REHABILITATION PRIOR ITY AREA ASSESSMENT ON DEATH …Muhammad Helmi Semarang 50275, Indonesia (PUI) - Center for Coastal Disaster , Diponegoro University, Jarot Marwoto, Heryoso Setiyono

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International Journal of Civil Engineering and Technology (IJCIET)Volume 9, Issue 11, November 201

Available online at http://www.iaeme.com/ijciet/issues.

ISSN Print: 0976-6308 and ISSN Online: 0976

©IAEME Publication

REHABILITATION PRIOR

ASSESSMENT ON

BASED MODELING

ISLANDS, KARIMUNJAWA

Oceanography Department, Faculty of Fisheries and Marine Science,

Diponegoro University

Center of Excellence for Science and Technology

Mitigation and Rehabilitation Studies (CoREM)

Alfi Satriadi, Agus Anugroho Dwi Suryoputro

Oceanograhy Department, Faculty of Fisheries and Marine Science,

ABSTRACT

This research applied

rehabilitation priority area of dead coral at

of GeoEye-1 satellite image was used to map the benthic habitat and distinguished the

dead coral area as rehabilitation target. The main factors that influence the dead

coral to become a rehabilitation priority area such as wave height, its strate

position for other benthic habitat and coastal protection, and preservation zona.

Certain data such as bathymetry, coastline, tide

develop the 2D numerical oceanography model and produce the four season of wave

height. This prototype research was conduct at Parang islets, Karimunjawa National

Park that inhabitant and have complex activities which impact to the coral reef

ecosystem, such as fishing, trap, tourism, aquaculture, harbor and sea lanes, and

coastal development. This rare and unique research was

survey. Significant accuracy for benthic habitat map has done using overall accuracy,

producer and user accuracy, and Kappa index methods. The position and spatial

distribution of rehabilitation pri

and can espouse the rehabilitation plan of the national park authority.

Keywords: Rehabilitation,

IJCIET/index.asp 2949 [email protected]

International Journal of Civil Engineering and Technology (IJCIET) 2018, pp. 2949–2961, Article ID: IJCIET_09_11_29

http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=

6308 and ISSN Online: 0976-6316

Scopus Indexed

REHABILITATION PRIORITY AREA

ON DEATH CORAL USING

ASED MODELING APPROACH AT PARANG

ISLANDS, KARIMUNJAWA NATIONAL PARK,

INDONESIA

Muhammad Helmi


Diponegoro University Semarang 50275, Indonesia

for Science and Technology (PUI) - Center for Coastal Disaster

tion and Rehabilitation Studies (CoREM), Diponegoro University

Agus Anugroho Dwi Suryoputro, Jarot Marwoto, Heryoso Setiyono

Hariyadi

hy Department, Faculty of Fisheries and Marine Science,

Diponegoro University Indonesia

This research applied a cell-based modeling approach on GIS method to map the

rehabilitation priority area of dead coral at the coral reef ecosystem. High re

1 satellite image was used to map the benthic habitat and distinguished the


coral to become a rehabilitation priority area such as wave height, its strate


data such as bathymetry, coastline, tide, and wind data ha


his prototype research was conduct at Parang islets, Karimunjawa National



This rare and unique research was espoused

. Significant accuracy for benthic habitat map has done using overall accuracy,


distribution of rehabilitation priority area of dead coral were successfully mapped

espouse the rehabilitation plan of the national park authority.

Rehabilitation, coral, remote sensing, GIS, and Karimunjawa

[email protected]

IJCIET_09_11_293

asp?JType=IJCIET&VType=9&IType=11

ITY AREA

USING CELL

T PARANG

NATIONAL PARK,


Center for Coastal Disaster

Diponegoro University, Indonesia

Heryoso Setiyono,

hy Department, Faculty of Fisheries and Marine Science,

based modeling approach on GIS method to map the

coral reef ecosystem. High resolution

1 satellite image was used to map the benthic habitat and distinguished the


coral to become a rehabilitation priority area such as wave height, its strategic


and wind data has collected to


his prototype research was conduct at Parang islets, Karimunjawa National



espoused by photo plot

. Significant accuracy for benthic habitat map has done using overall accuracy,


ority area of dead coral were successfully mapped

arimunjawa.

