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UNIVERSITI PUTRA MALAYSIA
A GEOSPATIAL APPROACH OF ESTIMATING VEGETATION ROUGHNESS FOR FLOOD MODELLING
IZNI BINTI MOHD ZAHIDI
FK 2017 84
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A GEOSPATIAL APPROACH OF ESTIMATING VEGETATION
ROUGHNESS FOR FLOOD MODELLING
By
IZNI BINTI MOHD ZAHIDI
Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,
in Fulfilment of the Requirements for the Degree of Doctor of Engineering
June 2017
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Copyright © Universiti Putra Malaysia
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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment
of the requirement for the degree of Doctor of Engineering
A GEOSPATIAL APPROACH OF ESTIMATING VEGETATION
ROUGHNESS FOR FLOOD MODELLING
By
IZNI BINTI MOHD ZAHIDI
June 2017
Chairman: Badronnisa Yusuf, PhD
Faculty: Engineering
2D hydrodynamic modelling has become a powerful tool to simulate the interaction
between flow and floodplains to balance the environmental requirements and flood
risks. However, vegetation roughness remains a major uncertainty. Although
roughness is known to vary with depth, it is seldom implemented due to its intricacies.
This research developed a practical method to estimate depth-varying vegetation
roughness using GIS and remote sensing. Since high point density LiDAR is not
widely accessible due to its cost, the low point density LiDAR data was combined with
QuickBird satellite image using supervised and rule-based Object-based Image
Analysis (OBIA) techniques to map the 14 km2 tropical vegetated floodplain in
Malacca, Malaysia. The rule-based results showed an 8% improvement in the overall
accuracy to 88.14% compared to the supervised classification. The McNemar results
further demonstrated that the rule-based classification accuracy was highly significant
compared to the supervised classification with 617 matches compared to 556 for
supervised. It was shown that even with low point density, the nDSM derived from
LiDAR still retains a good quality in order to improve the classification of paved
surface as well as grass and cropland. Thereafter, a regression analysis was conducted
for the trees and shrubs in combination with field measurements to estimate the
vegetation widths with high correlations. Vegetation width is the main variable in
calculating the vegetation density and consequently, the roughness coefficient. The
derived canopy covers for the shrubs were found to be representative of the field
measurements. The linear relationship of shrubs was found to be very strong at 0.98
and 0.95 for the Pearson correlation coefficient and R2, respectively. This implied that
the shrub widths can be estimated based on the canopy covers as the widths are
generally uniform throughout their heights and can be discriminated spatially.
Therefore, it is assumed that the shrubs with 100% canopy cover to have the width
equivalent to the plot width. On the other hand, the tree widths cannot be discriminated
spatially due to the obstruction by the canopy. Accordingly, the relationship between
the tree widths and NDVI was decided to be the best indicator. As a result, the tree
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widths can be calculated using a regression equation with the accuracy of 0.76 for
Pearson correlation and 0.58 for R2. Consequently, ArcGIS routines were developed
to automate the methodology to conveniently transfer to other ArcGIS interfaces for
other study areas and datasets. ArcGIS routines can generate roughness maps at any
preferred spatial resolution for 2D hydrodynamic modelling input. The calculated
depth-varying vegetation roughness coefficients were subsequently compared against
the literature. These values were plotted against the calculated Manning’s roughness
coefficients which were very close to the range of experimental values of 0.055 to
0.180. A minimum value of 0.03 was found for vegetation with the lowest density of
0.01 m-1 at 0.2 m depth and a maximum value of 0.20 for vegetation with the highest
density of 0.20 m-1 at 2 m flow depth. The mean absolute error (MAE) was 0.04 and
30% lower possibly due to the higher drag coefficients used by previous researchers.
The software TUFLOW was used to assess the depth-varying vegetation roughness
based on ecotope map and rule-based classification by comparing the modelled flood
depths with those recorded during the January 2011 flood event at the reference points
A, B, C and D. The simulation results showed improvements whereby the errors were
reduced using the depth-varying roughness approach regardless of land cover maps.
However, the details in the rule-based classification map contributed to better
estimates. The P values of t-test revealed that the overall differences of flood depths
and velocities on vegetated floodplains between the constant and depth-varying
roughness were statistically significant, wherein the maximum differences in flood
depths and velocities were 0.40 m and 0.25 m/s, respectively. The flood depth
difference was significant as it was bigger than the accuracy of LiDAR data (+/-
0.15m). This underscores the importance of spatially explicit and depth-varying
vegetation roughness. This research bridges between theoretical and practical
applications for evaluating vegetation restoration and thinning practice to optimise
vegetated floodplains as natural flood storage systems. It is useful in providing less
fieldwork and offers greater certainty over vegetated floodplains.
Keywords: Remote sensing, GIS, Tropical vegetated floodplain, Vegetation density,
Depth-varying roughness, Hydrodynamic modelling
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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai
memenuhi keperluan untuk ijazah Doktor Kejuruteraan
SATU PENDEKATAN GEOSPATIAL BAGI MENGANGGARKAN
KEKASARAN TUMBUH-TUMBUHAN UNTUK PEMODELAN BANJIR
Oleh
IZNI BINTI MOHD ZAHIDI
Jun 2017
Pengerusi: Badronnisa Yusuf, PhD
Fakulti: Kejuruteraan
Pemodelan hidrodinamik dua dimensi (2D) banyak digunakan untuk mensimulasikan
interaksi antara aliran air dengan dataran banjir bagi mengimbangi keperluan alam
sekitar dan risiko banjir. Namun, masih wujud ketidakpastian untuk faktor kekasaran
tumbuhan. Walaupun sedia dimaklumi bahawa kekasaran tumbuhan berbeza
bergantung pada kedalaman, faktor tersebut jarang dikaji kerana rumit. Kajian ini
membangunkan satu kaedah praktikal untuk menganggarkan kekasaran tumbuhan di
kedalaman yang berbeza dengan menggunakan sistem maklumat geografi (GIS) dan
penderiaan jauh. Memandangkan data LiDAR berketumpatan tinggi sukar diperoleh
disebabkan faktor kos, data LiDAR berketumpatan rendah digabungkan bersama imej
satelit QuickBird dengan menggunakan teknik analisis imej berdasarkan penyeliaan
dan peraturan untuk memetakan 14 km2 dataran banjir tropika di Melaka, Malaysia.
Keputusan teknik berdasarkan peraturan menunjukkan peningkatan ketepatan
sebanyak 8% kepada 88.14% berbanding teknik berdasarkan penyeliaan. Ujian
McNemar menunjukkan ketepatan teknik berdasarkan peraturan adalah signifikan
dengan 617 persamaan berbanding 556 persamaan bagi teknik berdasarkan penyeliaan.
Ia menunjukkan walaupun dengan ketumpatan rendah, nDSM yang dihasilkan
daripada data LiDAR masih cukup berkualiti untuk meningkatkan ketepatan
klasifikasi permukaan berturap serta rumput dan tanah ladang. Analisis regresi
kemudian dijalankan untuk pokok rimbun dan pokok renek dengan kombinasi
pengukuran di tapak untuk menganggar kelebaran tumbuhan dengan korelasi yang
tinggi. Kelebaran ialah pemboleh ubah utama dalam pengiraan kepadatan tumbuhan
dan seterusnya pekali kekasaran. Hasil kiraan spatial permukaan kanopi bagi pokok
renek ia mewakili ukuran di lapangan. Hubungan linear antara kedua-dua parameter
didapati sangat baik pada 0.98 dan 0.95 masing-masing bagi pekali kolerasi Pearson
dan R2. Ini menunjukkan kelebaran pokok renek boleh dianggarkan berdasarkan
permukaan kanopi memandangkan kelebaran pokok renek secara umumnya adalah
sama bagi sepanjang ketinggiannya dan boleh dibezakan secara spatial. Maka, pokok
renek dengan permukaan kanopi 100% dianggap mempunyai kelebaran yang sama
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dengan kelebaran plot ukuran. Sebaliknya, kelebaran pokok rimbun tidak boleh
dibezakan secara spatial disebabkan halangan oleh kanopi. Oleh itu, hubungan antara
kelebaran pokok rimbun dengan NDVI diputuskan sebagai pengukur yang terbaik.
Hasilnya, kelebaran pokok rimbun boleh dianggarkan melalui formula regresi dengan
ketepatan 0.76 bagi pekali kolerasi Pearson dan 0.58 bagi R2. Seterusnya, rutin ArcGIS
dibangunkan untuk mengautomasikan kaedah pemindahan data dengan mudah kepada
perisian ArcGIS lain. Rutin ArcGIS ini boleh menjana peta kekasaran mengikut
resolusi pilihan. Pekali kekasaran tumbuhan di kedalaman yang berbeza dikira dan
kemudiannya dibandingkan dengan sorotan kajian. Nilai-nilai tersebut diplot untuk
dibandingkan dengan pekali kekasaran Manning dan keputusannya sangat dekat
dengan nilai-nilai eksperimen iaitu daripada 0.055 ke 0.180. Nilai pekali kekasaran
minimum 0.03 didapati bagi tumbuhan dengan ketumpatan paling rendah iaitu 0.01 m-
1 di kedalaman 0.2 m manakala nilai pekali kekasaran maksimum 0.20 didapati bagi
tumbuhan dengan ketumpatan paling tinggi iaitu 0.20 m-1 di kedalaman 2 m. Kesilapan
purata adalah pada 0.04 dan 30% lebih rendah. Ini mungkin disebabkan oleh pekali
seretan lebih tinggi yang digunakan oleh penyelidik dalam kajian sebelum ini. Akhir
sekali, perisian TUFLOW digunakan untuk menilai kekasaran tumbuhan di kedalaman
yang berbeza berdasarkan peta guna tanah ekotop dan pengelasan berasaskan peraturan
dengan membandingkan model kedalaman banjir dengan data peristiwa banjir pada
Januari 2011 di lokasi rujukan A, B, C dan D. Hasil kajian menunjukkan
penambahbaikan melalui pendekatan kekasaran di kedalaman yang berbeza tanpa
mengambil kira peta guna tanah. Namun, peta guna tanah berasaskan peraturan
menjana anggaran yang lebih baik. Nilai P daripada ujian-t menunjukkan perbezaan
keseluruhan kedalaman banjir dan halaju antara pemalar dengan kekasaran di
kedalaman yang berbeza adalah signifikan. Perbezaan maksimum dalam kedalaman
banjir dan halaju adalah pada 0.40 m dan 0.25 m/s. Perbezaan kedalaman banjir adalah
signifikan disebabkan ia lebih besar dari ketepatan LiDAR (+/-0.15m). Ini menegaskan
kepentingan peta guna tanah yang lebih tepat dan kekasaran tumbuhan di kedalaman
yang berbeza. Kajian ini menghubungkan antara teori dengan aplikasi untuk menilai
kepadatan tumbuhan untuk mengoptimumkan dataran banjir yang dipenuhi tumbuhan
sebagai sistem penyimpanan banjir secara semula jadi. Perkara ini amat berguna bagi
mengurangkan kerja lapangan dan menawarkan kepastian yang lebih jitu tentang
dataran banjir yang dipenuhi tumbuhan.
