AIN SHAMS UNIVERSITY FACULTY OF ENGINEERING ... Thesis...AIN SHAMS UNIVERSITY FACULTY OF ENGINEERING...
Transcript of AIN SHAMS UNIVERSITY FACULTY OF ENGINEERING ... Thesis...AIN SHAMS UNIVERSITY FACULTY OF ENGINEERING...
AIN SHAMS UNIVERSITY
FACULTY OF ENGINEERING
IRRIGATION AND HYDRAULICS DEPARTMENT
MAPPING THE SPATIAL DISTRIBUTION OF WATER
QUALITY PARAMETERS IN LAKE NASSER USING
REMOTE SENSING TECHNIQUE
By
Eng. Mohammed Ali Hamed Ghareeb (B.Sc., Civil Engineering, Shoubra Faculty of
Engineering, Benha University, 2003)
(M.Sc., Civil Engineering Ain shams University, 2010)
A Thesis Submitted in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy in Civil Engineering
Supervised by
Prof. Dr. Aly Nabih El-Bahrawy Professor of Hydraulics
Irrigation and Hydraulics Department
Faculty of Engineering, Ain Shams University
Prof. Dr. Karima Mahmoud Attia Director
Water Resources Research Institute
National Water Research Center
Dr. Mohsen Mahmoud Yousry Secretary General
Nile Research Institute
National Water Research Center
Cairo, Egypt
2016
FACULTY OF ENGINEERING
IRRIGATION AND HYDRAULICS
MAPPING THE SPATIAL DISTRIBUTION OF WATER
QUALITY PARAMETERS IN LAKE NASSER USING
REMOTE SENSING TECHNIQUE
By
Eng. Mohammed Ali Hamed Ghareeb (B.Sc., Civil Engineering, Zagazig University, 2003)
(M.Sc., Civil Engineering, Ain shams University, 2010)
Examiners Committee
Signature
Prof. Dr. Medhat Saad Aziz ………….…. Director of Nile Research Institute
National Water Research Center
Ministry of Water Resources and Irrigation
Prof. Dr. Iman Elazizy ………….…. Professor of Hydraulics
Irrigation and Hydraulics Department
Faculty of Engineering, Ain Shams University
Prof. Dr. Aly Nabih El-Bahrawy ………….…. Professor of Hydraulics
Irrigation and Hydraulics Department
Faculty of Engineering, Ain Shams University
Prof. Dr. Karima Mahmoud Attia ………….…. Director of Water Resources Research Institute
National Water Research Center
Ministry of Water Resources and Irrigation
Date: -----/------/2016
(109سورة )الكهف( آية )
To my dear wife
To my lovely sons Ali and Ahmed
To my father and my mother
To my brother Ahmed and his family
STATEMENT
This dissertation is submitted to Ain Shams University for partial
fulfillment of the requirements for the Degree of Doctor of Philosophy in
Civil Engineering
The work included in this thesis was carried out by the author in the
department of Irrigation and Hydraulics, Ain Shams University.
No part of this thesis has been submitted for a degree or a qualification at
any other university or institution.
Date:
Name: Mohammed Ali Hamed
Signature:
CURRICULUM VITAE
Name
Mohammed Ali Hamed Ghareeb
Date of Birth
December, 06th, 1980
Present Position
Assistant Researcher, Nile Research Institute,
National Water Research Center.
Education
- From 1988 to 1992 Primary School
- From 1993 to 1995 preparatory School
- From 1996 to 1998 Secondary School
- From 1999 to 2003 Shoubra Faculty of
Engineering, Benha University
Degree Awarded
B.Sc. In Civil Engineering, Shoubra Faculty of
Engineering, Benha University, Egypt 2003. With
general grade Very Good with honor degree.
M.Sc. In Civil Engineering, Irrigation and Hydraulics,
Faculty of Engineering, Ain Shams University
Ain Shams University
Faculty of Engineering
Irrigation and Hydraulics
Name: Mohammed Ali Hamed Ghareeb
Thesis: Mapping the Spatial Distribution of Water Quality Parameters in
Lake Nasser Using Remote Sensing Technique
Abstract The main objective of this research is to use remote sensing in estimating
water quality parameters in Lake Nasser as it is considered the main and
strategic storage of fresh water in Egypt. Results of six field missions,
measured in different seasons, are used with Envisat/MERIS match-up
images to validate Case 2 water quality processors (Case2 Regional
(C2R), Eutrophic Lake and boreal Lake), with and without correcting
land adjacency effect using Improved Contrast between Ocean and Land
(ICOL) processor, to estimate optical parameters, Total Suspended Solids
(TSS) and Chlorophyll-a (Chl-a). Processors’ validation is based on using
of some statistical measures. For TSS, results show that, Eutrophic lake
processor without ICOL is suitable for the end of flood season, C2R and
eutrophic lake processor without ICOL is suitable for the falling period,
while all processors failed during rising period. For Chl-a, Eutrophic lake
processor with ICOL indicates good results during falling period and the
end of flood season. Also, regression models are created to estimate
optical and non-optical water quality parameters in different flood
seasons using atmospherically corrected MERIS images, where bands 5
and 6 are found to be the common predictors. Models are validated at the
end of flood season and applied to 913 images for period from 2002 to
2012 to create time series curves/maps for each parameter, after
excluding cloudy images. It is concluded that TSS and Total Phosphorus
concentrations indicate increasing until 2005 and started to decrease
again, Silica and TDS illustrate an opposite changing pattern and
transparency doesn’t have a specific pattern of change. It is also
concluded that period from September to November indicate the lowest
cloud probability and is suitable for preforming optical measurement
which helps in creating bio-optical model for Lake Nasser.
Keywords: Remote Sensing, Water Quality, Lake Nasser, Case2 Water
Quality Processors, regression models.
Supervisors : Prof. Dr. Aly Nabih El-Bahrawy
Prof. Dr. Karima Mahmoud Attia
Dr. Mohsen Mahmoud Yousry
ACKNOWLEDGEMENT
Praise is to Allah, Lord of the Worlds.
I will forever be grateful to many people either directly or indirectly for
the completion of this thesis.
I wish to express my deepest sense of gratitude and sincerest appreciation
to Prof. Dr. Aly El-Bahrawy, Professor of Hydraulics, Faculty of
Engineering, Ain Shams University, for his excellent advise, enthusiastic
guidance and continuous encouragement towards the successful
completion of this study.
This thesis would never have been completed without the supervision and
nurturing of my advisor, Prof. Dr. Karima Attia, Director, Water
Resources Research Institute, National Water Research Center. She
contributed tremendously to my scientific development. She always
challenged me to produce my best.
This thesis would never have been completed without the supervision and
nurturing of my advisor, Dr. Mohsen Yousry, Secretary-General, Nile
Research Institute, National Water Research Center.
I would also like to thank Prof. Dr. Medhat Aziz, Director of Nile
Research Institute, National Water Research Center, for his kind
assistance and providing the hardware and software facilities used to
perform this study.
I would like to express my deep thanks and appreciation to my dear wife,
Doha and my lovely sons, Ali and Ahmed, the source of my happiness,
encouragement and motivation.
I wish to express my deepest thanks, gratitude, and appreciation to my
mother and my father for their love, warm caring, support, praying for me
and great patience throughout the time of this study.
Last, I wish to express my deepest thanks, gratitude, and appreciation to
my twin brother Ahmed for his encouragement and for his support to
continue this thesis.
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TABLE OF CONTENTS TABLE OF CONTENTS .........................................................................i
LIST OF FIGURES ................................................................................. v
LIST OF TABLES ..................................................................................ix
LIST OF ABBREVIATIONS ................................................................xi
LIST OF SYMBOLS ........................................................................... xiii
CHAPTER (1) INTRODUCTION ....................................................... 1
1.1. General ........................................................................................ 1
1.2. Problem definition ....................................................................... 1
1.2.1. Sedimentation in Lake Nasser .............................................. 1
1.2.2. Water quality and suspended sediment ................................ 4
1.3. Study objectives .......................................................................... 5
1.4. Outline of the thesis..................................................................... 5
CHAPTER (2) LITERATURE REVIEW ........................................... 9
2.1. Introduction ................................................................................. 9
2.2. Remote Sensing Theory .............................................................. 9
2.2.1. Remote Sensing History ....................................................... 9
2.2.2. Principles of Remote Sensing ............................................. 10
2.2.3. Types of Remote Sensing ................................................... 12
2.2.4. Scale and Resolution .......................................................... 15
2.2.5. Interaction of EMR with the earth’s surface ...................... 16
2.3. Optics of water .......................................................................... 18
2.3.1. Interaction between light and water .................................... 19
2.3.2. Case 1 and Case 2 waters ................................................... 21
2.4. Remote Sensing and water quality ............................................ 21
2.4.1. Retrieval approaches ........................................................... 21
2.4.2. Satellite Missions ................................................................ 22
2.5. Application ................................................................................ 24
2.5.1. Suspended Sediments ......................................................... 25
2.5.2. Turbidity and Transparency ................................................ 25
2.5.3. Chlorophyll-a ...................................................................... 26
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2.5.4. Total Phosphorus (TP) and Total Nitrogen (TN) ............... 27
2.6. Remote sensing studies in Lake Nasser .................................... 29
2.7. Discussion ................................................................................. 30
CHAPTER (3) STUDY AREA AND DATA COLLECTION ......... 31
3.1. General ...................................................................................... 31
3.2. Study Area ................................................................................. 31
3.2.1. Aswan High Dam (AHD) ................................................... 31
3.2.2. Aswan High Dam Reservoir (AHDR) ................................ 33
3.2.3. AHDR morphology ............................................................ 33
3.2.4. AHDR operation policy ...................................................... 35
3.2.5. AHDR Monitoring Activities ............................................. 35
3.2.6. AHDR hydrological characteristics .................................... 38
3.3. Data Collection .......................................................................... 42
3.3.1. Field data ............................................................................ 42
3.3.2. Remote sensing data ........................................................... 53
CHAPTER (4) METHODOLOGY .................................................... 57
4.1. General ...................................................................................... 57
4.2. Field work ................................................................................. 57
4.3. Satellite images acquisition ....................................................... 57
4.3.1. Image processing tools ....................................................... 58
4.3.2. MERIS Processing tool (MPT) ........................................... 60
4.4. Validation of water quality processors ...................................... 61
4.5. Regression ................................................................................. 62
4.6. Time Series ................................................................................ 63
4.7. Statistical performance measures .............................................. 64
4.7.1. Definitions .......................................................................... 65
4.7.2. Interpretation of Performance measures ............................. 67
4.7.3. Criteria for good processor ................................................. 67
4.7.4. Ranking and Evaluation ...................................................... 68
CHAPTER (5) RESULTS AND DISCUSSION ................................ 69
5.1. Introduction ............................................................................... 69
5.2. Validation of Case 2 water quality processors .......................... 69
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5.2.1. Validation of TSS ............................................................... 69
5.2.2. Validation of Chl-a ............................................................. 82
5.3. Regression ................................................................................. 87
5.3.1. Correlation between measured parameters ......................... 87
5.3.2. Stepwise regression Model ................................................. 90
5.3.3. Validation of retrieval model .............................................. 95
5.4. Time series analysis ................................................................ 100
5.4.1. Image selection criteria ..................................................... 100
5.4.2. Flood seasons .................................................................... 103
5.4.3. Time series of Water quality parameters .......................... 105
CHAPTER (6) CONCLUSION AND RECOMMENDATIONS .. 113
6.1. Conclusion ............................................................................... 113
6.2. Recommendations ................................................................... 117
REFERENCES ................................................................................... 119
APPENDIX (A) MERIS IMAGES CHARACTERISTICS AND
WATER QUALITY ANALYSIS ....................................................... 125
A.1. MERIS image characteristics .................................................. 125
A.1.1. MERIS product processing levels .................................... 126
A.1.2. Resolutions ....................................................................... 126
A.1.3. MERIS Case 2 water quality processors .......................... 129
A.2. Water Quality Analysis ........................................................... 130
A.2.1. Water sampling ....................................................................... 130
A.2.2. Preservation ...................................................................... 130
A.2.3. Types of analysis .............................................................. 130
A.2.4. Laboratory measurements ................................................. 131
A.2.5. Reagents ............................................................................ 131
A.2.6. Equipment ......................................................................... 131
A.2.7. Laboratory equipment ....................................................... 132
A.2.8. General equipment ............................................................ 132
APPENDIX (B) MERIS PROCESSING TOOL (MPT) AND EXCEL
CUSTOM FUNCTIONS ..................................................................... 133
B.1. Introduction ............................................................................. 133
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B.2. MERIS PROCESSING TOOL (MPT) .................................... 133
B.2.1. Main Idea of MPT ............................................................ 133
B.2.2. MPT description ............................................................... 134
B.2.3. MPT Code ......................................................................... 138
APPENDIX (C) Stepwise Regression charts ..................................... 171
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LIST OF FIGURES
Figure ( 1-1) The Nile River Basin ....................................................... 2
Figure ( 1-2) Flow hydrograph of the main Nile and its tributaries ..... 3
Figure ( 1-3) Lake Nasser in two different seasons .............................. 4
Figure ( 2-1) Remote Sensing components (Miloud, 2012) ............... 11
Figure ( 2-2) The electromagnetic spectrum, (Tantirimudalige, 2002)
........................................................................................................... 12
Figure ( 2-3) The energy interactions, (Liliesand & Kiefer, 1993) .... 16
Figure ( 2-4) the spectral reflectance curves for clear water, vegetation
and bare soil, (Tantirimudalige, 2002) .............................................. 18
Figure ( 2-5) The relation between hydrologic optics and other
branches of optics (after Preisendorfer, 1976 and Kirk, 1994) ......... 19
Figure ( 2-6) Time line for Ocean color Sensors from 1978 to 2030
(CEOS, 2016) .................................................................................... 23
Figure ( 3-1) Layout of Aswan High Dam ......................................... 33
Figure ( 3-2) Lake Nasser Map........................................................... 34
Figure ( 3-3) Main component for different operation zones of AHDR
........................................................................................................... 35
Figure ( 3-4) Water level Upstream AHD from 1964 to 2011 ........... 40
Figure (3-5) AHDR inflow at Dongola station .................................. 40
Figure ( 3-6) Discharge and sediment concentration hydrographs at
Dongola station (1999-2003) ............................................................. 41
Figure ( 3-7) Water Quality samples’ Locations ................................ 44
Figure ( 3-8) Survey mission with respect to water level change in
period from 2002 to 2011 .................................................................. 45
Figure ( 3-9) Longitudinal Profile for TSS during different missions 47
Figure ( 3-10) Longitudinal Profile for Chl-a during different missions
........................................................................................................... 48
Figure ( 3-11) Longitudinal Profile for Transparency during different
missions ............................................................................................. 48
Figure ( 3-12) Longitudinal Profile for Turbidity during different
missions ............................................................................................. 49
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Figure ( 3-13) Longitudinal Profile for TP during different missions 50
Figure ( 3-14) Longitudinal Profile for SiO2 during different missions
........................................................................................................... 50
Figure ( 3-15) Longitudinal Profile for TDS during different missions
........................................................................................................... 51
Figure ( 3-16) Histogram of downloaded MERIS images ................. 56
Figure ( 4-1) Flow Chart of Research Methodology .......................... 58
Figure ( 4-2) Time series flowchart .................................................... 64
Figure ( 5-1) TSS for First Mission (mg/l) ......................................... 71
Figure ( 5-2) TSS for Third Mission (mg/l) ....................................... 72
Figure ( 5-3) TSS for Fourth Mission (mg/l) ..................................... 73
Figure ( 5-4) TSS for Fifth Mission (mg/l) ........................................ 74
Figure ( 5-5) TSS for Sixth Mission (mg/l) ........................................ 75
Figure ( 5-6) Measured verses predicted TSS Values for First, Third
and Fourth missions (mg/l) ................................................................ 76
Figure ( 5-7) Measured verses predicted TSS Values for Fifth and
Sixth missions (mg/l) ......................................................................... 77
Figure ( 5-8) Chl-a for Third Mission (mg/m3) .................................. 83
Figure ( 5-9) Chl-a for Fourth Mission (mg/m3) ................................ 84
