 10 points of mangrove, only 10 point compatible with mangrove. From the 7 points of mud, only 7 point compatible with mud. From the 10 points of sand, only 7 point compatible with sand. From the 10 points of seawall, only 8 point compatible with seawall. From the 10 points of stone, only 9 point compatible with stone. Table 5.2 Error Matrix Resulting from Classifying Training Set Pixels

Bedrock Mangrove Mud Sand Seawall Stone Total

Training Set Data (Known Cover Types) in Pixel Bedrock Mangrove Mud Sand Seawall Stone Total 0 0 1 0 0 7 6 0 0 0 0 0 10 10 0 0 0 0 0 7 7 2 0 0 2 0 11 7 0 0 0 1 1 10 8 2 0 0 1 0 12 9 10 10 7 10 10 10 57

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Table 5.3 Classification Error of Training Set Data Accuracy (%) Errors (%) Prod. Acc. User Acc. Commission Omission Bedrock 60 85.71 14.29 40 Mangrove 100 100 0 0 Mud 100 100 0 0 Sand 70 63.64 36.26 30 Seawall 80 80 20 20 Stone 90 75 25 10 The accuracy level of all classes (Overall Accuracy) 82.46% Classes

The error matrix in the Table 5.3 indicates an overall accuracy of substrates is 82.46%. Producer’s accuracies range from just 60% (“bedrock”) to 100 % (“Mangrove” and “Mud”) and user’s accuracies vary from 63.64% (“Sand”) to 100% (“Mangrove” and “Mud”). This error matrix is based on training data. The overall accuracies shows that the result are good, it means nothing more than that the training areas are homogeneous, the training classess are spectrally separable, and the classification strategy being employed works in the training areas. There are 95 points has been used for training data. Table 5.4 shown from the 10 points of bedrock, only 5 point compatible with bedrock. From the 25 points of mangrove, only 25 point compatible with mangrove. From the 7 points of mud, only 7 point compatible with mud. From the 27 points of sand, only 10 point compatible with sand. From the 12 points of seawall, only 6 point compatible with seawall. From the 14 points of stone, only 10 point compatible with stone.

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Table 5.4 Error Matrix Resulting from Classifying Ground Truth Pixels

Bedrock Mangrove Mud Sand Seawall Stone Total

Bedrock 5 0 0 1 3 1 10

Ground Truth Data in Pixel Mangrove Mud Sand Seawall 0 0 3 0 0 0 0 25 0 0 1 7 0 0 2 10 0 0 10 6 0 0 4 3 25 7 27 12

Stone 0 0 1 0 3 10 14

Total 8 25 9 13 22 18 95

Table 5.5 Classification Error of Ground Truth Data Accuracy (%) Errors (%) Prod. Acc. User Acc. Commission Omission Bedrock 50 62.5 37.5 50 Mangrove 100 100 0 0 Mud 100 77.78 22.22 0 Sand 37.04 76.92 23.08 62.96 Seawall 50 27.27 72.73 50 Stone 71.43 55.56 44.44 28.57 The accuracy level of all classes (Overall Accuracy) 66.32% Kappa coefficient 0.59 Classes

The overall accuracy of the classification is 66.32%. In fact, the only highly reliable category associated with this classification from both a producer’s and user’s perspective is “Mangrove”. From the Kappa coefficient indicator the “true” agreement (observed) versus “chance” agreement result shown that this accuracy have a “adequate” agreement. The Spectral analysis shown the stone, sand and bedrock have similar wavelength, that makes the accuracy of the image interpreatation have a adequate accuracy.

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Figure 5.8 Spectral Wavelength for Substrate Type

5.4

Shoreline Slope Classification Shoreline habitats, as the most important component of ESI map, were

classified by developing a simple cartographic model. Physical factors (Slope and substrate type) were combined together, an arithmetic overlay process in raster environment and finally the ESI map compared and ranked with sensitivity scale produced by NOAA. Shoreline slope is a measure of the steepness of the intertidal zone. A Digital Elevation Model (DEM) of the study area (Figure 5.9) was created using ArcGIS and analyzed from contour map.

