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         Geoplanning 
    Vol 5, No 2, 2018, 229-236                                                                                                                                                      Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi: 10.14710/geoplanning.5.2.229-236 

ANALYZING LAND USE PATTERN CHANGES IN MUKIM PENGERANG, 
JOHOR, MALAYSIA 

N. Che’Mana, A. F. Salihina 

aDepartment of Urban and Regional Planning, Faculty of Built Environment and Surveying, University Teknolog Malaysia, Malaysia 
 

Abstract: Urbanization and urban land-use transition have a competitive environment to 

ensure and provide good facilities for citizen benefit. Thus, quantifying the spatiotemporal 
pattern of urbanization is important for understanding its ecological impacts and can provide 
basic information for appropriate decision-making. The growth of urbanization in Mukim 
Pengerang, Johor, has undergone rapid changes in agriculture, settlements, townships and 
various activities. The changes of the land uses are due to the rapid economic development, 
which are the Refinery and Petrochemical Integrated Development (RAPID) project and 
Pengerang Integrated Petroleum Complex (PIPC). The industrialization projects boost the growth 
in land property and commercial which progressing in rapid development since the year 2012. 
Therefore, the main aim of this paper is to quantify the changes in landscape pattern or land use 
pattern between the year 2008 and 2017 occurred in Mukim Pengerang. In monitoring the 
spatial pattern changes, and the changes of landscape structure, the metrics landscape were 
analyzed with determination of the Shanon Diversity Index (SHDI), the number of patches (NP), 
Edge Density (ED) and Total Edge (TE) in the period of 8 years. The results show that the changes 
occurred with the three types of land use showed significant changes in the types of land use 
which are forest, agricultural and built-up area. The result of SHDI analysis shows the increment 
value between the year 2008 and 2017. This situation illustrates that the higher value of SHDI for 
an area, resulting in the higher level of land use. This is because the growing pattern of land use 
is reflected by a large number of patches due to the diversification of land use activities in the 
area. As a result, from the metrics statistics test verifies there was a significant change in land 
use that took place within 8 years. 

Copyright © 2018 GJGP-UNDIP  
This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 
 

How to cite (APA 6th Style): Che’Man, N., & Salihin, A. F. (2018). Analyzing Land Use Pattern Changes in Mukim Pengerang, Johor, 
Malaysia. Geoplanning: Journal of Geomatics and Planning, 5(2), 229-236. doi: 10.14710/geoplanning.5.2.229-236. 

 

1. INTRODUCTION  
City development and shifts of urban use are a challenge to ensure citizens’ welfare is adequately 

protected and available sufficiently. The rapid growth in Malaysia's is due to advances in economic 
development, particularly in the industrial sector such as the rubber and oil palm industries. Thus, as the 
industrial sector-based economic is growing, a township in Malaysia has started to growth drastically since 
the early 1970s. In line with the global economic growth and the high labor market in developed countries, 
had caused population migration to major cities to seize employment opportunities in the industrial sector. 
According to (Toosi et at., 2012), more than 50 percent of the world's population moves from rural to urban 
areas. To the extent that economic growth is reflected in urban growth, it is often manifested in changes in 
land use patterns. In general, some amount of growth can be captured in the existing building stock and 
associated land use patterns, but increasing growth tends to induce land use change. Therefore, urban 
development plays a role in providing economic facilities where it can support human life.  

In providing facilities for citizens, it requires a large land use spaces since the land usage is increasing 
from time to time. The changes in land use tend to occur due to urban development to meet the interests 
of the population. Increasing urban population increases the demand for land for urban activity (Samat, et 
al., 2011). Thus, the study on land use changes is quite interesting and important because, landscape 
fragmentation and the impact of such change is a growing need to uphold the natural biodiversity of the 
region. Geospatial technology used in this study could signify the importance of land cover changes over 

Article Info: 
Received: 19 July 2018 
in revised form: 20 Sept 2018 
Accepted: 1 Oct 2018 
Available Online: 25 Oct 2018 
 

Keywords:  
Land Use Pattern Change, Fragstat, 
Urbanization 
 

Corresponding Author: 
Noordini binti Che Man  
Department of Urban and Regional 
Planning, Faculty of Built 
Environment and Surveying, 
Universiti Teknologi Malaysia 
Email: b-noordini@utm.my  

OPEN ACCESS 

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http://doi.org/10.14710/geoplanning.5.2.229-236
mailto:b-noordini@utm.my


 
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the Pengerang area, which possibly helps to assess the dynamics changes of the area. It also could help to 
reveals if there is a reduction in natural vegetation cover in the study area. 