Kata kunci: Penderiaan jauh,, GIS, Dataran banjir tropikal, Ketumpatan tumbuhan,
Kekasaran kedalaman berbeza, Pemodelan hidrodinamik.
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ACKNOWLEDGEMENTS
“No man is an island” - John Donne
This thesis has been made possible with the guidance and help of several individuals
who have contributed in so many different ways towards the completion of this
research.
First of all, my utmost gratitude to my academic and industrial supervisors for giving
me the space to develop my own research but at the same time, always accessible:
Dr. Badronnisa Yusuf, Prof. Thamer Ahmed Mohamed and Dr. Helmi Zulhaidi
Mohd Shafri (Universiti Putra Malaysia)
Mike Cope and Matthew Kennedy (CH2M)
A huge thank you also goes to these organisations that have provided crucial data for
this research:
Malaysia Remote Sensing Agency
Malaysia Department of Irrigation and Drainage
Malacca Department of Irrigation and Drainage
Malacca Department of City and Regional Planning
Equally important, I would like to extend my appreciation to the Ministry of Higher
Education (Malaysia) for the financial support throughout the program of which
without, would have given me more things to stress about.
Last, but far from least, I would like to thank my family especially my parents, Zahidi
Yazid and Fadzlina Fadzil, who kept pestering me about finishing my studies, my
supportive husband, Rafiee Razak, who always stepped in when I needed to collapse
after pulling an all-nighter and our two boys, Youssef and Houd, who make me want
to be the best that I can be. Ultimately, thank you God for all the blessings He has
given me.
It truly has been a walk to remember, albeit quite a long one!
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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 Doctor of Engineering,
Water Resources Engineering.
The members of the Supervisory Committee were as follows:
Badronnisa Binti Yusuf, PhD
Senior Lecturer
Faculty of Engineering
Universiti Putra Malaysia
(Chairman)
Thamer Ahmed Mohamed, PhD
Professor
Faculty of Engineering
Universiti Putra Malaysia
(Member)
Helmi Zulhaidi Bin Mohd Shafri, PhD
Associate Professor
Faculty of Engineering
Universiti Putra Malaysia
(Member)
Matthew Kennedy, Master
Flood Risk Specialist
CH2M
(Member)
____________________________
ROBIAH BINTI YUNUS, PhD Professor and Dean
School of Graduate Studies
Universiti Putra Malaysia
Date:
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Declaration by graduate student
I hereby confirm that:
this thesis is my original work;
quotations, illustrations and citations have been duly referenced;
this thesis has not been submitted previously or concurrently for any other degree
at any other institutions;
intellectual property from the thesis and copyright of thesis are fully-owned by
Universiti Putra Malaysia, as according to the Universiti Putra Malaysia
(Research) Rules 2012;
written permission must be obtained from supervisor and the office of Deputy
Vice-Chancellor (Research and Innovation) before thesis is published (in the
form of written, printed or in electronic form) including books, Journals,
modules, proceedings, popular writings, seminar papers, manuscripts, posters,
reports, lecture notes, learning modules or any other materials as stated in the
Universiti Putra Malaysia (Research) Rules 2012;
there is no plagiarism or data falsification/fabrication in the thesis, and scholarly
integrity is upheld as according to the Universiti Putra Malaysia (Graduate
Studies) Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia
(Research) Rules 2012. The thesis has undergone plagiarism detection software.
Signature: ________________________ Date: __________________
Name and Matric No.: Izni Binti Mohd Zahidi (GS38121)
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Declaration by Members of Supervisory Committee
This is to confirm that:
the research conducted and the writing of this thesis was under the supervision;
supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate
Studies) Rules 2003 (Revision 2012-2013) are adhered to.
Signature:
Name of Chairman of
Supervisory
Committee:
Badronnisa Yusuf
Signature:
Name of Member of
Supervisory
Committee:
Matthew Kennedy
Signature:
Name of Member of
Supervisory
Committee:
Thamer Ahmed Mohamed
Signature:
Name of Member of
Supervisory
Committee:
Helmi Zulhaidi Mohd Shafri
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TABLE OF CONTENTS
Page
ABSTRACT i
ABSTRAK iii
ACKNOWLEDGEMENTS v
APPROVAL vi
DECLARATION viii
LIST OF TABLES xii
LIST OF FIGURES xiv
LIST OF ABBREVIATIONS xvii
CHAPTER
1 INTRODUCTION 1
1.1 Overview 1
1.2 Hydrodynamic Modelling 5
1.2.1 One-Dimensional Models 6
1.2.2 Two-Dimensional Models 6
1.3 Vegetation Roughness 7
1.4 Remote Sensing For Hydrodynamic Roughness Estimation 7
1.5 Problem Statement 10
1.6 Research Objectives 13
1.7 Limitations 13
2 LITERATURE REVIEW 14
2.1 Introduction 14
2.2 Vegetation Roughness 15
2.3 Vegetation In Hydrodynamic Modelling 24
2.4 Deriving Vegetation Properties For Roughness Estimation 26
2.4.1 Laboratory Experiments 26
2.4.2 Remote Sensing 27
2.5 Image Classification For Vegetated Floodplains 29
2.6 Summary 32
3 MATERIALS AND METHODOLOGY 33
3.1 Introduction 33
3.2 Study Area 35
3.3 Remote Sensing Data 36
3.3.1 QuickBird Satellite Image 36
3.3.2 LiDAR Raw Dataset 39
3.4 Hydraulic And Hydrologic Data 43
3.4.1 Floodplain DTM 46
3.4.2 River Model 46
3.4.3 Structures 49
3.4.4 Boundary Conditions 51
3.4.5 Land Cover 53
3.5 Object-based Image Classification 56
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3.5.1 Supervised Support Vector Machine Classification 57
3.5.2 Rule-based Classification 60
3.6 Accuracy Assessment Techniques 62
3.7 Site Measurements 63
3.8 Determination Of Manning’s Roughness Coefficients 63
3.9 Vegetation Hydrodynamic Roughness 70
3.10 Vegetation Roughness Routine 70
3.11 Model Build 73
3.11.1 Structure Validation 74
3.11.2 Model Calibration 77
3.11.3 Scenario Runs 80
3.12 Summary 81
4 RESULTS AND DISCUSSION 82
4.1 Introduction 82
4.2 Object-based Image Classification 82
4.2.1 Supervised Support Vector Machine Classification 83
4.2.2 Rule-based Classification 85
4.3 Site Validation 89
4.4 Vegetation Roughness Comparison 94
4.5 Hydrodynamic Modelling 99
4.6 Summary 106
5 CONCLUSION AND RECOMMENDATIONS 107
5.1 Conclusion 107
5.2 Recommendations For Future Studies 111
REFERENCES 112
APPENDICES 121
BIODATA OF STUDENT 186
LIST OF PUBLICATIONS 187
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LIST OF TABLES
Table Page
2.1 Floodplains Manning’s values (modified from Chow, 1959) 20
2.2 Methods available to determine the roughness coefficients of vegetation 22
3.1 QuickBird-2 image properties 37
3.2 Summary of TUFLOW key data sources 44
3.3 River gauging stations 44
3.4 Cross section data 47
3.5 River crossing structures along the modelled reach 49
3.6 Constant roughness .tmf file format 54
3.7 Depth-varying vegetation roughness .tmf file format 55
3.8 Example of depth-varying vegetation roughness Trees_Shrubs.csv file 55
3.9 Land cover 58
3.10 List of attributes (Source: ENVI, 2010) 60
3.11 Rule-set for each class 61
3.12 Base values of Manning’s nb (modified from Aldridge and Garrett, 1973) 68
3.13 Adjustment values for factors that affect roughness of floodplains
(modified from Aldridge and Garrett, 1973) 69
3.14 List of model runs 74
3.15 Differences between observed and calibrated water levels for stations
2322413 and 2222412 78
3.16 January 2011 model setup 80
4.1 Confusion matrix for SVM classification 84
4.2 Confusion matrix for rule-based classification 86
4.3 McNemar test results between supervised and rule-based classification 86
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4.4 Range of tree widths and corresponding mean NDVI 92
4.5 Coefficients of Pearson correlation and determination 94
4.7 Photo comparison against Arcement and Schneider (1989) 97
4.8 Roughness comparison for all field measurements against Arcement and
Schneider (1989) 98
4.9 Model results and mean MAE obtained at the reference points A, B, C and
D 103
4.10 t-test results between model 2a and model 2b 106
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LIST OF FIGURES
Figure Page
1.1 IFM principles (Source: DID, 2015) 1
1.2 Structural measures in mitigating floods (Source: DID, 2015) 2
1.3 Non-structural measures in mitigating floods (Source: DID, 2015) 2
1.4 Spectral reflectance (Source: Auracle Geospatial Science Inc., 2013) 8
3.1 Methodology flowchart 34
3.2 Study area 35
3.3 Different types of vegetation in the study area 35
3.4 QuickBird image of the study domain area 37
3.5 Typical reflectance spectra of vegetation (Source: Vodacek, 2015) 38
3.6 NDVI image against the original image 39
3.7 LiDAR header 40
3.8 LiDAR point clouds coloured by elevation 41
3.9 Multiple returns from a single pulse (Source: John A. Dutton Education
Institute, 2014) 41
3.10 Hillshaded, colour-ramped DSM (left) and DTM (right) 42
3.11 DEM profiles 42
3.12 (A) QuickBird image and (B) nDSM 43
3.13 Model schematic 45
3.14 Study area topography 46
3.15 Original DTM (black) and carved river channel DTM (red) at XS3 48
3.16 Upstream rating curve at station 2322413 (Pantai Belimbing) 51
3.17 Upstream flow at station 2322413 52
3.18 Downstream water level at station 2222413 (upstream SAMB gate) 52
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3.19 Ecotope map 53
3.20 (A) Segmentation and (B) Merging images 57
3.21 Spectral reflectance of each class 59
3.22 Plant dimensions for frontal area calculation (Source: Rahmeyer, 1998) 65
3.23 Flow resistance model (Source: Arcement and Schneider, 1989) 65
3.24 Vegetation classes as 5 m square plots 70
3.27 ArcGIS Model Builder elements 71
3.28 ArcGIS routine outline for depth-varying vegetation roughness calculation 71
3.29 ArcGIS user interface for shrubs roughness calculation 72
3.30 ArcGIS user interface for trees roughness calculation 73