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Figure 5.9 Digital Elevation Model (DEM) of Nusa Penida Island Slope map of the whole terrain shown Figure 5.9 was first derived from the DEM. This slope map clipped afterwards by the area of interest in order to delineate the slope map of the shoreline. The slope map is normally derived as a raster grid with each pixel in the raster represents a slope value measured with regard to the immediate neighborhood pixels. It is not easy to clip a raster map with an area of irregular shape. Hence, the raster area of interest was recoded with a constant value of one and eventually multiplied by the slope map using the map calculator of the GIS spatial analyst.

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Figure 5.10 Slope of Nusa Penida Island

The slope map of Nusa Penida Island is classified to three categories as steep (greater than 30o ), moderate (between 30o and 5o) and flat (less than 5o) (NOAA, 2002). The slope map was classified into a raster with those 3 classes only (Figure 5.10) with each qualitative class given a representative numerical value, as it is easier to work with numbers in the spatial overlay process. The concept that the sensitivity of any shoreline segment decreases with the increase in the slope of the intertidal zone, also taken into account while assigning the numerical values to the slope categories. Hence, flat intertidal zones have the highest number while areas wtih steep slope, in the other hand, have taken the lowest number.

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Figure 5. 11 Geology Map of Nusa Penida Island (Source : BAKOSURTANAL)

The geology map of Nusa Penida Island shows that the Nusa Penida Island dominantly composed from “Sentolo Formation (Tmps)” and “Alluvium (Qa)”. From the map above shows, northeast side of Nusa Penida Island is composed from “Qa”, Nusa Ceningan Island is totally composed from “Tmps”, and Nusa Lembongan Island is composed from “Tmps” in the middle of the land and the surrounding areas composed from“Qa”.

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5.5

Environmental Sensitivity Index Map

Figure 5.12 ESI Map of Nusa Lembongan Island and Nusa Ceningan Island

Figure 5.13 ESI Map of Nusa Penida Island

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In order to evaluate the reliability of the resultant eight classes, each of these classes is discussed briefly in the follow ing section taking into account the local conditions of the area, in which this class is found and how these conditions are physically

explaining the ESI rank. 1) ESI Rank 1: Impermeable vertical substrate Express Rank 1 is in the Lembongan village of Nusa Lembongan Island. southeast and east of Nusa Ceningan Island. Sampalan, Batununggal, Suana, Semaya, Pendem, Sekartaji, Batukandik, Batumadeg and Bungamekar villages of Nusa Penida Island. The area nearly vertical slope (greater than 30 degree) and composed with bedrock. The location of Figure 5. 14 in the Tanglad village. Legend ESI Map of Shoreline Vulnerabilty class 1. Bedrock, steep slope 2. Bedrock, moderate slope 3. Sand, flat slope 6. Stone and sand, moderate slope 7. Stone, flat slope 8. Seawall, moderate slope 9. Mud, flat slope 10. Mangrove, flat slope

Figure 5. 14 True Condition of Rank 1

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2) ESI Rank 2 : Impermeable non-vertical substrate The area southeast and east Nusa Ceningan Island. Lembongan village of Nusa Lembongan Island. Sampalan, Batununggal, Suana, Semaya, Pendem villages of Nusa Penida Island. Express Rank 2, this shoreline is similar to that in Rank 1, except the slope is less than 30°, composed with bedrock. The location of Figure 5.15 in the Nusa Ceningan Island. Legend ESI Map of Shoreline Vulnerabilty class 1. Bedrock, steep slope 2. Bedrock, moderate slope 3. Sand, flat slope 6. Stone and sand, moderate slope 7. Stone, flat slope 8. Seawall, moderate slope 9. Mud, flat slope 10. Mangrove, flat slope

