prediction on land use changes in mae …mulinet11.li.mahidol.ac.th/e-thesis/4736361.pdf · fac. of...

PREDICTION ON LAND USE CHANGES IN MAE TAENG

WATERSHED, CHIANG MAI PROVINCE

SURANG RATTANAPAN

A THESIS SUBMITTED IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

(TECHNOLOGY OF ENVIRONMENTAL MANAGEMENT) FACULTY OF GRADUATE STUDIES

MAHIDOL UNIVERSITY 2006

ISBN 974-04-7950-2 COPYRIGHT OF MAHIDOL UNIVERSITY

ACKNOWLEDGEMENT

The success of my thesis can been attributed to extensive support and

assistance from my major advisor, Assoc.Prof. Dr.Sura Pattanakiat for his meaningful

supervision, creation guidance, continuous discussion and couragement throughout the

course of this study. My thesis could not be absolutely done without his and my co-

advisor, Asst.Prof. Gritsanaruck Theeraraj and Asst Prof. Prasong Saguantam. I

deeply thank them for their valuables advice, kindness, inspiration, guidance and

encouragement in my thesis.

I wish to thank Dr. Chongrak Wachrinrat, Faculty of Forestry, Kasetsart

University, for kindness in examining the research instrument and providing

suggestions for improvement, and who was the external examiner of the thesis

defense.

I would like to special thank Miss Usawadee Phakularbdang who introduce me

about the Cellular Automata and always encourage and help me to encompass many

situations. Besides, thank Mr.Chidsanuphong Chartasa, Mr. Karn Kamonborisut and

Miss Chatchada Kaewpruksapimon who advice me using remote sensing, GIS

application and their helpful and kindness.

My special thank go to my friend, Miss Hathaikarn Sitta, Miss Tawisa

Jiyipong, Miss Khuanchanok Quecharoen, Miss Patcharawadee Khamrod and Miss

Napathida Hunhaboon who their friendship, kindness and standing me wherever

situation.

Finally, I am grateful and deep appreciate to my family: Mr. Sukon, Mrs. Riam

Rattanapan and grandmother: Mrs. Nark Maneeshine who are my inspiration, love,

encouragement, financial support and entirely care which made this thesis possible

and enabled me to undertake this thesis successfully. And my eldest brother, young

brother and elder sister-in-law for their understanding and kind supporting.

Surang Rattanapan

Fac. of Grad. Studies, Mahidol Univ. Thesis / iv

PREDICTION ON LAND USE CHANGES IN MAE TAENG WATERSHED, CHIANG MAI PROVINCE SURANG RATTANAPAN 4736361 ENTM/M M.Sc. (TECHNOLOGY OF ENVIRONMENTAL MANAGEMENT) THESIS ADVISORS: SURA PATTANAKIAT, Ph.D. (FORESTRY); GRITSANARUCK THEERARAJ, M.Sc. (TECHNOLOGY OF ENVIRONMENTAL MANAGEMENT)

ABSTRACT The objective of this study was to identify land use changes of Mae Taeng Watershed, Chiang Mai Province during 1990, 2000 and 2005 and to predict land use change in 2010 by applying Remote Sensing integrated with Markov Chain and Cellular Automata (CA-Markov). The results of the study showed that Mae Taeng Watershed covering an area 1,952.62 square kilometers has a pattern of land use that can be classified into evergreen forest, deciduous forest, forest plantation, paddy field, field crop, perennial and orchard, urban and built up land, water body and disturbed forest. The study of land use changes during 1990-2000 revealed that the area of field crop, perennial and orchard, forest plantation, disturbed forest, urban and built up land and water body have increased. While evergreen forest, deciduous forest and paddy field have decreased. And the study of land use changes during 2000-2005 revealed that each area of field crop, forest plantation and paddy field have increased. While deciduous forest, disturbed forest, perennial and orchard, evergreen forest, urban and built up land and water body have decreased. The prediction of land use of Mae Taeng Watershed in 2010 used land use in 1990, 2000 and 2000, 2005 as a base for calculations. The accuracy of the prediction in 2005 used the predicted land use map to compare with a land use map for 2005 classified by satellite interpretation. Overall accuracy of the prediction model was 71.09% and Kappa Index was 0.52. And the accuracy of the prediction in 2010 used the predicted land use map for 2010 based on land use data in 1990 and 2000 to compare with the predicted land use map in 2010 based on land use data in 2000 and 2005. Overall accuracy of the prediction model was 68.41 % and Kappa Index was 0.54.

KEY WORDS:PREDICTION/ LAND USE CHANGE/ MAETAENG WATERSHED 72 P. ISBN 974-04-7950-2

Fac. of Grad. Studies, Mahidol Univ. Thesis / v

การคาดการณรูปแบบการใชประโยชนที่ดนิในพืน้ที่ลุมน้ําแมแตง จ. เชียงใหม (PREDICTION ON LAND USE CHANGES IN MAE TAENG WATERSHED, CHIANG MAI PROVINCE)

สุรางค รัตนพันธ 4736361 ENTM/M

วท. ม. (เทคโนโลยีการบริหารส่ิงแวดลอม)

คณะกรรมการควบคุมวิทยานิพนธ: สุระ พัฒนเกยีรต,ิ Ph.D. (Forestry); กฤษณรักษ ธีรรัฐ, M.Sc. (Technology of Environmental Management)

บทคัดยอ การศึกษาครั้งนี้มีวัตถุประสงคเพื่อศึกษาการใชประโยชนที่ดินในป พ.ศ. 2533, 2543 และ 2548 และคาดการณการใชที่ดินในพื้นที่ ลุมน้ําแมแตง จ. เชียงใหม ป พ.ศ. 2553 โดยการประยุกตเทคโนโลยีการสํารวจระยะไกล (Remote sensing: RS) รวมกับแบบจําลอง Markov Chain and Cellular Automata (CA-Markov)

ผลการศึกษา พบวา ลุมน้ําแมแตง มีพื้นที่ 1,952.62 ตารางกิโลเมตร จําแนกประเภทการใชประโยชนที่ดินออกเปนปาไมผลัดใบ ปาผลัดใบ สวนปา นาขาว พืชไร พืชสวนและไมยืนตน ชุมชนและพื้นที่เปดโลง แหลงน้ํา และ ปาที่ถูกบุกรุก จากการศึกษาการเปลี่ยนแปลงการใชที่ดินในชวงป พ.ศ. 2533-2543 พบวา พืชไร พืชสวนและไมยืนตน สวนปา ปาที่ถูกบุกรุก ชุมชนและพื้นที่เปดโลง และแหลงน้ํา มีพื้นที่เพิ่มขึ้น ในขณะที่ ปาไมผลัดใบ ปาผลัดใบ และนาขาว มีพื้นที่ลดลง และ การเปลี่ยนแปลงการใชที่ดินในชวงป พ.ศ. 2543-2548 พบวา พืชไร สวนปา และนาขาว มีพื้นที่เพิ่มขึ้น ในขณะที่ ปาผลัดใบ ปาที่ถูกบุกรุก พืชสวนและไมยืนตน ปาไมผลัดใบ ชุมชนและพื้นที่เปดโลง และแหลงน้ํา มีพื้นที่ลดลง การคาดการณการใชที่ดินในลุมน้ําแมแตง จ. เชียงใหม ป พ.ศ. 2553 ใชขอมูลการใชประโยชนที่ดินป พ.ศ. 2533, 2543 และพ.ศ. 2543, 2548 เปนฐานในการคํานวณ และตรวจสอบความถูกตองของแบบจําลองการใชประโยชนที่ดินป พ.ศ. 2548 โดยนําผลจากการคาดการณการเปลี่ยนแปลงการใชประโยชนที่ดินตรวจสอบกับผลของการแปลภาพถายดาวเทียม พบวาคาความถูกตองรวม เทากับ 71.09% และคา Kappa Index เทากับ 0.52 และ ตรวจสอบความถูกตองของแบบจําลองการใชประโยชนที่ดินป พ.ศ. 2553 โดยนําผลการคาดการณการเปลี่ยนแปลงการใชประโยชนที่ดินป พ.ศ. 2553 ตรวจสอบความถูกตองของแบบจําลองจากการเปรียบเทียบฐานขอมูลการใชประโยชนที่ดินป พ.ศ. 2533, 2543 และพ.ศ. 2543, 2548 พบวาคาความถูกตองรวมเทากับ 68.41% และคา Kappa Index เทากับ 0.54 72 หนา ISBN 974-04-7950-2

CONTENTS

Page

ACKNOWLEDGEMENT iii

ABSTRACT (ENGLISH) iv

ABSTRACT (THAI) v

LIST OF TABLES viii

LIST OF FIGURES x

CHAPTER

I INTRODUCTION 1

1.1 Background 1

1.2 Objective of the study 2

1.3 Expected results 2

1.4 Scope of study 2

1.5 Conceptual Framework 3

II LITERATURE REVIEW 5

2.1 Mae Taeng Watershed, Chiang Mai Province 5

2.2 Land Use Changes 8

2.3 Geographic Information System and Remote Sensing 9

2.4 Accuracy Assessment 10

2.5 Markov Chain 12

2.6 Cellular Automata (CA) 12

2.7 Related researches 16

III MATERIALS AND METHODS 18

3.1 Materials 18

vii

CONTENTS (Cont.)

Page

3.2 Study design 18

3.2.1 Land Use Classification 20

3.2.2 Land Use Changes Detection 26

3.2.3 Prediction on Land Use Changes 26

3.2.4 Prediction Accuracy Assessment 27

3.2.5 Trend of Land Use Changes base on Socio-

Economic and Policy Aspect 27

IV RESULTS 28

4.1 Land use classification of Mae Taeng Watershed 28

4.2 Land Use Change Detection 34

4.3 Prediction of Land Use Change 42

4.4. Accuracy Assessment of Model 54

4.5 Trend of Land Use Changes base on Socio- Economic and

Policy Aspect 57

V CONCLUSION AND RECOMMENDATION 60

5.1 Conclusion 60

5.2 Recommendation 61

REFERENCE 63

APPENDIX 66

BIOGRAPHY 72

LIST OF TABLES

TABLE Page

2-1 Thematic Map Spectral Band 10

2-2 Error Matrix 11

3-1 Data sources for the study 20

3-2 Nomenclature of land use pattern of Mae Taeng Watershed 24

4-1 Land use pattern of Mae Taeng Watershed 29

4-2 Accuracy analysis of land use classification of Mae Taeng

Watershed 30

4-3 Land use changes of Mae Taeng Watershed between 1990 and

2000 based on cross-tabulation analysis 35


2005 based on cross-tabulation analysis 39

4-5 Probability of changing between 1990 and 2000 based on Markov

Chain Analysis 42

4-6 Transition area between 1990 and 2000 based on Markov Chain

Analysis 43

4-7 Probability of changing between 2000 and 2005 based on Markov

Chain Analysis 44

4-8 Transition area between 2000 and 2005 based on Markov Chain

Analysis 45

4-9 Prediction land use pattern of Mae Taeng Watershed in 2005 based

on land use data in 1990 and 2000 46

4-10 Comparison between land use map (ground truth) and prediction

land use map (CA_Markov) in 2005 48



ix

LIST OF TABLES (Cont.)

