Thesis_82488

8/10/2019 Thesis_82488

1/76

FACULTEIT GENEESKUNDE EN FARMACIE

Vakgroep Menselijke Ecologie

Master Programme in Human Ecology

Integration of Land Susceptibili ty Classi fication System

and Vegetation Change Detection--o-o--

A case study of Dakrong District, Vietnam

Thesis Presented to Obtain the Degree of Master in Human Ecology

Ngo Dang Tri(Roll No. 82488)

August 2007

Promoter: Prof. Dr. Luc Hens

8/10/2019 Thesis_82488

2/76

Declaration and Approval

I hereby declare that this thesis submitted for the Master degree in Human Ecology, at the

Vrije Universiteit Brussel (VUB), is my own original work and has not previously been

submitted to any other institution of higher education for award of any degree. All sources

consulted and quoted are acknowledged by means of a comprehensive list of references.

Signature ... Date .

(Ngo Dang Tri)

Approval

This thesis has been submitted for examination with my approval as the Vrije Universiteit

Brussels promoter.

Signature ... Date .

(Prof. Dr. Luc Hens)

8/10/2019 Thesis_82488

3/76

Dedicated

To my beloved parents Ng Thit ng

and o Th Hu

Knh tng cha m thn yu

8/10/2019 Thesis_82488

4/76

i

Acknowledgments

First of all, I would like to extend my profoundest gratitude to my promoter,

Prof. Luc Hens, for his invaluable comments and guidance through every stage of

my thesis from the formulation of the research proposal to the thesis writing.

With the sincerest regards, I wish to thank Dr. Ann Van Herzele for her ideas,

supervision and comments; I also wish to thank Prof. Rob De Wulf for his

comments in the beginning period of the thesis writing.

I would like to thank to Mr. Canters Frank and Mr. Tesfazghi Ghebre Egziabeherfor their teaching on Remote Sensing and GIS technical.

My gratitude is extended to all staff members of Landscape Ecology

Department, IG, VAST for their support of valuable data of study area. The

author also highly appreciates very helpful assistance from Mr. Nguyen Thanh

Tuan who works together with the author in data collected and processed.

My appreciations further go to the Human Ecology Programme coordinators and

teaching professors at Vrije Universiteit Brussel who equipped me with the

required interdisciplinary research knowledge and skills.

My appreciations also go to the Flemish Interuniversity Council (Vlaamse

Interuniversitaise Raad -VLIR) for providing me a scholarship that helped me

not only improve my academic career but also informally learn the cultural

diversity of global citizens.

Most especially, my deepest gratefulness is extended to my parents and siblings

for their inspiration and encouragement.

Last but never least is the heartfelt thanks to my betrothed, Nguyen Thi Nhu

Trang, for her continuous spiritual support to me, and for the hardship she bears

during my absence.

8/10/2019 Thesis_82488

5/76

ii

ABSTRACT

Dakrong is located in the up-stream area of ThachHan basin, which is one of the largest basins

in the centre of Vietnam. The declining of forest cover (1945 - 1990) in Dakrong leads to

adverse impacts on the environment of the entire ThachHan basin such as soil erosion,

siltation and runoff moderating ability. It also has flooding impacts on flood-plain area of the

basin.

In recognizing the role of vegetation cover, since 1989, the local government has made many

attempts in forest protection and plantation. Nevertheless, despite of gaining in forest cover,

adverse impacts of forest loss have not reduced clearly and significantly, natural disasters in

the entire basin are continuing uncontrollable. One important reason of this situation is:

though vegetation cover increased, it did not cover the sensitive areas to prevent degradation

of land and water, and to mitigate the risk of disasters. Or in other word, it is lack of a proper

land use plan for the Dakrong district.

This thesis develops a new land classification system, call Land susceptibility classification

(LSC), aiming at classifying land of the Dakrong district into different sensitive classestoward forestry management and conservation activities. Along with developing and

implementing the LSC, the thesis implements vegetation change detection (VC) in order to

detect the change of vegetation in Dakrong in the period 1989-2005. The integration of VC

and LSC shows how the change among cover types at a land area is and whether the

change is compatible with the recommended use for such land area where the change

occurred. Based on that, the thesis assesses the compatibility of the vegetation changewith

the recommendation of land use proposed by the LSC system, and provides information

supporting for master land use planning for the Dakrong district.

Keyword: Change detection, Dakrong, FAO land suitability classification, GIS, Image

classification, Land classification, Land susceptibility classification, NDVI image difference,

Post-classification change detection, Remote sensing, USDA land capability classification.

8/10/2019 Thesis_82488

6/76

iii

TABLE OF CONTENTS

Acknowledgments .................................................................................................................... i

Abstract .................................................................................................................................... ii

Table of contents .................................................................................................................... iii

List of figure ........................................................................................................................... vi

List of tables........................................................................................................................... vii

Abbreviation ......................................................................................................................... viii

Chapter 1.INTRODUCTION ................................................................................................1

1.1. Introduction ................................................................................................................................1

1.2. Statement of the research problem ...........................................................................................3

1.3. Research objectives ....................................................................................................................6

1.3.1. General objective ......................................................................................................... 6

1.3.2. Specific objectives ........................................................................................................ 6

1.4. Research questions .....................................................................................................................6

1.5. Definitions and concepts ............................................................................................................7

1.6. The research tools ......................................................................................................................8

1.6.1. Remote sensing (RS) ................................................................................................... 8

1.6.2. Geographical information system (GIS) ..................................................................... 8

1.7. Limitation and significance of the research .............................................................................9

1.7.1. Limitation of the research ........................................................................................... 9

1.7.2. Significance of the research ........................................................................................ 9

1.8. Research logical framework ....................................................................................................10

Chapter 2.LITERATURE REVIEW ..................................................................................12

2.1. Land classification ...................................................................................................................12

2.1.1. USDA land capability classification ......................................................................... 13

2.1.2. FAO land suitability classification ........................................................................... 15

2.1.3. Remarks ..................................................................................................................... 17

8/10/2019 Thesis_82488

7/76

iv

2.2. Vegetation cover change detection ......................................................................................... 17

2.2.1. Post-classification change detection ......................................................................... 18

2.2.1.1. Image classification methods ................................................................. 19

2.2.1.2. Land cover map comparison ..................................................................21

2.2.2. NDVI image difference ............................................................................................. 21

2.2.2.1. Radiometric normalization ..................................................................... 22

2.2.2.2. NDVI calculation and image difference .................................................23

2.2.3. A hybrid approach for change detection .................................................................. 23

Chapter 3.STUDY AREA ....................................................................................................25

3.1. Study area .................................................................................................................................25

3.1.1. Bio-physical conditions ............................................................................................. 253.1.1.1. Topology and hydrology ........................................................................ 25

3.1.1.2. Geomorphology......................................................................................26

3.1.1.3. Edaphology ............................................................................................26

3.1.1.4. Meteorology ...........................................................................................26

3.1.1.5. Land use ................................................................................................. 27

3.1.2. Socio-Economic ......................................................................................................... 27

3.1.2.1. Ethnic Groups ........................................................................................27

3.1.2.2. Health Care ............................................................................................273.1.2.3. Education ...............................................................................................28

3.1.2.4. Transportation ........................................................................................28

3.1.2.5. Cultivation practice and household incomes ..........................................28

Chapter 4.MATERIAL AND METHODS .........................................................................29

4.1. Materials ...................................................................................................................................30

4.2. Methods and process ................................................................................................................31

4.2.1. Land susceptibility classification .............................................................................. 31

4.2.1.1. Establishing factor maps ........................................................................33

4.2.1.2. Regression analysis ................................................................................ 37

4.2.1.3. Land susceptibility classification map ................................................... 37

4.2.2. Vegetation change detection ..................................................................................... 37

4.2.2.1. Pre-processing ........................................................................................38

4.2.2.2. Post-classification change detection....................................................... 39

4.2.2.3. NDVI image difference ..........................................................................40

4.2.2.4. The hybrid approach for change detection ............................................. 40

4.2.3. The compatibility of vegetation change .................................................................... 41

8/10/2019 Thesis_82488

8/76

v

Chapter 5.RESULTS AND DISCUSSION .........................................................................42

5.1. Results .......................................................................................................................................42

