Modeling of Land Use Change of Paddy Field in Karawang Regency Using Cellular Automata-Markov Chain

MODELING OF LAND USE CHANGE OF PADDY FIELD IN
KARAWANG REGENCY USING
CELLULAR AUTOMATA-MARKOV CHAIN

INDRIAN RIZKA AMALIA

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2015

ii

STATEMENT LETTER OF THESIS AND SOURCE OF
INFORMATION AND DEVOLUTION OF COPYRIGHT
Hereby I genuinely stated that the thesis entitled Modeling of Land Use
Change of Paddy Field in Karawang Regency Using Cellular Automata-Markov
Chain is an authentic work of my research supervised by supervisory committee
and never being presented in any forms and universities. All the information taken
and quoted from published or unpublished works of other writers has been
mentioned in the texts and attached in the references chapter at the end of thesis.

I hereby assign the copyright of this thesis to Bogor Agricultural University.

Bogor, Agustus 2015
Indrian Rizka Amalia
NRP G051110051

ii

RINGKASAN
INDRIAN RIZKA AMALIA. Model Perubahan Penggunaan Lahan Padi Sawah di
Kabupaten Karawang Menggunakan Cellular Automata-Markov Chain. Dibimbing
oleh WIDIATMAKA dan KHURSATUL MUNIBAH.
Konversi lahan padi sawah menjadi lahan pertanian lain dan lahan non
pertanian merupakan salah satu faktor yang dapat mengancam ketersediaan beras
di Indonesia. Salah satu yang merupakan daerah lumbung padi adalah Kabupaten
Karawang dengan kontribusi kepada ketersediaan beras nasional sebesar 784 000
ton.tahun-1 (Center for Agricultural Data and Information System 2013). Namun
Widiatmaka et al. (2013a) menunjukkan bahwa dalam satu dekade terakhir,
persentase konversi lahan padi sawah sebesar sekitar 1.88% dan diprediksikan akan
terus meningkat serta akan mengancam ketersediaan beras nasional. Oleh karena

itu, prediksi kondisi padi sawah menjadi hal yang penting. Hasil dari prediksi ini
akan membantu pemerintah atau pihak-pihak terkait dalam proses pengambilan
kebijakan terhadap penataan dan penggunaan lahan di Kabupaten Karawang.
Tujuan umum dari penelitian ini adalah untuk memprediksi perubahan lahan
padi sawah tahun 2024 di Kabupaten Karawang dengan model Cellular AutomataMarkov Chain. Tujuan umum ini terdiri dari beberapa tujuan khusus: (1)
menganalisis perubahan penggunaan lahan pada tahun 1994, 2004 dan 2014 di
Kabupaten Karawang, (2) memprediksi penggunaan lahan pada tahun 2024 di
Kabupaten Karawang. Penelitian ini terdiri dari empat prosedur yaitu (1)
pemrosesan data penggunaan lahan dari citra Landsat tahun akuisisi 1994, 2004 dan
2014; (2) analisis kesesuaian lahan; (3) proses pemodelan, dan (4) pembangunan
skenario model berdasarkan UU No 41 tahun 2009 tentang Perlindungan Lahan
Pertanian Pangan Berkelanjutan dan Perda tentang Tata Ruang Kabupaten
Karawang tahun 2011-2030.
Pada periode tahun 1994-2004, terjadi penurunan luas area dari pertanian
lahan kering, hutan, padi sawah dan badan air sedangkan kenaikan luas area terjadi
pada tambak dan lahan terbangun. Pada periode 2004-2014, terjadi penurunan luas
area dari hutan dan padi sawah sedangkan pertanian lahan kering, tambak, lahan
terbangun serta tubuh air mengalami kenaikan luas area. Hasil analisis pola
perubahan lahan padi sawah periode 1994-2014 menunjukkan adanya
kecenderungan konversi padi sawah menjadi lahan terbangun serta konversi padi

sawah menjadi lahan kering lalu kemudian menjadi lahan terbangun. Prediksi
penggunaan lahan pada tahun 2024 menunjukkan adanya penurunan luas pertanian
lahan kering, hutan dan padi sawah sedangkan kenaikan luas area terjadi pada
tambak, lahan terbangun dan badan air apabila dibandingkan dengan penggunaan
lahan tahun 2014. Luas area lahan padi sawah pada tahun 2024 tanpa penerapan
konservasi kesesuaian lahan; dengan konservasi area yang sesuai (S2); dan dengan
konservasi area yang sesuai (S2) dan sesuai-marjinal (S3) adalah berturut-turut 93
349.58 ha, 93 355.79 ha dan 93 358.57 ha. Produksi beras pada tahun 2024
diprediksikan adalah sebesar 863 261 ton dengan cadangan beras (surplus) pada
tahun 2024 adalah sebesar 517 577 ton atau menurun 28% apabila dibandingkan
dengan cadangan beras pada tahun 2014 (723 572 ton).
Kata Kunci: CA-Markov, Kabupaten Karawang, perubahan penggunaan lahan,
padi sawah