3.31 Structure schematisation in TUFLOW 75
3.32 Layered flow constrictions as applied in TUFLOW (Source: XPSWMM,
2015) 75
3.33 Examples of 1D model schematisation for structure validation in ISIS 77
3.34 Water level hydrographs between observed and calibrated data for station
2322413 79
3.35 Flow and water level hydrographs between observed and calibrated data for
station 2222412 79
4.1 Result of SVM classification for QB imagery and nDSM image 83
4.2 Result of rule-based classification for QB imagery and nDSM image 85
4.3 (A) QuickBird image against the classification results of (B) supervised and
(C) rule-based 88
4.4 Percentage area of each class 89
4.5 GPS point buffered as a 2.5m square vegetation plot 90
4.6 Relationship between range of measured ground cover percentage and
calculated canopy cover percentage 90
4.7 An example of estimating the shrub widths 91
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4.8 Point sampling for NDVI values 92
4.9 Relationship between mean NDVI and range of tree widths 92
4.10 An example of estimating the tree widths 93
4.11 Comparison between Chow’s (1959) constant Manning’s roughness
coefficients and the calculated depth-varying values 95
4.12 Photo comparison against Arcement and Schneider (1989) 97
4.13 Manning’s n distribution based on (1) look-up table and (2) vegetation
density approach at 1 m flow depth 100
4.14 Flood depths for models 1a, 1b, 2a and 2b 101
4.15 Flow velocities for models 1a, 1b, 2a and 2b 102
4.16 Difference in depths (model 2b - model 2a) 104
4.17 Difference in velocities (model 2b - model 2a) 105
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LIST OF ABBREVIATIONS
1D One-Dimensional
2D Two-Dimensional
3D Three-Dimensional
ADI Alternating Direction Implicit
ALS Airborne Laser Scanning
ASPRS American Society for Photogrammetry and Remote Sensing
CASI Compact Airborne Spectral Imager
CIR Colour Infrared
DBH Width Breast Height
DEM Digital Elevation Model
DID Department of Irrigation and Drainage
DSM Digital Surface Model
DTM Digital Terrain Model
FLC Form Loss Coefficients
GIS Geographic Information System
GPS Global Positioning System
IFM Integrated Flood Management
LAI Leaf Area Index
LiDAR Light Detection and Ranging
LP Low Point Index
MAE Mean Absolute Error
MLC Maximum Likelihood Classification
MSMA Urban Storm Water Management Manual
nDSM Normalised Digital Surface Model
NDVI Normalised Difference Vegetation Index
NIR Near Infrared
OBIA Object-Based Image Analysis
P Percentage Index
PPSM Points Per Square Metre
RBF Radial Basis Functions
RSO Rectified Skewed Orthomorphic
SAMB Syarikat Air Melaka Berhad
SVM Support Vector Machine
VHR Very High Resolution
VI Vegetation Index
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CHAPTER 1
1 INTRODUCTION
1.1 Overview
In its simplest form, flooding is water that has inundated usually dry areas (DEHP,
2012). This occurs when rainfall exceeds the land infiltration capacity, water exceeds
a watercourse capacity, or for low lying coastal areas, when storm surges or high tides
exceed normal levels. In all cases, flooding is known to cause devastating damages to
buildings, infrastructure, crops and agriculture. Flood victims often put the blame on
the authorities and request for compensation. This adds to the emergency operations
and cleaning costs. The emotional damage is even harder to put a figure on and may
cause long-term effects to the society.
A couple of examples in Malaysia were the infamous Cameron Highlands and
Kelantan floods in 2016 and 2013, respectively. Heavy rainfall resulting in a flash
flood killed people and destroyed many homes and vehicles. The disastrous episodes
were due to the massive deforestation and land clearing; for Cameron Highlands
particularly to make way for vegetable farms causing a great amount of sedimentation
built-up in the Bertam River and subsequently intense overflow (News Straits Times,
2013). With floods becoming more common throughout the country, the Malaysian
Department of Irrigation and Drainage (DID) is trying to adopt the Integrated Flood
Management (IFM) framework outlined in Figure 1.1. The IFM is an integrated
approach for effective and efficient flood mitigation management while maximising
the efficiency of floodplain and minimising damage to properties and loss of life.
Figure 1.1 : IFM principles (Source: DID, 2015)
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The framework incorporates both structural and non-structural flood mitigation
measures as illustrated in Figure 1.2 and Figure 1.3. The combination of different
measures supports the IFM concept of ‘living with floods’ by employing a basin
approach, maximising the positive aspects of water cycle, and integrating land and
water management. Flood hazard map is a substantial first step in formulating and
implementing any flood schemes. It is a risk assessment tool mitigating the drawbacks
of flooding. An understanding of the flow dynamic in watercourses and floodplains
such as flow depth, velocity, duration, and response time is crucial in flood
management. These parameters offer the locations and levels of hazard. A systematic
evaluation plays a key role in providing sensible outputs for designing a flood scheme.
Figure 1.2 : Structural measures in mitigating floods (Source: DID, 2015)
Figure 1.3 : Non-structural measures in mitigating floods (Source: DID, 2015)
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The IFM framework is not new. In many other regions, the interest in integrating river
and vegetation are growing as an effective approach to watershed management. The
benefits of having vegetation are numerous, ranging from rich biodiversity and
controls of erosion and sedimentation to providing nutrients for aquatic ecosystem
health (DEHP, 2012). Although it might not impact the extreme flood events where
even structural measures are overwhelmed, it has the possibility of lowering local
runoff for smaller flood events which take place more often. It is worth noting that
flow velocity is the more hazardous element in flooding. With vegetation obstructing
the flow, the localised flooding is amplified thereby creating a need for managing
vegetated floodplains effectively.
In balancing the hydrodynamic and environmental requirements, the effects of
different vegetation planting or cutting schemes on the flood extents, depths, and
velocities have to be assessed. For instance, trees planted on the river banks act as
buffer strips and while they can improve the water quality and riverine habitats, the
unkempt vegetation growth on the banks can increase flood levels by reducing the
carrying capacity of the flow course. The floodplain trees are a major cause of flood
resistance for vegetated floodplains. However, in other areas, the extra storage
provided by the vegetation can reduce the flood levels where it may benefit the
population. This highlights the significance of estimating vegetation in understanding
flood risk implications for restoring or cutting woody vegetation in riparian zones and
floodplains.
Vegetation within watercourses and floodplains undoubtedly influence how the fluvial
system behaves, particularly in reducing flow conveyance. Floodplain has a role in
reducing the peak discharge by channelling the excess water from the main river and
storing it for a time. Vegetation exerts increased resistance on the flow leading to
decreased velocity and increased water level (Zhang et al., 2013; Erduran and Kutija,
2003). The simple model Anderson et al. (2006) developed as a balanced
representation of primary vegetation properties such as density and height found that
flood waves propagation is more responsive to vegetation roughness for smaller floods
which occur more often thus making this subject even more critical.
Although the additional flow resistance of vegetation may increase the risk of flooding,
the positive effect is that vegetation can be manipulated for bank stabilisation and
erosion protection (Ministry of Forests, Lands and Natural Resource Operations,
British Columbia, 1999). Vegetation buffer can even provide some protection from
disastrous natural hazards such as tsunami (Chouhan and Rao, 2004). Additionally, the
reduced velocity encourages sedimentation and retention of nutrients in watercourses
which supports healthy ecosystems (Ministry of Natural Resources and Environment,
Putrajaya, 2009). Vegetation has also been used as a filter to improve surface water
quality (Helmers and Eisenhauer, 2006). Planting or removing more vegetation can
both increase or decrease the flood risk to different extents in different locations.
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Vegetation increases the flood depths and durations in the upstream of catchment while
the largely positive effects occur on the downstream end where the flood depths are
reduced. This is particularly the case for lowland areas where flow regulation and land
use change may affect the magnitude and frequency of flooding. On the receiving end,
the riparian vegetation controls the velocity of the runoff to the river. The trade-off is
between the lower flood depths and the slightly longer flood durations (Rutherford et
al., 2006). All these interacting factors should be taken into account for effective land
use planning.
Despite the impending aftermath, it is impossible to avoid floods altogether and there
is very little point trying to do so, as flooding is nature’s way of restoring biodiversity
by reviving floodplains, returning nutrients to the soil, wash off debris and sediments
as well as replenishing groundwater storage. Understanding the roles and limitations
of vegetated floodplains as natural flood storage systems adds another dimension to
land use planning (DEHP, 2012). This calls for objective management practices as part
of watershed planning that involves predicting the flow dynamic due to increased
resistance caused by vegetation.