TABLE Page



4-13 Comparison between the predicted land use map in 2010 based

on land use data in 1990, 2000 (interval 10 years) and the predicted

land use map in (interval 5 years) 2010 based on land use data

in 2000, 2005 53

LIST OF FIGURES

FIGURE Page

1-1 Conceptual framework in this research 3

1-2 Study site located on Mae Taeng Watershed, Chiang Mai

province 4

2-1 Mae Taeng Watershed’s boundary 7

2-2 Von Neumann Neighborhoods and Moore Neighborhood 14

2-3 Comparison between the basic Cellular Automata and Cellular

Automata adopted in GIS 15

3-1 The study design diagram 19

3-2 False color composite image of Landsat band 4-5-3 (R-G-B)

showing Mae Taeng Watershed in 1990 21





4-1 Land use pattern of Mae Taeng Watershed in 1990 31




2000 36


2005 40

4-6 Prediction land use pattern of Mae Taeng Watershed in 2005

based on land use data in 1990 and 2000 47



xi

LIST OF FIGURES (Cont.)

FIGURE Page



4-9 Composite between land use map in 2005 which interpreted

from Landsat image (A) and prediction land use in 2005 (B) 55

4-10 Composite between prediction land use map in 2010 based on

land use data in 1990 and 2000 (A) and prediction land use

map in 2010 based on land use data in 2000 and 2005 (B) 56

Fac. of Grad. Studies, Mahidol Univ. M.Sc. (Technology of Environmental Management) / 1

CHAPTER I

INTRODUCTION

1.1 Background

Land use is a pattern of human activities undertaken within a socio-economic

context, increasing of population, technology development and policy. While the

necessary use of the land is increasing, it has limitation and continuous declining. The

land use change is driven by the interaction in time between biophysical and human

dimensions that implied to past and present human activities. The watershed is one of

the sensitive areas affected by the land use changes. The land use in Mae Taeng

Watershed is used for a variety of activities. Land use patterns are including paddy

field, field crop, perennial and orchard, urban and built-up land, forest plantation.

Natural land area is covered by dry evergreen forest, hill evergreen forest, mixed

deciduous, dry dipterocarp forests. The forest in Mae Taeng Watershed tends to be

rapidly increasing in uses. From the LANDSAT-5(TM) in 1990 and 2005, the area of

forest was estimated to be 1,633.54 sq.km. and 1,304.90 sq.km, respectively. Every

year extensive areas of agricultural and natural forest in Mae Taeng Watershed are

degraded and turned into wastelands over time due to human interventions. Depletion

of forest has an important impact on socio-economic development and ecological

balance. High population growth rate in Mae Taeng Watershed is one of the main

causes for rapid deterioration of the physical environment and natural resource base.

We can understand the tendency of land use change in the future by the prediction on

land use change and forecast which based on models in the future for data supporting

land use planning and interactive environment to simulate “what-if” scenarios.

Therefore scenario analysis with land use models can support land use planning and

policy (1).

Various models have been used to predict the land use changes. They are

normally based on statistic approach. One of the well known models is Markov Chain,

Surang Rattanapan Introduction / 2 which is selected to use, in this study. However, Markov Chain is available only to

identify the whole area changes for each pattern. Therefore, the change of specific area

could not be classified. Thus, Cellular Automata Model which shown a potential

analysis spatial aspect is introduced to use in this study.

1.2 Objectives of the study

1.2.1 To identify land use pattern and changes of Mae Taeng Watershed in

1990, 2000 and 2005.

1.2.2 To predict the land use change in 2010. Use Markov Chain and Cellular

Automata Model.

1.3 Expected result

Land use map of Mae Taeng Watershed, Chiang Mai Province in 2010 to

support for land use planning and management.

1.4 Scope of the study

1.4.1 Study area

The study area covers Mae Taeng Watershed in Chiang Mai Province,

approximately 1,952.62 square kilometers as shown in Figure 1-2.

1.4.2 Scope of procedure

1. The land use pattern in Mae Taeng Watershed was classified using

satellite images with computer assisted interpretation and visual interpretation method.

For temporal study, three time periods of 1990, 2000 and 2005 have been selected to

assess the change of land use pattern.

2. Land use pattern in Mae Taeng Watershed can be classified into nine

land use categories including evergreen forest, deciduous forest, forest plantation,

paddy field, field crop, perennial and orchard, urban and built up land, water body and

disturbed forest.


3. To apply Markov Chain and Cellular Automata Model combine with

remote sensing technique to predict land use change. The results may provide some

useful information for planer.

1.5 Conceptual Framework

Figure1-1 Conceptual Framework in this research

Surang Rattanapan Introduction / 4

Figure1-2 Study site located on Mae Taeng Watershed, Chiang Mai Province


CHAPTER II

LITERATURE REVIEWS

2.1 Mae Taeng Watershed, Chiang Mai Province

Mae Taeng Watershed (2) is subcatchment of Ping Watershed is one of

twenty-five watershed in Thailand. Mae Taeng Watershed has been selected as the

study area for this study. It is the upper catchments basin Ping Watershed and

occupied a rapid change in land use. It approximately area is 1,952.62 square

kilometers, located at latitude 19˚ 05΄ to 19˚ 45΄north and longitude 95˚ 25΄to 99˚

05΄east. Mae Taeng Watershed is the north part of Chiang Mai Province. The origins

of Mae Taeng River are Dan Laos Mountain, border of Thailand nearby Myanmar,

flow through Wang Hang District and Chiang Dao and meet Mae Ping River close to

Administrative office of Mae Taeng District. Most of areas are mountains with few

patches of plain. Forests are quite abundant with the elevation between 350 – 1,200

meters above Mean Sea Level (MSL).

The area locates in 3 districts covers 15 tambon of Chiang Mai Province.

Its boundary is the followings as shown in Figure 2-1.

North bounded by The Union of Myanmar

South bounded by Mae Taeng District, Chiang Mai Province

East bounded by Pai District, Mae Hong Sorn Province

West bounded by Chiang Dao District, Chiang Mai Province

General Topography

Mae Taeng Watershed consists of mountain ranges and height slope. The

most area of watershed is mountain range approximately 90 percent. Slope more than

16 percent, the height of mountain is between 600-2,175 MSL and mean

approximately 1,000 meter. Every flat plain have the communities and agriculture.

Surang Rattanapan Literature Reviews / 6

Weather and Climate

The study area (3) is influence by tropical monsoon annually. The mean

annual minimum and mean annual maximum temperatures are 19.22° C and 30.61° C,

respectively, with a mean annual temperature of 24.92° C. According to the general

annual rainfall pattern, most areas of the country receive precipitation 1,446.16 mm a

year.

Geology

The areas are mountains including vary type of rocks. Most are granite rocks

especially in the southern. Sedimentary and metamorphic rocks can be founded

upward from the middle all over the northern of the area. Furthermore, there are

alluvial sediments along the stream.

Hydrology

As a general rule, tributaries in a stream system join at angles of less than

90°. This gives the system a branching pattern which is like that formed by the limbs

of a tree, and hence is called dendritic, which means treelike. The streams of area are

dendritic patterns because of the mountain topography. The origin of the stream is in

Wang Haeng District, close to Mynmar, southwestward to the Ping River at Mae

Taeng District and flows all year around.

Soil

The soil series in Mae Taeng Watershed include 14 difference classification

such as Lampang series, Lat Ya series, Tha Yang series, Tha Yang/ Lat Ya

association, Hang Dong series, Korat series, Hang Chat series undulating phase, Mae

Rim series undulating phase, Mae Rim series rolling phase, Mae Taeng series

undulating phase, Pak Chong series undulating phase, Alluvial complex, and Slope

Complex. However over 90% of the area is classified under soil type “Slope

Complex”.


Position ………...…...Reference System Geographic Projection…...……... Universal Transverse Mercator UTM……………Zone 47 North Datum……...…….. Indian 1975 Spheroid…………...…... Everest Grid cell size………..…25 meter Scale…………...…….. 1: 50,000

Source: Department of Environmental Quality Promotion, (No date)

Figure 2-1 Mae Taeng Watershed’s boundary

Source: Department of Environmental Quality Promotion, (No date)

Surang Rattanapan Literature Reviews / 8 2.2 Land Use Changes

Land use brings land to treat of human need agriculture and residential. (4)

Land use is continuous transition up to human use but soil property is not change.

Duchanee (5) found that type and pattern of land use proceeded from 3

important factors, land, society and economy. These factors influence suitability of

land use which identified by physical factors. Moreover, pattern of residential,

technologies, science of land use and social complex, attitude, cost of product, income,

market and labor influence the pattern of land use.

Abduliah (6) studied land cover in Mae Taeng Watershed, Chiang Mai

Province. He found that causes of human intervention in forest depletion are

population growth, cost of product, and market need. Furthermore, Pennapa (7) found

that land cover and land use change were influenced by population, cost of product,

and level of economic and social development, technology and policy.

Thongchai (8) said that causes of land use changes proceed from the depletion

of natural forest into another land use type by human. The important factor as

following;

• Socio-Economic such as increasing population, human

migration, support of economic plants for export, technological development for

increasing product, infrastructure development, forest industrial development,

problem of capitalist, depletion of forest, forest fire and insect disease.

• Laws and policy.

• Physical factor such as climate and water body.

The land covers change as an alteration in the surface components of a

landscape (9). The rate of the change occurring to land cover can be viewed as either

abrupt or gradual. The clearing of forest for cultivation or the fire burn left as a result

of a brushfire represent abrupt changes in land cover characterized by distinct

boundaries. These changes are relatively easy to detect and measure on satellite

imagery.

There are, however, far more subtle changes that take place in the landscape

that occurring without distinct boundaries such as the gradual deterioration in


vegetation cover associated either drought or overgrazing. This type of change is

usually more difficult to detect on satellite imagery because of a slight variation in the

spectral reflectance of large area.

2.3 Geographic Information System and Remote Sensing

2.3.1 Geographic Information Systems (GIS)

Geographic Information Systems (GIS) have been defined in various

ways such as its disciplinary, like;

Paul J. Curran (10) define that geographic information systems are

information system which is based on data referenced by geographic coordinates.