Figure ( 5-10) Measured verses predicted Chl-a Values (mg/m3) ...... 85
Figure ( 5-11) Cloud probability summary surface of Lake Nasser . 102
Figure ( 5-12) Percentage of excluded images due to clouds and sun
glint .................................................................................................. 103
Figure ( 5-13) Percentage of excluded images due to clouds and sun
glint .................................................................................................. 104
Figure ( 5-14) Monthly average TSS at Arkeen Cross section as
calculated from MERIS Images ...................................................... 105
Figure ( 5-15) Spatial distribution maps of monthly average
concentration of TSS, Transparency, and TP (mg/l) ....................... 107
Figure ( 5-16) TSS during Period from October to December (mg/l)
......................................................................................................... 108
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Figure ( 5-17) Transparency during Period from October to December
(mg/l) ............................................................................................... 109
Figure ( 5-18) Silica during Period from October to December (mg/l)
......................................................................................................... 110
Figure ( 5-19) Total Phosphorus during Period from October to
December (mg/l) .............................................................................. 111
Figure ( 5-20) TDS during Period from October to December (mg/l)
......................................................................................................... 112
Figure ( A-1) Envisat Satellite Configuration and Payload Instruments
(ESA, 2006) ..................................................................................... 125
Figure ( A-2) MERIS Global coverage (ESA, 2006) ....................... 129
Figure ( B-1) Sample of A processing graph .................................... 134
Figure ( B-2) Flow chart of MPT programming............................... 135
Figure ( B-3) MPT interface ............................................................. 137
Figure ( B-4) ArcMap Model Builder used to cloud coverage
percentage ........................................................................................ 168
Figure ( B-5) ArcMap Model Builder used to Extract Lake Nasser
Surface ............................................................................................. 169
Figure ( C-1) Stepwise Regression Analysis Results For 2003 ....... 172
Figure ( C-2) Stepwise Regression Analysis Results For 2006 ....... 173
Figure ( C-3) Stepwise Regression Analysis Results For 2007 ....... 174
Figure ( C-4) Stepwise Regression Analysis Results For 2009 ....... 175
Figure ( C-5) Stepwise Regression Analysis Results For 2010 ....... 176
Figure ( C-6) Stepwise Regression Analysis Results For 2011 ....... 177
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LIST OF TABLES
Table ( 2-1) Selected remote sensing application for monitoring
Suspended Sediment in different water bodies (Chang et al., 2015) ....... 26
Table ( 2-2) Selected remote sensing application for monitoring
Chlorophyll-a in different water bodies (Chang et al., 2015) .................. 27
Table ( 2-3) Selected remote sensing application for monitoring Nutrients
(TN and TP) in different water bodies (Chang et al., 2015) .................... 28
Table ( 3-1) Definition of main monitoring locations surveyed (El
Sammany, 2002) ...................................................................................... 37
Table ( 3-2) Measured Parameters for each mission ................................ 43
Table ( 3-3) Descriptive statistics of measured water quality parameters 52
Table ( 3-4) Characteristics of Match-up images. .................................... 55
Table ( 5-1) Summary of statistical performance measures for TSS ........ 78
Table ( 5-2) Ranking values of water quality processors for TSS ............ 81
Table ( 5-3) Summary of statistical performance measures for Chl-a ...... 86
Table ( 5-4) Ranking values of water quality processors for Chl-a .......... 86
Table ( 5-5) Pearson correlation matrix .................................................... 89
Table ( 5-6) Rule of Thumb for Interpreting the Size of a Correlation
Coefficient (Hinkle et al., 2003) .............................................................. 90
Table ( 5-7) Retrieval Models for water quality parameters (First, Second
and third missions) ................................................................................... 93
Table ( 5-8) Retrieval Models for water quality parameters (Fourth, Fifth
and Sixth missions) .................................................................................. 94
Table ( 5-9) Retrieval Models Validation for different seasons (TSS, Chl-
a, Turbidity and transperancy) ................................................................. 98
Table ( 5-10) Retrieval Models Validation for different seasons (TP, SiO2,
and TDS) .................................................................................................. 99
Table ( 5-11) Start/End of Flood Seasons ............................................... 104
Table ( A-1) MERIS products description (ESA, 2006)......................... 127
Table ( A-2) MERIS spectral bands and applications (ESA, 2006). ...... 128
xi
LIST OF ABBREVIATIONS
TSS Total Suspended Solids
Chl-a Chlorophyll- a
TDS Total Dissolved Solids
TP Total Phosphorus
AHD Aswan High Dam
AHDR Aswan High Dam Reservoir
C2R Case 2 Regional
MPT MERIS Processing Tool
CIR Color Infra-Red
MSS Multispectral Scanner
TM Thematic Mapper
MR Microwave Radiometer
SAR Synthetic Aperture Radar
EMR Electromagnetic Radiation
NIR Near Infrared
ERTS Earth Resources Technology Satellite
CZCS Coastal Zone Color Scanner
OCTS Ocean Color Temperature Scanner
POLDER POLarization and Directionality of the Earth's
Reflectances
SeaWiFS Sea-viewing Wide Field-of-view Sensor
MERIS MEduim Resolution Imaging Spectrometer
MODIS Moderate Resolution Imaging Spectroradiometer
xii
SPM Suspended Particulate Matter
Rrs Remote Sensing Reflectance
GIS Geographic Information System
MSL Mean Sea Level
DGPS Differential Global Positioning System
ESA European Space Agency
DDS Data Dissemination System
ICOL Improved Contrast between Ocean and Land
SMAC Simplified Method for Atmospheric Corrections
APHA America Public Health Association
FB Fractional Bias
r Correlation Coefficient
NMSE Normalized Mean Square Error ()
MG Geometric Mean Bias
MV Geometric Mean Variance
Fac2 Factor of two
NDWI Normalized Difference Water Index
MNB Mean Normalized Bias
SSC Suspended Sediment Concentration
MIM Matrix Inversion Method
TN Total Nitrogen
TOC Temperature Total Organic Carbon
xiii
LIST OF SYMBOLS
EI () Incident Energy joule
ER () Reflected Energy joule
EA () Absorbed Energy joule
ET () Transmitted Energy joule
ρ (λ) Spectral reflectance ------
Kd The vertical attenuation coefficient m−1
c () The beam attenuation coefficient m−1
Ed (z, ) Depth of the ambient downwelling irradiance W/m2
1
CHAPTER (1)
INTRODUCTION
1.1. General
Traditional water sampling methods are able to quantify temporal
changes in water quality at specific points. However, they are unable to
effectively quantify water quality across the entire surface area. In lakes
with high spatial variation, like Lake Nasser, point samples are often not
representative of the whole water body, which may mislead the lake
management process. Also, due the expense of traditional monitoring
programs, often, only critical locations are selected for monitoring. The
advantage of remote sensing is that it can cover large areas of both
waterbodies and ground; hence water quality can be estimated over vast
greater area.
1.2. Problem definition
Lake Nasser is considered the main and strategic storage of fresh water in
Egypt, so that, the preservation and continues monitoring of water quality
in lake is very important and represent a national goal. Although the
water quality is still good, sediment load comes with flood and human
activities such as agriculture, fisheries and transport threaten lake water
quality. So that, more attention should be bayed to monitor, protect and
manage water resources in Lake Nasser using a scientific approach.
Remote sensing technique is considered a powerful tool for assessment
and monitoring of lake condition and can supply timely based and
reliable information to help in decision making.
1.2.1. Sedimentation in Lake Nasser
The Nile River water flow is received from three main distinct
watersheds, Figure ( 1-1); Equatorial lakes plateau in the South, Sudd
region in the Center, and Ethiopian Highlands in the East. Figure ( 1-2)
illustrates the nominal flow hydrographs of White Nile, Blue Nile, and
2
Atbara River in addition to the main Nile. These hydrographs show the
average flows over more than 80 years. It is noted that the coming flows
from Blue Nile is highly suspended with sediments, especially during
summer months. This case can contribute in changing the water quality in
Lake Nasser throughout the year.
Figure (1-1) The Nile River Basin
The Nile River has two distinct hydrological phenomena; a short high
flow period takes about three months represents the flood season, the
flow comes from Blue Nile and Atbara river represent the main source of
3
water in Lake Nasser, in this period water is muddy and carries a large
quantity of sediments, about 95% of sediment load in Lake Nasser; and a
long period takes about nine months, water in this period is clear and the
sediment concentration is low, the sediment load coming from the White
Nile and tributaries is relatively small about 5% of the total annual
sediment load of the main river.
Figure (1-2) Flow hydrograph of the main Nile and its tributaries
Two remote sensing images (MERIS images) are shown in Figure ( 1-3)
representing lake in two different seasons. The left image is representing
the lake in 05 May 2010 shows lake in low flow period and shows a clear
4
water with no suspended sediments, the right image represents lake in 03
September 2010 which shows in flood period, the sediment carried by
water can be obviously observed from the remote sensing image in the
southern part of lake.
05 May, 2010 (Before flood) 03 September, 2010 (After flood)
Figure (1-3) Lake Nasser in two different seasons
1.2.2. Water quality and suspended sediment
Suspended Sediment in water has two opposite effects on water quality.
The first effect is that the suspended sediments in water, especially the
finer ones, absorb some pollutants such as phosphorus, nitrogen, organic
compounds and pathogenic, that can help in improving water quality to a
certain degree. The second effect is that sediment can be source of
pollutant, as it can carry and store other pollutants, such as bacteria,
pesticides, residues and viruses which will affect the water purity,
transparency and quality (Yang, 2003).
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1.3. Study objectives
The main objectives of this study are to develop a method using remote
sensing techniques to be used in spatial mapping of water quality
parameter to monitor water quality in Lake Nasser. The Medium
Resolution Imaging Spectrometer (MERIS) will be used to fulfill the
research objectives.
The objectives can be summarized in the following points:
- Validate the use of the existing Case 2 water quality processors
(Case 2 Regional, Boreal Lakes, and Eutrophic lakes) in lake
Nasser, and to what extent it can be applied in different seasons to
obtain optical water quality parameters such as Total Suspended
Solids (TSS), Chlorophyll-a (Chl-a).
- Determining a regression model that correlates different water
quality parameters and remote sensing reflectance of different
MERIS image bands.
- Time series analysis and Spatial and temporal mapping of water
quality parameters to determine seasonal variations.
- A cloud coverage and sun glint analysis for the lake Nasser area
will be performed to find the most suitable months for coupling
between RS and water quality; this will be helpful for future
researches.
1.4. Outline of the thesis
CHAPTER (1): Introduction
This chapter contains a general introduction of problem statement, effect
of sedimentation and different water quality parameters, research
objectives and contents of every chapter.
CHAPTER (2): Literature review
This chapter gives a literature review for using remote sensing in
assessment and monitoring, especially for water quality of water bodies
6
all over the world, also gives a literature about the previous studies for
Lake Nasser with remote sensing such as evaporation.
CHAPTER (3): Study area and data collection
This chapter will contain a description of Lake Nasser as a study area; its
location and the current monitoring program, in addition to its physical
characteristics, hydrologic and sedimentation characteristics. Also, it
contains a description of both field and remote sensing data used in the
research
CHAPTER (4): Methodology
This chapter will give a full description of the methodology used
throughout the research in both validation of case 2 water quality
processors and regression analysis between water quality parameters and
different bands reflectance, in addition to description of tools used in
through the current research. Also, it will contain a description of
statistical performance measures used to evaluate results.
CHAPTER (5): Results and discussion
This chapter illustrates in details the results obtained through the research
for validation of Case 2 water quality processor to estimate TSS and Chl-
a in Lake Nasser through different seasons, the results of stepwise
regression will be illustrated to get a regression model for each water
quality parameter and time series analysis. The chapter also contains
analysis of cloud coverage percentage over the surface area of the lake
which will help in excluding the cloudy images from time series analysis;
also it can help in identifying the best month for coupling water quality
measurements and remote sensing. Also, the chapter will contain results
of time series analysis for different water quality parameters.
CHAPTER (6): Conclusion and recommendations
This chapter summarizes the study scope and procedures in addition to
most important conclusions. Further studies and future works are also
recommended in this chapter.
7
REFERENCES
This chapter includes the biography and references used in the study
APPENDIX (A): MERIS Images Characteristics and water quality
analysis
It contains a description of MERIS image characteristics in means of its
spatial and temporal resolution, its processing levels and common uses of
it. In addition to a detailed description of Case 2 water quality processors
used through the research. It also includes methods and standards used in
sampling.
APPENDIX (B): MERIS Processing Tool (MPT) and Excel Custom
Functions
It contains a description of MERIS Processing Tool; which have been
programed in order to automate the processing of MERIS images. Also it
contains the programing code for excel custom functions to calculate
different statistical measures. In addition to some tools created by
ArcMap to automate the processing of images.
APPENDIX (c): Stepwise Regression charts.
9
CHAPTER (2)
LITERATURE REVIEW
2.1. Introduction
The aim of this chapter is to give a detailed review of remote sensing
theory and its application in lakes/reservoirs, rivers, estuaries and coastal
zones, especially in water quality. It will also give a review about the
researches pertain the use of remote sensing techniques in Lake Nasser.
2.2. Remote Sensing Theory
Remote sensing, also called earth observation, is a technique used to have
information about objects by collecting and analyzing data without being
in direct contact with the object or area. In general, remote sensing can be
defined as a tool used for observing and studying the Earth from space, it
can observe land surface, seas and oceans, the atmosphere and its
dynamics. It offers a means of obtaining large amounts of data, but its
value is in the information that is obtained from the acquired data and
how it is used, stored and expanded in the spatial, temporal and spectral
modes.
2.2.1. Remote Sensing History
Earth observation is an activity that started a long time ago, may be with
the land surveys conducted physically using various measuring
instruments. Gradually observation from air was possible and then
developed to satellite observation, (Tantirimudalige, 2002).
The first aerial photograph was taken by the Parisian photographer called
Gaspard Tournachon in 1858, a, from a balloon at a height of 80 m. The
earliest existing aerial photograph was taken in 1860 from a balloon over
Boston. In order to obtain meteorological data kites were used to obtain
aerial photographs from about 1882.
During World War I, Photography from aircraft received highest
attention especially in the interest of military reconnaissance to observe
10
enemy troop movements. Aerial photographs were used, during World
War II, to map sea bed conditions along the coastline to better plan the
maneuvering of landing craft. Furthermore, infrared film made it possible
to distinguish between green vegetation camouflage nets.
After the wars in the 1950s, color infra-Red (CIR) photography was
found. In 1956, Colwell conducted experiments on the use of CIR to
recognize and classify of vegetation types and to detect the diseased and
stressed or damaged vegetation. The significant progress in radar
technology was found also in 1950s. Hence, Studying the Earth from
space developed from pure research, to even daily applications.
The first satellite to be used for observing and monitoring of the earth
was launched in July, 1972. Early indications from NASA say that the
satellite is sending good pictures back to Earth, (Mitsch, 1973).
Today, remote sensing satellites have several applications such as
Weather, Environmental monitoring, water quality and Mapping.
2.2.2. Principles of Remote Sensing
In Remote Sensing, four elements are essential, see Figure ( 2-1):
1- Platform to hold instruments, for example, aircrafts and satellites.
2- A target object to be observed, the target is the earth.
3- An instrument or sensor to observe the Earth such as cameras,
scanners, radars, etc.
4- The information obtained from analysis of acquired data can help
in increasing our knowledge about the earth such as the cloud
cover, land use, and land cover, characteristics of waterbodies,
and much more).
11
Figure (2-1) Remote Sensing components (Miloud, 2012)
Detection and classification of different objects on the earth surface
means detecting and recording of radiant energy emitted or reflected by
these objects or surface material, which depends on the physical,
chemical, and structural properties of material, surface roughness and
radiant energy characteristics such as, angle of incidence, intensity, and
wavelength of radiant energy.
A general understanding of the electromagnetic radiation (EMR) and
electromagnetic spectrum is necessary to consider the technology of
remote sensing. EMR can be defined as the energy propagated through
space between electric and magnetic fields. Visible light was shown by
Maxwell as a part of the electromagnetic spectrum which also contains
gamma rays, X-rays, ultraviolet rays, infrared rays, microwaves, radar
and radio waves. Figure ( 2-2)
In general when electromagnetic energy hits any matter, the energy is
conserved according to basic physical principles in one or more of the
following states:
- Absorbed energy by the matter and it contributes in heating it
12
- Emitted energy, or commonly remitted energy, it is depended on
the structure and temperature of the matter. It has different values
depending on the matter and wave length.
- Scattered, deflected to one side and lost ultimately to absorption
of further scatter, or
- Reflected, returned unchanged to the medium (Colwell et al.,
1963).
Figure (2-2) The electromagnetic spectrum, (Tantirimudalige, 2002)
2.2.3. Types of Remote Sensing
Remote sensing can be classified according to either energy source or
sensor type into passive or active systems. Active systems use their own
energy source such as microwaves (radar), where the passive systems
depend upon external source of illumination, such as sun or self-emission
of the observed object.
