Geospatial Modeling of Vegetation Cover Changes on a Small Island (Case Study: Tanakeke Island, Takalar District, South Sulawesi)
GEOSPATIAL MODELING OF VEGETATION COVER CHANGES
ON A SMALL ISLAND
Case Study: Tanakeke Island, Takalar District, South Sulawesi
M. AKBAR AS
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014
STATEMENT
I, M. Akbar AS, hereby declare that this thesis entitled
Geospatial Modeling of Vegetation Cover Changes on a Small Island
Case Study: Tanakeke Island, Takalar District, South Sulawesi
Is a result of my work under the supervision of advisory board and that it has not
been published before. The content of this thesis has been examined by the
advisory board and external examiner.
Bogor,
June 2014
M. Akbar AS
G051110071
RINGKASAN
M. AKBAR AS. Permodelan Geospasial Perubahan Tutupan Vegetasi di Pulau
Kecil, Studi Kasus: Pulau Tanakeke, Kabupaten Takalar, Sulawesi Selatan.
Dibawah bimbingan oleh BUCE SALEH dan IBNU SOFIAN.
Indonesia telah kehilangan lebih dari 1,2 juta hektar mangrove sejak tahun
1980 ketika tutupan hutan mangrove masih 4,2 juta hektar (FAO 2007). Luas
hutan mangrove di Pulau Tanakeke, Kabupaten Takalar, Sulawesi Selatan,
Indonesia pada awal tahun 1980 adalah 1.770 hektar, dan setelah dikonversi
menjadi areal tambak luas lahan tinggal 500 hektar. Fenomena degradasi hutan
mangrove di Pulau Tanakeke perlu mendapat perhatian khusus, mengingat pulau
ini merupakan salah satu pulau di Sulawesi Selatan yang memiliki hutan
mangrove yang sangat berharga di wilayah pesisir. Tujuan dari penelitian ini
adalah untuk menganalisis dinamika perubahan tutupan mangrove (selama 41
tahun) dengan menggunakan citra Landsat multi-temporal dan memproyeksi
perubahan tutupan mangrove pada tahun 2023.
Penelitian ini dilakukan dari bulan Agustus 2013 sampai April 2014,
termasuk tahap persiapan data, pengolahan data, model simulasi, penulisan tesis
dan publikasi. Lokasi penelitian terletak di Pulau Tanakeke, Kabupaten Takalar,
Sulawesi Selatan. Untuk melakukan studi ini, ada empat data citra Landsat yang
digunakan, yaitu Landsat MSS (28 Oktober 1972), Landsat Thematic Mapper
(TM) (26 Agustus 1993), Landsat Enhanced TM Plus (ETM+) (21 Juli 2003), dan
Landsat 8 (Operational Land Imager, OLI) (27 April 2013) diperoleh dari United
States Geological Survey (USGS). Peta probabilitas untuk perubahan tutupan
mangrove menggunakan data Multi Criteria Evaluation (MCE), yaitu pasang
surut, permukiman, dan substrat.
Metodologi penelitian ini terdiri dari tiga langkah utama, yaitu: (1) analisis
citra: koreksi geometrik dari citra Landsat, analisis awal, dan klasifikasi; (2)
ground truth; dan (3) model Cellular Automata Markov (CA-Markov), sedangkan
pengolahan data citra satelit terdiri dari lima langkah utama, yaitu: (1) koreksi
geometrik, (2) analisis awal, (3) klasifikasi citra, (4) accuracy assessment, dan (5)
post classification. Data hasil analisis citra dari dua peta penggunaan lahan yang
berasal dari waktu yang berbeda digunakan dalam membuat model Markov
Chain. Peta penggunaan lahan dengan interval 21 tahun, memakai peta
penggunaan lahan dari 1972-1993 digunakan untuk proyeksi tahun 2003 dan
kemudian divalidasi menggunakan peta penggunaan lahan tahun 2003. Peta
penggunaan lahan dengan interval 10 tahun (1993-2003) digunakan sebagai
proyeksi untuk tahun 2013 dan divalidasi menggunakan peta penggunaan lahan
tahun 2013. Sementara itu, peta penggunaan lahan dengan interval 31 tahun,
(1972-2003) sebagai proyeksi untuk 2013 dan divalidasi menggunakan peta
penggunaan lahan tahun 2013. Proyeksi mangrove tahun 2023 diperoleh
berdasarkan tingkat akurasi dari ketiga interval yang dipilih. Jenis filtering 5x5
digunakan untuk melihat perubahan cell yang terjadi dalam memprediksi. Jika
hasil validasi yang diperoleh adalah >75%, maka dapat digunakan untuk
memprediksi mangrove untuk 10 tahun ke depan.
Penilaian kuantitatif dilakukan dengan menggunakan data ground truth
melalui pendekatan stratified random sampling. Matriks error yang dihasilkan
dari akurasi klasifikasi, yaitu 87,78%. Produser dan pengguna akurasi untuk
mangrove diperoleh 93%. Hasil analisis dinamika spasial pada tahun 1972-2013
menunjukkan bahwa luas mangrove di Pulau Tanakeke mengalami penurunan
sebesar 1.595,55 ha (63,91%). Tingkat perubahan yang terjadi tidak seragam
antara tahun 1972-1993, 1993-2003, dan 2003-2013. Laju perubahan luas tutupan
mangrove selama tahun 1972-1993 merupakan yang tertinggi di Pulau Tanakeke,
dimana mengalami penurunan luas tutupan mangrove sebesar 1.166,61 ha
(46,73%). Pada tahun 1993 hingga 2003, mangrove di Pulau Tanakeke mengalami
penurunan seluas 252,49 ha (18,98%), dan pada tahun 2003-2013 terjadi
penurunan seluas 176,45 ha (16,37%).
Hasil simulasi perubahan tutupan mangrove pada tahun 1993 dan 2003
(selang 10 tahun) digunakan untuk memprediksi perubahan tutupan mangrove
pada tahun 2013 dan dibandingkan dengan tutupan mangrove yang sebenarnya
pada tahun 2013 (peta hasil klasifikasi). Hasil perhitungan akurasi yang diperoleh
dengan menggunakan iterasi 35 dan grid 30, yaitu 86,87%, dan dengan
menggunakan iterasi 35 dan grid 60 diperoleh akurasi, yaitu 84,85%. Perubahan
tutupan mangrove pada tahun 1972 dan 1993 (selang 21 tahun) dibuat untuk
memprediksi perubahan tutupan mangrove pada tahun 2003. Hasil simulasi
perubahan tutupan mangrove dibandingkan dengan tutupan mangrove yang
sebenarnya pada tahun 2003 (peta hasil klasifikasi). Hasil perhitungan akurasi
yang diperoleh dengan menggunakan iterasi 25 dan grid 30, yaitu 81,96%, dan
dengan menggunakan iterasi 25 dan grid 60 akurasi yang diperoleh, yaitu 79,89%.
Perubahan tutupan mangrove pada tahun 1972 dan 2003 (selang 31 tahun) dibuat
untuk memprediksi perubahan tutupan hutan mangrove pada tahun 2013. Hasil
simulasi perubahan tutupan mangrove kemudian dibandingkan dengan tutupan
mangrove yang sebenarnya pada tahun 2013 (peta hasil klasifikasi). Hasil
perhitungan akurasi diperoleh dengan menggunakan iterasi 10 dan grid 30, yaitu
83,70%, dan dengan menggunakan iterasi 10 dan grid 60 diperoleh akurasi, yaitu
81,87%.
Berdasarkan hasil proyeksi dengan menggunakan interval tahun yang
berbeda, diperoleh data dengan akurasi yang lebih tinggi pada interval 10 tahun
dengan menggunakan iterasi 35. Dalam membuat model proyeksi untuk 10 tahun
ke depan (2023), digunakan interval waktu 10 tahun (2003-2013) dengan iterasi
35 dan grid 30 meter, maka diperoleh proyeksi luas tutupan lahan mangrove pada
tahun 2023, yaitu 662,28 ha (mengalami penurunan sebesar 238,83 ha).
Rekomendasi berdasarkan penelitian ini bahwa dengan menggunakan
Model CA-Markov dari hasil interpretasi visual dalam grid 60 m, dapat digunakan
untuk memproyeksikan perubahan luas tutupan mangrove di Pulau Tanakeke
karena memiliki akurasi yang baik. Pengembangan areal tambak dan penebangan
mangrove untuk arang harus dibatasi di Pulau Tanakeke guna melestarikan
mangrove untuk pengembangan pariwisata.
Kata kunci: Mangrove, Cellular Automata Markov, Penginderaan jauh, Dinamika
geospasial
SUMMARY
M. Akbar AS. Geospatial Modeling of Vegetation Cover Changes on a Small
Island (Case Study: Tanakeke Island, Takalar District, South Sulawesi).
Supervised By BUCE SALEH and IBNU SOFIAN.
Indonesia has lost more than 1.2 million hectare of mangrove since 1980
when mangrove forest cover was still 4.2 million hectare (FAO 2007). The
mangrove forest in Tanakeke Island, the district of Takalar, South Sulawesi,
Indonesia in the early 1980s was 1,770 hectares, and after a conversion into
middle and failed fish farms, only 500 hectares remain. The phenomenon of
mangrove forests degradation in Tanakeke Island needs to receive specific
attention, considering that the island is one in South Sulawesi having precious
mangrove forests in a coastal region. The objective of this study was to analyze
dynamics of mangrove cover changes (41 years) using multi-temporal Landsat
imagery and projection of mangrove cover changes in 2023.
This research was conducted from August 2013 to April 2014, including the
stages of data preparation, data processing, simulation model, thesis writing and
publication. The study area is located in Tanakeke Island, Takalar District, South
Sulawesi. To conduct the study, Landsat data, including four Landsat MSS (28
October 1972), a Landsat Thematic Mapper (TM) images (26 August 1993), a
Landsat Enhanced TM Plus (ETM+) image (21 July 2003), and a Landsat 8
(Operational Land Imager, OLI) image (27 April 2013) were acquired from the
United States Geological Survey (USGS). The probability maps for mangrove
cover changes in the study area using the multi criteria evaluation were the tidal,
settlement, and substrate.
The methodology of this study consisted of three main steps: (1) image
analysis: geometric correction of Landsat images, preliminary analysis, and
classification; (2) ground truth; (3) Cellular Automata Markov model (CAMarkov) while the data processing was composed of five main steps: (1)
geometric correction, (2) preliminary analysis, (3) image classification, (4)
accuracy assesment, and (5) post classification. Data input used for Markov
Chain on two land use maps derived from two different times. Land use map with
interval of 21 year, using land use map of 1972 to 1993, is used for projection
2003 and then was validated using land use map on 2003. A land use map with
10-year interval, which was the map of 1993 to 2003 used for projection of 2013,
and then was validated using land use map on 2013. Meanwhile, land use map
with a 31-year interval, which used a land use map of 1972 to 2003, for projection
2013, was validated using land use map on 2013. The results of the third interval
were selected based on the level of accuracy for projection change of mangrove in
2023. The cellular change of state was then determined in which 5x5 filter was
used. If the result of validation standard agreement was >75%, it could be used to
predict the future trend of mangrove for the next 10 years.
