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Tropical Forest Monitoring using Synthetic Aperture Radar

- Theories and Applications -

January 2002

Josaphat Tetuko SRI SUMANTYO

Graduate School of Science and Technology

Chiba University

ii

If you download/copy/refer this manuscript,

Please, donate for Indonesian Children Scholarship: *)

Pandhito Panji Foundation

c/o Innes Indreswari (Executive Secretary)

TAPLUS BNI 1946, ITB Branch

Jalan Ganesha 10 Bandung 40132 Indonesia

No. Account 236.003094537.901

*) This announcement is not including in real dissertation.

v

Declaration

This document certifies that the research and its results in this dissertation

have never been submitted elsewhere for an award of any degree or

diploma.

Chiba, January 2002

Candidate

(Josaphat Tetuko SRI SUMANTYO)

Supervisors

(Prof. Dr. Nobuo TAKEUCHI) (Ass. Prof. Dr. Ryutaro TATEISHI)

vii

To my lovely wife Innes Indreswari, son Johannes Pandhito Panji Herdento,

parents Michael Suman Juswaljati and Florentina Srindadi

for their love, support and encouragement.

Chiba, January 2002

ix

Abstract

Recently, remote sensing technology, especially Synthetic Aperture Radar (SAR)

sensor, has been an efficient and helpful tool to monitor tropical forest area where always in

cloudy condition. However, SAR data are not easily interpreted due to the complex relations

of the radar scattering mechanism between microwaves and tropical forest parameter (i.e.

diameter of tree trunk, thickness of burnt coal seam, surface roughness and soil moisture).

Hence, in this research, numerical methods were developed to analyse the interaction of L

Band microwaves with a tree trunk of tropical forest and burnt coal seam of forest fire scars.

In analysis of scattered wave from a tropical tree trunk, the proposed method

approximates a trunk as an infinite length of two and three layers of cylindrical dielectric

media. These layers are skin and heartwood; and skin, xylem and heartwood. The

horizontally and vertically polarised scattered fields are derived in order to calculate the

relationship between trunk diameter and backscattering coefficient. The analysis result is

confirmed by simulating the scattered wave from a tree trunk using Finite Difference Time

Domain (FDTD) method. The model uses the equations of scattered electromagnetic fields

that are derived from Maxwell’s equations. Both analysis and simulation results are similar.

Then the relationship is used to estimate tree trunk diameters of pine forest around Saguling

lake and tropical forest at Mount Gede Pangrango National Park, west Java, Indonesia from

Japanese Earth Resources Satellite (JERS-1) SAR data.

In analysis of scattered wave from burnt coal seam, two types of methods (simple and

complicated) are conducted to analyse scattered waves from burnt coal seam in order to

estimate thickness of forest fire scars. The model is composed of three media namely; free

space (air), burnt coal seam and peat (a perfectly conductor). For computation purposes, the

equivalent circuit of this model is conducted using classical transmission line circuit method

for a simple analysis, and the advanced stationary-phase approximation is used to analyse

scattered wave from complicated rough burnt coal seam. The relationship between

backscattering coefficient and thickness of burnt coal seam is obtained. The analysis result is

confirmed by simulation using FDTD method. The simulation is done using a two-

dimensional finite-difference model for scattered waves from the burnt coal seam. Both

analysis and the simulation results are similar. Subsequently, the developed model is applied

to estimate the thickness of burnt coal seam in central Borneo fire events in 1997 using

JERS-1 SAR data. The estimated result agrees with ground measurement that was collected

in period of 1995 to 1997.

xiii

Acknowledgements

The author wishes to express his gratitude to the members of his committee and to the

Department of Geoinformation Analysis and Department of Sensor and Atmospheric

Radiation, Center for Environmental Remote Sensing (CEReS), Chiba University for

supporting this research. Special thanks are due to Associate Professor Ryutaro TATEISHI

and Professor Nobuo TAKEUCHI for their continuing guidance and invaluable support

throughout the course of this research.

He would also like to thank the following friends and colleagues for their friendship and

enlightening conversations during his graduate studies: Dr. Peter Gunin, Dr. Nicolai Kharin,

Dr. WEN Cheng-Gang, Dr. ZHU Lin, Dr. PARK Jong-Hyun, Dr. Masayuki MATSUOKA, Dr.

PARK Jong-geol, Dr. Ketut Wikantika, Dr. Hussein HARAHSHEH, Dr. Kamal Sarabandi,

WU Jian-yu, Shin ICHIHASHI, ZHANG Xian-Ji, Satoshi AKAGAWA, KIM Dong-Hee,

Renchin Tsolmon, Hiroshi SATOH, Hussam AL-BILBISI, ABAN Jose Edgardo, Alessandra

DE CONTI, Ngigi Thomas CATHUNGU, Mitsuhiko EBATA, Yasunobu SHIMAZAKI,

Hokuto KANO, Jun OSOZAWA, Mohamed ABOEL GHAR, Adi Junjunan Mustafa, Yosuke

ORISHIMO, Rokhmatuloh, Faten Ali Kayed KHRAISHA, Hideyuki HANAWA, Kentaro

HARAGUCHI, and Wihartini NAZORI.

He also grateful to Dr. Kasdi Subagyo and Nuraini (Centre for Soil and Agroclimate

Research) for ground data of central Borneo; Dr. Abdul Hadi (Universitas Lambung

Mangkurat) and Prof. Kenichiro YASHIRO for valuable discussion; Maksum (Cicurug

National Park), Lucia Tri Erowadanti SRI SUMANTYO (Universitas Sebelas Maret) for

tropical tree samples; Franciscus Dwi Koco SRI SUMANTYO (Sarana Putra Makmur) and

Pandhito Panji Foundation (Research Center and Indonesian Databank) for ground data,

topographic and digital maps of the study area in this study; Prof. Koichi ITO, Dr. Ichiro IDA

xiv

and Dr. Kazuyuki SAITO (Chiba University) for their assistance to measure the dielectric

constant of tropical forest samples; MITI/NASDA for the courtesy of JERS-1 VNIR and SAR

data; CNES for SPOT-HRV data; BAKOSURTANAL for topographic and Digital Elevation

Model (DEM) data; Pandhito Panji Foundation (PPF), Okamoto Scholarship Foundation

(OSF), Satoh International Scholarship Foundation (SISF) and Atsumi International

Scholarship Foundation (AISF) for supporting this research.

xv

Contents

Title (in English) i

Title (in Japanese) iii

Declaration v

Dedication vii

Abstract (in English) ix

Abstract (in Japanese) xi

Acknowledgements xiii

Contents xv

List of Appendices xix

List of Figures xxi

List of Tables xxviii

Chapter I. Introduction

1.1 Motivations and objectives 1

1.2 Deforestation and forest fire 2

1.3 Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar 6

1.4 Physical characteristic measurements 8

References 13

Chapter II. Analysis of Scattered Waves from Two Layers of Tree Trunk

2.1 Introduction 17

2.2 Analysis 18

2.3 Simulation 22

2.4 Results and discussion 29

2.5 Application 33

2.5.1 Study area 33

2.5.2 Data processing 33

2.6 Conclusions 37

References 38

xvi

Chapter III. Analysis of Scattered Waves from Three Layers of Tree Trunk

3.1 Introduction 41

3.2 Analysis 42

3.3 Simulation 47

3.4 Results 49

3.5 Application 55

3.5.1 Study area 55


3.6 Conclusions 63

References 65

Chapter IV. Analysis of Scattered Waves from Burnt Coal Seam

4.1 Introduction 67

4.2 Analysis 78

4.3 Simulation 82

4.4 Results 84

4.5 Application 89

4.5.1 Study area 89


4.6 Conclusions 96

References 98

Chapter V. Analysis of Scattered Waves from Rough Burnt Coal Seam

5.1 Introduction 101

5.2 Analysis 102

5.2.1 Scattered fields on burnt coal seam surface (1) or interface 1 106

5.2.1.1 Scattering field on medium 1 106


5.2.2 Scattering field on peat surface or interface 2 110

5.2.3 Scattering field on burnt coal seam surface (2) or interface 1 113



5.2.4 Scattering coefficient 117

xvii

5.3 Results and Discussion 124

5.4 Application 126

5.4.1 Study area 126


5.5 Conclusions 128

References 129

Chapter VI. Summary and Recommendations

6.1 Summary 131

6.2 Future work and Recommendations 132

Appendices 135

Publication list 183

Biography 187

xix

List of Appendices

Appendix A Analysis of Scattered Waves on Two Layers of Tree Trunk (TM

mode)

135

Appendix B Analysis of Scattered Waves on Three Layers of Tree Trunk (TM

mode)

139

Appendix C Finite Difference Time Domain Method (TE mode) 143

Appendix D Ground Data Around Opening Peatland Area (One Million

Hectares Peatland Project, central Borneo, Indonesia)

149

Appendix E Wave Analysis in Cylindrical Coordinate System (TM mode) 157

Appendix F Ground Data : Mount Gede Pangrango National Park 159

Appendix G Derivation of the Scattered Fields in the Medium 1 at Air and

Burnt Coal Seam Interface

165

Appendix H Derivation of the Scattered Fields in the Medium 2 at Burnt Coal

Seam and Peat Interface

169

Appendix I Derivation of the Scattered Fields in the Medium 2 on Air and


173

Appendix J Derivation of the Scattered Fields in the Medium 1 on Air and


177

Appendix K Derivation of Horizontally and Vertically Polarized Surface-

Current Density

181

xxi

List of Figures

Figure 1.1 Distribution of tropical forest at Indonesia archipelago 2

Figure 1.2 The spread of forest plantations across Indonesia and the species

planted in each province

3

Figure 1.3 Borneo’s forest cover and 1997-1998 fire hot spots 5

Figure 1.4 Instruments onboard on JERS-1 satellite (source: NASDA) 7

Figure 1.5 Synthetic Aperture Radar on JERS-1 satellite (source: NASDA) 7

Figure 1.6 Photograph of dielectric constant measurements

(a) Tree trunk 9

(b) Burnt coal seam 9

Figure 1.7 Dielectric constant of burnt coal seam 10

Figure 1.8 (a) Dielectric constants measurement of tropical forest tree trunk

(skin): r-real part and i-imaginary part

11

(b) Dielectric constants measurement of tropical forest tree trunk

(xylem): r-real part and i-imaginary part

12

Figure 2.1 Photograph and Geometry of the analysis.

(a) Photograph of a cross section of pine (Pinus merkusii) trunk. 19

(b) Geometry of scattered waves from a pine trunk. 19

Figure 2.2 Simulation model. 25

Figure 2.3 Pulse of incident wave.

(a) Gaussian pulse. 27

(b) Fast fourier transformed Gaussian pulse. 27

Figure 2.4 Geometry of incident wave 28

xxii

Figure 2.5 Scattered waves in simulation space from tt ∆= 50 s to t∆300 s. A

and B are scattered waves from skin and heartwood, C and D are

scattered wave from trapped waves in skin layer, E is forwarded wave

that occurred by clipping pulse that flows on the trunk surface and

scattered to back of trunk, F and G are heartwood and skin,

respectively. P is the observed point.

30

Figure 2.6 Scattered waves at observed point P: A and B, C and D are scattered

pulse from skin and heartwood, and trapped wave in the skin layer,

respectively.

