APPLICATION OF GEOINFORMATION TECHNOLOGY FOR EVALUATION

OF CASSAVA PLANTATIONS: A CASE STUDY OF BANTEAY

MEANCHEY PROVINCE, CAMBODIA

SOPHEAK PEN

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE MASTER DEGREE OF

GEOINFORMATICS

FACULTY OF GEOINFORMATICS

BURAPHA UNIVERSITY

APRIL 2016

COPYRIGHT OF BURAPHA UNIVERSITY

The thesis of Sopheak Pen has been approved by the examining committee

to be partial fulfillment of the requirement for the Master Degree of Science Program

in Geoinformatics of Burapha University

Advisorv Committee

(Dr. Supan Karnchanasutham)

1as socr/af66ofdrro, Dr. Kaew Nualchawee)

Co-advisor

This thesis has been approved by the Faculty of Geoinformatics to be partial

fulfillment of the requirements for the Master Degree of Science Program in

Geoinformatics of Burapha University

(Dr. Supan Karnchanasutham)

APRIL.2016

ean of the Facultv of Geoinformatics

This study

I would like to express my sincere gratitude and deepest appreciation to Her Royal

Highness Princess Mahachakri Sirindhorn, who provided the scholarship for me to

study at Burapha University, 2549.

ACKNOWLEDGEMENTS

First of all, I would like to express my sincere gratitude and deepest

appreciation to Her Royal Highness Princess Mahachakri Sirindhorn, who provide the

scholarship for me to study at Burapha University.

I would like to express my special gratitude and deep appreciation to

Dr. Supan Karnchanasutham, Assoc. Prof. Dr. Kaew Nualchawee, Dr. Narong

Pleerux, who always kindly helped me to minimize difficulties, for their guidance and

valuable advice throughout this study.

I would like to thank Mr. Preecha Boonkhaw and Mr. Narathip Phengphit

who helped and facilitated in the process of data collection for their valuable advice

and assistance on technique.

I am very grateful to all the professors and members of the Faculty of

Geoinformatics who helped and supported me while I was studying here.

Finally, I would like to express my deepest gratitude to my beloved mother,

Nget Eng, and father, Pen Chhoern, who always give me the warmest and greatest

love and support. Unforgettablely, I offer special thanks to all my relatives and

friends for their help and encouragement.

Sopheak Pen

56910038: MAJOR: GEOINFORMATICS; M.Sc. (GEOINFORMATICS)

KEYWORDS: GEOINFORMATION TECHNOLOGY/ CLASSIFICATION/

CASSAVA

SOPHEAK PEN: APPLICATION OF GEOINFORMATION

TECHNOLOGY FOR EVALUATION OF CASSAVA PLANTATIONS: A CASE

STUDY OF BANTEAY MEANCHEY PROVINCE, CAMBODIA. ADVISORY

COMMITTEE: SUPAN KARNCHANASUTHAM, D.Tech.Sc., KAEW

NUALCHAWEE, Ph.D., NARONG PLEERUX, Ph.D. 70 P. 2015.

Cassava is currently the most important upland crop of Cambodia. It is an

agricultural product that can be processed into various other products such as ethanol,

animal feed and cassava starch or flour for human consumption. The objectives of this

research were to: (1) classify the cassava plantation areas in Banteay Meanchey

Province, Cambodia using LANDSAT 8 (OLI) and SMMS (HJ-A1) images, and: (2)

compare the cassava plantation areas between LANDSAT 8 (OLI) and SMMS (HJ-

A1). The Maximum likelihood classification technique was applied to this research.

Land use types were evaluated into seven classes: cassava, field crop, forest, water,

perennial tree/fruit tree, rice and urban.

The results revealed that the cassava areas from LANDSAT 8 (OLI) were

83,757.37 hectares or 13.54 % of the study area. Meanwhile, the cassava areas from

SMMS (HJ-A1) were 97,215.33 hectares or 15.72 % of the study area. The overall

accuracy of LANDSAT 8 (OLI) and SMMS (HJ-A1) was 81.48 % and 75.56 %

respectively. Therefore LANDSAT 8 (OLI) can be used to classify land use/land

cover with higher accuracy than SMMS (HJ-A1).

.

ABBREVIATIONS

GPS Global Positioning System

GIS Geographic Information Systems

RS Remote Sensing

FAO Food and Agriculture organization

HJ-1A Huan Jing 1A

MAFF Cambodian Ministry of Agriculture Fish.

SMMS Small Multi-Mission Satellite

USGS United States Geological Survey

UTM Universal Transverse Mercator

CONTENTS

Page

ACKNOWLEDGMENT iv

ABSTRACT v

CONTENTS vi

LIST OF TABLES viii

LIST OF FIGURES ix

CHAPTER

1. INTRODUCTION 1

Introduction 1

Statement and Significant of the problem 3

Objectives of the Study 4

Scope of Study 4

The Study Area 4

Benefits of the Study 5

2. LITERATURE REVIEW 7

Cassava 7

Cassava production in Cambodia 8

Cultivation Practices 9

Geographic Information System 10

Remote sensing 15

Huanjing-1A (HJ-1A) 17

Orbit Characteristics of HJ-1A 19

LANDSAT 8 (OLI) 20

Global Positioning System (GPS) 22

Literature Review 24

3 RESEARCH METHODOLOGY 28

Geocorrection of LANDSAT imagery 30

Data exploration and preprocessing 31

Collecting training samples 32

Evaluating training samples 33

vii

Creating the signature file 34

Examining the signature file 34

Editing the signature file 34

Applying classification 34

Post-classification processing 35

Equipment of Analysis 36

4 RESULTS 38

Land use from SMMS (HJ-A1) 38

Land use from LANDSAT 8 (OLI) 41

Land use comparison of SMMS (HJ-A1) and LANDSAT 8 (OLI) 45

Values accuracy of model 46

5 DISCUSSION, CONCLUSION AND RECOMMENDATION 49

Discussion 49

Conclusion 50

Recommendation 51

REFERENCES 52

APPENDIX 57

ABBREVIATIONS 60

BIOGRAPHY 61

LIST OF TABLES

Tables Page

2-1 Payload Parameters 19

2-2 Orbit parameters 19

2-3 LANDSAT 8 spectral band/wavelengths 21

2-4 Composition Band 22

3-1 Used software 37

4-1 Land use of Banteay Meanchey province from SMMS (HJ-A1) 2015 39

4-2 Land use of Banteay Meanchey province from LANDSAT 8 (OLI) 42

4-3 Comparison of land use in Banteay Meanchey province by LANDSAT 8

(OLI) and SMMS (HJ-A1) satellite images 45

4-4 Dislocation evaluation the accuracy of the classification of land use by

SMMS (HJ-A1) satellite 47

4-5 Dislocation evaluation of the accuracy of the classification of land use in

LANDSAT 8 (OLI) satellite image 48

LIST OF FIGURES

Figures Page

1-1 Map of Banteay Meanchey Province 5

2-1 Components of GIS 11

2-2 GIS Application 15

3-1 Classification of Workflow 28

3-2 SMMS (HJ-A1) 29

3-3 Enhanced LANDSAT 8 imagery in 2015 30

3-4 Collecting training samples 32

3-5 Evaluation training sample editing classes 33

3-6 Editing classes 33

3-7 Creating the signature file 34

4-1 Percentage of land use Banteay Meanchey SMMS (HJ-A1) 39

4-2 Area of land use Banteay Meanchey SMMS (HJ-A1) 40

4-3 SMMS (HJ-A1) Map of Banteay Meanchey showing location of cassava

plantation fields of studies 40

4-4 SMMS (HJ-A1) Map classification of Banteay Meanchey showing

location fields of studies 41

4-5 Percentage of land use in Banteay Meanchey from LANDSAT 8 (OLI) 43

4-6 Area of land use in Banteay Meanchey from LANDSAT 8 (OLI) 43

4-7 The LANDSAT 8 (OLI) satellite map of Banteay Meanchey showing

location of cassava plantation fields of study 44

4-8 The LANDSAT8 (OLI) satellite image map of Banteay Meanchey

province showing location fields of study 44

4-9 Comparison of land use in Banteay Meanchey province by LANDSAT 8

(OLI) and SMMS (HJ-A1) satellite images 46

4-10 Map showing sample locations to determine accuracy 47

CHAPTER 1

INTRODUCTION

Introduction

Cambodia is a country in Southeastern Asia, bordering the Gulf of Thailand,

between Thailand, Vietnam, and Laos. It has twenty four provinces and a capital city.

Cambodia occupies 181,035 square kilometers in the southwestern part of the

Indochina peninsula, and is located between 10° and 15° latitude north and 102° and

108° longitude east. The country shares 803 kilometers of border with Thailand on the

north and west, 541 kilometers of border with Laos on the northeast, 1,228 kilometers

of border with Vietnam on the east and southeast, for a total of 2,572 kilometers of

land borders; coastline along the Gulf of Thailand about 443 square kilometers. The

population of Cambodia is 15,184,116 million (Cambodia T. , 2014). Cambodia has a

tropical climate with two distinct monsoon seasons the rainy season starts in mid-

April and continues to October. Average annual rainfall is 1,250-1,750 millimeters

(Cambodia Ministry of Agriculture Fish, 2014). Sihanouk Ville has the highest

average annual rainfall of 2,996 millimeters and the average rainfall in Banteay

Meanchey Province is 1,000 millimeters, with peak rainfall occurring in September/

October and the lowest rainfall in February. As for the temperature, it is lowest in

December/January with average minimum temperature of 21 degrees celsius and the

highest in April with average minimum of 36 degrees celsius (Yem, 2010). Cambodia

is located in the trops so is suitable for several kinds of economic plants such as

soybeans, green bean, rice, cassava, corn (maize) and sugarcane. Agriculture has been

the first priority of the government’s development strategies since1993. Agriculture is

a fundamental sector of the Cambodian economy and small farmers dominate the

agricultural sector of the country.