Muhammad Helmi, Alfi Satriadi, Agus Anugroho Dwi Suryoputro, Jarot Marwoto, Heryoso Setiyono

and Hariyadi

http://www.iaeme.com/IJCIET/index.asp 2950 [email protected]

Cite this Article: Muhammad Helmi, Alfi Satriadi, Agus Anugroho Dwi Suryoputro,

Jarot Marwoto, Heryoso Setiyono and Hariyadi, Rehabilitation Priority Area

Assessment on Death Coral Using Cell Based Modeling Approach at Parang Islands,

Karimunjawa National Park, Indonesia, International Journal of Civil Engineering

and Technology (IJCIET) 9(11), 2018, pp. 2949–2961.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=11

1. INTRODUCTION

Coral reefs can reduce wave energy and maintain coastal stability from erosion threats. This

ability also plays an important role in protecting benthic habitats and other aquatic ecosystems

that live behind, such as sea-grass, seaweed, and mangroves. Coral reefs support

continuity aquatic life, such as spawning, nursery ground, and feeding ground areas. Its live

support almost all basic human needs, such as the areas of livelihoods, food, medicines,

aquaculture, tourism, and environmental services. Coral reefs are presently under high

pressure and threat. About 75% coral reefs in the world and 85% in the coral reef triangle

areas have been treat [1] by several of natural and anthropogenic factors [2; 3; 4; 5]. Its

conditions are also not preferable in Indonesia, the threat reaches 95% [1]. There are 60

million people in Indonesia live in the coastal area and depends on the coral reef and brings a

threat to sustaining coral reefs in this region [1].

The purpose of this research is 1) to learn the widespread and spatial distribution of Dead

Coral with Algae (DCA) compared to current live coral; 2) Mapping where the location and

spatial pattern of priority rehabilitation areas of DCA; and 3) To assess whether this cell-

based spatial modeling method can be used and to find out DCA rehabilitation priority area.

This method used in Parang Islets, Karimunjawa Islands, Central Java. Parang Islets consists

of Parang and Kumbang Islands, which unite by clear shallow water with 48.9 ha of coral reef

ecosystems. These populated islands are the western part of Karimunjawa Islands National

Park. The anthropogenic activities in those shallow waters were very complex and have

affected on coral reef ecosystem damage, such as fishing, ecotourism, harbor, and sea-lanes,

aquaculture, services, etc. In this conservation area, there is a common utilization zone and a

preservation zone. In some locations found eroded sandy beaches and coral damage due to

waves that need immediately handled. Scientific data and publications of coral reef conditions

on these islands are rarely found. Parang Islets is an ideal site as a study area, prototypes

for this modeling method implementation.

The damage and threats to coral reefs need to be offset by an intensive rehabilitation

program and implemented in a large area [2; 6]. The area of coral reef damage is significantly

wide, while less rehabilitation capability. This is what causes the rehabilitation program to

require comprehensive and proper planning to meet a high level of effectiveness and success.

This spatial model integrates input data, which were produced by comprehensive mapping

methods, such as remote sensing for DCA mapping, 2D wave numerical model, cell-based

modeling to generate a maximum wave map and participatory mapping for preservation zone

mapping.

This mapping focuses on DCA instead of living corals. This cell-based spatial modeling

application has been widely used for rehabilitation planning on land but rarely applied in

seawater. This research expected to contribute the coral reef researchers, planners, and

managers in the use of approaches and methods to determine DCA rehabilitation priority

areas.

Rehabilitation Priority Area Assessment on Death Coral Using Cell Based Modeling Approach at

Parang Islands, Karimunjawa National Park, Indonesia


2. MATERIALS AND METHODS

The materials and methods section contains sufficient detail as describe below.

2.1. Study area

Research area was located at the Parang Islets that consist of Parang and Kumbang Islands.

The islets are in the western part of Karimunjawa Islands National Park, Central Java,

Indonesia as seen in Figure 1.

Figure 1 Study area

2.2. Pre-processing of GeoEye-1 Satelite Image

A satellite image that used in this research was GeoEye-1, acquisition date July 16th

, 2011

(GeoEye Inc, USA). The technical specification of the image was 4 multispectral band, spatial

resolution 1.84 m, 11 bits, 4500 columns and 6394 rows of pixels on the Geotiff format. Pre-

processing conduct radiometric and geometric correction of the GeoEye-1 satellite image.

