UNIVERSITI PUTRA MALAYSIA

GENERATION AND EVALUATION OF DIGITAL ELEVATION MODEL DERIVED THROUGH SPACEBORNE SYNTHETIC APERTURE RADAR

INTERFEROMETRY

KU MOHD NOH BIN HJ KU RAMLI

FK 2007 82

GENERATION AND EVALUATION OF DIGITAL ELEVATION MODEL DERIVED THROUGH SPACEBORNE SYNTHETIC

APERTURE RADAR INTERFEROMETRY

By

KU MOHD NOH BIN HJ KU RAMLI

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfilment of the Requirements for the

Degree of Master of Science

August 2007

Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment of the requirement for the degree of Master of Science

GENERATION AND EVALUATION OF DIGITAL ELEVATION MODEL DERIVED THROUGH SPACEBORNE SYNTHETIC

APERTURE RADAR INTERFEROMETRY

By

KU MOHD NOH BIN HJ KU RAMLI

August 2007

Chairman : Professor Shattri bin Mansor, PhD

Faculty : Engineering

Many success stories of spaceborne InSAR have been reported all over the

world in various earth sciences applications. However, only a limited number

of articles were reported for applications in tropical regions. Therefore the

objectives of this research are: i) To generate DEM from repeat pass

spaceborne InSAR by using SAR data from European Earth’s Resource

Satellite (ERS-1 and ERS-2), Canadian Earth Resource Satellite (Radarsat)

which are both in C-band systems and L-band Japanese Earth Resource

Satellite (JERS-1) with emphasise on coherence optimisation; ii) To increase

the coherence level through slope enhancement technique; and iii) To

assess the potential, suitability and limitations of InSAR DEM generated from

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the above data and conducting accuracy assessment of the InSAR DEM in

Malaysian condition.

Two test sites (Site A-Selangor and Site B-pahang) were selected for their

variability in vegetations cover and landforms. Three pairs of ERS tandem

data (pair 1, pair 2 and pair 3), one pair of JERS-1 data (pair 5) and one pair

of standard mode Radarsat S7 data (pair 7) were acquired for site A-

Selangor. On the other hand, one pair ERS tandem data (pair 4) and one pair

JERS-1 data (pair 6) were acquired for site B-Pahang. Despite of having

many pairs of data, the generation of DEM for site A-Selangor was not very

successful. The ERS tandem data has revealed a very low coherence level

(< 0.3) for almost all vegetations cover with exception for urban and

suburban areas (> 0.5). The clearly visible interferogram pattern could be

seen in higher coherence areas. However, the atmospheric artifacts can be

observed clearly on the interferogram through ‘fuzzy’ fringe pattern especially

along the coastline. The Radarsat and JERS-1 data have shown a poorer

coherence and interferogram quality, eventhough slightly better results for

JERS-1 comparatively, presumably due to the long temporal separation of 24

days and 44 days between the two successive data acquisitions. ERS

tandem data over site B-Pahang has shown a promising result with 65% of

the ERS scene especially in urban and areas with low vegetations was

successfully generated. A long dry month and non-windy condition was

assumed to be the contributing success factor. The forested areas especially

in high relief areas remain ingenerated. The use of Radarsat and JERS-1

data did not improve the DEM generation. The temporal separation between

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the data pair was long enough to observe changes within the canopy and

hence the data pairs were totally uncorrelated.

DGPS field observations were conducted to improve the accuracy of InSAR

derived DEM and for the accuracy assessment. The third order polynomial

warping with thirteen GCP (X, Y) and fourteen GCP (Z) were used for the

transformation of DEM from relative to absolute height with the RMSE of 2.4

m, 0.52 m and 1.78 m in X, Y and Z respectively. Quantitative accuracy

assessments by using RMSE and R2 regression correlation analysis were

performed on the InSAR DEM against DGPS points, spot height check

points, contour generated DEM, SRTM Mean Sea level DEM and SRTM

WGS84 DEM. A Root Mean Square Error (RMSE) of 12.619, 15.662,

15.421, 15.016 and 15.694 were reported for the analysis accordingly. The

corresponding R2 results of 0.8598, 0.913, 0.914, 0.8948 and 0.8938,

obtained from similar evaluation pairs proved they are highly correlated. The

results were satisfactory and agreeable to the results from most researches.