The territory of Banteay Meanchey is a Cambodian province in the

northwest of the country. In 1988 the province of Banteay Meanchey was split off

from Battambang, and its capital is named Sisophon, approximately 359 kilometers

from Phnom Penh by National Road Number 5. The district is subdivided into 7 and 2

cities, communes 64 and 649 villages (National Institute of Statistics). One of the

2

most known places in that province is Poi Pet, a town on the Thailand/Cambodia

border. It is the key crossing point between the two countries. The total population is

745,618 or (5.242%) of the total population of 14,363,519 in Cambodia (provincial

government data, 2011) with a growth rate of 5.93 %, which consists of 402,201

males (49.11%), and 414,181 females (50.89%). The above number also consists of

654, 033 (93%) farmers, 8,228 (1.17%), fishermen, 35,162 (5%), traders, and 5,814

(0.83%), government officers (Tourism of Cambodia , 2015).

Cassava is currently the most important upland crop of Cambodia. It is an

agricultural product that can be processed into various other products such as ethanol,

animal feed or cassava starch or flour for human consumption. Cassava (Manihot

esculenta Crantz), a native to South America (Allem, 2002), is an important storage

root crop worldwide (Ceballos et al., 2004; El-Sharkawy, 2012). It is a key

component of the diet of over 800 million people across several continents (El

Sharkawy, 2012). The crop is a high starch producer with levels of up to 90% of its

total storage root dry mass (Jansson et al., 2009). Cassava is the third most important

source of calories in the tropics after rice and maize. Cassava is one of the most

important upland crops of Cambodia that a farmer plants after rice. More than 85 % of

the cultivated area is planted to rice, maize, soybean, sesame, peanut and cassava.

Agriculture is also the most important sector for employment, employing more than

half of the country’s total labor force. Agriculture is more important for the rural poor

as it provides their most important source of income (World Bank, 2009).

Land cover is the physical material at the surface of the earth. Land covers

include grass, asphalt, trees, bare ground, water, etc. This study uses Remote Sensing

data from Landsat imagery covering the study area. Remote sensing (RS) and

Geographic Information Systems (GIS) provide secure and established foundations

for measurement, mapping and analysis of natural resources in the world. There are

various ways to classify land cover based on remote sensing and GIS such as

supervised classification, unsupervised classification or combination of supervised

and unsupervised procedures with other sources like economic, social and historical

information as hybrid classification methods which are well known and established

today (Nguyen, 2015).

3

This research thesis is concerned with the application of recent technology

in Remote Sensing and the Geographical Information Systems in the evaluation of

cassava plantation areas in Banteay Meanchey Province in Cambodia. The capability

of Remote Sensing to capture up-to-date information for large areas and the ability of

GIS to store and manage data from various sources means the technology is an

important tool in the field of agricultural and another fields.

Statement and Significance of the Problem

Cambodia’s agriculture sector is growing, but suffers from constraints such

as low labor productivity, low yields, variable water resources, inefficient land titling,

inactive technology transfer, limited access to credit and inadequate rural

infrastructure (CDC, 2010), which make it very difficult to identify cassava plantation

areas.

There are many questions that could be asked which may lead to some

solutions to the above problems. The purpose of this thesis is to identify the land

surface use and approach to economic cassava plantation in Banteay Meanchey

province of Cambodia. The basic hypothesis of this research that Banteay Meanchey

province (the northern high. land of Cambodia), with its tropical climate, is import

(the seed) and export cassava products gateway which is very important in sharing

economic benefits together with Thailand. It is proposed that effective measurement

of land area dedicated to cassava, what areas are used specifically for cassava will

lead to economic benefits for farmers in this province. The analysis is divided into

two main parts. The first section of this report introduces the reasons for choosing this

area as the objective of the thesis. It will explain land use using Geographic

Information Systems and Remote Sensing satellite imagery. The second section is a

case evaluation of cassava plantation area for farmers – increasing the land plated and

increasing their income. The effects, implementation strategies, and specific strengths

and weaknesses associated with each approach are described, including a comparison

from the previous work. This study entails an examination of the land use developed

in an increasing total area of cassava plantation. In general the associated findings of

the research is located within this final section.

4

Objectives of the Study

The main objective of this study is the classification of cassava planted area

in Banteay Meanchey Province using Maximum Likelihood Supervisor classification

gathered using LANDSAT 8 (OLI) and SMMS (HJ-1A) images in 2015.

1. Classification of cassava planted areas from LANDSAT 8 (OLI) SMMS

(HJ-1A) in 2015.

2. Comparison of cassava planted areas from LANDSAT 8 (OLI) and

SMMS (HJ-1A) in 2015

Scope of Study

This study classifies the cassava plantation areas in Banteay Meanchey

Province, Cambodia, using LANDSAT 8 and SMMS (HJ-1A) data, by Maximum

Likelihood Classification of Supervised method and comparison of the cassava

plantation area obtained and classified with LANDSAT 8 and SMMS (HJ-1A).

The Study Area

This study was carried out in Banteay Meanchey Province, with an area of

6185.84 square kilometers, located between 13°45′- 13°75′ N latitude and 101°06-

105°21′E longitude. This province was chosen because it has the largest cassava

planted area in Cambodia after Kampong Cham and Battambang provinces.which

have approximately 62,151 hectares of cassava planted in 2014 and share a border

with Sa Kaeo Province of Thailand to the west and North, Oddar Meancheay to the

North, Siem Reap to the East, and Battambang to the South. The town of Sisophorn is

about 359 kilometers from Phnom Penh via national road number 5. The climate of

the area can be characterized as warm tropical, wet and dry climate with extreme low

and high temperatures of 23 degrees celsius to 36 degrees celsius and rainfall in this

province is 885.30 millimeters per year (Tourism, 2015).

5

Figure 1-1 Map of Banteay Meanchey Province

Benefits of the Study

1. The output of this study is very important for farmers and residents in the

area, first to gain useful knowledge of the area devoted to cassava and areas suitable

for agriculture by using Geographic Information System and Remote Sensing satellite

imagery.

2. To know the area planted at with cassava in Banteay Meanchey.

3. The farmers can divide their time to cultivate a variety of crops and

realize that multi-cropping including cassava allows farmers to expand the area

cultivated (cassava also allows a higher yield, improving their livelihoods. These

6

maps could serve as input or guides to the planning and formulation of sustainable

management strategies.

4. To obtain a Comparison of the area planted with cassava between

LANSAT 8 (OLI) and SMMS (HJ-1A) in 2015.

5. To create a map of cassava planted areas after classification at Banteay

Meanchey, Cambodia.

CHAPTER 2

LITURATURE REVIEW

Agriculture is a fundamental sector of Cambodia economy. Small farmers

dominate the agriculture sector of the country and most farmers in Banteay Meanchey

province plant cassava rather than rice. Cassava is a source of good income for people

there because yields and production are high especially near the border with Thailand.

Cassava production management and farmland division can be improved using

Remote Sensing and Geographical Information System technology in the of

evaluation cassava plantation. Remote Sensing and GIS are effective tools to

generate, analyze, and display these multi-disciplined spatially correlated data. In the

last several decades, methodologies have been developed and studies have been

conducted for effective analysis using Remote Sensing and GIS in classification of the

cassava plantation.

Cassava

“Cassava (Manihot esculenta Crantz) was introduced from Brazil, its

country of origin, to the tropical areas of Africa, the Far East and the Caribbean

Islands by the Portuguese during the 16th and 17th centuries. In the Gold Coast (now

Ghana), the Portuguese grew the crop around their trading ports, forts and castles and

it was a principal food eaten by both Portuguese and slaves. By the second half of the

18th century, cassava had become the most widely grown and used crop of the people

of the coastal plains. The Akan name for cassava 'Bankye' could most probably be a

contraction of 'Aban Kye' - Gift from the Castle” (Korang-Amoakoh et al., 1987).

Cassava is called “yuca” in Spanish, “mandioca” in Portuguese, “cassava” in

Haitian Creole, and “Manioc” in French. It is consumed in a variety of ways, but only

after some form of processing. Cultivars are classified into two groups based on the

amounts of hydrogen cyanide present. Sweet types contain less than 50 mg kg-1HCN

(fresh weight) and are generally sold as fresh roots, whereas bitter types have higher

amounts of HCN along with higher yields and starch content (Conceição, 1981).

8

Cassava is the fourth most important source of food energy in the tropics.

More than two-thirds of the total production of this crop is used as food for humans,

with lesser amounts being used for animal feed and industrial purposes. The ingestion

of high levels of cassava has been associated with chronic cyanide toxicity in parts of

Africa, but this appears to be related to inadequate processing of the root and poor

overall nutrition. Although cassava is not a complete food it is important as a cheap

source of calories. (Cock, 1982)

Cassava is an important cash crop and food crop of resource-limited farmers

in Africa, Asia, and Latin America and the Caribbean. The storage roots are utilized

either fresh, as in the case of sweet cultivars low in cyanogen glycosides, or after

processing into dry products such as flour, starch, and animal feed in the case of bitter

cultivars high in cyanogen glycosides (Emmanuel Okogbenin, Tim L. Setter, Morag

Ferguson, Rose Mutegi, Hernan Ceballos, Bunmi Olasanmi, and Martin Fregene,

2013).

