Monitoring of Changes of Coastal Conditions as a Result of Increased

Industrial Activities: Case Study - Lamong Bay

Cherie Bhekti Pribadi

, Teguh Hariyanto

and Akbar Kurniawan

Geomatics Engineering Department, Faculty of Civil, Environment, and Geo Engineering, Institut Teknologi Sepuluh

Nopember

Keywords: Coastal Condition, Industrial Activities, Lamong Bay

Abstract: Many kinds of activities in Lamong Bay area will have a big impact on the environment and surrounding

communities. Disposal of large quantities of mud and continuously, the construction and expansion of ports

in Lamong Bay was indicated to result in changes of functions around the coast of Lamong Bay. There are

several parameters that can be used to determine changes in coastal functions, one of which is the change of

land cover classes that occur around the coastal and marine areas. Remote sensing methods with satellite

imagery can be a solution for conducting research related to monitoring land cover, because this method is

more efficient and effective in large-scale research, and can be done temporally. The satellite image data used

in this study are various satellite images from 2002 to 2017. The results of the image processing land cover

class validated using the In-Situ data reference from the results of sampling in the field to obtain the linear

correlation value (R2).

1 INTRODUCTION

The port management system in Indonesia should be

able to provide optimal services to the parties

associated with the port. Environmental and

sustainability issues are a major issue related to the

issue of global warming, climate change and energy

consumption (Lam and Voorde, 2012). It’s affects the

changes in physical conditions around the coastal and

marine areas in Teluk Lamong.

Lamong River is one og nine major river in East

Java, Condition of water quality for a few important

parameters as water pollution indicator is exceeded

the boundary conditions which determine the

standard (Wahyuningsih, et.al, 2010). Lamong Bay

port will become an international category with plans

to shift the burden of maritime transportation

movements previously in the silver cape port which

is currently overcapacity, making the Lamong Bay

area the center of the industrial area.

There are several parameters which can be used to

determine changes in coastal functions, one of which

is the change in land cover classes that occur around

the coastal and marine areas. Remote sensing

methods with satellite imagery can be a solution to

research the problem of land cover mapping in coastal

and marine areas, because this method more efficient

and effective in large-scale research. Some multi

temporal satellite imagery can be used from 2002 to

2017 to examine the problem, has good and multi

temporal spatial resolution.

The final result will be expected to provide

information about land cover classes, along with

changes that affect the recommendations for coastal

and marine management in Lamong Bay in order to

optimize the results of mapping research using

satellite image technology and can be useful for the

management of coastal and marine areas in context of

regional development sustainable coastal areas.

2 METHODS

2.1 Study Area

The location of this study took Lamong Bay, which is

located in a geographical position 7°11’13” LS and

112°41’ 24” BT. The study area is presented in the

following figure :

Pribadi, C., Hariyanto, T. and Kurniawan, A.

Monitoring of Changes of Coastal Conditions as a Result of Increased Industrial Activities: Case Study - Lamong Bay.

DOI: 10.5220/0008373100290032

In Proceedings of the 6th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management (ISOCEEN 2018), pages 29-32

ISBN: 978-989-758-455-8

Figure 1: The study area

2.2 Data Acquisition

The data used in this study are Landsat-7 satellite

image data path / row: 118/065 in 2002 and Landsat-

8 satellite image data path / row: 118/065 in 2017.

Data in a period of 15 years was used to determine the

major changes of physical condition such as land

cover changes that occur in the coastal area.

2.3 Data Processing

2.3.1 Digital Classification

Digital classification that used in this image

classification process is a supervised classification

with the type of maximum likelihood classification

using image processing software. Guided

classification is the stage of making comparisons

between each pixel image with each category in key

interpretation is done numerically by determining the

training area. Ground truth is carried out in this

classification process aims to take samples of field

data that are used as reference data to test the accuracy

of image classification using confusion matrix

calculations. Remote sensing techniques, including

the use of conventional aerial photography, can be

used effectively to complement surveys based on

ground observation and enumeration, so the potential

of a timely and accurate inventory of the current use

of the Nation's land resources now exists (Anderson,

et.al, 1976).

2.3.2 Sub-setting Study Area

The research area used in Lamong Bay and its

surroundings, the image used must be subset based on

the specified study area.

2.3.3 Ground Truth

In this research, field survey or groundtruth is

something that must be done. It aims to determine the

condition of sedimentation in field and condition of

the waters in general. Field data is used as a basis for

the interpretation of satellite imagery that represents

the area so that it can support the process of making

land cover maps. The results of the digital

classification are determined by the truth or accuracy

through a test of classification accuracy which is done

by calculating the confusion matrix using image

processing software. Things that must be done before

performing the classification accuracy test are

groundtruth which aims to check in the field about the

truth of the image classification results with the

appearance of the land cover objects in the field.

3 RESULT AND DISCUSSION

Based on research conducted by Ecoton, type of river

from Lamong River which has a high sedimentary

estuary, makes this area as a transit and feeding area

for migra birds from continental Europe to Australia.

Assessment of Land cover changes and

transformation for 15 years in the study area is

extracted from the Landsat 7 images (30 m) for the

period of 2002 and Landsat 8 images (30 m) for the

period 2017 using the Maximum Likelihood

Classifier (MLC) algorithm of supervised image

classification technique. Thus, the result describes the

different land cover feature classes based on the

Level-II category of USGS-LULC classification table

with overall classification accuracies of 79.93% and

81.67% and overall Kappa coeffient statistical values

of 0.69 and 0.76 respectively. The information of land

cover changes and transformation is an essential

source for coastal resources management in the

coastal area.