The qualitative assessment was performed and presented graphically to

further emphasis on the quality of the results.

ERS tandem data is capable to provide a medium scale DEM at 1: 100 000

or smaller with accuracy within 20-25 m and contour interval of 40 m. This is

absolutely complementing the present techniques of producing DEM

especially in areas that is constraint by the persistent cloud cover through out

the year. This complementary approach is considered as the best approach

to expedite the generation of DEM for the whole country. Spaceborne ERS

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tandem data eventhough with one day temporal separation is still insufficient

to ensure a highly correlated image pairs for 100% success in generating

DEM. The DEM quality will be tremendously improved with single pass

InSAR where the data was acquired by the two sensors simultaneously

which is currently available only in airborne and shuttle environment.

Therefore, single pass data acquisition should ideally be the preference for

this part of the world such that have been proven by airborne Airsar Topsar

Pacrim I and II missions in 1996 and 2000 and SRTM.

V

Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk ijazah Master Sains

PENJANAAN DAN PENGANALISAAN DEM YANG DIHASILKAN MELALUI KAEDAH INTERFEROMETRI

SYNTHETIC APERTURE RADAR ANGKASA

Oleh

KU MOHD NOH BIN HJ KU RAMLI

Ogos 2007

Pengerusi : Profesor Shattri bin Mansor, PhD

Fakulti : Kejuruteraan

Kejayaan penjanaan DEM daripada teknik interferometri (InSAR) telah

banyak dilaporkan dalam pelbagai bidang aplikasi sains bumi di serata dunia.

Namun begitu, tidak banyak kajian yang telah dijalankan untuk aplikasi di

kawasan tropika yang penuh dengan tumbuhan. Oleh itu, objektif

penyelidikan ini ialah untuk: i) Menjana DEM InSAR angkasa dengan

menggunakan data dari European Earth’s Resource Satellite (ERS-1 and

ERS-2), Canadian Earth Resource Satellite (Radarsat) yang mana kedua-

duanya adalah sistem jalur C dan Japanese Earth Resource Satellite (JERS-

1) yang menggunakan jalur L dengan mengoptimakan coherence; ii) Untuk

meningkatkan tahap coherence dengan menggunakan teknik penonjolan

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cerun (slope enhancement technique); dan iii) Menilai keupayaan,

kesesuaian dan kekurangan DEM InSAR yang dihasilkan dengan

menggunakan data yang dihasilkan di atas dan menjalankan penganalisaan

ketepatan di persekitaran Malaysia.

Dua kawasan kajian (kawasan A-Selangor dan kawasan B-Pahang) telah

dipilih kerana kepelbagaian bentuk dan litupan buminya. Tiga pasang data

tandem ERS (pasangan 1, 2 dan 3), sepasang data JERS-1 (pasangan 5)

dan sepasang data Radarsat (pair 7) telah diperolehi untuk Tapak A-

Selangor. Sementara itu, sepasang data tandem ERS (pasangan 4) dan

sepasang data JERS-1 (pasangan 6) telah diperolehi untuk kawasan Tapak

B-Pahang. Walaupun kawasan A-Selangor mempunyai banyak pasangan

data, penjanaan DEM kawasan ini tidak begitu berjaya. Data tandem ERS

telah menunjukkan kadar koheren yang rendah (<0.3) pada semua litupan

bumi kecuali bagi kawasan perbandaran dan sub-perbandaran (>0.5).

Bentuk interferogram yang jelas telah ditunjukkan pada kawasan yang

mempunyai kadar koheren yang tinggi. Walaupun begitu, kesan atmospheric

artifacts dapat dilihat dengan jelas melalui bentuk interferogram yang tidak

jelas terutamanya di sepanjang pantai. Kedua-dua data radarsat dan JERS-1

L band telah menunjukkan kualiti koheren dan interferogram yang rendah,

walaupun JERS-1 menghasilkan kualiti data yang sedikit lebih baik

berbanding radarsat, berkemungkinan disebabkan kerana jarak masa yang

panjang di antara kedua-dua data bagi setiap pasangan iaitu 24 hari dan 44

hari masing-masing. Data tandem ERS kawasan B-Pahang telah

menunjukkan hasil yang memberansangkan di mana penjanaan DEM telah

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berjaya dilaksanakan bagi 65% daripada kawasan bertindih pasangan data

tersebut terutamanya di kawasan perbandaran dan kawasan bertumbuhan

rendah. Kemarau yang panjang dan keadaan tiada angin bertiup adalah

merupakan di antara faktor utama kejayaan penjanaan DEM di kawasan ini.