TYDAC Technologies Inc. (11) define that “Geographic Information

Systems are software package which can be used to create and analyze spatial

information. With such system, map, air photos and diagrams describing natural and

man-made features can be translated into an electronic code which can be recalled

modified and analyze.”

A simple definition is that a GIS is an organized collection of computer

hardware, software, and geographic data designed to efficiently capture, store, update,

manipulated, analyze, and display all form of geographically referenced information.

2.3.2 Remote Sensing (RS)

Remote sensing is the sciences and art of obtaining information about

and object, area, or phenomenon through the analysis of data acquired by a device that

is not in content with the object, area, or phenomena under investigation (12).

Remote sensing is one of the fastest growing, most exciting and

powerful techniques to scientists concerned with environmental questions form field

as diverse as geology, forestry, agriculture and land use change.

The Thematic map is usually of an environmental factor for which there

are few traditional data sources, for example land cover, estuarine sediment load, soil

moisture, vegetation biomass and rainfall (13). Thematic map data are available in

Surang Rattanapan Literature Reviews / 10 seven narrow bands. Six bands have improved spatial (ground) resolution of 30 meter

and Band 6 (thermal infrared) has a resolution of 120 meter. Each of which has

difference specific object visibility as shown in Table2-1.

Table 2-1 Thematic Map Spectral Band

Band

No

Description

Spectral Feature and Applications

1 Blue 0.45-0.52 Good water penetration, strong chlorophyll absorption.

Mapping of coastal water areas. Differentiation between soil

and vegetation.

2 Green 0.52-0.60 Matches green reflectance peak of healthy vegetation; Sensing

the health of vegetation.

3 Red 0.63-0.69 Chlorophyll absorption band, very strong vegetation

absorption. Differentiation between plant species thanks to

the chlorophyll absorption assessment.

4 Near-IR 0.76-0.90 Complete absorption by water; High land/water contrast, very

strong vegetation reflectance. Survey water body delineation.

5 Near-middle

infrared

1.55-1.75 Very moisture sensitive. Differentiation between clouds

and snow cover. Measurement of vegetation moisture and soil

moisture; Reflectance of most rock surfaces.

6 Thermal IR 10.40-

12.50

Thermal imaging and mapping. Information on plant heat stress.

Thermal data on geologic information.

7

Middle

Infrared

2.08-2.35

Good geological discrimination. Hydrothermal mapping. Rock

type discriminations (Mineral and petroleum).

Source: Lillesand et al., (12)

2.4 Accuracy Assessment

Accuracy assessment is the procedure used to quantify the reliability of a

classified image and use to make sure the model reacts as expected (14). The result

from the prediction was compared to the ground truth and the maximum likelihood

classification of 2005 and assume that the maximum likelihood produce a completely

correct result.


An error matrix is way to effectively compare two maps quantitatively. Its

consists of a square array of numbers set out in row and columns that express the

number of sample units assigned to each land use type as compared to what is on the

actual ground. The comparison was done by using the error matrix and Kappa Index to

identify overall accuracy of model as shown in Table 3.2.

Table 2-2 Error matrix

Ground truth data 1 2 3 r Row Total

1 n11 n12 n13 n1k n1+

2 n21 n22 n23 n2k n2+

3

n31 n32 n33 n3k

n3+

k nk1 nk2 nk3 nkn nk+ Pred

ictio

n da

ta

Column Total n+1 n +2 n+3 n+k n

Sources: Congalton and Green, 1999

Overall Accuracy =

Procedure’s Accuracy =

User’s Accuracy =

Overall Kappa =

When N = Total number of observations in error matrix,

k = Total of rows, columns in error matrix,

nii = The number of observations in row i column i

ni+ = Total of observations in row i (right of matrix),

n+i = Total of observations in column i (bottom of the matrix)

i

ii

nn

∑

∑ ∑

=++

= =++

⋅−

⋅−

k

iii

k

i

k

iiiii

nnN

nnnN

1

2

1 1

)(

)(

j

jj

nn

+

n

nk

iii∑

=1

Surang Rattanapan Literature Reviews / 12 2.5 Markov Chain

Markov Chain (15) is mathematics model described each of change process

by use i.e., land use data in the past and present for prediction trend of land use change

in the future.

The Markov Chain was use to analyze the probability of each land use

patterns. The probability of moving from one state j to another state k is called a

transition probability, Pjk, and it is given for every ordered set of states. These

probabilities can be represented in the form of a transition matrix P.

When (Vj) × (Pjk) = Proportion of Land use of Second date.

Pjk = F (Land use human activities).

= Matrix of probability of land use change (Matrix).

Vj = Proportion of land use of first date (Vector).

J = Type of land use in first date.

K = Type of land use in Second date.

2.6 Cellular Automata (CA)

CA was originally conceived by Ulam and Von Neumann in the 1940s to

provide a formal framework for investigating the behaviors of complex, extended

systems.

A CA (16) is an array of identically programmed automata, or cells which

interact with one another in a neighborhood and have definite state.

P11, P12, P13… P1m P21, P22, P23… P2m

:

V1, V2, V3 …Vn Vj × Pjk =


Michael (17) said that CA operationalzed to model life itself as part of the

“Game of life”. Data models are also based on the uniform grid cell tessellation of

geographic space. The CA incorporate an explicit set of transition rules, defined by the

modeler and designed to allow for dynamic modeling. CA have been the focus of great

attention over the years because of their ability to generate a rich spectrum of very

complex patterns of behavior out of sets of relatively simple underlying rules.

Moreover, they appear to capture many essential features of complex self-organizing

cooperative behavior observed in real systems.

2.6.1 Rules of the Game of Life

CA (18) has been used as models in many areas of Physical Sciences,

Biology and Mathematics. As well as social sciences, one of the simplest examples of

CA is Conway’s Game of Life, for example can be implemented in such a way that the

automata live or die according to the following criteria.

1. A dead cell with exactly three live neighbors becomes a live cell.

2. A live cell with two or three live neighbors stays alive.

3. In all other cases, a cell dies or remains dead.

2.6.2 Component of Cellular Automata

Lattice (Cell space)

The cell space is composed of individual cell. Theoretically, these cells

may be in any geometric shape. Yet, most CA adopts regular grids to represent such

space, which make CA very similar to a raster GIS.

Surang Rattanapan Literature Reviews / 14

Cell States

The states of each cell may represent any spatial variable, the various

types of land use. The states that change with respect to time are considered as

functional for example population and land use. The cells have just two states: alive

and dead.

Time

A CA will evolve at a sequence of discrete time steps. At each step, the

cells will be updated simultaneously based on transition rules.

Transition rules

In CA transition rules are deterministic and unchanged during

evolution. Enforcing the rule in coordinating with neighborhood affects the state in

new generation direc

Neighborhoods

There are two types of neighborhoods in conventional CA. Each cell

has two neighbors in one-dimensional CA. Where as in two dimensional CA model

there are two ways to define it. Von Neumann has considered four neighboring cells as

neighbors. Moore considered eight neighboring cells as neighbors as shown in Figure

2-2.

Von Neumann Neighborhood Moore Neighborhood

Figure 2-2 Von Neumann Neighborhoods and Moore Neighborhood

CA models are also based on the uniform grid cell of geographic space.

And like the simple raster GIS model (19)

The following Figure 2.3 shows the comparison between the basic CA

and CA adopted in GIS. The left side is characteristics of basic CA and right side is

modification in these characteristics of CA adopted in GIS.


Figure 2-3 Comparison between the basic Cellular Automata and Cellular Automata

adopted in GIS

Note: the figure is taken from Rinaldi (19)

Surang Rattanapan Literature Reviews / 16 2.7 Related Researches

Jinn-Guey Lay (20) adapted the spatial evolution concept embedded in CA

and applied it to land use change study in Tansui Watershed. Digital land use data of

two separate years (1971 and 1977) were complied and analyzed using GIS software.

The CA can help to reveal the complex process of land use change, as shown in the

various changes result from a same pattern of neighborhood. Such finding indicates

that a deterministic approach of CA may not fit with real world situation. The purpose

of this analysis is to identify the relationship between land use change and surrounding

environment. Finding from this analysis may reveal the dynamic process of land use

change and thus enhance our understanding on transition rules, the heart of a CA.

Joseph (21) studied about modeling land use and land cover dynamics in the

Ecuadorian Amazon were determined using a time-series of remotely sensed data

using an experimental classification scheme resulting in a time-series data set

including land use and land cover images for 1973, 1986, 1989, 1996, and 1999. The

model works by simulating the present by extrapolating from the past using the image

time-series, validating the simulations the remotely sensed time-series of past

conditions and through field observations of current conditions, allowing the model to

iterate to the year 2010.

Sura (22) applied Markov Chain Model and Cellular Automata by choosing

satellite imageries in 1989 and 2003 to classify coastal land use patterns of Krabi

province and predict land use change for the year 2013, based on the data of 2003. The

result shows that, from 1989 to 2003, the area of Para rubber plantation, mangrove

forest, and tropical rain forest are obviously decreased. Meanwhile, others area, oil

palm plantation, and opened area are obviously increased.

Noppon (23) was integrated a cellular automata model with remote sensing to

keep track of land use change of Ban Laem District, Phetchaburi Province between

1994 and 2004. The model focuses on three categories of the land use i.e., existing

mangrove, human community and activities, degraded or regenerated mangrove area.

A principle rule of cellular automata is that all the states can be switched to another

state by considering their surrounding environments, transition rules of Moor’s

neighborhood are applied to each cell (pixel) in each year. The results of the prediction


are compared to a maximum likelihood classification to test the model. Results of the

study showed that the CA model produces an overall accuracy of 91.07%. However,

the category mangrove area only showed 42.98 % prediction accuracy.

Usawadee (24) applied Geo-Informatics integrated with Markov Chain and

Cellular Automata (CA-Markov) to classify and predict land use of Krabi Province in

2004 by using land use in 1990 and 2000 as base for calculating. The accuracy of

prediction model was operated using the prediction land use map to compare with land

use and was classified using the hybrid classification process. Overall accuracy of the

prediction model was 74.83% and Kappa Index was 0.629.

Surang Rattanapan Materials and Methods / 18

CHAPTER III

MATERIALS AND METHODS 3.1 Materials

The hardware and software utilized in this study are as the following.

3.1.1. Hardware

1. Notebook computer

- CPU Intel Pentium M 1.6 GHz

- 512 Mb SDRAM.

- Hard disk 60 GHz.

2. Printer

3. Camera

3.1.2. Software

1. Operation System: Microsoft Windows XP

2. GIS software: Arc View GIS 3.3

3. ERDAS IMAGIN 8.7

4. ARCGIS 9.1

5. IDRISI 3.2

3.2 Study design

The study combines five main procedures including: land use classification,

land use change detection, prediction on land use change, accuracy assessment and

trend of land use change based on socio-economic and policy aspect as shown in

Figure 3-1.