Also remote can be classified according to the wavelengths into three
categories as follows:
1- Visible and near infrared Remote Sensing, this type can be
classified as a passive system where it depends on the reflected
energy from the sun. It helps in understanding of land surface
conditions such as vegetation, rivers, lakes, and urban areas
13
distribution. Also it can be used in field of water quality
monitoring.
2- Thermal Infrared Remote Sensing, this type classified as a passive
system where it depends on collecting the thermal infrared rays
radiated from land surface heated by sunlight. It can be used in
identifying the high temperature area such as volcanic activities
and fires areas.
3- Microwave (radar) Remote Sensing, this type can be classified as
either active or active microwave systems. In active microwave
system, the observation satellite emits microwaves and observes
microwaves reflected by different object in the earth, this type is
suitable to observe mountains and valleys. While the passive
microwave system measures the naturally radiated microwaves
from land surface, this type can be useful in observing sea surface
temperature, snow accumulation and thickness of ice.
There are many types of both passive and active sensors; each type has its
own application, the different types will be illustrated below as listed in
(NASA, 2015).
Passive sensors include different types of radiometers and spectrometers
as follows:
- Accelerometer: it is an instrument that able to measure both
translational or angular acceleration.
- Radiometer: this instrument can quantitatively measure the
intensity of electromagnetic radiation in some bands within the
spectrum.
- Imaging radiometer: it is a radiometer that can provide a two-
dimensional array of pixels to produce an image. It uses an array
of detectors for Scanning images and it can be performed
mechanically or electronically.
- Spectrometer: it is used to detect, measure, and analyze the
spectral characteristics of incident electromagnetic radiation.
14
- Spectroradiometer: it is a radiometer that can measure the
intensity of radiation in multiple wavelength bands (i.e.,
multispectral).
- Hyperspectral radiometer: it is an advanced multispectral
sensor and can detect hundreds of very narrow spectral bands in
the visible, near-infrared, and mid-infrared portions of the
electromagnetic spectrum.
- Sounder: it measures vertical distributions of atmospheric
parameters such as temperature, pressure, and composition from
multispectral information
Most active sensors operate in the microwave portion of the
electromagnetic spectrum, which enables them to penetrate the
atmosphere under most conditions. The below list is describing the
different types of active sensors:
- Radar: it can be an airborne or spaceborne which emits a series
of microwave pulses from an antenna. It detects and measures the
backscattered microwaves reflected back to sensor. The distance
or range to the target can be determined using time required for
the energy to travel to the target and return back to the sensor. A
two dimensional image can be produced by recording both range
and magnitude of the reflected energy from all targets.
- Ranging Instrument: this system uses a different technique which
depending on a pair of identical microwave instruments on two
different platforms. Signals are transmitted from each instrument
to the other, with the distance between the two determined from
the difference between the received signal phase and transmitted
(reference) phase.
- Scatterometer: it is high-frequency microwave radar used
especially to measure backscattered radiation. It can be used to
derive maps of surface wind speed and direction over ocean
surfaces.
15
- Lidar: it is a system used in light detection and works on the
principle of radar, but uses light from a laser.
- Laser altimeter: it is an instrument that uses a LIDAR to
measure the height of the platform above the mean Earth’s
surface; it can be used to determine the topography of the
underlying surface.
- Sounder: it can measure the vertical distribution of precipitation
and other atmospheric characteristics such as temperature,
humidity, and cloud composition
Today, there is a big list of remote sensing satellites that use passive and
active sensors. They are now onboard which provide the scientists with
hundreds of daily images for earth surface with different spatial, spectral,
temporal, and radiometric resolution
2.2.4. Scale and Resolution
Scale and resolution are very important properties of both aerial
photographs and satellite images, these properties can be calculated by
characteristics of lens and the platform flying height.
- Scale
Remote sensing information is scale dependent. Scale of remote sensing
image can depend on many factors such as the effective focal length of
the optical device used in acquiring image, altitude of the remote sensing
satellite and the factor of magnification used to reproduce the image.
- Resolution
The acquired remote sensing images can be described by three different
types of resolutions such as spatial, spectral and radiometric resolution.
The fourth one is depending on the space vehicle which is the temporal
resolution.
Four types of resolutions considered in remote sensing work will be
described as follows:
16
1- Spatial Resolution is by the minimum object size that can be
detected or recorded by sensor.
2- Spectral Resolution is defined by the used channel bandwidth.
High spectral resolution was characterized by a narrow
bandwidth; it provides more accurate discrimination of objects.
3- Radiometric Resolution is determined by the number of discrete
levels into which signal strength maybe divided (quantization).
4- Temporal Resolution is can be defined as the time interval
between two successive visits at the same place by the remote
sensing satellite.
2.2.5. InteractionofEMRwiththeearth’ssurface
The incident radiation from the sun can be reflected either by the surface,
transmitted into the surface or absorbed and emitted by the surface,
Figure ( 2-3). Hence a several changes were happened to the EMR in its
direction, magnitude, wavelength, polarization and phase. These changes
can be detected by the remote sensor and can be interpreted by scientists
to obtain useful information about the object of interest.
Incident energy = reflected energy + absorbed energy + transmitted
energy
EI () = ER () + EA () + ET () (1)
Figure (2-3) The energy interactions, (Liliesand & Kiefer, 1993)
17
Remote sensing are principally concerned with the reflected radiation
which emitted by the different objects in the earth. The magnitude of
reflected radiation depends on the type of surface reflecting it. This leads
to what is called spectral signatures.
The ratio of reflected energy with respect to the incident energy is known
as spectral reflectance, [ρ (λ)], it is a wavelength dependent. Each feature
in the earth has its own spectral reflectance characteristics. it is
responsible for the color of different objects, for example tree color is
green as they reflect more in the green wavelength.
The spectral reflectance is dependent on wavelength; it has different
values at different wavelengths for a particular terrain feature. The
reflectance characteristics of the earth’s surface features are expressed by
spectral reflectance, which is given by:
ρ (λ) = [ER(λ) / EI(λ)] x 100 (2)
Where,
ρ (λ) = Spectral reflectance at a specific wavelength.
ER (λ) = the reflected energy from object at a specific wavelength.
EI (λ) = the incident energy upon the object as a function of wavelength.
The spectral reflectance curve is the relation between ρ (λ) and λ. The
variation in the curve depends on the physical conditions and chemical
decomposition of each land feature. Wide range of values was observed
for the same material, so that, averaging is required to get a generalized
spectral response for the objects under study. Spectral response pattern
can be identified by spectral signature which indicates the characteristic
of each land feature. Figure ( 2-4) represents the spectral reflectance
curves for clear water, vegetation and bare soil.
18
Figure (2-4) the spectral reflectance curves for clear water,
vegetation and bare soil, (Tantirimudalige, 2002)
2.3. Optics of water
This section will explain the fundamentals of water optical properties,
including the interaction of light with natural waters in lakes, rivers,
estuaries, and how this process can used to acquire information about the
constituents in the water. Optics is that branch of Physics which studies
the characteristics of light (Visible Part of EMR). Since the behavior of
light is significantly affected by the nature and characteristics of the
medium through which it is passing, there are different branches of optics
dealing with different kinds of physical systems. Figure ( 2-5) presents the
different physical systems that interact with the EMR (Kirk, 1994).
Hydrological optics is the branch of optics that quantitatively studies the
interactions of radiant energy from the Earth’s oceans, estuaries, lakes,
reservoirs, and other water bodies (Mobley, 1995). Hydrological optics
can be subdivided into oceanographic and limnological optics depending
on the type of water under consideration whether it is salty, marine or
fresh, inland waters (Kirk, 1994).
19
Figure (2-5) The relation between hydrologic optics and other
branches of optics (after Preisendorfer, 1976 and Kirk, 1994)
2.3.1. Interaction between light and water
The interaction of the visible light in water depends on the characteristics
of the water and its optical properties. The water optical properties are
connected with the ambient light under the radiative transfer theory
(Mobley, 1995). According to (Mobley, 1995), Preisendorfer classified
the optical properties of water in two classes: inherent and apparent.
2.3.1.1. The inherent optical properties
The inherent optical properties depend on the medium and its
characteristics. There are two things that can happen to light passing
through water: it can be absorbed or scattered. Thus, if the effects
happened to light due to absorption and scattering were identified, the
characteristics of the water constituents can be known. Some
measurements were needed to know the quantity of light absorbed and
scatted by water. The ability of aquatic medium to scatter and absorb the
light at a given wavelength can be specified in terms of the scattering
20
coefficient, absorption coefficient, and the volume scattering function
(Kirk, 1994).
2.3.1.2. The apparent properties
The apparent properties depend upon both the medium and the ambient
light field. There are two critical apparent optical properties of water, the
vertical attenuation coefficient (Kd), also called spectral diffuse
attenuation coefficient, and the spectral irradiance reflectance (𝜆). The
beam attenuation coefficient, 𝑐 (𝜆), is different than the diffuse
attenuation coefficient, (Kd). 𝑐 (𝜆) refers to the radiant power lost from a
single, narrow beam of photons while the Kd refers to the decrease with
depth of the ambient downwelling irradiance 𝐸d (𝑧, 𝜆), which includes
photons heading in all downward directions (Mobley, 1995).
The most optically significant constituents in natural waters (lakes, ocean
and rivers) include, dissolved substances and particulate matter, including
phytoplankton (Mobley, 1995). These constituents will affect the
absorption and scattering processes of the light energy passes through
water.
The solar radiation that penetrates water is crucial to support life in
aquatic ecosystems. The response of water in the EMR differs by
wavelength and by the type of particles in it. Clear water reflects more in
short wavelengths and continuously reduces its reflection with increasing
wavelength. Therefore, clear water is commonly studied in the visible
spectrum, since it is there where water reflects the most. In the Near
Infrared (NIR) the reflectance of deep, clear water is nearly zero.
The challenges for satellite remote sensing are to estimate concentrations
of optical water quality parameters from reflectance and to differentiate
the reflectance generated by each parameter. Given the change of color
and consequently the change in reflectance of water, depending upon the
constituents it has, reflectance can in theory determine those constituents.
For example, in low concentration of chlorophyll, reflectance is highest
21
at blue wavelengths, this being the reason that clean ocean water has a
blue color. When there is greater concentration of chlorophyll, the
maximum R(λ) shifts from the blue to the green wavelengths; hence the
color of the water turns green. In high-sediment concentrations, R(λ) are
relatively flat from blue to yellow. Waters with high concentrations of
yellow matter will have high reflectance in the yellow part of the
spectrum.
2.3.2. Case 1 and Case 2 waters
(Morel & Prieur, 1977) defined water in ocean, coastal zones and inland
lakes into Case 1 and Case 2 waters, depending on its optical properties,
and refined by (Gordon & Morel, 1983). Case 1 waters are those
influenced by phytoplankton and their derivative products which play a
dominant role in defining the optical properties of the water. In Case 2
waters, the optical properties of water are influenced mainly by
suspended sediments transported by flood in river and reservoir, or from
particles and/or dissolved color. In Case 2 waters, phytoplankton
products may also be presented in significant amount or not.
2.4. Remote Sensing and water quality
Water quality parameters were studied using remote sensing earlier by
1970s. Many approaches and algorithms were developed using data
collected either by remote sensing satellites or by field optical
measurements. This section will discuss the approaches used, in addition
to a brief illustration about the past, present and future satellite missions.
2.4.1. Retrieval approaches
It is essential for retrieve water quality using remote sensing to have a
model between the different parameters concentrations and information
acquired using sensing satellites such as radiance or reflectance. Three
approaches were used in relating water quality to remote sensing,
empirical, semi-analytical and analytical approaches as stated by (Morel
& Gordon, 1980). The description of each approach is presented below:
22
1- Empirical approach
It depends on constructing statistical models between measured spectral
values and measured water quality parameters, these models can be
obtained using a linear or nonlinear regression. This approach is the most
widely used in estimating water quality parameters.
2- Semi-analytical approach
This approach typically uses an analytical model to derive the in-water
optical properties of backscattering and absorption from remote sensing
reflectance, but also require the use of empirical relationships between
these optical properties and seawater constituents. Semi-analytical
approaches are typically used in optically complex Case 2 waters such as
coastal and inland lakes and used to estimate chlorophyll-a and colored
dissolved organic matter.
3- Analytical approach
This approach is based on theoretical algorithms which uses apparent
optical properties and inherent optical properties to model the reflectance
and vice versa. There are a very few purely analytical algorithms because
they require detailed knowledge of a host of complex and often poorly
understood relationships between water components and their specific
optical properties (Morel, 1980; Morel & Maritorena, 2001).
2.4.2. Satellite Missions
Remote sensing was used in water quality mapping for first time in
1970’s with lunching with the launch of the Earth Resources Technology
Satellite (ERTS-1) by NASA to obtain a periodical multispectral images
over environmental targets and use these images to some investigations
that dealing with a variety of variables pertinent to Earth’s resources,
(Bukata, 2005).
23
Figure (2-6) Time line for Ocean color Sensors from 1978 to 2030
(CEOS, 2016)
In 1978, NASA had been successfully launched the first ocean color
sensor called Coastal Zone Color Scanner (CZCS). Although CZCS was
an experimental mission, it continued to provide data for selected sites
until 1986. A gap of ten years was found until lunching of Ocean Color
Temperature Scanner (OCTS) and POLarization and Directionality of the
Earth's Reflectances (POLDER) sensors in 1996 and Sea-viewing Wide
Field-of-view Sensor (SeaWiFS) in 1997. In the last years, some color
sensors were put onboard such as MERIS, MODIS Aqua and Terra, and
PROBA. Figure ( 2-6) illustrates past, current, and future water-color
missions. Data used in the figure were extracted form (CEOS, 2016).
Various types of remote sensing satellite rather than presented in Figure
( 2-6) also were used in monitoring water quality status in different water
bodies such as Landsat, IKONOS, ASTER, ALOS and SPOT.
GCOM-C3
FY-3G
FY-3RM
GCOM-C2
Meteor-M N3
Sentinel-3 C
FY-3F
FY-3E
Sentinel-3 B
FY-3D
GCOM-C
Sentinel-3 A
FY-3C
FY-3B
OCEANSAT-2
HJ-1A
FY-3A
Aqua
PROBA
MERIS
Terra
SeaWiFS
OCTS
CZCS
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
20
10
20
14
20
18
20
22
20
26
20
30
24
2.5. Application
The previous sections discuss the principles of remote sensing and the
interaction between the light and water which is effective in getting water
quality parameters via remote sensing techniques. Here in this section, a
various application of using remote sensing in retrieving water quality
parameters will be discussed:
A valuable literature review paper was prepared by (Chang et al., 2015)
to address the critical researches for monitoring surface water quality
status and ecosystem state in relation to the nutrient cycle in a 40-year
perspective. They do a great effort to collect the researches that have
been done in different water bodies to estimate water quality parameters
using empirical, semi-analytical and analytical approaches.
Regarding empirical approach, (Chang et al., 2015) reviewed selected
research which uses the empirical approach to retrieve water quality
using remote sensing platforms and sensors. The selected researches were
covering the period from 1998 to 2012, different empirical methods such
as regression, Artificial Neural Networks and Genetic Algorithms.
For semi-analytical approach, the papers selected in review of (Chang et
al., 2015) were covering the period from 1996 to 2012 to monitor various
types of water bodies using both air- and space-borne multispectral and
hyperspectral data.
Several selected studies that uses the analytical approach was also
reviewed in (Chang et al., 2015), using different techniques in finding the
relationship between inherent and optical properties such as Matrix
Inversion Method (MIM), Analytical Optical Modelling and Bio-optical
Model. The selected researches covered to period from 1998 to 2011.
In addition, they listed the researches that have been done to estimate
concentrations water quality parameters such as suspended sediments,
Chlorophyll-a, Total Phosphorus (TP), Total Nitrogen (TN), Temperature
Total Organic Carbon (TOC) and Microcystin.
25
2.5.1. Suspended Sediments
Suspended sediment is considered one of optical water quality parameters
so that most of the researches were tries to estimate its value using
different images and different modelling approaches. The review that
done by (Chang et al., 2015) cited some of these researches. Table ( 2-1)
gives a more information about these research such as which water body
it was conducted for, the used remote sensing platform, the inversion
method used to correlate between suspended sediment and remote
sensing reflectance, and the correlation coefficient.
Some study cases were added to Table ( 2-1) used to estimate suspended
sediment in two water bodies in Egypt. (Farag, 2011) constructed an
empirical Log-linear equation to estimate suspended sediment in Lake
Burullus, Egypt using MERIS image. (Azab, 2012) was studied
suspended sediment in Lake Edko, Egypt using MODIS Image.
Suspended sediment was correlated to remote sensing data acquired form
worldview-2 satellite in Rosetta branch, Egypt by (El Saadi et al. 2014).