Penjanaan DEM di kawasan berhutan tebal terutama di tanah tinggi tetap

tidak berjaya. Penggunaan data Radarsat dan JERS-1 tidak dapat

membantu meningkatkan kualiti penjanaan. Jarak masa di antara kedua-dua

data bagi setiap pasangan adalah terlalu panjang sehingga membolehkan

pengesanan perubahan pada tumbuhan yang seterusnya mengakibatkan

pasangan data tersebut tidak mempunyai kaitan langsung.

Cerapan DGPS di lapangan telah dijalankan bertujuan meningkatkan kualiti

DEM InSAR dan juga untuk analisa kejituan. Polinomial darjah ketiga telah

digunakan untuk menukar DEM berketinggian relatif kepada DEM

berketinggian absolut menggunakan 13 GCP (X,Y) dan 14 GCP (Z) DGPS

dengan memperolehi nilai RMSE 2.4 m, 0.52 m, 1.78 m bagi X, Y dan Z

masing-masing. Analisa kejituan secara kuantitatif dengan menggunakan

RMSE dan korelasi regresi R2 telah dijalankan pada DEM InSAR berbanding

titik cerapan DGPS, spot height check points, DEM daripada kontor, DEM

SRTM aras purata laut dan DEM SRTM WGS84. Hasil RMSE 12.619,

15.662, 15.421, 15.016 dan 15.694 telah diperolehi bagi setiap

perbandingan mengikut turutan yang telah dijalankan. Nilai R2 yang

diperolehi melalui perbandingan yang serupa telah menghasilkan nilai

0.8598, 0.9130, 0.9140, 0.8948 dan 0.8938 masing-masing dan ini

menunjukkan nilai korelasi yang tinggi bagi setiap perbandingan yang

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dijalankan. Hasil yang diperolehi adalah memuaskan dan selari dengan hasil

yang diperolehi oleh penyelidik yang lain. Analisa kualitatif telah juga

dijalankan secara grafik untuk lebih menguatkan lagi kualiti yang dihasilkan.

Data tandem ERS berkemampuan untuk menyediakan DEM skala

pertengahan pada 1: 100 000 ataupun lebih kecil dengan kejituan 20-25 m

dan sela kontor optima pada 40 m. Ini sudah tentu sekali akan saling

melengkapi dengan teknik yang sedia diamalkan sekarang ini terutama

sekali di kawasan yang di penuhi awan di sepanjang tahun. Kaedah saling

melengkapi ini adalah kaedah yang paling bersesuaian untuk

mempercepatkan penjanaan DEM di seluruh negara. Data tandem ERS

angkasa, walaupun berselang hanya sehari di antara keduanya masih lagi

belum mencukupi untuk menjamin imej berkualiti tinggi yang boleh menjana

100% kawasan tindihan pasangan data tersebut. Kualiti DEM yang

dihasilkan akan dapat dipertingkatkan secara mendadak dengan

menggunakan single pass InSAR di mana cerapan data dilakukan secara

serentak dengan menggunakan dua penderia. Teknik ini pada masa ini

hanya terdapat pada sistem pesawat dan shuttle sahaja. Oleh yang

demikian, pengambilan data secara single pass adalah yang terbaik untuk

menyelesaikan masalah penghasilan DEM di kawasan tropika seperti mana

yang telah dibuktikan melalui data SRTM dan misi pengambilan data

pesawat Airsar/Topsar Pacrim I dan II NASA pada tahun 1996 dan 2000.

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ACKNOWLEDGEMENTS

Praise and glory to Allah the Almighty for his blessing which permits me to

successfully complete this thesis. I would like to convey my deepest gratitude

to my supervisor Professor Dr. Shattri bin Mansor for his precious guidance,

encouragement and constructive criticisms throughout this research study.