Hydrodynamic modelling has become a popular tool to simulate the impacts of
different resistance due to the land cover on the overbank flow patterns and flood water
levels in watercourses and floodplains. There has been much advancement in
computational power and numerical algorithms since its inception, but the
performance of hydrodynamic modelling is still influenced by a number of
uncertainties which have been the basis of many studies. Roughness, which represents
the resistance of different land covers, has been demonstrated in many studies to affect
the flood depths and velocities, but it also remains one of the main uncertainties and
this has been well-documented (Medeiros et al., 2012; Noorayanan et el., 2012;
Stephens et al., 2012; Straatsma and Huthoff, 2011; O’Hare et al., 2010; Gu et al.,
2007; Stoesser et al., 2005; Werner et al., 2005, Wu et al., 2009).
In calibrating a hydrodynamic model, the roughness coefficient is normally adjusted
to minimise the deviation between prediction and observed data. However, it is very
subjective and loses its meaning in the process of becoming a measure to represent the
energy and momentum losses of the model as a whole. Horrit and Bates (2001) equally
stressed that the relationship between parameters used in calibration with the physical
representation may not be a simple one and often used to make up for poor model
build. A major downside of this computationally intensive process is that the calibrated
results can be manipulated without improving the model representation. This defeats
the purpose of model validation.
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This study contributes an original and proven methodology incorporating the cost-
efficient remote sensing data and Geographic Information System (GIS) to improve
the accuracy of tropical vegetated floodplain classification and roughness estimation.
Remote sensing and GIS allow the conversion of retrieved variables to roughness that
can be estimated for each grid cell through linear regression models relating vegetation
spatial attributes to vegetation density parameters such as vegetation width. This
provides an objective and relatively quick calculation of vegetation roughness instead
of the current subjective selection based on past studies or time-consuming field
measurements. Ultimately, this methodology enables roughness maps to be generated
at different spatial resolutions and used directly in 2D hydrodynamic modelling. The
quality of a roughness map can be evaluated by comparing the simulated flow
properties with those observed in the field.
1.2 Hydrodynamic Modelling
Hydrodynamic modelling can predict flows and water levels based on the conservation
laws of physics described by differential equations. Numerical methods are applied to
solve the equations by discretising the domain in which the schematisation scheme
differs from one software to another. In general, hydrodynamic modelling ranges from
one-dimensional (1D), 2D, to three-dimensional (3D) with their own strengths and
limitations. The 1D model generates results of flows in only one direction and the
velocity as an average value. The 2D model provides results of flows in two directions,
x and y, while the velocity is calculated as an average in either direction. The most
complex 3D model produces results of flows in an additional direction, z, whereas the
velocity can be calculated vertically.
The choice between the different models depends on a number of factors, mainly on
the study purpose, cost, time and level of accuracy or spatial variability. The 1D model
would yield good results in a relatively short time, but it is more suitable for narrow
floodplains where the width is typically smaller than three times the width of the main
channel (EA, 2009). This implies that the floodplain behaves in the same way as the
river channel. However, flooding is generally turbulent in nature and the assumption
that the floodplain flow is parallel to the main channel can be unrealistic. Another flaw
worth noting is that the cross-sectional averaged velocity contributes little when there
are large variations in velocity magnitude which is usually the case during flooding.
As the 3D model is derived from the averaged turbulent flow equations of Reynolds-
averaged Navier-Stokes, it would be useful to investigate the flow details. However,
the intricacies involved and high computational time prevent them from being
practically applied in the industry. The computational time is primarily influenced by
how refined the model needs to be which is a compromise between accuracy and
practical applicability. For a practical look at the model performance with aggregated
roughness coefficients, the 2D model is deemed more fitting as it could attribute
roughness specifically to vegetation and reduce the known difficulties within the 1D
roughness. A clear advantage is the model would be able to display the variations of
water levels, velocities, and the changing flow patterns due to vegetation.
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1.2.1 One-Dimensional Models
Attaining a firm understanding of the 2D numerical method requires identifying the
origins of the 1D model. The 1D model treats river channels as a set of cross sections
perpendicular to the flow direction while the floodplain is represented as extended
cross sections. The 1D model is based on the 1D Saint Venant or shallow water
equations with conveyance computed using a uniform flow law. These equations are
derived by integrating the Navier-Stokes equations over the flow cross-sectional
surface, assuming the flow direction is parallel to the river centre line.
The original Navier-Stokes equations describe fluid flow, combining five partial
differential equations with one for the conservation of mass, three for the conservation
of momentum and one for the conservation of energy. The equations are based on the
steady state and unsteady gradually varied flow models, derived under the following
assumptions:
Velocity is uniform
The flow is one-dimensional with no vertical accelerations
Pressure distribution is hydrostatic
Water levels are horizontal
Channel bed slope is small
One of the principle advances in 1D modelling is the conveyance estimation technique
such as the Conveyance Estimation System (HR Wallingford, 2003; Samuels et al.,
2002) incorporated in commercial packages such as ISIS and InfoWorks RS. This
system focusses on riverine vegetation, momentum exchange between watercourses
and floodplains flows, as well as the behaviour of natural channels. However, the
remaining drawbacks to the 1D schematisation are the floodplain flow is assumed to
be in 1D which is often not the case and the cross-sectional averaged velocity has a
less tangible physical meaning when there are large variations in the magnitude across
the floodplain.
1.2.2 Two-Dimensional Models
The 2D model is based on the 2D shallow water equations which are also known as
2D Saint Venant equations due to its 2D non-linear extension. Non-linear here means
that they do not satisfy the principle of superposition which subject shallow water
flows to shock waves, known to be discontinuous solutions of the shallow water
equations. These shocks on floodplains are generally in the form of hydrodynamic
jumps and are described as transition flows from supercritical to subcritical, caused by
either terrain changes or friction. The 2D solution algorithm solves the depth-averaged
2D shallow water equations which are the momentum and continuity equations for free
surface flow. They are derived using the hypotheses of vertically uniform horizontal
velocity and negligible vertical acceleration, namely the hydrostatic pressure
distribution. It is assumed that the wave length is much greater than the flow depth.
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1.3 Vegetation Roughness
As previously discussed, roughness is an essential input to a hydrodynamic model and
can be expressed by the Darcy-Weisbach f, Chezy C and the Manning’s n. However,
Manning’s n is widely used in hydrodynamic modelling as demonstrated by a number
of industrial software packages such as HEC-RAS, ISIS, MIKE and InfoWorks. The
roughness coefficient is usually applied as a bulk representation, but many agreed with
Chow (1959) that the resistance can no longer be defined as a deterministic value in
fluctuated flows which is often the case in flooding. This is important as flood
mitigation schemes depend heavily on predicted flood depths and velocities to design
for infrastructure and operation.
It is widely known that vegetation imposes higher resistance than the bed grain
particles, particularly for floodplain flow. Riparian vegetation similarly has a
significant impact on the floodplain flow. Riparian vegetation exists at the interface
between the river and the floodplain (Rutherford et al., 2006). It is known to play a
major role in flow resistance by increasing turbulent intensity as well as causing flow
retardance through additional loss of energy and momentum. Riparian regions impede
the surface runoff which in return can lower the downstream flood peak. While the
vegetated floodplain would impact the depth and duration of flooding, the riparian
vegetation would influence the timing of the flood delivery (Rutherford et al., 2006).
Vegetation is conventionally represented as rigid cylinders indicating that the total
shear stress equals the total of the bed shear stress and the equivalent shear stress due
to vegetation drag. Vegetation resistance highly relates to the flow depth and
corresponds to the vertical density variation. Gu et al. (2007) demonstrated how a
uniform roughness coefficient can be assumed when the ratio of flow depth and
effective height of the vegetation is two. On the contrary, anything below two shows
significant varied roughness coefficients dependent on the flow depth. Therefore, a
bulk roughness coefficient does not reflect the momentum losses in the flow through
vegetation. Ebrahimi et al. (2008) concurred that roughness coefficients increase with
vegetation density and decrease when flow depth and velocity decrease. A good
estimation of Manning’s n is important as its influence on water levels is significant.
A higher Manning’s n generally causes a higher flood risk (De Doncker et al., 2009).
1.4 Remote Sensing for Hydrodynamic Roughness Estimation
A classic definition of remote sensing by Jensen (2000) is the art and science of
obtaining information about an object without being in direct physical contact with the
object. It is a scientific technology used to measure and monitor important biophysical
characteristics and human activities on the earth. A good example to explain this is the
function of the eyes in which the information is gathered through the reflectance
amount of visible light energy.
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Technically, remote sensing can be defined as a technique of electromagnetic waves
with respect to objects such as materials, areas, phenomena or processes on the earth’s
surface, observed from a distance, as illustrated in Figure 1.4. The process involves
acquiring information of radiation from parts of the earth’s surface by means of close
range, airborne, or spaceborne sensors placed on stationary or moving platforms such
as aircrafts and satellites.
Figure 1.4 : Spectral reflectance (Source: Auracle Geospatial Science Inc., 2013)
In remote sensing, the reflection and emission of earthly objects are recorded while
field data of a descriptive and physical nature are added to these parameters. These
parameters are then analysed by image processing and interpreted according to the
spectral and spatial properties of the image and transformed into comprehensible data
useful for the qualification, quantification and mapping of objects, phenomenon, and
processes occurring on the earth.
Digital images in remote sensing are generally made from a collection of picture
elements, normally referred to as pixels. The process of photography uses chemical
reactions on the surface of a light-sensitive film to detect energy variations within a
scene. Electronic sensors generate an electrical signal that corresponds to the energy
variations in the original scene. By developing an image, a record of its detected signals
is obtained. This spectral response pattern is also known as a signature which describes
the degree of reflected energy in different regions of the spectrum.