Figure 5.15 True Condition of Rank 2

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3) ESI Rank 3 : Semi-permeable substrate The location of Rank 3 in the Jungut Batu and Lembongan villages of Nusa Lembongan. Southwest of Nusa Ceningan Island, Sampalan, Batununggal, Karangsari and Suana villages of Nusa Penida Island. Express this shoreline is composed of low-sloping profile, the substrate is semi-permeable (fine-to medium-grain sand) and the slope is very low less then three degrees. These areas is very important places for touristic acvtivities with hotel and harbor sites found. Location of Figure 5.16 in Telaga village on Nusa Penida Island. Legend ESI Map of Shoreline Vulnerabilty class 1. Bedrock, steep slope 2. Bedrock, moderate slope 3. Sand, flat slope 6. Stone and sand, moderate slope 7. Stone, flat slope 8. Seawall, moderate slope 9. Mud, flat slope 10. Mangrove, flat slope

Figure 5.16 True Condition of Rank 3

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4) ESI Rank 6 : High permeability The east until south of Nusa Lembongan Island and south until southeast of Nusa Ceningan Island. Semaya, Tanglad, Sekartaji, Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island. Location of

Figure 5.17 in

Batununggal village on Nusa Penida Island. Express Rank 6 show this shoreline is intermediate to steep, between 10 and 20°. Composed of coarse grained sands, gravel of varying sizes and possibly shell fragments. Legend ESI Map of Shoreline Vulnerabilty class 1. Bedrock, steep slope 2. Bedrock, moderate slope 3. Sand, flat slope 6. Stone and sand, moderate slope 7. Stone, flat slope 8. Seawall, moderate slope 9. Mud, flat slope 10. Mangrove, flat slope

Figure 5.17 True Condition of Rank 6

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5) ESI Rank 7 : Flat, permeable substrate Nusa Lembongan and Nusa Ceningan Island and in the Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island. Location of Figure 5.18 in Pendem village on Nusa Penida Island. Show the Rank 7 have the highly permeable substrate is dominated by sand and gravel to boulder-sized components. The beach usually flat, less than three degrees. Legend ESI Map of Shoreline Vulnerabilty class 1. Bedrock, steep slope 2. Bedrock, moderate slope 3. Sand, flat slope 6. Stone and sand, moderate slope 7. Stone, flat slope 8. Seawall, moderate slope 9. Mud, flat slope 10. Mangrove, flat slope

Figure 5.18 True Condition of Rank 7

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6) ESI Rank 8 : Impermeable substrate East until south of Nusa Lembongan Island, northwest of Nusa Ceningan Island. Ped, Nyuh, Toyapakeh, Sakti, Batukandik, Sekertaji, Semaya villages of Nusa Penida Island. This shore line is similar to that in Rank 2. Location of Figure 5.19 in Nyuh village on Nusa Penida Island. Express Rank 8, the substrate is compacted and hard, composed of bedrock, man-made materials (seawall), or stiff clay, and the slope is greater than 15°. Usually found along bays. The seawall in these areas (Ped village) is developed very long because the erosion is occurred (see Figure 5.20) Legend ESI Map of Shoreline Vulnerabilty class 1. Bedrock, steep slope 2. Bedrock, moderate slope 3. Sand, flat slope 6. Stone and sand, moderate slope 7. Stone, flat slope 8. Seawall, moderate slope 9. Mud, flat slope 10. Mangrove, flat slope

Figure 5.19 True Condition of Rank 8

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Figure 5.20 Erosion Destruction

7) ESI Rank 9 : Flat, semi-permeable substrate The location in Jungut Batu village of Nusa Lembongan island, west and north of Nusa Ceningan Island. Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island. Location of Figure 5.21 in Nusa Ceningan Island. Express as Rank 9 show these are dominated by very soft mud or muddy sand, in the wetland areas with a flat slope, less than three degrees.

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Legend ESI Map of Shoreline Vulnerabilty class 1. Bedrock, steep slope 2. Bedrock, moderate slope 3. Sand, flat slope 6. Stone and sand, moderate slope 7. Stone, flat slope 8. Seawall, moderate slope 9. Mud, flat slope 10. Mangrove, flat slope

Figure 5.21 True Condition of Rank 9

8) ESI Rank 10 : Vegetated emergent wetlands In the Lembongan village of Nusa Lembongan Island, north of Nusa Ceningan Island, and in the Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island. Location of Figure 5.22 in northeast of Nusa Lembongan Island. Show as Rank 10, these shoreline elements include mud to sand, marshes, mangroves and other vegetated wet lands with flat slope, less than three degrees.

