ENVIRONMENTAL SENSITIVITY INDEX ASSESSMENT USING FORMOSAT-2

Subagio et al.

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ENVIRONMENTAL SENSITIVITY INDEX ASSESSMENT USING

FORMOSAT-2 SATELLITE IN LABUAN COASTAL, BANTEN

Widitya Putri Fitriyanny Subagio 1)

,Abd. Rahman As-Syakur 2)

, Wandito

Himawan Soedomo 3)

, And I Wayan Sandi Adnyana 4)

1) Research Center for Marine Technology – Ministry of Marine Affairs and Fisheries

2) ,4) Environmental Research Center Udayana University

3) The Agency for the Assessment and Application Technology

Email : [email protected]

ABSTRACT Labuan coastal has highly risk of pollution case that might be distributed both from activity in

north area and land activity throughout coastline. Buffer process produced three zones of

sensitivity area. Sensitive area located in Karang Kebua Island and Popole Island. Average

sensitive area located throughout coastline in Pagelaran sub district, Panimbang sub district and

north part of Labuan sub district. Less sensitive area located throughout coastline in south part

of Labuan sub district. From numerical simulation result, Popole Island could get direct impact

from steam power plant (PLTU Labuan) activity. Meanwhile, coastline area throughout Labuan

sub district, Pagelaran sub district and Panimbang sub district have potential to be polluted from

pollutant in north area that might be brought through current circulation. Environmental

Sensitivity Index (ESI) map can be used to decide which area is the most susceptible and

sensitive concern with pollution in order to support regional planning strategy for stakeholders

and decision makers.

Keywords: Environmental Sensitivity Index, Labuan coastal, FORMOSAT-2 satellite data,

Geographical Information System (GIS)

I. INTRODUCTION

Coastal areas, by virtual of their position at the interface between truly terrestrial

ecosystems and aquatic systems, belong to the most dynamic and important ecosystems

on Earth (Yang et al., 1999). They are also the foci of human settlement, industry, and

tourism. Large coastal population and intense development are exacerbating

environmental stress and degradation of the coastal ecosystems, thus placing an elevated

burden on organizations responsible for the planning and management of these highly

sensitive areas (Yang, 2008).

Coastal maps are widely regarded to be an essential data source for coastal

management planning (Mumby et al., 1999). To a large extent, management objectives

can be defined in terms of coastal area, either because of their intrinsic value or because

of their significance in habitat characterization. Remote sensing could be used to

mapping coastal environment. Remotely sensed optical signatures have proved useful

for mapping mangrove, Coral Reefs, Macroalgae and other coastal habitat (Green et al.,

2000).

Labuan coastal located between industrial and tourism activity in northern area

(Cilegon, Carita, Anyer areas) that might bring some pollutants through ocean

circulation process to Labuan coastal and also Ujung Kulon National Park in the south

which designated for conservation area. It is important to arrange Environmental

Sensitivity Index (ESI) map in order to get information where is the most sensitive area

concern with pollutant in Labuan coastal. In the present study ESI analysis maps are

prepared to collect information regarding environmental baseline status of the study

Environmental Sensitivity Index Assessment Using Formosat-2 Satellite

In Labuan Coastal, Banten

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area pertaining to physical, human, biological resources, land use/land cover, and socio-

economic attributes which form important attribute for ESI maps (Sexena et al., 2004).

The most sensitive area in Labuan coastal needs special monitoring because this

area will be very susceptible with pollutant. Result of Environmental Sensitivity Index

map can be used to support stakeholders and decision makers to arrange regional

planning strategy in Labuan coastal.

Formosat-2, a satellite owned by the Taiwan National Space Organisation (NSPO),

was launched in May 2004. It provides images with features very close to Venμs: spatial

resolution of 8 m in four spectral bands centered at 488, 555, 650 and 830 nm, spatial

resolution of 2 m in panchromatic spectral, and field of view of 24 km (Hagolle, et al.,

2008). The orbital cycle is completed within one day. The sensor may deviate from

nadir in order to point at sites close to the ground track. Therefore, accessible locations

at Earth's surface are observed under a unique viewing direction (Bsaibes et al., 2009).

Remote sensing image analysis systems and geographic information systems

(GIS) show great promise for the integration of a wide variety of spatial information as

a support to tasks ESI. Remote sensing often requires other kinds of ancillary data to

achieve both its greatest value and the highest level of accuracy as a data and

information production technology. GIS can provide this capability (Star and Estes,

1990). GIS can make order to develop the required capability of natural resources

mapping and periodical monitoring (Muzein, 2006). As the coastal sensitive response

community moves towards development of automated sensitivity maps, it is important

to define what comprises the ESI mapping system and how this information is being

developed and distributed using GIS technology (NOAA, 2002).