Cassava production in Cambodia

Cambodian cassava is mainly grown in the central and southeastern part of

the country. Especially in Kampong Cham and Kampong Thom province, while some

is also grown along the Mekong River and Siem Reap, Kampong Speu, Kampong

Thom, Battambang, and Banteay Meanchey. There are two main local varieties in

Cambodia, one is sweet, the other is bitter. Mong Reththy Tapioca (MRT) plantation,

located in Sihanouk ville, in southwest Cambodia, introduced RAYONG 60 and

KASETSART 50 in 2000. In areas near the border, the farmers introduced some

Vietnamese varieties (bitter) in Kampong Cham province, and a Thai company

introduced some Thai varieties (bitter) in Battambang and Banteay Meanchey

Provinces. Because of a lack of extension, and farmers in many provinces have

difficulty finding cassava markets for animal feed and industrial raw material so, they

generally don‟t like planting the new bitter varieties; they just want to sell sweet roots

for human consumption in the local market fresh. The new varieties are not widely

grown yet (Cambodia Ministry of Agriculture and Fishing, 2010).

9

Cultivation Practices

Cassava is adaptable to diverse climates and can be grown in soil with low

fertility. It is planted either as a single crop or intercropped with maize, legumes,

vegetables, rubber or other plants. Cassava is normally planted during February–April

and harvested in eight to 12 months depending on market price and the availability of

labor for harvesting.

Cultivation practices in western and eastern Cambodia are similar, with a

few notable differences due to different soil and climate conditions. In Banteay

Meanchey province, with about 62151 hectares (Department of Agriculture Banteay

Meanchey 2014) of cassava, The yield and production were especially high the near

Thailand border. There are some Thai cassava varieties in Cambodia such as Rayong

60 and Karsetsart 50 or other varieties from Thai buyers‟. The farmers in Banteay

Meanchey near Thailand border have introduced high yielding varieties, so cassava is

mono-cropped and usually planted in December, with the earliest planting in the

middle of November and the last in February. The first ploughing starts in early

December before the forecast rain, followed by a second ploughing and row making

in the middle of January. Most farmers hire a local tractor owner to plough and hire

laborers to make rows for planting. Most have their land ploughed twice, which

results in a greater yield, while about 5 percent do it only once due to lack of financial

resources. Planting seeds usually takes place in March. The majority of farmers use

their own cassava seeds from the previous harvest (Hing vutha, Thun Vathana, 2009).

Herbicide is necessary in Malai and needs to be applied at least twice because weeds

grow high and thick. The first application is made in the middle of May and the

second a month and a half later. A third application of herbicide might be made,

depending on weed condition and farmers financial resources. Finally, some branches

are normally cut a month or so before harvesting to admit enough sunlight for the root

to grow bigger. Cassava is mostly planted with other crops, especially rubber etc.

Farmers mostly use more a tractor instead of labor for land preparation in order not to

disturb the other crops in western and north western areas.

10

Geographic Information System

Geographic Information System (GIS) is a powerful tool for collecting,

storing, retrieving, transforming and displaying spatial data from the real world

(Burrough., 1986). Many people offer definitions of GIS. In the range of definitions

presented below, different emphases are placed on various aspects of GIS. Some miss

the true power of GIS, its ability to integrate information and to help in making

decisions, but all include the essential features of spatial references and data analysis.

Geographic Information System (GIS) integrates hardware, software and data for

capturing, managing, analyzing, and displaying all forms of geographically referenced

data. GIS allows the viewing, understanding, questioning, interpreting, and

visualizing data in many ways that reveals relationships, patterns, and trends in the

form of maps, globes, reports, and charts. It answers questions and solves problems

by looking at data in a way that is quickly understood and easily shared. GIS

technology can be integrated into any enterprise information system framework. GIS

can be used to map the changes in an area to anticipate future conditions, decide on a

course of action, or to evaluate the results of an action or policy. By mapping where

and how things move over a period of time, one can gain insight into how they

behave. Dana Tomlin's definition, from the Geographic Information Systems and

Cartographic Modeling (Englewood Cliffs, NJ: Prentice-Hall, 1990) is a broad

definition. A considerably narrower definition, however, is more often employed. In

common parlance, a geographic information system, or GIS, is a configuration of

computer hardware and software specifically designed for the acquisition,

maintenance, and use of cartographic data. (From Jeffrey Star and John Estes, in

Geographic Information Systems. Englewood Cliffs, NJ: Prentice-Hall, 1990). A

geographic information system (GIS) is an information system that is designed to

work with data referenced by spatial or geographic coordinates. In other words, a GIS

is both a database system with specific capabilities for spatially-referenced data, as

well as a set of operations for working with data. In a sense, a GIS may be thought of

as a higher-order map (Understanding GIS: The ARC/INFO Method, 1990). A GIS is

an organized collection of computer hardware, software, geographic data, and

personnel designed to efficiently capture, store, update, manipulate, analyze, and

display all forms of geographically referenced information.

11

Components of Geographic Information System

A working Geographic Information System seamlessly integrates five key

components: hardware, software, data, people, and methods. (Ebook, 2014)

User

Procedure

Data

Software Hardware

Figure 2-1 Components of GIS (Mapsofindia, 2012)

1. Hardware

Hardware includes the computer on which a GIS operates the monitor on

which results are displayed, and a printer for making hard copies of the results.

Today, GIS software runs on a wide range of hardware types, from centralized

computer servers to desktops. Computers are used in stand-alone or networked

configurations. The data files used in GIS are relatively large, so the computer must

have a fast processing speed and a large hard drive capable of saving many files.

Because a GIS outputs visual results, a large, high resolution monitor and a high-

quality printer are recommended.

2. Software

GIS software provides the functions and tools needed to store, analyze, and

display geographic information. Key software components include tools for the input

and manipulation of geographic information, a database management system

(DBMS), tools that support geographic query, analysis, and visualization, and a

graphical user interface (GUI) for easy access to tools. The industry leader is

ARC/INFO, produced by Environmental Systems Research, Inc. The same company

GIS

12

produces a more accessible product, ArcView that is similar to ARCINFO in many

ways.

3. Data

Possibly the most important component of a GIS is the data. A GIS will

integrate spatial data with other data resources and can even use a database

management system, used by most organizations to organize and maintain their data,

and to manage spatial data. There are three ways to obtain the data to be used in a

GIS. Geographic data and related tabular data can be collected in-house or produced

by digitizing images from photographs or published maps. Data can also be purchased

from a commercial data provider. Finally, data can be obtained from the national

government at no cost.

4. People

GIS users range from technical specialists who design and maintain the

system to those who use it to help them perform their everyday work. The basic

techniques of GIS are simple enough to master that even students in elementary

schools are learning to use GIS. Because the technology is used in so many ways,

experienced GIS users have a tremendous advantage in today‟s job market.

5. Methods

A successful GIS operates according to a well-designed plan and business

rules, which are the models and operating practices unique to each organization.

Spatial data

It should be noted that spatial data is at the heart of every GIS application.

Spatial data stores the geographic location of particular features, along with

information describing what these features represent. The location is usually specified

according to some geographic referencing system (e.g., latitude, longitude) or simply

by an address. Spatial data may define some physical characteristics, such as location

or position, or it may also define a property such as the area of a forest and

agricultures (which results from defining the various positions of its boundaries).

(Davis, 1996) In GIS, spatial data is classified as three main types: point, line, and

polygon.

13

1. A point is a convenient visual symbol (an X, dot or other graphic), but

it does not reflect the real dimensions of the feature. Points may indicate specific

locations (such as a given address, or the occurrence of an event) or which are

usually too small to depict properly at the chosen scale features (such as a building).

2. A line is a one-dimensional feature with a starting and an ending point.

Lines represent linear features, either real (e.g., roads or streams) or imaginary (eg.

borders).

3. A polygon is an enclosed area, a two-dimensional feature with at least

three sides (and therefore with an area). For example, it may represent a parcel of

land, agricultural fields, or a political district

Attribute data

Since the data collected and stored in the database determine the kind of

questions that can be asked of the data, it is necessary to understand the scales of

measurement in which data are recorded. The measurement scales normally used are

nominal, ordinal, interval and ratio.

Nominal Scale – The nominal scale is the lowest level of measurement

which is used to distinguish among features. Nominal data could be a name or a

description of features. For instance, a lake could be differentiated from a sand dune.

In a tropical area, there could be regions identified with sugar cane fields or rice

paddy fields. Basically, each name or description is distinct.

Ordinal Scale – Ordinal scales allows for data to be ranked in either an

ascending or descending order. A hierarchy of rank could be established depending

on the features under consideration. For example, a country could have cities ranked

as small, medium and large. In addition, the country may have parks that are ranked

as being minor, intermediate and major. Although the ordinal scale permits

differentiation on the basis of rank, it does not show or specify the magnitude of

difference.

Interval Scale – With the interval scale of measurement the distance

between the ranks is known. To employ an interval scale an arbitrary starting point is

used. The widely used example of the Celsius temperature scale explains the interval

14

scale. For example, it cannot be said that 38 degrees Celsius is twice as hot as 19

degrees Celsius, because 0 degrees Celsius is arbitrary.

Ratio Scale – A ratio scale is more advanced than the interval scale because there can

be an absolute starting point. For example, 78 miles is twice as far as 39 miles

(Lakhan, 1996).

Electronic maps and images

An essential component of any GIS is some kind of map or image of an area.

This can be a digital map, satellite image or aerial photograph. Many GIS will use a

full range of map data and images as a base to link information against. The maps are

produced either as raster or vector data.