5.1.1. Land Susceptibility Classification ............................................................................. 42

5.1.2. Vegetation change detection ..................................................................................... 43

5.1.2.1. Change detected using the post-classification ........................................ 43

5.1.2.2. Change detected using NDVI image difference .....................................45

5.1.2.3. Change detected using the hybrid approach ...........................................46

5.1.2.4. Accuracy assessment for change detection ............................................47

5.1.3. Compatibility of vegetation change ........................................................................... 48

5.2. Discussion ..................................................................................................................................49

5.2.1. Question 1: Why should we develop a new classification system oriented

towards forestry management and conservation activities for the Dakrong

district as well as other up-stream areas?................................................................. 49

5.2.2. Question 2: How is the efficiency of different methods of vegetation cover/use

change detection?....................................................................................................... 50

5.2.3. Question 3: How does vegetation change during the period 1989-2005 in the

study area? Arguably although forest and trees in Vietnam increased, but it did

not cover the sensitive areas. Is the criticism right for Dakrong? .............................52

Chapter 6.CONCLUSIONS AND RECOMMENDATIONS ...........................................55

6.1. Conclusions ...............................................................................................................................55

6.2. Recommendations ....................................................................................................................56

REFERENCES .......................................................................................................................58

APPENDIX .............................................................................................................................64

8/10/2019 Thesis_82488

9/76

vi

LIST OF FIGURES

Figure 1.

Chain of the effects of deforestation ......................................................................2

Figure 2.

Changes on forest area in Vietnam during the period 1943-2003 ..........................4

Figure 3.

Percentage of forest area in the Dakrong district in 1945, 1990, 2000 ..................4

Figure 4.

Flowchart to produce the compatible vegetation change map .............................11

Figure 5.

Framework of the post-classification change detection method ..........................18

Figure 6.

Framework of the NDVI image difference method .............................................22

Figure 7.

Framework of the hybrid approach for change detection .....................................24

Figure 8.

Quang Tri province located in Vietnam ...............................................................25

Figure 9.

Hill shade map of Dakrong district ......................................................................25

Figure 10.

The framework to establish the compatible vegetation change map ....................29

Figure 11.

Sensitive land classes ...........................................................................................31

Figure 12.

The framework for developing the susceptibility classification map ...................32

Figure 13.

Post-classification method for change detection in period 1989 - 2005 ..............40

Figure 14.

The Hybrid method for change detection in period 1989 - 2005 .........................41

Figure 15.

The framework to establish the compatible vegetation cover map ......................41

Figure 16.

Land susceptibility classification map .................................................................42

Figure 17.

Vegetation map and the distribution of land cover types in 1989 ........................44

Figure 18.

Vegetation map and the distribution of land cover types in 2005 ........................44

Figure 19.

General trend of cover change (1989-2005) .........................................................45

Figure 20.

Map of change/no-change detected by NDVI image difference ..........................46

Figure 21.

Vegetation change map detected using the hybrid method ..................................47

Figure 22.

Compatible vegetation change map .....................................................................48

8/10/2019 Thesis_82488

10/76

vii

LIST OF TABLES

Table 1

The severity of the huge flood in Quang Tri occurred in 1999 ..............................5

Table 2

Land capability classes (USDA system) ..............................................................14

Table 3

A treatment oriented scheme for hilly marginal lands .........................................15

Table 4

Land suitability classes and subclasses ................................................................16

Table 5

FAO land suitability classification applied in Vietnam .......................................16

Table 6

Outline of images and maps used in this study ....................................................30

Table 7

Proposed factors for the land susceptibility classification system .......................33

Table 8

Sensitivity classes .................................................................................................42

Table 9

Change detected using the post-classification method .........................................45

Table 10

Change detected using the hybrid approach .........................................................46

Table 11

Accuracies of the post-classification and the NDVI image difference ................47

Table 12

Compatible vegetation change .............................................................................49

8/10/2019 Thesis_82488

11/76

viii

ABBREVIATIONS

CDE Centre for Development and Environment

CVC Compatibility of Vegetation Change

ESRI Environmental Systems Research Institute

FSSPP Forest Sector Support Program and Partnership

GIS Geography Information System

GTZ German Agency for Technical Cooperation

IG, VAST Institute of Geography, Vietnam Academic Science and Technology

ISRIC International Soil Reference and Information Centre

IUCN The World Conservation Union

LSC Land Susceptibility Classification

MARD Ministry of Agriculture and Rural Development Vietnam

MRC Mekong River Commission

NLWRA National Land and Water Resources Audit

QTDoSTE Quang Tri Department of Science, Technology and Environment

QTDoS Quang Tri Department of Statistic

QTSA Quang Tri Statistical Annual

RS Remote Sensing

RSI Research System Inc

SMRP Sustainable Management of Resource in the Lower Mekong Basin

UNDP United Nations Development Programme

VC Vegetation cover/use Change

VFPD Vietnam Forest Protection Department

VNSCFC Vietnam National Steering Committee on Flood control

8/10/2019 Thesis_82488

12/76

8/10/2019 Thesis_82488

13/76

Chapter 1. Introduction

2

Figure 1. Chain of the effects of deforestation (Graff, 1993)

Deforestation(especially insloping land)

Loss of bio -diversity

Reducedcooking

(Fuel) woodscarcity

Increased fuel-wood &fodder collection time

Reduced livestockfodder

Use of dung & cropresidues as fuel

Reduced manure andmulch

Increased soilerosion

Reduced soil fertility

Increased totalrunoff

Reduced runoff incritical periods

Water pollution &salinity

Downstreamsedimentationrivers/reservoirs

Increased flooding

Drinking water effects

Reduce fisheries

Reduced transportcapacity

Reduced irrigationcapacity

Reduced hydro-electricity

8/10/2019 Thesis_82488

14/76


3

This study applies Remote Sensing and Geographic Information System techniques in order

to develop new land classification oriented forestry; to detect the change of vegetation

cover/use (1989-2005),and incorporate the achieved results aiming toassess the compatibility

of Vegetation change in Dakrong (1989-2005) as well assupporting information for master

land use planning for the Dakrong district. The term compatibility refers to the suitability

of the change of vegetation in a land unit with the recommended use for such land unit.

Results of this thesis provide important information for assessing forestation plans and

programmes of the government as well as in proposing a master land use plan.

1.2. Statement of the research problem

Vegetation cover retards soil loss and erosion, retains moisture in the soil, and ensures a

gradual supply of water to streams and rivers (Narendra et al., 1992). When vegetation cover

is destroyed or altered, especially in upper catchments, it leads to erosion, soil and water

degradation, landslides, siltation of water courses and reservoirs, flash floods, and salt water

intrusion (CDE, 1997). Declining of forest in upper-stream areas can lead to increase in the

rate of occurrence and severity of floods and droughts in downstream areas (ARCADIS,

2000). Figure 1 illustrates the multitude of physical effects of one form of land degradation

deforestation. This demonstrates how forest decline directly impacts on wood resources and

indirectly affects on soil and water resources. Many of the potential benefits of soil and waterconservation measures, whether for deforestation or other problems, are shown in this figure

(Graaff, 1993).

Many international agreements and conferences have been organized with attempts to combat

adverse impacts of forests and trees loss. The most well-known are the Ramsar Convention on

Wetlands (1971), the World Commission on Environment and Development (1987), the

United Nations Conference on Environment and Development (UNCED, 1992), United

Nations Millennium Declaration (2000), World Summit for Sustainable Development (WSSD,

2002), the World Forestry Congress (generally taken place every six years, 1926-2003), and

Ministerial Meeting on Forests (1995, 1999, 2005).

In Vietnam, before 1943, forest covered more than 43% (14 million ha) of total land surface.

After 33 years (in1976), the forest cover decreased to 33.8% (11 million ha). From 1976 to

1989, the rapid rate of population growth and socio-economic development activities

continuously resulted in forest loss and degradation as shown in figure 2 (30% (1985); 27%

(1990).