Figure3.1 The study design diagram


3.2.1 Land Use Classification

In this study, image processing was conducted in five steps as image

import, preprocessing, image enhancement and nomenclature, image classification,

and image post analysis.

3.2.1.1 Image import

Landsat Thematic Mapper data of path/row131/047 and

131/046 of 1990, 2000 and 2005 was used as data based for the land use classification

scheme. A category of the secondary data including spatial data and non spatial data as

shown in Table 3-1, Figure 3-2, Figure 3-3, and Figure 3-4.

Table 3-1 Data sources for the study

RASTER IMAGE

Data Path/Row Bands Resolution Date Source

Landsat -5 (TM)

131/047 and 131/046 1-5,7 25 m 1990 GISTDA*

Landsat- 5 (TM)

131/047 and 131/046 1-5,7 25 m 2000 GISTDA*

Landsat- 5 (TM)

131/047 and 131/046

1-5,7

25 m 2005

GISTDA*

VECTER DATA

Data Type Scale Date Source

Political boundary Polygon

1 : 50,000 No reference

DEQP**

Remark *GISTDA (Geo-Informatics and Space Technology Development Agency (Public Organization)),

**DEQP (Department of Environment Quality Promotion)


Figure 3-2 False color composite image of Landsat band 4-5-3 (R-G-B) showing

Mae Taeng Watershed in 1990

Source: LANDSAT imageries analysis


3.2.1.2 Pre-processing

The preprocessing state removes systematic errors from the

data. The operations are the correction of radiometric and geometric errors, namely

calibration of the detected signal and registration of the image data to true surface

position. In geometric correction of this study, the co-register by image to image

method was utilized. The image was rectified to common Universal Transverse

Mercator coordinate system (UTM) zone 47, Everest spheroid and Indian 1975 datum.

3.2.1.3 Image Enhancement and Nomenclature

The aim of image enhancement to improvement to the

image, can be divided a variety of land cover objects such as vegetation water that

operate on individual pixels without reference to their spatial context, and that also

make use of spatial information. Normally, each of which is repeatable by the

wavelength reflectance. The first type can generally be referred to as contrast

modification, and the second as spatial filtering and nomenclature was determination

of image classification frame. It was classified into nine categories as shown in Table

3-2.

Table 3-2 Nomenclature of land use pattern of Mae Taeng Watershed

Type Code Land use pattern

1 1 Evergreen forest

2 2 Deciduous forest

3 3 Forest plantation

4 4 Paddy field

5 5 Field crop

6 6 Perennial and orchard

7 7 Urban and built up land

8 8 Water body

9

9

Disturbed forest


3.2.1.4 Image Classification

Image classification is the process of making quantitative

decision from image data, grouping pixels of the image into classes to represent

different physical object. The procedures of the classification consist of unsupervised

classification and supervised classification.

Unsupervised classification

Unsupervised classification is performed using algorithm

called the Iterative Self-Organizing Data Analysis Technique (ISODATA). I

performed an unsupervised classification with 30 clusters.

Supervised classification

The supervised classification performed by the method of

Maximum likelihood is to delineate a given pixel to the class that generated from the

spectral signature analysis. For avoiding bias, each training area was not least than 30

pixels and distributed around study area. In this study, it was classified nine land use

categories. The random recheck by field observation convincing the correct

classification.

3.2.1.5 Image Post Classification

Image post classification was required to correct the

classification manually. A mistake of interpretation by supervised classification was

eliminated in this step. Previous classifications were used only digital number (DN)

with statistic analysis for classification group of data. It used to regroup a cluster to

our interested categories. In this study, according to the characteristic of land cover in

Mae Taeng Watershed, it can be classified land use pattern into nine categories.

3.2.1.6 Classification Accuracy Classification Accuracy was calculated by confusion matrix

accuracy method. That was comparing classification result with ground truth.

Numbers of check points were calculated by binomial probability theory. (Equation 3-

1)(Clark University, 2003). Then correcting accuracy was not less than 80%.


2~))((2

E

qpZN = (Equation 3-1)

When N = Number of samples

Z = the standard score required for the desired level of

confidence in the assessment (Z= 1.96 for 95%

confidence, Z= 1.86 for 80% confidence)

P = Expected or calculated accuracy (in percentage)

q = 100-p

E = Allowable error

3.2.2 Land Use Changes Detection

The land use changes detection of Mae Taeng Watershed, from 1990 to

2000 and 2000 to 2005, were analyzed using cross-tabulation analysis. The land use

changes area was also identified.

3.2.3 Prediction on Land Use Changes

The land use data in 1990 and 2000 were basely used to identify the

prediction of land use changes in 2005, 2010. And use the land use data in 2000 and

2005 were basely used to identify the prediction of land use changes in 2010 for

compared the prediction of land use change in 2010 interval 10 and 5 years, as

following;

3.2.3.1 Prediction using Markov Chain Model

The land use data in 1990 and 2000 were manipulated by

Markov Chain Model in order to identify the probability and transition values of

changing, which might be occurred in 2005 and 2010.

And the land use data in 2000 and 2005 were manipulated by

Markov Chain Model in order to identify the probability and transition values of

changing, which might be occurred in 2010.

3.2.3.2. Prediction using Cellular Automata Model

The probability and transition values of change from Markov

Chain manipulation were then utilized by 5×5 cellular matrix analysis. According to


this matter, each pixel could be identified for their own class. Thus, the prediction land

use map in 2005 and 2010 (based on land use data in1990 and 2000) and 2010 (based

on land use data in 2000 and 2005) was then be classified.

3.2.4 Prediction Accuracy Assessment

An error matrix is a way to effectively compared two maps

quantitatively. The comparison was done by using the error matrix and Kappa Index to

identify overall accuracy of model. Land use map (by model) in 2005 was used to

make sure the model reacts as expected. The result from the prediction was compared

to the maximum likelihood classification of 2005 by using pairwise comparison. It

was assumed that the maximum likelihood classification produced a completely

correct result. And the accuracy of the prediction in 2010 was compared by the

prediction land use map 2010 based on land use data in1990, 2000 and 2000, 2005.

The comparison was done by using the accuracy assessment. The error

matrix and Kappa Index were used to identify overall accuracy of model in this study.

3.2.5 Trend of Land Use Change based on Socio-Economic and Policy

Aspect

The predicting model and map of land use in 2010 is sometime not

adequate for suitable future land use planning although the result model had been

verified. Thus, trend of land use change based on socio-economic and policy

aspect,was also analysis using in-depth interviewing method.

The sample was drawn from local key informant that are;

1. Village leader (deputy of the group of people utilizing water from

Mae Taeng Watershed in agricultural part).

2. Other stakeholder such as NGOs (member of committee of the

federation of northern agriculturalist (network of the northern people organization) and

chairman of the deputy of people in natural resources and environmental part of

Chiang Mai Province.

Surang Rattanapan Results / 28

CHAPTER IV

RESULTS

4.1 Land use classification of Mae Taeng Watershed

The result of land use pattern classification from Landsat imageries in 1990,

2000, 2005 can be briefly identified as following;

It was found that land use pattern in 1990 were mainly classified as evergreen

forest with the area of 1,483.19 square kilometers or 75.96 %. Meanwhile the other

were included deciduous forest, disturbed forest, field crop, paddy field, perennial and

orchard, urban and built up land, forest plantation and water body with the area of

150.35, 141.47, 103.82, 50.48, 10.14, 9.04, 3.63 and 0.50 square kilometers,

respectively as shown in Table 4-1and Figure 4-1.

The land use pattern in 2000 were mainly classified as evergreen forest with

the area of 1,259.16 square kilometers or 64.49 %. Meanwhile the other were included

field crop, disturbed forest, deciduous forest, perennial and orchard, forest plantation,

paddy field, urban and built up land and water body with the area of 228.20, 165.69,

128.47, 59.64, 46.21, 38.34, 25.50 and 1.41 square kilometers, respectively as shown

in Table 4-1 and Figure 4-2.

The land use pattern in 2005 were mainly classified as evergreen forest with

the area of 1,182.33 square kilometers or 60.55 %. Meanwhile the other were included

field crop, disturbed forest, deciduous forest, forest plantation, paddy field, perennial

and orchard, urban and built up land and water body with the area of 310.94, 138.77,


in Table 4-1 and Figure 4-3.


Table 4-1 Land use pattern of Mae Taeng Watershed

Year 1990 Year 2000 Year 2005 Land use pattern

km² % km² % km² %

Evergreen forest 1,483.19 75.96 1,259.16 64.49 1,182.33 60.55

Deciduous forest 150.35 7.70 128.47 6.58 122.57 6.28

Forest plantation 3.63 0.19 46.21 2.37 72.34 3.70

Paddy field 50.48 2.59 38.34 1.96 56.25 2.89

Field crop 103.82 5.32 228.20 11.69 310.94 15.92

Perennial and orchard 10.14 0.52 59.64 3.05 45.40 2.32

Urban and built up 9.04 0.46 25.50 1.31 22.90 1.17

Water body 0.50 0.03 1.41 0.07 1.12 0.06

Disturbed forest 141.47 7.25 165.69 8.49 138.77 7.11

Total area (km²)

1,952.62 100.00 1,952.62 100.00

1,952.62 100

Source: LANDSAT imageries analysis and ground survey

The accuracy of the interpretation was clarified using number of 62 ground

check point in 2005. Thus, the accuracy of land use classification identified in 2005 is

80.64 % as shown in Table 4-2.

The overall accuracy of interpretation in 2005 it was found that the overall

accuracy was 80.64 % and the Kappa index was 0.74. The missing accuracy was from

interpretation, land use changes influenced by economic such as the higher price of

agriculture product. The expended of continuous agriculture area that it’s depend on

the increasing type of product and Mae Taeng Watershed has been affected from

natural disaster such as flood disaster and landslide. The disaster affect and economic

growth are a factor of the land use change.

Surang Rattanapan Results / 30 Table 4-2 Accuracy analysis of land use classification of Mae Taeng Watershed

Reference Data

Cla

ss1

Cla

ss2

Cla

ss3

Cla

ss4

Cla

ss5

Cla

ss6

Cla

ss7

Cla

ss8

Cla

ss9

Total area

Class1 25 2 - 1 - - - - 3 31 Class2 - 5 - - - - - - - 5 Class3 - - 3 - - - - - 1 4 Class4 - - - 2 - - - - - 2 Class5 - - - 2 8 - - - - 10 Class6 - - - - 1 2 - - - 3 Class7 - - - - - - 2 - - 2 Class8 - - - - - - - 1 - 1 C

lass

ifica

tion

Dat

a in

200

5

Class9

- - - - 2 - - - 2

4 Total area 25 7 3 5 11 2 2 1 6 62

Source: Ground check point in 2005

Remark: Class 1 Evergreen forest

Class 2 Deciduous forest

Class 3 Forest plantation

Class 4 Paddy field

Class 5 Field crop

Class 6 Perennial and orchard

Class 7 Urban and built up land

Class 8 Water body

Class 9 Disturbed forest

Overall accuracy = 80.64 % Overall Kappa = 0.74


Figure 4-1 Land use pattern of Mae Taeng Watershed in 1990


Surang Rattanapan Results / 34 4.2 Land Use Change Detection

The land use changes differentiations were manipulated using cross-

tabulation analysis. The result can be identified as following.