SUMMARY
SUMMARY
INDRIAN RIZKA AMALIA. Modeling of Land Use Change of Paddy Field in
Karawang Regency Using Cellular Automata-Markov Chain. Supervised by
WIDIATMAKA and KHURSATUL MUNIBAH.
Paddy field conversion into non agricultural land or another agricultural land

is one of factor that threat rice sufficiency in Indonesia. One of region as center of
rice production is Karawang Regency with contribution for national rice supply at
784 000 ton.year-1 (Center for Agricultural Data and Information System 2013).
However, Widiatmaka et al. (2013a) stated that in one last decade, paddy field
conversion was at 1.88% and this percentage would higher in next years. The
phenomenon of paddy field conversion will affect rice sufficiency both for regional
and national consumption. Therefore, the prediction of future condition of paddy
field is being important thus it will help government or related parties in regional
policy making and application.
The general objective of this research is to predict land use change of paddy
field in year 2024 in Karawang Regency by using Cellular Automata-Markov Chain
model. The general objecitve consists of several objectives: (1) analyze land use
change of year 1994, 2004 and 2014 in Karawang Regency, (2) predict the condition
of land use of year 2024 in Karawang Regency. There were four procedures in this
research which were (1) land use data processing of Landsat of year 1994, 2004 and
2014; (2) land suitability analysis; (3) modeling process; and (4) scenarios building
based on Regional of Sustainable Land for Agricultural Food Crops No.41 of year
2009, and local government regulation about Regional Spatial Planning of Karawang
Regency year 2011-2030.
The result showed that in the period of 1994-2004, there were decreasing of

area on dryland farming, forest, paddy field and water body while the increasing of
area were occured on fishpond and settlement. In the period of 2004-2014, there were
decreasing of area of forest and paddy field while dryland farming, fishpond,
settlement and water body had been increased. During period 1994-2014 the trend of
change showed the tendency of paddy field conversion into built-up area, and paddy
field conversion into dryland farming that would be converted next into built-up area.
The result of predicted land use of year 2024 showed the decreasing area of dryland
farming, forest and paddy field while fishpond, settlement and water body had been
increased if it were compared to those in year 2014. The total area of paddy field in
year 2024 with scenario BAU (business as usual) was 93 349.58 ha; scenario of
preservation of S2 area was 93 355.79 ha; scenario of preservation of suitable area
(S2) and marginal suitable area (S3) was 93 358.57 ha. Rice production would be
predicted as 863 261 ton with surplus in year 2024 would be 517 577 ton or decrease
about 28% compare to rice surplus in year 2014 (723 572 ton).
Keywords: CA-Markov, Karawang Regency, land use change, paddy field

iv

Copyright © 2015, Bogor Agricultural University
All rights reserved

Any unauthorized quotation of all contents or any part thereof is strictly prohibited.
Citation is only for educational purpose, research, scientific writing, reports
writing, critique and problem analysis; and citations would not give any
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Announcing and duplicating either in part or in whole of this publication in any
form without permission of Bogor Agricultural University are strictly prohibited.

i

MODELING OF LAND USE CHANGE OF PADDY FIELD IN
KARAWANG REGENCY USING
CELLULAR AUTOMATA-MARKOV CHAIN

INDRIAN RIZKA AMALIA

A Thesis submitted for degree of Master of Science
in Information Technology for Natural Resources Management Study Program

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY

BOGOR
2015

ii

External Examiner: Prof Dr Ir Kukuh Murtilaksono, MS

iii

Thesis Title : Modeling of Land Use Change of Paddy Field in Karawang
Regency Using Cellular Automata-Markov Chain
Name
: Indrian Rizka Amalia
NRP
: G051110051

Approved by
Advisory Board

Dr Ir Widiatmaka, DEA

Supervisor

Dr Dra Khursatul Munibah, MSc
Co-Supervisor

Endorsed by

Program Coordinator of Information
Technology for Natural Resources
Management Study Program

Dean of Graduate School

Dr Ir Hartrisari Hardjomidjojo, DEA

Dr Ir Dahrul Syah, MScAgr

Date of Examination:

Date of Graduation:

August 18, 2015

August 28, 2015

iv

ACKNOWLEDGEMENTS
All praise to The Greatest Lord Allah SWT for giving me opportunity to
accomplish my thesis. The success of this thesis would not have been possible
without contributions and supports from many people in which I will not be able to
mention each one of them.
I would like to express my heartfelt gratitude to Mr. Dr Ir Widiatmaka, DEA
and Mrs. Dr Dra Khursatul Munibah, MSc as my supervisor and co-supervisor for
their enormous help and guidance throughout the completion of my thesis, also
Mr. Prof Dr Ir Kukuh Murtilaksono, MS for the exceptional advices. Last but not
least, I would like to thank to my father, mother, brother, sisters and my entire
family also my college friends in Bogor Agricultural University and Brawijaya
University for all pray, supports, and encouragement.
Hopefully, this thesis could give positive contribution to society and increase

knowledge to readers.

Bogor, August 2015
Indrian Rizka Amalia

.

v

TABLE OF CONTENTS
LIST OF TABLES

vi

LIST OF PICTURES

vi

LIST OF APPENDIX

vi

1 INTRODUCTION
Background
Problem Formulation
Objectives
Benefits

1
1
2
2
2

2 LITERATURE REVIEW
Land Use Change Modeling
Markov Chain Concept
Cellular Automata Concept and Application

3
3
3
5

3 METHOD
Research Conceptual Flowchart
Date and Location
Procedures

8
8
8
9

4 RESULT AND DISCUSSION
Land Use Dynamics of year 1994, 2004 and 2014
Land Suitability Analysis
CA-Markov Model Validation
CA-Markov Model Performance

15
15
20
22
25

5 CONCLUSION
Conclusion
Recommendation

31
31
31

REFERENCES

31

CURRICULUM VITAE

36

vi

LIST OF TABLES
1
2
3
4
5
6
7
8
9
10
11
12
13

The example of transition probability matrix
Matrix relational of objectives, analysis and output
Land suitability of paddy field
Criteria of land suitability for dry land farming
Criteria of land suitability for brackish water fishpond
Criteria of land suitability for built-up/built-up area
Criteria of land suitability for forest
Land use dynamics change of year 1994, 2004 and 2014
Transition probability matrix of year 1994-2004 (TPM1994-2004)
Kappa validation of model
Transition probability matrix of year 2004-2014 (TPM2004-2014)
The total of each land use in year 2024 based on model scenario
The comparison between rice production and consumption in year 2024