31

Figure 2.7 Relationship between tree trunk diameter and backscattering

coefficient

32

Figure 2.8 Map of the study area 34

Figure 2.9 Photograph of pine forest in the study area and the supervised

classification results of JERS-1 SAR data (path 106, row 312, 13

May 1997).

35

Figure 3.1 Tree trunk media 42

Figure 3.2 Geometry of analysis 43

Figure 3.3 Geometry of simulation space. Remarks: P is observed point. A, B

and C are heartwood, xylem, and skin, respectively. Simulation space

is divided into INX x INY grids of meshes.

48

Figure 3.4 Distribution of scattered electric field intensity SyE with tt ∆= 50 to

t∆300 s.

51

xxiii

Figure 3.5 Distribution of scattered electric field SyE in tt ∆= 300 s, where A, B

and C are scattered wave from skin, xylem and heartwood,

respectively. P is observed point. E, F and G are skin, xylem and

heartwood, respectively. D is forwarded wave that is occurred by

clipping wave that flows on the trunk surface and scattered to the

backward of tree trunk.

52

Figure 3.6 Scattered electric field intensities at observed point P. A, B and C are

scattered pulse from skin, xylem and heartwood, respectively.

53

Figure 3.7 Analysis and simulation results for four species of Indonesian tropical

forest, where the diameter of tree trunk is equal with 2c, where c is

radius of tree trunk.

54

Figure 3.8 JERS-1 VNIR data of the study area (Path 107 Row 312, 19970930):

Gede Pangrango National Park, west Java, Indonesia. Remark: A and

B show northern and southern part of the National Park, respectively.

56

Figure 3.9 JERS-1 SAR data of the study area (Path 107 Row 312, 19970810):

Gede Pangrango National Park, west Java, Indonesia. Remark: A and

B show northern and southern part of the National Park, respectively.

57

Figure 3.10 Altitude distribution of the study area : Mount Gede Pangrango

National Park, west Java, Indonesia.

58

Figure 3.11 Location of the study area: Gede Pangrango National Park (part of

area A in figure 3.8 and 3.9)

59

Figure 3.12 Classification result assigned the distribution of classes in the study

area. Test area shows classes distribution in ecosystem zones and its

terrain conditions. Ecosystem zones are settlement and paddy (sp),

sub-montane (sm) and montane (mt).

61

xxiv

Figure 4.1 Photographs of field survey expeditions in period 1995 to 1997. A

and B show main vegetations that found around study sites;

Tengkawang (Dipterocarpaceae spp.) and purun grass, respectively.

C shows burnt forest that remained burnt tree trunk and burnt coal

seam. D shows staffs measured thickness of coal seam.

68

Figure 4.2 (a) Digital map of the study area: One Million Hectares Peatland

Project (PLG), central Borneo, Indonesia (DEPHUTBUN 1999).

69

(b) Study area: master plan of ‘One Million Hectares Peatland Project

(PLG)’ at central Borneo, Indonesia. This figure shows thickness

of coal seam that collected in field survey expeditions in period

1995 to 1997. Dotted lines show the area covered by JERS-1 SAR

and SPOT HRV data.

70

(c) Distribution of the thickness of coal seam: One Million Hectares

Peatland Project, District B (PLG-B) at central Borneo, Indonesia

71

(d) Distribution of the thickness of coal seam: One Million Hectares

Peatland Project, District D (PLG-D) at central Borneo, Indonesia

72

Figure 4.3 SPOT HRV data of fire events in the study area

(a) 6 June 1997 (prior to the fire). 74

(b) 29 July 1997 (during fire). 75

(c) 7 August 1997 (during fire). 76

(d) 8 September 1997 (after fire). 77

Figure 4.4 Geometry of analysis

(a) Analysis model. Remarks: ① : burnt coal seam-obstacles

scattering; ② : obstacles – burnt coal seam scattering

79

(b) equivalent circuit 79

xxv

Figure 4.5 Geometry of wave propagation in two media. 80

Figure 4.6 Measurement of burnt coal seam sample using dielectric constant kit

HP85070B (see sub-figure)

80

Figure 4.7 Simulation model. 83

Figure 4.8 Scattered waves in simulation space. 85

Figure 4.9 Intensity of scattered wave in observed point Q.

(a) Electric field SyE 87

(b) Magnetic field SzH 87

Figure 4.10 Relationship between burnt coal seam thickness and backscattering

coefficient in two dimensional analysis and simulation.

88

Figure 4.11 (a) Raw data of JERS-1 SAR: 15 May 1996 90

(b) Raw data of JERS-1 SAR: 3 February 1997 91

(c) Raw data of JERS-1 SAR: 29 July 1997: dotted line is the study

area in this study

92

Figure 4.12 Composite of JERS-1 SAR data: red – 29 July 1997, green – 3

February 1997, blue – 15 May 1996

93

Figure 4.13 A SPOT-HRV data and supervised classification results of a JERS-1

SAR data (path 95, row 305, 27 July 1997).

95

Figure 5.1 Geometry of the scattered waves analysis. 103

Figure 5.2 Relationship between the backscattering coefficient and the thickness

of burnt coal seam.

125

Figure A.1 Geometry of analysis of scattered TM mode wave (two layers) 135

Figure B.1 Geometry of analysis of scattered TM mode wave (three layers) 139

Figure C.1 Portion of the finite-difference grid. 145

Figure D.1 Opening of peatland area at central Borneo, August 1996 (a). 149

xxvi

Figure D.2 Opening of peatland area at central Borneo, August 1996 (b). 149

Figure D.3 Vegetations around peat swamp at Mentangai river near Bunter lake

‘One million hectares peatland project (PLG)’, August 1996.

150

Figure D.4 Vegetation at peatland (± 8m) around Kurun river (Black water

river), PLG area, August 1996.

150

Figure D.5 Converting peatland area to be agricultural area at Dadahup, central

Borneo, August 1996.

151

Figure D.6 Canals at Tabukan, central Borneo, August 1996. 151

Figure D.7 Staffs are boring peatland to explore the depth and type of peatland at

central Borneo, August 1996.

152

Figure D.8 Converted peatland area at Siantan, west Borneo, 1995. 152

Figure D.9 Peatland identification with ± 8m pipe at backswamp around Kurun

river (black water river), central Borneo, August 1996.

153

Figure D.10 Burnt tengkawang and pule grass around Bunter lake, reaches of

Mentangai river, central Borneo, August 1996.

153

Figure D.11 Purun grass as main vegetation at backswamp (Kurun river) to

indicate that this area is ferrit land, August 1996.

154

Figure D.12 Burnt peatland at Berengbengkel, Kecamatan Pakandut, Kodya

Palangkaraya, central Borneo, 1995.

154

Figure D.13 Paddy field at Sakalagun, central Borneo, 1995. 155

Figure F.1 Map of field survey that was held in July 2000. 159

Figure F.2 Mount Gede (right) and Pangrango (left), west Java, Indonesia (figure

F.1 ①)

160

xxvii

Figure F.3 Hill of Mount Gede (900m asl), Rasamala forest, rock, and tea

plantation around Cipelang river that flows from Mount Gede to

Selabintana, Sukabumi (figure F.1 ②).

160

Figure F.4 Tea plantation around Mount Gede at Pasiripis district, Pondok

Halimun, Cipelang Selabintana Sukabumi (figure F.1 ③).

161

Figure F.5 Hill of Mount Gede with variation of grass, fern, and rasamala.

Pondok Halimun district in Gede Pangrango National Park. Land

surface around this area is wavy (figure F.1 ④)

161

Figure F.6 Tea and tobacco plantation at Wanasari district, Sukabumi, with

background Mount Gede Pangrango (figure F.1 ⑤).

162

Figure F.7 Rasamala forest and tea plantation near Cipelang river (850m

asl)( figure F.1 ⑥)

162

Figure F.8 Variation of Montane forest, fern, moss and bush (950m asl)( figure

F.1 ⑦)

163

Figure F.9 Small tree trunk of rasamala that nominates hill of Mount Gede

Pangrango (1030m asl) in Cicurug National Park (figure F.1 ⑧).

163

Figure F.10 Rasamala (Altingia exelsa) in Mount Gede Pangrango with diameter

50 – 200cm and height about 7 – 20m. It grows at attitude 700 –

1750m asl (figure F.1 ⑨).

164

xxviii

List of Tables

Table 1.1 Specification of JERS-1 Synthetic Aperture Radar (SAR) 8

Table 1.2 Specification of the dielectric probe kit HP85070B 9

Table 1.3 List of tropical forest species at Indonesia 10

Table 2.1 Classification and estimation results 36

Table 3.1 Dielectric constants of Indonesian tropical forest trees at the

frequency of JERS-1 SAR ( 275.1=f GHz)

47

Table 3.2 Relationship between backscattering coefficients and tree trunk

diameters of rasamala forests in the study area

62

Table 4.1 Thickness of burnt coal seam in the study area 96

Table 5.1 Thickness of burnt coal seam in the study area 127

1

Chapter I

Introduction

1.1. Motivations and objectives

Tropical forest is covering less than 7% of the terrestrial surface and has the important

role in carbon cycle (Tucker et al. 2000) and mega-biodiversity are living inside, especially in

Indonesian tropical forest (Tateishi et al. 2000). Figure 1.1 shows the tropical forest

distribution at Indonesia (WRI 1999 and DEPHUTBUN 1997). The type of tropical forest that

cover Indonesian area are lowland forest, montane forest, mangrove forest, swamp forest, and

lowland monsoon forest. Kyoto protocol (UN 1997) was signed in 1997, but deforestation is

being still done in many places in the world, especially at tropical forest area. Twelve million

hectares of tropical forest was cleared annually (FAO 1997). Another problem is leak of tools

and methods to manage and monitor the tropical forest in a large area. Indonesia is large

archipelago that consists of more than 1,700 large islands with land territory covers

approximately 1.9 millions square kilometres. It is very difficult to estimate age or volume of

tree trunk using conventional techniques that spent much time and cost to collect its ground

data.

Recently, remote sensing technology has been an efficient and helpful tool to monitor

tropical forest and plantation in a large area. The main problem in monitoring tropical areas,

as Indonesia, is cloudy condition. The best instrument to monitor these areas is Synthetic

Aperture Radar (SAR), as it works effectively in spite of cloudy conditions. However, SAR

data are not easily interpreted due to the complex relations of the radar backscattering

mechanism between microwaves and tropical forest parameter (i.e. diameter of tree trunk,

thickness of burnt coal seam, roughness of soil surface, soil moisture) (Kamal 1989). In this

2

Figure 1.1. Distribution of tropical forest at Indonesia archipelago

study, the author attempts to find the relationship between the radar backscattering

coefficients and its parameters that are found in tropical forest area. Figure 1.2 shows the

spread of forest plantation types across Indonesia and the species planted in each province

with major species are Acacia mangium, Agathis spp., Pinus merkusii, Gmelina arborea,

Shorea spp, Swietenia macrophylla etc (Nair 2000). In this research, the author also applies

the proposed method to monitor these species using remote sensing techniques or SAR sensor.