4.2.1 Land use pattern change between 1990 and 2000

The comparison of land use change between 1990 and 2000 was

analyzed using cross-tabulation analysis. It was found that land use patterns which

most gradually increased was field crop with the area of 124.39 square kilometers was

maintained into field crop and changed into perennial and orchard, evergreen forest,

disturbed forest, urban and built up land, forest plantation, paddy field, deciduous

forest and water body with the area of 39.95, 23.46, 21.29, 6.93, 5.08, 3.08, 2.69, 1.18

and 0.19 square kilometers, respectively. Meanwhile, land use pattern which most

gradually decreased was evergreen forest with the area of 224.09 square kilometers

was maintained into evergreen forest and changed into field crop, disturbed forest,

forest plantation, perennial and orchard, deciduous forest, paddy field, urban and built

up land and water body with the area of 1,168.26, 136.23, 111.99, 33.98, 12.66, 8.52,

6.55, 4.23 and 0.76 square kilometers, respectively. The differentiations of each class

tended to be the other as shown in Table 4-3 and Figure 4-4.

Land use change between 1990 and 2000. It was found that forest

became disturbed forest and then the disturbed forest continuously became into

agricultural. The cultivations are only in wet season and the area was ignored in dry

season. There is burning of weed without fire control before the cultivated season, so

the fire invaded the forest area. This makes continuously increasing of invaded area. A

lot of small invaded areas has been linked together then become a larger area. Most of

people in the area earn a living in agriculture especially in field crop such as rice

farming, corn, bean, red bean and garlic. The expanding of cultivation area was

because of the higher price of agriculture product.


Table 4-3 Land use changes of Mae Taeng Watershed between 1990 and 2000 based on

cross-tabulation analysis

LAND USE 2000

Cla

ss1

Cla

ss2

Cla

ss3

Cla

ss4

Cla

ss5

Cla

ss6

Cla

ss7

Cla

ss8

Cla

ss9

Tot

al

area

Class1 1,168.26 8.52 33.98 6.55 136.23 12.66 4.23 0.76 111.99 1,483.18 Class2 10.67 115.34 0.00 0.66 5.68 3.64 0.07 0.11 14.12 150.29 Class3 0.10 0.00 3.01 0.00 0.30 0.12 0.00 0.09 0.00 3.62 Class4 7.20 0.82 0.00 23.91 6.94 5.91 5.38 0.00 0.29 50.45 Class5 21.29 1.18 3.08 2.69 39.95 23.46 5.08 0.19 6.93 103.85 Class6 0.20 0.63 0.00 0.63 1.43 5.72 1.39 0.00 0.15 10.15 Class7 0.03 0.01 0.19 1.10 0.11 0.76 6.88 0.00 0.00 9.08 Class8 0.12 0.04 0.00 0.00 0.10 0.00 0.01 0.23 0.00 0.50

LA

ND

USE

199

0

Class9

51.22 1.96 5.95 2.78 37.50 7.37 2.47 0.02 32.23 141.50 Total area 1,259.09 128.50 46.21 38.32 228.24 59.64 25.51 1.40 165.71 1,952.62 Total area

change -224.09 -21.79 42.59 -12.13 124.39 49.49 16.43 0.90 24.21 512.02

Source: Data analysis

Remark: 1. Area unit is square kilometer

2. Positive change = increasing aspect

Negative change = decreasing aspect

3. Row are classes from 1990 image

Column are classes from 2000 image

4. Class 1 Evergreen forest



Class 4 Paddy field

Class 5 Field crop



Class 8 Water body



Figure 4-4 Land use changes of Mae Taeng Watershed between 1990 and 2000

Source: Analysis


Legend

When

Class 1 Evergreen forest



Class 4 Paddy field

Class 5 Field crop


Class 7 Urban and built up area

Class 8 Water body


Figure 4-4 (Cont.) Legend of Land use changes of Mae Taeng Watershed between

1990 and 2000


4.2.2 Land use pattern change between 2000 and 2005

The comparison of land use change between 2000 and 2005 was

analyzed using cross-tabulation analysis. It was found that land use pattern which most

gradually increased was field crop with the area of 82.78 square kilometers was

maintained into field crop and changed into evergreen forest, disturbed forest, paddy

field, forest plantation, perennial and orchard, urban and built up land and deciduous

forest with the area of 191.17, 12.92, 7.37, 7.10, 6.19, 1.71, 1.66 and 0.10 square

kilometers, respectively. Meanwhile, land use patterns which most gradually

decreased was evergreen forest with the area of 76.76 square kilometers was

maintained into evergreen forest and changed into field crop, disturbed forest, forest

plantation, paddy field, perennial and orchard and urban and built up land with the

area of 1,119.74, 66.47, 51.70, 13.35, 5.53, 1.24 and 1.06 square kilometers,

respectively. The differentiations of each class tended to be the other as shown in

Table 4-4 and Figure 4-5

Trend of land use change between 2000 and 2005. It was found that

forest became disturbed forest and then the disturbed forest continuously became into

agricultural and turned into wastelands agree with land use change between 1990 and

2000 but its have different trend . It was found that disturbed forest was trend to

decrease since the policy of the Department of Forestry. However, the Department of

Forestry has been set up a policy of pinery forest plantation to compensate the

disturbed forest. This caused more forest plantation area in 2005.

The predicted land use map in 2010 based on land use data in 1990 and

2000 using CA_Markov Model. But land use change between 2000 and 2005 has

another factor influence to trend of land use change of 5 years. Thus, limitation of

changed period of the model there would be any exterior factor influenced to land use

change in the future. The prediction of model base on difference data by comparison

of 10-year database (land use data in 1990 and 2000) and 5-year database (land use

data in 2000-2005) for prediction land use change in 2010 of the CA_Markov Model.


Table 4-4 Land use changes of Mae Taeng Watershed between 2000 and 2005 based on

cross-tabulation analysis

LAND USE 2005

Cla

ss1

Cla

ss2

Cla

ss3

Cla

ss4

Cla

ss5

Cla

ss6

Cla

ss7

Cla

ss8

Cla

ss9

Tot

al

area

Class1 1,119.74 0.00 13.35 5.53 66.47 1.24 1.06 0.00 51.70 1,259.09 Class2 0.93 114.38 0.00 0.43 6.33 1.29 0.22 0.00 4.92 128.50 Class3 0.21 0.00 37.51 0.00 4.34 0.02 0.17 0.00 3.96 46.21 Class4 0.07 0.00 0.00 32.41 3.10 0.45 1.90 0.00 0.42 38.35 Class5 12.92 0.10 6.19 7.10 191.17 1.71 1.66 0.00 7.37 228.22 Class6 0.40 0.00 2.42 6.00 9.34 38.17 1.31 0.27 1.73 59.64 Class7 0.59 0.00 0.00 4.27 2.95 1.12 16.47 0.00 0.11 25.51 Class8 0.00 0.00 0.04 0.00 0.42 0.11 0.00 0.84 0.00 1.41

LA

ND

USE

200

0

Class9

47.47 8.06 12.81 0.51 26.90 1.30 0.11 0.00 68.53 165.69 Total area 1,182.33 122.54 72.32 56.25 311.02 45.41 22.90 1.11 138.74 1,952.62 Total area

change -76.76 -5.96 26.11 17.90 82.80 -14.23 -2.61 -0.30 -26.95 253.62

Source: Data analysis

Remark: 1. Area unit is square kilometer

2. Positive change = increasing aspect

Negative change = decreasing aspect

3. Row are classes from 2000 image





Class 4 Paddy field

Class 5 Field crop



Class 8 Water body



Figure 4-5 Land use changes of Mae Taeng Watershed between 2000 and 2005

Source: Analysis


Legend

When

Class 1 Evergreen forest



Class 4 Paddy field

Class 5 Field crop


Class 7 Urban and built up area

Class 8 Water body


Figure 4-5 (Cont.) Legend of land use changes of Mae Taeng Watershed between

2000 and 2005

Surang Rattanapan Results / 42 4.3 Prediction of Land Use Change

4.3.1 Prediction using Markov Chain Model

4.3.1.2 The predicted using Markov Chain Model based on

land use data in 1990 and 2000


Markov Chain Model in order to identify the probability of changing and transition

area as shown in Table 4-5 and Table 4-6.

Table 4-5 Probability of changing between 1990 and 2000 based on Markov Chain

Analysis

LAND USE 2005

Cla

ss1

Cla

ss2

Cla

ss3

Cla

ss4

Cla

ss5

Cla

ss6

Cla

ss7

Cla

ss8

Cla

ss 9

Class 1 0.7326 0.0055 0.0206 0.0030 0.1143 0.0000 0.0000 0.0001 0.1238

Class 2 0.0456 0.7403 0.0000 0.0030 0.0204 0.0236 0.0000 0.0012 0.1658

Class 3 0.0158 0.0000 0.7726 0.0000 0.1391 0.0294 0.0000 0.0431 0.0000

Class 4 0.1147 0.0100 0.0000 0.5392 0.1507 0.0975 0.0878 0.0000 0.0000

Class 5 0.1768 0.0001 0.0234 0.0155 0.4303 0.2465 0.0250 0.0017 0.0806

Class 6 0.0000 0.0571 0.0000 0.0578 0.1653 0.5917 0.1216 0.0000 0.0065

Class 7 0.0000 0.0000 0.0222 0.1577 0.0000 0.0922 0.7279 0.0000 0.0000

Class 8 0.1653 0.0685 0.0000 0.0000 0.2144 0.0000 0.0133 0.5385 0.0000

LA

ND

USE

200

0

Class 9

0.3255 0.0102 0.0316 0.0162 0.3209 0.0089 0.0058 0.0000 0.2809

Source: Markov Chain Analysis

Remark: 1. Row are classes from 2000 image


2. Class 1 Evergreen forest Class 6 Perennial and orchard

Class 2 Deciduous forest Class 7 Urban and built up land

Class 3 Forest plantation Class 8 Water body

Class 4 Paddy field Class 9 Disturbed forest

Class 5 Field crop


Table 4-6 Transition area between 1990 and 2000 based on Markov Chain Analysis

LAND USE 2005

Cla

ss1

Cla

ss2

Cla

ss3

Cla

ss4

Cla

ss5

Cla

ss6

Cla

ss7

Cla

ss8

Cla

ss 9

Class 1 1475785 11039 41530 6087 230306 0 0 302 249480

Class 2 9375 152207 0 622 4199 4861 0 243 34091

Class 3 1168 0 57118 0 10281 2175 0 3187 0

Class 4 7038 614 0 33080 9245 5981 5387 0 0

Class 5 64566 42 8555 5664 157135 90019 9147 618 29430

Class 6 0 5447 0 5515 15772 56462 11607 0 616

Class 7 0 0 905 6436 0 3764 29701 0 0

Class 8 371 154 0 0 482 0 30 1210 0

LA

ND

USE

200

0

Class 9

86297 2693 8380 4304 85064 2362 1531 0 74476




2. Data analysis based on raster format




Class 4 Paddy field

Class 5 Field crop



Class 8 Water body


Surang Rattanapan Results / 44 4.3.1.2 The predicted using Markov Chain Model based on land use data in 2000 and 2005


Markov Chain Model in order to identify the probability of changing and transition

area as shown in Table 4-7 and Table 4-8.