Radiometric correction is needed to remove atmospheric scattering, absorption and to

minimize the light attenuation interference by the atmospheric constituents [7; 8]. A

radiometric correction was done using Dark Object Subtraction (DOS) method to correct or

remove the additive haze component or path radian on each band of the GeoEye-1 image. It

performs the optically of deep water pixels as a dark target. A geometric correction was

performed using orthorectification method based on input data including GeoEye-1 RPC

(rational polynomial coefficient) sensor model, 10m resolution of DEM (Digital Elevation

Model) and GCP (Ground Control Points) from GPS Survey. DEM data was created from

spot height and the contour line of topographic map scale 1:25000 published by BIG

(Indonesia Geospatial Information Agency) on 2012. Geometry reference was developed from

8 GCPs that measure using Garmin76 GPS Map using Datum WGS84 and projection

SUTM49.


and Hariyadi


2.3. Mapping of death coral with algae

Specular reflection in the form of sunglint on the visible band was corrected using infrared

using sunglint correction [9]. Water column impact [7; 9; 10; 11; 12] on each image band was

normalized with apply the Depth Invariant Index [7; 12]. That correction was done to prepare

the multispectral input band for benthic habitat classification. Unsupervised classification type

has been select and done to produce 25 classes that represent tentative benthic habitat spatial

distribution. Those classes reduced to become eight classes of benthic habitat based on field

survey data, i.e. live coral, DCA, sea-grass, rubble, sand, an associate of DCA and rubble, an

associate of rubble and sea-grass, and associate of sand and rubble. DCA class selected and

extracted as a separate layer from the benthic habitat map. To assess the accuracy of DCA has

conducted a field survey and accuracy assessment, as bellow;

2.3.1. Field Survey

Field survey conducted at 98 sampling sites using photo plot technique on 2015 and 2016

using an underwater camera. Spatial distribution of photo plots is in Figure 1. Purposive

sampling site selected based on variation classes of benthic habitat map. Benthic habitat types

at the field recorded and its position marked by using Garmin76 GPS Map.

2.3.2. Accuracy Assessment

Accuracy assessment was done for all benthic habitat using Overall Accuracy method, and for

each class of benthic habitat, accuracy assessed using User and Producer Accuracy [11; 12;

13; 14; 15; 16]. Overall accuracy, user accuracy, and producer accuracy need to performed a

statistical test to determine that the value of the resulting test does not happen by chance. The

test was Kappa Index [10; 14], which is one of the discrete multivariate techniques to study

accuracy used statistical analysis Khat, as the algorithm below (equation 1 and 2) :

� = �∑ ��∑ (��.��) ��

��∑ (��.��)

�� (1)

K equation represents that r: Number of rows in the confusion matrix: �� Number of

observations on the first line and on the main diagonal; ��: Number of observations on the

first line; �� : Number of observations in the first row; and N: Total number of observations

(pixels) of the matrix.

2.4. Distance from the front coral

DCA at the front coral area is the rehabilitation priority area due to its location face up the

wave energy. The highest wave at the benthic habitat is starting from the front coralline.

Mapping of the front coralline was done to generate distance class area that represents the

wave energy at benthic habitat. The front coralline was digitized true the outer line of benthic

habitat area. Euclidian Distance method was done to produce the 5m x 5m grid distance

image. Masking distance image with DCA area conducted to create the distance class image

at the coverage of DCA. Reclassified the distance class image at DCA area to become five

distance classes was done to provide the scoring value on each distance class.

2.5. Preservation zone mapping

Preservation area was selected from a digital map of Karimunjawa National Park Sea

Zonation Plan that was produced by the Indonesia Ministry of Forestry in 2012. This research

prepared the map, which has two classes consist of preservation area and non-preservation

area in the DCA area. The geospatial technique used to integrate the preservation area and the

preservation zone to create the preservation zone map. Its map converted to resolution 5m x

5m of raster data to prepare the input data on a geospatial model.




2.6. Maximum wave height mapping

Indonesian region is known as “the Maritime Continent” [17] located on the confluence of

Indo-Australian Plate, Pacific Plate, and Eurasian Plate. Because of its unique position, the

Indonesian maritime continent experiences seasonal variation influenced by monsoon wind.

There are four monsoon seasons every year, i.e. southeast (SE) monsoon, northwest (NW)

monsoon, and 2 transition seasons. The SE monsoon (NW monsoon) is associated with

easterlies from Australia (westerlies from the Eurasian continent) that usually occur in Jun,

July, and August (December, January, February) [18]. Since the Java Sea becomes the major

pathways for monsoon wind, the characteristic of ocean parameters in Karimunjawa Islands is

strongly influenced by monsoon wind. Therefore, the wave variation in Karimunjawa Islands

is separated by 4 different characteristics influenced by season.