5
10
12
12
13
13
13
18
23
24
26
28
30

LIST OF PICTURES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

Filter size 3x3; filter size 5x5; states of cell (Eastman 2003)
Research conceptual flowchart
Research operational flowchart
Land use map of year 1994
Land use map of year 2004
Land use map of year 2014
Land use dynamics change of 1994, 2004 and 2014
Land use change distribution year 1994-2004
Land use change distribution year 2004-2014
Rasterized of land suitability map
Land suitability of paddy field based on scenarios
Result of model validation at each iteration
Kappa value and time duration of model performance at each iteration
Predicted land use of year 2024 (Business As Usual)
Predicted land use of year 2024 (Scenario 1)
Predicted land use of year 2024 (Scenario 2)
Projection of population in Karawang Regency

7
8
9
15
16
17
18
19
20
21
22
23
24
26
27
28
30

LIST OF APPENDIX
PICTURES
1

Ground observation map in Karawang Regency

32

1

1 INTRODUCTION
Background
Land is important resource in human life, because it is a base for many human
activities such as built-up area, animal husbandry, forestry, agriculture,
transportation and recreation (Xie et al. 2005). The use of land usually coupled with
urbanization and economical condition, and it becomes dominant factors of land
conversion. Land conversion which leads land use change is driven by the
interaction between biophysical and human dimensions (Veldkamp and Verburg
2004) which sometimes give bad impact to ecosystem (Rahdary et al. 2008). One
of the factors of land use change is urbanization, as the implication of city growth,
industrial, residential and commercial use. As population increase, the proportion
of urbanization will also increase and it will affect the dynamic of land use change
(Jenerette and Wu 2001). One of consequence of rapid urbanization and population
growth is the loss of agricultural land (Xie et al. 2005). Land conversion from
agricultural into built-up area is one of factor which threat food security in
Indonesia. There are some factor influences in agricultural land conversion, such as
government policy in Urban Land Use Plan (Rencana Tata Ruang Wilayah),
industrial growth, population growth which is related with built-up area (Apriyana
2011) and land owning (Sudaryanto and Rusastra 2006).
It is estimated that loss of agricultural land in Indonesia more than 150 000
ha year-1 (Apriyana 2011). During period of 1999-2002, land conversion in
agriculture sector is 187 720 ha year-1 which consists of 110 164 ha land conversion
into non agriculture and 77 556 ha land conversion into horticulture (Septiana et al.
2013). Sudaryanto and Rusastra (2006) stated that loss of agricultural land will
cause deficit in regional income about 15% or 27.7 million USD. In Indonesia case,
discussion of agricultural land conversion is mainly related to paddy field
conversion, as it is staple food for most Indonesian people. The total area of paddy
field in Indonesia is decreasing each year more than 1 000 000 ha in the period of
2008-2012 (Center for Agricultural Data and Information System 2013).
In West Java Province which is the third largest paddy field in Indonesia,
the loss of paddy field is estimated more than 20 000 ha during 2008-2012 (Center
for Agricultural Data and Information System 2013). Karawang Regency is the
second largest paddy field area in West Java and well known as center of rice
production with the contribution to national rice supply at 784 000 ton year-1 in
2011 (Bappeda 2011). However, data from recent decade show high rate of paddy
field conversion into other land use in Karawang Regency at 1.88% each year and
this percentage will potentially higher in the future (Widiatmaka et al. 2013a). This
conversion may affect the ability of Karawang Regency to fulfill internal
consumption and decrease the ability to fullfill external consumption at 10% over
10 years (Widiatmaka et al. 2013b). The rate of paddy field conversion has been
worrying regarding to land suitability aspect. Spatial identification of total area of
paddy field in Karawang Regency showed change in year 2002 and 2008. The total
area of paddy field in 2002 is 60.22% from total area while in year 2008 has been
decreased at 49.39% (Septiana et al. 2013).

2

The development of Karawang as industrial area and its geographic location
close to capital city of Jakarta induces a quick rural development of Karawang
Regency. This condition may lead into decreasing of rice production and the ability
of Karawang Regency to fulfill its own food sufficiency. The prediction towards
future development and extension of paddy field is necessary especially for
government and urban planner in order recommend proper regulation to protect
food sustainability. Future condition of land use in general can be predicted through
modeling. The models of land use change can be developed through Remote
Sensing and GIS at macro-scale (Veldkamp and Verburg 2004), integrated with
Cellular Automata-Markov Chain (Xie et al. 2005). The approach of this model is
to simulate spatial patterns of land use change at a temporal resolution (Jenerette
and Wu 2001). The predicted condition of land use that is modeled by Cellular
Automata-Markov Chain will simulate future change of agricultural land in
Karawang Regency. The prediction result hopefully will give another point of view
to related parties towards the condition of land use and preservation of paddy field
in future years.
Problem Formulation
The problem formulation of this research is considered from the rapidness
conversion of paddy field in Karawang Regency, while in other hand, Karawang
Regency is one of center of rice production for West Java and contribute not only
for its internal consumption but also external (other region) consumption. The
increasing of another land usage such as dryland farming, plantation and built-up
area are assumed to be one of factor that induces paddy field conversion with
regards on the increasing of opulation, social and economic. Karawang Regency is
both agricultural and industrial region, thus it is predicted that the rate of paddy
field conversion will be higher. This condition will threat the existance of paddy
field in future years and affect the fulfillment of rice consumption both in Karawang
Regency and other region.
Objectives
The general objective of this research is to predict land use change of paddy
field in year 2024 in Karawang Regency by using Cellular Automata-Markov Chain
model. The general objective consist of several objectives which are:
1. Analyze land use change of year 1994, 2004 and 2014 in Karawang Regency
2. Predict the condition of land use of year 2024 in Karawang Regency
Benefits
Benefits of this research are to improve the knowledge of future trend of land
use change especially paddy field conversion in Karawang Regency. Hopefully the
result of prediction will give another point of view to government or related parties
towards the application of regional regulation in Karawang Regency especially in
terms of paddy field preservation.