In this paper, the level of land use change in Mukim Pengerang, Johor Malaysia was examined by using 
landscape metrics analysis method in FRAGSTAT 4.2, a spatial pattern analysis software. Landscape metrics 
analysis is used to obtain the applicable changes using the patch analysis. The use of spatial landscape 
metrics analysis techniques is to examine the percentage of changes of land use type and the shape of 
spatial changes in understanding the form and stage of land use change within the eight years. This process 
has led to land-use change or landscape fragmentation. Landscape metrics is an approach to estimate the 
landscape pattern. Various matrices are available for the examination of the relationship between spatial 
structures. It is also used in this study to understand patterns of land use change and to give a clear picture 
of the surrounding change and the comparison within the 2 years. 

There are several researches by previous researchers related to landscape pattern/fragmentation. Li et 
al. (2017) has characterized the landscape patterns in Beijing City, China during 2000 and 2010 using four 
landscape metrics, i.e. patch density (PD), edge density (ED), Shannon’s diversity index (SHDI) and the 
aggregation index (AI) which two of the metrics used are similar with the research (PD and SHDI). As result, 
new construction land was found in the original forest land and grassland, leading to a slight increase of PD 
and SHDI. As overall results, showed that landscape patterns in Beijing City were greatly changed along 
with the process of urbanization during 2000–2010 and showed obvious spatial differentiation. Similar 
research using landscape metrics by (Liu et al., 2010) which examine the size, pattern and nature of land 
use changes. The study demonstrating of landscape metrics which could show the characteristics of the 
urban expansion in Lianyunggang, China. As result, every expansion of urban development had their 
different modes and types of land use which can show the different changes of landscape patterns.  

A study related to landscape pattern using land use and land cover (LULC) analysis by Jaybhaye et al. 
(2016) reveals that there was reduction in natural vegetation cover from 1989 to 2015 in Anjaneri Hill, 
India. The fragmentation analysis for the study area was based on the parameters of class area, percentage 
of land, number of patches, patch density, total edge length, edge density, and largest patch index. The 
results obtained from the study revealed an increase in the fragmentation and significant degradation of 
forests. Similarly, Pang et al. (2010) investigated the changing characteristics of landscape patterns in Zoige 
County, from 1986 to 2005. Through analysis of LULC driving forces, finally got the conclusions: the climate 
change and human disturbance factors, including increasing temperature, over-grazing, drainage of water 
systems, were both responsible for the wetland degradation in Zoige County. 

The study of LULC along with fragmentation at the landscape level can help improve understanding of 
the pace at which conversion of landscape elements is happening and the impacts on ecosystem services as 
studies of LULC are courser in nature and would not show how each land use is reducing in size, proximity 
and shape among other things that determine ecosystem services as result on study by Tolessa et al. 
(2016). The study was conducted to examine composition and configuration of forested landscape in the 
central highlands of Ethiopia using satellite images of over a period of four decades, and FRAGSTAT raster 
dataset was used to analyze fragmentation.  

Nong et al. (2014) investigated urban growth patterns of the Hanoi capital City of Vietnam from 1993-
2001 which to quantify the speed, growth modes, and resultant changes in landscape pattern of 
urbanization and examine the diffusion- coalescence and the landscape structural homogenization 
processes in Hanoi. Through the landscape pattern analysis and comparison with other cities, the result 
show that the urbanization in Hanoi is limited by its infrastructure systems which make the urban growth 
not evenly distributed, limiting their competitive advantage disproportionately high transport cost, growing 
congestion and land market distortions. Kabba & Li (2011) investigated land use changes, and their 
ecological effects in Wuhan (1987-2005) by using Remote Sensing techniques extracted land use data, 
whilst the spatial analyst software, Fragstats quantified ecological metrics at both landscape and class 
levels. The results showed increased urban and agricultural land uses (1987-2005); with urban land 
increasing more than 250 percent. Other than that, socioeconomic factors and ecological metrics indeed 
explained land use changes and their effects in Wuhan. 

Other related research by Karami (2014), was carried out in the Zagros vegetative region in the west of 
Iran to quantify structure and spatial pattern of land uses and forest fragmentation in the Zagros 
Mountains region. The mosaic analysis method was used for quantifying landscape metrics. The result of 

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the study shows that the fragmentation of natural land uses such forest and rangelands should be reducing 
and maintain large patches of natural vegetation to sustainable land management in this region. Singh et al. 
(2014) study presents the results of a set of landscape metrics derived from remotely sensed data aiming to 
characterize the historical trends of landscape changes in the Allahabad district in the period 1990–2010. 
This study demonstrates the probable use of remote sensing, GIS and FRAGSTAT in assessing spatial 
structure and change in landscape. 