Raster maps

These are images made by a series of colored dots on a screen (pixels), just

like high quality digital photographs. Raster maps can be thought of as „unintelligent‟

as you can only obtain information that is visually represented on them. Raster maps

take up a lot of computer storage space but can be very useful as background maps to

other information.

Vector maps

Each vector map feature is recorded using XY coordinates. These may be a

single point (like a trig point), lines (such as roads) or polygons (such as buildings or

woods). The referenced features of vector maps make it possible for a GIS to link

information from spreadsheets and databases to the maps. Vector data is stored in

themed layers such as roads, water, or settlement.

Working GIS

A GIS stores information about the world as a collection of thematic layers

that can be linked together by geography. This simple but extremely powerful and

versatile concept has proven invaluable for solving many real-world problems from

modeling global atmospheric circulation, to predicting rural land use, and monitoring

changes in rainforest ecosystems. Geographic information contains either an explicit

geographic reference such as a latitude and longitude or national grid coordinate, or

an implicit reference such as an address, postal code, census tract name, forest stand

identifier, or road name. An automated process called geocoding is used to create

15

explicit geographic references (multiple locations) from implicit references

(descriptions such as addresses). These geographic references can then be used to

locate features, such as a business or forest stand, and events, such as an earthquake,

on the Earth's surface for analysis.

Figure 2-2 GIS Application

Remote Sensing

Remote Sensing is a powerful tool that could be used to address the problem

of thematic maps which are out of date and have to be updated. The capabilities of

Remote Sensing to map and extract information about earth resources for various

applications are well documented. Among those prominently used is land cover

mapping, considered as one of the most important, most direct and well established

applications of remote sensing (Cambell, 1987).

Remote Sensing provides spatial coverage by measurement of reflected and

emitted electromagnetic radiation, across a wide range of wavebands, from the earth‟s

surface and surrounding atmosphere. The improvement in technical tools of

meteorological observation, during the last twenty years, has created a favorable

substratum for research and monitoring in many applications of sciences of great

16

economic relevance, such as agriculture and forestry. Each waveband provides

different information about the atmosphere and land surface: surface temperature,

clouds, solar radiation, processes of photosynthesis and evaporation, which can affect

the reflected and emitted radiation, detected by satellites (Saha, 2003). The challenge

for research therefore is to develop new systems extracting this information from

remotely sensed data, giving to the final users, near-real-time information. Over the

last two decades, the development of space technology has led to a substantial

increase in satellite earth observation systems. Simultaneously, the Information and

Communication Technology (ICT) revolution has rendered increasingly effective the

processing of data for specific uses and their instantaneous distribution on the World

Wide Web (WWW). The meteorological community and associated environmental

disciplines such as climatology including global change, hydrology and oceanography

all over the world are now able to take advantage of a wealth of observational data,

products and services flowing from specially equipped and highly sophisticated

environmental observation satellites. An environmental observation satellite is an

artificial Earth satellite providing data on the Earth system and a Meteorological

satellite is a type of environmental satellite providing meteorological observations.

Several factors make environmental satellite data unique compared with data from

other sources, and it is worthy to note a few of the most important:

1. Because of its high vantage point and broad field of view, an

environmental satellite can provide a regular supply of data from those areas of the

globe yielding very few conventional observations.

2. The atmosphere is broadly scanned from satellite altitude and enables

large scale environmental features to be seen in a single view.

3. The ability of certain satellites to view a major portion of the atmosphere

continually from space makes them particularly well suited for the monitoring and

warning of short-lived meteorological phenomena, and;

4. The advanced communication systems developed as an integral part of

the satellite technology permit the rapid transmission of data from the satellite, or

their relay from automatic stations on earth and in the atmosphere, to operational

users. These factors are incorporated in the design of meteorological satellites to

provide data, products and services through three major functions.

17

5. Remote sensing of spectral radiation which can be converted into

meteorological measurements such as cloud cover, cloud motion vectors, surface

temperature, vertical profiles of atmospheric temperature, humidity and atmospheric

constituents such as ozone, snow and ice cover, ozone and various radiation

measurements.

6. Collection of data from in situ sensors on remote fixed or mobile

platforms located on the earth‟s surface or in the atmosphere, and;

7. Direct broadcast to provide cloud-cover images and other meteorological

information to users through a user-operated direct readout station.

The first views of earth from space were not obtained from satellites but

from converted military rockets in the early 1950s. It was not until 1 April 1960 that

the first operational meteorological satellite, TIROS-I, was launched by the USA and

began to transmit basic, but very useful, cloud imagery. This satellite was such an

effective proof of concept that by 1966 the USA had launched a long line of

operational polar satellites and its first geostationary meteorological satellite. In 1969

the USSR launched the first of a series of polar satellites.

Huanjing-1A (HJ-1A)

HJ-1A (Huan Jing = Environment) satellites are small Chinese Earth

observation satellites. The main application fields for China are environmental

monitoring and prediction, solid waste monitoring, disaster monitoring and prediction

(flood, drought, typhoon and wind damage, sand storm, earthquake, land creep, frost

and grassland fires, coal fires, crop pest monitoring, ocean disaster monitoring)

(eoPortail Directory, 2010). The first two satellites, HJ-1A and HJ-1B, was

successfully launched in China on September 6, 2008. Both were manufactured by the

China Spaces at Company, and use the China Aerospace Science and Industry

Corporation (CAST) 968 satellite bus with a designed service life of three years. The

HJ-1A was equipped with an electro-optical imager with a 30-meter resolution and a

700-km swath, and a hyper spectral imager with a 100-meter resolution and a 50-km

swath (Kevin Pollpeter, 2014). HJ-1A was the first micro-satellite constellation for

Environment and Disaster Monitoring of China. The same multispectral imagers

named HJ-1/CCD with four bands (R, G, B, NIR) and large swath are installed on

18

both HJ-1A and HJ-1B. The HJ-1/CCD is the main sensor of the constellation. The

satellite constellation is composed of a number of small satellites, the ground system,

and the application system. It provides all-weather (3 to 100 meter) imagery. The

Huan Jing constellation consists of two small optical satellites, the HJ-1A and the HJ-

1B.

HJ-1A is also the Small Multi-Mission Satellite (SMMS) of the Asia Pacific

Space Cooperation Organization (APSCO) and it will be an important data resource

of APSCO space application. SMMS is a joint venture payload between China, Iran,

South Korea, Mongolia, Pakistan, Thailand and Bangladesh under the Asian-Pacific

organization (Global Master Change Directory, 2012). China is establishing its

disaster and environment monitoring capability mainly depending on ordinary

technologies, and meanwhile, China will also launch the small satellites to monitor

the earth environment, and apply the satellite remote sensing technology to conduct

all weather, around-the-clock and high time-resolution disaster and environmental

monitoring. The small satellite constellation for disaster and environment monitor is

composed of 4 optical small satellites and 4 small satellites with synthetic aperture

radar. During the period of the Tenth Five-Year Plan, China will launch 2 optical

satellites and 1 radar satellite. The resolutions of the CCD camera, the infrared camera

and the synthetic aperture radar are 30m, 150m, and 20m, respectively.

The average re-visit time is 32 hours. China, Thailand and Iran are working

on a joint Small Multi Mission Spacecraft (SMMS) devoted to civilian remote-

sensing and communications experiments. The SMMS satellite will carry a low-

resolution charge-coupled device (CCD) camera and an experimental

telecommunications system. The SMMS will give Iran and Pakistan a semi-

autonomous space-imaging capability. The 470-kg (1,034-lb) spacecraft is set for

launch on a Chinese booster by 2004-05 into a 650-km. (400-mi.) Sun-synchronous

polar orbit (Zulu, 2011).

19

Table 2-1 Payload parameters (Rahman, 2012)

Satellite Payload Band

no.

Spectral

range (µm)

Spatial

resolution

(m)

Swath

width

(km)

Side-

lookin

g

ability

Repetition

cycle

(days)

Data

transmis

sion rate

(Mbps)

HJ-1A

CCD

Camera

1 0.43～0.52 30

700

83,757 4

120

2 0.52～0.60 30

3 0.63～0.69 30

4 0.76～0.9 30

Hyperspect

ral Imager －

0.45～0.95

(110-128

bands)

100 50 ±30 4

Orbit characteristic of HJ-1A

Sun-synchronous circular orbit, altitude = 649 km, inclination = 97.95º,

LTDN (Local Time of Descending Node) equator crossing at 10:45 hours. HJ-1A are

in a coplanar orbit with a phasing of 180º. In the final stage of the constellation, 4

satellites will be distributed in the same orbital plane at phase angles of 90.Table 2

below describes some orbital characteristics of HJ-1A.

Table 2-2 Orbit parameters (Rahman, 2012)

Satellite HJ-1A

Orbit Sun synchronous recurrent frozen orbit

Altitude 650 km

Inclination 97.95

Repetition cycle 31 days

Descending node (Local time) 10:30 AM

On-board capacity 16 Gbits

20

LANDSAT 8 (OLI)

LANDSAT 8 consist of two major segments the observatory and the ground

system. The observatory consists of the spacecraft bus and its payload of two earth

observing sensors, the operational land imager (OLI) and the thermal infrared sensor

(TIRS). OLI and TIRS collect LANDSAT 8 science data. The two sensors will

coincidently collect multispectral digital images of the global land surface including

coastal regions, polar ice, islands, and the continental areas. The spacecraft bus stores

the OLI and TIRS data on an onboard solid-state recorder and then transmits the data

to ground receiving stations, these two sensors provide seasonal coverage of the

global landmass at a spatial resolution of 30 meters (visible, NIR, SWIR), 100 meters

(thermal), and 15 meters (panchromatic). LANDSAT 8 (OLI) is a joint initiative of

NASA and the U.S. Geological Survey to maintain a robust archive of Landsat data

and imagery, which provides an uninterrupted multispectral record of the earth‟s land

surface and it lets us analyze everything from terrain types to crop growth to natural

disasters all around the world. Table 2-3 below, describes some of the spectral bands

and wavelengths within the satellite image.