II. METHOD

Figure 1. Reseach Location.in Pandeglang Regency (inset figure) (Source:

www.bapedalbanten.go.id)

This research located in Labuan coastal in western coast of Banten province with

specific location 6° 15' 40" - 6° 41' 30" S / 105° 35' 00" - 106° 00' 00" E (Figure 1).

FORMOSAT-2 satellite data recorded from National Space Agency of Taiwan in

9th

August 2007 was used as data base to produce Environmental Sensitivity Index Map.

This satellite data has 2 m x 2 m resolution (panchromatic band (0.45~0.90μm)), and 8

m x 8 m resolution (multispectral band (0.45~0.52μm (Blue); 0.52~0.60μm (Green);

0.63~0.69μm (Red); 0.76~0.90μm (Near Infra Red). Ground truth data were available

from Ministry of Marine Affairs & Fisheries, Research Center of Marine Technology

http://www.bapedalbanten.go.id/

Subagio et al.

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(Pratama et al., 2007) taken on 5th

– 7th

August 2007. Ground truth data were consisted

of water quality data (temperature, salinity, tubidity, total suspended solid), sea grass

area, coral reef area, coastal characteristic, and important place. These data were taken

using rapid coastal habitat assessment methods (English et al., 1997 in Pratama., 2007)

in 22 stations to support data analysis.

Research process was divided into three parts: (1) data collection, (2) data

processing (Image processing using brovey transformation, land use classification using

supervised classification, pollutant source and distribution using numerical model

simulation) and (3) arrangement of Environmental Sensitivity Index Map using

Geographical Information System (buffering process methods).

2.1. Brovey Transform

Based on Neteler et al. (2004), brovey transform can combine both panchromatic

image and multispectral image to produce high quality image with equation (1) :

1

1 2 3

*bfused pan

b b b

DNDN DN

DN DN DN………………………………………………..(1)

Where DNfused (Digital Number after fusion process); DNb1 (Digital Number band

1 multispectral); DNb2 (Digital Number band 2 multispectral); DNb3 (Digital Number

band 3 multispectral); DNpan (Digital Number panchromatic image).

2.2. Supervised Classification

A supervised classification was a methods of clustering pixels in a data set into

classes corresponding to user-defined training classes. Training classes are groups of

pixels or individual spectra and it was selected as representative areas or materials that

mapped in the output (CCRS, 2003).

In this research, supervised classification has determined with mahalanobis

distance method which has assumed that all pixel were classified to the closest region of

interest. Region of interests have been determined by choosing pixels from image

satellite data. Groups of pixel were chosen to represent one region or class. Furthermore

with mahalanobis distance process, groups of pixel in several region or class were

classified to describe some region or class from image satellite data. In some cases,

some pixels may be classified in the same class with other pixel although it was not the

exactly region. To avoid that condition, visual interpretation and ground truth data are

needed to support such classification.

2.3. Numerical Simulation Model

Numerical simulation model has been done to predict source and distribution of

pollutant. Total simulation is 31 days and it divided into thermal distribution model

(source pollutant from PLTU Labuan) and current circulation model in equation (2), (3)

and (4) based on Kouitas (1988) in Soedomo (2006):

Continuity equation

0u v

t x y ………………………………………… (2)



4

Momentum equation

2

h h

U U UU V g fV v U

t x y x ………………………………………….(3)

Thermal distribution equation

( ) ( )x y

C CU CV C Cv R R C

t x y x x y y……………………………….

(4)

Where η (elevation); u (velocity x direction); v (velocity y direction); g (gravity

component); f (friction component); C (temperature); Rx & Ry (diffusion component).

2.4. Buffering Process

Buffering technique refers to the creation of a zone of a specified width around a

point or a line or a polygon area. It is also referred to as a zone of specified distance

around coverage features (Mandagere, 2008).

Buffer technique formed new buffer zone that covered buffered object (point, line

or polygon) based on the distance that already set before buffering process. Buffer zone

can be used to define spatial closeness function from one object to another object.

Spatial data of buffer zone is liable with several spatial operation and attribute.

In this case, buffer zone combined each score of parameters become total

Environmental Sensitivity Index (ESI) scoring. From total Environmental Sensitivity

Index (ESI) scoring, the most and the least sensitive area could be predicted.

Figure 2. Determining of buffer zone distance (Source : http://www.sli.unimelb.edu.au)

2.5. Environmental Sensitivity Index

Environmental Sensitivity Index is scoring method to decide the most sensitive

area based on combination score coverage of coral reef, coverage of sea grass,

important place and coastal characteristic (Sloan, 1993) based on equation (5):

Subagio et al.