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The successful application of remote sensing is based on the integration of multiple,
interrelated data sources and analysis procedures. All successful remote sensing
applications should consist of the following steps:
1. Clear definition of the problem
2. Assessment of the potential for addressing the problem with remote sensing
3. Determination of suitable remote sensing data acquisition procedures
4. Evaluation of the data interpretation procedures and the references required for
calibration and verification
5. Identification of the criteria to gauge the quality of information derived
While Very High Resolution (VHR) satellite images, particularly QuickBird image
used in this research, may be sufficient for general mapping applications, it is often not
the case when more details are required to make informed decisions. In 2D
hydrodynamic modelling for instance, surface roughness should represent the terrain
profiles and obstructions. Even with a highly accurate Digital Terrain Model (DTM)
which is a prerequisite, the hydrodynamic model requires vegetation to be reasonably
classified (NOAA, 2012). Thus, vegetation is required to be categorised into different
sub-classes as the different properties impose different roughness. This is when
spectral information is inadequate to segregate the types of vegetation due to severe
spectral resemblances and overlaps within the classes (Htun et al., 2011; Dengsheng et
al., 2010; Stibig et al., 2003; Blaschke and Strobl, 2001). Subsequently, Object-Based
Image Analysis (OBIA) is often preferred for classification due its better performance
(Blaschke et al., 2014; Hamedianfar and Shafri, 2014a; Zhang et al, 2013; Li et al.,
2011; Myint et al., 2011; Blaschke, 2010; Forzieri et al., 2010; Baatz and Schape,
2000). The fundamentals are the segmentation and merger of the homogenous pixels
based on their size, distance, texture, spectral similarity, and form (Li et al., 2011;
Baatz and Schape, 2000). OBIA can be carried out as supervised or rule-based.
The supervised technique can be performed by different supervised algorithms such as
K-nearest neighbour or Support Vector Machine (SVM). SVM has become a popular
algorithm in the remote sensing community due to its ability to provide good
classification results with a small amount of training samples (Hamedianfar et al.,
2014b; Heumann, 2011; Mountrakis et al., 2011). This algorithm which was
introduced by Boser et al. (1992) is then run for the supervised classification based on
the training samples defined by the user. Adversely, the rule-based technique is based
on human knowledge and reasoning about each feature classes (ENVI, 2008). The user
is required to define a rule-set that can include one or several attributes to identify each
class.
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Although VHR multispectral imagery is becoming more affordable and popular, Laser
Imaging Detection and Ranging (LiDAR) data acquisition can be very expensive for
vegetation studies alone and the readily available LiDAR data is often those below 4
points per square metre (PPSM) for DEM creations. PPSM, also known as point
density, is an important parameter of LiDAR. It is measured directly as the ratio
between the number of points and the covered area (Balsa-Barreiro and Lerma, 2014).
Recent LiDAR systems provide at least three returns per pulse in which any multiple
returns most likely represent vegetation (NOAA, 2012). Many of the studies
mentioned so far utilised high point density LiDAR data which are not easily obtained
due to its cost. Therefore, recent research has been centred on using VHR multispectral
imagery in combination of low point density LiDAR for land use classification and
vegetation mapping with the help of object-based methodology (Alexander et al.,
2014; Brubaker et al., 2014; Machala and Zejdova, 2014; Bujan et al., 2013; Takahashi
et al., 2010; Geerling et al., 2007).
1.5 Problem Statement
Flooding has long been recognised as one of the most damaging and costly natural
hazards. Flooding at a large scale does not just upset social and economic activities,
but also poses a risk to lives. Recovery and intangible damage such as the
psychological distress incurred can be very costly to the communities as much as to
individuals. It can also drive away potential investments which can be very devastating
particularly for developing countries such as Malaysia.
Common local practices to mitigate current intense overflows include river
canalisation and flood diversions although a greater focus is on flood warning, flood
management and non-structural measures (DID, 2011b). Over relying on structural
measures can increase vulnerability especially when extreme flood events exceed the
design measure. Excessive canalisation works, which often need to be carried out on
an annual basis, cause instability to river crossing structures and other infrastructure
within the vicinity. Such works also result in the loss of aquatic and riparian flora and
fauna habitat. While the aim is to lower the water levels, this also lowers the water
tables near the channel bed and consequently decreases groundwater nutrient influxes
into the river.
To make things worse, the canalisation works upstream may significantly impact the
downstream flow conditions. For this reason, constructing dams is becoming an
integral part to regulate the flow and provide excess floodwater storage. There are at
least 60 existing dams throughout Malaysia and counting. Any construction within the
river vicinity such as dams, barrages, and weirs also accumulate sediments and
interrupt the river sediment balance even when the dam cuts off sediment supply to the
downstream, causing scouring (DID, 2011b). This demonstrates how limiting flooding
in one place can increase flooding somewhere else. A short-term approach to cope with
siltation is dredging which goes back to the canalisation works discussed earlier,
resulting in a high maintenance cycle. Hence, the artificial methods employed at
present are not a sustainable solution.
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With climate change affecting the hydrological cycle through the temperature increase,
there will be a drop of effective precipitation leading to drier soil conditions and more
outflow sediments when it rains (Moui et al., 2012). Additionally, the more frequent
short-duration intense rainfalls will produce more direct runoff. While it is impossible
to avoid floods altogether, it becomes even more important to understand the dynamic
of vegetated watercourses and floodplains and to be able to limit the flood effects by
working with nature instead of against it. Educated land use planning can help allocate
appropriate land uses in areas that are more flood prone. This can complement the
engineering advances to help protect the floodplains and make them more resilient.
Being a developing country, Malaysia has the advantage of learning from the
experiences of developed countries. Take Japan for example. Years after World War
II when Japan was generating considerable wealth, they invested a lot in river
construction works such as river canalisation and dams but failed to take the
environment into account, leading to frequent floods, deteriorating water quality and
ecological health in the subsequent decades (Takahasi and Uitto, 2004). This echoes
the situation in the country where the majority of rivers can be classified as alluvial
streams (DID, 2012). This means the rivers are capable in adjusting to the natural
processes without any external influences to balance the hydrological regime and
hydrodynamic behaviours. Therefore, any changes to alter the natural river responses
may prompt gradual or drastic changes to the sedimentation rate, river slope, depth,
width and flow resistance to name a few. This can result in bigger impacts of flooding
similar to what happened in Japan.
Another example can be observed in Australia and the Netherlands where the flooding
issue used to be addressed by removing vegetation or raising the dykes (DEHP, 2012).
Australia subsequently commenced revegetation works and the Dutch created more
space for the floodwater by lowering floodplains, putting groins and excavating
secondary channels. However, Makaske et al. (2011) stressed the need to integrate
hydrodynamic evaluation of river engineering measures and vegetation succession in
riverine ecosystem rehabilitation plans as they found the growth of vegetation in the
Dutch Rhine River branches may result in up to 0.6 m higher river flood levels due to
the higher vegetation roughness.
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The presence of vegetation is known to amplify the turbulent intensity and
consequently produces a significant amount of energy and momentum losses and flow
retardance (Tsihrintzis and Madiedo, 2000). The standard approach in hydrodynamic
modelling is to assign a constant value to a vegetation cluster. Although this is
satisfactory for flood extent forecast, the simplistic approach may not be able to
accurately predict the depth-varying flow dynamic for more detailed applications. By
being able to quantify the vegetation better for hydrodynamic modelling, much of the
ambiguity surrounding the key aspects of roughness uncertainty can be removed.
Obtaining meaningful results from an improved hydrodynamic modelling will greatly
complement vegetation management in mitigating flood risk and conserving the
ecosystem, as a change of roughness coefficients across the model grids may change
the balance and extensively change the flow dynamic of floodplain storage,
conveyance and backwater effects (Helmers and Eisenhauer, 2006; Werner et al.,
2005). It is equally important to develop a practical methodology that can be easily
replicated to manage vegetation quantitatively so that people can benefit from positive
and sustainable socioeconomic and environmental effects. This is made possible
through the application of remote sensing and GIS. This leads to the main hypothesis
of this study: the geospatial approach in approximating spatially explicit and depth-
varying vegetation roughness can provide a practical alternative in modelling
vegetated floodplains.
In essence, while there is a considerable amount of resolved studies on vegetation
properties and their impacts on flow dynamic (Aberle and Jarvela, 2013; Sun and
Shiono, 2009; Anderson et al., 2006; Helmers and Eisenhauer, 2006; Rowinski and
Kubrak, 2002; Jarvela, 2002; Petryk and Bosmajian, 1975), the same cannot be
claimed for modelling vegetated watercourses and floodplains. Manning’s n is
dependent on the retardance factor which is a function of the vegetation properties such
as flexibility, height, thickness and density. To represent the variation of Manning’s n,
measurements are essential. Although conventional field survey would be the best
estimates, it is often not possible for large areas as it is time and cost consuming. Aberle
and Jarvela (2013) summed it up perfectly that the vegetation-flow interactions are
gaining much attention for the past few years, but none is yet suitable for practical
applications.
As a result, the geospatial approach is potentially the pragmatic way forward as it is
capable in developing and handling extensive spatial as well as tabular data. The
application of GIS and remote sensing has been extended to numerous fields that it is
only natural to utilise them in improving the estimation of vegetation roughness. This
study provides better understanding and improvements in the integrated river basin
study through the geospatial approach for various applications, as discussed. It is a
valuable step in providing a practical alternative to quantify depth-varying vegetation
roughness coefficients in 2D hydrodynamic modelling by producing detailed flood
maps for improved sustainable development and well-controlled vegetation
management.
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It is important to start right and not overlook the importance of environmental elements
such as vegetation. Rural areas are likely to be developed eventually and it will be
advantageous to be able to quantify more accurately how changing the natural
landscape may alter the flood dynamic. Incorporating vegetation as a flood mitigation
measure will also support the river’s self-resilient capacities and prompt a river to
function as naturally as possible as an alluvial stream is able to adapt to the natural
processes and balance the changing hydrological regime and flow dynamic.
Consequently, the environmental health can be preserved as well as the agricultural
lands, flora, and fauna habitat. Therefore, this research attempts to take a step forward
in quantifying the relationship between vegetated river basins and the varying
hydraulic using the geospatial approach.
1.6 Research Objectives
It is now established that vegetation can influence the impacts of flooding to a certain
degree, but the question is how to quantify the flood sensitivity to the amount of
vegetation in practice and how significant is the application of depth-varying
roughness? The main research objective is to develop a practical methodology via a
geospatial approach to calculate depth-varying vegetation roughness to be used in 2D
hydrodynamic modelling for practical applications. The specific study objectives are:
1. To improve land cover and vegetation classification geospatially by using
QuickBird satellite image and low point density LiDAR elevation for vegetation
density estimation on plot level.