Sincere thanks and appreciation are also extended to other Supervisory

Committee Members comprising Associate Professor Dr. Noordin Ahmad

and Dr. Muhamad Radzali Mispan for their continuous advices and

comments.

My highest appreciations go to Mr. Darus Ahmad, Director of MACRES and

Dato’ Nik Nasruddin Mahmood, former Director of MACRES for their

continuous support and encouragement.

I have no words to express my sincere appreciation to Atlantis Scientific Inc.

Ottawa, Canada especially to Dr. Marko Van de Kooij and Dr. Shinya Sato

for their experts’ guidance and comments in the subject matter and

processing of the data using APP and EVInSAR software. I am very much

indebted to European Space Agency (ESA), Frascatti, Italy for graciously

providing ERS data to materialise this research.

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Not to forget, thanks to all staff of UPM who directly and indirectly involve in

making this study a success especially the staff of Engineering faculty and

Institute of Advanced Technology (ITMA).

Last but not the least, my deepest admiration to my dear wife; Noorma Aziz,

daughters; Ku Siti Farhana, Ku Siti Syahidah, Ku Nazurah Husna and Ku

Nurul Izzah, and son; Ku Ahmad Lutfi, not only for their patience, kind

understanding and supports while preparing the thesis, but also providing

courage and inspiration without which this research study would not have

been a successful one.

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I certify that an Examination Committee has met on 23rd August 2007 to conduct the final examination of Ku Mohd Noh bin Ku Ramli on his Master of Science thesis entitled “Generation and Evaluation of Digital Elevation Model Derived Through Spaceborne Synthetic Aperture Radar Interferometry” in accordance with Universiti Pertanian Malaysia (Higher Degree) Act 1980 and Universiti Pertanian Malaysia (Higher Degree) Regulations 1981. The Committee recommends that the student be awarded the degree of Master of Science. Members of the Examination Committee were as follows: Kamaruzaman Jusoff, PhD Professor Faculty of Forestry Universiti Putra Malaysia (Chairman) Ahmad Rodzi Mahmud, PhD Associate Professor Faculty of Engineering Universiti Putra Malaysia (Internal Examiner) Helmi Zulhaidi Mohd Shafri, PhD Lecturer Faculty of Engineering Universiti Putra Malaysia (Internal Examiner) Mazlan Hashim, PhD Professor Faculty of Engineering and Geoinformation Science Universiti Teknologi Malaysia (External Examiner)

_____________________________ HASANAH MOHD. GHAZALI, PhD Professor and Deputy Dean School of Graduate Studies Universiti Putra Malaysia

Date: 28 April 2008

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This thesis was submitted to the Senate of Universiti Putra Malaysia and has been accepted as fulfilment of the requirement for the degree of Master of Science. The members of the Supervisory Committee were as follows: Shattri Mansor, PhD Professor Engineering Faculty Universiti Putra Malaysia (Chairman) Noordin Ahmad, PhD Associate Professor Engineering Faculty Universiti Putra Malaysia (Member) Muhamad Radzali Mispan, PhD Senior Research Officer Malaysian Agriculture Research and Development Institute (MARDI) (Member) ______________________

AINI IDERIS, PhD Professor and Dean School of Graduate Studies Universiti Putra Malaysia

Date: 8 May 2008

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DECLARATION I declare that the thesis is my original work except for quotations and citations which have been duly acknowledged. I also declare that it has not been previously, and is not concurrently, submitted for any degree at Universiti Putra Malaysia or other institution.

KU MOHD NOH BIN HJ KU RAMLI

Date: 20 March 2008

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TABLE OF CONTENTS

Page

ABSTRACT IIABSTRAK VIACKNOWLEDGEMENTS XAPPROVAL SHEETS XIIDECLARATION FORM XIVTABLE OF CONTENTS XVLIST OF TABLES XVIILIST OF FIGURES XVIIILIST OF ABBREVIATIONS XXII

1 INTRODUCTION 1 1.1 General 1 1.2 Rational and Justification 7

1.2.1 The Need for Updated Information 7 1.2.2 Complementing and Expediting Map Revision Process 8 1.2.3 Technological Advancement 9