Quantitative assessment was performed using ground truthing data with a
stratified random sampling approach. An error matrix was generated with an
overall classification accuracy of 87.78%. Producer‟s and user‟s accuracy for
mangrove was 93%. The result of spatial dynamic analysis from 1972 to 2013
indicated that mangrove in the Tanakeke Island decreased by 1,595.55 ha
(63.91%). The rate of change, however, was not uniform from 1972 to 1993, 1993
to 2003, and from 2003 to 2013. Deforestation rate during 1972 to 1993 was the
highest in Tanakeke Island, and mangrove area decreased by 1,166.61 ha
(46.73%). From 1993 to 2003, mangrove in Tanakeke Island decreased by 252.49
ha (18.98%), and from 2003 to 2013 there was a decreased of 176.45 ha
(16.37%).
The simulation result of mangrove cover changes in 1993 and 2003 (interval
10 years) was made to predict mangrove cover change in 2013. The simulation
result of mangrove cover change was compared with the actual mangrove cover in
2013 (map classification). Calculation results were obtained using iteration 35 and
grid 30 by 86.87%, iteration 35 and grid 60 by 84.85% overall accuracy.
Mangrove cover changes in 1972 and 1993 (21 years interval) were made to
predict mangrove cover change in 2003. The simulation result of mangrove cover
change was then compared with the actual mangrove cover in 2003 (map
classification). Calculation results were obtained using iteration 25 and grid 30 by
81.96%, iteration 25 and grid 60 by 79.89% overall accuracy. Mangrove cover
changes in 1972 and 2003 (interval 31 years) were made to predict mangrove
cover change in 2013. The simulation result of mangrove cover change was then
compared with the actual mangrove cover in 2013 (map classification).
Calculation results were obtained using iteration 10 and grid 30 by 83.70%,
iteration 10 and grid 60 by 81.87% overall accuracy
Based on the results of the projection by using different intervals, the data
with higher accuracy were obtained during ten-year intervals with in which 35
iterations were used. In making a projection for the next 10 years (2023), an
interval of 10 years (2003 to 2013) with iterations 35 was used. The simulation
model trend, mangrove cover change is a major concern which continued to be
broken from 2013 to 2023 as illustrated in the total area of mangrove cover is
predicted to be 662.28 ha in 2023 (decreased by 238.83 ha) by using grid 30 m.
The recommendation based on the study is that by using visual
interpretation results in a grid of 60 m, CA-Markov model can be used in
projecting change of mangrove in Tanakeke Island because it has good accuracy.
Development of aquaculture and charcoal should be restricted in Tanakeke Island
to conserve mangrove for tourism development.
Keyword: Mangrove, Cellular Automata Markov, Remote Sensing, Geospatial
Dynamic
Copyright © 2014, Bogor Agricultural University
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Citation does not inflict the name and honor of Bogor Agricultural University.
It is prohibited to republish and reproduce all part of this thesis without any
written permission from Bogor Agricultural University.
GEOSPATIAL MODELING OF VEGETATION COVER CHANGES
ON A SMALL ISLAND
Case Study: Tanakeke Island, Takalar District, South Sulawesi
M. AKBAR AS
A Thesis submitted for the Degree of Master of Science in
Information Technology for Natural Resources
Management Program Study
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014
External Examiner:
Dr Antonius Bambang Wijanarto
Research Title
Name
Student ID
: Geospatial Modeling of Vegetation Cover Changes on a Small
Island (Case Study: Tanakeke Island, Takalar District, South
Sulawesi)
: M. Akbar AS
: G051110071
Approved by,
Advisory Board
Dr Ir Buce Saleh, MS
Supervisor
Dr Ibnu Sofian, MEng
Co-Supervisor
Endorsed by,
Program Coordinator of
MSc in IT for Natural Resources
Management
Dr Ir Hartrisari Hardjomidjojo, DEA
Date of examination:
June 13, 2014
Dean of Graduate School
Dr Ir Dahrul Syah, MScAgr
Date of Graduation :
ACKNOWLEDGEMENT
In the name of Allah SWT, the Most Gracious and the Most Merciful.
Alhamdulillah, all praises to Allah for the strengths and His blessing in
completing this thesis, I would like to express my highly appreciation to the
following:
1. Dr Ir Buce Saleh, MS as my supervisor for his supervision support. His
invaluable help in the forms of constructive comments and suggestions
throughout the experimental and thesis works have contributed to the success
of this research and Dr Ibnu Sofian, MEng as the co-supervisor for his ideas
and knowledge regarding this topic during my research.
2. Dr Antonius Bambang Wijanarto as the external examiner for his positive
inputs and ideas.
3. Dr Ir Hartrisari Hardjomidjojo, DEA, as the program coordinator and all my
lecturers for giving knowledge and experience.
4. Dr Nurjannah Nurdin, ST for helping provide data and suggestions during my
research. Your kindness means a lot to me. Thank you very much.
5. All my friends at MIT, IPB, for their help and support in finishing my
assignment and study.
6. Staff of Center for Regional Development and Spatial Information,
Hasanuddin University for all the support and help.
7. MIT secretariate and all of its staff members for helping me to arrange the
administration, technical procedures, and facilities.
8. All of my family. Thank you for always supporting me.
Bogor, July 2014
M. Akbar AS
LIST OF CONTENTS
LIST OF TABLES
vii
LIST OF FIGURES
vii
1. INTRODUCTION
Background
Objectives
Problem Identification
1
2
2
2. LITERATURE REVIEW
Mangrove Ecosystem
Influencing Factors for Mangrove
Remote Sensing of Mangrove Vegetation
Dynamics of Mangrove Forest
Markov Chain
Cellular Automata (CA)
3
3
4
4
5
6
3. METHODOLOGY
Time and Location
Data Collection
Method
Image processing analysis
Geometric Correction
Preliminary Analysis
Image Classification
Accuracy Assessment
Post Classification
Mangrove cover projection
Markov chain analysis
Multi Criteria Evaluation (MCE)
CA-Markov analysis
Kappa accuracy
7
7
8
9
9
10
10
10
11
11
11
12
12
12
4. RESULT AND DISCUSSION
Mangrove Cover Dynamics
Image Classification
Accuracy Assessment
Post Classification
Mangrove Cover Projection
Projection of mangrove cover in 2023
13
13
15
16
22
24
26
28
30
5. CONCLUSION AND RECOMMENDATION
Conclusion
Recommendation
31
32
Interval of 10 years (1993to2003) for the projection in 2013
Interval of 20 years (1972to1993) for the projection in 2003
Interval of 30 years (1972to2003) for the projection in 2013
REFERENCES
32
CURRICULUM VITAE
34
LIST OF TABLES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Factors influencing loss of mangrove in Indonesia.
Satelite data.
Confusion matrix.
Criteria and categorization of factors for mangrove.
Areal estimates of major land use/cover types.
Areal and percentages of land use/cover changes.
Areal of land use/cover changes from the 1972 to 1993.
Percentages of land use/cover changes from the 1972 to 1993.
Areal of land use/cover changes from the 1993 to 2003.
Percentages of land use/cover changes from the 1993 to 2003.
Areal of land use/cover changes from the 2003 to 2013.
Percentages of land use/cover changes from the 2003 to 2013.
Areal of land use/cover changes from the 1972 to 2013.
Percentages of land use/cover changes from the 1972 to 2013.
Matrix kappa Index of Agreement (KIA).
Mangrove cover change projection using a grid 30 m.
3
8
10
12
14
14
17
17
17
18
18
19
19
20
30
30
LIST OF FIGURES
1
2
3
4
5
6
7
8
9
Temporal and spatial hierarchical organization of key ecosystem
components in mangrove forests including leaves, trees, forests
and watershed regions.
Map of the study area, Tanakeke Island.
Flow chart of methodology.
Flow chart of image processing.
Flow chart of Markov Chain model.
Flow chart of Cellular Automata-Markov (CA-Markov) model
Land use/cover maps of Tanakeke Island (a) in 1972, (b) 1993,
(c) 2003,and (d) 2013.
Actual condition adjusted to Landsat 8 images of 2013 in
Tanakeke Island.
Post classification land use/cover map of Tanakeke Island: (a) in
1972 to 1993, (b) 1993 to 2003, (c) 2003 to 2013, (d) 1972 to
2013.
5
7
8
9
11
13
15
16
21
10
11
12
13
14
15
16
17
18
Tidal data of Tanakeke Island in 2014.
Probability maps of (a) Substrat, (b) Tidal, (c), Settlement, (d)
Result from Boolean approach.
Interval of 10 years: (a) iteration for grid 30 m, (b) iteration for
grid 60 m.
Interval of 10 years: (a) mangrove cover in 2013 (Grid 30 m),
(b) mangrove cover simulation in 2013 (Grid 30 m), (c)
mangrove cover in 2013 (Grid 60 m), (d) mangrove cover
simulation in 2013 (Grid 60 meter).
Interval of 21 years: (a) iteration for grid 30 m, (b) iteration for
grid 60 m.
Interval of 21 years: (a) mangrove cover in 2003 (Grid 30 m),
(b) mangrove cover simulation in 2003 (Grid 30 m), (c)
mangrove cover in 2003 (Grid 60 m), (d) mangrove cover
simulation in 2003 (Grid 60 meter).
Interval of 31 years: (a) iteration for grid 30 m, (b) iteration for
grid 60 m.
Interval of 31 years: (a) mangrove cover in 2003 (Grid 30 m),
(b) mangrove cover simulation in 2003 (Grid 30 m), (c)
mangrove cover in 2003 (Grid 60 m), (d) mangrove cover
simulation in 2003 (Grid 60 meter).
Condition of mangrove cover in Tanakeke Island: (a) Mangrove
cover in 2013, (b) Projection mangrove cover in 2023.
23
23
24
25
26
27
28
29
31
1
1 INTRODUCTION
Background
Mangrove forests found in the inter-tidal zone in the tropics and subtropics,
play an important role in stabilizing shorelines and in helping reduce devastating
impacts of natural disasters such as tsunamis and hurricanes. They also provide
important ecological and social goods and services including breeding and
nursing grounds for marine and pelagic species, food, medicine, fuel, and building
materials for local communities. These forests, however, are declining at an
alarming rate, perhaps even more rapidly than inland tropical forests, and much of
what remains is in degraded condition (Wilkie and Fortune 2003).