Multiple band regression was used.
Table ( 2-1) indicates that the correlation coefficient is very good for most
of the reviewed researches except for small cases it gives a bad
correlation.
2.5.2. Turbidity and Transparency
Turbidity and transparency have been affected by the suspended sediment
concentration, so that it is noted both turbidity and transparency have a
good correlation with remote sensing data. (Allan et al., 2007) conducted
a study to evaluate different water quality parameters such as (TP, TN,
Chl-a, turbidity and transparency) in Rotorua Lakes using remote sensing
techniques. During this research, landsat TM was used in two different
seasons and found that Turbidity and Transparency have a very good
correlation with remote sensing data, where the correlation coefficient
between natural log of Turbidity and Transparency is -0.931 and 0.939
respectively.
26
Table (2-1) Selected remote sensing application for monitoring
Suspended Sediment in different water bodies (Chang et al., 2015)
Location Reference
Remote
sensing
Platforms
Inversion
method R
2
Six northern
Mississippi
reservoirs, U.S.
Ritchie et al., 1976 Landsat Linear
regression 0.88
Lake Chicot,
Arkansas, U.S.
Ritchie & Schiebe,
1986 Landsat MSS
Linear
Regression 0.78
Multiple
Regression 0.83
Delaware Bay-
U.S. Keiner & Yan, 1998 Landsat TM
Log-linear
equation 0.54
Neural
Network 0.97
North Sea,
Europe Gomez, 2000 AVHRR
Linear
Regression
Summer
(0.94)
Winter
(0.024)
The Great Miami
River, Ohio Senay et al., 2001 CASI
Linear
Regression 0.79
Lake Balaton,
Central Europe Tyler et al.,2006 Landsat TM
Linear
Regression 0.89
Istanbul, Turkey Ekercin,2007 IKONOS Linear
Regression 0.83-0.97
The Peace-
Athabasca Delta,
Canada
Pavelsky & Smith,
2009 SPOT, ASTER
Empirical
Formula 0.78-0.82
Mayaguez Bay,
Puerto Rico
Rodríguez Guzmán
& Gilbes Santaella,
2009
MODIS Exponential
Equation 0.73
Lake Mazalah,
Egypt Farag, 2011 MERIS
Log-linear
equation 0.56
Lake Edko
Egypt Azab, 2012 MODIS
Exponential
Regression 0.85
Rosetta Branch,
Nile River, Egypt El Saadia et al. 2014 WorldView-2
Multiple
Regression 0.388
2.5.3. Chlorophyll-a
Chlorophyll-a is also optical water quality parameters, so that, it indicate
a very good correlation with remote sensing data as indicated in Table
( 2-2) cited by (Chang et al., 2015) for some of selected studies.
27
Table (2-2) Selected remote sensing application for monitoring
Chlorophyll-a in different water bodies (Chang et al., 2015)
Location Reference Remote Sensing
Platforms
Inversion
Method R
2
A group of lakes in
Finland
Kallio et al.,
2001
Airborne Imaging
Spectrometer for
different Applications
(AISA)
Two-band Ratio
Equation 0.72-0.89
Black Sea Kopelevich et
al., 2002
Coastal Zone Color
Scanner (CZCS) Regression 0.81
A group of
Minnesota lakes,
U.S.
Brezonik et
al., 2005 Landsat TM
Linear
Regression 0.88
West Florida Shelf
and Grand Bahamas
Bank
Cannizzaro
and Carder,
2006
Lambertian “graycard”
reflector
Semi- analytical
Model _
Istanbul, Turkey Ekercin, 2007 IKONOS Linear
Regression 0.88-0.99
West lake, China Torbick et al.,
2008 Landsat 7 ETM
Linear
Regression 0.82
Lake Malawi Chavula et
al., 2009 MODIS
Two-band and
three- band
Models
0.58
Burdekin Falls
reservoir, Australia
Campbell et
al., 2011 MERIS
Analytical
model 0.92
Group for
Mediterrane an
Lakes
Gomez et al.,
2011 CHRIS and MERIS
Single Band
Regression 0.99
Group of Lakes in
Eastern China
Duan et al.,
2010 MERIS
Two-band
Regression 0.92-0.93
Fremont Lakes,
Nebraska, US
Moses et al.,
2012
Airborne Imaging
Spectrometer for
different Applications
(AISA)
Two-band or
Three-band
Models
0.83-0.98
Lake Okeechobee,
Florida, U.S.
Chang et al.,
2012a MODIS
Genetic
Programming 0.72
Rosetta Branch,
Nile River, Egypt
El Saadia et
al. 2014 WorldView-2
Multiple
Regression 0.388
2.5.4. Total Phosphorus (TP) and Total Nitrogen (TN)
Total phosphorus (TP) and Total Nitrogen (TN) were known as nutrients,
the presence of nutrients have a direct effect in increasing the growth of
28
algae and in turn may increase the eutrophication of water body, so that
many researches were conducted during years to estimate values of both
parameters using remote sensing techniques Table ( 2-3) highlights some
of researches done to estimate both TP and TN as cited by (Chang et al.,
2015). It is noted that the correlation between both TP and TN with
remote sensing data is very good in some cases and in other cases is poor.
Table (2-3) Selected remote sensing application for monitoring
Nutrients (TN and TP) in different water bodies (Chang et al., 2015)
Location Reference Remote Sensing
Platforms
Inversion
Method R2
Manzala
Lagoon, Egypt
Dewidar &
Khedar, 2001 Landsat 5 TM
Multiple
Linear
Regression
Model
TN (0.298)
TP (0.421)
Guanting
Reservoir,
Beijing, China
He et al.,
2008 Landsat 5 TM Regression
TN (0.75)
TP (0.61)
Lake Kemp,
Texas, U.S.
El-Masri &
Rahman,
2008
MODIS Linear
Regression TP (0.56)
Kucukcek
Lake, Turkey
Alparslan et
al., 2009
Landsat-5 TM +
SPOT-Pan, IRS-
1C/D LISS+ Pan,
and Landsat-5 TM
Multiple
Regression
TN (0.835)
TP (0.788)
Koise River,
Japan
He et al.,
2010 QuickBird
Linear
Regression TN(0.94)
Quintang
River, China
Wu et al.,
2010 Landsat TM Regression TP (0.77)
Baltic Sea,
Eastern part of
Sweden
Anderson,
2012 Landsat TM Regression TP (0.41)
Taihu Lake,
China
Chen &
Quan, 2012
Landsat TM (Band
1,2,3,4)
Linear
Regression
TN (0.24)
TP (0.63)
Tampa Bay,
USA
Chang et al.,
2012b MODIS
Genetic
Programming TN (0.75)
Tampa Bay,
USA
Chang et al.,
2013a MODIS
Genetic
Programming TP (0.58)
Rosetta
Branch, Nile
River, Egypt
El Saadia et
al. 2014 WorldView-2
Multiple
Regression TP (0.58)
29
2.6. Remote sensing studies in Lake Nasser
This part of literature will focus on the researches that use remote sensing
to describe the behavior of Lake Nasser in many driplines. The first study
of Lake Nasser using remote sensing have been done by (Aguib et al.,
1989) to monitor sedimentation in AHDR, they proposed a technique for
sediment process calculation using a hydrodynamic model and remote
sensing data. Since this date, many researches were conducted to study
the Lake in many fields.
Evaporation losses were studied by (Shafik, 2004) using two sets of
Landsat images to represent the whole lake surface in two different dates,
the images were processed to calculate the lake surface area at different
water levels. Evaporation losses were calculated using surface area and
other meteorological data.
(Ebaid & Ismail, 2010) conducted a study to evaluate the reduction of
evaporation in Lake Nasser by disconnecting the secondary Channels
(Khores). The study estimates the evaporation using the integration
between remote sensing and GIS techniques. The aerodynamic method
was used in order to estimate the evaporation volume from surface of
lake and evaporation from secondary channels. The results show that 2.4
billion m3/year can be reduced from the total evaporation losses by
disconnecting the secondary channels.
(Hassan, 2013) estimate the daily evaporation rate in Lake Nasser using
Surface Energy Balance Algorithm for Land. The algorithm was applied
to different seven Landsat TM for lake, the results were compared with
six traditional methods used for estimating the evaporation rate. A good
performance of the mass transfer method was noted.
(Mostafa & Sousa, 2006) used Geographic Information System (GIS) and
remote sensing techniques to propose a suitability map of the area around
lake based on different environmental aspects, three Landsat images were
used in different date (1984, 1996 and 2001) to track the changes in lake
30
shape in different years and the water level fluctuation which helps in
identifying the agriculture areas around the lake.
(Hassan & Fahmy, 2005) estimated surface suspended sediment using the
second order polynomial equation developed by (Ritchie et al., 1991).
The model was applied to two Landsat images represent the high and low
water levels. The author stated that the proposed equation at low water
levels suspended sediment in surface layer were ranging between 2 to 5
mg/l, while it isn’t representing the suspended sediment value during
flood.
2.7. Discussion
The main objectives of the literature review chapter is to explain what is
meant by remote sensing and its history especially in field of estimating
surface water quality, in addition to the used techniques in evaluating
different water quality parameters. Also the chapter contains the
researches that had been done in Lake Nasser in different fields which
guide us that there is only one published research in the field of water
quality, this research used an equation developed by (Ritchie et al., 1991)
to estimate TSS in Lake Nasser while the current research will introduce
a full study to retrieve water quality parameters via remote sensing
techniques. The current study will introduce a different regression models
that can be used especially for Lake Nasser in different seasons.
31
CHAPTER (3)
STUDY AREA AND DATA COLLECTION
3.1. General
The current chapter will illustrate the study area characteristics and a full
description of data collected to achieve the research objectives.
3.2. Study Area
The Aswan High Dam (AHD) has its great effect on the economic
development of Egypt by means of storing water during flood in the
upstream reservoir, known as Aswan High Dam Reservoir (AHDR), to
supply water needed for irrigation, producing hydroelectric power and
protecting the downstream reaches from flood damage. Protection of
water in AHDR from pollution is a big challenge, so that, Egypt
government take some important decisions to protect it against pollution,
such as, isolating it from any human activities. Decree 203/2002
identified a 2 km buffer zone around, where it is not allowed to have any
agricultural, industrial and tourist activities. Wadi Alaqi, as a part of Lake
Nasser was considered as a Biosphere Reserve of international
importance according to Law 102/1983 which stated that this area should
remain free of any development, changes and disturbance in land-use or
activity that may affect the natural site (Zaghloul et al., 2012). This
research will focus on study of the spatial and temporal concentration of
different water quality parameters such as suspended sediment,
chlorophyll, total phosphorus, etc. A complete description of study area
characteristics is carried out within this chapter.
3.2.1. Aswan High Dam (AHD)
The AHD was constructed in the period between 1964 and 1971, created
a large reservoir, upstream the dam, the reservoir is extended in Egypt
and Sudan, the Egyptian part is known as Lake Nasser while the
Sudanese part is known as Lake Nubia. This research will focus on the
32
Egyptian part of reservoir (Lake Nasser) as a study area. Lake Nasser is
considered the second largest man-made lake all over the world (ICOLD,
1984). Thus, it has to be set under comprehensive studies particularly the
water quality parameters to make sure of the quality and clarity of the
stored water using a scientific approach to help in monitoring, protecting
and managing water resources. Satellite remote sensing techniques can be
used for assessment and monitoring of lake conditions where it can
supply timely and reliable information to decision-maker to help in
putting sustainable management and development plans.
AHD was located 7 Km upstream Aswan Old Dam (AOD). The
construction of AHD took place in two steps. The first step was the
diverting the river into a diversion channel in the eastern bank of the Nile
and the construction of cofferdams along the dam path to close off that
part of its course where the main body of the dam was to be built (Said,
1993). The second step was the building of the main body of the dam.
This step continued till 1968 and the hydroelectric power plant from 1967
to 1971 (Shahin, 2002). The dam is a rock-fill dam made of granite rocks
and sands and provided with a vertical cutoff wall consisting of very
impermeable clay. The dam has a trapezoidal cross section, its crest
width is 40 m and its base width is 980 m. A layout of the dam can be
seen in Figure ( 3-1).
33
Figure (3-1) Layout of Aswan High Dam
3.2.2. Aswan High Dam Reservoir (AHDR)
After the construction of the upstream cofferdam in 1964, the reservoir
slowly started to fill. The AHDR is designed to store water at a maximum
water level of 182 m above (MSL). The mean depth of the reservoir is 25
m while the maximum depth reaches up to 110 m at its maximum level.
The total capacity of AHDR is 162 km3 at its maximum water with a total
length of 500 km where 333 km in Egypt and 167 km Sudan. The
average width of AHDR is 13 km, and its surface area is about 6500 km2
at its maximum water level.
3.2.3. AHDR morphology
In general, the morphology of a lake is described by bathymetric map
which is required to determine the major morphological parameters. This
map is prepared by a survey of the shoreline by standard surveying
methods combined with aerial photography or remote sensing images.
34
Lake Nasser has a number of side channels called in Arabic Khors
extending in both the Egyptian and Sudanese parts as shown in Figure ( 3-
2). Hence, the reservoir is considered dendritic in shape characteristics.
The total number of important Khors is about 100 in both Lake Nasser
and Nubia Lake. Khors Allaqi, Kalabsha and Toshka are the largest in
area.
Figure (3-2) Lake Nasser Map
35
3.2.4. AHDR operation policy
The reservoir total capacity is divided into three zones, according to its
operation policy. The dead storage zone of 31 km3, as shown in Figure
( 3-3), which is used for sediment depositions which comes with the river
flow during the flood, the dead zone is identified as the volume of the
reservoir allocated below level of 147 m above (MSL). The second zone
is the live storage zone, it extended between levels of 147 m and 175 m
above (MSL) divided into a buffer zone lies between elevation 147 and
150 m and a conservation zone between 150 to 175 m, the total capacity
of live zone is about 90 km3. The third zone is used as for flood buffer
and control; its volume is about 41 km3 and it is allocated between levels
of 175 m and 182 m above (MSL), which is considered the maximum
water level of the reservoir. The crest of emergency spillway is found at
level of 178 m which is considered as the separation level between the
flood buffer zone and flood control zone (Fahmy, 2001; Sadek et al.,
1997). The policy of AHDR is to maintain the level of the reservoir at
175 m at the end of the hydrologic year (July 31st).
Figure (3-3) Main component for different operation zones of AHDR
3.2.5. AHDR Monitoring Activities
Since 1973, the first mission was conducted by High Aswan Dam
Authority (HADA) incorporation with Nile Research Institute (NRI), to
Inflow
(178 m)
(182 m)
(175 m)
(150 m)
(147 m)
Dead Storage (31 BCM)
Conservation Zone
Flood Buffer zone
Flood control zone
Buffer Zone
Live Storage
(90 BCM)
Flood Storage
(41 BCM)
36
collect data about the sediment deposition in Lake Nasser. The main
goals of this mission were designed to conduct hydrographic survey,
measure flow currents, and collect suspended matters and bed material
samples at specific monitoring cross sections along the lake in both Egypt
and Sudan parts.
Total number of 29 cross sections was hydrographically surveyed in
order to estimate sedimentation in AHDR. Table ( 3-1) indicates names
and location from upstream AHD gauge. In earlier time of monitoring
program, the survey equipment used in the survey was theodolites and
primary echo-sounders. So that, only the indicated cross sections was
used in monitoring the sedimentation. By the time, the survey equipment
and techniques were developed. A real time data acquisition system, such
as Mini-Ranger systems (Falcon IV), was used since 1990. Differential
global positioning system (DGPS) has been used in survey since 1998.
This development in survey equipment enables the survey team to
increase monitoring cross section and also increase the accuracy of
estimating the sedimentation volume.
At the beginning of the monitoring program only hydrographic survey
was conducted in addition to measuring velocity, suspended matters and
bed material samples which usually collected at different vertical
locations depending on the total depth at sampling point. Samples were
collected at east, middle and west of each cross section. At the end of
1970s, water quality was taken into account in the monitoring program, a
fewer parameters, in particular physiochemical, were measured at the
beginning and by the time the measured parameters were increased to
include more water quality variables such as nutrients and major ions. (El
Sammany, 2002) gives a detailed description of all the actual variables
measured at each cross section as well as the number of times a particular
variable was measured at each cross section.
37
Table (3-1) Definition of main monitoring locations surveyed (El
Sammany, 2002)
Region Serial
No. Cross Section Name
Cross
Section
Number
Location U.S.