3

2 LITERATURE REVIEW
Land Use Change Modeling
Land use change models are tools for understanding the causes and the
implication of land use dynamics. Models of land use change can help land use
planning, thus it is possible to informed land use projection as part of decision. Land
use change model can represent the complexity problem in real world and by using
integrated geographic information system, the model can be spatially understood.
Spatial information from model gives high quality information for public decision
maker, thus the arrangement and evaluation of decision will be more qualified
(Veldkamp and Lambin 2001). Briassoulis (2000) noted that the use of models in
land use planning is defined as decision support, explanation, prediction, impact
assessment and prescription. Land use change model assess and project the future
role of land use and land cover in the functioning of earth system under some
scenario conditions (Veldkamp and Lambin 2001). The awareness of change is
essential in order to get better overview and understanding about landscape dynamic
of known period and time. Understanding the landscape patterns, the changes,
interaction between human and nature or spatial interaction become important
factors in modeling land use change and for decision making (Rahdary et al. 2008;
Veldkamp and Lambin 2001). There are six factors considered important in
modeling land use change i.e level of analysis, cross-scale dynamics, driving
factors, spatial interaction and neighborhood effects, temporal dynamics and level
of integration (Veldkamp and Verburg 2004). One of method that relates all of those
factors is Cellular Automata to simulate multiple land use types (White and Engelen
2000).
Markov Chain Concept
Markov Chain is approach in order to get transition probability matrix from
past periods of land use to predict land use in future periods. Cellular Automata
arises in iterative part of land allocation and in part of cell filtering stage 5x5 where
neighborhood influences are considered (Eastman 2003). Probability is generated
from past changes, to be applied to predict future changes. Markov Chain can be
divided into two steps such as to obtain transition probability matrix of various land
use types in detailed information about inter-class transition among different land
use types, and to predict land use based on probability values which result is
temporal predict information (Fan et al. 2008). Markov Chain is defined as
stochastic process which its properties at time t, Xt depends on its value at time t1, Xt-1. The equation is showing the transitional probability that gives the probability
on process, makes transition from state ai to state aj in one time period, as follow
(Weng 2002):
P{

=

|

=

}

4

The main factor in Markov Chain is transition probability, a conditional
probability for system to undergo transition to new state and gives current state of
the system. Tang et al. (2007) explained that input information in Markov Chain
equation is land use distribution at the beginning time and the end of a discrete time
period. Transition matrix is representing the changes among two land use occurred
during that period. The changing happened in two different land use will form
transition matrices. Mondal and Southworth (2010) denotes that transition
probability can be illustrated as Pij where it shows the probability of each class
changing to every class or remaining the same from time step i to time step j. Ozah
et al. (2007) explained a homogenous Markov Chain has properties as following:
• For each time period, every object in systems is in exactly one of
defined states
• Objects move from one state to next state according to transition
probabilities which depend only on current state (previous history is
not being considered)
• Transition probabilities do not change over time
Ozah et al. (2007) explained that Markov Chain can be used to predict future
matrix of vector (tn) based on past matrix of vector (t1) by transition probability
matrix, while transition probability matrix is derived from probability changing
between past matrix vector and previous (t1 and t0).
=

. =

.



.
.
. ..
.

Where:
n
= number of time steps
m
= number of states
= vector initial states at an initial time t
= vector of states at the next time t+1
P
= transition probabilities matrix
The result from Markov Chain is transition probability matrix from time one
(t0) to time two (t1) which will be the basis for projecting future time period.
Transition probability matrix is consists of the row represents older land use
categories and the columns represent the newer categories (Eastman 2003) (Table
1).

5

Table 1 The example of transition probability matrix
t0
(early year)
Class 1
Class 2
Class 3
Class 4
Class 5

Class 1
0.0000
0.9900
0.0000
0.0000
0.0000

Class 2
0.0000
0.0000
0.0000
0.0000
0.0000

t1 (predicted year)
Class 3
Class 4
0.0000
0.0000
0.0000
0.0000
0.0000
0.0009
0.0000
0.0000
0.0000
0.0000

Class 5
0.0000
0.1000
1.0000
1.0000
1.0000

Cellular Automata Concept and Application
Cellular Automata (CA) is one of application in spatial dynamics modeling
that mainly in land use change simulation (White and Engelen 2000). The approach
of CA is not only deal with the dynamic process modeling but also provides the
decision making processes. Significant properties of Cellular Automata is its
simplicity, thus there are many problem that can be approached by cellular structure
and rules such as spatially complex systems (e.g.: landscape processes), modeling
in space and time (e.g. ecological systems, population dynamics) and emergent
phenomena (e.g. evolution, earthquakes). Cellular Automata can simulate the
dynamic processes spatially, affinity with Geographic Information System (GIS)
and Remote Sensing (RS) technique (Veldkamp and Lambin 2001). The integration
between CA, GIS and RS can gives sophisticated result to model land use change,
since the basis work of them is the same type of raster data (Fan et al. 2008)
Cellular Automata model is embedded on landscape rasterized into discrete
cells. Each time of step, current state of each individual cell is updated based on
rules (deterministic or probabilistic) which are dependent on focal cell and
neighboring cells (Jenerette and Wu 2001). Set of identical elements of cells is
located in regular and discrete space. Each cell associates with state and form a
finite set. In discrete time steps, the model changing the states of its entire cell as
the transition rule, homogeneously applied at every step. The new state of cells is
depends on the previous state of cells (Fan et al. 2008). Cellular Automata are
neighborhood based cell while transition between it is determined by states in its
neighborhood and probability transition. Standard cellular automaton can be
defined as function that defines change of state from time past time to future and
influenced by neighborhood of all cells (Li and On Yeh 2001). Cellular Automata
as modeling approaches has basic elements comprises of series of cells or grid, set
of local states, a neighborhood and transition rules. Samat et al. (2011) define
Cellular Automata state as follow:
1=
2=
LU . =

3=
4=
5=
ℎ

6

,

=

(

,

.