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ESI CC SG CR IP …………………………………….. (5)

Where ESI (Total Environmental Sensitivity Index score; range 1-20); CR (Score

of coral reef cover percentage; range 1-5); SG (Score of sea grass cover percentage;

range 1-5); CC (Score of coastal characteristic; range 1-5); IP (Score of important place;

range 1-5). Score 1 is the least sensitive area and score 5 is the most sensitive area. This

scoring index showed in Table 1. Determination of total Environmental Sensitivity

Index score will be divided into total Environmental Sensitivity Index score in three

small islands (Karang Kebua Island, Popole Island and Liwungan Island) and total

Environmental Sensitivity Index score in region throughout coastline area in three

subdistrict (Labuan sub district, Pagelaran sub district, Panimbang sub district

Table 1. Environmental Sensitivity Index Value (Sloan, 1993)

Principal parameters Score

Coastal Characteristic

Muddy coastal 5

Sheltered tidal flat 4

Exposed tidal flat 3

Grained sandy coastal 2

Exposed rocky shore 1

Coral Ecosystem

Coverage hard coral & other family 80-100% 5





Sea Grass Ecosystem

Coverage sea grass 80-100% 5





Important Place

Tourism resort (include diving & snorkeling

site)

5

Planting area (include fishpond) 4

Settlement and Power Station 3

Fisheries area 2

Port / Harbor 1

Environmental Sensitivity Index

Very sensitive area 16-20

Sensitive area 11-15

Average sensitive area 6-10

Less sensitive area 1-5



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III. RESULTS AND DISCUSSION

3.1. Brovey Transform and Land Use Classification Result

Figure 2 showed the result of satellite image before and after brovey

transformation. Image after brovey transformation (Figure 3 right) indicated clearer

image than that without brovey transform (Figure 3 left).

Figure 3. Comparisson image without brovey transform (left) and with brovey transform

(right) (Source: data processing result)

Land use classification using supervised classification methods classified each

pixel based on training area or region of interest (CCRS,2003).

Supervised classification result consist of 14 classes only because there are so many

pixel in image satellite data that have similar value therefore supervise classification

process classified similar pixel into one region. Therefore each detail class were

digitized using the integration between supervised classification result, high image

quality from Brovey transform result and also field data. This process will change raster

data to vector data using digitations process. After digitations, Labuan coastal is

classified into 22 classes in vector type (Fig 4).

Figure 4. Supervised classification result vector data type (source: data processing

result)

Panimbang Pagelaran

Labuan Karang Kebua Island

Popole Island

LiwunganIsland

SUNDA STRAIT

Subagio et al.

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From land use classification, the area can be divided into 4 parameters there are

coral distribution, sea grass distribution, coastal characteristic, and important place.

3.2. Source of Pollutan Using Numerical Model Simulation

Result of this numerical model can be seen in Figure 5 and Figure 6.

Figure 5. Result of thermal pollution distribution model in day 1, day 15, day 31

(source: data processing result)

Figure 6. Result of current circulation throughout coastline area in day 1, day 15, day 31


Simulation model for thermal pollution showed that thermal pollutants from

power station could be distributed until Popole Island. Hot sea water from power

station outlet has temperature until 36oC. High temperature could disturb coral reef

and sea grass ecosystem in Popole Island. Simulation model for current distribution

showed that current system flow from north part to south part. Industrial and tourism

activity from Cilegon, Carita and Anyer area could be distributed throughout Labuan

coastline area. This result is appropriate with research of Pariwono (1999) whom

said that current system in Sunda Strait is always flows from north part to the south

part.

3.3. Arrangement of Environmental Sensitivity Index Map

Total Environmental Sensitivity Index score in region throughout coastline

area only consist of coastal characteristic and important place therefore these areas

are divided into 17 buffer zones (Figure 7) to make a combination score between

score of important place and coastal characteristic.



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Figure 7. Buffering zones to arrange buffering process


Figure 7. Buffer Zone (source: data processing result)

Total Environmental Sensitivity Index (ESI) Score in Small Islands

Based on buffer process and ESI table score in that mention in table 1, ESI

score in Karang Kebua Island consist of combination score of coverage coral reef

(coverage 50%; score 3), important place (tourism; score 5) and coastal

characteristic (muddy coastal; score 5) so that total ESI score is 13 (sensitive area).

ESI score in Popole Island consists of combination score of coverage coral

(coverage 63%; score 4), important place (tourism; score 5); coverage sea grass

(coverage 50%; score 3) and coastal characteristic (grained rubble; score 2) so that

total ESI score is 14 (sensitive area). ESI score in Liwungan Island consists of

combination score of coverage coral reef (coverage 13%; score 1) and coastal

characteristic (grained rubble; score 2) so that total ESI score is 3 (Less sensitive

area).