As a complex and unique ecosystem, mangrove has important functions
and benefits for coastal environments (Bengen 1999), some of which are a muffler
of waves and storms, a protector of abrasion, a trapper of mudguard, and
sediment, a producer of some detritus from the leaves and branches of mangrove
trees, a nursery, feeding, and spawning ground of various types of fish, shrimp
and other marine biota, a producer of wood for constructions materials, firewood,
charcoal materials, paper materials, and furniture-making materials, and a supplier
of fish larvae, shrimp and other marine biota.
Indonesia has lost more than 1.2 Mha of its mangrove since 1980 when
mangrove forest cover was still 4.2 Mha (FAO 2007). Among the immediate
impacts of mangrove loss has been the decline in fish production since mangrove
are vital fish nurseries and rapid erosion in the absence of buffer against high
waves. 60% of this loss has been attributed to conversion of mangrove into
brackish water aquaculture ponds, which peaked in the 1980‟s and 1990‟s during
a period of aquaculture expansion known as the blue revolution.
There is no agreement for the extent of mangrove vegetation in Indonesia,
but various forums usually use the number of 4.25 million ha for a measure. The
occurence of mangrove forest reduction is generally caused by the exploitation
and transferring of appropriation of theland. Approximately 9 years ago, the
extensive vast of mangrove forest in Indonesia was about 4.13 million ha, but now
only 2.49 million ha (60%) remains.
South Sulawesi is facing massive mangrove forest destruction, for over the
past 30 years deforestation and pollution have taken their toll and damaged almost
90 percent of the total original areas. Before 1980s, the mangrove forest in South
Sulawesi was over 214,000 hectares. The conversion of the mangrove forest into
fishpond reduced the area into 23,000 hectares in the early 1991, or a decrease of
61 percent. The conversion of mangrove forests into fish farms was a result of a
government policy in the early 1980s that instructed the offices of local fisheries
and maritime affairs to carry out intensification of shrimp cultivation, which was
then a major export commodity and earned the state a huge amount of money.
The mangrove forest in Tanakeke Island, Takalar District, South Sulawesi,
Indonesia in the early 1980s was 1,770 hectares, and after conversion, for middle
and failed fish farms, only 500 hectares remain. The phenomenon of mangrove
forests degradation in Tanakeke Island deserve specific attention, given that
2
Tanakeke Island is one small island in South Sulawesi with precious mangrove
forests in its coastal region.
Remote sensing could play an important and effective role in the assessment
and monitoring of mangrove forest cover dynamics. While remote sensing data
analysis does not replace field inventory, it provides supplementary information
quickly and efficiently. The use of remotely sensed data offers many advantages
including synoptic coverage, availability of low-cost or free satellite data,
availability of historical satellite data, and repeated coverage. In addition, recent
advances in the hardware and software used for processing a large volume of
satellite data have helped increase the usefulness of remotely sensed data.
Moreover, it is extremely difficult to get into vast swamps of mangrove forests,
and conducting a field inventory is time consuming and costly. A number of
studies conducted in small islands have begun to develop and apply remotesensing techniques mainly for mapping purposes (Nayak et al. 2001).
The present work used the Cellular Automata-Markov (CA-Markov) model
to project the likely temporal distribution of the mangrove landscape in the prespecified coastal region driven by some well-known local socioeconomic factors.
The quantitative information achieved from such a projection might be useful for
forest managers to devise better plans for long-term management of mangrove
ecosystems in Tanakeke Island.
Objectives
The aim of this study was to analyze dynamics of mangrove cover changes
(41 years) using multi-temporal Landsat imagery and projection of mangrove
cover changes in 2023.
Problem Identification
As a result of socio-economic development and population growth, some
parts of the mangrove forests were cleared for other land uses. Since the early
1997, a number of aquaculture fields, especially large-scale shrimp farms, have
been constructed by clearing the mangrove forests. The destruction of mangrove
forests for shrimp farming has continuously degraded ecological and socioeconomic services of mangrove forests with associated environmental impacts.
In the case of Tanakeke island , development of coastal shrimp farming
ponds in an intertidal area is considered as a major factors behind mangrove
delineation. Shrimp cultivation is the largest anthropogenic cause of mangrove
deforestation in Tanakeke Island. The process of shrimp farming is economically
lucrative for the coastal communities. Particularly, with huge demands of
commercially produced shrimps in Indonesia, it guarantees a high economic
return. Moreover, clearing of mangrove trees for charchoal on a large scale and
mangrove forests diverted for establishment of new coastal settlements are the
second largest anthropogenic cause.
3
2 LITERATURE REVIEW
Mangrove Ecosystem
Deforestation led by increasing demand for land and climate change events
such as the rise of sea level and reduction in freshwater flow are considered as
major players behind the continuous annihilation of mangrove; however, climate
change events may have an impact of only 10-15% reduction of mangrove
habitats in the distant future whereas the immediate threat comes from
uncontrolled exploitation and deforestation (Alongi 2008). This has resulted in
serious concerns among conservationists and has exposed the coastal communities
to a further increasing threat of climate change and hydro meteorological
disasters.
It is easier to understand the benefits and important values of the mangrove
ecosystem by classifying it into three main functions namely:
1. Physical-chemical functions: mangrove ecosystem will physically
maintain and stabilize the shoreline and the banks of the river, protect it
from waves and currents, and accelerate the formation of new land.
Mangrove ecosystem is also a source of nutrient elements which consist of
nitrogen, magnesium, sodium, calcium, sulfur and phosphor.
2. Biological functions: mangrove ecosystem functions as nursery ground,
feeding ground, and spawning ground of a few shrimp, fish, birds, monitor
lizards, snakes, as well as a living place of epiphytes and parasitic plants
such as orchids, ferns nails, and other various lives.
3. Economic functions: mangrove ecosystem can be a place for recreational
fish and shrimp ponds, salt ponds, and a food manufacturer.
Influencing Factors for Mangrove
Erosion is severe in the coast of Java islands and other provinces such as
Lampung, Northeast Sumatra, Kalimantan, West Sumatra (Padang), Nusa
Tenggara, Papua, South Sulawesi, and Bali. On the contrary, Irrawaddy delta
mangrove in Myanmar are presently affected by increased sedimentation and
coastal accretion. It is interesting that deposition in coastal areas may cause a
decrease in the tidal prism in rivers running through the mangrove, resulting in the
closing of tidal creeks and the degradation of the forest (Brown 2007).
Sedimentation rate of the Irrawaddy is the fifth highest in the world.
Table 1 Factors influencing loss of mangrove in Indonesia
Factors Influencing
Anthropogenic factors
Agropolitan conversation
Shrimp pond conversation
Clear felling
Wood and fodder
Categories
Severe Impact
Severe Impact
Severe Impact
Moderate Impact
4
Factors Influencing
Wars and conflicts
Urbanization
Environmental factors
Rising Sea level
Increase in salinity
Erosion and sedimentation
High tide and tsunami
Cyclones
Erosion of shoreline
Source: Brown, 2007
Categories
No Significant Impact
Severe Impact
Moderate Impact
No Information
Moderate Impact
Severe Impact
Low Impact
Severe Impact
Remote Sensing of Mangrove Vegetation
Remote sensing is an indispensable tool for assessing and monitoring
mangrove forests primarily because many mangrove swamps are inaccessible or
difficult to reach for a field survey. Remote sensing data providing synoptic
coverage and those of historical satellites dating back to the 1960s are available.
Global mapping initiatives have failed to map the extent and rate of deforestation
with sufficient details. For example, only extensive mangrove areas were mapped
as part of the Global Land Cover of 2000 survey (Stibig et al. 2007).
Remote sensing of mangrove vegetation is based on two important
properties namely mangrove growing in coastal areas and the chlorophyll of their
leaves (chlorophyll). The optical properties absorb the red light and very strongly
reflect the infrared spectrum (Susilo 2000). Based on the results of the reflection
properties of electromagnetic waves on the object, the use of satellite observations
of vegetation with bands work on the electromagnetic wavelength reflectance
produced high values. Remote sensing is becoming an important tool for
monitoring rapid changes in the biosphere, including changes in vegetation cover.
Mangrove habitat maps have been used for three general management
applications: resource inventory, change detection and the selection and inventory
of aquaculture sites. The mangrove distribution maps can be made from
investigation in situ or obtained from remote sensing images and GIS techniques
(Kairo et al. 2002).
Dynamics of Mangrove Forest
The modeling approach is suitable for simultaneously evaluating the effects
of environmental changes and disturbances on ecological processes such as tree
recruitment, establishment, growth, productivity, and mortality. Such estimates on
the sustainability of mangrove resources may contribute to the evaluation of
impacts of mangrove degradation on socio-economic systems (Davis et al. 2005).
According to hierarchy theory (Holker and Breckling 2002), processes at a
particular organization level can be explained by constraints at higher levels along
with mechanisms at lower levels of organization. Thus, it is essential to evaluate
5
the climatic and landform characteristics of coastal regions which result in local
and often gradual environmental gradients that represent top-down constraints of
mangrove forest development. At the same time, tree performance, growth
response, and interactions among trees affect bottom-up patterns of forest
development (Smith 1992).
Figure 1 Temporal and spatial hierarchical organization of key ecosystem
components in mangrove forests including leaves, trees, forests and
watershed regions.
Markov Chain
In the Markov Chain method, transition probability is used as the basis for
the transitional provisions to the possible changes of each future cell and suitable
for detecting dynamic change in land. This change is determined by current and
past conditions. In a probability statistics theory, which was analyzed in the
Markov process, it was a time-varying phenomena of randomly assigned to a
particular state (Baja 2012). As noted by Houet (2006) the Markov Chain process
controls temporal dynamics among the cover types through the use of transition
probability. In general, change detection to land cover phenomena can be built by
probability information analysis based on Markov Chain where its process allows
the giving of output like transition probability and transition matrix of different
images. This output can be used in modelling and detection through a cellular
automata model.
Markov method is one of the applications used to detect changes to predict
future changes based on a probability that a given piece of land will change from
one state to another. Markov analyzed two qualities of land cover of different
times. It produced a transition area matrix and transition probability matrix. The
probability of information in the land cover change modeling is often based on
Markov chains. Markov chain models have been used to model landscape changes
6
in understanding and predicting the behavior of complex systems (Tang et al.
2007) using discrete state spaces.
However, Markov chain model has some limitations in explaining the
interaction between the changes that occur. Another limitation is that the model
cannot answer the question why the changes occur. Markov model can only be
used to determine when and what type of land use or land cover will change.
Markov chain model has been combined into GIS (Geographic Information
System) through the integration of the remote sensing technology and GIS data.
