study of shoreline vulnerability in nusa penida islands

Transcription

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
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
iii
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
iv
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
v
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.
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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
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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
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
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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) ................
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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 ....................................................................
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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 ...................................
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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 ...............................................
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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
xiii
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
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Figure 5.15
Figure 5.16
Figure 5.17
Figure 5.18
Figure 5.19
Figure 5.20
Figure 5.21
Figure 5.22
Erosion Destruction ......................................................................
True Condition of Rank 10 ...........................................................
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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
xvi
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
xvii
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
1
2
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
3
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.
4
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
6
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.
7
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
8
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
9
(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).
13
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).
14
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).
15
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.
16
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
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
20
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
23
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
24
(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
28
-
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.
30
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
32
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
3
Semi-Permeable Substrate
4
Medium Permeability
5
Medium-to-High Permeability
6
High Permeability
7
Flat, Permeable Substrate
8
Impermeable Substrate
9
Flat, Semi-Permeable Substrate
10
Vegetated Emergent Wetlands
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.
34
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).
35
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).
36
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
 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.
37
Rank of 3: Semi-Permeable Substrate
 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
Rank of 4: Medium Permeability
38
 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.
Rank of 5 : Medium-to-High Permeability
 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).
39
Rank of 6 : High Permeability
 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.
40
Rank of 7 : Flat, Permeable Substrate
 It is flat (less than three degrees).
 The highly permeable substrate is dominated by sand, although there may
be gravel components.
41
Rank of 8 : Impermeable Substrate
 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.
Rank of 9 : Flat, Semi-Permeable Substrate
 The substrate is flat (less than three degrees) and dominated by mud.
 Width can vary from a few meters to nearly one kilometer.
42
Rank of 10 : Vegetated Emergent Wetlands
 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.
43
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).
44
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.
45
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
46
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
47
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).
48
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
49
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
50
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
51
52
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 :
53
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
54
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.
55
Figure 5.5 Shoreline Habitat Classification on Nusa Penida Island
Figure 5.6 Shoreline Habitat Classification on Nusa Ceningan Island
56
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.
57
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
58
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.
59
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.
60
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.
61
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.
62
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.
63
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”.
64
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
65
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
66
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
class
3. Sand, flat slope
9. Mud, flat slope
Figure 5.15 True Condition of Rank 2
67
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
class
3. Sand, flat slope
9. Mud, flat slope
68
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
class
3. Sand, flat slope
9. Mud, flat slope
69
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
class
3. Sand, flat slope
9. Mud, flat slope
70
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
class
3. Sand, flat slope
9. Mud, flat slope
71
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.
72
Legend
class
3. Sand, flat slope
9. Mud, flat slope
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.
73
Legend
class
3. Sand, flat slope
9. Mud, flat slope
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.
74
75
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
76
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
77
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
78
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.
79
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).
80
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
82
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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study of shoreline vulnerability in nusa penida islands

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