1.2. Deforestation and forest fire

Deforestation in tropical regions went at a quite stable pace during 1980 – 1995, of

0.7% per year, which is about 12 million hectares annually (including reforestation) of

tropical forest is lost each day. This boils down to 33,000 hectare per day. Nearly half of all

species of plants and animals on earth are living in tropical forests. According to some experts

something like 100 species become extinct each day, and most of them as a direct result of

deforestation. Indonesia has second deforestation speed in the world, so it has high

South China Sea

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arbo

rea

Para

seri

anth

es f

alca

tari

a Sh

orea

spp

Lam

pu

ng

Aca

cia

man

gium

G

mel

ina

arbo

rea

Para

seri

anth

es f

alca

tari

a Sh

orea

spp

.

Wes

t ja

va

Aca

cia

man

gium

A

gath

is s

pp

Man

grov

e sp

ecie

s Pi

nus

mer

kusi

i Sh

orea

spp

. Sw

iete

nia

mac

roph

ylla

T

ecto

na g

rand

is

Sou

thea

st S

ula

wes

iA

caci

a m

angi

um

Gm

elin

a ar

bore

a Sw

iete

nia

mac

roph

ylla

T

ecto

na g

rand

is

Sou

th S

ula

wes

i G

mel

ina

arbo

rea

P

aras

eria

nthe

s fa

lcat

aria

Eas

t Ja

va

Aga

this

spp

M

elal

euca

caj

uput

i Pa

rase

rian

thes

fal

cata

ria

Pinu

s m

erku

sii

Schl

eich

era

oleo

sa

Swie

teni

a m

acro

phyl

la

Tec

tona

gra

ndis

Cen

tral

Jav

a

Aga

this

spp

M

elal

euca

caj

uput

i Pa

rase

rian

thes

fal

cata

ria

Pinu

s m

erku

sii

Schl

eich

era

oleo

sa

Swie

teni

a m

acro

phyl

la

Tec

tona

gra

ndis

Mol

ucca

s

Cen

tral

Su

law

esi

Sout

heas

t Su

law

esi

Sout

h

Sula

wes

i

Eas

t

Nus

a T

engg

ara

Eas

t

Kal

iman

tan

Cen

tral

K

alim

anta

n

Sout

h

Kal

iman

tan

Wes

t

Kal

iman

tan

Eas

t Ja

va

Cen

tral

Ja

va

Wes

t Ja

va

Lam

pung

Sout

h Su

mat

era

Jam

bi

Wes

t

Sum

ater

a

Ria

u

Nor

th

Sum

ater

a

D.I

. Ace

h

4

contribution in environmental destruction and indirectly influence to the carbon dioxide

production over the earth (WRI 1999). In this reason, the author attempts to analyse the

scattered waves from burnt coal seam to estimate the thickness of burnt coal seam. In near

future, this result can be applied to estimate the carbon dioxide volume that is produced by

deforestation or forest fire area, especially at Indonesian region.

The large-scale forest fire was occurred in Borneo island, Indonesia in period of 1997 to

1998 (Nakayama et al. 1999, Siegert et al. 2000, Liew et al. 1999). This is coinciding with the

El-nino Southern Oscillation (ENSO) of 1997-1998 (Muraleedharan et al. 2000a). It has

become air pollution episode due to the out-of-control biomass burning for agricultural

purposes started in June 1997 that to be a severe environmental problem for itself and the

neighbouring countries. Its impact on the health and ecology in the affected areas is expected

to be substantial, costly and possibly long lasting (Fang et al. 1999). Figure 1.3 shows the

distribution of forest fire at Borneo island, Indonesia in 1997 – 1998, where about one million

hectares of forest area was devastated (Charles et al. 2000).

Many researchers attempted to know hotspot of forest fire and quantity of air pollution

using many sensors with study sites were countries around Indonesia (Fang et al. 1998,

Miroslav et al. 1999, Wolfgang et al. 1999). Biomass fires have been responsible for several

regional haze episodes in southeast Asia, and most of the fires were in areas where peat is the

dominant biomass fuel (Muraleedharan et al. 2000b). Muraleedharan reported that the

chemical components present in the haze in southeast Asia with special emphasis on those

substances with potential health impacts (Muraleedharan et al. 2000a). Based on these reasons,

this research attempts to analyse the relationship between the thickness of burnt coal seam and

radar backscattering coefficient. Then the result will be applied to estimate the thickness of

burnt coal seam using Synthetic Aperture Radar (SAR) data. The study area was chosen

5

Figure 1.3. Borneo’s forest cover and 1997-1998 fire hot spots

1997

1998

Frontier forest under low threat

Threatened frontier forest

Non-frontier forest

Others

6

around ‘One Million Hectares Peatland Project (PLG)’ at central and south Borneo area. To

realise this purpose, Japanese Earth Resources Satellite (JERS-1) SAR data (L band) were

employed.

1.3. Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar

JERS-1 is a Japanese National Space Development Agency (NASDA) satellite whose

mission objectives of JERS-1 are twofold; Firstly, the assessment of the newly developed

onboard sensors and the spacecraft itself, and secondly, the establishment of an integrated

system for observing the Earth that is focused on observations of earth resources, geology,

agriculture, forestry, land use, sea ice monitoring and coastal monitoring.

The spacecraft contains two instruments: a Synthetic Aperture Radar (SAR) and an

Optical Sensor (OPS), see figure 1.4. The SAR is an active microwave sensor that transmits

microwave and detects the wave that is reflected back by objects (see figure 1.5 and table 1.1).

It enables fine-resolution, high contrast observation and accurate determinations of

topographical features. Since it is totally reliant on microwave data (1.275 GHz), it operates

independent of weather conditions and cloud cover. The OPS separates the light reflected

from the ground into seven spectral bands from visible to short wave infrared. It is made up of

two sensors: the Visible and Near Infrared Radiometer (VNIR) and the Short Wave Infrared

Radiometer (SWIR).

JERS-1 was launched on 11 February 1992 from Tanegashima Space Centre in

Kagoshima, Japan on a 2 stage H-1 launch vehicle. The satellite had approximate dimensions

1.0m x 1.8m x 3.1m with payload weights approximately 1.4 tons. The spacecraft has a solar

array that is approximately 8.0m x 3.4m. JERS-1 had an onboard Mission Data Recorder

(MDR) that allows it to collect data even when a ground station is not in view. JERS-1 had

already exceeded its 2 years design life until 11 October 1998.

7

Figure 1.4. Instruments onboard on JERS-1 satellite (source: NASDA)

Figure 1.5. Synthetic Aperture Radar on JERS-1 satellite (source: NASDA).

R

R′

8

Table 1.1. Specification of JERS-1 Synthetic Aperture Radar (SAR)

Specification

Flight attitude 568km Flight inclination angle 97.7o

Frequency 1.275GHz (L band) Wavelength 23.5cm Polarisation HH Off nadir angle 35o

Incidence angle 38.7o

Swath width 75km Azimuth resolution 18m (3 looks) Range resolution 18m Peak Power 325 W (specification 1.3kW) Band width 15MHz Antenna size 2.2m x 12m

1.4. Physical characteristic measurements

Table 1.2 shows specification of the dielectric probe kit HP85070B (HP 2000). The

dielectric constant of each sample is important parameter in modelling and analysis of the

relationship between backscattering coefficient and tropical forest characteristics (i.e. tree

trunk diameter, thickness of burnt coal seam, roughness of soil surface, and soil moisture).

Figure 1.6 shows photographs of dielectric constant measurements that were done by the

author to obtain the dielectric constant of samples, where figure 1.6 (a) and (b) shows the

measurement of dielectric constant of tropical forest tree trunk media and burnt coal seam,

respectively. In field survey that was done in 1999, the tree trunk samples of 17 species of

tropical forest plants were collected in Indonesia. Table 1.3 shows the list of these samples.

The measurement was done in frequency range from 0.3 to 3GHz. Figure 1.7 and 1.8 shows

the measurement results from samples of burnt coal seam and tropical forest tree trunks,

respectively. Tree trunk media were skin, xylem and heartwood. The dielectric constant of

9

Table 1.2. Specification of the dielectric probe kit HP85070B

Specification

Band width 200 MHz ~ 20 GHz

Operating temperature - 40 ~ +200oC

Probe 3.5mm connector type

Flexible cable 1m length

Terminal 50Ω Open / Short / Load: 3.5mm connector

Probe stand 24inch, diameter 1/2inch

(a) Tree trunk (b) Burnt coal seam

Figure 1.6. Photograph of dielectric constant measurements

each medium was measured, but the results of heartwood measurement were similar with

water. Consequently, figure 1.8 shows results from the measurement of skin and xylem only.

10

Table 1.3. List of tropical forest species at Indonesia

No Trade name Botanical name Family 1 Acacia Acacia mangium - 2 Coconut Cocos mucifera - 3 Mahagony Swietenia mahagony - 4 Rasamala Altingia exelsa noronhae Hamamelidaceae 5 Pine Pinus Merkusii Pinaceae 6 Rattan Calamus - 7 Tekik Parasianthes lebbeck Benth. Mimosaceae 8 Tamarind Tamarindus indica - 9 Teak Tectona grandis - 10 Orange Citrus aurantium sinensis - 11 Petai Parkia Speciosa Hassk Mimosaceae 12 Mango Magnifera indica Anacardiaceae 13 Mete Anacardium occidentale - 14 Mulwo - - 15 Munggur Enterolobium saman prain - 16 Randu (capok tree) Cinnamomum spp. Lauraceae 17 Sengon Albizzia chinensis Merr. Mimosaceae

Figure 1.7. Dielectric constant of burnt coal seam

-1

0

1

2

3

4

0.24 0.84 1.44 2.04 2.64

Frequencies [GHz]

Die

lect

ric c

onst

ants

real

imaginary

11

0

1

2

3

4

5

6

7

0.240.54

0.84

1.14

1.44

1.74

2.04

2.34

2.642.94

Frequency [GHz]

Die

lect

ric c

onst

ant

acacia (r) acacia (i)

coconut (r) coconut (i)

mahogany (r) mahogany (i)

rasamala (r) rasamala (i)

pine (r) pine (i)

rattan (r) rattan (i)

tekik (r) tekik (i)

tamarind (r) tamarind (i)

teak (r) teak (i)

orange (r) orange (i)

petai (r) petai (i)

mango (r) mango (i)

mete (r) mete (i)

mulwo (r) mulwo (i)

munggur (r) munggur (i)

randu (r) randu (i)

sengon (r) sengon (i)

Figure 1.8 (a) Dielectric constants measurement of tropical forest tree trunk (skin): r – real

part and i – imaginary part.

i

r

12

0

2

4

6

8

10

12

14

16

18

0.24 0.84 1.44 2.04 2.64

Frequency [GHz]

Die

lect

ric c

onst

ant

teak (r)

teak (i)mahagony (r)

mahagony (i)pine (r)pine (i)

rasamala (r)rasamala (i)

Figure 1.8 (b) Dielectric constants measurement of tropical forest tree trunk (xylem): r – real

part and i – imaginary part

i

r

13

References

1. CHARLES V. B., JAMES S., 2000, Trial by Fire, Forest Fire and Forestry Policy in

Indonesia’s Era of Crisis and Reform. World Resource Institute (WRI), Forest Frontiers

Initiatives in Collaboration with WWF-Indonesian & Telapak Indonesia Foundation

(Washington: WRI).

2. DEPHUTBUN, 1997, Land Use by Concensus Map, Directorate General Forest Inventory

and Land Use Planning, Indonesian Ministry of Forestry and Estate (Jakarta: dephutbun).