8/10/2019 Thesis_82488

15/76


4

43%

34%

30% 27% 28% 36%

33%

0%

10%

20%

30%

40%

50%

1943 1976 1985 1990 1995 1999 2003

Percentage of Forest cover

Figure 2. Changes on forest area and coverage in Vietnam during the period 1943 2003

(FSSPP, 2006)

In recognizing the role of vegetation cover, since 1989, Vietnams government has made

many attempts on afforestation and forest protection programs such as Program 327, 5-million

ha Afforestation Program, Afforestation program PAM, and German sponsored projects. In

2003, Vietnam forest covered reached 36%, increased more than 9% in comparison with 1990

(figure 2). This increase in forest cover elevated Vietnam to the top 10 world gainers of forest

cover during the period 1990-2000 (IUCN, 2006).

Dakrong is a remote district in the Quang Tri, one of the poorest provinces located in central

Vietnam. In the past, Dakrong was rich of forest cover. As many areas in Vietnam and the

world, forest cover of Dakrong was destroyed and/or conversed to other use. Figure 3 shows

the forests decline during the period 1945 1990 (from 46% decline to 21% of total area).

46%

21%

32%

1945 1990 2000

Figure 3. Percentage of forest area in the Dakrong district in 1945, 1990, 2000 (QTDoS, 2000)

Dakrong falls in up-stream area of ThachHan basin which is the largest basin and occupies

more than 70% of the Quang Tri province territory. Reduction of forest cover in Dakrong

leads to serious effects on the entire ThachHan basin. The declining of forest cover from 46%

to 21% in Dakrong (1945 - 1990) (figure 3) leads to adverse impacts on the environment such

as soil erosion, siltation and runoff moderating ability. It has also flooding impacts on flood-

plain area in the south-eastern areas of Quang Tri (Thuong, 1993). For example, during the

period 1900-1950, only one huge flood happened (experienced in 1928) but there were 4 huge

floods recorded during the period 1950 2000. These four huge floods occurred in 1953,

1975, 1983, and 1990 (VNSCFC, 2002).

8/10/2019 Thesis_82488

16/76


5

Since 1989, the Quang Tri government made efforts to mitigate the situations through

stabilizing and recovering the vegetation cover. Like many regions in Vietnam, Dakrongs

vegetation cover is recovering progressively. According to QTDoS (2000), vegetation cover

has been increasing from 21% (1990) to 32% (2000) (figure 3). Nevertheless, several reports

(Phong, 2001; Tuan, 2001 and VNSCFC, 2002) indicate that despite of gain in forest cover,

the adverse impacts of forest loss have not reduced clearly and significantly, natural disasters

are still uncontrollable. The most noticeable is the huge flood in Quang Tri occurred in 1999

(table 1), which is reputed as the consequent of forest loss in the Dakrong district . These reports also

criticize forestation projects have been implemented without an appropriate location plan.

Table 1 The severity of the huge flood in Quang Tri occurred in 1999 (UNDP, 1999)

Effect Indication Unit Damage

PeoplePeople killed and missing No. 58

People injured No. 4

HousingHouses collapsed No. 2,229

Houses flooded and damaged No. 59,112

EducationSchools collapsed Room 183

Schools damaged Room 1,640

Agriculture

Paddy flooded Ha 4,999

Paddy destroyed Ha No data

Other crops flooded Ha 7,945

FisheryShips and boats sunk and destroyed No. 148

Fish and shrimp lost Ton 295

Total Economic Loss US$ 18,000,000

In this regard, developing a proper land use plan is very important for implementing a

forestation program. Land classification and vegetation change detection are the two

instruments provide important information for developing a proper land use plan.

There are many existing land classification systems. However, among them the USDA land

capability classification and the FAO land suitability classification are most widely used. They

are also the only land classification systems which have been used in Vietnam. These systems

are oriented towards agricultural crop development (these systems will be reviewed in chapter

3). Whereas, Dakrong is a mountainous district, which should be classified by a land

classification system oriented towards forestry. Therefore, it is necessary to develop a new

land classification system for the Dakrong district, which should be oriented towards forest

management and conservation activities without neglecting effectively using of land.

8/10/2019 Thesis_82488

17/76


6

Along with implementation of a land classification, monitoring the conversion of vegetation

cover (vegetation change detection) is also very important in forestry planning. Remote

sensing technique is an extremely valuable tool for vegetation change detection and vegetation

cover mapping. It becomes more valuable for mountainous area such as Dakrong district due

to inaccessible areas in the district. However, the application of remote sensing technique for

vegetation cover change in Vietnam is still limited. Therefore, it is necessary to investigate the

efficiency of different change detection methods. This thesis applied some different methods

of vegetation change detection to detect the change of vegetation of Dakrong in the period

1989-2005 and also find the most efficient method among them.

1.3. Research objectives

1.3.1. General objective

The general objective of this study is to support information for master land use planning

in the Dakrong district.

1.3.2. Specific objectives

The specific objectives of this thesis are:

To develop a new land classification system which oriented towards forest

management and conservation activities, called Land susceptibility classification

system. Based on this system, a land susceptibility classification map is generated.

To identify and quantify the major changes in vegetation cover/use in the study area

during the periods of 1989-2005. Based on this system, a vegetation change map

(period 1989-2005) is generated.

To assess the compatibility of the vegetation changewith the recommendation of land

useproposed by the land susceptibility classification system.

1.4. Research questions

Why should we develop a new classification system oriented towards forestry

management for the Dakrong district as well as other up-stream areas? Does the new

classification system fill up the limitation of USDA land capability classification

system and FAO land suitability classification?

8/10/2019 Thesis_82488

18/76


7

How is the efficiency of different methods of vegetation changes detection? (How are

the different between the results of 3 methods of change detections: the NDVI

difference; the post-classification and the hybrid method?).

How does vegetation change during the period 1989-2005 in the study area? Arguably

although forest and trees in Vietnam increased, but it did not cover the sensitive areas.

Is the criticism right for Dakrong?

1.5. Definitions and concepts

The information about the compatibility of vegetation change can be achieved by

incorporating the results of two methods: (1) Land susceptibility classification and (2)

Vegetation change detection.

(1) Land susceptibility classification (LSC): is a new land classification system oriented

towards forestry management and conservation activities. This thesis established the LSCin

order to classify a territory into different sensitive classes of land. Each class is

different from others by the degree of sensitivity towards land degradation and water

runoff moderate ability.In other word, each class is different from others by the strict

degree of vegetation cover demand to prevent land, water resources degradation and

mitigate disaster. The expected result of LSC is LSC map, which represents sensitive

degree of each area and the type of land use should be practiced on the identified area

(Conservation forest, Production forest, Agriculture, or other cover).

(2) Vegetation change (VC) detection method: is applied to investigate the change of

vegetation cover/use between two different years: 1989 and 2005. The result of this

method is VC map, which represent how vegetation-cover types (Conservation forest,

Production forest, Agriculture, and other lands) change in the periods. There are many

existing change detection methods, some of the most popular methods will bereviewed in chapter 3.

Finally, the two achieved results will be intersected to create the compatibility of vegetation

changes (CVC) map. The CVC map is the final target of this study which enables to show

how the vegetation changed and whether or not the changes are suitable with

recommendations for land use. This result expresses to some extent the efficiency of

afforestation programs toward land, water resources protection and disaster prevention as well

as economic development. The result also provides important information supporting for

proposing a master land use plan.

8/10/2019 Thesis_82488

19/76


8

1.6. The research tools

Remote Sensing and GIS are the main analytical tools used in this thesis. Remote sensing

(RS) and Geography Information Systems (GIS) are very suitable for a multi disciplinary

research like this study. In this study, RS&GIS are used as the main tools for data analyzeprocesses. The major role of RS in this study is detecting the vegetation cover/use change.

Meanwhile, GIS is used to generate Land susceptibility classification map and Compatibility

of vegetation change (CVC) map.

1.6.1. Remote sensing (RS)

Remote sensing is the science and art of obtaining and interpreting information about the earth

surface and atmosphere through the analysis of data acquired by a device that is not in contact

with the earth, which measures the intensity of electromagnetic radiation reflected or emitted

from the earth (Cracknell et al., 1991).

For design of meaningful conservation strategies, comprehensive information on the changes

in distribution with time is required. It is nearly impossible to acquire such information purely

on the basis of field assessment and monitoring. Remote sensing provides a systematic,

synoptic view of earth cover at regular time intervals, and has been useful for this purpose

(Nagendra, 2001). The use of RS in forest resource assessment is important because they offer

us some advantages such as: information collection of the forest with low cost, short time and

less human resources.