OLI and TIRS sensors are mounted on the LANDSAT Data Continuity

Mission spacecraft (LDCM). The Operational Land Imager (OLI) and Thermal

Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution

of 30 meters for Bands 1 to 7 and 9. New band 1 (ultra-blue) is useful for coastal and

aerosol studies. New band 9 is useful for cirrus cloud detection. The resolution for

Band 8 (panchromatic) is 15 meters. Thermal bands 10 and 11 are useful in providing

more accurate surface temperatures and are collected at 100 meters. Approximate

scene size is 170 km north-south by 183 km east west (106 miles by 114 miles).

21

Table 2-3 LANDSAT 8 spectral band/wavelengths (Source: Credit U.S. Geological

Survey Department of the Interior USGS)

Landsat Data

Continuity

Mission(LDCM)

Lunch

February 11,2013

Bands Wavelength

(µm)

Resolution

(m)

Band 1 – Coastal aerosol 0.43 - 0.45 30

Band 2 – Blue 0.45 – 0.51 30

Band 3 – Green 0.53 - 0.59 30

Band 4 – Red 0.64 - 0.67 30

Band 5 - Near Infrared (NIR) 0.85 - 0.88 30

Band 6 – SWIR 1 1.57 - 1.65 30

Band 7 – SWIR 2 2.11 – 2. 29 30

Band 8 - Panchromatic 0.50 - 0.68 15

Band 9 – Cirrus 1.36 - 1.38 30

Band 10 – Thermal Infrared (TIRS)1 10.60 – 11.19 100

Band 11 - Thermal Infrared (TIRS) 2 11.50 -12.51 100

Band Combinations for LANDSAT 8 (OLI)

LANDSAT 8 (OLI) image look incredible now, while of the bands from

previous Landsat mission are still incorporated, there are a couple of new ones, such

as the coastal blue band water penetration/aerosol detection and the cirrus cloud band

for cloud masking and other application. Below is a rundown of some common band

combination applied to LANDSAT 8 (OLI), displayed as a red, green, blue (RGB).

22

Table 2-4 Composition Band (Source: Credit U.S. Geological Survey Department of

the Interior USGS)

Natural Color 4 3 2

False Color (Urban) 7 6 4

Color Infrared (Vegetation) 5 4 3

Agriculture 6 5 2

Atmospheric Penetration 7 6 5

Healthy Vegetation 5 6 2

Land and Water 5 6 4

Natural with Atmospheric Removal 7 5 3

Shortwave Infrared 7 5 4

Vegetation Analysis 6 5 4

Global Positioning System (GPS)

The working of Global Positioning System (GPS) is a satellite navigation

system providing worldwide coverage. A group of 24 satellites, circling twice-daily

20,000 km above the earth's surface, transmit coded signals that are picked up by GPS

receivers. The constellation of navigation satellites around the earth enables position

to be determined anywhere at any time, and in any weather condition for free. By

recognizing the codes for each satellite, the receiver can determine the time taken for

the signal to be transmitted. The GPS uses this information to then calculate the

distance to each satellite. Once four or more satellites are located, the GPS

"triangulates" the distances to provide a location on the earth's surface, i.e. longitude,

latitude, and elevation. However, the signal is still prone to a number of errors that

can reduce the positional accuracy. These include atmospheric errors, multi-path

errors, satellite and receiver errors, and intentional errors.

Atmospheric errors are introduced as the signal passes through the

atmospheric layers. Charged particles and moisture droplets delay the signal, leading

to timing inaccuracies. Atmospheric errors may range from 3 to 50 m, depending on

the time of day and the arrangement of satellites in the sky. A "dual-frequency" GPS

23

minimizes these errors through computer modeling or by comparing the relative

speeds of two different signals - but these receivers are costly. Multi-path errors occur

when the signal bounces off obstructions, such as buildings or sheds, before reaching

the receiver. Such errors may exceed 100 m in certain situations. Complex signal

rejection procedures - or simply using the GPS in wide-open spaces - should

minimize these errors. Satellite (or "ephemeris") errors result when the broadcast

orbit differs from the actual orbit. The US Department of Defense uses radar to

determine these errors, and any updated positional information can be added to the

satellite code to reduce this error. Receiver errors result largely from noise or the use

of inaccurate clocks inside the GPS unit - but can be minimized with more expensive

clocks (Amod Ashok Salgaonkar, Trivesh Suresh Mayekar, Avinash Rambhau Rasal,

Kiran Rasal, Balkrishna Hotekar, Rakesh Jadhav, Amar Gaikwad). GPS is not a single

unit. It is a system and has the following three major components:

1. Satellites: There are 24 satellites & 3 spare satellites. The exact location

of each of the satellites at any given moment is known. Very accurate clocks are

installed onboard these satellites. The satellites send radio signals continuously

towards earth. These signals contain several pieces of information such as satellite ID

number, time stamp, exact position of satellite etc.

2. Ground Control Stations: These are five control stations to monitor the

satellites. These stations enable the information on earth to be transmitted to the

satellites. Control stations track satellites & update the position of each satellite

continuously. These stations ensure the accuracy of the system.

3. GPS receivers: GPS units are referred to as receivers. These units receive

radio signals from satellites, which contain important information such as time stamp,

satellite ID number, satellite position etc. The receiver knows exactly when the signal

leaves the satellite (time stamp) and when the signal arrives at the receiver. Hence, it

is possible to calculate the distance from satellites as distance → time × velocity of

light. The receiver also knows the exact position of satellite via the signal. The

receiver is therefore able to determine its exact distance from satellite.

24

Literature Review

Land suitability and crop substitution modeling for cassava (Tamkuan,

2013).in Thailand shows that it is importing highly expensive energy sources such as

petroleum products. However, cassava and sugarcane can be used as both a food and

an alternative energy source. Therefore, this study suggests the use of biofuel crops to

be utilized as a source of energy. Kampaeng-phet province was selected as the

research area in this research because it had potential for many industries to support

growing these crops and extent the plantation. This study has three objectives. The

first objective was land evaluation for cassava considering 10 factors including soil

texture, soil depth, soil drainage, soil fertility, soil pH, surface water, irrigation,

rainfall, temperature and slope. Then, the weights of factors were investigated by

analytic hierarchy process (AHP) and Fuzzy AHP. It was found that Fuzzy AHP had

more accuracy than AHP to evaluate the land suitability for cassava plantation. The

second objective was to classify agricultural areas using HJ-1A satellite images. the

two methods of classification in this study were pixel base classification (maximum

likelihood), and object based classification. For this objective, it was found that object

based classification had an overall accuracy of 76.27% which was more than pixel-

based classification (64.55%). The last objective was to make crop substitution model

for extending cassava plantations with regard to land suitability, and economics

(revenue and profit). The different scenarios showed many different options for

planting biofuel crops. Crop substitution modeling with regard to land suitability

suggested an area suitable to growing cassava of 278.68 sq.km. The model regarding

profit suggested an area suitable for substitution to cassava of 1196.76 sq.km.

Some implications on agricultural land use as affected by land quality in

Sakon Basin, Northeast Thailand were also examined (Mongkolsawat, 2011). Crop

requirements are normally confined to certain land qualities which in turn reflect land

use pattern in these areas. Exploring land quality under a given land use was

conducted with the objective of identifying the land quality limitations and its

consequences on land use patterns. The study area, Amphoe Wanon Niwat, is located

in Sakon Nakhon basin and significantly differs in land use pattern when compared to

the extensive areas in the Northeast. The study used 1995 Landsat TM and 2002

orthophotography to identify the changes in land use patterns of the areas. Evaluation

25

of land suitability for cassava was conducted based on integrated land qualities

concerned using GIS. With the established GIS database, the overall insight into each

land quality affecting the crops could be determined. The spatial land qualities and

their associated attributes were used to analyze the causes and their consequences on

land use patterns. The work demonstrates that an analysis of satellite data and aerial

orthophotos can provide detailed, spatially explicit identification of land qualities

causing the consequent agricultural land use pattern in the Sakon Nakhon Basin. The

shallow lateritic soils, improper land use and mis-management of land have

significantly caused the current land use patterns with relatively low agricultural

productivity.

There was also a study on estimation of cassava producing areas using

Remote Sensing and Geographic Information Systems in the northeast region of

Thailand (Rajendra, 1999). The study on cassava plantation area and production was

conducted in the northeast region of Thailand using integrated Satellite Remote

Sensing (SRS) and Geographic Information Systems (GIS). Although SRS and GIS

are considered to be efficient tools for resource inventorying and monitoring, little

work has been done in Thailand with regards to large-area crop monitoring and

production estimation. The objective of the study was to explore the use of NOAA

AVHRR data for mapping cassava plantation areas. GIS was employed to create

geographical database, such as soils, topography, and land use and also for improving

the results of image classification. The study, conducted over the two crop seasons of

1995 and 1996 indicated that NOAA-AVHRR data can be used to map the cassava

plantation areas at the regional scale in Thailand. The results of the study were

compared with existing cassava statistics produced from the Thai Tapioca

Development Institute (TTDI) and the Office of Agricultural Economics (OAE),

Thailand. The estimated cassava plantation areas from the study were underestimated

by - 9.7 and - 16.4 percent to that of TTDI and OAE, respectively, for 1995 and

overestimated by 4.0 percent but underestimated by - 14.4 percent, respectively, for

the year 1996.