3. FANG, M., HUANG, W., 1998, Tracking the Indonesian forest fire using

NOAA/AVHRR images, International Journal of Remote Sensing, Vol.19, No.3, pp.387-

390 (London: Francis and Taylor).

4. FANG, M., ZHENG, M., WANG, F., To, K.L., JAAFAR, A.B., TONG, S.L., 1999, The

solvent-extractable organic compounds in the Indonesia biomass burning aerosols –

characterisation studies. Atmospheric Environment, Vol. 33, pp. 783-795 (Pergamon).

5. FAO, 1997, State of the World's Forests 1997, Food and Agriculture Organization of the

United Nations, p.16 (Rome: FAO).

6. HP, 2000, Measurement Tutorial HP85070M. Dielectric Constant Measurement System,

Japan Hawlett Packard (Tokyo: HP)

7. KAMAL SARABANDI, 1989, Electromagnetic Scattering from Vegetation Canopies.

Radiation Laboratory, Department of Electrical Engineering and Computer Science, The

University of Michigan (Dissertation) (Michigan: University of Michigan Press).

8. LIEW, S.C., KWOH, L.K., PADMANABHAN, K., LIM, O.K., LIM, H., 1999,

Delineating land/forest fire burnt scars with ERS interferometric synthetic aperture radar,

Geophysical Research Letters, Vol.26, No. 16, pp.2409-2412 (American Geophysical

Union)

14

9. MIROSLAV R., HASNAH H., 1999, Air quality in Brunei Darussalam during the 1998

haze episode. Atmospheric Environment, Vol.33, pp. 3651-3658 (Pergamon).

10. MURALEEDHARAN, T. R., MIROSLAV R., ALLAN W. ANTHONY C., 2000a,

Chemical characterisation of the haze in Brunei Darussalam during the 1998 episode.

Atmospheric Environment, Vol. 34, No. 17, pp. 2725-2731.

11. MURALEEDHARAN, T.R., MIROSLAV R., ALLAN W., ANTHONY C., 2000b,

Emissions from the combustion of peat: an experimental study. Atmospheric Environment,

Vol. 34, pp. 3033-3035 (Pergamon).

12. NAIR, K.S.S. (Editor), 2000, Insect pests and diseases in Indonesian forests – An

assessment of the major threats, research efforts and literature, Center for International

Forestry Research (Jakarta: Grafika Desa Putera).

13. NAKAYAMA, M., MAKI, M., ELVIDGE, C.D., LIEW, S.C., 1999, Contextua l algoritm

adapted for NOAA-AVHRR fire detection in Indonesia, International Journal of Remote

Sensing, Vol. 20, No. 17, pp. 3415-3421 (London: Taylor and Francis).

14. SIEGERT, F., HOFFMANN, A.A., 2000, The 1998 forest fires in east Kalimantan

(Indonesia): A quantitative evaluation using high resolution, multitemporal ERS-2 SAR

images and NOAA-AVHRR hotspot data, Remote Sensing of Environment, Vol. 72, No.1,

pp.64-77 (New York: Elsevier).

15. TATEISHI, R. and HASTINGS, D. (Editors), 2000, Global Environmental Databases –

Present Situation; Future Directions, Chapter 6 Biodiversity Data and Information,

International Society for Photogrammetry and Remote Sensing (ISPRS), pp. 126-156, 1st

edition (Hongkong: ISPRS).

16. TUCKER, C.J. and TOWNSHEND, J.R.G., 2000, Strategy for monitoring tropical

deforestation using satellite data, International Journal of Remote Sensing, Vol. 21, No.6

& 7, pp.1461-1471.

15

17. UN, 1997, Kyoto Protocol to the United Nations Framework Convention on Climate

Change, United Nations.

18. WOLFGANG V.H.H., TORSTEN S., SIGURD S., CHAN A.K., LIM J.T., 1999,

Climate-relevant aerosol parameters of South-East-Asian forest fire haze. Atmospheric

Environment, Vol. 33, pp.3183-3190 (Pergamon).

19. WRI, 1999, World Resources 1998-1999: Chapter 11 Forests and Land Cover, World

Resources Institute (WRI) – Food and Agriculture Organisation of the United Nations

(FAO) – International Tropical Timber Organisation (ITTO) (Washington: WRI).

17

Chapter II

Analysis of Scattered Waves from Two Layers of Tree Trunk

2.1. Introduction

Pine (Pinus Merkusii) is the important plant in Indonesia, especially Java and Sumatra

islands as a source of turpentine or volatile oil (Coppen et al. 1993), see figure 1.2.

Turpentine is distilled from the pine resin. Traditionally, turpentine has been employed as a

solvent or cleaning agent for paints and varnishes. Most turpentine nowadays is used as a

source of chemical isolations that are then converted into a wide range of products. Many of

these, including the biggest single turpentine derivative and synthetic pine oil, are employed

for fragrance and flavour use, although there are also many important non-aromatic

applications such as polyterpene resins. Pine oil is used in disinfectants, cleaning agents and

other products having a pine odour (FAO 1995). Turpentine is obtained via tapping of living

pine trees (whether natural stands or plantations). Pine is multi purposes plant, if its tapped

then the felled trees provide income from sale of the logs for timber or pulp.

Indonesia is large archipelago that consists of more than 1,700 large islands with land

territory covers approximately 1.9 million square kilometres. It is very difficult to estimate

age or volume of pine using conventional techniques that spent much time and cost to collect

its ground data. Recently, remote sensing technology has been an efficient and helpful tool to

monitor forest and plantation in a large area. The main problem in monitoring tropical areas,

as Indonesia, is cloudy conditions. The best instrument to monitor these areas is synthetic

aperture radar (SAR), as it works effectively in spite of cloudy conditions. However, SAR

data are not easily interpreted due to the complex relations of the radar backscattering

mechanisms between microwaves and pine trunk. In this study, the author attempts to find the

18

relationships between the radar backscattering coefficients and the diameter of pine trunk that

are found in Indonesian forests and plantations.

In this study, a simple analysis of scattered wave from a pine trunk was done in order

to estimate the relationship between diameter of tree trunk and its backscattering coefficients

oσ . In section 2.2, the modelling and formulation of scattering problems on pine are

discussed. In section 2.3, the simulation of transverse electric (TE) wave propagation is done

using Finite Difference Time Domain (FDTD) method. In section 2.4, the analytical results

are verified by comparing them with the simulated results. The application of this research

will be introduced in section 2.5. Finally, conclusions are given in section 2.6.

2.2. Analysis

Figure 2.1(a) shows photograph of pine trunk that found at west Java, Indonesia.

Actually, its tree trunk is composed of two media; skin and heartwood. In this study, the

analysis of scattered wave from its tree trunk is discussed in order to investigate the

correlation of backscattering coefficient oσ and the diameter of tree trunk. This scattering

problem in its tree trunk is analysed using mode expansion method (Tetuko et al. 2001). The

two-dimensional model of tree trunk is shown in figure 2.1(b). Two layers of media compose

this model of a tree trunk with infinite length in z-axis. The radii of heartwood and skin layer

are a and b, respectively. Several trunks of pine trees that were found around the study area,

were measured and the results showed that approximately each component had relations

a=0.5b. The properties of skin are determined by complex dielectric constant rε and complex

permeability rµ . The dielectric constant rε of several skin medium of pine samples were

measured experimentally by the author using dielectric probe kit HP85070B and the result

was 3.1-j0.4 (see figure 1.8 (a)). The water content of heartwood is high, consequently,

heartwood was assumed to be an infinite length of perfect conductor or electromagnetic fields

19

Figure 2.1. Photograph and geometry of the analysis.

heartwood

skin

(a) Photograph of a cross section of pine (Pinus merkusii) trunk.

heartwood

y

x z

skin

a

b

r

(region I) (region II)

scattered waves

incident waves

transmitted waves

observed point (P)

SEφ

mEφ

IEφφ

(b) Geometry of scattered waves from a pine trunk.

20

in heartwood is zero. Here, incident wave is assumed as a plane wave that has transverse

electric (TE) mode and incident angle φ with respect to direction of observed point P from

origin of coordinate. This wave propagation is –x direction. Based on this figure and

Appendix E, the z component of the magnetic fields in free space and skin are determined as

Incident wave ∑∞

=

=0

cos)(m

momm

Io

Iz mjrkJUHH φ ( br > ) (2.1)

Scattered wave ∑∞

=

=0

)2( cos)(m

ommIo

Sz mrkHbHH φ ( br > ) (2.2)

Transmitted wave φmkrNakrJaHHm

mmmmIo

mz cos)()(

0∑

∞

=

′+= ( bra ≤≤ ) (2.3)

Where the wave number of skin is rrokk εµ= and ok is wave number in free space. ma to

mb are amplitude coefficients. IoH is initial amplitude of incident magnetic field. mJ , mN ,

and )2(mH are m-th order of Bessel function, Neumann function, and 2nd kind of Hankel

function. Where

==

=),3,2,1(2

)0(1

Lm

mUm (2.4)

By substituting (2.1) to (2.3) into Maxwell’s equations below

t∂∂=×∇ E

H ε (2.5)

the electric field of each medium was derived as

∑∞

=

′−=0

cos)(m

momm

o

IooI mjrkJU

j

HkE φ

ωεφ ( br > ) (2.6)

∑∞

=

′−=0

)2( cos)(m

ommo

IooS mrkHb

j

HkE φ

ωεφ ( br > ) (2.7)

∑∞

=

′′+′−=0

cos)()(m

mmmm

Iom mkrNakrJa

jkH

E φωεφ ( bra ≤≤ ) (2.8)

21

Further, by substituting (2.1) to (2.3) and (2.6) to (2.8) into the boundary condition of each

interface between media given below:

r=a 0=mEφ (2.9)

r=b SIm EEE φφφ += (2.10)

Sz

Iz

mz HHH += (2.11)

the amplitude coefficient mb of scattered wave from tree trunk SEφ was obtained as;

( )

′−

′−−=

)()(

)()(

)2()2( bkHbkHZ

bkJbkJZjUb

ommomrm

ommomrmm

mm

βα

βα (2.12)

where

rrrZ εµ=

)()()()( kbJkaNkaJkbN mmmmm ′′−′′=α

)()()()( kbJkaNkaJkbN mmmmm ′−′=β

Finally, by substituting the amplitude coefficient mb of (2.12) into (2.7), the scattered electric

field is obtained.

In the same manner, fields of transverse magnetic (TM) mode that scattered from two

layers of tree trunk could be derived (see Appendix A) and are obtained as below. Where the

electric fields are

Incident wave ( )∑∞

=

=0

cosm

momm

Io

Iz mjrkJUEE φ ( br > ) (2.13)

Scattered wave ( ) ( )∑∞

=

=0

2 cosm

ommIo

Sz mrkHbEE φ ( br > ) (2.14)

Transmitted wave ( ) ( ) ∑∞

=

′+=0

cosm

mmmmIo

mz mkrNakrJaEE φ ( bra ≤≤ ) (2.15)

and the magnetic fields are

22

( )∑∞

=

′=0

cosm

momm

o

IooI mjrkJU

j

EkH φ

ωµφ ( br > ) (2.16)

( )∑∞

=

′=0

)2( cosm

ommo

IooS mrkHb

j

EkH φ

ωµφ ( br > ) (2.17)

( ) ( ) ∑∞

=

′′+′=0

cosm

mmmm

Iom mkrNakrJa

jkE

H φωµφ ( bra ≤≤ ) (2.18)

Finally, amplitude coefficient mb for transverse magnetic mode is derived as

( ) ( )

( ) ( )bkHbkHZ

bkJbkJZjUb

ommomrm

ommomrmm

mm

)2()2( βα

βα

−′

−′−= (2.19)

where

( ) ( ) ( ) ( )kaNkbJkbNkaJ mmmmm −=α

( ) ( ) ( ) ( )kaNkbJkbNkaJ mmmmm ′−′=β

To confirm the analysis result, the simulation of scattered wave from a pine tree trunk

using Finite Difference Time Domain (FDTD) method is discussed in the next section.