AHD (km)
Su
da
nes
e B
ord
ers
1 EL-Daka 23 487.000
2 Okma 19 466.000
3 Malek EL-Nasser 16 448.000
4 EL-Dowaishat 13 431.000
5 Ateere 10 415.500
6 Semna 8 403.500
7 Kajnarity 6 394.000
8 Morshed 3 378.500
9 Gomai D 372.000
10 Madeek Amka 28 368.000
11 Amka 27 364.000
12 Second Cataract 26 357.000
13 Abdel-Qader 25 352.000
14 Doghame 24 347.000
15 Dabrosa 22 337.500
Eg
yp
tia
n B
ord
ers
16 Arkin 21 331.100
17 Sara 20 325.000
18 Adendan N/A 307.000
19 Abu-Simbel N/A 282.000
20 Toushka N/A 256.000
21 Ibreem N/A 228.000
22 Korosko N/A 182.500
23 Wadi- Alarab N/A 171.000
24 EL-Madeek N/A 130.000
25 Garf Hussien N/A 90.000
26 Morwaw N/A 60.000
27 Kalabsha N/A 41.000
28 Dahmeet N/A 30.000
29 High Dam N/A 0.500
38
The AHDR monitoring program is basically proposed and designed for
sediment deposition in the reservoir. Since sediment deposition is
influenced by flood conditions, estimating of sediment deposition
frequency has been chosen to be once per year for both sediment
deposition and water quality variables. In addition, the timing for
sampling within the day of sampling has not been defined according to a
certain criteria and/or understanding of timing effects on water quality
sampling. For example, thermal stratification pattern is changing from
time to another within the same day, as it is affected by incoming solar
radiation. In addition, photosynthesis process, carried out by algae and
aquatic plants, is largely affected by the timing of the day since the
available light penetration is changing from time to another within the
day of sampling.
3.2.6. AHDR hydrological characteristics
AHDR was designed essentially for purposes of flood protection, water
conservation and hydropower generation. The released water from dam is
designed to meet the irrigation requirements, which varies from season to
another accordingly with the crop pattern. In addition, the reservoir is
operated to control the annual flood by drawing down the reservoir to a
predetermined level on a specified date each year. Additional constraints
on the operation of the reservoir are imposed by the need to limit the
magnitude of releases so as to avoid downstream degradation and/or
hindrance to navigation.
3.2.6.1. Water levels
There is only one gauging station measuring daily water level all over the
year and it is located just upstream the AHD. But, during the survey
missions, the water levels at the sampling locations are measured and
estimated from the vertical elevation of the nearest landmark to each
sampling location. Figure ( 3-4) illustrates the daily water levels U.S.
AHD from 1964 to 2011. Water level data were extracted from Nile
research Institute (NRI) database, the figure shows that reservoir started
39
to fill in 1964 until it reached its full storage capacity at level of 175 m
according to the indicated by the operation policy of the reservoir. Period
from 1975 to 1981, the maximum and minimum water levels in the
reservoir is extremely fixed as the level ranging from 171.13 m to 173.03
for minimum and from 175.71 m to 177.47 m. reservoir reach its
minimum capacity in 1988 as the water level reached to value of 150.62
m, just above the buffer zone limit. A rapid increase in reservoir water
level occurred in the same year due to high flood resulted in increasing
the water level to 168.82 m. the water level increased gradually until it
reaches its maximum value 181.60 m. The rising stage starts by the end
of July and reaches its peak around the middle of September and the
falling stage, of which the discharge starts to decrease during months
October to June.
3.2.6.2. Discharges
The average monthly inflow discharges at Dongola gauging station is
shown in Figure ( 3-5). Dongola is the first main gauge station located on
the main Nile below the confluence of Atbara river, it is located 750 km
U.S. AHD, Dongola play a key role in calculating water balance of
AHDR. Figure represents the inflow from 1964/1965 to 2009/2010, the
data used in the figure was extracted form (Abdel-Latif and Yacoub,
2011). The relatively high flood occurred in 1964/1965, 1974/1975 and
1988/1999.
40
Figure (3-4) Water level Upstream AHD from 1964 to 2011
Figure (3-5) AHDR inflow at Dongola station
This research will focus in the period from 2002 to 2011. In this period,
the reservoir reached its lowest level of 168.57 m in July 2006, but
fortunately, the large flood of during the successive years in 2006/2007
41
and 2007/2008 allowed the reservoir to rise again and reach up to a
maximum storage level of 180.11 m in October 2007. The minimum
flood was occurred in 2003/2004 and the maximum occurred in
2006/2007.
3.2.6.3. Sediment Data
After the construction of AHD, Dongola Station has been selected as a
measuring station to monitor suspended sediment. The rising and falling
rating curves for AHDR were constructed using sediment data available
for the period 1966-1982 (EL-Moattassem and Abdel-Aziz, 1988).
Figure (3-6) Discharge and sediment concentration hydrographs at
Dongola station (1999-2003)
The equations for estimating the suspended sediment hydrograph at
Dongola Station are as follows:
(i) For rising stage flow discharge hydrograph:
𝑄𝑠 = 5.753 × 10−6 𝑄1.98 (2)
(ii) For falling stage flow discharge hydrograph:
𝑄𝑠 = 2.695 × 10−7 𝑄2.347 (3)
Where Q is the discharge at Dongola Station in million m3/day and Qs is
the sediment load in 109 kg/day. By applying these equations, the
Discharge and Sediment Concentration at Dongola Station
( 1999 - 2003)
0
200
400
600
800
1000
1200
1400
1600
1800
2000
AU
G.
SE
P.
OC
T.
NO
V.
DE
C.
JA
N
FE
B.
MA
R.
AP
R.
MA
YJU
N.
JU
L.
AU
G.
SE
P.
OC
T.
NO
V.
DE
C.
JA
N
FE
B.
MA
R.
AP
R.
MA
YJU
N.
JU
L.
AU
G.
SE
P.
OC
T.
NO
V.
DE
C.
JA
NF
EB
.
MA
R.
AP
R.
MA
Y
JU
N.
JU
L.
AU
G.
SE
P.
OC
T.
NO
V.
DE
C.
JA
NF
EB
.
MA
R.
AP
R.
MA
Y
JU
N.
JU
L.
1999/2000 2000/2001 2001/2002 2002/2003
Months
Dis
ch
arg
e (
M.m
^3/d
ay)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Sed
imen
t C
on
cen
trati
on
pp
m
Flow Discharge
Sediment Concentration
42
suspended sediment concentration hydrographs at the inlet boundary of
the study area of the reservoir (Dongola Station) can be estimated for the
period from 1999 to 2003 as shown in Figure ( 3-6).
From Figure ( 3-6), it is clear that the concentration of suspend sediment
entering Aswan High Dam Reservoir also has a seasonal variation like
the flow hydrograph, the peak discharge and peak suspended sediment
concentration do not occur simultaneously. The suspended sediment
concentration rises to a maximum (5000 ppm) many days before the peak
of water discharge. The lag time between the peak of the water discharge
and the suspended sediment concentration varies from year to year, and
on average is approximately 10 days.
3.3. Data Collection
3.3.1. Field data
Water quality data used through this research was collected during annual
field survey for Lake Nasser carried out by High Aswan Dam Authority
(HADA) incorporation with Nile Research Institute (NRI), the main goal
of this annual trip to perform a hydrographic survey, water quality
measurements, and study of growing weeds in lake and around the lake
shore line. (El Sammany, 2002) gives a full description and main
drawbacks of the methodology used in these field trips.
Figure ( 3-7a) shows samples’ location for water quality monitoring, and
the location of each sample upstream AHD was shown as well, in
addition to its geodetic coordinates. The indicated location is for all
survey missions except for 2010 mission as samples were taken every
five kilometers from the Egyptian-Sudanese border until reaches near
Allaqi section as indicated in Figure ( 3-7b). Five vertical samples were
taken at 0.50 m below water surface, 25%, 50%, 65% and 80% of water
depth at each location. Water sample at depth 0.5 m under water surface
is used through this research as it is the effective layer in remote sensing.
43
Water quality data used through this research was collected during six
survey missions in the period from 2003 to 2011, as shown in Table
( 3-2). Seven water quality parameters are measured during the indicated
field mission. The parameters are Total suspended sediment (TSS),
Chlorophyll-a (Chl-a), Turbidity, Transparency, Total phosphorus (TP),
Silica (SiO2), Total dissolved Solids (TDS). Figure ( 3-8), shows the
period of each survey with changing water levels in Lake Nasser during
the period 2001- 2011. The first three survey missions and the sixth
mission were conducted in the period in which lake is in the case of
maximum level after the end of flood. The fourth mission was conducted
during the Lake level falling period, while the fifth mission was in the
rising period. Table ( 3-2) shows dates and the measured water quality
during each mission. As discussed before, not all water quality
parameters had been measured every year, a description for different
water quality parameters measured and the lake condition in each mission
will be illustrated in the following sections in details by means of water
level and measure water quality parameters during every survey mission.
Table (3-2) Measured Parameters for each mission
Dates TSS Chl-a Turbidity Transp. TP SiO2 TDS
First Mission 23Oct 03
15 Nov 03 ●
● ● ● ● ●
Second mission 27 Nov06
15 Dec 06 ● ● ● ● ●
Third Mission 15 Nov 07
24 Nov 07 ● ●
● ● ● ●
Fourth Mission 24 Apr 09
08 May 09 ● ●
● ● ●
Fifth mission 21 Aug 10
31 Aug 10 ●
● ● ●
●
Sixth Mission 04 Oct 11
10 Oct 11 ●
44
a)
b)
Figure (3-7) WaterQualitysamples’Locations
45
Figure (3-8) Survey mission with respect to water level change in
period from 2002 to 2011
First mission was conducted from October 23, 2003 to November 15,
2003. Table ( 3-2) indicates that six parameters were measured during this
mission. Water level in this period ranging from 177.28 m to 177.91 m
above MSL, this period water level in lake reaches its maximum value.
Table ( 3-2) indicates that TSS, Turbidity, Transparency, TP, SiO2 and
Total Dissolved Solids (TDS) were measured during first mission.
Parameters measured during second mission were only five parameters
(Turbidity, Transparency, TP, SiO2 and TDS). Mission duration was
between November 27, 2006 and December 12, 2006. The recorded
Water level at upstream AHD gauge had a constant value of 176.46 m
above MSL for all survey periods. This period was lied in the end of
flood period as the water level begun to decrease and suspended sediment
started to settle in the bottom of lake and by time water became clearer,
and hence the water quality changed accordingly. During 2006, Lake
reached to its minimum water level value of 168.57 m, which represented
the lowest water level occurred in recent years.
23
-Oct
-03
15
-Nov-0
3
27
-Nov-0
6
12
-Dec
-06
15
-Nov-0
7
24
-Nov-0
7
24
-Ap
r-09
08
-May
-09
21
-Au
g-1
0
31
-Au
g-1
0
04
-Oct
-11
10
-Oct
-11
Fir
st M
issi
on
Sec
on
d M
issi
on
Th
ird
Mis
sion
Fou
rth
Mis
sion
Fif
th M
issi
on
Six
th M
issi
on
46
The period of measurement for third mission was lied between November
15, 2007 to November 24, 2007. The flood during 2007 had its maximum
value in recent years as it reached to 88.78 BCM which resulted in
increasing water level upstream AHD to maximum value of 180.11 m
occurred in October 19, 2007. Water level through the period of
measurements was ranging from 179.85 m to 179.71 m at the start and
end of mission respectively, which means that it lies in the start of fall
period. Measured water quality parameters were (TSS, Chl-a,
Transparency, TP, SiO2, and TDS)
Fourth mission was surveyed in the period from April 24, 2009 to May
08, 2009. Water level was ranging from 175.62 m to175.1 m above MSL,
that period was lied, as previously illustrated in Figure ( 3-8), in the
middle of falling period. Table ( 3-2) shows the ranges of TSS, Chl-a,
Secchi depth, TP and SiO2 measured during that survey.
Parameters measured during fifth mission were only five parameters
(TSS, Turbidity, Transparency, TP, and TDS), and the measurements
were taken in period from August 21, 2010 to August 31, 2010. Water
level at upstream AHD gauge increased from 171.70 m in the beginning
of measuring period to 171.90 m above MSL. that period represented the
start of rising stage which occurred due to flood, water level started to
increase until it reaches its maximum value at the end of rising stage and
water is carrying large amount of sediments during this period.
Sixth mission was conducted during period from October 4, 2011 to
October 10, 2011. Only TSS was measured during that mission. Water
level values were ranging from 173.93 m to 173.95 m above MSL.
Table ( 3-3) presents a descriptive statistics for all missions, table shows
the maximum and minimum values for each parameter in addition to its
standard deviation and mean. Also, it shows the total number of samples
taken during each field mission.
The maximum TSS value occurred during the fifth mission which took
place during the start of flood period where water was fully loaded by
47
sediments. The minimum value of TSS occurred in the fifth mission as
well, see Figure ( 3-9), the reason of this is that the mission was just
before the flood and all sediments settled down. The maximum value of
standard deviation occurred in the fifth mission as there were big spatial
variations in concentration of sediment between start and end of the lake.
The maximum TSS concentrations for other mission was ranging from 14
to 24 mg/l, which was very small compared to the maximum value during
flood. The minimum values ranged from 2 to 6 mg/l, the variation in the
minimum concentration of TSS was not big as the northern part of the
lake is not influenced by flood sedimentation. The measured
concentrations of Chl-a during third and fourth missions were ranging
from 10 to 13 mg/m3 for maximum and 3.5 to 4.5 mg/m
3 for minimum,
Figure ( 3-10) indicates that there is a small variation of Chl-a
concentration during these two missions.
It is clear that transparency is affected by both turbidity and TSS, as
transparency is a measure of water clarity. Figure ( 3-11) indicted that the
maximum and minimum values were altered along the lake compared to
TSS and Turbidity. It is obvious from Table ( 3-3) that values of
maximum and minimum transparency are 1.30 m and 0.17 m during start
of flood, fifth mission. While it ranges between 1.64 m to 4.70 m during
other missions.
Figure (3-9) Longitudinal Profile for TSS during different missions
0
20
40
60
80
100
0 50 100 150 200 250 300 350
TS
S (
mg
/l)
Distance from AHD (Km)
First Mission
Third Mission
Fourth Mission
Fifth Mission
Sixth Mission
48
Figure (3-10) Longitudinal Profile for Chl-a during different
missions
Turbidity had high values during fifth mission, where maximum and
minimum values were 78.40 NTU and 1.35 NTU respectively. While
during other two missions; first and second, it approximately had the
same longitudinal profile as they were conducted during the same lake
condition, see Figure ( 3-12).
Figure (3-11) Longitudinal Profile for Transparency during different
missions
0
2
4
6
8
10
12
14
0 50 100 150 200 250 300 350
Ch
l-s
(mg/m
3)
Distance from AHD (Km)
Third Mission
Fourth Mission
0
1
2
3
4
5
0 50 100 150 200 250 300 350
Tra
nsp
are
ncy
(m
)
Distance from AHD (Km)
First Mission
Second Mission
Third Mission
Fourth Mission
Fifth Mission
49
Figure (3-12) Longitudinal Profile for Turbidity during different
missions
Total phosphorus (TP) ranging from minimum value of 0.07 mg/l to 0.15
mg/l during the start of flood season, fifth mission, while it had the
lowest concentrations compared to other missions. Maximum TP
concentrations were observed during the second mission, after end of
flood, where it ranged from 0.11 mg/l to 0.52 mg/l. it is observed that
profile of TP during end of flood season took the same trend as TSS
while it was approximately flat during other seasons, Figure ( 3-13).
Figure ( 3-14) presents the longitudinal profile for Silica (SiO2). During
first three missions (end of flood season) SiO2 ranges from 6.10 mg/l to
18.10 mg/l while during falling period it had low values compared with
the others. SiO2 ranges from 4.20 mg/l to 7.80 mg/l as shown in Table
( 3-3)
0
20
40
60
80
100
0 50 100 150 200 250 300 350
Tu
rbid
ity (
NT
U)
Distance from AHD (Km)
First Mission
Second Mission
Fifth Mission
50
Figure (3-13) Longitudinal Profile for TP during different missions
Figure (3-14) Longitudinal Profile for SiO2 during different missions
0
0.1
0.2
0.3
0.4
0.5
0.6
0 50 100 150 200 250 300 350
TP
(m
g/l
)
Distance from AHD (Km)
First Mission
Second Mission
Third Mission
Fourth Mission
Fifth Mission
0
10
20
30
40
0 50 100 150 200 250 300 350
SiO
2 (
mg
/l)
Distance from AHD (Km)
First Mission Second Mission Third Mission Fourth Mission
51
Figure (3-15) Longitudinal Profile for TDS during different missions
According to Table ( 3-3), TDS ranged from 131.0 mg/l observed during
first mission to 170.3 mg/l during the end of flood season, TDS
longitudinal profiles, Figure, indicates that the concentration during
flood, TDS had higher values in the southern part of the lake compared to
the southern part. That case was altered while it starts to decrease in the
southern part until the end of flood season.
