III. METHODOLOGY
3.1. Time and Location
This research was conducted from January until May 2006 in Maros. The study area is located in South Sulawesi, Indonesia, approximately between 4° 45’
- 5° South and 119° 21’ - 119° 42’ 30’’ East.
Research Area
Fig3.1. Maros Location
3.2. Equipment And Data Requirement 3.2.1. Data Collection
The types of data needed for this research were: a. Primary Data
Landsat TM is to extraction of land physical characteristic data and coastal identification to determining of coastal zones.
b. Secondary Data It is difficult to be extracted on imagery, but need for this research. They are:
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Physical Social Economic
- Topographic Map - Salinity
- Brightness of water - Current Velocity
- Depth of Sea - Population
- Sechi Disk Depth - Sea Surface Temperature
- DO, PH Demography Agriculture and fishing
productivity
c. Other Data Necessary data as well as map related with this study is needed, such as:
- Topographic map 1 : 50.000 scale, Maros region as base map to plotting criteria and data.
- Administrative map for determining administrative boundary.
3.2.2. Hardware, Software and Equipment Used
The following application software, hardware were used for the overall processes in this study.
Table 3.1. List of hardware, software and equipment.
PC Pentium IV 1,8 GHZ, 256 MB
For software running and reporting Hardware
Canon I - 255 For printing
ArcView 3.3 version For spatial data processing and
analyzing PC ArcInfo 3.5 version
For spatial data processing and analyzing
ER Mapper 6.4 version For raster data processing
Ilwiss For raster and tabular data processing
Definite Multi criteria analyzing
Excel Tabular data analyzing
Software
MS Word Report
Equipment Global Position System GPS
Determine Position
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3.3. Framework of Research 3.3.1. Framework of Coastal Ecosystem Development Decision Making
The decision-making problem of land suitability analysis for coastal ecosystem development coastal land use planning is analyzed using integration of
remote sensing, GIS and MCDM. Fig 3.2 depicts the conceptual flow of the
research approach completely.
3.3.2. Data Compilation
Data used for this research are primary and secondary data. Primary data was derived from remote sensing imagery Landsat TM, while secondary data
was derived from field check and some institutions. Compiling data is needed to recognize research location and to build model of coastal landuse development
planning.
3.3.3. Image Preprocessing
For acquiring better geometric and radiometric aspects from Landsat TM imagery, geometric and radiometric corrections should be accomplished. It is the
first step prior to analysis of the remotely sensed data that is to remove errors. Geometric correction is a replacing pixel position as truly condition.
Geometric correction that is commonly used to make digital remote sensor data truly useful is geometric rectification. It is a process using Ground Control Point
GCPs, which are selected transform the geometry of the image so that each pixel corresponds to a position in a real world coordinate system.
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Diagram Alir Penelitian lihat file diagram
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RMS Roat Mean Square is differentiating between original coordinates based on line and row of GCP coordinates that are measured with square of
deviation of GCP on satellite imagery. Equation following: RMS
error =
√ x
i
– x
orig
2 + y
i
– y
orig 2
Where: x orig and y orig are original coordinate line and row. xi and yi are GCP from Imagery corrected.
Raw Image Radiometric Correction
Geometric Correction Corrected Image
Image Calibration Atmospheric Correction
Ground Control Point GCPs Rectification
Resampling to common grid
Fig 3.3. Image Preprocessing
Radiometric correction is one of histogram adjustment as a way to minimize bias. The effect of atmospheric scattering caused by particles is a
problem in imagery and should be removed or minimized to avoid bias in each spectral band. Radiometric correction is performed to reduce bias effect of
atmosphere on satellite imagery for good display, it means that pixel value on imagery is represented actual real value of field.
Radiometric correction on this research was done by displaying all histogram used to known actual value. The lowest spectral value must be 0 zero
as well as bit-coding sensor. The equation used for radiometric correction is:
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BV
ijout
= BV
i – l, j, k
+ BV
i+j, j,k
2 Where : BV
ijout :
Correction Result BV
i – l, j, k :
Actual Spectral Value BV
i+j, j,k :
Actual Value
3.4. Field Checking
- Determine sample point, it was done for compiling field data, which were difficult to derived by using remote sensing imagery.