Land suitability for cassava and assessing cassava cropping area with

satellite data and Geographic Information Systems (Mongkolsawat W. P., 2008) was

also studied. Cassava is a major annual crop in Northeast Thailand requires minimal

26

cultural attention and cash input, it is tolerant to drought, and is an efficient extractor

of nutrients in infertile soil. Over 50% of the cassava production in Thailand

originates from the Northeast. Land suitable for cassava should be identified to

support the increase of its yield through effective land utilization. The study was made

with the aims of analyzing the integration of land qualities to evaluate land based on

FAO guidelines, with respect to cultivation practices and minimal soil loss as well as

economic viability. The study area, Northeast Thailand, covers an estimated 170,000

km2

with over 1 million ha of cassava cropped area and is characterized by gently

undulating topography. The overall evaluation of land for cassava in the Northeast

was based on the integrated requirements of crop, management, conservation and

economic viability.

Evaluation of cassava planting potential with Remote Sensing and

Geographic Information System (GIS) (Zhang Chao, 2010) was also studied. Along

with the development of the starch and grain alcohol industries, biomass energy

production has recently become important. Cassava is an important biomass energy

plant. In this paper, Geographic Information System (GIS) and remote sensing (RS)

are used, along with knowledge of the growing environment needed for cassava and

the farmland and ecology protection policy of China, to evaluate the cassava-growing

potential of Winning County. The processing of spatial data is done first. Then, the

evaluation principles are defined according to the spatial data and the required growth

conditions. The evaluation data are obtained by spatial data analysis according to the

evaluation principles. Lastly, the cassava planting potential results are verified by

referencing these to cassava planting statistical data for 2005 provided by Nanning

City Government of Guangxi province.

Another study referred to is conformity of agricultural land use and physical

stability in Khon Kaen, Northeast Thailand. This study established a database of land

suitability for cassava (S.Sukchan, 2003). The evaluation of land in terms of its

suitability for cassava is based on the procedure as described in FAO guidelines for

land evaluation for agriculture. The study area, Khon Kaen Province, covers an area

of about 1,088,599 ha. The cassava requirements include a number of land qualities

which affect the plant growth and yield. The land qualities, on which the suitability

are based, consist of rainfall, irrigated area, soil texture and drainage, soil depth, base

27

saturation, cation exchange capacity, available phosphorus, landform, slope and

salinity of soil. Each of land quality in terms of spatial data were digitally encoded in

Geographic Information System databases to create thematic layers. With the

selection criteria, the overlay of those land qualities was digitally performed to

produce resultant, polygonal layer, each of which is a land unit. The land suitability

model applied to the resultant layer provides the suitability class. The result indicated

that the most extensive areas are marginally suitable and cover areas of about 37% of

the province

CHAPTER 3

RESEARCH METHODOLOGY

This chapter gives an overview of data collection, data requirements and the

method applied for processing as well as the modeling approach, geographical dataset

creation and analysis techniques adopted. The findings of this research can be adapted

to form the basis for driving statics in the classification of cassava plantation in

Banteay Meanchey Province and, subsequently, over broader areas.

The methodology workflow to achieve the objectives are shown below

Figure 3-1 Classification of workflow

29

This chapter will discuss data acquisition and the processing of data

collected in preparation for its inclusion in analysis for classification of cassava

plantation. The discussion begins with a brief overview of the satellite image.

Figure 3-2 SMMS (HJ-1A)

The figure above is a Satellite image from Chines Huan Jing-1A (HJ-1A),

derived on the 02 March, 2015 by code LT51280512011018BKT00 path 07 and row

105. The reference datum of this image was WGS 1984 and the map projection was

UTM zone 47N.

30

Figure 3-3 Enhanced LANDSAT 8 Imagery in 2015

The satellite image from LANDSAT 8 shown above was derived in four

parts on the 03rd ,07th February, 2015 and 02,nd 18th March, 2015 by LANDSAT 8

code LC81270502015054LGN00, LC81270512015038LGN00 path 127 and row

50,51 and LC81280502015077LGN00, LC81280512015061LGN00 path 128 and row

50, 51 using the OLI and TIRS. The reference datum of this image was WGS 1984

and pat the map projection was UTM zone 47N.

Geocorrection of LANDSAT Imagery

Geometric correction of satellite images involves modeling the relationship

between the image and ground coordinate systems. There are both systematic and

non-systematic geometric errors present in satellite imagery (Jenson, 1996). The

31

systematic errors in Landsat imagery are well documented, and are primarily

functions of scan skew, mirror scan velocity, panoramic distortion, platform velocity,

perspective and earth rotation (Mather, 1999; Jenson, 1996). Data on sensor

characteristics and ephemeris information are modeled and applied to the raw imagery

as part of the systematic correction performed by the Landsat receiving stations

(Masek et al., 2001). Assuming an accurate ephemeris based correction software

model is implemented, systematic errors are corrected in commercially available

Landsat imagery. Non-systematic errors are mainly caused by variation through time

in the position and attitude angles of the satellite platform (Janson, 1996).

The stage in Geo-correcting an image is to select an appropriate map

reference system for the area of interest. Correction of all data sources to a single map

projection will allow accurate and easy integration, so choosing the correct system is

critical to success. To Geo Correct your scanned field slip you will input four control

points with known x and y coordinates into the corners of the images. The coordinate

values used will be junctions of North -South and East-West oriented divisions of the

British National grid, which can be assumed to have a precise position and value.

Geocorrection rectification is assigning coordinates to known locations

Ground Control Point and GCPs. The Data pixels must be related to ground locations

such as in UTM coordinates using two main methods: Image to Image correction

involves matching the coordinate systems of one digital image to another image

acting as a map reference

Data exploration and preprocessing

Data exploration

The classification analysis is based on the assumption that the band data and

the training sample data follow normal distribution. To check the distribution of the

data in a band, use the interactive Histogram tool on the Spatial Analyst toolbar. To

check the distribution of individual training samples, use the Histograms tool on the

Training Sample Manager (ArcGIS Resource, 2013).

32

Stretching of band data

The classification process is sensitive to the range of values in each band. To

have the attributes of each band considered equally, the value range for each band

should be similar. If the value range of one band is too small (or too large) relative to

the other bands, it can use the mathematical tools in the Spatial Analyst toolbox to

stretch it. For example, we can use the Times math tool to multiply the band with a

constant value to stretch its value range.

Creating a multiband image

The Image Classification toolbar works with a multiband image layer. To

load individual bands to a new multiband image, use the Composite Bands tool.

Collecting training samples

In supervised classification, training samples are used to identify classes and

calculate their signatures. Training samples can be created interactively using the

training sample drawing tools on the Image Classification toolbar. Creating a training

sample is similar to drawing a graphic in Arc Map except training sample shapes are

managed with Training Sample Manager instead of in an Arc Map graphic layer.

To create a training sample, select one of the training sample drawing tools (for

example, the polygon tool) on the Image Classification toolbar and draw on the input

image layer. The number of pixels in each training sample should not be too small or

too large. If the training sample is too small, it may not provide enough information to

adequately create the class signature. If the training sample is too large, you might

include pixels that are not part of that class. If the number of bands in the image is n,

the optimal number of pixels for each training sample would be between 10n and

100n.

Figure 3-4 Collecting training samples

33

Evaluating training samples

When training samples are drawn in the display, new classes are

automatically created in the Training Sample Manager. The manager provides it with

three tools to evaluate the training samples the Histograms tool, the Scatterplots tool,

and the Statistics tool. You can use these tools to explore the spectral characteristics

of different areas. You can also use these tools to evaluate training samples to see if

there is enough separation between the classes

Figure 3-5 Evaluation training sample editing classes

Depending on the outcome of the training sample evaluation, it may be

necesssary to merge the classes that are overlapping each other into one class. This

can be done using the Merge tool in the manager window. In addition, it can rename

or renumber a class, change the display color, split a class, delete classes, save and

load training samples, and so forth. The following image shows how to merge two

classes

Figure 3-6 Editing class

34

Creating the signature file

Once determined, the training samples are representative of the desired

classes and are distinguishable from one another, a signature file can be created using

the Create Signature File tool in the manager window.

Figure 3-7 Creating the signature file

Examining the signature file

The dendrogram tool allows examining the attribute distances between

sequentially merged classes in a signature file. The output is an ASCII file with a tree

diagram showing the separation of the classes. From the dendrogram, it can determine

whether two or more classes or clusters are distinguishable enough; if not, it might

decide to merge them in the next step.

Editing the signature file

The signature file should not be directly edited in a text editor. Instead, it

should use the Edit Signatures tool in the Multivariate toolset. This tool allows you to

merge, renumber, and delete class signatures.

Applying classification

To classify the image, the Maximum Likelihood Classification tool should

be used. This tool is based on the maximum likelihood probability theory. It assigns

each pixel to one of the different classes based on the means and variances of the class

signatures (stored in a signature file). The tool is also accessible from the Image

Classification toolbar.

35

The Interactive Supervised Classification tool is another way to classify

images. This tool accelerates the maximum likelihood classification process. It allows

one to quickly previewing the classification result without running the Maximum

Likelihood Classification tool.

Post-classification processing

The classified image created by the Maximum Likelihood Classification tool

may misclassify certain cells and create small invalid regions. To improve

classification, it may want to reclassify these misclassified cells to a class or cluster

that is immediately surrounding them. The most commonly used techniques to clean

up the classified image include filtering, smoothing class boundaries, and removing

small isolated regions. A more visually appealing map results from the data cleanup

tools.