Maximum of wave height is an image that represents a maximum value of the wave height

on a four-season composite of wave height. Maximum wave height image was generated

using 5m x 5m cell-based modeling on GIS that integrated southeast (SE) monsoon,

northwest (NW) monsoon, and two transition seasons of wave height. Wave height of each

season prepared in the 5m x 5m raster data format based on the spline type interpolation

method from point data of wave height. The wave height was developed from a 2D

oceanographic numerical model that used certain data input, such as coastline, bathymetry

from BIG, 2016, daily speed and wind direction (European Centre for Medium-Range

Weather Forecasts, 2014 - 2015) and tide prediction data per 25 minutes for 1-year data on

2016.

Wave modeling needs wind data that was corrected to prepare it condition from wind in

the land to become wind in the sea surface. Correction of the wind data based on [19] as

below:

Height correction; If the measured data at the measurement station not located at 10 m

altitude, it needs to be converted to a height of 10 m through the equation 3 and 4 :

�(��) = �(�) ��

(2)

with z less than 20 m.

Stability correction; This correction is done because of the temperature difference between

air and seawater (equation 5).

U =RT U(10) (5)

Where : RT = 1,1 and U in units m/sec.

Location effect correction; Mostly wind measurement done on the mainland, therefore it is

necessary to make correction of location effect. The relationship between the wind above the

sea and the wind above the nearest land is given by equation 6.

Uw = UL x RL (6)

- Shear stress, Wind speed is converted to a wind stress factor (UA) with the equation 7.

UA = 0.71 U1,23

(m/sec) (7)

The formation of waves is not only generated from the same direction as the wind

direction, but also in various angles to the wind direction. The fetch calculation method of

these angles then determined by the magnitude, and the effective fetch gave by equation 8.


and Hariyadi


α

α

cos

cos

Σ

Σ=

ieff

XF

(8)

Determination of fetch withdrawal direction is based on wind coming directly at the study

area. From UA data, the length of fetch and its duration through the above equation can be

known high (H) and wave period (T) [19], in equation 9 and 10.

2

1

2-10616.1 FUH A×= (9)

3

1

1- )(10 × 6.238 = FUT A (10)

Wave height data was modeled for 1 year to represent four monsoon seasons. The

composite of height wave was cropped base on DCA area, to represent the maximum wave

height on the DCA area and classified to become 5 class using equal interval classification

type.

The three geospatial data that prepared integrated to produce Rehabilitation Priority Index

(RPI) map using a geospatial model with represent in Table 1.

Table 1 Rpi map geospatial model criteria.

Variables Class and score Weighted

Distance from the front Coral (Dfc) (5), (4) (3) (2) (1) 0.2

Preservation Zona (Pz) Preservation zone (5) and non

preservation zone (1)

0.5

Maximum of Wave Height (Mwh) (5), (4) (3) (2) (1) 0.3

The equation of the cell based modeling to develop the Rehabilitation Priority Index Map

(RPI), as folow (equation 11) :

Rpi = (0.2 x Dfc) + (0.5 x Pz) + (0.3 x Mwh) (11)

Five class of Rpi map has been developed using equal interval method.

2.7. Rehabilitation priority area mapping

Rehabilitation priority area was developed using 5m x 5m cell-based modeling with the task

data processing as follow (Figure 2);

Figure 2 Data processing flow diagram




3. RESULTS AND DISCUSSION

3.1. Satellite Image Processing

Ortho-rectification of the GeoEye-1 satellite image (spatial resolution 0.5m x 0.5m) produce

RMSE 0.87, that means it and the benthic habitat map which produces have field horizontal

accuracy 0.44 m. Figure1 shows the orthorectified of the GeoEye-1 image in the true color

composite type. Sunglint correction and water column correction result based on bottom

correction method and Lyzenga transform is shown in Figure 3.

Image 3b show the Sun Glind correction has been removed the glint on the image that was

impacted by the sunshine on the sea surface water. The bright pixels has been reduced and

produce a seamless image for visual and digital image processing purpose. Image 3c has a

water depth impact that represents the white color or bright tone for the pixel of sand in the

shallow water area and darker blue in the deeper area. Area of the sand pixels are good and

easily view to understand the impact of water column depth impact on the shallow water

compared to the other benthic habitat such as coral reef, sea-grass or macro alga pixels area.