2.3. Simulation

The JERS-1 SAR operated in horizontal (H) polarization on both transmits and

receives. Hence horizontal polarization or transverse electric (TE) mode is considered in this

study. Hence, electromagnetic field components are considered as )0,,( yx EE and ),0,0( zH .

Figure C.1 (in Appendix C) shows the position of field components in the finite-difference

grid (unit of sample spacing). By referring this figure, the detail scattered electromagnetic

fields SE and SH that are derived from Maxwell’s equations are discussed in Appendix C,

where the electromagnetic fields are shown in (2.20) to (2.22). In the derivation of these

equation, t∆ is time step, and x∆ , y∆ are sample spacing in x and y-axis, respectively. Here,

the notation of Yee (Yee 1966) is used to replace tnH ∆+ )( 21

as )( 21+nH . Similarly, scattered

23

fields ),( yxS,tE and ),(, yxtSH are expressed as ),( jiS,nE and ),(, jinSH , where tnt ∆= ,

xix ∆= and yjy ∆= . Sampling lead to the characteristic staggered finite-difference grid. In

this grid, the electromagnetic field components are offset by 2t∆ in time and 2x∆ and

2y∆ in space. In the former, the field components are updated sequentially in time.

( ) ( ) ( )( ) ( ) ( )

( )( ) ( )

( ) ( )y

jiHjiHjitji

jit

jiEjitjijitji

jiE

nSz

nSz

nSx

nSx

∆−+−++

+∆+++∆

+

++∆+++∆+−

=+

−−

−

21

21,

21

21,

21

21

21

211,

21

21

21

21

21,

,,,2,1

,

,,2,1,2,1

,

21

21

εσε

εσεσ

( ) ( ) ( )( ) ( )( ) ( ) ( )

( ) ( ) ( )( ) ( )( ) ( ) ( )jiE

jitjijijijitji

jiEjitji

jijijitji

nIx

o

nIx

o

,,2,1

,,,2,

,,2,1

,,,2,

21,

21

21

21

21

21

21

211,

21

21

21

21

21

21

++∆++

+−+++∆+−

++∆++

+−+−+∆+− −

εσεεεεσ

εσεεεεσ

(2.20)

( ) ( ) ( )( ) ( ) ( )

( )( ) ( )

( ) ( )

( ) ( ) ( )( ) ( )( ) ( ) ( )2

11,

21

21

21

21

21

21

21

21,

21

21,

21

21

21

211,

21

21

21

21

21,

,,2,1

,,,2,

,,

,2,1

,

,,2,1

,2,1,

21

21

++∆++

+−+−+∆+−

∆+−−++

+∆+++∆

−

++∆+++∆+−

=+

−

−−

−

jiEjitji

jijijitji

x

jiHjiH

jitji

jit

jiEjitji

jitjijiE

nIy

o

nSz

nSz

nSy

nSy

εσεεεεσ

εσε

εσεσ

( ) ( ) ( )( ) ( )( ) ( ) ( )2

1,

21

21

21

21

21

21

,,2,1

,,,2,+

+∆+++−+++∆+

− jiEjitji

jijijitji nIy

o

εσεεεεσ

(2.21)

( )

( ) ( ) ( ) ( )( )

( ) ( ) ( )( )( )

( ) ( ) ( )( )21

21,

21

21,

21

21

21

21

21,

21,

21

21

21,

21,

21

212

121,

21

21,

,,,

,

,1,,

,,1,

,

,

21

21

21

21

++−++++

−++−

+−++∆++

∆+

+−++∆++

∆−++

=++

−+

−

+

jiHjiHji

ji

jiEjiEyji

t

jiEjiExji

tjiH

jiH

nI

z

nI

zo

nSx

nSx

nSy

nSy

nSz

nS

z

µµµ

µ

µ

(2.22)

24

where ),( jiµ , ),( jiε , ),( jiσ are the characteristics of wave propagation media in

simulation space. SE and SH are scattered electromagnetic fields that derived from

Maxwell’s equations. oε and oµ are dielectric constant and permeability of free space,

respectively. IE and IH are space functions of incident electromagnetic fields. In this

research, horizontal polarization or transverse electric wave was considered. Consequently, in

this simulation, electromagnetic field components were considered as )0,,( yx EE and

),0,0( zH .

When implementing the finite-difference scheme, boundary conditions must be treated

in a special manner. Two different kinds of boundaries: the internal boundaries (i.e.,

boundaries within the medium marked by a change in material properties) and the external

boundaries (i.e., the grid edges). The conditions at internal boundaries (i.e., at the interfaces

between different media) are usually satisfied implicitly. However, to ensure numerical

stability, the material properties must be averaged for components on boundary. For

transitions between similar materials, the averaging may be omitted. However, it is necessary

at an interface between media with greatly different material properties (for example, at an air

– skin interface) in order to maintain the stability. The finite-difference model is implemented

in two-dimensions (2-D) as shown in figure 2.2. In this figure, simulation space is sampled

into INX x INY grids. FDTD method can only simulate a finite space, but real scattering

problems are often in the infinite formations. In this case, artificial external boundaries must

be applied in the FDTD method. To prevent these artificial boundaries from reflecting

electromagnetic waves, absorbing boundary conditions are used. In 1981, Gerrit Mur

introduced simple absorbing boundary conditions to truncate FDTD meshes (Gerrit 1981).

The second kind of Mur method was applied in this analysis, because it involves small

calculation-memory size and its accuracy is assured (Uno 1998). In this study, for example,

electric field in i=1 is determined as;

25

Figure 2.2. Simulation model.

tree trunk

B

P

1 2 3 4 … … … INX-1 INX

1

2

3

4

:::

::

INY-1

INY

abso

rbin

g bo

unda

ry c

ondi

tion

simulation space

A

incident wave

26

( )

( )

)1,2(1,

),2(1,

2)1,2(1,

)1,1(1,

),1(1,

2

)1,1(1,

22

2),2(

1,),1(

1,2

),1(2,

),2(,

),2(2,

),1(,

−−

+−

−+−

+−−

+−

−

+−

∆+∆∆

∆∆+

−+

−

∆+∆

∆+

−+

∆+∆

∆−∆+

−−=

jnS

EjnS

EjnS

EjnS

EjnS

E

jnS

Extvy

tvxj

nSEj

nSE

xtv

x

jnS

EjnS

Extv

xtvj

nSEj

nSE

yyyyy

yyy

yyyy

(2.23)

In the same manner, the other components of electric field in i=INX, j=1, and j=INY can be

derived, where ν is wave speed.

Figure 2.2 depicts the simulation model where A and B are heartwood and skin layer,

respectively. This model was done in two-dimension, where each medium has infinite length

in z-axis. This simulation space is divided into INX x INY grids (unit of cell size). Incident

wave is a plane wave of intensity as that shown by Gaussian pulse, where the power spectrum

of it is smooth and it is easy to sample. This pulse propagates from left to right of the

simulation space in light speed. The Gaussian pulse is defined by function

≤≤=

−−

otherwise

tetpt

,0

)20(,)( 0)( 2

0 ττα

(2.24)

Where t is running time, 0τ is pulse width, and ( )204 τα = . Figure 2.3 (a) and (b) show

Gaussian pulse with 90 109 −×=τ s and its spectrum, respectively.

The geometry of incident wave is shown in figure 2.4, where plane wave propagates at

angle φ with respect to x-axis. The propagation direction of the incident wave is determined

as

φφ sinˆcosˆ0 yxr += (2.25)

where x and y are unit vectors. Hence the incident electromagnetic fields are derived as

( )000

0 ˆ),( tcrtpZE

tH Iz +⋅+= rr (2.26)

)ˆ()ˆcosˆsin(),( 000 trtpyxEtI +⋅++−= crrE φφ (2.27)

27

Figure 2.3. Pulse of incident wave.

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5 2.0

Times

Gau

ssia

n pu

lse

0τt

(a) Gaussian pulse.

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

0 2 4 6 8 10

FFT

inte

nsiti

es

[dB

]

0τf(b) Fast fourier transformed Gaussian pulse.

28

Figure 2.4. Geometry of incident wave

where c is speed of light. 0E and 0Z are initial intensity of incident electric field and wave

impedance in free space, respectively. )(tp is pulse function excited by Gaussian pulse

(2.24). By substituting the components of electromagnetic fields in (2.26) and (2.27), and

considering the Yee’s notation, each component of electromagnetic fields of incident wave is

acquired as follows;

( ) ( ) ( ) ( )cdcyjxitnpZE

jiH nIz −∆++∆++∆+⋅=+++ φφ sincos),( 2

121

21

0

021

21, 2

1

(2.28)

( ) ( ) ( )cdcyjxitnpEjiE nIx −∆++∆++∆⋅−⋅=++ φφφ sin1cos)sin()1,( 2

102

1,

(2.29)

( ) ( ) ( )cdcyjxitnpEjiE nIy −∆++∆++∆⋅⋅=++ φφφ sincos1cos),1( 2

102

1, (2.30)

where cdt −=0 means that the pulse head is at a distance d from the origin of coordinate at

initial time ( 0=t s). For each time step, the value of excitation is added to the value

calculated from the finite-difference scheme.

To arrange the time step and cell size, first, frequency maxf must be decided from

figure 2.3(b). Here maxf is frequency in 120dB from maximum intensity of frequencies

spectrum. It means that the accuracy of calculation is assured to six digits. By referring to this

figure, 6.2max =f GHz is obtained. By using empirical equation max10 fvyx =∆=∆ (Uno

1998), 21025.1 −×=∆=∆ yx m is obtained, where v is wave speed, in this study it is assumed

IEφ

IzH

y

z x

0r0φ

d

29

to be the same with wave speed in free space ( smc 8103×= ). Finally, according to Courant

condition (Uno 1998)

( ) ( )22 111 yxtc ∆+∆≤∆ (2.31)

the time step t∆ is obtained as st 11105.2 −×=∆ . Finally, the two dimensional backscattering

coefficient oσ is defined as

GHzf

Iy

Sy

E

E

lR

275.1

2

2

0 2

=

= πσ (2.32)

where IyE is observed or incident electric field intensity in frequency 275.1=f GHz on the

trunk surface. SyE is the intensity of electric field in observed point. l is effective surface of

tree trunk for scattered wave, where it is assumed as bπ . To simplify the analysis, the trunk

number in targeted area is not considered and the obtained backscattering coefficient is

considered as the average backscattering coefficient of total tree trunk in a unit area.