Interest on the study on landscape fragmentation is not only cover on land use changes but also 
influence on disease emergence. A study by Ferrell & Brinkerhoff (2018) which to identify patterns and 
drivers of vector-borne disease risk that may operate at different scales in Virginia. Although the study is 
not related or similar to this research, but it is quite interesting which the land use or land cover variables 
could be used for other type of researches. These and other examples show that the landscape 
fragmentation research attracting more researcher and moving towards interdisciplinary endeavors. 

 

2. DATA AND METHODS 
2.1 Study Area 

Mukim Pengerang located in the east of Johor state. There was a rapid development in surrounding 
areas that become a new growth area in the state of Johor and intended to place as the catalyst for growth 
in Johor. Among the major developments or mega projects in the region are the development of the gas 
and oil industry, Refinery and Petroleum Integrated Development (RAPID) and Pengerang Integrated 
Petroleum Complex (PIPC) (Figure 1). It is about 20,000 acres and the construction started in the year 2012 
and growing rapidly which influenced the development of the surrounding area. In the meantime, it 
encouraged the provision of housing and institutional requirements for the residents. Therefore, there is an 
extension of land use for land development and land use change as a result of the major developments. 
Total populations in this mukim are 125,544 people (year 2010) and the population density is 82.53 people 
per km2. With the economic development and increasing of population, the surrounding land use in Mukim 
Pengerang has changed between 2008 and 2017.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

Figure 1.  Industrial Project of PIPC effect the surrounding of Mukim Pengerang 

 

 

 

 

 

Mukim 

Pengerang 

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2.2 Methods 
In monitoring spatial pattern changes, land use data was prepared using ESRI ArcGIS 10 software to 

oversee the classification of land use classes. Geographical Information Systems (GIS) approaches have 
added a new dimension to the understanding of these changes, not least the urban landscape (Wu, 2008; 
Wu et al., 2006; Yuan et al.,2005). GIS could offer the platform on which data on such images are stored, 
processed and analyzed for decision making. The land use data collected and prepared for this study was in 
shapefile format and classified by classes. There are several land uses classes that used in this study as 
shown in Table 1. Based on the land use data (Figure 2), the dominant land use classes in this area for both 
years are agriculture which is about 223,292.90 acres (year 2008) and 220,078.35 acres (year 2017). While, 
for other land use classes which is built-up area 12,648.99 acres (year 2008), 21,709.25 acres (year 2017) 
and forest 28,829.41 acres (year 2008), 28,016.05 acre (year 2017). As shown in this data, the acreage of 
agriculture becomes decreased as the built-up area becomes expanded in 2017. Thus, (Table 1) shows the 
agriculture land use class has converted into a built-up area for development in this area.  
 

Table 1. Land Use Classification 

Land Use Classes Description 

Agriculture Almost all are green gardens, small size corn and fruit gardens that are generally located in 
gardens. 

Forest Mixed of plants with a higher density of trees and plants. 

Built-up Residential, commercial, industrial, transportation and facilities. 

 
Figure 2. Raster Image of Mukim Pengerang, 2008 and 2017 

 

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To obtain the applicable changes using matrix statistical analysis, patch analysis was used. The used 
of spatial metrics analysis techniques is to examine the percentage of changes in the type of land use and 
the shape of spatial change (Figure 2). This spatial change study can be applied using GIS to make the 
results more efficient. This is because by using GIS, spatial metrics changes not only be produced in the 
category of classes but also by the diversity of area which either homogeneous or heterogeneous. 
Classification technique is too accurate in ensuring precise change-detention results. Furthermore, the data 
collected in two differences years (2008 and 2017) give a comparison of the fragmentation result between 
both years.  

Spatial pattern analysis software FRAGSTATS 4.2 is applied to calculate landscape metrics of each 
class type and total landscape after it was converted into raster image from shapefile format. FRAGSTAT 4.2 
provides a very comprehensive set of spatial statistics and descriptive metrics of the pattern at the patch, 
class, and landscape levels (Haines-Young & Chopping, 1996). In analyzing the fragmentation of landscape 
in the study area and correlated the changes throughout the years, quantify landscape metrics was used at 
both landscape and class levels. There are several class-level metrics as shown in Table 2. 

 

Table 2. Class-Level Metrics 

Index (Unit) Formula Description 

NP (Number of Patches) 
(#) 

 
 
 
 
 
 
 
Where:  
ni = number of patches of the corresponding 
land use class 
 

 
 
 
Is the number of patches of the corresponding 
patch type (class).  
 