1.6.2. Geographical information system (GIS)

GIS is a collection of computer hardware, software, and geographic data for capturing,

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

(ESRI 2006). Currently, GIS is one of the most powerful tools used in resource management.Considering GIS powers for integrating geo-referenced data, and the possibility of the

complex analyses connected with attribute information and spatial information, GIS is the best

suitable system for land classification. Other advantages of GIS in comparison with tradition

method for developing a land classification are the capacity of interpolation, the capacity of

saving time, money and human resources.

The following RS/GIS soft-wares and hard-wares used for the thesis purpose:

Software: ENVI 4.2, Arcinfo Workstation 9.0, ArcView 3.2, Mapinfo 7.5 and SPSS.

Hardware: GPS, Personal computer: PIV, 512MB ram.

8/10/2019 Thesis_82488

20/76

8/10/2019 Thesis_82488

21/76

8/10/2019 Thesis_82488

22/76

11

Figure 4. Flowchart to produce the compatible vegetation chang

Existing map (1)

Creating factor maps (2)

Elevation

Slope

Soil depth

Roughness

Test areas

Land susceptibility classification map (5)

Use the equation for entire area;

Sub-classification for class 3 (4)

Class = ?

Class = 1?

Class = 2?

Class = 3?

SC = a*elevation + b*Slope + c*Soil depth

+ d*Roughness + e

Regression analysis

to predict coefficients (3)

Factors value =?

Elevation

Slope

Soil depth

Roughness

Vegetation C

Compatible vegetation change map (10)

Overlay/GIS

Vegetation map1989

Vegetation2005

Landsat TM1989

Landsat T2005

Image classification (6

Land susceptibility classification Vegeta

Integration of the land classification and the vegetation cover

8/10/2019 Thesis_82488

23/76

Chapter 2. Literature review

12

Chapter 2. LITERATURE REVIEW

As introduced in chapter 1, the main idea of this study is the integration of a land

classification and a vegetation change detection aim to provide information for preliminary

assessing forestation plans and programs of the government as well as providing information

for a master land use planning. This chapter reviews two existing land classification systems

which are most widely used and also to be the only land classifications applied in Vietnam.

This chapter also reviews some methods of change detection.

2.1. Land classification

Forestation is an essential activity to combat adverse impacts of vegetation loss. However,

forestation is also a costly activity and hardly affects socio-economic and environment

aspects, so it requires a proper land use master plan. Always, a land classification is one of the

key components of master planning. A Classification system is defined as a systematic

framework for putting objects into distinct groups or classes based on certain diagnostic

criteria (Kuechler and Zonneveld, 1988).

There are many land classification systems was adopted for specific purposes. Some well-

known land classification systems are USDA land capability classification, FAO Land

suitability classification, USBR Land Suitability for Irrigation, Agro-ecological Zoning, soil

survey interpretations, parametric indices, yield estimates, agro-ecological zoning, fertility

capability soil classification system, the LESA system, and soil potential ratings. Theseclassifications are useful only when used for their intended purposes (Rossiter, 1994). Out of

these classification systems, the USDA land capability classification and the FAO land

evaluation framework (FAO 1976) are the most widely used ones.

This section, firstly, reviews the USDA land capability classification and the FAO land

evaluation framework. After that, a general remark is stated in the section 2.1.3.

8/10/2019 Thesis_82488

24/76


13

2.1.1. USDA land capability classification

USDA land capability classification is the earliest and best known system of land capability

mapping. The system was developed by the United States Natural Resources Conservation

Service to assess the extent to which limitations such as erosion, soil depth, wetness and

climate hinder the agricultural use (Graaff, 1993). The land classification has been adapted for

use in many other countries (Hudson, 1981). This is undoubtedly the most used land

classification system in the world, and the land evaluator will very often encounter it

(Rossiter, 1994).

USDA is applied mainly in agricultural land use planning, with emphasis on its conservation

requirements (FAO, 1984). The objective of the classification is to divide an area of land into

units (the basic units are the capability units) according to their ability to support general kinds

of land use without degradation or significant off-site effects, for farm planning (Rossiter,

1994). This system recognises eight classes arranged from Class I characterised by no or very

slight risk of damage to the land when used for cultivation, to Class VIII, very rough land that

can be safely used only for wildlife, limited recreation and watershed conservation (Graaff,

1993). Increasing class number restricts the intensity of land use (Graff, 1993) (table 2). The

value of land capability classification lies in identifying the risks attached to cultivating theland and in indicating the soil conservation measures that are required (Morgan, 2005).

However, it must be emphasized that the prime concern of the classification is the risk of

erosion, and not productivity (Beek, 1980).

Sheng (1972, 1975, and 1986) has improved the land capability classification by making the

conservation recommendations more specific. The scheme classified lands into whether they

are cultivable and then by conservation treatments required. The lands are divided mainly

according to degree of slope and soil depth although stoniness, wetness and gully dissection

are also considered (Camirand and Evelyn, 2003). His treatment-oriented scheme developed

in Taiwan and tested on hilly land in Jamaica, includes six slope classes and four soil depth

classes. Based on suitability for tillage, this provides seven different options for land use or

recommended treatment (table 3) (Sheng, 1986).

8/10/2019 Thesis_82488

25/76


14

Table 2 Land capability classes (USDA system) (Graaff, 1993)

Class Characteristics and recommended land use

I Deep, productive soils easily worked, on nearly level land; not subject to overland

flow; no or slight risk of damage when cultivated; use of fertilizers and lime, cover

crops, crop rotations required to maintain soil fertility and soil structure.

II Productive soils on gentle slopes; moderate depth; subject to occasional overland

flow; may require drainage; moderate risk of damage when cultivated; use of crop

rotations, water-control systems or special tillage practices to control erosion.

III Soils of moderate fertility on moderately steep slopes, subject to more severe

erosion; subject to sever risk of damage but can be used for crops provided plant

cover is maintained; hay or other sod crops should be grown instead of row crop.

IV Good soils on steep slopes, subject to severe erosion; very severe risk of damage

but may be cultivated if handled with great care; keep in hay or pasture but a grain

crop may be grown once in five or six years.

V Land is too wet or stony for cultivation but of nearly level slope; subject to only

slight erosion if properly managed; should be used for pasture of forestry but

grazing should be regulated to prevent plant cover being destroyed.

VI Shallow soils on steep slopes; use for grazing and forestry; grazing should be

regulated to preserve plant cover; if plant cover is destroyed, use should be

restricted until land cover is re-established.

VII Steep, rough, eroded land with shallow soils; also includes droughty or swampy

land; severe risk of damage even when used for pasture or forestry; strict grazing orforest management must be applied/

VIII Very rough land; not suitable even for woodland or grazing; reserve for wildlife,

recreation or watershed conservation

Classes I-IV denote soils suitable for cultivation;

Classes V-VIII denote soils unsuitable for cultivation.

8/10/2019 Thesis_82488

26/76


15

Table 3 A treatment oriented scheme for hilly marginal lands (Sheng, 1986)

Slope

Soil

depth

Gentle

< 12%

Moderate

12-27%

Strong

27-36%

Very

strong

36-47%

Steep

47-58%

Very

steep

> 58%

Deep (>90 cm) C1 C2 C3 C4 FT F

Moderately deep

(50-90 cm)C1 C2 C3

C4

P

FT

AFF

Shallow

(20-50 cm)C1

C2

P

C3

PP AF F

Very shallow

(= 45%;

FT: Fruit trees; if widely spaced, interspaces to be grass covered;AF: Agro-forestry;

F: Forestry.

2.1.2. FAO land suitability classification

The FAO suitability classification aims to show the suitability of each land unit for each land

use. Land suitability classification specifies the suitability of land for particular crops(Morgan, 2005). In FAO's Framework for Land Evaluation, land is first classed as suitable (S)

or not suitable (N). These suitability classes can then be further sub-divided. In practice, three

classes (S1, S2 and S3) are often used to distinguish land that is highly suitable, moderately

suitable and marginally suitable for a particular use. Two classes of 'not suitable' can usefully

distinguish land that is unsuitable for a particular use at present but which might be useable in

future (N1), from land that offers no prospect of being so used (N2) (FAO, 1976). Principally,

the system classifies all lands into order, class, subclass, and units according to the degree ofsuitability and types of limitations, as given in table 4.