2.4. Results and discussion

In simulation space, see figure 2.2, infinite length of tree trunk is considered. This tree

trunk is composed of two media; skin and heartwood. The radius of tree trunk b varies from 0

to 40 grids (or 0 to 0.5 m). The simulation space edges are surrounded by artificial absorbing

boundary condition (Mur method). Incident wave is a plane wave of intensity as that shown

by Gaussian pulse, which propagates from left to right of the simulation space in speed of

light. Parameters of simulation are the simulation space grids 300== INYINX , space-

increments 21025.1 −×=∆=∆ yx m, time- increment 11105.2 −×=∆t s, maximum intensity of

initial electric field 100 V/m, and running time tt ∆= 600 s. Figure 2.5 shows scattered wave

from pine tree trunk with tt ∆= 50 to t∆300 s. Additionally, figure 2.5 at tt ∆= 300 s shows

30

Figure 2.5. Scattered waves in simulation space from tt ∆= 50 s to t∆300 s. A and B are

scattered waves from skin and heartwood, C and D are scattered wave from

trapped waves in skin layer, E is forwarded wave that occurred by clipping pulse

that flows on the trunk surface and scattered to back of trunk, F and G are

heartwood and skin, respectively. P is the observed point.

A B

C D

E

F G P

31

Figure 2.6. Scattered waves at observed point P: A and B, C and D are scattered pulse from

skin and heartwood, and trapped wave in the skin layer, respectively.

details of scattered wave, where A and B are scattered waves from skin and heartwood,

respectively. C and D are scattered wave from trapped waves in skin layer. E is forwarded

wave. Then F and G are heartwood and skin, respectively. The observed point P is at 1.5 m

from centre of tree trunk. This point is used to observe the intensities of scattered

electromagnetic fields. In this study, the author observed only the horizontal polarisation

component (transverse electric wave) or electric field SyE in backscattering direction

( oo 0=φ ). The backscattered electric field is computed and is shown in figure 2.6. In this

figure, A and B, C and D show scattered waves from skin and heartwood, and trapped wave

in the skin layer, respectively. Further, fast Fourier transform is employed to obtain the

electric field intensity of preferred frequency, in this case, frequency of Japanese Earth

Resources Satellite (JERS-1) SAR, 275.1=f GHz, is used. Finally, the backscattering

coefficient is calculated using (2.32) and the results are shown in figure 2.7 (£ - simulation).

-20

-10

0

10

20

0.0 2.5 5.0 7.5 10.0 12.5

Times (ns)

Inte

nsity

(V/m

) A B

C D

32

Figure 2.7. Relationship between tree trunk diameter and backscattering coefficient

-16

-14

-12

-10

-8

-6

-4

-2

0

2

0.254 0.262 0.270 0.278 0.286 0.294

Diameter of tree trunk [m]

Bac

ksca

tterin

g co

effic

ient

[dB

]

analysis

simulation

a

b

pine, a=0.5b

33

In the analysis, direction of scattered wave is the same as observed point P or oo 0=φ .

Consequently, SEφ in analysis is equivalent to SyE that calculated in the simulation. By

substituting the parameters of JERS-1 SAR in the equations of analysis, the analysis results

are obtained and are as shown in figure 2.7 (¢ - analysis). The results compare well with

simulation ones for a tree trunk (£ - simulation). However, a small error was found. It is

considered that the error is generated by the Finite Difference Time Domain (FDTD)

calculation error caused by calculation using the sampled space. In this analysis, the soil is

assumed as perfectly conductor, hence figure 2.7 shows only pure backscattering coefficient

of a tree trunk. In the next section, this result will be applied to estimate pine trunk diameter

in the study area using JERS-1 SAR data.

2.5. Application

2.5.1. Study area

The study area is pine forest around Saguling lake, west Java, Indonesia (figure 2.8).

The region has altitude ranging from 7m to 127m. Biomes of this area are pine forest, mixed

vegetation area, settlement and paddy fields called ladang (dry paddy fields). The soil

condition around the study area is wet. The ground data of the study area were collected in

1999 (Ketut 2001). The annual average rainfall of this area was 233 days, while the annual

average temperature is 23oC.

2.5.2. Data processing

The JERS-1 SAR data was examined in order to estimate the diameters of pine tree

trunk in the study area. The data (path 106, row 312) was acquired on 13 May 1997 during the

34

Figure 2.8. Map of the study area

Indian Ocean

Jakarta

Bogor

I N D O N E S I A

Bandung

Bandung

Saguling Lake

highway

N

0 5 km

107o 48` E 107o 18` E

6o 42` S

7o 00` S

to Jakarta

study area (pine forest)

Sumatera island

Java island

35

Figure 2.9. Photograph of pine forest in the study area and the supervised classification results

of JERS-1 SAR data (path 106, row 312, 13 May 1997).

Legend

kilometres

1. river/paddy field

2. mixed vegetation area

3. forest 1

4. forest 2

5. forest 3

6. dry land (ladang) 7. settlement

6o55`S

6o55`S

107o27È 107o28È 107o29È

10

10

10

10

10

10

15

15

15 20

15

20

15 15

20

20 20

20 25

25

25

30

30

30

35 35

35

35

40

40

40

45

45

0 1 2 3

Altitude units in metres

Pine forest in the study area

36

Table 2.1. Classification and estimation results

Class names Backscattering coefficient oσ (dB)

Diameter of tree trunk (m)

Standard deviation (m)

forest 1 -10 0.260 0.0025

forest 2 -8 0.265 0.0025

forest 3 -5 0.270 0.0025

dry season in the study area. This image was processed at level 2.1 or standard geocoded data

and was resampled to the Universal Transverse Mercator (UTM) projection by the Earth

Observation Research Centre (EORC) of the National Space Development Agency (NASDA)

of Japan. Firstly, a 3x3 median filter was employed and second ly, process used a 5x5 average

filter to reduce inherent speckle noise (Sunar et al. 1998). At the same time, the data was also

referenced to the UTM co-ordinate system, through a polynomial rectification using 30

ground control points collected from topographic maps scale of 1:25.000

(BAKOSURTANAL 1990). This procedure yielded a geometric accuracy of 0.1 pixels. Then

the spatial resolution of SAR data was resampled to 12.5m.

A supervised classification was performed to classify the satellite data into seven

classes. The topographic maps and ground data were used to select training sites (Ketut 2001),

i.e. river and paddy field, bush, forest 1, forest 2, forest 3, mixed forest, and settlement (figure

2.9). By supervised classification, the average pixel intensity I of each class was obtained.

These values were substituted in the equation 2.68log20 −= Ioσ dB (Shimada 1998) to

obtain backscattering coefficients. By plotting the results on figure 2.7, the average diameter

of each class was obtained (see table 2.1). The estimation result shows the diameter of pine in

37

the study area was between 0.26m and 0.27m and has standard deviation 0.0025m. A sampling of

10 locations yielded similar results to the ground data that collected in 1999 (Ketut 2001).

2.6. Conclusions

A simple numerical analysis was conducted to analyse the rela tionship between the

backscattering coefficients oσ and diameter of pine tree trunk. The analysis results were

confirmed by simulation using Finite Difference Time Domain (FDTD) method. These results

are in good agreement. A variation of this analysis, it could be applied to estimate diameter of

tropical tree trunk from Synthetic Aperture Radar (SAR) data, which this information is very

important to estimate the forest volumes or biomass effectively and accurately. These results

succeeded in estimating tree trunk diameter of pine (Pinus merkusii) that is widely distributed

in the west Java forest, Indonesia from JERS-1 SAR data.

While this study focused on single site in Indonesia, it is reasonable to expect that this

method or variations should be successful in estimating tree trunk diameters in similar forest

regions of the world using SAR data.

38

References

1. BAKOSURTANAL, 1990, Topographic maps; 1209-223, 1209-241, 1209-242.

Indonesian National Coordination Agency for Surveys and Mapping, 1st edition

(Cibinong: Bakosurtanal).

2. COPPEN, J.J.W., GAY, C., JAMES, D.J., ROBINSON, J.M. and SUPRIANA, N., 1993,

Variability in xylem resin composition amongst natural populations of Indonesian Pinus

merkusii. Phytochemistry, 33, 129-136.

3. DAVID, P., STELLA, E.B., STHEPHEN, J.M., 1997, Terrain influences on SAR

backscatter around Mt. Taranaki, New Zealand. IEEE Transactions on Geoscience and

Remote Sensing, 35, 924-932.

4. FAO, 1995, Flavours and fragrances of plant origin. Food and Agriculture Organisation

(FAO), United Nations, Chapter 8, 1st edition (Rome: FAO-UN).

5. GERRIT MUR, 1981, Absorbing boundary conditions for the finite-difference

approximation of the time-domain electromagnetic-field equation. IEEE Transactions on

Electromagnetic Compatibility, 23, 377-382.

6. KETUT WIKANTIKA, 2001, Spectral and textural aspects of multisensor and

multitemporal satellite data for land use / land cover mapping in a tropical area. Ph.D

Dissertation, Chiba University, January 2001 (Chiba: Chiba University).

7. SHIMADA, M., 1998, User’s guide to NASDA’s SAR products. Earth Observation

Research Centre, National Space Development Agency (NASDA), 2nd edition (Tokyo:

NASDA).

8. SUNAR, F., TABERNER, M., MAKTAV, D., KAYA, S., MUSAOGLU, M., and YAGIZ,

E., 1998, The use of multi temporal radar data in agriculture monitoring: a case study in

Kyocegiz-Dalaman ecosystem, Turkey. International Archives of Photogrammetry and


39

9. TETUKO S.S., J., R. TATEISHI, K. WIKANTIKA, 2001, A method to estimate tree

trunk diameter and its application to discriminate Java-Indonesia tropical forests.

International Journal of Remote Sensing, 22, 177-183.

10. UNO TORU, 1998, Finite difference time domain method for electromagnetic field and

antenna analyses. 1st edition (Tokyo: Corona).

11. YEE, K. S., 1966, Numerical solution of initial boundary value problems involving

Maxwell’s equations in isotropic media. IEEE Transactions on Antennas Propagation, 14,

302-307.

41

Chapter III

Analysis of Scattered Waves from Three Layers of Tree Trunk

3.1 Introduction

In Chapter II, the author has discussed about the analysis of the scattered wave from

two layers of a tree trunk. The analysis was only focusing in two layers, but actually the tree

trunk has three main layers (Kamal 1989). Hence in this study, the analysis of scattered wave

from three layers of a tree trunk is discussed to explore the complex relationships of the radar

backscattering mechanisms between microwaves and tropical vegetation types. Many

researchers have developed electromagnetic modelling of vegetations (Kamal 1989, Li et al.

1999), but here the author attempts to develop a method to find the relationships between the

radar backscattering coefficients and the characteristics of tropical forest vegetation,

particularly species that are found in Indonesian tropical forests.

In this study, analysis of scattered wave from a tree trunk has been done in order to

estimate the relationship between the diameter of a tree trunk and its backscattering

coefficients oσ . In section 3.2, the modelling and formulation of scattering problems in three

layers of a tropical tree trunk are discussed. Then the simulation of transverse electric (TE)

wave propagation in a tropical tree trunk is done using the Finite Difference Time Domain

(FDTD) method (Uno 1998, Yee 1966), where the Mur method (Mur 1981) is applied as the

42

Figure 3.1. Tree trunk media

absorbing boundary condition to absorb the outgoing electromagnetic waves in a simulation

space edges are discussed in section 3.3. In section 3.4, the analytical results are verified by

comparing them with the simulated results. The application of the proposed method is

discussed in section 3.5. Finally, conclusions are given in section 3.6.