The integration of the GIS based on Markov model and cellular automata can
represent spatial interaction for land cover and land use change, and can also be
used to determine or predict the land use change in the spatial dimension. Based
on the previous research using Markov chain model to model the land use or land
cover change, however, this research focused on the coral reef as the spatial
dimension as well as the Markov model.
Cellular Automata (CA)
Cellular automata model is an environmental simulation model based on a
tool consisting of a regular grid of cells based on defined neighborhood
interacting with surrounding cells only. Thus, cellular automata will be most
appropriate in a process where immediate surroundings of the cell have been
affected in the cell. Cellular automata models consist of a simulation environment
represented by a gridded space (raster). Cellular automata system is a raster-based
tool and consists of a regular grid of cells. A model is a simplification of reality.
This means some assumptions are used regarding its components namely cells
space (the space on which the automaton exists), cell state (in which the
automaton resides and thus constrained its state), neighborhood (the cells
surrounding the automaton), transition rules (the behavior of the automaton), and
temporal space (discrete time steps in which the automaton evolutes) to assure as
realistic representation of reality as possible. The formalism of CA consists of cell
space, cell state, neighborhoods, transition rule, and temporal space.
The characteristics of the CA model are time, space, and discrete states. The
variable only has a finite number of states, and the rules of state changes are
manifested as local characteristics. The modeling of M-CA process was with and
without the integration of the landscape features: riparian wetlands and hedgerows.
Modelling the transition rules at the field scale through suitability maps which
integrate both local spatial dependencies and driving factor at larger scale appears
as a necessary step to restitute the landscape pattern with a CA based on
continuity relations to model changes (Houet and Hubert 2006)
According to Soe and Le (2006), a multi criteria technique can be used to
drive factors for predicting a future scenario in which decision of land allocation
is done by considering the different criteria. Driving factor is a common method
to assess and aggregate many criteria. It is used as a procedure that multiplies
each of the factors.
7
3 METHODOLOGY
Time and Location
This research was conducted from August 2013 to April 2014, including:
data preparation, data processing, simulation model, thesis writing and
publication. The study area is located in Tanakeke Island, Takalar District, South
Sulawesi, Indonesia. Tanakeke Island has a land area of 4,312 ha. The
geographical boundary of the study area was between 119016‟55”East and
05029‟34” South (Figure 2).
Figure 2 Map of the study area, Tanakeke Island
Data Collection
A set of Landsat data, including four landsat MSS (October 28, 1972), a
Landsat Thematic Mapper (TM) images ( August 26 1993), a Landsat Enhanced
TM Plus (ETM+) image (July 21, 2003), and a Landsat 8 (Operational Land
Imager, OLI) image (April 27, 2013) acquired from the US Geological Survey
(USGS), was used in this study (Table 2). The Landsat MSS data have four
spectral bands, with a spatial resolution of 60 m. landsat TM data have seven
spectral bands, with a spatial resolution of 30 m for bands 1-5 and 7.
The TM band 6 (thermal infrared) was acquired at 120 m resolution but was
resampled to 30 m pixels. The Landsat ETM+ data consisted of eight spectral
bands with a spatial resolution of 30 m for bands 1-7. The ETM+ band 6 (thermal
infrared) was acquired at 60 m resolution but was resampled to 30 m pixels. The
8
Landsat 8 data have nine spectral bands with a spatial resolution of 30 m for
bands 1-7 and 9. The ETM+ and OLI band 8 (panchromatic band) have a spatial
resolution of 15 m. The spectral bands were generally between the optical and
short-wavelength-infrared regions, except for band 9 of Landsat 8 data, which had
a cirrus wavelength between 1.36 and 1.38 μm.
Table 2 Satelite data
Satelite
Resolution
Landsat MSS
60
Landsat TM
30
Landsat ETM (+)
30
Landsat 8
30
Path and Row
122/064
114/064
114/064
114/064
Acquisition
28 October 1972
26 August 1993
21 July 2003
27 April 2013
Method
The methodology of this study consisted of three main steps (Figure 3)
consisting of: (1) image analysis: geometric correction of Landsat images,
preliminary analysis, and classification; (2) ground truth; (3) Cellular Automata
Markov model (CA-Markov).
. Multitemporal image
Geometric correction
Markov analysis
Preliminary analysis
CA-Markov analysis
Classification
Projection of mangrove cover
changes
Ground truth
Land Use/Cover maps
(Multitemporal)
Figure 3 Flow chart of methodology
9
Image Processing Analysis
The Data Processing was composed of five main steps (Figure 4) including:
(1) geometric correction, (2) preliminary analysis, (3) image classification, (4)
accuracy assesment, and (5) post classification.
Landsat MSS,TM ,ETM+,8
(1972,1993,2003,2013)
Geometric Correction
GCP
Preliminary Analysis
Ground Truth
Image Classification
No
Accuracy
Assesment
Yes
Land Use/Cover Map
(1972,1993,2003,2013)
Post Classification
Dynamic of Mangrove
Cover
Figure 4 Flow chart of image processing
Geometric Correction
The varying pixel sizes of the different images were changed into a common
map grid based on a reference image/map. Evenly-distributed GCPs (Ground
Control Points) were selected in different images and registered with the reference
images/maps. A RMS (Root Mean Square) error of less than 0.5 pixels was
10
accepted for the transformation. Resampling was performed by converting
different pixel sizes into the same final image pixel sizes.
Preliminary Analysis
After satellite images were geometrically corrected, preliminary analysis
methods could be applied for image enhancement, composite RGB, and cropping.
The land use/cover classes consisted of mangrove, aquaculture, dry land
vegetation, settlement, and shallow water.
Image Classification
Image classification is a process of grouping all of the pixels into an image
consisting of classes. Time series data Landsat imagery were used in this research
to obtain information about actual land use from each time series. A classification
process in this research was to determine the number of classes as representatives
of the entities. Visual classification is a semi-automatic method using a screen
digitizing technique. Detection performed on objects by making delineation of the
outer limits of the group had the same color which differentiates objects from the
others.
Accuracy Assessment
Accuracy test was used to make any calculation matrix for each error
(confusion matrix) on any kind of habitat shallow water cover resulting from the
analysis satellite imagery. The following is a table of confusion matrix form
(Table 3).
Table 3 Confusion matrix
Classified to class
Reference (sample point)
1
1
N11
2
N21
K
NK1
Column Total (N+j)
N+1
Producer‟s Accuracy NKK/N+1
Source: (Congalton 1999)
2
N12
N22
NK2
N+2
N22/N+2
K
N1K
N2K
NKK
N+K
NKK/N+K
Row Total
(Ni+)
User‟s
Accuracy
N1+
N2+
NK+
N
N11/N1+
N22/N2+
NKK/NK+
Image validation was counted based on the above table such as Overall
Accuracy (OA), Producer’s Accuracy (PA), and User’s Accuracy (UA). OA is a
percentage of sample units that were classified accurately. PA and UA are ways of
representing individual category accuracies instead of just the OA. PA is a
percentage of probability average of a sample unit that refers to distribution of
each class that had been classified in the field, while UA is a percentage of sample
unit that actually represented the classes in the field. The confusion matrix in
11
Table 3 helped make the transition of equation and mathematical notation easy to
understand.
Post Classification
From these classification maps, changes in the extent of mangrove forests
during the periods of 1972 to 1993, 1993 to 2003, 2003 to 2013, and 1972 to 2013
were examined to gain geographic understanding of the spatiotemporal evolution
of land use in the region.
Mangrove Cover Projection
Markov Chain analysis
Marcov Chain. A markovian process is one in which the state of a system at
time (t2) can be predicted by the state of the system at time (t1). In this research,
where the result of image processing is land use/cover map in 1972, 1993, 2003,
and 2013, Markov model would generate transition probability matrix and
transition area matrix (Figure 5).
Data inputs used for Markov chain were two land use/cover maps derived
from different times. Land use/cover map with an interval of 21 years, where land
use/cover map of 1972 to 1993 was projected for 2003, was validated using land
use/cover map in 2003. Land use/cover map with a 10-year interval, where land
use/cover map of 1993 to 2003 was projected for 2013, was validated using land
use/cover map of 2013. Land use/cover map with a 31-year interval, where land
use map of 1972 to 2003 was projected for 2013, was validated using land
use/cover map in 2013. The results of the third interval would be selected based
on the level of accuracy for projection change of mangrove in 2023. The raster
data with pixel 30x30 meter and 60x60 meter were applied for all variabels that
were involved in the model.
Land Use/Cover
Map (t1)
Land Use/Cover
Map (t2)
Markov Chain
Analysis
Markov RGF
Transition
Probabilities Matrix
Transition Area
Matrix
Figure 5 Flow chart of Markov Chain model
12
Multi Criteria Evaluation (MCE)
A suitability map shows the degree of suitability for a particular purpose at
any location. It is most often produced from multiple images since most
suitability problems incorporate multiple criteria. This type of Boolean suitability
evaluation is often referred to as constraint mapping since each criterion is defined
by a Boolean image indicating areas that are either suitable for use (value 1) or
constrained from use (value 0).
Table 4 Criteria and categorization of factors for mangrove
Factor
Settlement
Substrate
Tidal
Value
1
0
1
0
1
0
Category factors
Non Settlement
Settlement
Silty
Sand
Inter-tidal
Non Inter-tidal
CA-Markov analysis
The present states of the cellular and neighboring sections determine the
future state. The specific realization processes are as follow: (1) determination of
conversion rules. By converting interpreted data from vector to raster, via a GIS
overlay analysis, the transition probability matrix as well as transition and
conditional probability image of mangrove types can be determined. (2)
construction of CA filters. The cellular change of state is then determined using a
5x5 filter which means a rectangular space that is composed with one cellular
section. This filter has a remarkable influence on the change in the cellular state.
(3) determination of start time and iteration number.
According to the characteristics of the individual CA and Markov models,
they can be combined with each other. The study area will be divided into several
sections and each section is a cellular one. Each cellular section has a
corresponding state and neighbors.
There are three data inputs for CA-Markov model (Figure 6): (1) basis land
cover image that is land use map. (2) markov transition matrix areas that is loaded
from transition area matrix. (3) transition suitability image collection that is
collection of land suitability images. CA-Markov is nonlinear formula with the
computarization was iterated and stop end kappa accuracy value.