,

.

, ,,

.

,

)

Where:
.
,
,
,
, ,,
,

= states of cell i,j at early time
= the potential of cell i,j to change at time t+1 of predicted cell
= states of cell i,j at time t of actual cell
= suitability indexes of cell i,j at time t
= probability of cell i,j to change from state x to state y at time t
= neighborhood index of cell i,j

As stated by Samat et al. (2011), one of important rule in CA concept is
transition rules. The transition rules can be approximated with Markov Chain. The
research from Munibah (2008) used the integration between CA-Markov which it
is first originally presented by Eastman (2003). Decision rules in CA-Markov can
be explained by two rules (Munibah 2008):
Decision rule 1: explains about suitability score in center pixel where
neighborhood pixels also influent same suitability condition with center pixel. If
decision allows it then land use type of center pixel is remain the same otherwise it
will enter Decision rule 2.
Decision rule 2: explains about the change in center pixel as if (a) it has
highest score: 1st highest, 2nd highest,….n, (b) the neighborhood pixels has the same
suitability score with center pixel. If decision allows it then the center pixel will has
more probability to be changed to other land use type. The final decision is decided
by the Transition Rules from Markov Chain result. If the probability of change is
high then the central cell will be changed into other land use type. Otherwise, the
center cell will stay the same, or going back to Decision rule 1 to recalculate the
other possibilites of change.
There are several factors involved in Cellular Automata model such as
Transition Probability which is from Markov Chain, Suitability factors and
Neighborhood influences. The explanation of those factors explained as follows:
1. Suitability Indexes
Suitability indexes is approached by analyzing land suitability. Land
suitability is key factor in determining site allocation and land use planning. Land
suitability helps user to evaluate whether piece of land is allocated properly or there
is a tendency to be changed. Land suitability maps in Celllular Automata model
gives spatial information integrates with the model where changes a particular pixel
from one land use class in time step i to another class in time step j based on the
state on local neighborhood (Mondal and Southworth 2010). The assessment of land
suitability can be conducted by integrating GIS and Decision Support System (Li
and On Yeh 2001). There are several methods for assessing land suitability i.e.
Multi-Criteria Evaluation by weighting factors and constraint (Li and On Yeh 2001;
Samat et al. 2011) and by matching manually of the criteria of land suitability
(Munibah, 2008). Classification structure for land suitability is based on matching
each criteria consists of S1 (very suitable), S2 (suitable), S3 (marginal suitable), N
(not suitable) (Djaenudin et al. 2011).

7

2. Neighborhood Indexes
Cell neighborhood influences plays important rule in Cellular Automata
model. If neighborhood influences is high then the probability of central cell (in
filter) to be changed or to be selected is also high (Li and On Yeh 2001).
Neighborhood influences is moving inside cell filtering size of 3x3 (Moore
Neighborhood) or 5x5 (Extended Moore Neighborhood) (Figure 1) (Samat et al.
2011; Munibah 2008). The process in neighborhood cells is considering a change
in central cell (black) according to state and rule in surrounding cells (white) (Figure
1). Eastman (2003) explained the influence of neighborhood cells through the case
of Conway’s Game of Life where there are some rules:
- An empty cell becomes alive if there are three living automata in 3x3
neighborhood surrounding cell.
- The cell will stay alive as long as there are 2 or 3 living neighbors. Less than
that, the cell will die from loneliness, but more than that, the cell will do
competition to gain resources.

Figure 1 Filter size 3x3; filter size 5x5; states of cell (Eastman 2003)

8

3 METHOD
Research Conceptual Flowchart
The issue pointed in this research was the rapidness of paddy field conversion
into non-paddy field i.e. because of the increasing of population and the economic
acitivity (Figure 2). The rapidness of paddy field conversion will affect the
availability of rice, as it is staple food for most Indonesian people. The availability
of rice has connected with rice sufficency where Karawang Regency not only fullfill
its internal consumption but also external consumption by other region. The
condition of paddy field in future year will become main topic in this research since
this condition may affect the food security in Indonesia. Towards this issue, the
prediction of paddy field existance by using CA-Markov becomes important.

Figure 2 Research conceptual flowchart
The government as controlling agent has policy in zoning, protecting and
regulating paddy field and its conversion through Regulation of Sustainable Land
for Agricultural Food Crops No.41 of year 2009, and Regional Spatial Planning of
Karawang Regency period 2011-2030. Hopefully the prediction result can support
the application of government regulation or related parties, and also enrich the
information towards the trend of land use change generally and paddy field
especially in Karawang Regency.
Date and Location
Research was conducted from April 2014-May 2015. Study area was in
Karawang Regency located at 107°02’-107°40’ E and 5°56’-6°34’ S. Data

9

processing, CA-Markov model processing and result analysis was conducted in
Laboratory of GIS/RS, Study Program of Information Technology for Natural
Resources Management, Bogor Agricultural University.
Procedures
There were four main procedures in this research such as: (1) Data Image
Processing, (2) Land Suitability Assessment, (3) CA-Markov Chain Model, (4)
Modeling Scenarios Application (Figure 3). The procedures in this research has
been correlated with research objectives, data necessities and expected output
(Table 2).