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Legend ESI Map of Shoreline Vulnerabilty class 1. Bedrock, steep slope 2. Bedrock, moderate slope 3. Sand, flat slope 6. Stone and sand, moderate slope 7. Stone, flat slope 8. Seawall, moderate slope 9. Mud, flat slope 10. Mangrove, flat slope

Figure 5.22 True Condition of Rank 10

CHAPTER VI DISCUSSION

6.1

Accuracy Test of Image Multispectral Classifications Results Table 5.2 ~ 5.5 shows the results of accuracy test image interpretation

results where the accuracy of the satellite image interpretation is 66.32% with the Kappa coefficient of 0.59.

It is pressumably caused by 4 factors such as

classification error according to complex interaction of spatial structure of topography, error an definition information from the spectral class, ground truth data and error on the satellite image itself. The accuracy level for using interpretation of satellite image is acceptable if the value is not less than 75 % (Mumby et al., 2003). It means, it need to use satellite data with high spatial resolution ( less than 10 meters) because the object is very small ( less than 0.06 mm ~ greater than 256 mm). Figure 5.8 shown the spectral wavelength is 0.46 ~ 0.82µm for bedrock, seawall and stone. Smith et al., (1990) reported that the spectral wavelength of stone or rock is 0.4 ~ 0.7 μm and spectral wavelength of sand is 0.45 ~ 0.52 μm (Wahyudi, 2008). Hence, the satellite image disoriented separate the object because the patterns of spectral wavelength is almost similar.

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6.2

The Sensitive Shoreline Area on Nusa Penida Island Based on

Environmental Sensitivity Index (ESI) To define the sensitive shoreline area based on ESI, the satellite data was combined with slope data. The geology map (Figure 5. 11) shows that Nusa Penida Island composed from “Sentolo Formation” and in the side north-east Nusa Penida Island is composed from “Alluvium”, Nusa Ceningan Island is totally composed from the “Sentolo Formation”, and Nusa Lembongan Island is composed from “Sentolo Formation” in the middle of the land and the outside of the middle is surrounding composed from“Alluvium”. The Geology Map here is to validate the Slope map data. Figure 5.14 shows the slope map of Nusa Penida Island characteristics where is the steep slope located in the southeast, west, southeast and northwest of Nusa Penida Island. Nusa Lembongan Island is composed from moderate and flat slope. The south east of Nusa Ceningan Island is steep slope and southwest until east is composed from moderate and flat slope. Less sensitive area is Rank 1 located in the southwest of Nusa Lembongan Island, southeast and east of Nusa Ceningan Island. Sampalan, Batununggal, Suana, Semaya, Pendem, Sekartaji, Batukandik, Batumadeg and Bungamekar villages of Nusa Penida Island. These islands is composed of bedrock with impermeable vertical substrate it means the wave need to scuffeling hard to smashed the bedrock with solid substrate type and it need a long time process. NOAA (2002) reported, the intertidal zone is steep (greater than 30° slope), with very little width. Sediment accumulations are uncommon and usually ephemeral, since waves remove the debris that has slumped from the eroding