1.3 Objectives 9 1.4 Scopes of Research 10 1.5 Software 11 1.6 Overview Of The Thesis 12

2 LITERATURE REVIEW 14

2.1 Synthetic Aperture Radar (SAR) 14 2.1.1 SAR System Configuration 17 2.1.2 Factors Influencing Radar Backscattering Return 19

2.2 Satellites and Map Coordinate Systems 23 2.2.1 Malaysian Map Projection Systems 23 2.2.2 Vertical Datum 24

2.3 Differential Global Positioning System (DGPS) 26 2.4 Synthetic Aperture Radar Interferometry (InSAR) 29

2.4.1 Development and Applications of InSAR 29 2.4.2 Various Techniques of InSAR Implementations 49

2.5 Summary 55 3 METHODOLOGY 56

3.1 Introduction 56 3.2 Selection of Study Sites 57 3.3 Satellite Data Selection and Acquisitions 59

3.3.1 European Earth Resource Satellites (ERS-1 and ERS-2) 62 3.3.2 Japanese Earth Resource Satellite (JERS-1) SAR Data 64 3.3.3 Radarsat SAR Data 65 3.3.4 Landsat ETM Data Acquisition and Processing 66

3.4 Vector Data Acquisitions and Processing 68 3.5 Field Work 71 3.6 JERS-1 Raw Signal Data Processing 74 3.7 InSAR Processing 77

3.7.1 Selection of Input Data 77 3.7.2 Coregistration 86 3.7.3 Generate and Filter Interferogram 87 3.7.4 Enhance Interferogram Phase 91

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3.7.5 Optimisation of Interferogram and Coherence Using Slope Enhancement Technique 91

3.7.6 Phase Unwrapping 92 3.7.7 Generate DEM from Phase 96 3.7.8 Post Processing 98

4 RESULTS AND DISCUSSIONS 101

4.1 Introduction 101 4.2 Interferogram and Coherence Analysis 102

4.2.1 ERS Tandem Over Selangor 102 4.2.2 Radarsat Over Selangor 106 4.2.3 JERS-1 Over Selangor 107 4.2.4 JERS-1 Over Pahang 109 4.2.5 ERS Tandem Over Pahang 110

4.3 Coordinate system transformation 117 4.4 Quantitative Assessment of DEM 120 4.5 Qualitative Analysis 134

4.5.1 Permanent Scatterer Features 134 4.5.2 Series of Small Hills 137

4.6 Recommended Mapping Parameters 139 4.6.1 Contour Interval 139 4.6.2 Accuracy 140 4.6.3 Mapping Scale 140

5 CONCLUSION AND RECOMMENDATIONS 141

5.1 Conclusion 141 5.2 Recommendations 146

REFERENCES 148 APPENDICES 155 BIODATA OF THE AUTHOR 195

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LIST OF TABLES

Table Page 1.1 History of optical sensors with stereo viewing capability (Konecny, 2002). 4