Kappa accuracy
Kappa „ mathematical accuracy is :
∑
∑
∑
(Congalton and Mead in Wassahu 2010)…….(1)
13
Where
N
r
Xii
x+1
x1+
:
: the total Number of cell in the matrix,
: the number of rows in the matrix,
: the number in row i and column i
: the total observations for column i, and
: the total observations in row i
Mangrove Map (t2)
Raster Data
Transition Area Matrix
(t1+t2)
CA-Markov Simulation
MCE
(Probability Map)
Mangrove Projection
Kappa Validation
ON A SMALL ISLAND
Case Study: Tanakeke Island, Takalar District, South Sulawesi
M. AKBAR AS
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014
STATEMENT
I, M. Akbar AS, hereby declare that this thesis entitled
Geospatial Modeling of Vegetation Cover Changes on a Small Island
Case Study: Tanakeke Island, Takalar District, South Sulawesi
Is a result of my work under the supervision of advisory board and that it has not
been published before. The content of this thesis has been examined by the
advisory board and external examiner.
Bogor,
June 2014
M. Akbar AS
G051110071
RINGKASAN
M. AKBAR AS. Permodelan Geospasial Perubahan Tutupan Vegetasi di Pulau
Kecil, Studi Kasus: Pulau Tanakeke, Kabupaten Takalar, Sulawesi Selatan.
Dibawah bimbingan oleh BUCE SALEH dan IBNU SOFIAN.
Indonesia telah kehilangan lebih dari 1,2 juta hektar mangrove sejak tahun
1980 ketika tutupan hutan mangrove masih 4,2 juta hektar (FAO 2007). Luas
hutan mangrove di Pulau Tanakeke, Kabupaten Takalar, Sulawesi Selatan,
Indonesia pada awal tahun 1980 adalah 1.770 hektar, dan setelah dikonversi
menjadi areal tambak luas lahan tinggal 500 hektar. Fenomena degradasi hutan
mangrove di Pulau Tanakeke perlu mendapat perhatian khusus, mengingat pulau
ini merupakan salah satu pulau di Sulawesi Selatan yang memiliki hutan
mangrove yang sangat berharga di wilayah pesisir. Tujuan dari penelitian ini
adalah untuk menganalisis dinamika perubahan tutupan mangrove (selama 41
tahun) dengan menggunakan citra Landsat multi-temporal dan memproyeksi
perubahan tutupan mangrove pada tahun 2023.
Penelitian ini dilakukan dari bulan Agustus 2013 sampai April 2014,
termasuk tahap persiapan data, pengolahan data, model simulasi, penulisan tesis
dan publikasi. Lokasi penelitian terletak di Pulau Tanakeke, Kabupaten Takalar,
Sulawesi Selatan. Untuk melakukan studi ini, ada empat data citra Landsat yang
digunakan, yaitu Landsat MSS (28 Oktober 1972), Landsat Thematic Mapper
(TM) (26 Agustus 1993), Landsat Enhanced TM Plus (ETM+) (21 Juli 2003), dan
Landsat 8 (Operational Land Imager, OLI) (27 April 2013) diperoleh dari United
States Geological Survey (USGS). Peta probabilitas untuk perubahan tutupan
mangrove menggunakan data Multi Criteria Evaluation (MCE), yaitu pasang
surut, permukiman, dan substrat.
Metodologi penelitian ini terdiri dari tiga langkah utama, yaitu: (1) analisis
citra: koreksi geometrik dari citra Landsat, analisis awal, dan klasifikasi; (2)
ground truth; dan (3) model Cellular Automata Markov (CA-Markov), sedangkan
pengolahan data citra satelit terdiri dari lima langkah utama, yaitu: (1) koreksi
geometrik, (2) analisis awal, (3) klasifikasi citra, (4) accuracy assessment, dan (5)
post classification. Data hasil analisis citra dari dua peta penggunaan lahan yang
berasal dari waktu yang berbeda digunakan dalam membuat model Markov
Chain. Peta penggunaan lahan dengan interval 21 tahun, memakai peta
penggunaan lahan dari 1972-1993 digunakan untuk proyeksi tahun 2003 dan
kemudian divalidasi menggunakan peta penggunaan lahan tahun 2003. Peta
penggunaan lahan dengan interval 10 tahun (1993-2003) digunakan sebagai
proyeksi untuk tahun 2013 dan divalidasi menggunakan peta penggunaan lahan
tahun 2013. Sementara itu, peta penggunaan lahan dengan interval 31 tahun,
(1972-2003) sebagai proyeksi untuk 2013 dan divalidasi menggunakan peta
penggunaan lahan tahun 2013. Proyeksi mangrove tahun 2023 diperoleh
berdasarkan tingkat akurasi dari ketiga interval yang dipilih. Jenis filtering 5x5
digunakan untuk melihat perubahan cell yang terjadi dalam memprediksi. Jika
hasil validasi yang diperoleh adalah >75%, maka dapat digunakan untuk
memprediksi mangrove untuk 10 tahun ke depan.
Penilaian kuantitatif dilakukan dengan menggunakan data ground truth
melalui pendekatan stratified random sampling. Matriks error yang dihasilkan
dari akurasi klasifikasi, yaitu 87,78%. Produser dan pengguna akurasi untuk
mangrove diperoleh 93%. Hasil analisis dinamika spasial pada tahun 1972-2013
menunjukkan bahwa luas mangrove di Pulau Tanakeke mengalami penurunan
sebesar 1.595,55 ha (63,91%). Tingkat perubahan yang terjadi tidak seragam
antara tahun 1972-1993, 1993-2003, dan 2003-2013. Laju perubahan luas tutupan
mangrove selama tahun 1972-1993 merupakan yang tertinggi di Pulau Tanakeke,
dimana mengalami penurunan luas tutupan mangrove sebesar 1.166,61 ha
(46,73%). Pada tahun 1993 hingga 2003, mangrove di Pulau Tanakeke mengalami
penurunan seluas 252,49 ha (18,98%), dan pada tahun 2003-2013 terjadi
penurunan seluas 176,45 ha (16,37%).
Hasil simulasi perubahan tutupan mangrove pada tahun 1993 dan 2003
(selang 10 tahun) digunakan untuk memprediksi perubahan tutupan mangrove
pada tahun 2013 dan dibandingkan dengan tutupan mangrove yang sebenarnya
pada tahun 2013 (peta hasil klasifikasi). Hasil perhitungan akurasi yang diperoleh
dengan menggunakan iterasi 35 dan grid 30, yaitu 86,87%, dan dengan
menggunakan iterasi 35 dan grid 60 diperoleh akurasi, yaitu 84,85%. Perubahan
tutupan mangrove pada tahun 1972 dan 1993 (selang 21 tahun) dibuat untuk
memprediksi perubahan tutupan mangrove pada tahun 2003. Hasil simulasi
perubahan tutupan mangrove dibandingkan dengan tutupan mangrove yang
sebenarnya pada tahun 2003 (peta hasil klasifikasi). Hasil perhitungan akurasi
yang diperoleh dengan menggunakan iterasi 25 dan grid 30, yaitu 81,96%, dan
dengan menggunakan iterasi 25 dan grid 60 akurasi yang diperoleh, yaitu 79,89%.
Perubahan tutupan mangrove pada tahun 1972 dan 2003 (selang 31 tahun) dibuat
untuk memprediksi perubahan tutupan hutan mangrove pada tahun 2013. Hasil
simulasi perubahan tutupan mangrove kemudian dibandingkan dengan tutupan
mangrove yang sebenarnya pada tahun 2013 (peta hasil klasifikasi). Hasil
perhitungan akurasi diperoleh dengan menggunakan iterasi 10 dan grid 30, yaitu
83,70%, dan dengan menggunakan iterasi 10 dan grid 60 diperoleh akurasi, yaitu
81,87%.
Berdasarkan hasil proyeksi dengan menggunakan interval tahun yang
berbeda, diperoleh data dengan akurasi yang lebih tinggi pada interval 10 tahun
dengan menggunakan iterasi 35. Dalam membuat model proyeksi untuk 10 tahun
ke depan (2023), digunakan interval waktu 10 tahun (2003-2013) dengan iterasi
35 dan grid 30 meter, maka diperoleh proyeksi luas tutupan lahan mangrove pada
tahun 2023, yaitu 662,28 ha (mengalami penurunan sebesar 238,83 ha).
Rekomendasi berdasarkan penelitian ini bahwa dengan menggunakan
Model CA-Markov dari hasil interpretasi visual dalam grid 60 m, dapat digunakan
untuk memproyeksikan perubahan luas tutupan mangrove di Pulau Tanakeke
karena memiliki akurasi yang baik. Pengembangan areal tambak dan penebangan
mangrove untuk arang harus dibatasi di Pulau Tanakeke guna melestarikan
mangrove untuk pengembangan pariwisata.
Kata kunci: Mangrove, Cellular Automata Markov, Penginderaan jauh, Dinamika
geospasial
SUMMARY
M. Akbar AS. Geospatial Modeling of Vegetation Cover Changes on a Small
Island (Case Study: Tanakeke Island, Takalar District, South Sulawesi).
Supervised By BUCE SALEH and IBNU SOFIAN.
Indonesia has lost more than 1.2 million hectare of mangrove since 1980
when mangrove forest cover was still 4.2 million hectare (FAO 2007). The
mangrove forest in Tanakeke Island, the district of Takalar, South Sulawesi,
Indonesia in the early 1980s was 1,770 hectares, and after a conversion into
middle and failed fish farms, only 500 hectares remain. The phenomenon of
mangrove forests degradation in Tanakeke Island needs to receive specific
attention, considering that the island is one in South Sulawesi having precious
mangrove forests in a coastal region. The objective of this study was to analyze
dynamics of mangrove cover changes (41 years) using multi-temporal Landsat
imagery and projection of mangrove cover changes in 2023.
This research was conducted from August 2013 to April 2014, including the
stages of data preparation, data processing, simulation model, thesis writing and
publication. The study area is located in Tanakeke Island, Takalar District, South
Sulawesi. To conduct the study, Landsat data, including four Landsat MSS (28
October 1972), a Landsat Thematic Mapper (TM) images (26 August 1993), a
Landsat Enhanced TM Plus (ETM+) image (21 July 2003), and a Landsat 8
(Operational Land Imager, OLI) image (27 April 2013) were acquired from the
United States Geological Survey (USGS). The probability maps for mangrove
cover changes in the study area using the multi criteria evaluation were the tidal,
settlement, and substrate.
The methodology of this study consisted of three main steps: (1) image
analysis: geometric correction of Landsat images, preliminary analysis, and
classification; (2) ground truth; (3) Cellular Automata Markov model (CAMarkov) while the data processing was composed of five main steps: (1)
geometric correction, (2) preliminary analysis, (3) image classification, (4)
accuracy assesment, and (5) post classification. Data input used for Markov
Chain on two land use maps derived from two different times. Land use map with
interval of 21 year, using land use map of 1972 to 1993, is used for projection
2003 and then was validated using land use map on 2003. A land use map with
10-year interval, which was the map of 1993 to 2003 used for projection of 2013,
and then was validated using land use map on 2013. Meanwhile, land use map
with a 31-year interval, which used a land use map of 1972 to 2003, for projection
2013, was validated using land use map on 2013. The results of the third interval
were selected based on the level of accuracy for projection change of mangrove in
2023. The cellular change of state was then determined in which 5x5 filter was
used. If the result of validation standard agreement was >75%, it could be used to
predict the future trend of mangrove for the next 10 years.