Table 4-7 Probability of changing between 2000 and 2005 based on Markov Chain

Analysis

LAND USE 2010

Cla

ss1

Cla

ss2

Cla

ss3

Cla

ss4

Cla

ss5

Cla

ss6

Cla

ss7

Cla

ss8

Cla

ss 9

Class 1 0.7559 0.0000 0.0234 0.0097 0.1164 0.0022 0.0018 0.0000 0.0906

Class 2 0.0162 0.7566 0.0000 0.0074 0.1091 0.0222 0.0037 0.0000 0.0848

Class 3 0.0074 0.0000 0.6900 0.0000 0.1546 0.0007 0.0060 0.0000 0.1413

Class 4 0.0034 0.0000 0.0000 0.7185 0.1469 0.0213 0.0903 0.0000 0.0197

Class 5 0.1004 0.0010 0.0481 0.0551 0.7120 0.0133 0.0129 0.0000 0.0573

Class 6 0.0085 0.0000 0.0513 0.1275 0.1983 0.5440 0.0279 0.0058 0.0367

Class 7 0.0294 0.0000 0.0000 0.2130 0.1472 0.0559 0.5489 0.0000 0.0055

Class 8 0.0000 0.0000 0.0316 0.0000 0.3667 0.0941 0.0000 0.5077 0.0000

LA

ND

USE

200

5

Class 9

0.3168 0.0538 0.0855 0.0034 0.1795 0.0087 0.0007 0.0000 0.3516







Class 4 Paddy field

Class 5 Field crop



Class 8 Water body



Table 4-8 Transition area between 2000 and 2005 based on Markov Chain

Analysis

LAND USE 2010

C

lass

1

Cla

ss2

Cla

ss3

Cla

ss4

Cla

ss5

Cla

ss6

Cla

ss7

Cla

ss8

Cla

ss 9

Class 1 1430002 12 44230 18336 220241 4100 3498 0 171297

Class 2 3172 148386 0 1450 21389 4364 731 0 16628

Class 3 853 0 79832 0 17883 85 691 0 16347

Class 4 302 0 0 64655 13216 1919 8126 3 1772

Class 5 49960 493 23921 27438 354289 6599 6410 0 28504

Class 6 616 0 3729 9261 14405 39515 2024 423 2664

Class 7 1078 0 0 7801 5393 2049 20107 0 201

Class 8 0 0 56 0 653 168 0 905 0

LA

ND

USE

200

5

Class 9

70316 11943 18975 759 39855 1922 164 0 78040




2. Data analysis based on raster format




Class 4 Paddy field

Class 5 Field crop



Class 8 Water body



4.3.2 Prediction using Cellular Automata Model

The probability and transition values of change from 4.3.1 by Markov

manipulation were then utilized by 5×5 cellular matrix analysis. According to this

matter, each pixel could be identified for their own class. Thus, the prediction land use

map in 2005 and 2010 was then be classified.

4.3.2.1 The predicted land use map in 2005 based on land

use data in 1990 and 2000

It was found that the land use pattern in 2005 based on land

use data in 1990 and 2000 were mainly classified as evergreen forest with the area of

1,047.73 square kilometers or 53.66 %. Meanwhile the other were included field crop,

disturbed forest, deciduous forest, perennial and orchard, forest plantation, paddy

field, urban and built up land, and water body with the area of 321.22, 243.73, 106.67,


in Table 4-9, Figure 4-6.

Table 4-9 Prediction land use pattern of Mae Taeng Watershed in 2005 based on land


Land use pattern Area Square Kilometers % of total area

Evergreen forest 1,047.73 53.66 Deciduous forest 106.67 5.46 Forest plantation 54.91 2.81 Paddy field 37.41 1.92 Field crop 321.22 16.45 Perennial and orchard 102.75 5.26 Urban and built up land 35.08 1.80 Water body 3.12 0.16 Disturbed forest 243.73 12.48 Total area 1,952.62 100.00 Source: Calculated


Figure 4-6 Prediction land use pattern of Mae Taeng Watershed in 2005 based on land


Source: CA-Markov model


When compared the predicted land use map and ground

truthing map, it was found that the predicted area lower than ground truthing area were

included evergreen forest, paddy field, forest plantation and deciduous forest with the

area of 134.6, 18.84, 17.43 and 15.90 square kilometers, respectively. Meanwhile, the

predicted area higher than ground truthing area were included disturbed forest,

perennial and orchard, urban and built up land, field crop and water body with the

area of 104.96, 57.35, 12.18, 10.28 and 2.00 square kilometers, respectively as shown

in Table 4-10.

Table 4-10 Comparison between land use map (ground truth) and prediction land use

map (CA_Markov) in 2005

Area Square Kilometers Land use pattern 2005

(ground truthing) 2005

(CA_Markov) Different area

Evergreen forest 1,182.33 1,047.73 -134.60 Deciduous forest 122.57 106.67 -15.90 Forest plantation 72.34 54.91 -17.43 Paddy field 56.25 37.41 -18.84 Field crop 310.94 321.22 10.28 Perennial and orchard 45.4 102.75 57.35 Urban and built up land 22.9 35.08 12.18 Water body 1.12 3.12 2.00 Disturbed forest 138.77 243.73 104.96 Total area

1,952.62 1,952.62

373.54 Source: Calculated


4.3.2.2 The predicted land use map in 2010 based on land use data

in 1990 and 2000

It was found that the land use pattern in 2010 based on land

use data in 1990 and 2000 were mainly classified as evergreen forest with the area of

988.99 square kilometers or 50.65%. Meanwhile the other were included field crop,

disturbed forest, perennial and orchard, deciduous forest, forest plantation, urban and

built up land, paddy field and water body with the area of 331.44, 216.89, 125.83,

105.72, 89.79, 46.74, 43.88 and 3.34 square kilometers, respectively as shown in

Table 4-11, Figure 4-7.

Table 4-11 Prediction land use pattern of Mae Taeng Watershed in 2010 based on


Land use pattern Area Square Kilometers % of total area Evergreen forest 988.99 50.65 Deciduous forest 105.72 5.41 Forest plantation 89.79 4.60 Paddy field 43.88 2.25 Field crop 331.44 16.97 Perennial and orchard 125.83 6.44 Urban and built up land 46.74 2.39 Water body 3.34 0.17 Disturbed forest 216.89 11.11 Total area

1,952.62 100.00 Source: Calculated


4.3.2.3 The predicted land use map in 2010 based on land use data

in 2000 and 2005

To check the efficiency and limitation of changed period of the

model there would be any exterior factor influenced to land use change in 2010 of the

CA_Markov Model. The comparison of 10-year database (land use data in 1990 and

2000) and 5-year database (land use data in 2000 and 2005).

It was found that the land use pattern in 2010 base on land use

data in 2000 and 2005 were mainly classified as evergreen forest with the area of

990.86 square kilometers or 50.75%. Meanwhile the other were included field crop,

disturbed forest, deciduous forest, forest plantation , paddy field , perennial and

orchard, urban and built up land and water body with the area of 429.75, 195.90,


in Table 4-12, Figure 4-8.

Table 4-12 Prediction land use pattern of Mae Taeng Watershed in 2010 based on


Land use pattern Area Square Kilometers % of total area Evergreen forest 990.86 50.75 Deciduous forest 101.20 5.18 Forest plantation 96.02 4.92 Paddy field 79.03 4.05 Field crop 429.75 22.01 Perennial and orchard 36.17 1.85 Urban and built up land 23.04 1.18 Water body 0.65 0.03 Disturbed forest 195.90 10.03 Total area

1,952.62 100.00 Source: Calculated


When compared the predicted land use map in 2010 based on

land use data in 1990 and 2000 (interval 10 years) and the predicted land use map in

2010 based on land use data in 2000 and 2005 (interval 5 years), it was found that the

predicted area interval 5 years lower than the predicted area interval 10 years were

perennial and orchard, urban and built up land, disturbed forest, deciduous forest and

water body with the area of 89.66, 23.7, 20.99, 4.52, 2.69 square kilometers,

respectively. Meanwhile, the predicted area interval 5 years higher than the predicted

area interval 10 years were field crop, paddy field, forest plantation and evergreen

forest with the area of 98.31, 35.15, 6.23, 1.87 square kilometers, respectively as

shown in Table 4-13.

Table 4-13 Comparison between the predicted land use map in 2010 based on land use data

in 1990 ,2000 (interval 10 years) and the predicted land use map in 2010 based

on land use data in 2000 ,2005 (interval 5 years)

Area Square Kilometers Predicted land use map in 2010

Land use pattern Based on land use data in 1990, 2000

(interval 10 years)

Based on land use data in 2000, 2005

(interval 5years)

Different area

Evergreen forest 988.99 990.86 1.87 Deciduous forest 105.72 101.20 -4.52 Forest plantation 89.79 96.02 6.23 Paddy field 43.88 79.03 35.15 Field crop 331.44 429.75 98.31 Perennial and orchard 125.83 36.17 -89.66 Urban and built up land 46.74 23.04 -23.7 Water body 3.34 0.65 -2.69 Disturbed forest 216.89 195.90 -20.99 Total area

1,952.62 1,952.62

283.12 Source: Calculated

Surang Rattanapan Results / 54 4.4 Accuracy Assessment of Model

The accuracy of prediction in 2005 was analyzed by comparing with the land

use map in 2005 classified by satellite interpretation and ground truthing. It was found

that the prediction land use in 2005 of Mae Taeng Watershed using CA_Markov

Model was shown the overall accuracy of 71.09 % and Kappa Index of 0.52 as shown

in Figure 4-9.