8/10/2019 Thesis_82488

27/76


16

Table 4 Land suitability classes and subclasses (FAO. 1976)

Order Class Subclass Unit

S1: Highly suitable

S2: Moderately suitable

S3: Marginally suitable

etc.

S2m

S2e

etc.

S2e-1

S2e-2

etc.

N1: Currently not suitable

N2: Permanently not suitable

According APO (2004), since 1994, Vietnam follows the FAO framework on land

classification and land evaluation. In 1996, Vietnam published the findings of the National

Program on Vietnam land use evaluation for productive use and ecological stability. Under

this program, different sustainable land-use types of Vietnam were classified jointly by the

experts of Tran An Phong and other agencies. As a result of this program, the soil scientists of

Vietnam completed the land suitability classification for land-use planning in different

ecological zones of the whole country. The main land-use types representing Vietnams

agricultural production systems were classified as table 5. This system takes into consideration

agriculture development and improving crop productivity more than forestry and resource

conservation.

Table 5 FAO land suitability classification applied in Vietnam (Tran an Phong, 2001)

No Major land use types Area(million ha)

Suitability rating

S1 S2 S3

1 Paddy rice 4.38 1.57 1.70 1.11

2 Annual industrial and subsidiary crops 1.66 0.41 0.77 0.48

3 Perennial industrial tree crops and fruit trees 1.84 0.54 0.73 0.57

4 Grass land/pasture 0.53 0.15 0.22 0.16

5 Agro-forestry systems 0.58 0 0.44 0.146 Aquaculture 0.42 0.42 0 0

N: Not suitable

S:Suitable

8/10/2019 Thesis_82488

28/76


17

2.1.3. Remarks

With a district that has of its total area are characterized by hill and mountain as Dakrong

(Trai et al. 2001), consideration for conservation should be emphasized, and forestry planning

should be promoted over agricultural development.

USDA land capability classification system and Sheng scheme as well as FAO land suitability

classification system, which was applied to classify land in Vietnam are oriented towards

agricultural crop development. Therefore, it is necessary to develop a new land classification

system for the Dakrong district, which should be oriented towards forest management.

In additional, one limitation of USDA land capacity classification and FAO land suitability

classification system is the problem of parameter value. The first system classifies land

mapping unit mainly according to slope and soil depth parameters while FAO land suitability

classification then added more land parameters such as soil fertility, water ability, or rainfall

intensity However, both of these systems have a constraint of ranging parameter values

leading to an imprecise result in classifying land units.

For example, it is easily recognized in table 3 that the same value is assigned for the slope

ranging from 47% to 57% (steep slopping) but a different value for the slope ranging from

36% to 47% (very strong sloping). Here, a slope at the margin of the class which have more

similarity are put in different categories. It means that if a land unit has soil depth 15cm then if

the slope is 46%, then the recommended use will be P (Pasture). Whereas, if the slope is 48%

then the recommended use for the land unit will be F (forestry) and the same recommended

use for a land unit that has slope of 57% (which is quite a different value from 48%).

2.2. Vegetation cover change detection

Along with classifying land, detecting the change of vegetation cover/use in order to

investigate vegetation change is very important for forestation management. Many studies

(Brandon and Bottomley, 1998; Chen, 2000; Diouf and Lambin, 2001; Kuntz and Siegert,

1999; Lambin, 1994; Mendoza S. and Etter R, 2002; and Vance and Geoghegan, 2002) have

emphasised the importance of investigating land cover dynamics as a baseline requirement for

sustainable management of natural resources. The knowledge of where are the changes is

essential for the formulation of appropriate management strategies (Phong, 2004).

8/10/2019 Thesis_82488

29/76


18

Change detection is a process of identifying and analyzing the differences of an object or a

phenomenon through monitoring at different times (Morshed, 2002). Many change detection

methods have been developed and used for various applications. However, they can be

broadly divided into: from to and change/no change approaches. Each method of

change detection has advantages and disadvantages its self. In which, the post-classification

change detection (represents for from - to method) and NDVI image difference (represents

for Change - No change methods) are most widely used.

2.2.1. Post-classification change detection

The post-classification change detection is the most intuitive and common method. In this

method, two images from different dates are classified and labelled. The area of change is then

extracted through the direct comparison of the classification results (Lunetta and Elvidge,

1999) (figure 5).

Figure 5. Framework of the post-classification change detection method

Where: (1) Refer to 2.2.1.1. Image classification methods;

(2) Refer to 2.2.1.2. Land cover map comparison.

The principal advantage of the post-classification lies in the fact that the two dates of imagery

are separately classified; thereby minimising the problem of radiometric calibration between

dates (Coppin et al. 2004). Another advantages of the post-classification include the detailed

of from-to information (Chen, 2000; Lunetta and Elvidge, 1999). It bypasses the difficulties

associated with the analysis of images acquired at different times of year or sensor (Chen,

2000).

(1) Image

classification

Image date 1

Classified map

date 1

(2) Comparison

Change map

(1) Image

classification

Image date 2

Classified map

date 2

8/10/2019 Thesis_82488

30/76


19

The main disadvantage of the post-classification approach is the dependency of the land cover

change results on the individual classification accuracies (Chen, 2000). The problem is that

the errors, which are cumulative, from each of the individual land cover maps are incorporated

into the final change product (AMNH, 2004a). In other word, this approach can produce a

large number of erroneous change indications since an error on either data gives a false

indication of change (Singh, 1989). Therefore, it is imperative that the individual classification

be as accurate as possible (Chen, 2000).

From the framework of the post-classification method (figure 5), it has been easily found that

there are two steps to generate vegetation change map: Image classificationand Comparison

of the two image classified maps.

2.2.1.1. Image classification methods

The very important step in the post-classification method is classification process, which

involves translating the pixel values in a satellite image into meaningful categories.

Digital image classification is the process of assigning pixel to classes (Jensen, 1996).

Usually, each pixel is treated as an individual unit composed of values in several spectral

bands. By comparing pixel to one another and to pixels of known identity, it is possible toassemble groups of similar pixels into classes that match to the informational categories of

interest to users of remotely sensed data. Digital image classification can be group into

automated, manual and hybrid approaches.

(1) Automated approach

The majority methods of image classification fall in automated category which is emphasized

by supervised and unsupervised classification algorithm.

Supervised classification algorithm:With supervised classification, we identify examples of

the Information classes (i.e., land cover type) of interest in the image. These are called

"training sites". The image processing software system is then used to develop a statistical

characterisation of the reflectance for each information class. This stage is often called

"signature analysis"and may involve developing a characterisation as simple as the mean or

the rage of reflectance on each bands, or as complex as detailed analyses of the mean,

variances and covariance over all bands. Once a statistical characterisation has been achieved

8/10/2019 Thesis_82488

31/76


20

for each information class, the image is then classified by examining the reflectance for each

pixel and making a decision about which of the signatures it resembles most (Eastman, 1995).

There are several types of supervised classification algorithms. Some of the more popular

ones are: parallelepiped, minimum distance, maximum likelihood, and mahalanobis distance.

Studies (RSI, 2003) proved that maximum likelihood supervised classification has a high

accuracy and is one of the most popular methods of classification in remote sensing. The

method assumes that the statistics for each class in each band are normally distributed and

calculates the probability that a given pixel belongs to a specific class. Unless a probability

threshold is selected, all pixels are classified. Each pixel is assigned to the class that has the

highest probability.

Unsupervised classification algorithm: Unsupervised classification is a method which

examines a large number of unknown pixels and divides into a number of classed based on

natural groupings present in the image values. Unlike supervised classification, unsupervised

classification does not require analyst-specified training data. The basic premise is that values

within a given cover type should be close together in the measurement space (i.e. have similar

gray levels), whereas data in different classes should be comparatively well separated (i.e.

have very different gray levels) (PCI, 1997; Lillesand and Kiefer, 1994; Eastman, 1995). The

two most frequently used algorithms are the K-mean and the ISODATA clustering algorithm.