Figure 3 Research operational flowchart

10

10

Table 2 Matrix relational of objectives, analysis and output
No
1

Objective
Objective I
Analyze land use change of year 1994,
2004 and 2014 in Karawang Regency

Data Necessity
path:122 and row: 64 of
-Landsat TM, t=1994
-Landsat ETM+7, t=2004
-Landsat 8, t=2014
-Ground observation (point location
and interviewing)

Analysis
-Supervised classification
-Accuracy assessment of interpretation result by Kappa

Output
Land use of t=1994, t=2004 and
t=2014
Type: raster

2

Objective II
Predict land use of year 2024 in
Karawang Regency by using CAMarkov Chain

(a) Model Validation
-Land use of t=2004
-Land use of t=1994
-Land suitability map resulted from
matching method between physical
land characteristics of:
- Soil Map 1:50.000
- Tabular data of soil and rainfall
- Base Map 1:50.000

Pre-analysis:
-Analyze Markov Chain of land use t=1994 and t=2004
with result Transition Probability Matrix (TPM1994-2004)
-Weighting score for land suitability map collection in
interval 1-255
CA-Markov Model Analysis:
-Multi-objective allocation procedure (MOLA) of
TPM1994-2004 and land suitability map collection
-Neighborhood analysis with moving filter size 5x5
-Perform iteration of: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20
-Model validation

Model of CA-Markov, define as:
-Transition Probability Matrix
-Moving filter size 5x5
-Optimum iteration (based on
Kappa)
Type: raster

(b) Model Performance
-Land use of t=2004
-Land use of t=2014

3

Objective III
Analyze the condition of paddy field of
year 2024 in Karawang Regency

-Output from 2(b)
-Data of demography
-Land suitability of paddy field

Pre-analysis:
- Analyze Markov Chain of land use t=2004 and t=2014
with result Transition Probability Matrix (TPM2004-2014)
CA-Markov Model Analysis:
-Using the same analysis as 2(a)
-Model Scenario application, stated as:
1. Conserve the area of paddy field with land suitability
of S2
2. Conserve the area of paddy field with land suitability
of S2 and S3

Predicted land use of t=2024
Type: raster

-The prediction of paddy field
existance

11

1. Data Image Processing
Landsat satellite imagery t=1994, t=2004 and t=2014 were being corrected in
radiometric aspect and geometric aspect by rectify/resampling pixel position in
image, and geo-referencing the image into the selected projection system. Optical
band of imageries had 30 m of resolution. Satellite imageries was interpreted by
supervised classification with Maximum Likelihood method. There were several
characteristics in interpretation such as tone, pattern, texture, shape, size, shadow
and association. Accuracy assessment was being conducted by Kappa coefficient
(к) for accuracy assessment which relies on image training area. Training area was
deliniation based on ground observation with 60 samples of training area with
random sampling method. Kappa Accuracy mathematically can be measured as
follow where maximum value of coefficient is 1 (Liu and Mason 2009):
к=
Where:
Cii
Nri
Nci
N

∑

− ∑
− ∑

= value of diagonal matrix of contingency from row-i and column-i
= sum of pixel in row-i
= sum of pixel in column-i
= total number of pixels

Ground observation was important in order verify image interpretation result.
Data from ground truth will increase the total image interpretation/classification
accuracy (Liu and Mason 2009). Ground truth also occurred to collect field data
such as area observation and interviewing local people about historical trend of
previous land usage.
2. Land Suitability Assessment
Land suitability assessment in this research based on physical characteristic.
Land suitability was analyzed for land use considering several uses. The approach
for land suitability was by matching each land characteristics with suitability
classes. Classification structure for land suitability in this research based on FAO
criteria consist of S1 (very suitable), S2 (suitable), S3 (marginal suitable), N (not
suitable) (Djaenudin et al. 2011). Criteria of S1 is defined as land which has no
significant limiting factor that will affect land productivity; S2 is defined as land
which has few limiting factor that will affect land productivity; S3 is defined as
land which has more limiting factor than S2 that will affect land productivity where
to overcome the limiting factor needs more effort and cost; N is defined as not
suitable land because of difficulties in overcome the limiting factors (Ritung et al
2007).
As the input for model, land suitability should be quantified as score. Score
for S1, S2, S3 and N were divided linear stretching in range integer of 1-255 where
S1=255, S2=170, S3=85 and N=1. The score quantification was was built based on
assumption of land productivity. This concept stated that productivity of land
suitability of S1 is ≥ 80% from optimum production; S2 is 60%-80% from optimum
production; S3 is 40%-60% from optimum production; N is 50
soil depth
Land
preparation
• Rock surface
%
40
solid

3-8
F2

>8-15
F3

>25
F4

Table 4 Criteria of land suitability for dry land farming
Land
characteristic
Effective soil
depth

Unit

Effective soil
depth

Suitability Classes
S2
S3

cm

>75

>50-75

>25-50

m asl
%

fine clay, fine
silt
≤500
0-8

fine clay, fist
silt
>500-750
>8-15

fine clay,
rough silt
>750
>15-40

cm

>75

>50-75

>25-50

Soil texture
Elevation
Slope

S1

fine clay, fist
fine clay,
silt
rough silt
dryland
Existing
paddy
dryland farming farming, open
landuse
field, forest
land
Source: Widiatmaka and Hardjowigeno (2007) with modification.
Soil texture

-

fine clay, fine
silt

N
≤25
rough, sandy
>40
≤25
rough, sandy
built-up area,
fishpond, water
body

13

Table 5 Criteria of land suitability for brackish water fishpond
Suitability Classes
Land
Unit
characteristic
S1
S2
S3
Elevation
m asl
0-10
>10-20
>20-30
Distance from
m
250-500
fishpond
Slope
%
0-3
Existing landuse
Fishpond
Source: Widiatmaka and Hardjowigeno (2007) with modification.