Quantitative assessment was performed using ground truthing data with a
stratified random sampling approach. An error matrix was generated with an
overall classification accuracy of 87.78%. Producer‟s and user‟s accuracy for
mangrove was 93%. The result of spatial dynamic analysis from 1972 to 2013
indicated that mangrove in the Tanakeke Island decreased by 1,595.55 ha
(63.91%). The rate of change, however, was not uniform from 1972 to 1993, 1993
to 2003, and from 2003 to 2013. Deforestation rate during 1972 to 1993 was the
highest in Tanakeke Island, and mangrove area decreased by 1,166.61 ha
(46.73%). From 1993 to 2003, mangrove in Tanakeke Island decreased by 252.49
ha (18.98%), and from 2003 to 2013 there was a decreased of 176.45 ha
(16.37%).
The simulation result of mangrove cover changes in 1993 and 2003 (interval
10 years) was made to predict mangrove cover change in 2013. The simulation
result of mangrove cover change was compared with the actual mangrove cover in
2013 (map classification). Calculation results were obtained using iteration 35 and
grid 30 by 86.87%, iteration 35 and grid 60 by 84.85% overall accuracy.
Mangrove cover changes in 1972 and 1993 (21 years interval) were made to
predict mangrove cover change in 2003. The simulation result of mangrove cover
change was then compared with the actual mangrove cover in 2003 (map
classification). Calculation results were obtained using iteration 25 and grid 30 by
81.96%, iteration 25 and grid 60 by 79.89% overall accuracy. Mangrove cover
changes in 1972 and 2003 (interval 31 years) were made to predict mangrove
cover change in 2013. The simulation result of mangrove cover change was then
compared with the actual mangrove cover in 2013 (map classification).
Calculation results were obtained using iteration 10 and grid 30 by 83.70%,
iteration 10 and grid 60 by 81.87% overall accuracy
Based on the results of the projection by using different intervals, the data
with higher accuracy were obtained during ten-year intervals with in which 35
iterations were used. In making a projection for the next 10 years (2023), an
interval of 10 years (2003 to 2013) with iterations 35 was used. The simulation
model trend, mangrove cover change is a major concern which continued to be
broken from 2013 to 2023 as illustrated in the total area of mangrove cover is
predicted to be 662.28 ha in 2023 (decreased by 238.83 ha) by using grid 30 m.
The recommendation based on the study is that by using visual
interpretation results in a grid of 60 m, CA-Markov model can be used in
projecting change of mangrove in Tanakeke Island because it has good accuracy.
Development of aquaculture and charcoal should be restricted in Tanakeke Island
to conserve mangrove for tourism development.
Keyword: Mangrove, Cellular Automata Markov, Remote Sensing, Geospatial
Dynamic
Copyright © 2014, Bogor Agricultural University
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Citation does not inflict the name and honor of Bogor Agricultural University.
It is prohibited to republish and reproduce all part of this thesis without any
written permission from Bogor Agricultural University.
GEOSPATIAL MODELING OF VEGETATION COVER CHANGES
ON A SMALL ISLAND
Case Study: Tanakeke Island, Takalar District, South Sulawesi
M. AKBAR AS
A Thesis submitted for the Degree of Master of Science in
Information Technology for Natural Resources
Management Program Study
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014
External Examiner:
Dr Antonius Bambang Wijanarto
Research Title
Name
Student ID
: Geospatial Modeling of Vegetation Cover Changes on a Small
Island (Case Study: Tanakeke Island, Takalar District, South
Sulawesi)
: M. Akbar AS
: G051110071
Approved by,
Advisory Board
Dr Ir Buce Saleh, MS
Supervisor
Dr Ibnu Sofian, MEng
Co-Supervisor
Endorsed by,
Program Coordinator of
MSc in IT for Natural Resources
Management
Dr Ir Hartrisari Hardjomidjojo, DEA
Date of examination:
June 13, 2014
Dean of Graduate School
Dr Ir Dahrul Syah, MScAgr
Date of Graduation :
ACKNOWLEDGEMENT
In the name of Allah SWT, the Most Gracious and the Most Merciful.
Alhamdulillah, all praises to Allah for the strengths and His blessing in
completing this thesis, I would like to express my highly appreciation to the
following:
1. Dr Ir Buce Saleh, MS as my supervisor for his supervision support. His
invaluable help in the forms of constructive comments and suggestions
throughout the experimental and thesis works have contributed to the success
of this research and Dr Ibnu Sofian, MEng as the co-supervisor for his ideas
and knowledge regarding this topic during my research.
2. Dr Antonius Bambang Wijanarto as the external examiner for his positive
inputs and ideas.
3. Dr Ir Hartrisari Hardjomidjojo, DEA, as the program coordinator and all my
lecturers for giving knowledge and experience.
4. Dr Nurjannah Nurdin, ST for helping provide data and suggestions during my
research. Your kindness means a lot to me. Thank you very much.
5. All my friends at MIT, IPB, for their help and support in finishing my
assignment and study.
6. Staff of Center for Regional Development and Spatial Information,
Hasanuddin University for all the support and help.
7. MIT secretariate and all of its staff members for helping me to arrange the
administration, technical procedures, and facilities.
8. All of my family. Thank you for always supporting me.
Bogor, July 2014
M. Akbar AS
LIST OF CONTENTS
LIST OF TABLES
vii
LIST OF FIGURES
vii
1. INTRODUCTION
Background
Objectives
Problem Identification
1
2
2
2. LITERATURE REVIEW
Mangrove Ecosystem
Influencing Factors for Mangrove
Remote Sensing of Mangrove Vegetation
Dynamics of Mangrove Forest
Markov Chain
Cellular Automata (CA)
3
3
4
4
5
6
3. METHODOLOGY
Time and Location
Data Collection
Method
Image processing analysis
Geometric Correction
Preliminary Analysis
Image Classification
Accuracy Assessment
Post Classification
Mangrove cover projection
Markov chain analysis
Multi Criteria Evaluation (MCE)
CA-Markov analysis
Kappa accuracy
7
7
8
9
9
10
10
10
11
11
11
12
12
12
4. RESULT AND DISCUSSION
Mangrove Cover Dynamics
Image Classification
Accuracy Assessment
Post Classification
Mangrove Cover Projection
Projection of mangrove cover in 2023
13
13
15
16
22
24
26
28
30
5. CONCLUSION AND RECOMMENDATION
Conclusion
Recommendation
31
32
Interval of 10 years (1993to2003) for the projection in 2013
Interval of 20 years (1972to1993) for the projection in 2003
Interval of 30 years (1972to2003) for the projection in 2013
REFERENCES
32
CURRICULUM VITAE
34
LIST OF TABLES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Factors influencing loss of mangrove in Indonesia.
Satelite data.
Confusion matrix.
Criteria and categorization of factors for mangrove.
Areal estimates of major land use/cover types.
Areal and percentages of land use/cover changes.
Areal of land use/cover changes from the 1972 to 1993.
Percentages of land use/cover changes from the 1972 to 1993.
Areal of land use/cover changes from the 1993 to 2003.
Percentages of land use/cover changes from the 1993 to 2003.
Areal of land use/cover changes from the 2003 to 2013.
Percentages of land use/cover changes from the 2003 to 2013.
Areal of land use/cover changes from the 1972 to 2013.
Percentages of land use/cover changes from the 1972 to 2013.
Matrix kappa Index of Agreement (KIA).
Mangrove cover change projection using a grid 30 m.
3
8
10
12
14
14
17
17
17
18
18
19
19
20
30
30
LIST OF FIGURES
1
2
3
4
5
6
7
8
9
Temporal and spatial hierarchical organization of key ecosystem
components in mangrove forests including leaves, trees, forests
and watershed regions.
Map of the study area, Tanakeke Island.
Flow chart of methodology.
Flow chart of image processing.
Flow chart of Markov Chain model.
Flow chart of Cellular Automata-Markov (CA-Markov) model
Land use/cover maps of Tanakeke Island (a) in 1972, (b) 1993,
(c) 2003,and (d) 2013.
Actual condition adjusted to Landsat 8 images of 2013 in
Tanakeke Island.
Post classification land use/cover map of Tanakeke Island: (a) in
1972 to 1993, (b) 1993 to 2003, (c) 2003 to 2013, (d) 1972 to
2013.
5
7
8
9
11
13
15
16
21
10
11
12
13
14
15
16
17
18
Tidal data of Tanakeke Island in 2014.
Probability maps of (a) Substrat, (b) Tidal, (c), Settlement, (d)
Result from Boolean approach.
Interval of 10 years: (a) iteration for grid 30 m, (b) iteration for
grid 60 m.
Interval of 10 years: (a) mangrove cover in 2013 (Grid 30 m),
(b) mangrove cover simulation in 2013 (Grid 30 m), (c)
mangrove cover in 2013 (Grid 60 m), (d) mangrove cover
simulation in 2013 (Grid 60 meter).
Interval of 21 years: (a) iteration for grid 30 m, (b) iteration for
grid 60 m.
Interval of 21 years: (a) mangrove cover in 2003 (Grid 30 m),
(b) mangrove cover simulation in 2003 (Grid 30 m), (c)
mangrove cover in 2003 (Grid 60 m), (d) mangrove cover
simulation in 2003 (Grid 60 meter).
Interval of 31 years: (a) iteration for grid 30 m, (b) iteration for
grid 60 m.
Interval of 31 years: (a) mangrove cover in 2003 (Grid 30 m),
(b) mangrove cover simulation in 2003 (Grid 30 m), (c)
mangrove cover in 2003 (Grid 60 m), (d) mangrove cover
simulation in 2003 (Grid 60 meter).
Condition of mangrove cover in Tanakeke Island: (a) Mangrove
cover in 2013, (b) Projection mangrove cover in 2023.
23
23
24
25
26
27
28
29
31
1
1 INTRODUCTION
Background
Mangrove forests found in the inter-tidal zone in the tropics and subtropics,
play an important role in stabilizing shorelines and in helping reduce devastating
impacts of natural disasters such as tsunamis and hurricanes. They also provide
important ecological and social goods and services including breeding and
nursing grounds for marine and pelagic species, food, medicine, fuel, and building
materials for local communities. These forests, however, are declining at an
alarming rate, perhaps even more rapidly than inland tropical forests, and much of
what remains is in degraded condition (Wilkie and Fortune 2003).