(2) Manual approach

Manual classification method uses skills that were originally developed for interpreting aerial

photographs. It relies on the interpreter to employ visual cues such as tone, texture, shape,

pattern, and relationship to other objects to identify the different land cover classes.

The primary advantage of manual interpretation is its utilization of the brain to identifyfeatures in the image and relate them to features on the ground. The brain can still beat the

computer in accurately identifying image features.

The disadvantage of manual interpretation is that it tends to be tedious and slow when

compared with automated classification and because it relies solely on a human interpreter and

is also subjective. Another drawback of this method is that it is only able to incorporate 3

bands of data from a satellite image since the interpretation is usually done using a colour

image comprised of red, green, and blue bands (AMNH, 2004b).

8/10/2019 Thesis_82488

32/76


21

(3) The hybrid approach for Image classification

The hybrid approach combines the advantages of the automated and manual methods to

produce a land cover map that is better than if just a single method was used. One hybrid

approach is to use the automated classification methods to do an initial classification and then

use manual methods to refine the classification and correct obvious errors. With this approach

you can get a reasonably good classification quickly with the automated approach and then

use manual methods to refine the classes that did not get labelled correctly (AMNH, 2004b).

2.2.1.2. Land cover map comparison

After classified and labelled two images from different dates, the area of change is deduced by

comparing the classification results. It is the simple step in the post-classification change

detection and be executed through GIS.

2.2.2. NDVI image difference

Almost methods in change/no change approaches are based on some types ofImage algebra

change detection techniques (image difference or image rationing). Singh (1989) and Coppin

et al(2004) have identified image differenceas the most accurate change detection technique.

This technique is performed by subtracting images from two dates pixel by pixel. Then

threshold boundaries between change and no-change pixels are determined for the difference

image to produce the change map (Singh, 1989).

Among change/no change detection methods, NDVI image difference is emphasized as one

of the most widely used. The reason is the method only requires data from the red and near

infrared portion of the electromagnetic spectrum, and it can be applied to virtually all

multispectral data types. A large number of comparative studies on different change detection

methods including NDVI image difference have been carried out (Fung and Siu, 2000; Hayes

and Sader, 2001; Lunetta, 2002; Michener and Houhoulis, 1997; Petit et al2001; Yuan and

Elvidge, 1998). Except for the study by Yuan and Elvidge, (1998), most studies conclude that

NDVI image difference method yields highest accuracy. Studies by Lyon (1998), and Lunetta

(2002) reported that NDVI difference was the best method for vegetation change detection in

biologically complex ecosystems.

To detect the change in this method, firstly, one of the images is applied to a radiometric

normalization to the other image. Secondly, NDVI formulation will be used for NDVI

calculation for both images. Finally, a subtraction is performed between these NDVI images

to generate a different image (figure 6).

8/10/2019 Thesis_82488

33/76


22

The major advantage of NDVI image difference as well as other spectral-based detection

techniques is that they are based on the detection of physical changes between image dates.

This avoids the errors introduced in the post-classification change detection where

inaccuracies in the land cover classification are accumulated into land cover change analysis.

In additional, NDVI difference was least affected by topographic factors (Lyon, 1998, and

Phong, 2004). However, the disadvantage of NDVI image difference laid on the loss of

fromto information. The result is just a representation of change and no change

information.

Figure 6. Framework of the NDVI image difference method

2.2.2.1. Radiometric normalization

Remotely sensed data acquired by satellite sensors are usually influenced by a number of

factors, such as change in radiometric performance over time, variation in solar illumination

conditions, atmospheric scattering and absorption and changes in atmospheric conditions

(presence of clouds) (Mas, 1999; Song, 2000; Yang and Lo, 2000; Du, Teillet and Cihlar,

2002). Therefore, if any two datasets are to be used for quantitative analysis based on

radiometric information, as in the case of multi-date analysis for detecting surface changes,

they ought to be adjusted to compensate for radiometric divergence (Mas, 1999).

Image date 1 Image date 2

Radiometric

normalization to

Image date 2

NormalizedImage date 1

NDVI

calculation

NDVI

calculation

NDVI Image

date 1

NDVI Image

date 2

Image difference

Change map

8/10/2019 Thesis_82488

34/76


23

Two approaches have been developed to achieve these radiometric compensation (radiometric

normalisation) are absolute and relative. The absolute approach requires knowledge of the

sensor spectral profile and atmospheric properties at the time of image acquisition for

atmospheric correction and sensor calibration (Du, 2002; Song, 2000; Yang and Lo, 2000).

This approach is not only costly but also impractical since for most of historical satellite

images these data are not available (Du, 2002).

The relative approachknown as relative radiometric normalisation is more preferred since it

bypasses the shortcomings of absolute approach. In this approach digital numbers of multi-

date images are normalised band by band to a reference image selected by the analyst (Yang

and Lo, 2000). A number of relative radiometric correction methods have been developed for

land cover change detection. They can be divided into three groups: statistical adjustment,

histogram matching, and linear regression normalization. The latter includes various methods

such as image regression (IR), pseudo- invariant feature (PIF), radiometric control set (RCS),

and no change set determined from scattergram (NC). The RCS method is selected for this

study since it favors better change detection (Yang and Lo, 2000; Phong, 2004).

2.2.2.2. NDVI calculation and image difference

After radiometric normalization, NDVI have to be calculated for both images. The equationfor calculating NDVI is denoted as: NDVI = (NIR Red)/(NIR + Red).

And then, a subtraction is performed between these NDVI images to generate the difference

image: NDVI difference image = NDVI date2 NDVI date1.

2.2.3. A hybrid approach for change detection

In common, a hybrid method is the combination between change/no change method and

from - to method in order to minimize the shortcomings of the two mentioned methods,

thereby enhancing the results of the change analysis.

In this method, a change/no change method is used to determine the changed areas. If a

pixel falls in the changed areas, it will be labelled as 1. Otherwise, it will be labelled as 0. This

results in a binary mask image. A traditional post-classification comparison can then be

applied to yield from-to change information. The mask image is overlaid onto the date image

two, and only those pixels that were detected as having changed are classified in the both date

images (figure 7).

8/10/2019 Thesis_82488

35/76

8/10/2019 Thesis_82488

36/76

Chapter 3. Study area

25

Chapter 3. STUDY AREA

3.1. Study area

Dakrong is a remote, mountainous district of Quang Tri, one of the poorest provinces located

in the central Vietnam. Dakrong district is located in the upper catchment basin of the Quang

Tri and Thach Han Rivers. Dakrong spread out from the latitude1618 to 1651 North, and

from the longitude 10642 to 1079 east (figure 8 and 9).

N

300000

300000

600000

600000

900000

900000

1200000

1200000

1500000

1500000

1800000

1800000

2100000

2100000

24000

00

24

00000

100 0 km 200

Figure 8. Quang Tri province

located in Vietnam

Figure 9. Hill shade map of Dakrong district

3.1.1. Bio-physical conditions

3.1.1.1. Topology and hydrology

The topography of Dakrong is characterised by a ridge of low mountains, which extends

south-east from the Annamite Mountains, and forms the boundary between Quang Tri and

Thua Thien Hue provinces. The north-eastern sections of the area are predominantly low-lying

hills. The southern and western sections are rougher in the upstream or highest river

catchments. The lowest and highest point in Dakrong is 9m and 1551m above sea level

respectively.

8/10/2019 Thesis_82488

37/76


26

In central Vietnam, the foothills extend to the coastline, and the coastal plain is compressed or

non-existent. As a result of the coastal topography rivers in the study area are often short,

slope and narrow. Predominant flow direction is east or north-east towards the sea (Trai et al.,

2001).

3.1.1.2. Geomorphology

The study area is situated within the Viet-Lao Caledon enfolded syncline of central Vietnam.

This syncline is confined between the lines of the Ma River fault to the north and the Tam Ky-

Hiep Duc fault to the south. This syncline complex developed from the Cambrian Period to

the beginning of the Devonian Period (William et al. 2001).

Most of the mountains are composed of granite which is common in the region. Lower

mountains are composed of sedimentary rocks from the Ordovician-Silurian Age, including

hyaline rock, stratified arenaceous rock, stratified sandstone, and argillaceous rock (William et

al., 2001).