2. To develop a geospatial method for the estimation of vegetation density and depth-
varying roughness for trees and shrubs using remotely sensed data and field
measurements.
3. To assess and validate the 2D hydrodynamic modelling results between constant
and depth-varying roughness using detailed and ecotope land cover maps.
1.7 Limitations
In making the research as meaningful as possible, the limitations have been reduced to
a minimum. However, it is worth noting that the regression analysis developed in this
research was based on the same modelled area and limited to the 0.6 m QuickBird
image and LiDAR dataset of 1.4 PPSM. It is possible that a new regression analysis
can be carried out using other datasets that may or may not have impacts on the current
model. Additionally, this research has classified vegetation into three general classes
of trees, shrubs as well as grass and cropland which are the main vegetation that
significantly predict hydraulic roughness (Forzieri et al., 2012). Although more
detailed classification might lead to better assessments, this would be too
computationally demanding for practical purposes. Another limitation is the drag
coefficient used in the roughness calculation. A number of studies have used a higher
range of drag coefficients which also vary with depths, though they are few and far
between compared to those who have used constant values. Therefore, a constant value
of 1.5 was selected for the roughness calculation.
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REFERENCES
Abdul Rahman, M. Z. (2011). Estimation of Composite Hydrodynamic Roughness over
Land with High Density Airborne Laser Scanning (Master thesis). Delft University of
Technology, Delft, Netherland.
Aberle, J., & Jarvela, J. (2013). Flow resistance of emergent rigid and flexible floodplains
vegetation. Journal of Hydraulic Research, 51(1): 33-45.
Abood, M. M., Yusuf, B., Mohamed, T. A., & Ghazali, A. H. (2006). Manning roughness
coefficient for grass-lined. Suranaree Journal of Science and Technology, 13(4): 317-
330.
Abu-Aly, T. R., Pasternack, G. B., Wyrick, J. R., Barker, R., Massa, D., & Johnson, T.
(2014). Effects of LiDAR-derived, spatially distributed vegetation roughness on two-
dimensional hydraulic in a gravel-cobble river at flows of 0.2 to 20 times bankfull.
Geomorphology, 206: 468-482.
Aldridge, B. N., & Garrett, J. M. (1973). Roughness Coefficients for Stream Channels in
Arizona. U.S. Geological Survey Open-File Report.
Alexander C., Bocher, P.K., Arge, L., & Svenning, J.C. (2014). Regional-scale mapping
of tree cover, height and main phenological tree types using airborne laser scanning
data. Remote Sensing of Environment, 147: 156-172.
Anderson, B. G., Rutherford, I. D., & Western, A. W. (2006). An analysis of the influence of riparian
vegetation on the propagation of flood waves. Environmental Modelling and Software,
21(9): 1290-1296.
Arcement, G. J., & Schneider, V. R. (1989). Guide for selecting Manning’s roughness
coefficients for natural channels and floodplains. USGS Water-Supply Paper, 2339(1):
38.
Arcement, G. J.,& Schneider, V. R. (1987). Roughness coefficients for densely vegetated
floodplains. US Geological Survey - Water Resources Investigations Report, 85-4247.
Aubrecht, C., Hofle, B., Hollaus, M., Kostl, M., Steinnocher, K., & Wagner,W. (2010).
Vertical roughness mapping - ALS based classification of the vertical vegetation
structure in forested areas. The International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Sciences, Vol. XXXVIII-7B: 35-40.
Baatz, M., & Schape A. (2000). Proceedings from Angewandte Geographische
Informationsverabeitung XII, Beitragezum AGIT-Symposium: Multiresolution
Segmentation: An Optimization Approach for High Quality Multi-Scale Image
Segmentation. Salzburg.
Balse-Barreiro J., & Lerma J. L. (2014). Empirical study of variation in LiDAR point
density over different land covers. International Journal of Remote Sensing, 35 (9):
3372-3383.
Baptist, M., Babovic, V., Rodríguez Uthurburu, J., Keijzer, M., Uittenbogaard, R. E.,
Mynett, A., & Verwey, A. (2007). On inducing the equations for vegetation resistance.
Journal of Hydraulic Engineering, 45(4): 435–450.
Barnes, H. H. Jr. (1967). Roughness Characteristics of Natural Channels. U.S. Geological
Survey, Water-Supply Paper 1849: Washington, D.C.
Bernardi, D., & Schippa, L. (2015). Proceedings from E-proceedings of the 36th IAHR
World Congress: A Vegetation Roughness Model to Evaluate Flood Risk along Secchia
River. Hague, Netherlands.
Blaschke. T. (2010). Object based image analysis for remote sensing. ISPRS Journal of
Photogrammetry and Remote Sensing, 65 (1): 2-16.
© COPYRIG
HT UPM
113
Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., & Queiroz Feitosa,
R. (2014). Geographic object-based image analysis - towards a new paradigm. ISPRS
Journal of Photogrammetry and Remote Sensing, 87: 180-91.
Blaschke, T., & Strobl, J. (2001). What’s wrong with pixels? Some recent developments
interfacing remote sensing and GIS. Journal of Spatial Information and Decision
Making, 14 (6): 12-17.
Boser, B., Guyon, I., & Vapnik, V. (1992). Proceedings from Fifth Annual Workshop on
Computational Learning Theory: A Training Algorithm for Optimal Margin
Classifiers.
Brivio, P. A., Colombo, R., Maggi, M., & Tomasoni, R. (2002). Integration of remote
sensing data and GIS for accurate mapping of flooded areas. International Journal of
Remote Sensing, 23(3): 429-441.
Brubaker, K. M., Johnson, S. E., Brinks, J., & Leites, L. P. (2014). Estimating canopy
height of deciduous forests at a regional scale with leaf-off, low point density LiDAR.
Canadian Journal of Remote Sensing, 40 (2): 123-134.
Bujan, S., Gonzalez-Ferreiro, E., Barreiro-Fernandez, L., Sante, I., Corbelle, E., &
Miranda, D. (2013). Classification of rural landscapes from low-density LiDAR data:
Is it theoretically possible?. International Journal of Remote Sensing, 34 (16): 5666-
5689.
Carmona-Moreno, C., Moreno-Ruiz, J. A., Bartholome, E., & Negre, T. (1999).
Proceedings from 19th EARSeL Symposium on Remote Sensing in the 21st Century
Space Vegetation Software: A Remote Sensing Tool for Environmental Applications at
Regional Level. Spain.
CH2M (2016). St. Helen’s Mill Options Modelling. United Kingdom.
Chadwick, J. (2011). Integrated LiDAR and IKONOS multispectral imagery for mapping
mangrove distribution and physical properties. International Journal of Remote
Sensing, 32 (21): 6765-6781.
Chen, S. C., Kuo, Y. M., & Li, Y. H. (2011). Flow characteristics within different
configurations of submerged flexible vegetation. Journal of Hydrology, 398: 124-134.
Chouhan, R., & Rao, N. (2004). Proceedings from Geo-Information for Disaster
Management: Vegetation Detection in Multispectral Remote Sensing Images:
Protective Role-Analysis of Coastal Vegetation in 2004 Indian Ocean Tsunami.
Antalya.
Chow, V. T. (1959). Open Channel Hydraulics. New York: McGraw-Hill Book Co.
Cobby, D. M., Mason, D. C., & Davenport, I. J. (2001). Image processing of airborne
scanning laser altimetry data for improved river flood modelling. ISPRS Journal of
Photogrammetry and Remote Sensing, 56: 121-138.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. New Jersey:
Lawrence Earlbaum Associates.
Corona, P., Cartisano, R., Salvati, R., Chirici, G., Floris, A., Martino, P. D., Marchetti, M.,
Scrinzi, G., Clementel, F., Travaglini, D., & Torresan, C. (2012). Airborne laser
scanning to support forest resource management under alpine, temperate and
Mediterranean environments in Italy. European Journal of Remote Sensing, 45: 27-37.
Cosh, M. H., & Brutsaert, W. (2003). Microscale structural aspects of vegetation density
variability. Journal of Hydrology, 276: 128-136.
Cowan, W.L. (1956). Estimating hydraulic roughness coefficients. Agricultural
Engineering, 37(7):473-475.
© COPYRIG
HT UPM
114
De Doncker, L., Troch, P., Verhoeven, R., Bal, K., Meire, P., & Quintelier, J. (2009).
Determination of the Manning roughness coefficient influenced by vegetation in the
river Aa and Biebrza river. Environmental Fluid Mechanics. 9(5): 549-567.
Dengsheng, L., Hetrick, S., Moran, E., & Li, G. (2010). Detection of urban expansion in an
urban-rural landscape with multitemporal QuickBird images. Journal of Applied
Remote Sensing, 4 (1) 041880.
Department of Environment and Heritage Protection (DEHP) (2012). Natural Assets for
Flood and Cyclone Resilience - Synthesis of Scientific Evidence on the Role of Natural
Assets to Reduce the Human Impacts of Floods and Cyclones. Australia.
Department of Agriculture (DOA) (2014). Peta Tinjauan Tanah-Tanih, Alor Gajah dan
Melaka Tengah. Melaka.
Department of Irrigation and Drainage (DID) (2015, December 5). Retrieved from
http://www.water.gov.my/the-services-mainmenu-252/flood-mitigation-mainmenu-
323/programme-aamp-activities-mainmenu-199?lang=en&start=1
Department of Irrigation and Drainage (DID) (2011a). Laporan Banjir Tahunan 2011.
Malaysia.
Department of Irrigation and Drainage (DID) (2011b). Review of the National Water
Resources (2000 - 2050) and Formulations of National Water Resources Policy.
Malaysia.
Department of Irrigation and Drainage (DID) (2012). Second Edition of Urban Stormwater
Management Manual. Malaysia.
Ebrahimi, N. G., Fathi-Moghadam, M., Kashefipthe, S. M., Saneie, M., & Ebrahimi, K.
(2008). Effects of flow and vegetation states on river roughness coefficients. Journal
of Applied Sciences, 8: 2118-2123.
Eastman, J.R. (2001). IDRISI32: IDRISI for Workstations, Release 2. Worcester: Clark
University.