120
130
140
150
160
170
180
0 50 100 150 200 250 300 350
TD
S (
mg/l
)
Distance from AHD (Km)
First Mission Second Mission Third Mission Fifth Mission
52
Table (3-3) Descriptive statistics of measured water quality
parameters
First
Mission
Second
Mission
Third
Mission
Fourth
Mission
Fifth
Mission
Sixth
Mission
n 10.00 12.00 11.00 11.00 16.00 9.00
TSS (mg/l)
Max 20.00 - 28.00 14.00 84.00 24.00
Min 6.00 - 3.00 5.00 2.00 2.00
Mean 10.20 - 12.82 7.45 23.06 8.67
Std Dev. 5.20 - 8.87 2.70 25.99 8.40
Chl-a (mg/m3)
Min - - 13.00 10.00 - -
Max - - 3.50 4.50 - -
Mean - - 7.50 8.05 - -
Std Dev. - - 3.07 2.16 - -
Transparency
(m)
Min 0.80 0.40 0.45 1.25 0.17 -
Max 4.70 3.50 4.00 2.00 3.10 -
Mean 2.80 2.08 2.05 1.64 1.30 -
Std Dev. 1.43 1.22 1.41 0.22 0.97 -
Turbidity
(NTU)
Min 0.75 1.06 - - 1.35 -
Max 16.50 37.30 - - 78.40 -
Mean 5.13 10.77 - - 21.27 -
Std Dev. 6.07 11.67 - - 24.54 -
TP
(mg/l)
Min 0.08 0.11 0.12 0.09 0.07 -
Max 0.19 0.52 0.40 0.15 0.15 -
Mean 0.11 0.25 0.25 0.12 0.09 -
Std Dev. 0.04 0.14 0.11 0.02 0.02 -
SiO2
(mg/l)
Min 6.10 7.90 12.80 4.20 - -
Max 14.20 18.10 16.40 7.80 - -
Mean 9.97 13.93 14.67 5.74 - -
Std Dev. 2.61 3.34 1.17 1.04 - -
TDS
(mg/l)
Min 138.0 142.35 131.00 - 140.16 -
Max 153.0 170.30 164.00 - 160.00 -
Mean 148.4 154.48 143.73 - 151.08 -
Std Dev. 5.13 9.40 12.92 - 6.99 -
53
3.3.2. Remote sensing data
The Medium Resolution Imaging Spectrometer (MERIS) will be used to
fulfill the objectives of the research. MERIS instrument is a multispectral
sensor that produces images with 15 spectral bands acquired in the visible
and near infra-red wavelengths. Each band has a programmable width
and position within the 390 nm to 1040 nm spectral range of the
electromagnetic spectrum (ESA, 2006). The primary mission of MERIS
is to measure the color of water in the oceans, seas, coastal zones and
inland lakes. Knowledge of water color can be interpreted into a
measurement of water quality parameters such as concentration of
suspended sediment and chlorophyll pigment, and atmospheric aerosol
loads over water (ESA, 2006). MERIS was provided in three main spatial
resolutions; Full-Resolution (FR), Reduced Resolution (RR) and Low
Resolution (LR). Each pixel in an FR image represents a ground area of
260 m × 290 m, while it represents an area of 1,040 m × 1,160 m in an
RR image, and in LR the pixel represents an area of 4,160 m × 4,640 m.
More details about MERIS images will be found in Appendix (A).
3.3.2.1. Data Sources
Remote sensing images used in the current research are available from
European Space Agency (ESA) through TIGER Initiative project no. 14
under title of “Enhancing the Sediment Transport Modelling in the Nile
Basin Reservoirs” and it was downloaded from different sources as
follow:
1- EOLI-SA is a free interactive tool that allows users to access and
download many remote sensing images from different platforms
especially ESA’s Earth Observation data products.
2- The ESA satellite-based Earth Observation Data Dissemination
System (DDS) installed in Nile Research Institute (NRI) which
provided for the dissemination of Envisat data products especially
MERIS Images.
54
3- MERIS FRS regional extraction tool (merisfrs-merci-
ds.eo.esa.int).
3.3.2.2. Used MERIS images
MERIS images used through this research is divided into two groups: the
first group is match-up images and the other one is data used for creating
time series for water quality parameters in Lake Nasser, each group can
be described as follows:
- Match-up images
MERIS full resolution (FR) level 1B scenes were used in this research.
Table ( 3-4) shows the match-up between MERIS images and field trips,
one image per each field trip was selected, the specific dates were chosen
taking into account to select the most clear and cloud free images and to
be at the middle of the period as much as it can. There are three reasons
to follow this approach in matching up process; first reason is because
this research was proposed after samples was taken, the second is that the
change of water quality parameters occurred slowly except in flood
season, the third reason is because the sampling date affected by the
whole survey plan of monitoring program such as hydrographic survey,
so that, it is not possible to get samples on the basis of scheduled MERIS
overpasses. Six images were selected for purposes of validation of Case 2
water quality processors and regression analysis between reflectance and
other water quality parameters.
- Images used for time series
Remote sensing images (MERIS images) for period of ten years from
December, 2002 to March, 2012 were downloaded with permission of
European Space Agency (ESA) under the umbrella of TIGER II project.
A full swath Level 1 MERIS images were downloaded using MERIS
FRS regional extraction tool, it is an internet site which provides a
services for accessing Envisat MERIS Level 1 and Level 2 Full
Resolution Full Swath data products.
55
MERIS images were downloaded by specifying Lake Nasser bounding
box coordinates in the extraction tool, the total downloaded images was
1824 images, a filter process were conducted to the downloaded images
in order to get rid of the images that not cover all area of Lake Nasser and
repeated image. A subset process for filtered images had been done to
reduce the extents of image to area of Lake Nasser, which helps in
reducing time needed for image processing.
Table (3-4) Characteristics of Match-up images.
First
Mission
Second
Mission
Third
Mission
Fourth
Mission
Fifth
Mission
Sixth
Mission
Period From 23-Oct-03 27-Nov-06 15-Nov-07 24-Apr-09 21-Aug-10 04-Oct-11
To 15-Nov-03 12-Dec-06 24-Nov-07 08-May-09 31-Aug-10 15-Oct-11
Matchup
image 07-Nov-03 01-Dec-06 19-Nov-07 01-May-09 21-Aug-10 10-Oct-11
Sun zenith 44.65-43.58 50.24- 49.1 47.16-45.98 28.97-
27.92
29.54-
28.65
35.04-
34.13
Sun azimuth 152.11-
149.45
153.94 :
151.69
155.19-
152.76
102.99 :
98.52
109.27-
104.76
148.34-
144.72
View zenith 17.64- 8.37 17.69 : 8.43 27.87-
19.64
30.39-
21.87 14.92- 5.20
30.44-
22.36
View
azimuth
283.77-
283.22
283.77-
283.21
284.34-
283.75
101.83-
101.41
102.66-
102.18
284.41-
283.81
Figure ( 3-16) shows a histogram for Downloaded MERIS images for
period from December 2002 until February, 2012. The total number of
images used in time series, after performing filter process, is 913 images.
The frequency of images per month, as shown in figure, is ranging from
12 images to one image per month. There are some months that have zero
frequency, especially in the first two years of lunching Envisat.
56
Figure (3-16) Histogram of downloaded MERIS images
0
2
4
6
8
10
12
14
De
c-2
00
2
May
-20
03
Oct
-20
03
Mar
-20
04
Au
g-2
00
4
Jan
-20
05
Jun
-20
05
No
v-2
00
5
Ap
r-2
00
6
Sep
-20
06
Feb
-20
07
Jul-
20
07
De
c-2
00
7
May
-20
08
Oct
-20
08
Mar
-20
09
Au
g-2
00
9
Jan
-20
10
Jun
-20
10
No
v-2
01
0
Ap
r-2
01
1
Sep
-20
11
Feb
-20
12
Fre
qu
en
cy
Months
57
CHAPTER (4)
METHODOLOGY
4.1. General
This chapter is devoted to present description of research methodology
used to fulfill the objectives of this research, whether used in sampling
and field/lab analysis, acquisition of MERIS images, or in the validation
and regression analysis. In addition, the tools used for images processing
will be described as well. Figure ( 4-1) represents a flow chart for research
methodology which consists of two main parts; field work and satellite
images acquisition. Figure highlights only the main steps of
methodology; the detailed steps of methodology will be described in the
following sections.
4.2. Field work
Field work was carried out through survey missions of Lake Nasser,
water samples were taken at specified locations in Lake Nasser. Water
samples were collected vertically to represent water column at each
sampling point, five vertical samples were taken at 0.50 m below water
surface, 25%, 50%, 65% and 80% of water depth at each location. Water
sample at depth 0.5 m under water surface will be used through this
research as it is the effective in remote sensing. A laboratory tests for
different parameters were done according to the Standard Methods for
the Examination of Water and Wastewater (APHA, 2012) to get
concentration of each parameter. The sampling procedure and both field
and laboratory equipment needed in measuring different in situ water
quality parameters will be described later in Appendix A.
4.3. Satellite images acquisition
Satellite images acquisition is considered the main part of methodology
to fulfill the objectives of this research. The spatial coverage of the image
was greater than the current study area, so that, subset process for images
58
was performed to reduce image spatial coverage to the bounding box
coordinates of Lake Nasser, hence, the time needed for image processing
was reduced.
There two main objectives of this thesis, the first is to validate the use of
Case2 water quality processors in Lake Nasser, while the second is to
find a relationship between different water quality parameters and
Remote sensing reflectance extracted from MERIS images.
Match-up images were specified based on the dates of field survey as
indicated in Table ( 3-4). Both validation and regression models will be
based on comparing measured data with data extracted from the
atmospherically corrected match-up images.
Figure (4-1) Flow Chart of Research Methodology
4.3.1. Image processing tools
Many tools will be used in order to process MERIS image, it will be
described as follows:
59
- BEAM (V4.10)
BEAM is an open-source toolbox used for processing, analyzing and
viewing of remote sensing images. It is originally developed to facilitate
and utilize image data acquired from Envisat's optical instruments
(Brockmann, 2014). It is composed of the three main components, 1)
VISAT which is an application used in processing, analyzing and
visualization of remote sensing raster images. 2) A set of tools that can be
run either from BEAM command line or found in VISAT, 3) A rich Java
API that can be used in programming of new BEAM plugins or
developing new remote sensing applications.
- Case 2 water processors
These processors are a BEAM plugins; it has been developed by GKSS
Research Center, Institute for Coastal Research, and Brockmann Consult,
under contract of the European Space Agency. It uses an algorithm
developed by (Doerffer and Schiller, 2008), the algorithm is found as a
neural network which relates the radiance detected by MERIS to surface
reflectances and then calculates the concentration of optical water quality
constituents.
Case 2 Regional (C2R) processor (V1.5.8) is A BEAM plugin will be
used through the current research, similarly, Lakes processor (V1.58) will
be used too. Lakes processors include two inland water modules for
boreal and eutrophic lakes.
Processors were validated using several remote sensing campaigns that
were conducted during the summer of 2007 in Finland (3 campaigns and
4 lakes), Germany (3 campaigns and 1 lake), and Spain (8 campaigns and
6 lakes). In addition to European lakes, validation activities were also
conducted in Lake Victoria, Africa, using data collected before the start
of the project, in 2003, 2004 and 2005 and in Lake Manzalah, Egypt,
with data collected in 2007 (Koponen et al., 2008).
60
- MERIS ICOL Processor (V2.9.2)
Improved Contrast between Ocean and Land (ICOL) is BEAM plugin
was developed by (Santer and Zagolski, 2009), can be implemented with
the case2 water quality processors to correct for adjacency effects
(increased radiance due to scattering and reflection of photons). It was
developed as a result of a detailed analysis of the adjacency effect
carrued out by (Santer and Schmechtig, 2000). More details about ICOL
processor can be found in Appendix (A).
- SMAC Processor (V1.5.203)
Simplified Method for Atmospheric Corrections (SMAC) is a semi-
empirical algorithm used to correct MERIS image from the atmosphere
effect by introducing some approximations to the radiative transfer in the
atmosphere. The algorithm is developed by (Rahman and Dedieu, 1994).
- Cloud Probability Processor (V1.5.203)
It is a BEAM plugin used to implement the detection of clouds in MERIS
L1b products.
- ArcMap
ArcMap is used through this research in many processes such as
computing the percentage of cloudy areas over the lake surface,
calculating the monthly average water quality parameter over Lake
Nasser surface based on the results of regression models, creating
shapefiles for lake surface and extracting the monthly average water
quality parameter based on it. All these processes were applied to the 913
images used in the time series either by using ArcMap Model Builder or
by writing scripts by python programming language. Python is a free and
open source language, it used as a scripting language in ArcGIS
geoprocessing.
4.3.2. MERIS Processing tool (MPT)
MERIS Processing tool (MPT) was created using Visual Basic for
Application (VBA) found in Excel in order to automate the process of
61
subset, reproject, and preform water quality processors. In addition to
preform atmospheric correction, band math and cloud probability. The
description of MPT will be shown later on in Appendix (B). It helps in
processing large number of MERIS images needed for validation of
water quality processors, regression analysis and time series.
4.4. Validation of water quality processors
Water quality processors are mainly developed in order to estimate the
optically active substances found in water such as Chlorophyll-a (Chl-a),
Total Suspended Sediment (TSS). Case2 water processors such as C2R,
eutrophic lake and boreal lake processors will be validated in Lake
Nasser and the effect of ICOL processor will be taken into account.
Different processors will be applied to the match up images for different
field missions as follows:
1- ICOL processor was carried out to match-up images in order to
correct the adjacency effect of water pixels.
2- Case 2 water processors (Case 2 Regional, eutrophic lake and
boreal lake processors) were applied to the raw match-up images,
i.e., without using ICOL processor. Also, Case 2 water processors
were applied to images resulted from ICOL processor.
3- Data were extracted from the resulted images for two cases (with
and without using ICOL processor) at the same locations of in situ
measurements.
4- A comparison between the predicted values of TSS and Chl-a
with the field measurements will be conducted to validate
processors in Lake Nasser.
5- An evaluation of each processor will be performed in order to
select the best water quality processor to be used in the lake and
to what extent it can be applied in different seasons to obtain
water quality.
6- The evaluation procedure will be based on many statistical
performance measures like Fractional Bias (FB), Correlation
62
Coefficient (r), Normalized Mean Square Error (NMSE),
Geometric Mean Bias (MG), Geometric Mean Variance (MV)
and Factor of two (Fac2).
7- The description of statistical measures terms in addition to the
evaluation and ranking methods of water quality processors will
be shown below in section 4.7
4.5. Regression
Regression gives, in many cases, strong and best relationships between
surface reflectance and different optical water quality parameters. Many
examples can be found in the remote sensing literature that claim the
ability to measure water quality variables that do not directly affect light
reflectance or are present in natural waters at such low concentrations
that they do not affect reflectance signals measured by satellite sensors.
Through current research, many parameters can be correlated to the
extracted surface reflectance of 15 bands of match-up MERIS images.
According to Table ( A-2) which shows bands application it can be notice
that bands from 11 to 15 contain data for O2 absorption and it can be used
in atmospheric correction (ESA, 2006), so that only first ten bands will
be used in regression analysis, method of analysis was presented in
Figure ( 4-1) and it can be described below:
1- Surface reflectance will be calculated by applying a Simplified
Model for Atmospheric Correction (SMAC) to the raw match-up
images. SMAC is found also as plugin in BEAM
2- Extracting surface reflectance from the atmospherically corrected
MERIS images at the location of in situ measurements.
3- Pearson correlation matrix between measured water quality
parameters will be calculated to check influence of optical water
quality parameters such as TSS and Chl-a on other parameters.
4- Regression analysis using stepwise regression will be applied
separately for each field mission in order to automate the choice
of variable and regression model based on
63
5- Validating the resulted regression model, the validation will be
performed only for the end of flood as this period has different
measurements during this period.
4.6. Time Series
This section will describe the method and tools created in order to make a
time series curves of each water quality parameters. Concentration of
each will be calculated using the validated models resulted from stepwise
regression. Dates of each season will be defined using the upstream AHD
hydrograph; the images will be categorized for each season to apply the
different regression models. Figure ( 4-2) represents a flow chart for steps
used to create time series curves.