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cliffs. They are often found interspersed with other shoreline types. There is strong vertical zonation of intertidal biological communities. Express Rank 2 in the area southeast and east Nusa Ceningan Island. Lembongan village of Nusa Lembongan Island. Sampalan, Batununggal, Suana, Semaya, Pendem villages of Nusa Penida Island. Slope of the intertidal zone is usually less than 30 degrees, resulting in a wide intertidal zone; it can be less than five degrees and the intertidal zone can be up to hundreds of meters wide. Sediments can accumulate at the base of bedrock cliffs, but are regularly mobilized by storm waves. As with ESI = 1, these shorelines rank low because they are exposed to high wave energy. However, they have a flatter intertidal zone, sometimes with small accumulations of sediment at the high-tide line. Along coastal plain areas, the equivalent shoreline type consists of scarps in relict marsh clay. Biological impacts can be immediate and severe, particularly if erosion cover tidal pool communities on rocky platforms (NOAA (2002). The location of Rank 3 in the Jungut Batu and Lembongan villages of Nusa Lembongan. Southwest of Nusa Ceningan Island, Sampalan, Batununggal, Karangsari and Suana villages of Nusa Penida Island. In this rank, sediments are well-sorted and compacted (hard). On beaches, the slope is very low, less than five degrees. This shoreline rank includes exposed sand beaches on outer shores, sheltered sand beaches along bays and lagoons, and sandy scarps and banks along lake and river shores. Cleanup on fine-grained sand beaches is simplified by the

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hard substrate that can support vehicular and foot traffic. Infaunal densities vary significantly both spatially and temporally (NOAA, 2002). The east until south of Nusa Lembongan Island and south until southeast of Nusa Ceningan Island. Semaya, Tanglad, Sekartaji, Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island express 6. In this area, The substrate is highly permeable (gravel-sized sediments) with penetration up to 100 cm. The slope is intermediate to steep, between ten and 20 degrees. There is high annual variability in degree of exposure, and thus in the frequency of mobilization by waves. Sediments have lowest trafficability of all beaches. Fine-grained gravel beaches are composed primarily of pebbles and cobbles (from 4 to256 mm), with boulders as a minor fraction. Little sand is evident on the surface, and there is less than 20 percent sand in the subsurface. There can be zones of pure pebbles or cobbles, with the pebbles forming berms at the high-tide line and the cobbles and boulders dominating the lower beachface. Sediment mobility limits the amount of attached algae, barnacles, and mussels to low levels. NOAA (2002) reported, for many gravel beaches, significant wave action (meaning waves large enough to rework the sediments to the depth of penetration) occurs only every few years, leading to long-term persistence of subsurface. Shell fragments can be the equivalent of gravel along Gulf of Mexico and South Atlantic beaches. The distinction can also be made on the basis of grain size and extent of rounding of the sediments on a shoreline. The gravel is rounded or wellrounded only on those beaches regularly mobilized during storms. Large-grained gravel beaches have boulders dominating the lower intertidal zone. The amount of

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attached algae and epifauna is much higher, reflecting the stability of the large sediments. A boulder-and-cobble armoring of the surface of the middle to lower intertidal zone is common on these beaches. Nusa Lembongan and Nusa Ceningan Island and in the Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island, express 7. In this area They are flat (less than three degrees) accumulations of sediment. The highly permeable substrate is dominated by sand, although there may be silt and gravel components and width can vary from a few meters to nearly one kilometer. The tidal flats commonly occur with other shoreline types, usually marsh vegetation, on the landward edge of the flat. However, erosion can penetrate the tops of sand bars and burrows if they dry out at low tide. Because of the high biological use, impacts can be significant to benthic invertebrates exposed to the water-accommodated fraction or smothered. Cleanup is always difficult because of the potential for erosion deeper into the sediment, especially with foot traffic (NOAA, 2002). East until south of Nusa Lembongan Island, northwest of Nusa Ceningan Island. Ped, Nyuh, Toyapakeh, Sakti, Batukandik, Sekertaji, Semaya villages of Nusa Penida Island, express 8. On this rank, substrate is hard, composed of bedrock, man-made materials, or stiff clay. The type of bedrock can be highly variable, from smooth, vertical bedrock, to rubble slopes which vary in permeability. Slope is generally steep (greater than 15 degrees), resulting in a narrow intertidal zone. There is usually a very high coverage of attached algae and organisms.