1.2 The status of world’s topographic mapping (Konecny, 2003). 13

1.3 The status of world’s topographic maps updating (Konecny, 2002). 13

2.1 Radar wavelenghts and designations (Adapted from Lillesand, 1994). 16

2.2 Spaceborne SAR systems (Jensen, 2000). 18

2.3 Recommended perpendicular baseline (Solaas, 1994). 46

3.1 List of ERS Tandem data used in the analysis. 63

3.2 List of JERS-1 data. 65

3.3 List of Radarsat SAR data. 66

3.4 Tie points data summary. 88

4.1 Coherence extraction from various cover types. 115

4.2 Weather parameters on the date of data acquisition. 116

4.3 The perpendicular baseline length vs. height sensitivity change. 117

4.4 Analysis on number of GCP and polynomial transformations. 118

4.5 Results of chi square test on GPS base stations. 121

4.6 RMSE evaluation between DGPS points and DEM. 126

4.7 RMSE evaluation between DEM and spot height check points. 129

4.8 RMSE evaluation between InSAR DEM and other DEM. 132

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LIST OF FIGURES Figure Page

2.1 Operation of SAR (Jensen, 2000). 15

2.2 SAR systems configuration (Price, 1999). 18

2.3 Penetration capability of radar system at X, C and L bands (Dallemand et al., 1993). 20

2.4 Radar scattering interactions in a forest canopy (Dobson et al., 1992). 20

2.5 Foreshortening (RSI, 1995). 22

2.6 Layover (RSI, 1995). 22

2.7 Shadow (RSI, 1995). 22

2.8 Satellite orbit in space (NASA, 2007). 24

2.9 Geoidal height (orthometric) and Ellipsoidal height (spheroidal) (CCRS, 2008). 26

2.10 The gridded geoid map for Southern Peninsular Malaysia (Zarina et al., 1996).

27 2.11 The concept of DGPS (Dana, 2006). 29

2.12 Wavelength and phase information. 35

2.13 Gausse plane (Heitz, 1998). 36

2.14 A schematic diagram of InSAR (Adapted from Gens et al., 1996). 39

2.15 Baseline configuration. 45

2.16 Along track interferometry (Adapted from Gens et al., 1996). 51

3.1 The location of study areas shown on a map of Central Region Peninsular Malaysia. 60

3.2 ERS Tandem data listing and baseline length information for ascending pass

data (night time). 63 3.3 Thumbnails images, path, row and RSP number. 64

3.4 Browse image. 66

3.5 Landsat TM, ERS and contour data coverage over topogaphic maps index. 67

3.6 Contour and attributes. 69

3.7 Spot heights. 69

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3.8 Discontinuity in 2-D DEM colour resulted from wrong contour attributes. 70

3.9 DEM of six map sheets. 71

3.10 OMNISTAR DGPS equipments. 72

3.11 Calibrating OMNISTAR DGPS at the base station. 73

3.12 Distribution of DGPS base stations and DGPS GCP. 74

3.13 Flow chart for APP processing. 76

3.14 InSAR processing flowchart. 79

3.15 Master data - Amplitude and phase image (Left) and amplitude image (Right). 81

3.16 Master data- Zoom-in a section of amplitude and phase image. 81

3.17 Master data- Zoom-in a section of amplitude image. 82

3.18 Master data - Resampled amplitude and phase image. 82

3.19 Master data - resampled amplitude image. 83

3.20 Slave data - Amplitude and phase image (left) and amplitude image only (Right). 83

3.21 Slave data - Zoom-in a section of amplitude and phase image. 84

3.22 Slave data - Zoom-in a section of amplitude image. 84

3.23 Slave data - Resampled amplitude and phase image. 85

3.24 Slave data - Resampled amplitude image. 85

3.25 Interferogram without flat earth phase correction applied. 90

3.26 Interferogram with flat earth phase correction applied. 90

3.27 Enhanced interferogram. 93

3.28 Coherence image. 94

3.29 Phase unwrapping editing. 95

3.30 Phase discontinuity. 95

3.31 Unwrapping errors shown in purple. 97

3.32 Mask lines, negative and positve phase residues and unwrapping windows. 97

3.33 Advanced geodetic correction and height trend removal. 100

4.1 ERS Interferogram image of pair 1 over Selangor. 103

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4.2 ERS coherence image of pair 1 over Selangor. 103

4.3 ERS Interferogram image of pair 2 over Selangor. 104

4.4 ERS coherence image of pair 2 over Selangor. 104

4.5 ERS Interferogram image of pair 3 over Selangor. 105

4.6 ERS coherence image of pair 3 over Selangor. 105

4.7 Radarsat interferogram image over Selangor. 106

4.8 Radarsat coherence image over Selangor. 106

4.9 JERS-1 interferogram image over Selangor. 108

4.10 JERS-1 coherence image over Selangor. 108

4.11 JERS-1 interferogram image over Pahang. 109

4.12 JERS-1 coherence image over Pahang. 110

4.13 Interferogram before slope enhancement. 111

4.14 Interferogram after slope enhancement. 112

4.15 ERS Interferogram image over Pahang. 113

4.16 ERS coherence image over Pahang. 114

4.17 Geocoded master image. 122

4.18 Geocoded slave image with mask applied. 123

4.19 Geocoded InSAR DEM. 124

4.20 Correlation between InSAR DEM and DGPS geoid height. 126

4.21 Correlation between contour DEM and DGPS geoid height. 127

4.22 Correlation between SRTM MSL DEM and DGPS geoid height. 127

4.23 Correlation between SRTM WGS84 DEM and DGPS geoid height. 128

4.24 Correlation between INSAR DEM and spot heights check points. 129

4.25 Correlation between contour DEM and spot height check points. 130

4.26 Correlation between SRTM MSL DEM and spot height check points. 130

4.27 Correlation between SRTM WGS84 DEM and spot height check points. 131

4.28 Correlation between InSAR DEM and contour generated DEM. 132

4.29 Correlation between InSAR DEM and SRTM MSL DEM. 133

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4.30 Correlation between InSAR DEM and SRTM WGS84 DEM. 133