3.1.1.3. Edaphology

According to William et al. (2001), in Dakrong, the following soils are typical:

Hills:yellow fertile soils developed on sedimentary rocks;

Lower Mountains and Hills: red/yellow fertile soils developed on sedimentary

rocks, with fine soil composition;

Low Mountains:yellow fertile soils developed on effusive acid rock;

Mid-high Mountains:yellow and red alpine humus and fertile soils developed on

sedimentary rock, with rude soil composition, or yellow and red alpine humusdeveloped on effusive acid rock; and

Basins and River Washes:river and stream alluvium.

3.1.1.4. Meteorology

Temperature: Dakrong is located in an eastern tropical monsoons area,

experiencing an average annual temperature ranges from 22 to 24C. Winters are

cold and humid, due to north-easterly winds. Western winds lead to hot and dry

conditions in the summer (Trai et al. 2001).

8/10/2019 Thesis_82488

38/76


27

Precipitation:Dakrong has a high rainfall with average of 2,500-3,000 mm/year.

September and October are highest rainfall months (45% of the total rainfall). The

dry season are extended from February to ends in July (Trai et al. 2001).

Humidity: Relative humidity for this region averages 85-88 %. During the rainyseason, relative humidity is around 90%. In dry season, the minimum relative

humidity can be reached at 30% (Trai et al. 2001).

3.1.1.5. Land use

In 1997, the forest land covered 27,937 ha (27% of the total district land area), and the

agricultural land covers 8,681 ha (8.4% of total land). Unproductive land currently accounts

for 66,905 ha (64.6 %) of the total; unproductive lands include agriculturally exhausted lands

barren lands, and hills (QTDoS, 2000).

The high percentage of unproductive land area was originally created by slash-and-burn

cultivation. Although there have been determined efforts to reform land use practices, the

amount of unusable land is increasing as a result of continued slash-and-burn cultivation, and

is further compounded by progressive soil erosion (QTDoS, 2000).

3.1.2. Socio-Economic

The Dakrong district has 13 communes. The population density is dispersed along roads rather

than in villages. In the Dakrong district there are currently: 2,603 households; 14,489 people;

and 2% population growth per year (QTDoS, 2000).

3.1.2.1. Ethnic Groups

There are three ethnic groups: Kinh (majority Vietnamese) (33 %); Bru-Van Kieu (52 %); and

Pa-co (15 %). The Bru-Van Kieu ethnic minority, also known as the Van Kieu, are member ofthe Mon-Khmer language group, have the largest local population. The Pa-co ethnic minority,

a subgroup of the Ta-oi ethnic minority, closely akin to the Ba-hi ethnic minority and live in

the Ta Rut commune (QTDoS, 2000).

3.1.2.2. Health Care

Health facilities are sparse in this newly established district. In the 13 communes of the

district, there are only three commune health centres (Ta Rut, Ba Long and Mo O communes).The largest commune does not have a health centre (QTDoS, 2000).

8/10/2019 Thesis_82488

39/76


28

The health care facilities are understaffed and lack properly trained health care workers, and

the staff housing is primitive and inadequate. The most common ailments are malaria, goiter

and tuberculosis (QTDoS, 2000).

3.1.2.3. Education

The educational infrastructures are also poorly established and lack both schools and teachers.

The literacy rate in the Dakrong district is uncommonly low for Vietnam. Kindergarten

facilities do not exist in any of the 13 communes. However, each commune has a primary

school. Ba Long and Trieu Nguyen also have secondary schools within the primary school

facilities. Very few children attend secondary school. In total, there are 122 teachers but only

11 are ethnic minority people, all of whom teach at the primary school level (QTDoS, 2000).

3.1.2.4. Transportation

Currently, two communes (Ba Long and Hai Phuc) are not accessible by road, and the main

mode of transport to these two communes is the Quang Tri River. There are two existing roads

which are within the national road system and which cross the district: National Highways 9

and 14B (Trai et al. 2001).

3.1.2.5. Cultivation practice and household incomes

The main sources of income are agriculture and forestry. Average income is low, cultivation

practices are antiquated and arable land is scarce. Total food consumption per person is only

120 kg/year. Malnutrition and poverty are common, especially among ethnic minority people:

A sizeable portion of the districts population supplement their diets by gathering and hunting

in the watershed protection forest (QTDoS, 2000).

Animal husbandry is also a source of income, particularly the breeding of water buffaloes,

cows and pigs. Buffalo and cows are free ranging and are commonly used as draft animals for

timber exploitation and transportation (QTDoS, 2000).

8/10/2019 Thesis_82488

40/76

Chapter 4. Materials and Methods

29

Chapter 4. MATERIAL AND METHODS

As mention in Chapter 1, the compatibility of vegetation change (CVC) mapis t resulted from

the integration of the land susceptibility classification map and the vegetation change map.

Where:

The Land susceptibility map represents the susceptive level of a certain land area

and pinpoints the type of land use that is appropriate with such land area. The map

is established in GIS by implementing a land susceptibility classification system.

The Vegetation change mapshows how the vegetation changed in the period 1989-

2005. The map is established mainly in Remote Sensing environment by

implementing vegetation change detection.

Figure 10 describes the general framework of producing the CVC map. The Land

susceptibility map and the Vegetation change map are first deduced separately, and then, the

two maps are overlaid to produce the CVC map.

Figure 10. The framework to establish the compatible vegetation change map

Where: (1) Refer to 4.2.1. Land susceptibility classification;

(2) Refer to figure 16. Land Susceptibility classification map;

(3) Refer to 4.2.2. Vegetation change detection;

(4) Refer to figure 21. Vegetation change map using the hybrid method;

(5) Refer to figure 22. Compatible vegetation change map.

Overlay/GIS

Land susceptibilityclassification map (2)

Vegetation changemap (4)

Compatible vegetationchange map (5)

Land susceptibilityclassification (1)

Vegetation changedetection (3)

8/10/2019 Thesis_82488

41/76


30

4.1. Materials

To understand the background and to identify the problems, the general concepts,

development and implementations of land classification systems as well as cover change

detection around the world and Vietnam has been reviewed. This information was obtained

from books, study papers, internet, magazines, journals and reports.

Other very important data are satellite images, maps, relevant studies and reports in Quang Tri

province that were collected from the Global land cover facility, Quang Tri Department of

Science, Technology and Environment (QT DoSTE), and Landscape Ecology Department

(LED)-IG, VAST. Table 6 outlines the images and maps used in this study.

Table 6 Outline of images and maps used in this study

Data Name Date Format Scale Source

Existing

images

Landsat TM

Path/Row = 125/49

17/02/

1989

Raster 30m resolution Global land

cover facility

Landsat ETM+

Path/Row = 125/49

01/02/

2005

Raster 30m resolution LED-IG, Vast

Existing

maps

Contour 1990 Vector/line 1/25.000 LED-IG, Vast

River 2000 Vector/line 1/25.000 LED-IG, Vast

Soil 1998 Vector/ polygon 1/25.000 QT DoSTE

Administrative 2000 Vector/ polygon 1/25.000 LED-IG, Vast

Deducing

maps

DEM 2007 Grid 30m resolution Contour map

Slope 2007 Grid 30m resolution DEM

Roughness 2007 Grid 30m resolution DEM

Soil depth 2007 Grid 30m resolution Soil map

Soil type 2007 Grid 30m resolution Soil map

Water availability 2007 Grid 30m resolution River map

8/10/2019 Thesis_82488

42/76


31

4.2. Methods and process

4.2.1. Land susceptibility classification

In actual appearance, the classes describe difference strict degree of vegetation cover

requirement aiming to mitigate resource degradation and prevent disaster. The degree of

strictness of vegetation cover requirement is decreased from class 1 to class 3 and represented

in land susceptibility classification map. Susceptive class 3 can be subdivided into class 3a

(arable land) and class 3b (Non-crop land). This map is the expected result of the

classification system. For each class, general recommendations for sustainable land use are

given. The three classes are:

Susceptive class 1 (the most sensitivity class):Areas with very steep slopes and rugged

landforms, commonly uplands and headwater areas. These are critical areas for water

and soil resources management. These areas are the highest priority for forest

protection and forestation. Recommended land use: Conservation Forest. As a rule,

these areas should be under permanent forest cover.