As a complex and unique ecosystem, mangrove has important functions
and benefits for coastal environments (Bengen 1999), some of which are a muffler
of waves and storms, a protector of abrasion, a trapper of mudguard, and
sediment, a producer of some detritus from the leaves and branches of mangrove
trees, a nursery, feeding, and spawning ground of various types of fish, shrimp
and other marine biota, a producer of wood for constructions materials, firewood,
charcoal materials, paper materials, and furniture-making materials, and a supplier
of fish larvae, shrimp and other marine biota.
Indonesia has lost more than 1.2 Mha of its mangrove since 1980 when
mangrove forest cover was still 4.2 Mha (FAO 2007). Among the immediate
impacts of mangrove loss has been the decline in fish production since mangrove
are vital fish nurseries and rapid erosion in the absence of buffer against high
waves. 60% of this loss has been attributed to conversion of mangrove into
brackish water aquaculture ponds, which peaked in the 1980‟s and 1990‟s during
a period of aquaculture expansion known as the blue revolution.
There is no agreement for the extent of mangrove vegetation in Indonesia,
but various forums usually use the number of 4.25 million ha for a measure. The
occurence of mangrove forest reduction is generally caused by the exploitation
and transferring of appropriation of theland. Approximately 9 years ago, the
extensive vast of mangrove forest in Indonesia was about 4.13 million ha, but now
only 2.49 million ha (60%) remains.
South Sulawesi is facing massive mangrove forest destruction, for over the
past 30 years deforestation and pollution have taken their toll and damaged almost
90 percent of the total original areas. Before 1980s, the mangrove forest in South
Sulawesi was over 214,000 hectares. The conversion of the mangrove forest into
fishpond reduced the area into 23,000 hectares in the early 1991, or a decrease of
61 percent. The conversion of mangrove forests into fish farms was a result of a
government policy in the early 1980s that instructed the offices of local fisheries
and maritime affairs to carry out intensification of shrimp cultivation, which was
then a major export commodity and earned the state a huge amount of money.
The mangrove forest in Tanakeke Island, Takalar District, South Sulawesi,
Indonesia in the early 1980s was 1,770 hectares, and after conversion, for middle
and failed fish farms, only 500 hectares remain. The phenomenon of mangrove
forests degradation in Tanakeke Island deserve specific attention, given that
2
Tanakeke Island is one small island in South Sulawesi with precious mangrove
forests in its coastal region.
Remote sensing could play an important and effective role in the assessment
and monitoring of mangrove forest cover dynamics. While remote sensing data
analysis does not replace field inventory, it provides supplementary information
quickly and efficiently. The use of remotely sensed data offers many advantages
including synoptic coverage, availability of low-cost or free satellite data,
availability of historical satellite data, and repeated coverage. In addition, recent
advances in the hardware and software used for processing a large volume of
satellite data have helped increase the usefulness of remotely sensed data.
Moreover, it is extremely difficult to get into vast swamps of mangrove forests,
and conducting a field inventory is time consuming and costly. A number of
studies conducted in small islands have begun to develop and apply remotesensing techniques mainly for mapping purposes (Nayak et al. 2001).
The present work used the Cellular Automata-Markov (CA-Markov) model
to project the likely temporal distribution of the mangrove landscape in the prespecified coastal region driven by some well-known local socioeconomic factors.
The quantitative information achieved from such a projection might be useful for
forest managers to devise better plans for long-term management of mangrove
ecosystems in Tanakeke Island.
Objectives
The aim of this study was to analyze dynamics of mangrove cover changes
(41 years) using multi-temporal Landsat imagery and projection of mangrove
cover changes in 2023.
Problem Identification
As a result of socio-economic development and population growth, some
parts of the mangrove forests were cleared for other land uses. Since the early
1997, a number of aquaculture fields, especially large-scale shrimp farms, have
been constructed by clearing the mangrove forests. The destruction of mangrove
forests for shrimp farming has continuously degraded ecological and socioeconomic services of mangrove forests with associated environmental impacts.
In the case of Tanakeke island , development of coastal shrimp farming
ponds in an intertidal area is considered as a major factors behind mangrove
delineation. Shrimp cultivation is the largest anthropogenic cause of mangrove
deforestation in Tanakeke Island. The process of shrimp farming is economically
lucrative for the coastal communities. Particularly, with huge demands of
commercially produced shrimps in Indonesia, it guarantees a high economic
return. Moreover, clearing of mangrove trees for charchoal on a large scale and
mangrove forests diverted for establishment of new coastal settlements are the
second largest anthropogenic cause.
3
2 LITERATURE REVIEW
Mangrove Ecosystem
Deforestation led by increasing demand for land and climate change events
such as the rise of sea level and reduction in freshwater flow are considered as
major players behind the continuous annihilation of mangrove; however, climate
change events may have an impact of only 10-15% reduction of mangrove
habitats in the distant future whereas the immediate threat comes from
uncontrolled exploitation and deforestation (Alongi 2008). This has resulted in
serious concerns among conservationists and has exposed the coastal communities
to a further increasing threat of climate change and hydro meteorological
disasters.
It is easier to understand the benefits and important values of the mangrove
ecosystem by classifying it into three main functions namely:
1. Physical-chemical functions: mangrove ecosystem will physically
maintain and stabilize the shoreline and the banks of the river, protect it
from waves and currents, and accelerate the formation of new land.
Mangrove ecosystem is also a source of nutrient elements which consist of
nitrogen, magnesium, sodium, calcium, sulfur and phosphor.
2. Biological functions: mangrove ecosystem functions as nursery ground,
feeding ground, and spawning ground of a few shrimp, fish, birds, monitor
lizards, snakes, as well as a living place of epiphytes and parasitic plants
such as orchids, ferns nails, and other various lives.
3. Economic functions: mangrove ecosystem can be a place for recreational
fish and shrimp ponds, salt ponds, and a food manufacturer.
Influencing Factors for Mangrove
Erosion is severe in the coast of Java islands and other provinces such as
Lampung, Northeast Sumatra, Kalimantan, West Sumatra (Padang), Nusa
Tenggara, Papua, South Sulawesi, and Bali. On the contrary, Irrawaddy delta
mangrove in Myanmar are presently affected by increased sedimentation and
coastal accretion. It is interesting that deposition in coastal areas may cause a
decrease in the tidal prism in rivers running through the mangrove, resulting in the
closing of tidal creeks and the degradation of the forest (Brown 2007).
Sedimentation rate of the Irrawaddy is the fifth highest in the world.
Table 1 Factors influencing loss of mangrove in Indonesia
Factors Influencing
Anthropogenic factors
Agropolitan conversation
Shrimp pond conversation
Clear felling
Wood and fodder
Categories
Severe Impact
Severe Impact
Severe Impact
Moderate Impact
4
Factors Influencing
Wars and conflicts
Urbanization
Environmental factors
Rising Sea level
Increase in salinity
Erosion and sedimentation
High tide and tsunami
Cyclones
Erosion of shoreline
Source: Brown, 2007
Categories
No Significant Impact
Severe Impact
Moderate Impact
No Information
Moderate Impact
Severe Impact
Low Impact
Severe Impact
Remote Sensing of Mangrove Vegetation
Remote sensing is an indispensable tool for assessing and monitoring
mangrove forests primarily because many mangrove swamps are inaccessible or
difficult to reach for a field survey. Remote sensing data providing synoptic
coverage and those of historical satellites dating back to the 1960s are available.
Global mapping initiatives have failed to map the extent and rate of deforestation
with sufficient details. For example, only extensive mangrove areas were mapped
as part of the Global Land Cover of 2000 survey (Stibig et al. 2007).
Remote sensing of mangrove vegetation is based on two important
properties namely mangrove growing in coastal areas and the chlorophyll of their
leaves (chlorophyll). The optical properties absorb the red light and very strongly
reflect the infrared spectrum (Susilo 2000). Based on the results of the reflection
properties of electromagnetic waves on the object, the use of satellite observations
of vegetation with bands work on the electromagnetic wavelength reflectance
produced high values. Remote sensing is becoming an important tool for
monitoring rapid changes in the biosphere, including changes in vegetation cover.
Mangrove habitat maps have been used for three general management
applications: resource inventory, change detection and the selection and inventory
of aquaculture sites. The mangrove distribution maps can be made from
investigation in situ or obtained from remote sensing images and GIS techniques
(Kairo et al. 2002).
Dynamics of Mangrove Forest
The modeling approach is suitable for simultaneously evaluating the effects
of environmental changes and disturbances on ecological processes such as tree
recruitment, establishment, growth, productivity, and mortality. Such estimates on
the sustainability of mangrove resources may contribute to the evaluation of
impacts of mangrove degradation on socio-economic systems (Davis et al. 2005).
According to hierarchy theory (Holker and Breckling 2002), processes at a
particular organization level can be explained by constraints at higher levels along
with mechanisms at lower levels of organization. Thus, it is essential to evaluate
5
the climatic and landform characteristics of coastal regions which result in local
and often gradual environmental gradients that represent top-down constraints of
mangrove forest development. At the same time, tree performance, growth
response, and interactions among trees affect bottom-up patterns of forest
development (Smith 1992).
Figure 1 Temporal and spatial hierarchical organization of key ecosystem
components in mangrove forests including leaves, trees, forests and
watershed regions.
Markov Chain
In the Markov Chain method, transition probability is used as the basis for
the transitional provisions to the possible changes of each future cell and suitable
for detecting dynamic change in land. This change is determined by current and
past conditions. In a probability statistics theory, which was analyzed in the
Markov process, it was a time-varying phenomena of randomly assigned to a
particular state (Baja 2012). As noted by Houet (2006) the Markov Chain process
controls temporal dynamics among the cover types through the use of transition
probability. In general, change detection to land cover phenomena can be built by
probability information analysis based on Markov Chain where its process allows
the giving of output like transition probability and transition matrix of different
images. This output can be used in modelling and detection through a cellular
automata model.
Markov method is one of the applications used to detect changes to predict
future changes based on a probability that a given piece of land will change from
one state to another. Markov analyzed two qualities of land cover of different
times. It produced a transition area matrix and transition probability matrix. The
probability of information in the land cover change modeling is often based on
Markov chains. Markov chain models have been used to model landscape changes
6
in understanding and predicting the behavior of complex systems (Tang et al.
2007) using discrete state spaces.
However, Markov chain model has some limitations in explaining the
interaction between the changes that occur. Another limitation is that the model
cannot answer the question why the changes occur. Markov model can only be
used to determine when and what type of land use or land cover will change.
Markov chain model has been combined into GIS (Geographic Information
System) through the integration of the remote sensing technology and GIS data.