4.31 Features with permanent scatterer characteristics seen from Ikonos data. 135

4.32 Zoom-in road and railway junction on Ikonos imagery. 135

4.33 Road and railway junction as seen from ERS master data. 136

4.34 Railway line. 136

4.35 Series of small hills on ERS master data and zoom-in image. 137

4.36 Density of the forest. 138

4.37 3D perspective view of the small hills. 138

4.38 Density slicing of the series of small hills. 139

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LIST OF ABBREVIATIONS

3D Three Dimensional

AIRSAR NASA’s Airborne Synthetic Aperture Radar

APP Advanced Precision Processor software

ASEAN Association of South East Asean Nations

CAMS Computer Assisted Mapping Systems

CEOS Committee of Earth Observation System

CEOS PPR CEOS Platform Position Record

CRISP Centre for Remote Imaging, Sensing and Processing

DEM Digital Elevation Model

DGPS Differential Global Positioning System

DInSAR Differential SAR Interferometry

DTM Digital Terrain Model

DXF Data Exchange Format

EC European Community

ECI Earth Centered Inertial

ECR Earth Centered Rotation

EDM Electronic Distance Measurements

EMR Electromagnetic Radiation

ERS-1 European Earth Resource Satellite-1

ERS-2 European Earth Resource Satellite-2

ESA European Space Agency

EVInSAR EarthView SAR Interferometry software

GCP Ground Control Point

GIS Geographic Information System

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GlobeSAR Canadian Airborne SAR System

GPS Global Positioning System

GRS Ground Reference System

H Horizontally polarized

HH Horizontal polarized transmission, horizontal polarized reception

HV Horizontal polarized transmission, vertical polarized

reception InSAR SAR Interferometry

IKONOS IKONOS, launched on September 24, 1999 is the first commercial high-resolution satellite.

IPL Input Parameter List

LANDSAT ETM Enhanced Thematic Mapper sensor on board LANDSAT satellite records 7 spectral bands of 30m resolution and one panchromatic band of 12.5m resolution

LIDAR Light Detection and Ranging

JERS Japanese Earth Resource Satellite

JPL Jet Propulsion Laboratory

JUPEM Jabatan Ukur dan Pemetaan Malaysia

MACRES Malaysian Centre for Remote Sensing

MSL Mean Sea Level

MMU Multi Media University

MOSTI Ministry of Science, Technology and Innovation

MOU Memorandum of Understanding

MLD Multi Look Detected format

NASA National Aeronautics and Space Agency

NCC Network Control Centre

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PACRIM Pacific Rim Mission

PSInSAR Permanent Scatterers InSAR

R&D Research and Development

RADAR RAdio Detection And Ranging

RADARSAT Canadian Earth Resource Satellite

RESTEC Remote Sensing Technology Center of Japan

RMS Root Mean Square

RMSE Root Mean Square Error

RSO Rectified Skew Orthomorphic Projection System

RSP Reference System for Planning

S7 The RADARSAT image with standard mode 7

SA Selective Availability

SAR Synthetic Aperture Radar

SEASAT The first earth-orbiting satellite designed for remote sensing of the earth’s oceans and had onboard the first spaceborne SAR

SLAR Side Looking Airborne Radar

SLC Single Look Complex

SPOT System Probatoire d’Observation de la Terre

SRTM Shuttle Radar Topographic Mission

TOPSAR NASA’s Topographic SAR system onboard AIRSAR

TP Tie Point

UN United Nations

V Vertically polarized

VIR Visible and Infra Red

VH Vertical polarized transmission, horizontal polarized reception

XXIV