Susceptive class 2 (the moderate sensitivity class): They are usually at high elevation

with steep to very steep slopes. Landforms usually result in less erosion than Class 1.

Recommended land use: Production forestswhere mining and logging will be allowedwithin legal limits.

Susceptive class 3 (No sensitivity towards land and water degradation): includes

gentle slopes or flat areas. Such areas are appropriate for other land use practice such

as agriculture and other uses. The class can be divided into 2 subclasses: class 3a -

arable land and class 3b - non-crop land (barren lands).

The Land susceptibility classification system is developed based on a relationship between

susceptive classes and related factors (elevation, roughness, slope, and soil depth). This

relationship can be expressed as a function between susceptive classes and factors and denoted

as following equation:

As mention in section 2.1.3, all of the land classificationsystems in Vietnam were oriented toward agricultural

crop development. In order to classify land oriented

towards forest management and conservation, this thesis

develops a new land classification system, called Land

susceptibility classification system. The system classifies

territory into three difference classes. Each class is

different from others by the degree of sensitivity towards

land degradation and water runoff moderate ability.

Figure 11. Sensitive land classes

8/10/2019 Thesis_82488

43/76


32

SC = a1*x1+ a2*x2+ a3*x3 ++ an*xn+ an+1(equation 1)

Where: SC = Susceptive class;

x1 xn = Factors that has relation with Sensitive classes.

a1 an+1 = Coefficients that describes mathematical relationship orinteraction between susceptive class and factors;

Note: The equation was a linear regression because all of relationship factors are either direct ratio

or inverse ratio with susceptive class.

Two task blocks (Creating factor maps and regression analysis) have to be implemented to

develop the classification system. The framework for developing the classification system is

described in figure 12 following:

Figure 12.The framework for developing the susceptibility classification map

Where: (1) Refer to table 6. Outline of images and maps used in this study;

(2) Refer to 4.2.1.1. Creating factor maps;

(3) Refer to 4.2.1.2. Regression analysis;

(4) Refer to 4.2.1.3. Deducing land susceptibility classification map;(5) Refer to figure 16. Land susceptibility classification map.

Existing map (1)

Creating factor maps (2)

Elevation

Slope

Soil depth

Roughness

Test areas

Land susceptibility classification map (5)

Use the equation for entire area;

Sub-classi ication or class 3 (4)

Class = ?

Class = 1?

Class = 2?

Class = 3?

SC = a*elevation + b*Slope + c*Soil depth +

d*Roughness + e

Regression analysis

to predict coefficients (3)

Factors value =?

Elevation

Slope

Soil depth

Roughness

8/10/2019 Thesis_82488

44/76


33

4.2.1.1. Establishing factor maps

The following six factors were initially proposed (table 7). (The climate factor was rejected

because the Dakrong district has a uniform climate type). Elevation, Slope, Roughness and

Soil depth are used to classify study area into 3 classes (class 1, class 2 and class 3); and then,

Slope, Soil depth, Water availability and Soil type are used to sub-classify class 3 to class 3a

and class 3b.

Table 7 Proposed factors for the land susceptibility classification system

Criteria Value Relation to sensitivity land class

Elevation Real value (meter) The higher the elevation, the more sensitive of the landis.

Inverse ratio with Susceptive class.

Slope Real value (percent) The higher the slope, the more sensitive of the land is.


Roughness Real value(m/990m2)

The higher the roughness value, the more sensitive ofthe land is.


Soil depth Integer value (Soil

depth are assigned to

score values)

The more soil depth, the more sensitive of the land is.

Direct ratio with Susceptive class.

Soil type If soil type belongs to no salty, no sulphate and nobared soil group

Can be used for agriculture (class 3a).

WaterAvailability

Real value(m/990m2)

If River density > 500m/km2

Can be used for agriculture (class 3a).

(1) Elevation factor

The elevation is the altitude of a given spot measured in meters (continuous value) above sea

level. Areas in higher locations usually have higher average annual rainfall. Therefore such

areas have a greater erosive potential, increasing soil and water degradation. Therefore, the

elevation factor has an inverse ratio with susceptive class number:The higher of elevation, the

more susceptive of land, the smaller susceptive class number.

8/10/2019 Thesis_82488

45/76


34

Elevation factor is deduced from a Digital elevation model (DEM). DEM is a grid theme

where each grid cell contains elevation data. The DEM is generated by GIS interpolation

function (ArcInfo Topogrid). Linear and point information (the elevation contours and

elevation points from topography map) is transformed into spatial information (DEM). The

main ArcInfo commands used to generate DEM from topographic map layers are:

Arc: TOPOGRID Draft_dem 30

Topogrid: CONTOUR elev_ln elev

Topogrid: POINT elev_pnt elev

Topogrid: STREAM stream

Topogrid: LAKE lake

Topogrid: END

Arc: GRID

Grid: FILL draft_dem dem SINK 20

The ArcInfo command used for generated Elevation factor from DEM is:

Arc: GRID

Grid: Elev = DEM

(2)

Slope factor

Slope factor identifies the maximum rate of change in value from each cell to its neighbours.

The slope unit can be percentage or degree. The unit used here is percentage. A slope of 40%

means that, for each 100 meters of horizontal distance, the terrain rises or drops 40 meters

(vertical distance).

The steeper the land area is, the more it is susceptive to soil degradation. A piece of land with

a slope of 40% is much higher risk of soil degradation, than another piece with a slope of 5%,

suppose that other related factors are similar in both cases. Therefore, the slope factor has an

inverse ratio with susceptive class number: The higher the slope value, the more susceptive of

land, the smaller susceptive class number.

Slope factor is also used in subdivided processing to divide susceptive class 3 into subclass 3a

and subclass 3b. If slope of a certain land is more than 15% then the land lies in subclass 3b

(non-suitable for agriculture). Otherwise, the land will be determined as subclass 3a orsubclass 3b depending on other factors (water availability, soil depth and soil type).

8/10/2019 Thesis_82488

46/76

8/10/2019 Thesis_82488

47/76


36

The Soil depth factor is also used in subdivided processing to divide susceptive class 3 into

subclass 3a and subclass 3b. If soil depth of a certain land is less than 50cm then the land lies

in subclass 3b (non-suitable for agriculture). Otherwise, the land will be determined as

subclass 3a or subclass 3b depending on other factors (slope, water availability and soil type).

The soil depth layer can be extracted from soil map. The soil map for the Dakrong district is

stored in vector. ArcInfo software is used to deduce soil depth grid from soil depth vector

map. The main ArcInfo commands used to deduce soil depth factor are:

Arc: POLYGRID soil soil_depth depth

(5) Soil type factor

This factor is used to subdivide the susceptive class 3 into subclass 3a and subclass 3b. If soil

type of a certain land belong to the salty, sulphate or bared soil group, the land lies in subclass

3b (non-suitable for agriculture). Otherwise, the land will be determined as subclass 3a or

subclass 3b depending on other factors (slope, water availability and soil depth).

The soil type layer can be also extracted from soil map. The same as deducing soil depth

factor, ArcInfo is used to deduce soil depth grid from soil type vector map. The main ArcInfo

commands used to deduce soil depth factor are:

Arc: POLYGRID soil soil_type type

(6) Water availability factor (river density or dissection degree)

Water availability factor is also used in subdivided processing to subdivide susceptive class 3

into two subclasses: 3a and 3b. If river density of a certain land is less than 500m/km 2then the

land lies in subclass 3b (non-suitable for agriculture). Otherwise, the land will be determined

as subclass 3a or subclass 3b depending on other factors (slope, soil depth and soil type).

The water availability is difficult to quantify with traditional methods (ISRIC, 1993).

However, the use of GIS makes it feasible to derive the water availability factor from drainage

network. The unit for measuring water availability value is m/km2. The factor was derived

from drainage network using the line_density function available from the ArcInfo-GRID

module. The main ArcInfo commands used to deduce water availability factor are:

Grid: river_den = LINEDENSITY (drainage, #, 30, SIMPLE, #, 1000)

8/10/2019 Thesis_82488

48/76


37

4.2.1.2. Regression analysis

In order to predict the coefficients for equation 1 (ref 4.2.1) for Dakrong, test areas (samples)

is collected randomly and assigned a

Thesis_82488

Documents

Transcript of Thesis_82488