The accuracy of prediction land use map in 2010 based on land use data in

1990 and 2000 (interval 10 years) was analyzed by comparing with prediction land use

map in 2010 prediction land use map in 2010 based on land use data in 2000 and 2005

(interval 5 years). It was found that the prediction land use in 2010 of Mae Taeng

Watershed using CA_Markov Model was shown the overall accuracy of 68.41 % and

Kappa Index of 0.54 as shown in Figure 4-10.

To check the efficiency and limitation of changed period of the model there

would be any exterior factor influenced to land use change in 2010 of the CA_Markov

Model. The comparison of 10-year database (land use data in 1990 and 2000) and 5-

year database (land use data in 2000-2005), it was found that the land use pattern in

2010 based on difference database of 5-year and 10-year were 71.09 and 68.41 of

overall accuracy. The Kappa index values were 0.52 and 0.54 respectively. These can

be agreed even using different database. It can be concluded that the CA_Markov

Model can be applied in Mae Taeng Watershed for land use planning. It can be also

applied with economical model, natural disaster model, socio-economic and

government policy.


(B)

(A)

Figu

re 4

-9 C

ompo

site

bet

wee

n la

nd u

se m

ap in

200

5 w

hich

inte

rpre

ted

from

Lan

dsat

imag

e(A

) and

pre

dict

ion

land

use

in 2

005

(B)


(B)

(A)

Figu

re 4

-10

Com

posi

te b

etw

een

pred

ictio

n la

nd u

se m

ap in

201

0 ba

sed

on

land

use

dat

a in

199

0 an

d 20

00 (A

) and

pr

edic

tion

land

use

map

in 2

010

base

d o

n la

nd u

se d

ata

in 2

000

and

2005

(B)


4.5 Trend of Land Use Changes based on Socio-Economic and Policy Aspect

Using the in-depth interview with the specific sampling method for

qualitative study. The answerers were included Suchin Ngarmniyom (member of

committee of the federation of northern agriculturalist (network of the northern people

organization)), Boonyeun Wongfhan (deputy of the group of people utilizing water

from Mae Taeng Watershed in agricultural part) and Singhachai Thamphing (member

of committee of the federation of northern agriculturalist (network of the northern

people organization)).

The interviews of the key informant shown the land use pattern of Mae Taeng

Watershed, problem in the area, factors influence to land use change and trend of land

use in the future that can be concluded as shown below.

1. Land Use Pattern in Mae Taeng Watershed

In the past, most of land use in Mae Taeng Watershed was natural forest and

continuously decrease. As the area was quite abundance, there was invaded for

agriculture especially crop field. Because of the crop field, the soil of the area became

acid condition and low of phosphorus. Since the decreasing of the product, it had to

use more fertilizers and chemicals. The higher of production cost made the farmer had

more agricultural debt. There was still drift cultivation in some steep slope of the area

two times a year.

The land use pattern in Mae Taeng Watershed was related to slope of the area

and the folkway of each tribe. The cultivation in plain area was permanent with dike

and canal around the area. Most of the people on the plain were Thai, Thai-Yai and

Karen. The vegetations of the area were rice, corn, soy, green bean, garlic and chilli.

For the steep slope area, there was drift cultivation rely on only rain water. The

vegetations of the area were wheat, corn and cabbage. Perennial plantations were less

because of water shortage. Most of perennials were on the plain area of the lower of

Mae Taeng Watershed. The vegetations of the area were plum, tea tree, Chinese pear,

coffee, lychee and mango.

Surang Rattanapan Results / 58 2. Problem in the area

There was expanding of communities in every area of Mae Taeng Watershed.

The expanding of community went on the original. For the new settlement, there were

5-10 of households settled in the new invaded area. This was the few because of the

control of forestry officer. The officer will press the invader to go back to the old

community. The invaded area was damaged and fragile, so it needed time for

recovery. The area had chance to be invaded again. Some of invaders claimed that the

area were their own land before. The problem will be prolonged so the invasion

obviously covered. The invasion was close to road and infrastructure then people in

the area needed more income to support their life with facilities. The increasing of

invasion area had trend to link together by roads.

The increasing of expanding around the old cultivated area into highland had

occurred because the villager received information that the government will assign

office of the land to the owner of land before the cadastral surveying. So the

agriculturist continuously invaded to mark the land. There was also opium poppy

plantation mixed with field crop on highland. Even there were strict investigation and

suppression but with the high income.

3. The effective factor to trend change in the future.

There are several of races in Mae Taeng Watershed including Thai people

and Thai people of upper land which are Karen, Lee Sor, Mou Ser, Mong and Jean

How. The residents of the tribes are settled in the upper area of Mae Taeng Watershed.

The area is highland and located in conservative forest area. There are trends of

residential expanding. Most of people in the area earn a living in agriculture especially

in field crop such as rice farming, corn, bean, red bean and garlic. There are also

orchards such as mango, tea, coffee and plum in steep slope area. Most of the invasion

is because of expanding in the area to be cultivation area. There is no adjusting in the

area. The cultivations are only in wet season and the area was ignored in dry season.

There is burning of weed without fire control before the cultivated season, so the fire

invaded the forest area. This makes continuously increasing of invaded area. A lot of

small invaded areas has been linked together then become a larger area. This shows the

expanding of the communities in the future. In the plain area, the location of Thai


people, there is expanding of rice farming area. Most of the area is closed to road. The

large-size community with infrastructure development made a convenience in

invasion. The forest area had trend to be surrounded by agriculture area and may

completely cut off from the larger area.

Surang Rattanapan Conclusion and Recommendation / 60

CHAPTER V

CONCLUSION AND RECOMMENDATION

5.1 Conclusion

The study involves using remote sensing technique integrated with Markov

Chain and Cellular Automata Model to identify land use pattern and prediction model

for land use change of Mae Taeng Watershed, Chiang Mai Province.

Satellite image, path 131 row 047 and path 131 row 046, was use to represent

the area studied. The study was started by image processing that the satellite image

was image import, preprocessing, image enhancement, image classification, and image

post analysis to classify image. The result of the study was summarized as follows.

1. Mae Taeng Watershed, Chiang Mai Province covering an area of 1,952.62

square kilometers, can be classified into nine land use categories including evergreen

forest, deciduous forest, forest plantation, paddy field, field crop, perennial and

orchard, urban and built up land, water body and disturbed forest.

2. Land use change of Mae Taeng Watershed using cross-tabulation analysis

to compared pattern of land use in 1990 to 2000. It was found that, the area of field

crop, perennial and orchard, disturbed forest, urban and built up land and water body

are increased. Meanwhile, evergreen forest, forest plantation, deciduous forest and

paddy field are decreased.

3. Land use change of Mae Taeng Watershed using cross-tabulation analysis

to compared pattern of land use in 2000 to 2005. It was found that, the area of field

crop, forest plantation and paddy field are increased. Meanwhile, forest, disturbed

forest, perennial and orchard, deciduous forest, urban and built up land and water body

are decreased.

Fac. of Grad. Studies. Mahidol Univ. M.Sc. (Technology of Environmental Management) / 61

4. The predicted land use map in 2010 base on land use data in 1990 and

2000. It was found that the pattern of land use in 2010 were mainly classified as

evergreen forest with the area of 988.99 square kilometers or 50.65% meanwhile the

other were included field crop, disturbed forest, perennial and orchard, deciduous

forest, forest plantation, urban and built up land, paddy field and water body with the

area of 216.89, 125.83, 105.72, 89.79, 46.74, 43.88 and 3.34 square kilometers,

respectively.

5. The predicted land use map in 2010 base on land use data in 2000 and

2005. It was found that the pattern of land use in 2010 were mainly classified as

evergreen forest with the area of 990.86 square kilometers or 50.75% meanwhile the

other were included field crop, disturbed forest, deciduous forest, forest plantation,

paddy field, perennial and orchard, urban and built up land and water body with the

area of 429.75, 195.90, 101.20, 96.02, 79.03, 36.17, 23.04 and 0.65 square kilometers,

respectively.

6. The result from the prediction was compare to the classified by satellite

interpretation of 2005 by using pairwise comparison. The Overall accuracy of 71.09 %

and Overall Kappa of 0.52. Meanwhile the result from the land use map in 2010 based

on land use data in 1990 and 2000 (interval 10 years) and prediction land use map in

2010 based on land use data in 2000 and 2005 (interval 5 years). by using pairwise

comparison. The Overall accuracy of 68.41% and Overall Kappa of 0.54.

5.2 Recommendation

The land use patterns in Mae Taeng Watershed consisted of various land use

types. The training area should be represented most of type which occurred in the area.

Thus, the interpretation would be accepted of better results.

Satellite image of Landsat-5(TM) more than 25 meter for instances,

IKONOSE, Quick Bird or aerial photograph should be used to increase accuracy and

better classification

Surang Rattanapan Conclusion and Recommendation / 62

Beside physical factor has influence to land use change. Another factor such

as policy of government, natural disasters, socio-economic, etc. There would be any

exterior factor influenced to land use change. Thus, its have limitation of changed

period of the model in the future. So the land use has been continuously of exchange

all time. Thus, there was a limit of the prediction of the trend of land use change in

2010 based on the data in 1990 and 2000 using CA_Markov Model. As the model

calculated data in 1990 and 2000. They are normally based on statistic approach.

There may be any change of land use during 2000 and 2005 that the model cannot

recognize. Thus, the overall accuracy of model in 2010 by comparison of 10-year

database (land use data in 1990 and 2000) and 5-year database (land use data in 2000-

2005). So can be used correct database to support land use planning.

The prediction by Markov Chain and Cellular Automata model will be more

accurate. If increased other model in the analysis such as model of socio-economic,

disaster and government policy, etc.


REFERENCES

1. Veldkamp A., P.H. Verburg. Modeling land use change and environmental impact. Journal of environmental Management 72(2004) 1-3; 2004.

Available from:http://www.cptec.inpe.br/~summersc/ Apresentacao TV

eldkamp/ Veldkamp LAND USE CHANGE1.pdf [Accessed 2005 Jun 23].

2. สํานักงานนโยบายและแผนสิ่งแวดลอม. โครงการตดิตามและประเมินผลการดําเนินการและ คุณภาพสิ่งแวดลอม ในพื้นที่ลุมน้ําตัวอยาง (ลุมน้ําแมแตง เชิญ คลองยนั). บริษัท พรี ดิเวลลอปเมนท คอนซัลแตนท จํากัด; 2541

3. สํานักวิจยัการปาไม. สถานีวจัิยลุมน้ําแมแตง. Available from: http://www.dnp.go.th/Ferd/ferdTHAI/mtwatershedstation.htm[Access21 Dec 2005].