Filtering the classified output

This process will remove single isolated pixels from the classified image. It

can be accomplished by either the Majority Filter tool or the Focal Statistics tool with

Majority as the statistics type. The difference of the two tools is that the Majority

Filter tool assumes a 3 x 3 square neighborhood during the processing, while the

Focal Statistics tool supports more neighborhood types.

Smoothing class boundaries

The Boundary Clean tool clumps the classes and smooth the ragged edges of

the classes. The tool works by expanding and then shrinking the classes. It will

increase the spatial coherency of the classified image. Adjacent regions may become

connected.

Generalizing output by removing small isolated regions

After the filtering and smoothing process, the classified image should be

much cleaner than before. However, there may still be some isolated small regions on

the classified image. The generalizing process further cleans up the image by

removing such small regions from the image. This is a multi-step process which

involves several Spatial Analyst tools.

1. Run the Region Group tool with the classified image to assign unique

values to run each region on the image.

36

2. Open the attribute table of the new raster layer created by the Region

Group tool. Use the pixel counts to identify the threshold of small regions that you

want to remove.

3. Create a mask raster for the regions you want to remove. This can be

done by running the Set Null tool to set the regions with small numbers of pixels to a

null value.

4. Run the Nibble tool on the classified image. Use the mask raster created

from the Set Null tool from the previous step as the Input mask raster. This will

dissolve the small regions on the output image.

Equipment of Analysis

In this section, some key equipment in participatory research and analysis is

given. Computer tools to assist in the research, collection of data, and the writing of

this thesis and analysis with other programs of overall use include:

Hardware

Central Processing Unit (CPU) cor i5 2.6GHZ

Read and Memory (RAM) 4GHZ

Printer: for printing thesis

Camera: IPad Air

Global Position System (GPS)

Software Application

The ArcGIS was used to present the resulting map and image from the

analyses carried out on the research, and used Microsoft office for facilitates sit thesis.

The table below outlines the software

37

Table 3-1 Used Software

No. Software Relevance

1 ArcGIS 10

ArcGIS was used for displaying and subsequent

processing and enhancement of the Image as well as the

resulting maps

2

Microsoft

Office: MS word

& Excel

Excel was used in producing the bar chart, while

Microsoft word was used generally for the presentation

of the research in text, chart and map formats

CHAPTER 4

RESULTS

The object of this study form the basis for analysis present in this chapter.

The results are presented using map, charts and statistical tables and examines the

application of geospatial technology compared with LANDSAT 8 (OLI) and SMMS

(HJ-A1) 2015 image satellites. land use was classified in nine classes in Banteay

Meanchey province. Seven categories of land use which were identified these are:

cassava, field crops, forest, water, perennial trees/fruit trees, rice and urban using

maximum likelihood.

Land use from SMMS (HJ-A1)

Land use in Banteay Meanchey province for 2015, as measured by land use

classification of SMMS (HJ-A1) satellite images covers a total of about 6,185.64

square kilometers or about 618,564.44 hectares, by generation results as follows:

Cassava covers approximately 972.15 square kilometers or 97,215.33

hectares an about 15.72 percent of the total area.

Field crops cover approximately 742.74 square kilometers or 74,273.76

hectares or 12.01 percent of the total area.

Forest cover approximately 221.46 square kilometers or approximately

22,146.02 hectares or 3.58 percent of the total area.

Water covers approximately 283.40 square kilometers or 28,340.40 hectares

or 4.58 percent of the total area.

Perennial tree/Fruit tree with an area of approximately 1,075.64 square

kilometers or 107,564.40 hectares or 17.39 percent of the total area.

Rice covers approximately 2,531.59 square kilometers or 253,158.56

hectares or 40.93 percent of the total area.

Urban areas cover approximately 358.66 square kilometers or 35,865.98

hectares or 5.80 percent of the total area.

39

Table 4-1 Land use of Banteay Meanchey province from SMMS (HJ-A1) 2015

Land use Hectares Square

kilometers Percentage

Cassava 97,215.33

972.15 15.72

Filed crop 74,273.76

742.74 12.01

Forest 22,146.02

221.46 3.58

Water 28,340.40

283.40 4.58

Perennial trees/

Fruit trees 107,564.40

1,075.64 17.39

Rice 253,158.56

2,531.59 40.93

Urban 35,865.98

358.66 5.80

Total 618,564.44

6,185.64 100.00

Figure 4-1 Percentage of land use in Banteay Meanchey SMMS (HJ-A1)

Cassava

15.72%

Field crop

12.01%

Forest

3.58%

Water

4.58%

Perennial

Trees/Fruit Trees

17.39%

Rice

40.93%

Urban

5.80%

40

Figure 4-2 Area of land use in Banteay Meanchey SMMS (HJ-A1)

Figure 4-3 SMMS (HJ-A1) map of Banteay Meanchey showing location of cassava

plantation fields of studies

-

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

Cassava

Filed crop

Forest

Water

Perennial Trees/Fruit Trees

Rice

Urban

Field crop

41

Figure 4-4 Map SMMS (HJ-A1) classification of Banteay Meanchey showing

location of fields of study

Land use from LANDSAT 8 (OLI)

The tables below show Land use by satellite image of LANDSAT 8 (OLI)

in the 2015 using land use classification of LANDSAT satellite images covering a

total of 6,185.64 square kilometers or about 618,564.44 hectares and generates result

as follows:

Cassava covers approximately 837.57 square kilometers or 83,757.37

hectares or about 13.54 percent of the total area.

Field crops cover approximately 653.87 square kilometers or 65,386.54

hectares or 10.57 percent of the total area.

Forest covers approximately 268.25 square kilometers or 26,825.14 hectares

or 4.34 percent of the total area.

42

Water covers of approximately 421.43 square kilometers or 42,143.43

hectares or 6.81 percent of the total area.

Perennial trees/Fruit trees cover approximately 1,170.80 square kilometers

or 117,079.71 hectares or 18.93 percent of the total area.

Rice covers approximately 2,508.45 square kilometers or 250,845.26

hectares or 40.55 percent of the total area.

Urban areas cover approximately 325.27 square kilometers or 32,527.00

hectares or 5.26 percent of the total area.

Table 4-2 Land use of Banteay Meanchey province from LANDSAT 8 (OLI)

Land use Hectares

Square

kilometers Percentage

Cassava 83,757.37 837.57 13.54

Field crops 65,386.54 653.87 10.57

Forest 26,825.14 268.25 4.34

Water 42,143.43 421.43 6.81

Perennial trees/

Fruit trees 117,079.71 1,170.80 18.93

Rice 250,845.26 2,508.45 40.55

Urban 32,527.00 325.27 5.26

Total 618,564.44 6,185.64 100.00

43

Figure 4-5 Percentage of land use in Banteay Meanchey from LANDSAT 8 (OLI)

Figure 4-6 Area of land use in Banteay Meanchey from LANDSAT 8 (OLI)

Cassava

13.54% Field crop

10.57%

Forest

4.34%

Water

6.81%

Perennial

Trees/Fruit Trees

18.93%

Rice

40.55%

Urban

5.26%

-

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

Cassava

Filed crop

Forest

Water

Perennial Trees/Fruit Trees

Rice

Urban

Field crop

44

Figure 4-7 The LANDSAT 8 (OLI) satellite map of Banteay Meanchey showing

location of cassava plantation fields of study

Figure 4-8 The LANDSAT 8 (OLI)satellite image map of Banteay Meanchey

province showing location of fields of study

45

Land use comparison of SMMS (HJ-A1) and LANDSAT 8 (OLI)

Compare in land use Small Multi-Mission Satellite identified and analyze

that the Cassava area has increased by13,457.96 hectares or 2.18 percent, field crop

area has increased by 8,887.22 hectares or 1.44 percent, forest area has decreased

markedly by 4,679.12 hectares or 0.76 percent, water area has decreased markedly by

13,803.03 or 2.23 percent, Perennial Trees/Fruit trees have decreased markedly by

9,515.31 hectares or 1.54 percent, Rice area has increased 2,313.30 hectares or 0.37

percent, urban areas have increased markedly by 3,338.98 hectares or 0.54 percent.

Table 4-3 Comparison of land use in Banteay Meanchey province by LANDSAT 8

(OLI) and SMMS (HJ-A1) satellite images

SMMS (HJ-A1) LANDSAT 8 (OLI) Difference

Land use Hectares Square

kilometers Hectares

Square

kilometers Hectares

Square

kilometers

Cassava 97,215.33 15.72 83,757.37 13.54 13,457.96

2.18

Field crops 74,273.76 12.01 65,386.54 10.57 8,887.22 1.44

Forest 22,146.02 3.58 26,825.14 4.34 -4,679.12 -0.76

Water 28,340.40 4.58 42,143.43 6.81 -13,803.03 -2.23

Perennial

trees/Fruit

trees

107,564.40 17.39 117,079.71 18.93 -9,515.31 -1.54

Rice 253,158.56 40.93 250,845.26 40.55 2,313.30 0.37

Urban 35,865.98 5.80 32,527.00 5.26 3,338.98 0.54

Total 618,564.44 100.00 618,564.44 100.00

46

Figure 4-9 Comparison of land use in Banteay Meanchey province by LANDSAT 8

(OLI) and SMMS (HJ-A1) satellite images

Values accuracy of model

By adopting the land use map from the 2015 model, the models show that

the accuracy of the 2015 land-use maps can be obtained from the classification of

satellite data. The implementation of random sampling point out that the review result

of the 2015 land use model is accurate and found that the model generated an overall

accuracy of 75.56 percent from the land use classify SMMS (HJ-A1) satellite and the

model show an the overall accuracy of 81.48 percent land use from the LANDSAT 8

(OLI) satellite.