Engeny Water Management (2013). Stormwater Management - Catchment Analysis.
Australia.
ENVI (2008). ENVI Feature Extraction Module User’s Guide. United States.
ENVI (2010). ENVI User Guide. United States.
ENVI (2014, October 29). Segmentation algorithms background. Retrieved from
http://www.exelisvis.com/docs/BackgroundSegmentationAlgorithm.html
Environment Agency (2009). Desktop Review of 2D Hydraulic Modelling Package. United
Kingdom.
Erduran, K. S., & Kutija, V. (2003). Quasi-three-dimensional numerical model for flow
through flexible, rigid, submerged and non-submerged vegetation. Journal of
Hydroinformatics, 5: 189–202.
Fathi-Moghadam, M. (2006). Effects of land slope and flow depth on retarding flow in non-
submerged vegetated lands. Journal of Agronomy, 5: 536-540.
Fathi-Maghadam, M., Drikvandi, K., Lashkarara, B., & Hammadi, K. (2011).
Determination of friction factor for rivers with non-submerged vegetation in banks and
floodplains. Scientific Research and Essays, 6(22): 4714–4719.
Fenzl, R. N. (1962). Hydraulic Resistance of Broad Shallow Vegetated Channels (PhD
thesis). University of California, United States.
Fischenich, J. C. (1997). Hydraulic Impacts of Riparian Vegetation: Summary of the
Literature. United States.
Flippin-Dudley, S. J., Abt, S. R., Bonham, C. D., Watson, C. C., & Fischenich, J. C. (1998).
Evaluation of Flow-Resistance Equations for Vegetated Channels and Floodplains.
United States.
© COPYRIG
HT UPM
115
Foody, G. M. (2004). Thematic map comparison: evaluating the statistical significance of
differences in classification accuracy. Photogrammetric Engineering and Remote
Sensing, 70 (5): 627-634.
Forzieri, G., Castelli, F., & Preti, F. (2012). Advances in remote sensing of hydraulic
roughness. International Journal of Remote Sensing, 33: 630-654.
Forzieri, G., Guarnieri, L., Vivoni, E. R., Castelli, F., & Preti, F. (2011a). Spectral-ALS
data fusion for different roughness parameterizations of forested floodplains. River
Research and Application, 27: 826-840.
Forzieri, G., Degetto, M., Righetti, M., Castelli, F., & Preti, F. (2011b). Satellite
multispectral data for improved floodplains roughness modelling. Journal of
Hydrology, 407: 41-57.
Forzieri, G., Moser, G., Vivoni, E. R., Castelli, F., & Canovaro, F. (2010). Riparian
vegetation mapping for hydraulic roughness estimation using very high resolution
remote sensing data fusion. Journal of Hydraulic Engineering, 136: 855-867.
Freeman, G.E., Copeland, R.R., Rahmeyer, W., & Derrick, D.L. (1998). Proceedings from
1998 ASCE Wetland Engineering - River Restoration Conference: Field
Determination of Manning’s n Value for Shrubs and Woody Vegetation. Denver.
Geerling, G. W., Labrador-Garcia, M., Clevers, J. G. P. W., Ragas, A. M. J., & Smits, A. J.
M. (2007). Classification of floodplain vegetation by data fusion of spectral [CASI]
and LiDAR data. International Journal of Remote Sensing, 28 (19): 4263-4284.
Gidudu, A., Hulley, G., & Marwala, T. (2007). Proceedings from 28th Asian Conference
on Remote Sensing: Image Classification using SVMs: One-Against-One vs One-
Against-All. Kuala Lumpur.
Gu, F. F., Ni, H. G., & Qi, D. M. (2007). Roughness coefficient for unsubmerged and
submerged reed. Journal of Hydrodynamics, 19(4): 421-428.
Hamedianfar, A., & Shafri, H. Z. M. (2014a). Development of fuzzy rulebased parameters
for urban object-oriented classification using very high resolution imagery. Geocarto
International, 29(3): 268-292.
Hamedianfar, A., Shafri, H. Z. M., Mansor, S., & Ahmad, N. (2014b). Improving detailed
rule-based feature extraction of urban areas from WorldView-2 image and LiDAR
data. International Journal of Remote Sensing, 35(5): 1876-1899.
Helmers, M. J., & Eisenhauer, D. E. (2006). Overland flow modelling in a vegetative filter
considering non-planar topography and spatial variability of soil hydraulic properties
and vegetation density. Journal of Hydrology, 328: 267-282.
Heumann, B. W. (2011). An object-based classification of mangroves using a hybrid
decision tree - support vector machine approach. Remote Sensing, 3: 2440-2460.
Horrit, M. S., & Bates, P. D. (2001). Predicting floodplains inundation: raster-based
modelling versus the finite element approach. Hydrological Processes, 15: 825-842.
Horrit, M., & Pender, G. (2008). Research Outcomes in the Application of 2D Inundation
Models for Flood Risk Management. United Kingdom.
Htun, N. Z., Mizoue, N., & Yoshida, S. (2011). Classifying tropical deciduous vegetation:
A comparison of multiple approaches in Popa Mountain Park, Myanmar. International
Journal of Remote Sensing, 32 (24): 8935-8948.
Ishikawa, Y., Sakamoto, T., & Mizuhara, K. (2003). Effect of density of riparian vegetation
on effective tractive force. Journal of Forestry Research, 8: 235–246.
Jalonen, J., Jarvela, J., & Aberle, J. (2013). Leaf area index as vegetation density measure
for hydraulic analyses. Journal of Hydraulic Engineering, 139(5): 461–469.
James, C. S., Liu, W., & Myers, W. R. C. (2001). Conveyance of meandering channels with
marginal vegetation. ICE - Water and Maritime Engineering, 148(2): 97-106.
© COPYRIG
HT UPM
116
Jarvela, J. (2002). Proceedings from River Flow 2002: Determination of Flow Resistance
of Vegetated Channel Banks and Floodplains. Lisse.
Jensen, J. R. (2000). Remote Sensing of the Environment: An Earth Resource Perspective.
New Jersey: Prentice-Hall, Inc.
Johansen, B., & Tommervik, H. (2014). The relationship between phytomass, NDVI and
vegetation communities on Svalbard. Geoinformation, 27: 20-30.
Joshi, P. C. (2011). Performance evaluation of vegetation indices using remotely sensed
data. International Journal of Geomatics and Geosciences, 2(1): 231–240.
Kouwen, N., & Li, R. M. (1980). Biomechanics of Vegetative Channel Linings. Journal of
the Hydraulics Division, 106(6):1085-1103.
Leuven, R. S. E. W., Gerig, Y., Poudevigne, I., Geerling, G. W., Kooistra, L., & Aarts, B.
G. W. (2002). Cumulative impact assessment of ecological rehabilitation and
infrastructure facilities in floodplains along the middle reach of the River Waal.
Application of Geographic Information Systems and Remote Sensing, 201-216.
Li, G., Lu, D., Moran, E., Dutra, L., & Batistella, M. (2012). A comparative analysis of
ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification
in a tropical moist region. ISPRS Journal of Photogrammetry and Remote Sensing, 70:
26-38.
Li, G., Lu, D., Moran, E., & Hetrick, S. (2011). Land-cover classification in a moist tropical
region of Brazil with Landsat thematic mapper imagery. International Journal of
Remote Sensing, 32 (23): 8207-8230.
Machala, M., & Zejdova, L. (2014). Forest mapping through object-based image analysis
of multispectral and LiDAR aerial data. European Journal of Remote Sensing, 47: 117-
131.
Maheshwari, B., & McMahon, T. (1992) Modelling shallow overland flow in surface
irrigation. Journal of Irrigation and Drainage Engineering, 118: 201-217.
Makaske, B., Maas, G. J., Brink, C. V. D., & Wolfert, H. P. (2011). The influence of
floodplain vegetation succession on hydraulic roughness: Is ecosystem rehabilitation
in Dutch embanked floodplains compatible with flood safety standards? AMBIO, 40:
370-376.
Manners, R., Schmidt, J., & Wheaton, J. M. (2012). Multiscalar model for the determination
of spatially explicit riparian vegetation roughness. Journal of Geophysical Research:
Earth Surface, 118: 1-19.
Marshall, T. R., & Lee, P. F. (2012). Mapping aquatic macrophytes through digital image
analysis of aerial photographs: An assessment. Journal of Aquatic Plant Management,
32: 61-66.
Mason, D. C., Cobby, D. M., Horrit, M. S., & Bates, P. (2003). Floodplain friction
parameterization in two-dimensional river food models using vegetation heights
derived from airborne scanning laser altimetry. Hydrological Processes, 17: 1711-
1732.
Mtamba, J., van der Velde, R., Ndomba, P., Zoltan, V., and Mtalo, F. (2015). Use of
Radarsat-2 and Landsat TM images for spatial parameterisation of Manning’s
roughness coefficient in hydraulic modelling. Remote Sensing, 7(1): 836-864.
Medeiros, S., Hagen, S. C., & Weishampel, J. F. (2012). Comparison of floodplains surface
roughness parameters derived from land cover data and field measurements. Journal
of Hydrology, 452-453.
Ministry of Forests, Lands and Natural Resource Operations, British Columbia (1999).
Environmental Guidelines for Vegetation Management on Flood Protection Works to
Protect Public Safety and the Environment. Canada.
© COPYRIG
HT UPM
117
Ministry of Natural Resources and Environment, Putrajaya. (2009). Managing biodiversity
in the riparian zone. Malaysia.
Montakhab, A., Yusuf, B., Ghazali, A. H., Mohamed, T. A. (2012). Estimation of
vegetation porosity in vegetated waterways. ICE - Water Management, 166(6): 333-
340.
Moui, G., Shinoda, S., Golosov, V., Shiiba, M., Hori, T., Kanae, S., Takizawa, S., & Oki,
T. (2012). Ecological and hydrological responses to climate change in an urban-
forested catchment, Nagara River basin, Japan. Urban Climate, 1: 40-54.
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A
review. ISPRS Journal of Photogrammetry and Remote Sensing, 66: 247-259.