Total Environmental Sensitivity Index (ESI) Score in Region Throughout

Coastline. ESI score in region throughout coastline can be seen in table 2 below. Total

score of each parameters were based on Environmental Sensitivity Index Value in

table 1.

11 21 31 4

1 51 61 71 8

1

1001

91

11

12

13

14

15

161

17

Subagio et al.

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Table 2. ESI Score in Region Throughout Coastline

Buffer

Zone

Paramaters Total

Score

ESI Index

1 settlement (score:3) & muddy coastal (score:5) 8 Average Sensitive

2 planting area (score:4) & muddy coastal

(score:5)

9 Average Sensitive

3 tourism area (score:5) & muddy coastal

(score:5)

10 Average Sensitive


(score:5)

9 Average Sensitive

5 planting area (score:4) and rocky coastal

(score:1)

5 Less Sensitive

6 port (score:1) and rocky coastal (score:1) 2 Less Sensitive

7 planting (score:4) and rocky coastal (score:1) 5 Less Sensitive


(score:5)

9 Average Sensitive

9 Power plan (score:3) & muddy coastal (score:5) 8 Average Sensitive


(score:5)

9 Average Sensitive

11 settlement (score:3) & muddy coastal (score:5) 8 Average Sensitive


(score:5)

9 Average Sensitive


(score:5)

9 Average Sensitive

14 settlement area (score:3) & muddy coastal

(score:5)

8 Average Sensitive

15 forest area (score:4) & muddy coastal (score:5) 9 Average Sensitive

16 grass field (score:4) & rubble grain coastal

(score:2)

6 Average Sensitive

17 tourism area (score:5) & grained rubble coastal

(score:2)

7 Average Sensitive

Environmental Sensitivity Index Map can be arranged based on result of buffering

process and can be seen in Figure 8. From the figure can be seen that Karang Kebua

Island and Popole Island are categorized in sensitive area. Furthermore, steam power

plant (PLTU Labuan) located near Popole Island and from numerical simulation model

can be seen that outlet from power station (water with high temperature) can be

distributed until Popole Island. Change of temperature will give impact to the local

ecosystem in Popole Island such as coral reef and sea grass that very susceptible with

change of environment and very easy to disturb. Furthermore local government has

planned to develop this area become tourism area. This condition should be considered

by stakeholders to split the differences between power station activity and conservations

of environment in order to develop the best regional planning strategy.This condition

can be recommended to the stakeholders and decision makers to be considered in order

to arrange regional planning strategy based on environmental condition. For example do

some monitoring of coral reef and sea grass quality and condition in Popole Island and



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Karang Kebua Island, or make good quality of waste water management process in

PLTU Labuan.

Meanwhile, Karang Kebua Island could be polluted from pollutant that distributed

from industrial and tourism area in north part through current system distribution

(numerical model result). Furthermore, from current pattern (Figure 6) can be seen

clearly that this current flow throughout coastline area with shallow bathymetry in

Pagelaran and Panimbang sub district. This is also one of serious problems because

pollutant can be mixed with mud and it will hard to remove naturally. Whereas there are

many important places in coastline area for planting area and settlement. If pollutant

mix together with mud materials, so that the water quality could change. This condition

can be recommended to the stakeholders and decision makers to do some continuous

monitoring especially for water quality monitoring in two sub district.

Figure 8. Environmental Sensitivity Index Map (source: data processing result)

IV. CONCLUSIONS

1. Sensitive areas were located in Popole Island and Karang Kebua Island. These areas

are the most susceptible coastal area concern with pollutant and need high

awareness to protect this area from pollutant in order to avoid adverse consequences.

Land activity from power plant outlet can produced thermal pollutant that might

give impact to the surrounding environment especially to Popole Island. Changes of

water quality could give destructive impact to the local ecosystem of coral reef and

sea grass in this Island. Therefore plan of local government to develop this area

become tourism area for snorkeling and diving might be misallocated.

2. Average sensitive area were located in Pagelaran sub district, Panimbang sub district

and north part of Labuan sub district. These areas also have high potential to be

polluted especially in area dominated by mud material because pollutants can be

mixed and hard to remove naturally. Current circulation could bring pollutant from

Industrial city (Cilegon area) & tourism resort (Carita & Anyer area) throughout

Subagio et al.

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coastline area. This is very high risk condition because pollutant could be mixed

very well with mud material in shallow bathymetry.

3. Less sensitive area were located in southern part of Labuan sub district and

Liwungan Island. This means that these areas are the most unsusceptible coastal

area concern with pollutant. But Several locations in Labuan coastal area could be

already misallocated concern with pollutant source and distribution in this area.

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ENVIRONMENTAL SENSITIVITY INDEX ASSESSMENT USING FORMOSAT-2

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