N
>30
>500
built-up area

Table 6 Criteria of land suitability for built-up/built-up area
Suitability Classes
S2
S3
>15-25
>25-40

Land characteristic

Unit

Slope
Distance from river
ordo 1&2
Distance from street

%

S1
0-15

m

-

-

-

≤100

m
-

0-500

>500-1000

>1000

-

aluvial,
volcano area,

former
embankment,
volkaco area

former
embankment,
former
swamp

-

-

fishpond,
water
body

Landform

-

dryland
farming,
Existing landuse
built-up area
forest, paddy
field,
Source: Widiatmaka and Hardjowigeno (2007) with modification.

N
>40

Table 7 Criteria of land suitability for forest
Land
characteristic
Elevation

Unit

S1

Suitability Classes
S2
S3

N

m
asl

>2000

>1000-2000

≤1000

-

m

≤100

-

-

-

Distance from
river ordo 1&2
Distance from
shoreline
Slope

m

≤100

-

-

-

%

>45

-

-

Existing landuse

-

Forest

-

-

Built-up area, water
body

Source: Widiatmaka and Hardjowigeno (2007) with modification.

3. Cellular Automata-Markov Chain Model
Inputs for Markov Chain were:
1. Land use image of t1=1994; t2=2004; t3=2014
2. Defining number of time periods between t1 and t2; t2 and t3
3. Defining number of time periods to project forward from t3

14

Outputs from Markov Chain analysis were:
1. Transition probability matrix (TPM) which express probability of each land
use category that will change to every other category.
2. Transition areas matrix which express the total area (number of pixels)
which expected to change in next period.
3. A set of conditional probability images which express the probability that
each pixel will belong to the designated class in next period.
Inputs for Cellular Automata model were:
1. Basis land use image of t3
2. Transition Probability Matrix (TPM1994-2004 or TPM2004-2014)
3. Land suitability
4. Defining the moving filter size as 5x5 where central pixel where this filter
will move and weighted the pixel one by one.
5. Defining iteration of model: 2, 6, 8, 10, 12, 14, 16, 18, 20
Outputs from Cellular Automata analysis were:
1. Predicted of land use of t=2014
Firstly, CA-Markov modeling was conducted in order to validate the model at
various iteration. Validation was conducted by assessing predicted land use t=2014
with actual land use t=2014 using Kappa Accuracy assessment.
2. Predicted of land use change in future years t=2024
Secondly, CA-Markov modeling was conducted to predict land use t=2024 by using
iteration that had optimum accuracy result based on model validation. The result of
predicted land use then was used to analyze future condition of land use generally
and paddy field especially in Karawang Regency.
4. Modeling Scenarios Application
The scenario was defined to direct the flow of simulation and help model to
define its goal. Scenarios were constructed based on Regulation of Sustainable
Land for Agricultural Food Crops No.41 of year 2009 and Regional Spatial
Planning of Karawang Regency period 2011-2030.
Scenarios of modeling were stated as:
Scenario 1: Conserve the area of paddy field with land suitability of S2 for paddy
field in future years.
Scenario 2: Conserve the area of paddy field with land suitability of S2 and S3 for
paddy field in future years.

15

4 RESULT AND DISCUSSION
Land Use Dynamics of year 1994, 2004 and 2014
Result of satellite image interpretation indicate six land uses which were
dryland farming, fishpond, forest, paddy field, built-up area and water body
(Appendix 1) with the Kappa accuracy from this interpretation was 85.16%. The
result of image interpretation of 1994, 2004 and 2014 showed different trend of
land use (Figure 4, Figure 5, Figure 6). The different trend of land use was
influenced by the dynamics of socio-economic activities due to population
increasing each year in Karawang Regency. Generally, during period of 1994-2014,
there were some of land uses which had increased (Figure 7) while some others had
decreased.

Figure 4 Land use map of year 1994

16

Figure 5 Land use map of year 2004
During the periods of 1994-2004, the area of dryland farming, paddy field
and forest had decreased while fishpond, built-up area and water body had
increased. Dryland farming had decreased at 0.61%, followed by paddy field which
was decreased at 0,69% and forest which was decreased at 0.02% (Table 8). The
decreasing area of those three land use was in line with the increasing of other land
use which were fishpond, built-up area and water body. Built-up area had the
highest increasing at 39.98%, followed by fishpond which was increased at 1.05%
and water body which was increased at 0.01%. During the periods of 2004-2014,
the area of dryland farming had increased at 19.29% whereas in the previous years
decreased at 2.95% (Table 8). The area of built-up area had increased at 4.58% and
higher 6% than 10 years previously. The area of fishpond had increased slightly
each at 0.12%. Forest and paddy field had decreased each at 0.58% and 7.06%. The
result of land use dynamics showed that paddy field decrease in each year and
probably this condition will continue in future year. Wulandari et al. (2008)
explained that rate of paddy field conversion in Karawang Regency was average at

17

0.046% each year from 1995-2011 with low conversion rate less than 0.02%
occured in 1998, 2000 and 2007. The loss of paddy field gives bad impact not only
in food self-sufficiency but also in greater term of economic. Besides for food
production, paddy fields also provides multi-functionalities i.e. flood control,
groundwater recharge, soil erosion control, water quality purification, air
purification and cooling, wildlife habitat and social benefits. The value of this multifunctionalities is estimated more valuable than the value of rice production itself
(Yoon 2009).