Image 3d is the result of the Bottom Invariant Index Correction and Lyzenga Transform

that significantly reduce the depth impact of the water column. All sand pixels in the different

depth have an almost similar value that represents the similar color and tone on a true color

composite image. The Sun Glint Correction and both methods of Bottom Invariant Index

Correction and Lyzenga Transform be done to have a better benthic habitat map based on a

digital classification image.

Figure 3: True color composite image before (image a) and after Sunglint Correction (image b), and

the image before (image c) and after Bottom Invariant Index Correction and Lyzenga Transform

(image d).

3.2. Benthic Habitat

Equations provided in a text format, rather than as an image. The unsupervised classification

was used in this study for interpretation key of benthic habitat, as there is no study previously.


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Figure 4 on the left map is the benthic habitat map that was reclassified from 25 classes that

produce by supervised classification. The 8 classes of benthic habitat were chosen based on

98 photo plots survey of benthic type. Spatial distribution of benthic type photo plot on the

study area is shown in Figure 1.

Mapping of DCA and live corals is the main objective of high-resolution satellite data

processing not the number of benthic classification. Unsupervised has a simple processing

task to differentiate a complex shallow marine habitat based on remote sensing data. Figure 3

on the right map shows the DCA area in Parang Islands. DCA patches are distributed around

waters of the islets especially in the Parang islet.

Figure 4 Benthic habitat map and DCA spatial distribution.

The accuracy test of benthic habitat maps has been done on 98 survey sites using photo

plots, as shown in Figure 1. The accuracy of this map is categorized as good, with Overall

Accuracy = 83.7% or slightly higher than tolerable accuracy (80%). This accuracy indicates

that the overall mapping results are in accordance with the conditions in the field. The

following contingency matrix indicates the compatibility of benthic habitat on the map and

the field.

Table 2 indicates the number of stations that are correct (corresponding between the map

and the field) are more than inappropriate. The number on the diagonal indicates the correct

number of stations. The matrix shows 14 stations of DCA in accordance with the field, while

one station is not DCA, but live coral in the field. Detail condition of all classes is described

on the contingency matrix in Table 2.

Table 2 shows the results of statistical analysis using the Confusion Matrix Method. This

method generates User Accuracy and Producer Accuracy on each class by considering

Omission and Commision Error. Producer accuracy is more reliable because the calculation is

associated with the correct number of pixels x. In contrast, the calculation of User Accuracy is

associated with the number of pixels x interpretation results that are not necessarily true. The

k value is between 0-1. The value of k in the results of this study is not zero, indicating that

the accuracy value of this habitat benthic classification does not occur by chance




Table 2 Contingency matrix of accuracy assessment

*(1) Live Coral, (2) DCA, (3) Sea-grass, (4) Rubble, (5) Sand,(6) Associate of rubble and

sea-grass, (7) Associate of sand and rubble, (8) Associate of rubble and DCA.

This k value is not negative which indicates that the resulting classification is good. This

habitat benthic mapping has high accuracy indicated by the value of k = 0.81 which is close to

the value of 1. Based on the analysis of all accuracy test, the results showed that multispectral

GeoEye-1 satellite image can be used for habitat benthic mapping with significant accuracy.

Figure 4 shows the extent of benthic habitat (total classes) reaching 814.2 ha. The benthic

habitat area in shallow waters is wider than the land area of Parang (452.2 ha) and the

Kumbang Island (9.4 ha). The number of benthic habitat types found can be an indicator that

the region has high biodiversity. The area of live coral reefs found was 202 ha. In this region,

there is coral damage indicated by the presence of DCA. The DCA area was 204 ha (50%)

among coral reef ecosystem which shows a high degree of damage. Live corals were found

scattered near the front coral, thus contributing to the reduction of waves. However, the width

is narrow and relatively in a deeper position. Thus, the waves are reduced. DCA is commonly

found behind or in the inner part of living coral. The wave damp function increase when the

reef is rehabilitated.