3.2 Analysis

Actually, a tree trunk is composed of three media; skin, xylem, and heartwood,

referring to figure 3.1 (Kamal 1989). Additionally, a skin medium is structured by cork,

phleom and cambium layers. In this study, the scattering problems in a tropical tree trunk is

discussed in order to investigate the correlation of backscattering coefficient oσ and the

diameter of a tree trunk. This scattering problem in a tropical tree trunk is analysed by

cork

phleom

cambium

xylem

heartwood

skin

43

Figure 3.2. Geometry of analysis

deriving the scattered fields at each medium. The two-dimensional model of a tree trunk is

shown in figure 3.2. Three layers of media compose this model of a tree trunk with infinite

length in z-axis. The radii of heartwood, xylem and skin layer are a, b, and c, respectively.

Where these radii have relations as a=0.5c and b=0.9c.

In this study, the scattered wave from a tropical tree trunk is analysed. For this

purpose, several trunks of tropical trees were collected and measured. The properties of xylem

and skin are determined by 1rε , 1rµ and 2rε , 2rµ respectively. Where, riε and riµ (i=1,

2) are complex dielectric constant and complex permeability, respectively. The water content

of heartwood is high, consequently, heartwood may be assumed to be an infinite length of

b

c

a

x

y

P

φ

r

I (xylem)

II

IEφ

IzH

III (free surface, air) (skin)

(heartwood)

z

SEφ

1mEφ

2mEφ

Incident wave

Scattered wave

Transmitted

wave at I

Transmitted

wave at II

Observed point

44

perfect conductor or electromagnetic fields in heartwood is zero. Here, incident wave is

assumed as a plane wave that has transverse electric (TE) mode and incident angle φ with

respect to direction of observed point P from the origin of coordinate (0, 0). This wave

propagates in –x direction. Based on this figure, the φ component of the electromagnetic

fields in a free space, xylem and skin are determined as

xjkIo

Iz

oeHH = )( cr > (3.1)

∑∞

=

=0

)2( cos)(m

ommIo

Sz mrkHbHH φ )( cr > (3.2)

∑∞

=

′+=0

11111 cos)()(

mmmmm

Io

mz mrkNarkJaHH φ )( bra ≤< (3.3)

∑∞

=

′+=0

22222 cos)()(

mmmmm

Io

mz mrkNarkJaHH φ )( crb ≤< (3.4)

where the wave numbers of each medium are 111 rrokk εµ= and 222 rrokk εµ= , and ok

is the wave number in free space. ma1 to mb are amplitude coefficients. IoH is initial

amplitude of incident magnetic field. mJ , mN , and )2(mH are m-th Bessel function,

Neumann function, and 2nd kind of Hankel function. By referring to Appendix E, (3.1) is

transformed to be

∑∞

=

=0

cos)(m

momm

Io

Iz mjrkJUHH φ )( cr > (3.5)

where

==

=L,2,1,2

0,1

m

mUm (3.6)

By substituting (3.1) to (3.5) into source free Maxwell’s equations below

t∂∂=×∇ E

H ε (3.7)

45

the magnetic field of each medium is derived as

∑∞

=

′−=0

cos)(m

momm

o

IooI mjrkJU

j

HkE φ

ωεφ )( cr > (3.8)

∑∞

=

′−=0

)2( cos)(m

ommo

IooS mrkHb

j

HkE φ

ωεφ )( cr > (3.9)

∑∞

=

′′+′−=0

11111

11 cos)()(m

mmmm

Iom mrkNarkJa

j

HkE φ

ωεφ )( bra ≤< (3.10)

∑∞

=

′′+′−=0

22222

22 cos)()(m

mmmm

Iom mrkNarkJa

jHk

E φωεφ )( crb ≤< (3.11)

Further, by substituting (3.1) to (3.5) and (3.8) to (3.11) into the boundary condition of each

interface between media given below:

ar = 01 =mEφ (3.12)

br = 21 mm EE φφ = and 21 mz

mz HH = (3.13)

cr = ISm EEE φφφ +=2 and Iz

Sz

mz HHH +=2 (3.14)

the amplitude coefficient mb of scattered wave from tree trunk SEφ is obtained as;

[ ])()(

)()(

)2(7

)2(6

76

ckHckH

ckJckJjUb

ommomm

ommommm

mm ′

−

′−−=

αα

αα (3.15)

where

)()(

1

11 akJ

akN

m

mm ′

′=α (3.16)

( ) ( )bkJbkN mmmm 1112 ′−′= αα (3.17)

)()( 1113 bkJbkN mmmm αα −= (3.18)

( ) ( )bkJk

bkJk

mmmmm 232

222

1

14 ′−= α

εα

εα (3.19)

( ) ( )bkNk

bkNk

mmmmm 232

222

1

15 ′−= α

εα

εα (3.20)

46

( ) ( )

′−′= ckJckNkk

mm

mm

o

om 2

4

52

2

26 α

αεε

α (3.21)

)()( 24

527 ckJckN m

m

mmm α

αα −= (3.22)

Finally, by substituting the amplitude coefficient mb of (3.15) into (3.9), the scattered

electric field is obtained.

In the same manner, transverse magnetic (TM) mode of scattered waves from three

layers of tree trunk was derived as Appendix B. The electric fields in each medium will be


=

=0

cosm

momm

Io

Iz mjrkJUEE φ ( cr > ) (3.23)

Scattered wave ( )∑∞

=

=0

)2( cosm

ommIo

Sz mrkHbEE φ ( cr > ) (3.24)

Medium I ( ) ( ) ∑∞

=

′+=0

11111 cos

mmmmm

Io

mz mrkNarkJaEE φ ( bra ≤< ) (3.25)

Medium II ( ) ( ) ∑∞

=

′+=0

22222 cos

mmmmm

Io

mz mrkNarkJaEE φ ( crb ≤< ) (3.26)

and the magnetic fields will be


=

′=0

cosm

momm

o

IooI mjrkJU

j

EkH φ

ωµφ ( cr > ) (3.27)

Scattered wave ( )∑∞

=

′=0

)2( cosm

ommo

IooS mrkHb

j

EkH φ

ωµφ ( cr > ) (3.28)

Medium I ( ) ( ) φωµφ mrkNarkJaj

EkH

mmmmm

Iom cos

01111

1

11 ∑∞

=

′′+′= ( bra ≤< ) (3.29)

Medium II ( ) ( ) φωµφ mrkNarkJaj

EkH

mmmmm

Iom cos

02222

2

22 ∑∞

=

′′+′= ( crb ≤< ) (3.30)

By substituting these fields into the boundary conditions, the amplitude coefficient mb is

obtained as

( ) ( )

( ) ( )ckHckH

ckJckJjUb

ommomm

ommommm

mm ′−

−′=

)2(6

)2(7

76

αα

αα (3.31)

47

Table 3.1. Dielectric constants of Indonesian tropical forest trees at the frequency of

JERS-1 SAR ( 275.1=f GHz)

skin ( rrr jεεε ′′−′=2 ) xylem ( rrr jεεε ′′−′=1 ) species names

rε′ rε ′′ rε′ rε ′′

teak 3.1 0.4 11.5 2.6

mahogany 2.7 0.3 10.2 2.1

pine 3.4 0.4 13.6 3.0

rasamala 2.5 0.3 9.4 2.1

where the constants of (3.31) can be referred in Appendix B.

The dielectric constant rε of several sample of tree trunks were measured

experimentally using dielectric probe kit HP85070B (figure 1.6.(a)), and the results are shown

in figure 1.8 and table 3.1 which shows dielectric constants in frequency 275.1=f GHz

(JERS-1 SAR).

3.3 Simulation

In this study, the author considers the scattered waves from tropical tree trunk to

explore the relationship between backscattering coefficient oσ and the diameter of three

layers of tree trunk. The finite-difference model (Yee 1966) is implemented in two dimensions

(2-D) as shown in figure 3.3. In this figure, simulation space is sampled into INX x INY grids.

48

Figure 3.3. Geometry of simulation space. Remarks: P is observed point. A, B and C are

heartwood, xylem, and skin, respectively. Simulation space is divided into INX x

INY grids of meshes.

Trunk

C P

1 2 3 4 … … … … INX-1 INX

1 2 3 4

:::::

INY-1 INY

Incident wave

Abs

orbi

ng b

ound

ary

cond

ition

Simulation space

B A

49

Referring to the simulation in Chapter II with the same parameters, i.e. incident pulse using

Gaussian pulse, boundary condition using Mur method (Mur 1981). Finally, the two

dimensional backscattering coefficient oσ is defined by (3.32), where IyE is observed

electric field intensity on the trunk surface in frequency 275.1=f GHz.

GHzf

Iy

Sy

E

E

lR

275.1

2

2

0 2

=

= πσ (3.32)

Where R is distance from centre of tree trunk to the observation point. l is effective scattered

surface of tree trunk, in this case, is assumed as bπ . In the calculation of its backscattering

coefficient in the analysis, SyE and I

yE are equal to SEφ and IEφ respectively at incident

angle 0o.

3.4. Results

In simulation space, see figure 3.3, scattered wave from an infinite length of tree trunk

is considered. This tree trunk is composed of three media; skin, xylem, and heartwood. The

radius of tree trunk varies from 0 to 40 grids (or 0 to 0.5m). The simulation space edges

(external boundaries) are surrounded by artificial absorbing boundary condition (Mur

method), refer the simulation in Chapter II. Incident wave is a plane wave with intensity as

shown by Gaussian pulse, which propagates from left to right of the simulation space in speed

of light. Parameter of simulation is simulation space grids 300== INYINX ,

50

space-increments 21025.1 −×=∆=∆ yx m, time- increment =∆t 11105.2 −× s, maximum

intensity of initial electric field 100 V/m, and running time tt ∆= 600 s. Figure 3.4 shows

scattered wave from tree trunk with tt ∆= 50 to t∆300 s. Additionally, figure 3.5 shows

details of scattered wave when tt ∆= 300 s, where A, B, C, and D are scattered waves from

skin, xylem, heartwood, and forwarded wave that is occurred by clipping wave on the tree

trunk surface, respectively. Then E, F, and G are skin, xylem, and heartwood, respectively.

The observed point P is at 1.5 m from centre of tree trunk. This point is used to observe

intensities of scattered electromagnetic fields.

In this study, the author observed only the horizontal component (TE wave) or

electric field SyE in backscattering direction ( o0=φ ). The backscattered electric field is

computed and is shown in figure 3.6. In this figure, A, B, and C show scattered waves from

skin, xylem, and heartwood, respectively. Further, fast Fourier Transform is employed to

obtain power spectrum of it and electric field intensity of preferred frequency, in this case,

frequency of Japanese Earth Resources Satellite (JERS-1) SAR, 275.1=f GHz was used.

Finally, the backscattering coefficient is calculated using (3.32) and the result is shown in

figure 3.7 (r - simulation).

In the analysis, direction of incident wave is the same as of observed point P or

o0=φ . Consequently, SEφ is equivalent to SzE that obtained in simulation where the

distance between the centre of tree trunk to the observed point is 1.5 m. By considering the

51

Figure 3.4. Distribution of scattered electric field intensity SyE with tt ∆= 50 to t∆300 s.

52

Figure 3.5. Distribution of scattered electric field SyE in tt ∆= 300 s, where A, B and C are

scattered wave from skin, xylem and heartwood, respectively. P is observed point.