- Ground checking to determine the accuracy of interpretation result. Interpretation of remote sensing imagery was not exactly correct, hence
ground truth is needed to prove correcting of interpretation result. - Compiling data from other institutions that has relationship with this
research. Some data, such as fishing sector, socio and culture is needed to support this research.
3.5. Data Analysis
Data analysis involves two 2 kinds of data: secondary and primary data. Primary data include depth of sea, sea surface temperature and water quality, and
other physic variables. Meanwhile, secondary data include socio, economic and
ecology data. 3.5.1. Physical and Chemical Variables
Physical and chemical data are acquired from institution and field survey that has relationship with each theme depth of sea, sea surface temperature and
water quality. Every data will be plotted in certain location as one value.
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Distribution of values points can be generated by linear interpolation method, so each area will have a value that indicates variable above.
3.5.2. Social, Economic and Ecology
Socio, economic and ecology data are acquired from some institution. Here, mapping analysis is needed to acquire habitat, accessibility and utility
serving maps. Even the analysis is focus to physical criteria, the socio, economic and ecology is needed for additional information.
3.6. Determining Coastal Ecosystem Zones
Soedharma, et al 1992 has developed coastal zone classification, which its zone is divided into four 4 parts, namely: preservation, conservation, buffer
and uses zones. More detail about coastal zone classification can be seen in Table 3.2.
Table 3.2. Parameter, mapping unit and scoring variables.
Categorize and scoring No
Variable unit
weighting Preservation
Score Conservation
Score Buffer
Score Uses
Score
Land
1. Distance from
Beach M
2 100
4 100 – 150
3 150 – 200
2 200
1 2.
Vegetation 1 Mangrove
4 Mangrove
3 - 2 - 1 3.
Slope Type
1 0 – 15
4 15 – 25
3 25– 40
2 40
1 4. Substrate
Distribution 1 Seagrass
Coral Reef 4 Seagrass
Coral Reef 3 - 2 - 1
5. Population pressuring
4 Very Critical
4 Critical
3 Marginal
2 Not Critical
1
Marine
6. Sea
Surface temperature
oC 1
29 – 30 4
28 – 29 31 - 32
3 - 2 27
33 1
7. Salinity
ppt 1
31 – 32 4
30 – 31 32 – 33
3 - 2 30
33 1
Source: Soedharma, et al 1992 with modification
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Coastal ecosystem zones are determined using seven 7 physical variables; they are
1. distance from coastal line, 2. coastal vegetation,
3. slope, 4. sea surface temperature,
5. salinity, 6. substrate distribution
7. and population pressuring. A score was given for each variable to determine coastal ecosystem zones.
All thematic maps will be crossed overlaid to obtain new information coastal zone, in which score of mapping unit is calculated by using the formula
below: …….
1 ZC = 2 x SD + 2 x V + DB + S + PP
Where: ZC = Coastal Zone S = Slope
SD = Substrate Distribution PP = Population Pressuring
V = Vegetation DB = Distance from beach
Referring to scoring and weighting on Table 3.2, the total of minimal and maximal scores that were calculated by multiplying the minimal and maximal
scores with its weight can be seen in Table 3.3. Only conservation, buffer and uses zones can be developed for any
activities, but they still consider environment requirements to maintain sustainable coastal ecosystem. Preservation area is not allowed for any uses.
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Table 3.3. Weighting and scoring of zone classification
No Variables
Minimal Score
Maximal Score
Weight Total of
Minimal score Total of
Maximal score 1.
2. 3.
4. 5.
6. 7.
Distance from Beach Vegetation
Slope Substrate Distribution
Population Pressuring Sea Surface Temperature
Salinity 1
1 1
1 1
1 1
4 4
4 4
4 4
4 2
1 1
1 4
1 1
2 1
1 1
4 1
1 8
4 4
4 16
4 4
Total 11 44
3.7. Coastal Ecosystem Development 3.7.1. Resort Tourism Area