Mueller-Dombois, D. (1984). The Role of Terrestrial Vegetation in the Global Carbon
Cycle: Measurement by Remote Sensing, Classification and Mapping of Plant
Communities: A Review with Emphasis on Tropical Vegetation (pp. 21-88). London:
Wiley.
Myint. S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel
vs. object-based classification of urban land cover extraction using high spatial
resolution imagery. Remote Sensing of Environment, 115 (5): 1145-1161.
National Oceanic and Atmospheric Administration Coastal Services Center (NOAA)
(2012). LiDAR 101: An Introduction to LiDAR Technology, Data, and Applications.
United States.
Noarayanan, L., Murali, K., & Sundar, V. (2012). Manning’s n co-efficient for flexible
emergent vegetation in tandem configuration. Journal of Hydro Environment research,
6: 51-62.
O’Hare, M. T., McGahey, C., Bissett, N., Cailes, C., Henville, P., & Scarlett, P. (2010).
Variability in roughness measurements for vegetated rivers near base flow in England
and Scotland. Journal of Hydrology, 385: 361-370.
Petryk, S., & Bosmajian III, G. (1975). Analysis of flow through vegetation. Journal of
Hydraulic Division ASCE, 101(7).
Pouteau, R., & Collin, A. (2013). Spatial location and ecological content of support vectors
in an SVM classification of tropical vegetation. Remote Sensing Letters, 4 (7): 686-
695.
Rahmeyer, W. (1998). Flow Resistance due to Vegetation in Compound Channels and
Floodplains. United States.
Razak, K. A., Santangelo, M., Van Westen, C. J., Straatsma, M. W., & de Jong, S. M.
(2013). Generating an optimal DTM from airborne laser scanning data for landslide
mapping in a tropical forest environment. Geomorphology, 190: 223-125.
Rowinski, P. M., & Kubrak, J. (2002). Proceedings from River Flow 2002: Velocity Profiles
on Vegetated Floodplains. Lisse.
Rutherford, I., Anderson, B., & Ladson, A. (2006). Principles for Riparian Lands
Management, Managing the Effects of Riparian Vegetation on Flooding. Canberra:
Land and Water Australia.
Sanderson, R. Introduction to Remote Sensing (2015, November 24). Retrieved from
http://faculty.kfupm.edu.sa/crp/bramadan/crp514/readings/7%20-
%20Introd_Remote_Sensing_Dr_Sanderson_New_Mexico_State_Univ_38Pages.pdf
San Joaquin River (2015). Deriving Vegetation Characteristics from LiDAR Data and Two-
Dimensional Hydraulic Modelling of the San Joaquin River Floodplain. United States.
Shaw, E.M., Beven, K. J., Chappell, N. A., & Lamb, R. (2011). Hydrology in Practice (4th
Edition). UK: Taylor & Francis.
© COPYRIG
HT UPM
118
Shih, S.F., & Rahi, G.S. (1982). Seasonal variations of Manning’s roughness coefficient in
subtropical marsh. Transactions of ASAE, 25(1):116-119.
Smith, M. W. (2008). Relationship of trunk size to selected canopy size parameters for
native pecan trees. Horticultural Science, 43 (3): 784-786.
Soong, T. W., & Hoffman, M. J. (2002). Effects of Riparian Tree Management on Flood
Conveyance Study of Manning’s Roughness in Vegetated Floodplains with an
Application on the Embarras River in Illinois. United States.
Stephens, E. M., Bates, P. D., Freer, J., & Mason, D. (2012). The impact of uncertainty in
satellite data on the assessment of flood inundation models. Journal of Hydrology,
414(41): 162-173.
Stibig, H. J., Beuchle, R., & Achard, F. (2003). Mapping of the tropical forest cover of
insular Southeast Asia from SPOT4-vegetation images. International Journal of
Remote Sensing, 24 (18): 3651-3662.
Stoesser, T., Neary, V., & Wilson, C. (2005). Proceedings from WSEAS (The World
Scientific and Engineering Academy and Society) Conference on Fluid Mechanics:
Modelling Vegetated Channel Flows: Challenges and Opportunities. Corfu.
Stoesser, T., Wilson, C., Bates, P. D., & Dittrich, A. (2003). Application of a 3D numerical
model to a river with vegetated floodplains. Journal of Hydroinformatics, 5: 99-112.
Straatsma, M. W. (2006). Proceedings from ISPRS Remote sensing: From Pixels to
Processes: Floodplains Roughness Mapping Synergy: LiDAR and Spectral Remote
Sensing. Enschede.
Straatsma, M. W. (2005). Proceedings from ISPRS Workshop “Laser scanning 2005”:
Quantitative Mapping of Hydrodynamic Vegetation Density of Floodplains Forests
using Airborne Laser Scanning. Enschede.
Straatsma, M. W., & Baptist, M. J. (2008). Floodplain roughness parameterisation using
airborne laser scanning and spectral remote sensing. Remote Sensing Environment,
112: 1062-1080.
Straatsma, M. W., & Huthoff, F. (2011). Uncertainty in 2D hydrodynamic models from
errors in roughness parameterization based on aerial images. Physics and Chemistry of
the Earth, 36(7-8): 324-333.
Sun, X., & Shiono, K. (2009). Flow resistance of one-line emergent vegetation along the
floodplains edge of a compound open channel. Advances in Water Resources, 32(3):
430-438.
Syme, W. J. (2001). Proceedings from IEAust Water Panel Workshop on 2D Models:
TUFLOW - Two & One-Dimensional Unsteady FLOW Software for Rivers, Estuaries
and Coastal Waters. Sydney.
Syme, W. J. (2008). Proceedings from 9th National Conference on Hydraulics in Water
Engineering: Flooding in Urban Areas - 2D Modelling Approaches for Buildings and
Fences. Darwin.
Takahasi, Y., & Uitto, J. I. (2004). Evolution of river management in Japan: from focus on
economic benefits to a comprehensive view. Global Environmental Change, 14(3): 63-
70.
Tang, X., Knight, D. W., & Sterling, M. (2011). Analytical model for streamwise velocity
in vegetated channels. ICE – Engineering and Computational Mechanics, 164(2): 91-
102.
Tian, F., Brandt, M., Liu, Y. Y., Verger, A., Tagesson, T., Diouf, A. A., Rasmussen, K.,
Mbow, C., Wang, Y., & Fensholt, R. (2016). Remote sensing of vegetation dynamics
in drylands: evaluating vegetation optical depth (VOD) using AVHRR NDVI and in
© COPYRIG
HT UPM
119
site green biomass data over West African Sahel. Remote Sensing of Environment, 177:
265-276.
Tsihrintzis, V. A., & Madiedo, E. E. (2000). Hydraulic resistance determination in marsh
wetlands. Water Resources Management, 14(4): 285-309.
Tymkow, P., & Borkowski, A. (2008). Land cover classification using airborne laser
scanning data and photographs. International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences, 37: 185-190.
Uotani, T., Kanda, K., & Michioku, K. (2014). Experimental and numerical study on
hydrodynamics of riparian vegetation. Journal of Hydrodynamics, 26 (5): 796-806.
Van der Sande, C., De Jong, S., & De Roo, A. (2003). A segmentation and classification
approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood
damage assessment. International Journal of Applied Earth Observation and
Geoinformation, 217–229.
Verschoren, V., Meire, D., Schoelynck, J., Buis, K., Bal, K. D., Troch, Meire, P., &
Temmerman, S. (2016). Resistance and reconfiguration of natural flexible submerged
vegetation in hydrodynamic river modelling. Environmental Fluid Mechanics, 16(1):
245-265.
Vodacek, A. (2015, November 24). Special Topics: Environmental Applications of Remote
Sensing. Retrieved from
http://www.cis.rit.edu/class/simg553_01/agriculture.html. Accessed on 24 November
2015.
WBM Pty Ltd (2010). TUFLOW User Manual. Australia.
Warmink, J.J., Straatsma, M.W., & Huthoff, F. (2012). Proceedings from Flood Risk 2012
Conference: The Effect of Hydraulic Roughness on Design Water Levels in River
Models. Rotterdam.
Werner, M. G. F., Hunter, N. M., & Bates, P. D. (2005). Identifiability of distributed
floodplains roughness coefficients in flood extent estimation. Journal of Hydrology,
314(1-4): 139-157.
Ward, R. D., Burnside, N. G., Joyce, C. B., & Sepp, K. (2013). The use of medium point
density LiDAR elevation data to determine plant community types in Baltic coastal
wetlands. Ecological Indicators, 33: 96-104.
Watershed Sciences Inc. (2010). Minimum LiDAR Data Density Considerations for the
Pacific Northwest. United States.
Wilson, C., Yagci, O., Rauch, H. P., & Stoesser, T. (2006). Application of the drag force
approach to model the flow-interaction of natural vegetation. International Journal of
River Basin Management, 4(2): 137-146.
Wu, F., Shen, H., & Chou, Y. (1999). Variation of roughness coefficients for unsubmerged
and submerged vegetation. Journal of Hydraulic Engineering, 125(9): 934-942.
XPSWMM (2015, December 5). 2D Domains. Retrieved from
http://help.xpsolutions.com/display/xps2016/2D+Domains
Yen, B.C. (1992). Channel Flow Resistance: Centennial of Manning’s Formula, Hydraulic
Resistance in Open Channels (pp. 1-135). Littleton: Water Resources Publications.
Zhang, M., Li, C. W., & Shen, Y. (2013). Depth-averaged modelling of free surface flows
in open channels with emerged and submerged vegetation. Applied Mathematical
Modelling, 37(1-2): 540-553.
Zhang, M. L., Xu, Y. Y., Qiao, Y., Jiang, H. Z., Zhang, Z. Z., & Zhang, G. S. (2016).
Numerical simulation of flow and bed morphology in the case of dam break floods
with vegetation effect. Journal of Hydrodynamics, 28(1): 23-32.
© COPYRIG
HT UPM
120
Zhou, X., Guan, H., Xie, H., & Wilson, J. L. (2009). Analysis and optimization of NDVI
definitions and areal fraction models in remote sensing of vegetation. International
Journal of Remote Sensing, 20 (3): 721-751.