4. สถิตย วัชรเกียรติ . ระบบการแบงแยกการใชประโยชนท่ีดิน.มหาวิทยาลัยเกษตรศาสตร,กรุงเทพฯ; 2525.

5. ดรรชนี เอมพนัธุ. หลักการใชท่ีดินเบื้องตน. คณะวนศาสตร มหาวิทยาลัยเกษตรศาสตร กรุงเทพฯ; 2531. 6. Abduliah , A.H. Humam dimension . Submitted in Partial Fulfillment of the

Requirements of The Training on Land use and Land cover changes.

NRCT, Bangkok; 1994.

7. เพ็ญนภา เกียรติเกาะ. การศึกษาการเปลี่ยนแปลงสิ่งปกคลุมดิน และการเปลี่ยนแปลงการใช ท่ีดินของลุมน้ําพอง. [วิทยานิพนธปริญญาโท มหาวิทยาลัยเกษตรศาสตร]. กรุงเทพฯ; 2539.

8. ธงชัย จารุพิพัฒน. สถานการณปจจุบันของทรัพยากรปาไม. เอกสารประกอบคําบรรยาย วิชาภาพรวมทรัพยากรปาไม สาขาการบริหารทรัพยากรปาไม คณะวนศาสตร มหาวิทยาลัยเกษตรศาสตร, กรุงเทพ; 2542.

9. Lawawirojwong S. Expert classification for land cover mapping of Bang

Pakong Watershed [M.Sc. Thesis in Technology of Information System

Management]. Nakornpathom: Faculty of Graduate Studies, Mahidol

University; 2002.

Surang Rattanapan References / 64 10. Curran, P.J. Geographic information System Area 16(2):153-158; 1984.

11. TYDAC Technologies Inc. Systems overview and information, chapter2,

In Spatial Analysis Systems, reference guide version 3.6. Ontario;

P1-9; 1987.

12. Lillesand, T M., Kiefer R W. Remote sensing and image interpretation. (2rd

edition).New York; 1987.

13. Paul J. Curran . Principles of remote Sensing; P224; 1985.

14. Congalton, R.G. and R.A. Green. Assessing the accuracy of remotely sensed

data: principle and practices. New York: Lewis publishers; 1999.

15. Watcharakitti, S.K.Eadkeo, P. Interchandra, N. Ruangpanit, U. Kutintara

and A. pataratuma. Nampong Environmental Management Research

Project: Land Use; Working Document Number3. The Mekong

Secretariat; 1979.

16. Self organization system. Self organization system-lecture 4. Vrije Universiteite

Amsterdam; last modified November 20; 2001.

17. Michael N. DeMers. GIS Modelling in Raster. New Mexico State University;

2002.

18. http://www.math.com/students/wonders/life/life.html [Access 9August 2005]

19. Rinaldi, E. The Multi – Cellular Automata: A tool to build more sophisticated

models; 1998. Available from: http://www.itc.nl/library/ Papers2003

/msc/gfm/anuj.pdf. [Access 21 Jun 2004].

20. Jinn-Guey Lay. A Land Use Change Study using Cellular Automata. [Online].

Availablefrom: http://www.gisdevelopment.net/aars/acrs/2000/ts12/ts

12003 pf.htm. [Access 11 Jun 2004].

21. Joseph P. Messina1 and Stephen J. Walsh. Simulating Land Use and Land

Cover Dynamics in the Ecuadorian Amazon through Cellular

Automata Approaches and an Integrated GIS. Messina and Walsh;

2001. Availablefrom:opaper.pdf.[Access11 Jun 2004]


22. Pattanakiat S, Navanugraha C.and Lawawirojwong S. Remote Sensing for the

prediction on coastal land use change of Krabi Province, Thailand.

Proceeding of The 25th Asian Conference on Remote Sensing Volume 2:

2004 November 22-26; Sheraton Chiang Mai Hotel, Chiang Mai Thailand;

2004

23. Choosri N. Cellular Automata for prediction mangrove forest area changes: A

case study of Ban Laem District, Phetchaburi Province. [Master degree

thesis in science]. Nakhon Prathom: Faculty of Environmental and

Resource Study, Mahidol University; 2005.

24. Phakularbdang U. Prediction model for land use changes of krabi province.

[Master degree thesis in science]. Nakhon Prathom: Faculty of

Environmental and Resource Study, Mahidol University; 2005.

Surang Rattanapan Appendix / 66

APPENDIX

Fac. of Grad. Studies, Mahidol Univ M.Sc.(Technology of Environmental Management) /

67

APPENDIX A

THE ACCURACY OF PREDICTION IN 2005

Table A-1 Error matrix of prediction model in 2005

Land use pattern in 2005 (Landsat image)

Cla

ss 1

Cla

ss 2

Cla

ss 3

Cla

ss 4

Cla

ss 5

Cla

ss 6

Cla

ss 7

Cla

ss 8

Cla

ss 9

Row

tota

l User’s Accuracy

(%)

Class1 1511806 358 14013 8073 64554 0.10 1454 1504 97 1601859.10 94.38

Class2 1303 151680 0 626 7454 0.11 2105 309 38 163515.11 92.76

Class3 8401 0 60602 0 8729 0.31 77 996 0 78805.31 76.90

Class4 26 709 0 49008 3670 0.18 817 3399 1 57630.18 85.04

Class5 154751 6076 15389 12369 284590 0.44 2072 931 13 476191.44 59.76

Class6 5008 686 7351 10425 76205 0.65 58656 1442 439 160212.65 0.00

Class7 1106 0 12 8946 12252 0.51 5511 27821 0 55648.51 9.90

Class8 2 0 2657 0 1028 0.77 173 2 1194 5056.77 0.04

Pred

ictio

n La

nd u

se in

200

5

Class9 209314 36610 15667 545 39133 0.77 1772 225 0 303266.77 0.00

Column total 1891717 196119 115691 89992 497615 3.84 72637 36629 1782 2902185.84

Column total Producer’s

Accuracy %) 79.92 77.34 52.38 54.46 57.19 16.93 7.59 0.01 0.00

Source : Calculated

Remark: 1. Data analysis based on raster format





Class 5 Field crop


Overall Accuracy =

= [1511806+151680+60602+49008+284590 +0.65+5511+2] 2902185.84 = 71.09%

Overall Kappa =

= N X - Y

N²-Y

= [(2902185.84×2063200)- 3318364236476.08]

[(2902185.84)²- 3318364236476.08]

= 0.52

When

N = 2902185.84

X = 1511806+151680+60602+49008+284590+0.65+5511+2

= 2063200

Y = (1601859.10×1891717)+(163515.11×196119)+

(78805.31×115691)+(57630.18×89992)+

(476191.44×497615)+(160212.65×3.84)+

(55648.51×72637)+(5056.77×36629) +(303266.77×1782)

= 3318364236476.08

n

nk

iii∑

=1

∑

∑ ∑

=++

= =++

⋅−

⋅−

k

iii

k

i

k

iiiii

nnN

nnnN

1

2

1 1

)(

)(


69

APPENDIX B

THE ACCURACY OF PREDICTION IN 2010

Table B-1 Error matrix of prediction model in 2010

Prediction land use base on land use 1990 and 2000

Cla

ss 1

Cla

ss 2

Cla

ss 3

Cla

ss 4

Cla

ss 5

Cla

ss 6

Cla

ss 7

Cla

ss 8

Cla

ss 9

Row

tota

l User’s Accuracy

(%)

Class1 1311183 1612 34330 93 71364 14772 3522 8 145793 1582677 82.85 Class2 545 133698 0 925 2091 195 0 0 24338 161792 82.64 Class3 22098 301 76499 25 17708 15451 586 2989 18707 154364 49.56 Class4 13818 1307 68 57959 18695 20417 14054 31 288 126637 45.77 Class5 102174 17204 15555 4048 383973 92309 21251 1507 49378 687399 55.86 Class6 819 5429 40 215 636 46171 4502 192 191 58195 79.34 Class7 1150 353 1146 3174 351 1399 29602 58 14 37247 79.47 Class8 52 8 4 0 0 303 12 692 0 1071 64.61

Pred

ictio

n la

nd u

se b

ase

on la

nd u

se

1990

and

200

0

Class9 130805 9131 16702 3696 35294 10943 1880 226 106962 315639 33.89 Column total 1582663 169043 148675 70135 530112 201960 82769 6736 345676 3137769

Column total Producer’s

Accuracy %) 82.85 79.09 51.45 82.64 72.43 22.86 35.76 10.27 30.94

Source : Calculated

Remark: 1. Data analysis based on raster format





Class 5 Field crop


Overall Accuracy =

= [1311183+133698+76499+57959+383973+46171+ 29602+ 692 + 106962] 3137769 = 68.41%

Overall Kappa =

= N X - Y

N²-Y

= [(3137769×2146739)- 3052376346653.00]

[(3137769)²- 3052376346653.00]

= 0.54

When

N = 3137769

X = [1311183+133698+76499+57959+383973+46171+ 29602+692 + 106962]

= 2146739

Y = (1582677×1582663)+( 161792×169043)+

(154364×148675)+( 126637×70135)+

(687399×530112)+( 58195×201960)+

(37247×82769)+( 1071×6736) +(315639×345676)

= 3052376346653.00

n

nk

iii∑

=1

∑

∑ ∑

=++

= =++

⋅−

⋅−

k

iii

k

i

k

iiiii

nnN

nnnN

1

2

1 1

)(

)(


71 Overall Accuracy =

Procedure’s Accuracy =

User’s Accuracy =

Overall Kappa =

When N = Total number of observations in error matrix,

k = Total of rows, columns in error matrix,

nii = The number of observations in row i column i

ni+ = Total of observations in row i (right of matrix),

n+i = Total of observations in column i (bottom of the matrix)

n

nk

iii∑

=1

j

jj

nn

+

i

ii

nn

∑

∑ ∑

=++

= =++

⋅−

⋅−

k

iii

k

i

k

iiiii

nnN

nnnN

1

2

1 1

)(

)(

Surang Rattanapan Biography / 72

BIOGRAPHY

NAME Miss Surang Rattanapan

DATE OF BIRTH 9th December 1981

PLACE OF BIRTH Nakornsritammarat, Thailand

INSTITUTIONS ATTENDED Kasetsart University, 2001-2004

Bachelor of Science

(Forestry)

Mahidol University, 2004-2006

Master of Science

(Technology of Environmental

Management)

HOME ADDRESS 126 Moo 10, Tamyai Sub-district,

Thongsong District,

Nakornsritammarat Thailand

80110

Tel. +66 86703 6435

Tel. +66 29681 157

E-mail: [email protected]

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