The integration of the GIS based on Markov model and cellular automata can
represent spatial interaction for land cover and land use change, and can also be
used to determine or predict the land use change in the spatial dimension. Based
on the previous research using Markov chain model to model the land use or land
cover change, however, this research focused on the coral reef as the spatial
dimension as well as the Markov model.
Cellular Automata (CA)
Cellular automata model is an environmental simulation model based on a
tool consisting of a regular grid of cells based on defined neighborhood
interacting with surrounding cells only. Thus, cellular automata will be most
appropriate in a process where immediate surroundings of the cell have been
affected in the cell. Cellular automata models consist of a simulation environment
represented by a gridded space (raster). Cellular automata system is a raster-based
tool and consists of a regular grid of cells. A model is a simplification of reality.
This means some assumptions are used regarding its components namely cells
space (the space on which the automaton exists), cell state (in which the
automaton resides and thus constrained its state), neighborhood (the cells
surrounding the automaton), transition rules (the behavior of the automaton), and
temporal space (discrete time steps in which the automaton evolutes) to assure as
realistic representation of reality as possible. The formalism of CA consists of cell
space, cell state, neighborhoods, transition rule, and temporal space.
The characteristics of the CA model are time, space, and discrete states. The
variable only has a finite number of states, and the rules of state changes are
manifested as local characteristics. The modeling of M-CA process was with and
without the integration of the landscape features: riparian wetlands and hedgerows.
Modelling the transition rules at the field scale through suitability maps which
integrate both local spatial dependencies and driving factor at larger scale appears
as a necessary step to restitute the landscape pattern with a CA based on
continuity relations to model changes (Houet and Hubert 2006)
According to Soe and Le (2006), a multi criteria technique can be used to
drive factors for predicting a future scenario in which decision of land allocation
is done by considering the different criteria. Driving factor is a common method
to assess and aggregate many criteria. It is used as a procedure that multiplies
each of the factors.
7
3 METHODOLOGY
Time and Location
This research was conducted from August 2013 to April 2014, including:
data preparation, data processing, simulation model, thesis writing and
publication. The study area is located in Tanakeke Island, Takalar District, South
Sulawesi, Indonesia. Tanakeke Island has a land area of 4,312 ha. The
geographical boundary of the study area was between 119016‟55”East and
05029‟34” South (Figure 2).
Figure 2 Map of the study area, Tanakeke Island
Data Collection
A set of Landsat data, including four landsat MSS (October 28, 1972), a
Landsat Thematic Mapper (TM) images ( August 26 1993), a Landsat Enhanced
TM Plus (ETM+) image (July 21, 2003), and a Landsat 8 (Operational Land
Imager, OLI) image (April 27, 2013) acquired from the US Geological Survey
(USGS), was used in this study (Table 2). The Landsat MSS data have four
spectral bands, with a spatial resolution of 60 m. landsat TM data have seven
spectral bands, with a spatial resolution of 30 m for bands 1-5 and 7.
The TM band 6 (thermal infrared) was acquired at 120 m resolution but was
resampled to 30 m pixels. The Landsat ETM+ data consisted of eight spectral
bands with a spatial resolution of 30 m for bands 1-7. The ETM+ band 6 (thermal
infrared) was acquired at 60 m resolution but was resampled to 30 m pixels. The
8
Landsat 8 data have nine spectral bands with a spatial resolution of 30 m for
bands 1-7 and 9. The ETM+ and OLI band 8 (panchromatic band) have a spatial
resolution of 15 m. The spectral bands were generally between the optical and
short-wavelength-infrared regions, except for band 9 of Landsat 8 data, which had
a cirrus wavelength between 1.36 and 1.38 μm.
Table 2 Satelite data
Satelite
Resolution
Landsat MSS
60
Landsat TM
30
Landsat ETM (+)
30
Landsat 8
30
Path and Row
122/064
114/064
114/064
114/064
Acquisition
28 October 1972
26 August 1993
21 July 2003
27 April 2013
Method
The methodology of this study consisted of three main steps (Figure 3)
consisting of: (1) image analysis: geometric correction of Landsat images,
preliminary analysis, and classification; (2) ground truth; (3) Cellular Automata
Markov model (CA-Markov).
. Multitemporal image
Geometric correction
Markov analysis
Preliminary analysis
CA-Markov analysis
Classification
Projection of mangrove cover
changes
Ground truth
Land Use/Cover maps
(Multitemporal)
Figure 3 Flow chart of methodology
9
Image Processing Analysis
The Data Processing was composed of five main steps (Figure 4) including:
(1) geometric correction, (2) preliminary analysis, (3) image classification, (4)
accuracy assesment, and (5) post classification.
Landsat MSS,TM ,ETM+,8
(1972,1993,2003,2013)
Geometric Correction
GCP
Preliminary Analysis
Ground Truth
Image Classification
No
Accuracy
Assesment
Yes
Land Use/Cover Map
(1972,1993,2003,2013)
Post Classification
Dynamic of Mangrove
Cover
Figure 4 Flow chart of image processing
Geometric Correction
The varying pixel sizes of the different images were changed into a common
map grid based on a reference image/map. Evenly-distributed GCPs (Ground
Control Points) were selected in different images and registered with the reference
images/maps. A RMS (Root Mean Square) error of less than 0.5 pixels was
10
accepted for the transformation. Resampling was performed by converting
different pixel sizes into the same final image pixel sizes.
Preliminary Analysis
After satellite images were geometrically corrected, preliminary analysis
methods could be applied for image enhancement, composite RGB, and cropping.
The land use/cover classes consisted of mangrove, aquaculture, dry land
vegetation, settlement, and shallow water.
Image Classification
Image classification is a process of grouping all of the pixels into an image
consisting of classes. Time series data Landsat imagery were used in this research
to obtain information about actual land use from each time series. A classification
process in this research was to determine the number of classes as representatives
of the entities. Visual classification is a semi-automatic method using a screen
digitizing technique. Detection performed on objects by making delineation of the
outer limits of the group had the same color which differentiates objects from the
others.
Accuracy Assessment
Accuracy test was used to make any calculation matrix for each error
(confusion matrix) on any kind of habitat shallow water cover resulting from the
analysis satellite imagery. The following is a table of confusion matrix form
(Table 3).
Table 3 Confusion matrix
Classified to class
Reference (sample point)
1
1
N11
2
N21
K
NK1
Column Total (N+j)
N+1
Producer‟s Accuracy NKK/N+1
Source: (Congalton 1999)
2
N12
N22
NK2
N+2
N22/N+2
K
N1K
N2K
NKK
N+K
NKK/N+K
Row Total
(Ni+)
User‟s
Accuracy
N1+
N2+
NK+
N
N11/N1+
N22/N2+
NKK/NK+
Image validation was counted based on the above table such as Overall
Accuracy (OA), Producer’s Accuracy (PA), and User’s Accuracy (UA). OA is a
percentage of sample units that were classified accurately. PA and UA are ways of
representing individual category accuracies instead of just the OA. PA is a
percentage of probability average of a sample unit that refers to distribution of
each class that had been classified in the field, while UA is a percentage of sample
unit that actually represented the classes in the field. The confusion matrix in
11
Table 3 helped make the transition of equation and mathematical notation easy to
understand.
Post Classification
From these classification maps, changes in the extent of mangrove forests
during the periods of 1972 to 1993, 1993 to 2003, 2003 to 2013, and 1972 to 2013
were examined to gain geographic understanding of the spatiotemporal evolution
of land use in the region.
Mangrove Cover Projection
Markov Chain analysis
Marcov Chain. A markovian process is one in which the state of a system at
time (t2) can be predicted by the state of the system at time (t1). In this research,
where the result of image processing is land use/cover map in 1972, 1993, 2003,
and 2013, Markov model would generate transition probability matrix and
transition area matrix (Figure 5).
Data inputs used for Markov chain were two land use/cover maps derived
from different times. Land use/cover map with an interval of 21 years, where land
use/cover map of 1972 to 1993 was projected for 2003, was validated using land
use/cover map in 2003. Land use/cover map with a 10-year interval, where land
use/cover map of 1993 to 2003 was projected for 2013, was validated using land
use/cover map of 2013. Land use/cover map with a 31-year interval, where land
use map of 1972 to 2003 was projected for 2013, was validated using land
use/cover map in 2013. The results of the third interval would be selected based
on the level of accuracy for projection change of mangrove in 2023. The raster
data with pixel 30x30 meter and 60x60 meter were applied for all variabels that
were involved in the model.
Land Use/Cover
Map (t1)
Land Use/Cover
Map (t2)
Markov Chain
Analysis
Markov RGF
Transition
Probabilities Matrix
Transition Area
Matrix
Figure 5 Flow chart of Markov Chain model
12
Multi Criteria Evaluation (MCE)
A suitability map shows the degree of suitability for a particular purpose at
any location. It is most often produced from multiple images since most
suitability problems incorporate multiple criteria. This type of Boolean suitability
evaluation is often referred to as constraint mapping since each criterion is defined
by a Boolean image indicating areas that are either suitable for use (value 1) or
constrained from use (value 0).
Table 4 Criteria and categorization of factors for mangrove
Factor
Settlement
Substrate
Tidal
Value
1
0
1
0
1
0
Category factors
Non Settlement
Settlement
Silty
Sand
Inter-tidal
Non Inter-tidal
CA-Markov analysis
The present states of the cellular and neighboring sections determine the
future state. The specific realization processes are as follow: (1) determination of
conversion rules. By converting interpreted data from vector to raster, via a GIS
overlay analysis, the transition probability matrix as well as transition and
conditional probability image of mangrove types can be determined. (2)
construction of CA filters. The cellular change of state is then determined using a
5x5 filter which means a rectangular space that is composed with one cellular
section. This filter has a remarkable influence on the change in the cellular state.
(3) determination of start time and iteration number.
According to the characteristics of the individual CA and Markov models,
they can be combined with each other. The study area will be divided into several
sections and each section is a cellular one. Each cellular section has a
corresponding state and neighbors.
There are three data inputs for CA-Markov model (Figure 6): (1) basis land
cover image that is land use map. (2) markov transition matrix areas that is loaded
from transition area matrix. (3) transition suitability image collection that is
collection of land suitability images. CA-Markov is nonlinear formula with the
computarization was iterated and stop end kappa accuracy value.
Kappa accuracy
Kappa „ mathematical accuracy is :
∑
∑
∑
(Congalton and Mead in Wassahu 2010)…….(1)
13
Where
N
r
Xii
x+1
x1+
:
: the total Number of cell in the matrix,
: the number of rows in the matrix,
: the number in row i and column i
: the total observations for column i, and
: the total observations in row i
Mangrove Map (t2)
Raster Data
Transition Area Matrix
(t1+t2)
CA-Markov Simulation
MCE
(Probability Map)
Mangrove Projection
Kappa Validation