Higher NumP indicates greater fragmentation. 

PD (Patch Density)  
 
 
 
 
 
Where:  
ni = number of patches of class 
A= total of the class area (m2) 
 

 
 
The equals the number of patches of the 
corresponding patch type divided by total 
landscape area (m2).  

 

PLAND (%)  
 
 
 
 
Where: 
TLA= total landscape area 

 
 
It equals the percentage of the landscape 
comprised of the corresponding class type. 

SHDI (Shanon Diversity 
Index) 

 
 
 
 
 
 
 
 
m= number of patches included 
 
Pi= proportion of the landscape occupied by 
patch type (class) i  

 
Shannon’s diversity index is the amount of patch 
per individual 
 
The value of SHDI increases if the number of 
patches increases and the broad distribution 
borders between classes increases over time. 

Source: (McGarigal, 2002) and (McGarigal & Marks, 1995)  
 

 

 

*Ln Pi 

 

 

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3. RESULTS AND DISCUSSION 

3.1. Fragmentation Analysis 
In 2008 and 2017, the most changing classes (agriculture, forest, and built-up area) are chosen to 

compute spatial landscape at class level by means of FRAGSTAT software. Based on the result (Table 3), in 
Mukim Pengerang, agriculture is represented as a dominant class of landscape because it has a larger 
percentage of total area. Meanwhile, the statistic of agriculture showed that the percentage of landscape 
(PLAND) index decreased from 81.38 to 78.6, while the number of patches (NP) increased from 228 to 427 
during the whole period from 2008 to 2017. This combination result shows that there are breaking up of 
the agriculture areas into small areas. Besides, Patch Density (PD) result shows non-isolated in certain areas 
that caused break up patches. This shows that agriculture land use becomes decrease because of 
development expanded to the surrounding of the study area throughout the years. 
 

Table 3. Class-Level Metrics of 2008 and 2017 
Land Use Class Area 

(CA) 
2008 

Class Area 
(CA) 
2017 

NP (#) 
2008 

NP (#) 
2017 

PD 
2008 

PD 
2017 

PLAND 
(%) 2008 

PLAND 
(%) 2017 

Agriculture 94,690.34 93,792.79 228 427 0.37 0.19 81.378 78.6 

Forest 11,305.26 11,124.26 141 1,025 0.88 0.12 9.716 9.57 

Buit-up 3,874.74 4,689.51 8,322 6,948 1.18 5.82 3.33 3.93 

 
In regards to forest area during period 2008-2017, the number of patches (NP) increased from 141 

to 1025. Similarly, the percentage of the landscape (PLAND) index decreased from 9.72 to 9.57. Thus, it 
shows that forest land use becomes decreased and breaking up into smaller patch caused by the 
development of the surrounding area. While the value of the built-up area metrics shows a change in the 
increasing percentage of landscape index (PLAND) from 3.33 to 3.93. These show that urban development 
in the study area has taken place. Meanwhile, the number of patches (NP) has also decreased from 8,322 to 
6,948. However, the PD value for built-up is increasing. This shows that urban change is increasing by 2017 
and the shape of the development density is a group based on patch saturation value is increasing in 2017. 

 
Table 4. Metrics of landscape structure for selected indices at the landscape level, 2008 and 2017 

9 NP (#) PD SHDI 

2008 10,089 8.67 0.696 

2017 8,187 6.86 0.818 

 
For SHDI, based on the value for 2008 and 2017, it shows an increasing value (Table 4). This situation 

illustrates that the higher the SHDI value for land use, the higher the level of land use compositions. This is 

because the growing pattern of land use is reflected by a large number of patches due to the diversification 

of land use activities within a given area. Based on this study results, it shows an agreement with the 

findings of Singh et al. (2014) and Tolessa et al. (2016). These study and some previous study results related 

to land use changes therefore proved the capability of remote sensing and GIS to quantify changes in 

natural resource over time.  

4. CONCLUSION 
In conclusion, based on the result, land use change in Mukim Pengerang is more frequent in 2008 

compared to 2017. This resulted in the use of agricultural land and forests in 2017 due to urban 
development caused by the development of the petroleum industry project. Overall, from the metrics 
statistics, it was found that there was a significant change in land use over a period of 8 years. This study is 
an analysis that aims to know and understand the land use structure against the effects of ecology. This 
study was also conducted to determine the level of land use change. This is because measuring land use 
change or landscape is very important for understanding the structure of land use against relevant 
ecological effects. 
 

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