3.1 Assessment of Land Cover Change

between 2002 and 2017

Assessment of land cover change area in coastal area

of Lamong Bay can be seen in the following table

ISOCEEN 2018 - 6th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management

Table 1: Land Cover Change During 15 Years

Land

Cover

Feature

Area in

Period of

2002 (Ha)

Area in

Period of

2017 (Ha)

Area in

Change

During 15

years

% of

distribution

% of

distribution

% of Change

During 15

years

Road

15844.77 8079.48 -7765.29

17.69% 6.94% -29,02

Vacant

Land

9089.71 16937.31 7847.6

10.15% 14.56% 29.32

Settlement

30877.31 33236.28 2358.97

34.47% 28.57% 8.81

River

6704.23 6283.48 -420.75

7.48% 5.40% -1.57

Pond

17850.62 35280.38 17429.76

19.93% 30.32% 65.13

Vegetation

9217.27 16528.33 7311.06

10.29% 14.21% 27.32

Table 1 tell us classification results during 2002

to 2017 to identify the dynamics of change in land

cover in coastal area. The proportions of change in

land cover feature indicate where development

pressure has occurred and they demonstrate an

increase in settlement areas to the detriment of dense

vegetation. In 2002, many residential buildings.

There were some land cover changes area in 2002 to

2017. It occurs due to a change which the increase

occurred, namely the residential area of 2358.97 Ha.

Land cover change in 2002 to 2017 includes changes

vacant land to waters, vacant land to settlements,

vegetation to waters, vacant land to ponds, vegetation

to ponds, vegetation to road. The most dominant

changes are from vacant land to settlements amount

2700.12 Ha.

Figure 2: Map of land cover in 2000

Figure 3: Map of land cover in 2017

3.2 Accuracy Assessment

Accuracy assessment is an integral process of feature

extraction from the classified images. It highlights the

possible sources of errors in a classified image, thus

enhancing the quality of information derived from the

data (Lea and Curtis, 2010). The results of the digital

classification are determined by accuracy assessment

which is done by calculating the confusion matrix

using image processing software. From the results of

the confusion matrix calculation, the results of the

accuracy of all classification results for Landsat 8

satellite images in 2002 were 79.93% and Landsat 8

in 2017 was 81.67%. With the results of the

calculation of the accuracy of the classification, the

results of the land cover classification with five

classes are considered correct because the value is

more than 75%. Overall observation of accuracy

shows the reliability of land cover features in terms of

location area and spatial distribution in the study area.

Table 3: Accuracy assessment of classified Landsat 8

images for 2002 and 2017

Land Cover

Feature

Producer’s

accuracy

(%)

User’s

Accuracy

(%)

Commission

(%)

Omission

(%)

2002 2017 2002 2017 2002 2017 2002 2017

Road 76.38 67.92 22.32 77.42 77.68 22.58 23.62 32.08

Vacant Land 95.47 76.74 94.91 69.47 5.09 30.53 4.53 23.26

Settlement 74.44 91.92 90.06 88.78 9.94 11.22 25.56 8.08

River 80.49 89.82 37.92 74.00 62.08 26.00 19.51 10.18

Pond 83.75 76.86 98.71 90.74 1.29 9.26 16.25 23.14

Vegetation 96.12 100 88.57 90.48 11.43 9.52 3.88 0

Overall Accuracy 79.93 81.56

Overall Kappa Coefficient 0.69 0.76

Monitoring of Changes of Coastal Conditions as a Result of Increased Industrial Activities: Case Study - Lamong Bay

4 CONCLUSION

Based on results of this study, there were some land

cover changes area in 2002 to 2017. It occurs due to

a change in the function which the largest increase

occurred, namely the residential area of 2358.97 Ha.

Increase in pond area amount 17429.76 Ha, and

increase in fields area amount 7847.6 Ha. Land cover

change in 2002 to 2017 includes changes vacant land

to waters, vacant land to settlements, vegetation to

waters, vacant land to ponds, vegetation to ponds,

vegetation to road. The most dominant changes are

from vacant land to settlements amount 2700.12 Ha.

REFERENCES

Anderson, J. R., Hardy, E. E., Roach, 1976. J. T., Witmer,

R. E. A Land Use and Land Cover Classification

System for Use with Remote Sensor Data. United

States, Geological Survey Professional, p. 964.

Lam, J.S.L. and van de Voorde, E. 2012. Green Port

Strategy for Sustainable Growth and Development.

Transport Logistics for Sustainable Growth at a New

Level. International Forum on Shipping, Ports and

Airports (IFSPA), 27- 30.

Lea, C., Curtis, A.C. 2010. Thematic accuracy assessment

procedures: National Park Service Vegetation

Inventory, version 2.0. Natural Resource Report

NPS/2010/NRR––2010/204, National Park Service,

Fort Collins, Colorado, USA.

Wahyuningsih, S., Anwar, N., Edijatno, Karnaningroem,

N., 2010. A Comparative Study of Water Quality

Characteristics at East Java River.

ISOCEEN 2018 - 6th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management