Figure 6 Land use map of year 2014

18

140000

Total Area (ha)

120000
100000
80000
60000
40000
20000
0
Dryland
farming

Fishpond

Forest

1994

Paddy field

2004

Built-up
area

Water body

2014

Figure 7 Land use dynamics change of 1994, 2004 and 2014
Table 8 Land use dynamics change of year 1994, 2004 and 2014
Land Use

1994

2004

2014

ha

%

ha

%

ha

%

Dryland farming

24 462.68

12.78

23 305.46

12.17

28 946.83

15.12

Fishpond

17 917.31

9.36

18 403.51

9.61

18 625.55

9.73

7 733.55

4.04

7 690.61

4.02

6 580.80

3.44

Forest
Paddy field

123 666.10

64.60

122 345.37

63.91

108 835.08

56.85

Built-up area

16 484.26

8.61

18 499.95

9.66

27 256.63

14.24

Water body

1 172.52

0.61

1 171.51

0.62

1 171.51

0.62

Changes in land use implied by its distribution. Distribution change between
land use in 1994-2004 (Figure 8) and 2004-2014 (Figure 9) were cross tabulated to
determine relative frequency of each landcover class that changed into other classes
during period of time (Eastman 2015). Cross tabulation matrix as a core tool for
categorical map compared two categorical variables by tables, one variable in row
and other in columns to assess two maps relation in the terms of quantity (Pontius
and Millones 2011). Cross tabulation results showed image of change allocation
and frequencies classes which had remained the same shown in frequencies along
the diagonal whereas changed classes shown in off-diagonal frequencies.
Crosstabulation result shows a kappa index of agreement between the two maps
both in an overall sense and on a per-category basis (Pontius 2000).

19

Figure 8 Land use change distribution year 1994-2004
The result showed that distribution change of land use in period 2004-2014
was more dynamic than it occured at period 1994-2004. In the period of 2004-2014,
almost all of land uses had been changed. Paddy field had changed dynamically
both in period 1994-2004 and 2004-2014. It implied that paddy field was easily
converted into other land uses. As stated by Widiatmaka et al. (2013a), paddy field
and built-up area are two land categories which have high rate conversion. Paddy
field conversion seemed to be persistent, can not be restored immediately and tend
to irreversible. Moreover, government strategy for paddy field extensification
requires abundance preparation and will take long periods besides the lackness of
land availability (Irawan 2005), and it makes agricultural land preservation is
necessary important.

20

Figure 9 Land use change distribution year 2004-2014
Land Suitability Analysis
Land suitability map as the input for model were a collection of land
suitability of fishpond, forest, dryland farming, built-up area, paddy field and map
of water body existing (Figure 10). Each of land use suitability was given with
score, range from 1-255 based on criteria. Land suitability also used as the basis
decision making for model scenario with assumption that land suitability was the
ideal condition for gaining optimum production. There were two scenario that is
first scenario was the preserving of suitable area (S2), and second scenario was the
preserving of suitable area (S2) and marginal-suitable area (S3) (Figure 11).

21

Figure 10 Rasterized of land suitability map

22

The scenario was aimed to analyze the condition of future paddy field
existence towards the probability of its conversion into other land uses. The
scenario assumed that other land uses except paddy field will change following the
model while paddy field will be restricted only with the scenarios. These scenarios
considered the application of government regulation about agricultural area
preservation in order to maintain food sustainability and food sufficiency.

Figure 11 Land suitability of paddy field based on scenarios

CA-Markov Model Validation
Modeling phase were consists of Markov Chain and Cellular Automata. First,
Markov Chain was conducted on land use t=1994 and t=2004. Markov Chain was
based on the probability of changes occurred between 1994 and 2004. The result of
Markov Chain’s transition matrix seen as probability index and number of cells
expected to be transitioned in t=2014 (Table 9) or transition probability matrix of
year 1994-2004 (TPM1994-2004). Transition probability matrix was then used as one
of input for model. Based on transition matrix result, paddy field might change into
dryland farming, fishpond and built-up area. The probability of paddy field
conversion into built-up area was higher than fishpond or dryland farming and it
showed the trend of paddy field change in period 1994-2004. In case of dryland
farming, the probability of change was high on built-up area comparing to other
land uses. It also showed the trend of change of land use in period 1994-2004.
In period of 2008-2012, the statistics data showed that total area of paddy
field in Karawang was stable but tend to increase (Center for Agricultural Data and
Information System 2013). Towards this issue, the government has promote the
extensification program. The target of this program is to mantain rice sustainability
and cover up its loss. The extensification program will be conducted in two period
with target 374 125 ha. The aim of this strategy is to mantain food security in
Indonesia in future years (Panudju et al. 2013).

23

Table 9 Transition probability matrix of year 1994-2004 (TPM1994-2004)
Probability of changing to:
Dryland
farming

Fishpond

Forest

Paddy field

Built-up
area

Water body

0.8042
0.0000
0.0000
0.0189
0.0000
0.0000

0.0000
0.8800
0.0000
0.0566
0.0000
0.0000

0.0000
0.0000
0.9953
0.0000
0.0000
0.0000

0.0007
0.0600
0.0047
0.8406
0.0000
0.0000

0.1951
0.0300
0.0000
0.0817
0.9990
0.0010

0.0000
0.0300
0.0000
0.0022
0.0010
0.9990

Dryland farming
Fishpond
Forest
Paddy field
Built-up area
Water body

The first model was conducted to predict land use of 2014 and validate it with
actual land use of 2014 by Kappa. This step was aimed to assess model validation
by comparing Kappa value at each iteration. Iteration which was used in th