Three different processes will be performed to MERIS image using MPT
as follows:
1- Atmospheric correction using Simplified Method for Atmospheric
Correction (SMAC) to calculate surface reflectance of each image
band.
2- Band Math will be used in order to extract the lake surface using
Normalized Difference Water Index (NDWI)
𝑁𝐷𝑊𝐼 =𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒_5−𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒_10
𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒_5+𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒_10 (4)
This index was found by author by comparing the spectrum
characteristics of land pixels and water pixels, and it is found that
NDWI values greater than 0.1 which is identifying the water
surface.
3- Cloud probability processor will be used to classify clouds over
each pixel into three classes as cloud free, cloud uncertain or
cloudy.
Outputs from the previously described processes will pass through many
steps to create the time series curves. The cloud free percentage over the
whole lake surface will be calculated using an ArcMap model builder
64
represented in Appendix (B), Figure ( B-4). Images that have a cloud free
percentage lower than 95% of the lake surface will be excluded from
time series. Sun glint effect will be used also to exclude images from
time series. The lake surface shapefile will be extracted using an ArcMap
model builder represented in Appendix (B), Figure ( B-5). Regression
models for each water quality parameters will be applied to SMAC
output using MPT. A python code was written to calculate the average
monthly concentration for each parameter.
Figure (4-2) Time series flowchart
4.7. Statistical performance measures
The statistical methods used here to evaluate the use of water quality
processors was originated by (Fox, 1984) and modified by (Hanna,
1989), the method was used to evaluate several air quality models Hanna
(1993) and also used in statistical analysis on wheat experiments (Patryl
& Galeriua, 2011). The main idea is to find out the reliable and accurate
model when compared to the real and actual measured values.
65
Two types of performance measures will be used in evaluating and
selecting of water quality processor to be used in Lake Nasser, the first is
measure of difference which represents a quantitative estimate of the size
of difference between predicted and observed values, while the second is
measurement of correlation, it is a quantitative measure of association
between predicted and observed values. The correlation coefficient is a
popular method for evaluating models, perfect correlation between the
predicted and observed is a necessary condition for a perfect model, but it
is still not sufficient as the only criterion for model evaluation.
4.7.1. Definitions
In order to compare the computed water quality parameters from
different processors and observed values, several statistical performances
measures will be used such as:
Model Bias
Model Bias can be defined as the difference between computed
concentration (CP) and observed concentration (CO). It is given by:
OP CCBiasModel (5)
Correlation Coefficient (r)
The correlation coefficient is a numerical value that describes the
quantitative relation between two variables. A good model should
indicate correlation coefficient close to unity.
The correlation coefficient can be calculated as follows:
PoP CC
PPOO CCCCr
(6)
Mean Normalized Bias (MNB)
It averages the residual between computed and observed values
normalized by observed values. A best model should have a MNB value
close to zero.
66
%1001
o
op
C
CC
NMNB (7)
Fractional Bias (FB)
The fractional bias is a dimensionless number and is based on a linear
scale and refers to the arithmetic difference between CP and Co. It
indicates only systematic errors which lead to always under estimate or
overestimate the measured values (Patryl & Galeriua, 2011). It is written
in symbolic form as:
PO
PO
CC
CCFB 2 (8)
Values for the fractional bias range between -2.0 (extreme under-
prediction) to +2.0 (extreme over-prediction)
Normalized Mean Square Error (NMSE)
Normalized Mean Square Error (NMSE) is used to measure the scattering
of data. It can indicate both systematic and random error. The Smaller
value of NMSE indicates a better performance of the model. The NMSE
is given by:
PO
PO
CC
CCNMSE
2
(9)
Geometric Mean Bias (MG)
The geometric mean bias (MG) like FB, indicates only systematic errors
which leads to always under estimate or overestimate the measured
values but it is based on logarithmic scale (Patryl & Galeriua, 2011). MG
is given by:
PO CCMG lnlnexp (10)
Geometric Mean Variance (VG)
67
The geometric mean variance (VG) can indicate both systematic and
random error and is given by:
2lnlnexp PO CCVG (11)
Factor of two (Fac2)
The factor of two (Fac2) is defined as the percentage of the predictions
within a factor of two of the observed values. The ideal value for the
factor of two should be 1 (100%).
Fac2 = Fraction of data that satisfy 0.5 CP/CO 2
In order to calculate all statistical performance measure, a custom excel
function was created to save time needed for manual calculations.
Appendix (B) contains the programming code for these custom functions.
4.7.2. Interpretation of Performance measures
The three Water quality processors tested through this research will be
validated using specific criteria for each statistical measure. The quality
of an ideal and perfect processor should indicate values of statistical
measures as follows:
The values of both fractional bias and normalized mean square
error should equal to zero.
The fractional bias gives a positive weight for under predictions
and negative weight for over predictions.
The fractional bias for cases with factors of 2 under prediction
and over prediction are -0.67 and +0.67, respectively.
Geometric mean bias and geometric mean variance value should
equal to 1.
4.7.3. Criteria for good processor
The following criterion was suggested by (Kumar et al., 1993) for good
model and to accept its performance:
𝑁𝑀𝑆𝐸 ≤ 0.5 (12)
68
−0.5 ≤ 𝐹𝐵 ≤ +0.5 (13)
𝐹𝑎𝑐2 > 0.80 (14)
Also two additional criteria for MG and VG were given by (Ahuja &
Kumar, 1996) as a useful test of model performance:
0.75 ≤ 𝑀𝐺 ≤ 1.25 (15)
And 0.75 ≤ 𝑉𝐺 ≤ 1.25 (16)
4.7.4. Ranking and Evaluation
In order to evaluate which water quality processor is valid, the following
ranking method was implemented:
All statistical performance measures, declared above, will be
taken in the ranking process except Mean Normalize Bias as there
is no defined criterion for best model found in literature. Also
correlation coefficient will not be used in ranking.
One degree will be given for each performance measure that
complies with the criteria for best processor, and zero for which
not comply.
The sum of these degrees for each processor divided by number
of statistical measures, used in raking, expresses the ranking
degree each processor.
𝑅𝑎𝑛𝑘𝑖𝑛𝑔 𝑉𝑎𝑙𝑢𝑒 =𝑁𝑜. 𝑜𝑓 𝐶𝑜𝑚𝑝𝑙𝑖𝑒𝑑 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑠
𝑁𝑜. 𝑜𝑓 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑠 (17)
Six ranking degrees are proposed in the following list:
- Not valid 0
- Poor 0.2
- Accepted 0.4
- Good 0.6
- Very good 0.8
- Excellent 1
69
CHAPTER (5)
RESULTS AND DISCUSSION
5.1. Introduction
In this chapter, the most important results will be illustrated. First,
predicted concentration of TSS and Chl-a using Case 2 water quality
processors will be compared with the in situ measurements. Several
longitudinal profiles and scatter plots have been made and analyzed to
evaluate the behavior of each processor; the selection of the suitable
processor for Lake Nasser in different seasons will be based on using
some statistical performance measures. Second, Regression analysis
between measured water quality parameters and remote sensing
reflectance extracted from atmospherically corrected MERIS image will
be conducted. Regression analysis will be made for optical and non-
optical water quality parameters. Finally, the time series for each water
quality parameter will be created and analyzed.
5.2. Validation of Case 2 water quality processors
Validation of water quality processor will include only two parameters;
TSS and Chl-a as these processors were created to evaluate only
parameters whose change in concentration affects the color of water,
which called “optical parameters”. The following two sections will
discuss the results of validation.
5.2.1. Validation of TSS
Comparison between measured water quality parameters were discussed
previously in CHAPTER (3(. Figure ( 5-1) to Figure ( 5-5) show the
longitudinal profiles of TSS for C2R, Eutrophic and Boreal Lake
processors compared with the in situ measurements, in addition to the
spatial distribution of TSS. The upper part of each figure represents the
predicted TSS values without using ICOL processor and the lower is after
using ICOL.
70
The comparison between predicted TSS longitudinal profiles from the
three processors shows that Eutrophic lake processor gives higher
predicted values while Boreal lake processor gives a lowest value for all
survey missions, this for with and without using ICOL processor. The
spatial distribution of TSS represented in the same set of figures also
supports the previous notice. Figure ( 5-3) shows a quite different
behavior of boreal lake processor when using ICOL processor; most of
predicted values along the longitudinal profile are the lowest value but in
some locations in the lake gives values higher than the other two
processors.
Scatter plots between measured and predicted TSS values for three
processors are shown in Figure ( 5-6) and Figure ( 5-7). Summary of the
statistical performance measures are shown in, Table ( 5-1). The
underlined bold numbers cells indicate that the value lies within the limits
of good model. Table ( 5-2) shows the ranking of each processor
according to proposed ranking procedure.
71
Wit
ho
ut
usi
ng
IC
OL
Pro
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Figure (5-1) TSS for First Mission (mg/l)
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Case 2 Regional Processor Eutrophic Lake Processor Boreal Lake Processor
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Figure (5-2) TSS for Third Mission (mg/l)
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Figure (5-3) TSS for Fourth Mission (mg/l)
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Figure (5-4) TSS for Fifth Mission (mg/l)
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Figure (5-5) TSS for Sixth Mission (mg/l)
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a) 1
st Mission
b) 3
rd mission
c) 4
th mission
Figure (5-6) Measured verses predicted TSS Values for First, Third
and Fourth missions (mg/l)
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a) 5
th mission
b) 6
th mission
Figure (5-7) Measured verses predicted TSS Values for Fifth and
Sixth missions (mg/l)
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Table (5-1) Summary of statistical performance measures for TSS
Without ICOL With ICOL
C2R Eut_Lakes Bor_Lakes C2R Eut_Lakes Bor_Lakes
FIR
ST
MIS
SIO
N
No
v-0
3
r 0.971 0.949 0.956 0.922 0.931 0.747
MNB -52.324 -26.397 -63.823 -69.039 -56.623 -70.556
FB 0.645 0.264 0.882 0.916 0.635 1.044
NMSE 0.517 0.087 1.119 1.156 0.48 1.784
MG 2.144 1.389 2.853 3.646 2.595 3.94
VG. 1.869 1.165 3.205 6.792 3.16 8.683
Fac2 0.500 1.000 0.000 0.1 0.3 0.1
TH
IRD
MIS
SIO
N
No
v-2
00
7
r 0.97 0.966 0.969 0.963 0.959 0.975
MNB -14.667 15.212 -38.916 -26.077 -1.028 -45.592
FB 0.122 -0.146 0.452 0.234 -0.035 0.528
NMSE 0.03 0.054 0.305 0.078 0.035 0.402
MG 1.182 0.876 1.651 1.383 1.033 1.865
VG. 1.047 1.035 1.308 1.164 1.049 1.521
Fac2 1.000 1.000 1.000 0.909 1.000 0.545
FO
UR
TH
MIS
SIO
N
May
-20
09
r 0.671 0.672 0.684 0.694 0.698 0.689
MNB -6.497 27.673 -33.075 -37.627 -17.839 -46.841
FB 0.05 -0.259 0.367 0.421 0.147 0.579
NMSE 0.078 0.188 0.205 0.262 0.146 0.433
MG 1.122 0.82 1.586 1.718 1.31 2.02
VG. 1.118 1.14 1.398 1.55 1.257 1.929
Fac2 1.000 0.909 0.636 0.636 0.818 0.545
FIF
TH
MIS
SIO
N
Oct
-20
10
r 0.48 0.442 0.36 0.472 0.435 0.376
MNB 67.3 114.402 7.768 33.039 63.493 -8.247
FB 0.076 -0.084 0.656 0.142 -0.013 0.694
NMSE 0.681 0.619 2.287 0.748 0.667 2.384
MG 0.757 0.6 1.216 0.974 0.793 1.382
VG. 2.001 2.542 2.164 1.848 1.983 2.05
Fac2 0.750 0.375 0.563 0.813 0.625 0.625
SIX
TH
MIS
SIO
N
Oct
-20
11
r 0.976 0.972 0.968 0.973 0.967 0.968
MNB 52.622 133.157 14.522 18.2 69.197 -18.627
FB -0.257 -0.583 0.093 -0.135 -0.455 0.223
NMSE 0.092 0.468 0.107 0.046 0.359 0.138
MG 0.67 0.448 0.906 0.858 0.6 1.25
VG. 1.227 2.081 1.089 1.053 1.337 1.086
Fac2 1.000 0.556 1.000 1.000 0.778 1.000
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5.2.1.1. Validation of first mission
This mission has been performed in the period of maximum water level
at the end of the flood. The behavior of water quality processors is shown
in Figure ( 5-6a). The values of Mean Normal Bias are smaller than zero
for all processors with and without using ICOL, which indicates a general
underestimation. The maximum value of correlation coefficient (r) is
observed for C2R without using ICOL processor. The higher value of (r)
is not evident that it is the best case as the other statistical measures for
the same case is outside the accepted limits. The statistical measures
indicates that Eutrophic Lake processor without ICOL is the most
appropriate to use during this field mission, the ranking value of that
processors is 0.8 indicating a very good degree for application, the values
is inside the accepted limits except for Geometric Mean Bias (MG), but
its value still the smallest compared to other processors.
5.2.1.2. Validation of third mission
Figure ( 5-6b) shows that the relation between predicted and observed
TSS values is around the 1:1 line for Eutrophic lake processor with and
without using ICOL. The statistical measures for all cases indicate that
there are three cases suitable for predicting TSS; C2R without using
ICOL and Eutrophic lakes processor with and without ICOL. The ranking
value for these processors, according to Table ( 5-2), is equal to one
which indicates an excellent degree of ranking. The correlation
coefficient for three processors ranges from 0.959 to 0.97, which
indicates a good correlation for all three cases. C2R without using ICOL
processor also gives a very good ranking as the ranking value is equal to
0.8 and the correlation between measured and estimated values equal to
0.963. So, it can also be used to estimate TSS in the third mission.
5.2.1.3. Validation of fourth mission
Although R values is relatively small for all processors, Eutrophic Lake
processor and C2R processor without using ICOL processor indicate an
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excellent ranking of statistical performance measures, see Table ( 5-1),
which comply with the limits of good model. The most appropriate
processor according to the evaluation criteria is Eutrophic lake processor
without using ICOL.
5.2.1.4. Validation of fifth mission
The scatter plot represented in Figure ( 5-7a) shows the results of fifth
mission which performed at the start of flood period. The figure indicates
a different behavior for all processor that observed during the mission,
the relation between measured and predicted TSS values indicate that all
processors fail to estimate TSS value when it exceed 40 mg/l, where it
gives approximately a constant estimated value. Waters that carry a
heavy load of sediments can be classified as Case 3 waters (Liqin, 2014).
So that, in Lake Nasser both Case 2 and Case 3 water can be found
during the start of flood season. For values below 40 mg/l gives a good
trend with measured data. Some of statistical performance measures, such
as FB, GM, and Fac2, were complied with the criteria of good model but
it cannot give a good indication for best model. Also, the correlation
between estimated and measured is not good as it does not exceed 0.48.
5.2.1.5. Validation of sixth mission
Table ( 5-1) shows that there are three processors suitable for estimating
TSS in Lake Nasser and give an excellent ranking degree during the six
mission; the processors are boreal lake processor with and without using
ICOL and C2R processor with using ICOL. According to Table ( 5-2),
C2R without using ICOL processor can be taken into account for this
mission as the ranking value is 0.8 which means a very good comply with
the criteria of good model described before in chapter 4. Correlation
coefficient (r) for all four selected processors is ranging from 0.968 to
0.976, which gives a good correlation between field and estimated values.
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Table (5-2) Ranking values of water quality processors for TSS
Without ICOL With ICOL
C2R Eut_Lakes Bor_Lakes C2R Eut_Lakes Bor_Lakes
First Mission 0 0.8 0.0 0.0 0.2 0.0
Third Mission 1.0 1.0 0.6 0.8 1.0 0.2
Fourth Mission 1.0 1.0 0.4 0.4 0.6 0.2
Fifth Mission 0.4 0.2 0.2 0.6 0.2 0.0
Sixth Mission 0.8 0.2 1.0 1.0 0.4 1.0
5.2.1.6. Overall evaluation of TSS
The overall evaluation will be based on the ranking values, missions will
be grouped depending on the situation of lake and flood condition to
specify which processor will be used during different seasons.
First, third, and six missions were conducted during maximum water
level at the end of flood, the evaluation and selection of water quality
processors, for these mission, will be based on the sum of ranking values
divided by three. The overall ranking values are 0.6 for C2R without
ICOL, 0.67 for eutrophic lake processor without ICOL, and 0.6 for C2R
without ICOL processor. So that, the most appropriate processor that can
be used in this period is Eutrophic lake processor without using ICOL.