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NOAA (2002) reported cleanup is often required because natural removal rates are slow. Yet cleanup is often difficult and intrusive. Sheltered seawalls and riprap are the man-made equivalents.Usually, more intrusive cleanup is necessary for aesthetic reasons. In riverine settings, terrestrial vegetation along the river bluff indicates low energy and thus slow natural removal rates. The location in Jungut Batu village of Nusa Lembongan island, west and north of Nusa Ceningan Island. Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Islan, express 9. On this area, the substrate is flat (less than three degrees) and dominated by mud. The sediments are water-saturated, so permeability is very low, except where animal burrows are present. Width can vary from a few meters to nearly one kilometer. Sediments are soft, with low trafficability. Infaunal densities are usually very high. The soft substrate and limited access makes sheltered tidal flats almost impossible to clean. Usually, any cleanup efforts deeper into the sediments, prolonging recovery. Once erosion reaches these habitats, natural removal rates are very slow. They can be important feeding areas for birds and rearing areas for fish, making them highly sensitive to erosion impacts. In areas without a significant tidal range, sheltered flats are created by less-frequent variations in water level. These flats are unique in that low-water conditions can persist for weeks to months, providing a mechanism for sediment contamination in areas that can be subsequently flooded. Low riverine banks are often muddy, soft, and vegetated, making them extremely difficult to clean. Natural removal rates could be very slow, and depend on flooding frequency (NOAA. 2002).

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The very sensitive area is Rank 10 located in the Lembongan village of Nusa Lembongan Island, north of Nusa Ceningan Island, and in the Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island. Rank 10 location is almost composed of marshes, mangroves, and other vegetated wetlands are the most sensitive habitats because of their high biological use and value, difficulty to clean up, and potential for long-term impacts to many organisms. Mangrove forests are composed of salt-tolerant trees that form dense stands with distinct zonation: red mangroves (Rhizophora mangle) occur on the seaward exterior while black mangroves (Avicennia germinans) and white mangroves (Laguncularia racemosa) occur on forest interiors. The outer, fringing forests can be exposed to relatively high wave activity and strong currents; forests located in bays and estuaries are well-sheltered. Sediment types range from thin layers of sand and mud to muddy peat to loose gravel on limestone beachrock. Heavy wrack deposits in the storm swash line are very common. The topographic profile is generally very flat, and seagrass beds are common in shallow offshore areas. Attached to the prop roots are moderate densities of algae, snails, and crabs. At present, mangroves are considered a specific habitat type and are not grouped with shrub vegetation (NOAA, 2002).

CHAPTER VII CONCLUSIONS AND SUGGESTIONS

7.1 Conclusions The satellite data (AVNIR-2) analysis combine with slope data to make the ESI map to express Rank 1 ~ Rank 10. Less sensitivity area is Rank 1 located in the southwest of Nusa Lembongan Island, southeast and east of Nusa Ceningan Island. Sampalan, Batununggal, Suana, Semaya, Pendem, Sekartaji, Batukandik, Batumadeg and Bungamekar villages of Nusa Penida Island. Very sensitivity area is Rank 10 located in the Lembongan village of Nusa Lembongan Island, north of Nusa Ceningan Island, and in the Sakti, Toyapakeh, Nyuh and Ped villages of Nusa Penida Island. Image interpretation shows Nusa Penida Island is composed from stone (cobble, pebble, boulder, granule) and bedrock.

7.2 Suggestions 1. To continue the observation annually including wind speed data and wind direction to get exposure of shoreline. Bathymetry and tidal data also consider to predict the conditions of the island next view years. 2. To obtain complete ESI results have to consider to human resources and biological data. 81

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3. To using satellite data with high spatial resolution to identify the object more clearly. 4. To using satellite data with no cloud, shadow of cloud and cloud cover. 5. To make as much as training area to get good classification results. 6. To the government is to making shore protection projects design to retain and rebuild natural systems such as bluffs, dunes, wetlands, and beaches and to protect structures and infrastructure landward of the shoreline. Shore protection not only can reduce a storm’s potential physical and economic damages from waves, storm surge, and the resulting coastal flooding but also can mitigate coastal erosion and even help restore valuable ecosystems that may have been lost such as beaches, wetlands, reefs, and nesting areas. If a shoreline cannot provide a protective buffer, coastal wetlands are at risk: In fact, sediment overwash, salt water inundation and erosion may cause essential wetlands to disappear.

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