3.3. Maximum Wave High in the DCA Area

2D numerical oceanographic modeling produces four-wave height maps, as in Figure 5. The

map shows relatively high waves (0.45 - 0.67m) occur during the west season (NW) with the

direction of incoming waves from the west. High waves in west season are influenced by high

winds blowing from the west due to the influence of NW monsoon. For local people, this

western season is very popular with extreme sea conditions compared to other seasons, which

is characterized by high waves and strong ocean currents. This causes high waves to occur in

shallow water areas on the west and north sides of these small islands. On the western side of

the central part of the archipelago, high waves occur in areas close to the coral front, because

the waters are sheltered by corals that form the barrier reef in front of it. In the southern

waters, the wave height at this NW monsoon can be induced by the relatively broad barrier

reef and the Kumbang island as a barrier.

Benthic Habitat Map Producer

accuracy

User

accuracy

Overall

Accuracy

Kappa

index (k)

Field

Data

BH Class 1 2 3 4 5 6 7 8 Total

1 16 1 1 18 94.1 88.9

83.7 0.81

2 1 14 15 87.5 93.3

3 9 1 10 81.8 90.0

4 11 1 1 13 78.6 84.6

5 1 1 10 12 83.3 83.3

6 1 8 1 1 11 88.9 72.7

7 1 1 9 1 12 75.0 75.0

8 1 1 5 7 71.4 71.4

Total 17 16 11 14 12 9 12 7 98


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Figure 5: Four seasons of wave height and maximum wave height maps at coral reef ecosystems at

Parang Islands, Karimunjawa.

The high wave composite maps in the DCA area (Figure 5) clearly represent the

maximum wave conditions in the NW monsoon. This composite map is built from raster data

(5m x 5m spatial resolution) represented by the cell with the highest wave value of existing

four seasons.

3.4. Rehabilitation Priority Index Map

The following cell-based spatial data (5m x 5m) are used as inputs in modeling, i.e. DCA

area, maximum wave high, distance from front coral and preservation area (Figure 6). Figure

7 shows the high priority areas of rehabilitation (the priority index score of 80 - 100) was

found in the southwestern part of the waters of Parang Island and in the western part of

Kumbang Island (Figure 7). This priority area has an area of 17.2 ha and located in the

preservation zone. This rehabilitation area has objectives for protection and conservation. The

high priority DCA rehabilitation areas (the priority index score of 60 - 80) with an area of

15.8 ha spread across the northwest of Parang Island. These two priority areas of DCA are

consistent with the purpose of returning coral reef function as a protective coast and benthic

habitat behind it from strong sea wave drive in during the west season. DCAs in the eastern

region and some bays are not a priority area of rehabilitation since wave energy is relatively

low and the area is not a preservation area. The following is a graph of the area composition

of each class of rehabilitation priority areas in DCA.




Figure 6: Thematic maps of distance DCA from the front reef and preservation zone on cell format

(5m x 5m).

Figure 7 Map of DCA rehabilitation priority areas.

This research finds the main priority of the DCA rehabilitation area is relatively

significant, so needs a proper and gradual planning. The planning and implementation of this

DCA rehabilitation program can be carried out in an integrated and community-based manner

coordinated by park managers. Rehabilitation of DCA areas can be done in various ways

which by the method of transplantation, use of artificial reef or other available technique. This


and Hariyadi


research demonstrates that the GIS approach, especially cell-based modeling can be used for

planning the priority areas of DCA rehabilitation objectively to support the management of

coral reef conservation areas.

4. CONCLUSIONS

The live coral in the study area is 202.44 ha. This research found a high relative of DCA

(204.07 ha), that shows a high degree of damage. DCA scattered around Parang Island and

Kumbang area. In southwest and northwest of Parang Island, there are high priority areas for

DCA rehabilitation, with the area of 17.2 ha and 15.8 ha respectively. Mapping by using cell-

based modeling is effective for the area of DCA rehabilitation. This priority area for DCA

rehabilitation is an important input for conservation planners and managers in Karimunjawa

National Park.

ACKNOWLEDGMENTS

Thank Research Institutions and Community Service (LPPM) of Diponegoro University for

the funding (RPP scheme No: 275-037/UN7.5.1/PG/2017) of this research. We also thank PT.

Waindo SpecTerra for the support of using ArcGIS 10.3, and ERDAS - ER Mapper software.

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REHABILITATION PRIOR ITY AREA ASSESSMENT ON DEATH …Muhammad Helmi Semarang 50275, Indonesia (PUI) - Center for Coastal Disaster , Diponegoro University, Jarot Marwoto, Heryoso Setiyono