-

50,000.00

100,000.00

150,000.00

200,000.00

250,000.00

300,000.00

SMMS

LANDSAT

(HJ-A1)

8 (OLI)

47

Figure 4-10 Map showing sample locations to determine accuracy

Table 4-4 Dislocation evaluation the accuracy of the classification of land use by

SMMS (HJ-A1) satellite

Classifieds

Data

Reference data

Cassava Field

crop Forest Water

Perennial

trees/

Fruit trees

Rice Urban Total

Cassava 26 3 0 0 2 3 1 35

Field crop 4 13 1 1 1 2 0 22

Forest 0 0 5 1 0 0 0 6

Water 0 0 0 6 0 1 1 8

Perennial

trees/Fruit

trees

1 1 2 1 52 3 2 62

Rice 4 8 4 6 2 90 4 118

Urban 2 1 1 2 0 1 12 19

Total 37 26 13 17 57 100 20 270

Overall Accuracy for SMMS (HJ-A1) 204/270*100=75.56 %

48

Table 4-5 Dislocation evaluation of the accuracy of the classification of land use in

LANDSAT 8 (OLI) satellite image

Classifieds

Data

Reference data

Cassava Filed

crop Forest Water

Perennial

trees/

Fruit tree

Rice Urban Total

Cassava 28 3 0 0 0 3 1 35

Filed crop 4 16 0 0 0 2 0 22

Forest 0 0 5 0 1 0 0 6

water 0 0 0 6 0 1 1 8

Perennial

trees/Fruit trees 1 0 2 1 57 0 1 62

Rice 3 8 4 6 2 93 2 118

Urban 2 1 0 0 0 1 15 19

Total 38 28 11 13 60 100 20 270

Overall Accuracy for LANDSAT 8 (OLI) 220/270*100=81.48 %

CHAPTER 5

DISCUSSION, CONCLUSION AND RECOMMENDATION

The main purpose this chapter is to synthesize the result from chapter four

and provide answers to the objective of which aims to classify the area planted to

cassava in Banteay Meanchey province using Maximum Likelihood Supervised

Classification explore comparison of the cassava plantation area obtained between

LANDSAT 8 (OLI) and SMMS (HJ-1A). This chapter discusses the findings of

analysis and their significant, conclusions and recommendations for further study.

Discussion

This chapter discusses the classification of cassava planed area in Banteay

Meanchey. The results form land use analysis compares land use classified form

LANDSAT8 (OLI) and SMMS (HJ-A1) satellite imagery. Land use is divided into

seven categories, namely: cassava, field crops, forest, water, perennial trees/fruit trees,

rich and urban. The classification using SMMS (HJ-A1) satellites in 2015 revealed

the area of these seven types were 97,215.33, 74,273.76, 22,146.02, 28,340.40,

107,564.40, 253,158.56 and 35,865.98 hectares respectively.

Rice area was the dominate land use accounting for 40.93 percent of the

total area, followed by cassava, field crops, forest, water, perennial trees/fruit trees,

rice and urban accounting for 40.93 percent, 15.72, 12.01, 3.58, 4.58, 17.39, 40.93

and 5.80.percent, respectively.

The result of classification by LANDSAT 8 satellite imagery in the year

2015, show the area of seven types as 83,757.37, 65,386.54, 26,825.14, 42,143.43,

117,079.71, 250,845.26 and 32,527.00 hectares respectively. Rice accounted for 40.55

percent of the total area, but compared to the SMMS (HJ-A1) satellite, it was found

that the area decreased by 2,313.30 hectares representing 0.37 percent of the total

area, followed by field crops, urban and cassava and aquaculture accounting for 1.44,

0.54 and 2.18 percent. And another area has increased such as forests, water and

perennial trees/ fruit trees and aquaculture accounting for 4,679.12, 13,803.03 and

9,515.31 hectare respectively, which is 0.76, 2.23 and 1.54 percent respectively.

50

When compared SMMS (HJ-A1) with LANDSAT 8 images, it was found

that cassava, field crops, rice and urban areas had increased and area decreasing such

as forest, water and perennial trees/fruit trees. The accuracy of land use models

generates an overall accuracy of 75.56 percent from the land use classified SMMS

(HJ-A1) and the model covers an overall accuracy of 81.48 percent of land use from

LANDSAT 8 (OLI), which shows that the accuracy is at medium and high levels

between the data obtained from monitoring changes and data for location details of

the trial and evaluation of the accuracy of each point in the survey.

Conclusion

Classification of cassava plantations using LANDSAT 8 (OLI) satellite

imagery, a case study Banteay Meanchey Province, Cambodia, used controlled

(Supervised Classification) providing the highest probability (Maximum Likelihood

Classification) and suitabity for Banteay Meanchey Province where cassava

plantation cover approximately 837.57 square kilometers or 83,757 hectares

accounting for 15.72 percent of the total study area. Comparing the results of the

classification using SMMS (HJ-A1) satellite imagery reveals a different in percentage

of 2.18 found in the area under cultivation of cassava from SMMS (HJ-A1) which

showed approximately 975.15 square kilometers or 97,215.33 hectares representing

15.72 percent of the total area. The result of the inspection is accurate with, the

classification of LANDSAT 8 (OLI) and SMMS (HJ-A1) satellite images having

approximately 75.56 and 81.48 percent respectively. The accuracy levels are

considered moderate and high. This accuracy of this study is similar to the findings of

Yamamoto and Suckchan when studying the appropriate classification of land (Land

suitability) for rice, sugarcane and cassava using factors include area classification.

Results from monitoring the overall accuracy of the classification (Overall accuracy

assessment) found that the overall accuracy of the classification was 81.48% by

reason of LANDSAT 8 (OLI) was higher than SMMS (HJ-A1), since LANDSAT 8

(OLI) has wavelength available upon request. In addition the numerical values, which

amounted to 16 bits or 65,536, more detailed satellite images than SMMS (HJ-A1)

with the numerical value of 8-bit or 256.

51

This study revealed the area of cassava plantations which will provide useful

information to forecast the trends in cassava production. The results obtained from

this study serve as a good example of the application of remote sensing data and

geoinformation systems for agriculture. This application can be seen as a regional

pilot project to be adopted by many other provinces in Cambodia for various kinds of

agricultural cultivation such as rice, sugarcane, miaze, etc.

Recommendation

1. This study used satellite data from Landsat 8 and SMMS recorded from

different satellites at different times, so the results of the data analysis area deviated

from reality. Therefore, data should be taken form images of the same time and

analyzed to provide information with increased accuracy.

2. The classification of data using satellite images of cassava plantations

required the ability and experience of a translator. Although it did not affect the

accuracy of the maps of land uses, the accuracy of land use should be checked to

obtain current information and increased accuracy.

3. The distribution of numerical value Digital Number: DN of cassava, rice

and maize are similar for accuracy but must use high resolution satellite Landsat

images. Exploration of other factors related to crop planted, such as drainage of the

soil, depth of soil character of land index value NDVI

4. Continuing the trend of land use in the future can be must also use

appropriate resolution used as a guide in developing spatial databases and the

information may be used to plan development, including the preliminary management

of natural resources, area agriculture and environment.

5. The technology of remote sensing and GIS should be employed in major

studies, concerning nation issues such as agriculture and the environment etc.

52

52

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57

APPENDIX

CASSAVA PLANTATION IN BANTEAY MEANCHEY PROVINCE

58

Comparison of cassava plantations between LANDSAT 8 (OLI) Band

5-4-3 and SMMS (HJ-A1) Band 4-3-2

No Field survey SMMS (HJ-A1) 4-3-2 LANDSAT 8 (OLI)

5-4-3

1 Svay Check District,

TaBean Commune

X: 13°45'25" Y:102°58'38"

2 Svay Check District,

TaBean Commune

X: 13°46'51" Y:102°58'5"

3 Malai District, Ou Sralau

Commune

X: 13°33'48" Y:102°30'4"

59

Compares of cassava plantations between LANDSAT 8 (OLI) Band 5-

4-3 and SMMS (HJ-A1) Band 4-3-2

4 Malai District, Ou Sralau

Commune

X: 13°31'2" Y: 102°30'15"

5 Serei Saophoan District,

Kompong Svay Commune

X: 13°38'10" Y:102°56'44"

6 Malai District, Ou Sampor

Commune

X: 13°32'48" Y: 102°30'4"

BIOGRAPHY

Name Mr. Sopheak Pen

Date of Birth May 30, 1982

Place of Birth Commune #4 , Khan Toul Kork

Phnome Penh Municipality, Cambodia

Present Address Borey Chamkar Dong , # 38D, Chamkar Dong

Phnom Penh Municipality, Cambodia

Position held

2010-present Lecture in the Faculty of Science, Mean Chey

University of Computer Science, National Road

No 5, Banoy Village, Sangkat Teuk Thlar,

Serey Sophorn City, Banteay Meanchey

Province, Cambodia

Education

2001-2005 Bachelor of Computer Science, Royal

University of Phnom Penh

2009-2010 Pedagogy of National Institute Education,

Phnom Penh Municipality, Cambodia

2013-2015 Master of Science (M.Sc.geoinfomatics)

Faculty of Geoinformatics, Burapha University

Chon Buri, Thailand

Email address sopheakgeo@gmail.com

Format Checked by…………………………………

(Mrs. Waranya Chanasongkram)

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