E, F and G are skin, xylem and heartwood, respectively. D is forwarded wave that

is occurred by clipping wave that flows on the trunk surface and scattered to the

backward of tree trunk.

X [ m ]

Y [

m]

0 0 . 5 1 1 . 5 2 2 . 5 3 3 . 5

0

0 . 5

1

1 . 5

2

2 . 5

3

3 . 5

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0n=300 Rasamala (Altingia exelsa) Intensities S

yE

(dB)

P

A B

C D

E

F G

53

Figure 3.6. Scattered electric field intensities at observed point P. A, B and C are scattered

pulse from skin, xylem and heartwood, respectively.

parameter of JERS-1 SAR, the result of analysis is obtained and is shown in figure 3.7 (p -

rasamala). This result is obtained from the analysis of scattered wave from a tree trunk of

rasamala (Altingia exelsa) in analysis area. The analysis result compares well with simulation

ones. However, a small error was found. It can be considered that the error is generated by the

FDTD calculation error caused by calculation using the sampled grids. Additionally, in the

same figure, the results of analysis for teak (Tectona grandis), pine (Pinus merkusii), and

mahagony (Swietenia macrophylla) are depicted too.

From these results we know that the increment of tree trunk diameter was directly

proportional to increment of the backscattering coefficient. It means that backscattering is

influenced by the width of tree trunk surface and volume of it. When the surface is wide, the

-15

-10

-5

0

5

10

15

0.0 2.5 5.0 7.5 10.0 12.5

Times (ns)

Bac

ksca

ttere

d A

mpl

itude

(V/m

)

A B

C

54

Figure 3.7. Analysis and simulation results for four species of Indonesian tropical forest,

where the diameter of tree trunk is equal with 2c, where c is radius of tree trunk.

-20

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

0.252 0.262 0.272 0.282 0.292 0.302 0.312 0.322

Diameter of tree trunk [m]

Bac

ksca

tterin

g co

effic

ient

[dB

]

mahagonypinerasamalateaksimulation

55

reflected or scattered field intensity or energy will be higher, and in the contrary of it.

The application of this study will be applied to monitor rasamala (Altingia exelsa)

species that is mainly distributed around Gede Pangrango National Park, west Java, Indonesia

using JERS-1 Synthetic Aperture Radar (SAR, L band) data. The application will be

discussed in the next section.

3.5. Application

3.5.1 Study area

The study area was Cisarua tropical forest, part of Gede Pangrango National Park,

west Java, Indonesia (part of A area in figure 3.8 and 3.9). Figure 3.8 and 3.9 show JERS-1

Visible Near Infra Red (VNIR) and Synthetic Aperture Radar (SAR) data respectively that

acquired at the same area (Path 107 Row 312) on 30 September 1997 and 10 August 1997.

This area is one of the wettest parts of Java with an average annual rainfall of around 3000mm

to 4200mm, while the relative humidity varies between 80% and 90%. Figure 3.10 shows the

altitude distribution of the study area where the study area has altitude ranging from 627m to

2030m as seen in figure 3.11 (DEM 1990). Biomes of this area are the sub-montane (1100m

to 1500m) and montane (1500m to 2030m) tropical forest. In addition to that, settlement and

dry paddy fields called ladang are distributed in the region with altitude between 627m and

1100m above sea level (asl). Tea, which has tall canopy about 1m, is usually planted at

56

Figure 3.8. JERS-1 VNIR data of the study area (Path 107 Row 312, 19970930): Gede

Pangrango National Park, west Java, Indonesia. Remark: A and B show northern

and southern part of the National Park, respectively.

JERS-1 VNIR

Path 107 – Row 312

19970930

A

B

clouds

Study area

57

Figure 3.9. JERS-1 SAR data of the study area (Path 107 Row 312, 19970810): Gede

Pangrango National Park, west Java, Indonesia. Remark: A and B show northern

and southern part of the National Park, respectively.

A

B JERS-1 SAR

Path 107 – Row 312

19970810

Study area

58

Figure 3.10. Altitude distribution of the study area : Mount Gede Pangrango National

Park, west Java, Indonesia.

Study area

S6o45’

S6o50’

E106o55’ E107o00’

59

Figure 3.11. Location of the study area: Gede Pangrango National Park (part of area A in

figure 3.8 and 3.9)

Indonesia

106o56`E

6o38` S

6o44`S

107o02`E

N

800m

1000m

1400m

1200m

1600m

1600m

1200m

1400m

1000m

1400m

1600m

1 0 2km

60

altitude between 627m and 2030m by government companies. Appendix F shows part of the

ground data of the study area. The sub-montane tropical forest has the highest diversity of

plant life and is characterized by large trees forming diameter trunks between 0.2m and 0.4m.

The dominant species in this ecosystem is rasamala (Altingia exelsa). Besides a rich ground

flora containing begonias and ferns, many species of epiphytes are found growing

non-parasitically on twigs and branches (e.g. orchids, lianas, and herbs).

Montane tropical forest has a lower diversity of plants with fewer herb species than

the sub-montane zone. Common trees included rasamala; also noticeable are puspa (Schima

walichii) and conifers (Dacrycarpus imbricatus and Podacarpus neriifolius).

3.5.2 Data processing

The JERS-1 SAR data (figure 3.9) was examined in order to estimate the diameters

of rasamala in the study area. The data (path 107, row 312) was acquired on 10 August 1997

during the dry season in the study area. This data was processed at level 2.1 or standard

geocoded data and was resampled to Universal Transverse Mercator (UTM) projection by the

Earth Observation Research Centre (EORC) of National Space Development Agency

(NASDA) of Japan. Firstly, a 3x3 median filter was employed and the second process used a

5x5 average filter to reduce inherent speckle noise (Sunar et al. 1998). At the same time, the

image was also referenced to the UTM co-ordinate system, through a polynomial rectification

61

Figure 3.12. Classification result assigned the distribution of classes in the study area. Test

area shows classes distribution in ecosystem zones and its terrain conditions.

Ecosystem zones are settlement and paddy (sp), sub-montane (sm) and montane

(mt).

6o38 S

6o44 S

106o56 E 107o00 E

km

sp sp sm sm mt

test area

Altitude units in metres

62

Table 3.2. Relationship between backscattering coefficients and tree trunk diameters of

rasamala forests in the study area

Class names Backscattering

coefficient (dB) Trunk diameter (m) Standard deviation (m)

forest 1 -12 0.290 0.0025

forest 2 -9 0.295 0.0025

forest 3 -7 0.300 0.0025

using 30 ground control points collected from topographic maps scale of 1:25 000

(BAKOSURTANAL 1990). This procedure yielded a geometric accuracy of 0.1 pixels. Then

the spatial resolution of SAR image was resampled to 12.5m.

A supervised classification was performed to classify the data. The study area was

classified into six classes based on topographic maps (BAKOSURTANAL 1990). They were

namely forest 1, forest 2, forest 3, bush (tea), ladang (dry paddy fields), and settlement.

Figure 3.12 shows classification results and the terrain characteristics of each ecosystem zone

in the study area. The classification results contained only 1% error in comparison with the 30

training sites that were sampled from topographic maps. The terrain characteristics of the

study area were generated using digital elevation model (DEM 1990). Figure 3.12 shows that

forest classes distributed in the study area are at the altitude above 1100m or at the

sub-montane and montane zones.

63

The statistical value of each forest class was derived and then the backscattering

coefficient of each class was calculated using NASDA calibrated equation (Shimada 1998).

These results are shown in table 3.2. Where the species included in forest classes in each pixel

were assumed to be rasamala (Altingia exelsa), because it was the dominant species in the

study area (refer Appendix F). By comparing the backscattering coefficient of each forest

class to the curve of rasamala in figure 3.7, as seen in table 3.2, the trunk diameter of each

forest class was obtained. This result shows that trees in sub-montane and montane tropical

forest zones have a trunk diameter between 0.29m and 0.30m with standard deviation of each

class is 0.0025m. These results matched well with the ground data.

3.6 Conclusions

Numerical analysis was conducted to analyse the relationship between the

backscattering coefficients oσ and diameter of tropical tree trunk. The analysis results were

confirmed by simulation using Finite Difference Time Domain (FDTD) method. These results

are in good agreement. A variation of this analysis, it could be applied to estimate diameter of

tropical tree trunk from Synthetic Aperture Radar (SAR) data, which this information is very

important to estimate the forest volumes effectively and accurately. These results succeeded in

estimating tree trunk diameter of rasamala (Altingia exelsa) that is widely distributed in the

Cisarua tropical forest, part of Gede Pangrango National Park, west Java, Indonesia (Tetuko et

64

al. 2001) from JERS-1 SAR data. While this study focused on single site in Indonesia, it is

reasonable to expect that this method or variations should be successful in estimating tree

trunk diameters in similar tropical forest regions of the world using SAR data.

65

References

1. BAKOSURTANAL, 1990, Topographic maps; Cisarua 1209-142, Gunung Hambalang

1209-144. Indonesian National Coordination Agency for Surveys and Mapping, 1st edn

(Cibinong: BAKOSURTANAL).

2. DAVID, P., STELLA, E.B., STHEPHEN, J.M., 1997, Terrain influences on SAR

backscatter around Mt. Taranaki, New Zealand. IEEE Transactions on Geoscience and


3. DEM, 1990, Digital Elevation Model: Cisarua 1209-142, Gunung Hambalang 1209-144.

Indonesian National Coordination Agency for Surveys and Mapping, 1st edn (Cibinong:

BAKOSURTANAL).

4. KAMAL SARABANDI, 1989, Electromagnetic Scattering from Vegetation Canopies,

Ph.D Dissertation in the University of Michigan, Michigan: Michigan University.

5. LI, W., KOH, J., YEO, T., KOOI, P., 1999, “Analysis of Radiowave Propagation in a

Four- layered Anisotropic Forest Environment,” IEEE Transactions on Geoscience and

Remote Sensing, vol.37, pp.1967-1979.

6. MUR, G., 1981, “Absorbing boundary Conditions for the finite-difference approximation

of the time-domain electromagne tic- field equation,” IEEE Transactions on

Electromagnetic Compatibility, vol.23, no.4, pp.377-382.

7. SHIMADA, M., 1998, User’s guide to NASDA’s SAR products. Earth Observation

66

Research Centre (EORC), National Space Development Agency (NASDA), 2nd edn

(Tokyo: EORC-NASDA)

8. SUNAR, F., TABERNER, M., MAKTAV, D., KAYA, S., MUSAOGLU, M., and YAGIZ,

E., 1998, The use of multi temporal radar data in agriculture monitoring: a case study in

Kyocegiz-Dalaman ecosystem, Turkey. International Archives of Photogrammetry and


9. TETUKO S.S., J., TATEISHI, R., WIKANTIKA, K., 2001, “A method to estimate tree

trunk diameter and its application to discriminate Java-Indonesia tropical forest,”

International Journal of Remote Sensing, vol. 22, no.1, pp.177-183.

10. UNO TORU, 1998, Finite Difference Time Domain Method for Electromagnetic Field

and Antenna Analyses. 1st edition, Tokyo: Corona.

11. YEE, K. S., 1966, “Numerical solution of initial boundary value problems involving

Maxwell’s equations in isotropic media,” IEEE Transactions on Antennas Propagation,

vol.14, no.4, pp.302-307.

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