Fourth mission was conducted during the falling period, C2R and
eutrophic lake processor without ICOL can be used to estimate TSS
values as the overall rank value equal to one.
The evaluation of water quality processor to estimate TSS during rising
period, fifth mission, did not indicate a specific processor to estimate TSS
for all lake during this rising period as all processors failed to estimate
high concentrations.
The intensity of field mission during different seasons is not enough to
specify the suitable processor that can be used all over the year, for
example, during the rising and falling periods there are only one mission
per each period, so that the conclusion will be based on the results of one
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mission. This is not enough to assure that it is the selected processor is
the best.
5.2.2. Validation of Chl-a
Validation of water quality processor to estimate Chl-a concentration in
Lake Nasser will be based on the results from two field missions (third
and fourth). The validation will use the same procedure as followed for
TSS. Figures (5-8) and (5-9) show the longitudinal profiles of Chl-a,
estimate using three water quality processors, third and fourth missions
respectively.
The comparison of the three longitudinal profiles show that boreal lake
processor gives a higher values than C2R and eutrophic lake processors
with and without using ICOL processor while eutrophic lake processor
give lower values, that can be supported visually using the spatial
distribution maps shown in the same figures.
Figure ( 5-10) represents the scatter plot comparison between measured
and estimated Chl-a values. Tables (5-3) and (5-4) show different
statistical measured and ranking values respectively. The evaluation of
each mission will be discussed below for each mission.
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Figure (5-8) Chl-a for Third Mission (mg/m3)
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C2R EUT BOR In situ
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Figure (5-9) Chl-a for Fourth Mission (mg/m3)
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C2R EUT BOR In situ
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a) 3
rd mission
b) 4
th mission
Figure (5-10) Measured verses predicted Chl-a Values (mg/m3)
5.2.2.1. Validation of third mission
The correlation between measured and estimated value is very poor, as
indicated in Table ( 5-3), the values are not exceed 0.0231, also the values
of mean normalized bias are very high and indicate that there is a big
scatter in the data and the positive sign indicates that the processors give
an over prediction for all processed cases except for eutrophic lake
processor with using ICOL, the mean normalized bias has the lowest
value, in addition to the comply of good model criteria for FB, NMSE
and MG.
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Table ( 5-4) indicates a ranking value of 0.6 for eutrophic lake processor
with using ICOL, which mean, according to the ranking list shown
before, that it gives good results compared to the in situ measurements.
5.2.2.2. Validation of fourth mission
Table ( 5-3) indicates that C2R and eutrophic lake processors with using
ICOL comply with the criteria of good model for FB, NMSE and MG.
The overall ranking values indicate the same value of 0.6 for both
processors, which indicate a good ranking, while the correlation is very
poor and did not exceed 0.35 between measured and estimated Chl-a
values.
Table (5-3) Summary of statistical performance measures for Chl-a
Without ICOL With ICOL
C2R Eut_Lakes Bor_Lakes C2R Eut_Lakes Bor_Lakes
TH
IRD
MIS
SIO
N
No
v-2
00
7
R 0.0231 0.0021 0.000011 0.001 0.010 0.018
MNB 79.390 72.679 324.959 62.751 -7.671 115.335
FB -0.417 -0.362 -1.114 -0.301 -0.230 -0.996
NMSE 0.387 0.346 2.360 0.341 0.331 2.029
MG 0.630 0.666 0.286 0.719 0.774 0.352
VG. 1.578 1.543 7.040 1.520 1.517 5.182
Fac2 0.636 0.636 0.273 0.700 0.700 0.200
FO
UR
TH
MIS
SIO
N
May
-20
09
R 0.080 0.089 0.103 0.308 0.350 0.352
MNB 87.795 74.755 341.599 28.854 16.981 155.765
FB -0.513 -0.446 -1.190 -0.103 -0.005 -0.720
NMSE 0.383 0.305 2.418 0.284 0.264 1.278
MG 0.576 0.617 0.250 0.922 1.013 0.523
VG. 1.567 1.450 8.225 1.408 1.389 2.776
Fac2 0.727 0.727 0.000 0.636 0.636 0.545
Table (5-4) Ranking values of water quality processors for Chl-a
Without ICOL With ICOL
C2R Eut_Lakes Bor_Lakes C2R Eut_Lakes Bor_Lakes
Third Mission 0.4 0.4 0 0.4 0.6 0
Fourth Mission 0.2 0.4 0 0.6 0.6 0
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5.2.2.3. Overall evaluation of Chl-a
The statistical performance measures and overall ranking of Chl-a for
third and fourth missions indicate that eutrophic lake processor with
using ICOL process indicates good results.
Although the results are good for estimating Chl-a values, it cannot be
concluded that the processor is valid to estimate Chl-a concentration for
the entire year.
5.3. Regression
The relation between reflectance values and measured water parameters
can be found by three approaches empirical, analytical, semi analytical.
Through this research, the empirical approach will be used; regression
analysis will be conducted between different water quality parameters
and reflectance of different bands of MERIS image. The regression
analysis will not only be for optical parameters but it will also include the
non-optical parameters. Pearson correlation matrix will be calculated to
find the effect of optical parameter on others; strong correlation can be an
evident that non-optical parameter can be correlated to different bands’
reflectance.
5.3.1. Correlation between measured parameters
The linear correlation (Pearson correlation) between different water
quality parameters is shown in Table ( 5-5). The analysis will be shown
below, for each water quality parameter, to show its behavior with the
change of optical parameters such as TSS and Chl-a. The interpretation
of correlation coefficient and to how extent the parameters were linearly
correlated will be described using the guidelines guideline suggested by
Hinkle et al. (2003) as illustrated in Table ( 5-6).
Turbidity is a measure of water clarity and how much the suspended
material in water decreases the passage of light through the water. So
that, a very strong positive relationship between TSS and Turbidity was
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found, the correlation factor is 0.994 and 0.999 for First and fifth
missions respectively.
Transparency means how deep sunlight penetrates through the water and
measured with a Secchi disk. It is also considered as measurement of
water clarity as it depends on the amount of particles in the water. These
particles can be algae or suspended sediments, the more particles, the less
water transparency. So that transparency has a strong negative correlation
with TSS as shown in Table ( 5-5), Pearson correlation factor ranging
from a minimum value of -0.807 in fifth mission to -0.928 in third
mission.
Pearson correlation factor between Total phosphorus (TP) and other
water quality parameters is shown in Table ( 5-5), the correlation between
TSS and TP is a positive strong to very strong relation for second to fifth
mission, it ranges from 0.846 for fourth mission and 0.974 in third
mission while first mission indicates correlation factor of 0.696.
Silica (SiO2) indicates a positive correlation with TSS for first, second
and third missions as the values of correlation factor are 0.759, 0.576 and
0.748 for the three missions respectively. A different behavior of SiO2 is
found during fourth mission, as it gives a negative correlation with TSS
with a value of -0.570. According to the indicated values the relation can
be described as strong to moderately strong relationship.
Total Dissolved Salts (TDS) is showing, in common, a strong negative
correlation with TSS, as indicated in Table ( 5-5), for first and third
missions while gives a positive correlation for fifth mission. Values of
correlation are -0.970, -0.837 and 0.835 for the indicated missions
respectively.
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Table (5-5) Pearson correlation matrix
a- First Mission
TSS Turbidity Transparency TP Sio2 TDS
TSS 1 - - - - -
Turbidity 0.994 1 - - - -
Transparency -0.917 -0.889 1 - - -
TP 0.696 0.696 -0.737 1 - -
SiO2 0.759 0.713 -0.839 0.329 1 -
TDS -0.970 -0.947 0.897 -0.565 -0.799 1
b- Second Mission
Turbidity Transparency TP Sio2 TDS
Turbidity 1 - - - -
Transparency -0.882 1 - - -
TP 0.971 -0.933 1 - -
SiO2 0.576 -0.856 0.644 1 -
TDS -0.686 0.617 -0.607 -0.431 1
c- Third Mission
TSS Chl-a
Transparenc
y TP SiO2 TDS
TSS 1 - - - - -
Chl-a -0.687 1 - - - -
Transparenc
y -0.928 0.537 1 - - -
TP 0.974 -0.656 -0.938 1 - -
SiO2 0.748 -0.571 -0.831 0.801 1 -
TDS -0.837 0.524 0.943 -0.871 -0.878 1
d- Fourth Mission
TSS Chl-a Transparency TP SiO2
TSS 1 - - - -
Chl-a -0.733 1 - - -
Transparency -0.814 0.653 1 - -
TP 0.846 -0.498 -0.877 1 -
SiO2 -0.570 0.335 0.654 -0.610 1
e- Fifth Mission
TSS Transparency Turbidity TP TDS
TSS 1 - - - -
Transparency -0.807 1 - - -
Turbidity 0.999 -0.819 1 - -
TP 0.852 -0.660 0.847 1 -
TDS 0.835 -0.902 0.841 0.750 1
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The correlation between Chl-a and TSS as indicated in Table ( 5-5) is a
negative correlation with values -0.687 and -0.733 for both third and
fourth missions respectively. While it gives a positive correlation with
transparency with correlation not exceeds value of 0.653. TP also gives a
negative correlation with Chl-a with values of -0.656 and -0.498. Silica
(SiO2) is indicating a different behavior as it gives a negative correlation
with Chl-a in third mission with value of -0.571, and a positive
correlation is noted in fourth mission with value of 0.335. In general, the
correlation between Chl-a can be described as a moderately strong
correlation.
Table (5-6) Rule of Thumb for Interpreting the Size of a Correlation
Coefficient (Hinkle et al., 2003)
Size of Correlation
(r)
Interpretation
0.90 to 1.00 Very strong correlation
0.70 to 0.90 Strong correlation
0.50 to 0.70 Moderately strong correlation
0.30 to 0.50 Weak correlation
0.00 to 0.30 Negligible correlation
According to the previous analysis, it is found that there is a correlation
between optical water quality parameters and other non-optical
parameters, so that, the non-optical water quality parameters can be
correlated to remote sensing reflectance calculated from MERIS images.
5.3.2. Stepwise regression Model
Simple regression analysis between water quality parameters, measured
during six missions, and remote sensing reflectance calculated from
MERIS images will be performed using stepwise regression. Stepwise
Regression is an iterative process used to construct a regression model. It
is semi-automatic selection process of independent variables carried out
in two ways by including independent variables in the regression model
one by one at a time if they are statistically significant, or by including all
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the independent variables initially and then removing them one by one if
they prove to be statistically insignificant. Ten MERIS bands will be used
in stepwise regression, Figure ( C-1) to Figure ( C-6) in appendix (C)
represent Matlab output of stepwise regression for each water quality
parameter. Each figure contains a graph showing the coefficient with
error bar, t-stat, p-value for each band reflectance, where the bands
reflectance is designated by (X1 to X10). In addition to other factors that
can be used in evaluating the regression models such as RMSE, R-
squared, F and p values for overall model. Summary of regression
analysis are presented in Tables ( 5 7) and ( 5 8), retrieval models for
different water quality parameter are grouped by mission, where each
parameter has a different retrieval model for each mission. A description
of retrieval models for each water quality parameter will be discussed
below.
Retrieval models between TSS and surface reflectance show that TSS can
be correlated to different bands, as indicated in Table ( 5-7). It is found
that TSS can be correlated to reflec_5 for both first and fourth mission.
While reflec_6 and reflec_8 can be used to predict TSS for third and fifth
missions respectively. For sixth mission, TSS can be obtained by a linear
equation between both reflec_1 and reflec_5. The R-squared value is very
strong for all TSS retrieval models for all missions except fourth mission;
it has a minimum value of 0.881 and maximum value of 0.969. While for
fourth mission R-squared value is 0.578, the low value for fourth mission is
attributed to conducting this mission during falling period where the influence
of TSS is very low.
Chl-a was measured only twice; in third and fourth missions. The results
of stepwise regression show that reflec_8 can be used as a predictor of
Chl-a during the third mission, but with low R-squared value (0.431).
Stepwise regression is failed to get an equation for fourth mission.
Turbidity was measured in three missions; the retrieval models show that
R-squared value is not less than 0.823 which indicate a very good
92
correlation. Also, it shows that turbidity can be correlated to many bands
reflectance depending on the season of the mission.
Transparency is considered as a measure of water clarity, so that, its
retrieval models for it have a strong R-Squared values ranges from 0.893
to 0.984, it is noted that, retrieval models for different missions indicate
that reflec_5 can be used as a predictor for transparency, it can be used
alone as in fifth mission or it can be used with other band reflectance
such as reflec_4 and reflec_2 for first, second and third missions. Fourth
mission indicates a combination of other two bands which are reflec_3
and reflec_10.
Stepwise regression between TP and surface reflectance of different
MERIS images bands indicate that reflec_8 is the suitable for TP
prediction for both fourth and fifth missions. Where reflec_5 and reflec_2
are suitable for second and third missions respectively. The R-squared
value is ranging from 0.55 for fourth mission to 0.986 for third mission.
Silica (SiO2) was measured during four mission. Retrieval model for first
mission shows that reflec_8 give R-squared value of 0.47 and fourth
mission has R-squared value of 0.45 with reflec_1, this indicates that the
relation is not good but still is the best to predict SiO2 concentration
during these two missions. While second and third mission were give an
R-squared values of 0.796 and 0.847 respectively. Where the retrieval
models shows that reflec_5 and reflec_6 are used to estimate SiO2 for
second mission, while reflec_4 and reflec_9 for third mission. The
resulted equation did not show correlation to a common band reflectance.
Retrieval models for TDS indicate that reflec_6 can be used as a
predictor for first, second and fifth missions, the R-squared values are
0.808, 0.446 and 0.814 for these missions respectively. While both
reflec_2 and reflec_4 can be used during third mission.
93
Table (5-7) Retrieval Models for water quality parameters (First, Second and third missions)
Mission Parameter Retrieval Model R-Sq Ajd R-Sq RMSE F P
1st TDS 164.488-395.906*reflec_6 0.808 0.784 2.38 33.719 0.0004
Transparency 0.207+436.153*reflect_4-346.301*reflec_5 0.889 0.857 0.542 28.009 0.00046
SiO2 2.082+225.371*reflec_8 0.47 0.4 2.015 7.088 0.0287
Turbidity -0.056+10.066*reflec_4-9.331*reflec_9 0.823 0.773 0.0379 16.329 0.0023
TSS -11.412+316.079*reflec_5 0.917 0.906 1.858 87.943 1.37E-05
2nd
TDS 164.505-150.108*reflec_6 0.446 0.391 7.308 8.066 0.017
TP -0.0124+3.275*reflec_5 0.878 0.866 0.0495 72.259 6.89E-06
SiO2 6.542+233.258*reflec_5-155.372*reflec_6 0.796 0.751 1.664 17.594 0.00077
Transparency 2.45+50.634*reflec_2-38.832*reflec_5 0.983 0.977 0.178 254.316 1.21E-08
Turbidity 8.189-365.413*reflec_2+334.423*reflec_6 0.913 0.894 3.805 47.227 1.69E-05
3rd
Chl-a 9.81-76.269*reflec_8 0.431 0.638 2.444 6.825 0.028
TSS 1.914+258.003*reflec_6 0.969 0.965 1.66 276.867 4.57E-08
TP -0.019+8.246*reflec_2 0.986 0.984 0.014 654.597 1.03E-09
Transparency 3.297+99.144*reflec_2-71.577*reflec_5 0.978 0.972 0.235 174.839 2.50E-07
SiO2 11.769+102.322*reflec_4-85.277*reflec_9 0.847 0.809 0.51 22.193 5.40E-04
TDS 154.985+2038.49*reflec_2-1701.5*reflec_4 0.923 0.904 4.01 47.934 3.52E-05
94
Table (5-8) Retrieval Models for water quality parameters (Fourth, Fifth and Sixth missions)
Mission Parameter Retrieval Model R-Sq Ajd R-Sq RMSE F P
4th TSS 0.954+120.622*reflec_5 0.578 0.531 1.847 12.324 0.00666
SiO2 9.295-180.949*reflec_1 0.45 0.389 0.813 7.354 0.0239
TP 0.0321+3.121*reflec_4-1.144*reflec_8 0.94 0.926 0.0058 63.13 1.26E-05
Transparency 2.336-35.723*reflec_3+19.756*reflec_10 0.893 0.867 0.0815 33.553 0.000129
5th TSS -33.274+632.144*reflec_8 0.881 0.873 9.266 104.013 7.32E-08
TDS 134.257+176.93*reflec_6 0.814 0.8 3.122 61.215 1.77E-06
TP 0.052+0.437*reflec_8 0.55 0.518 0.016 17.108 0.0011
Transparency 5.409.39.844*reflec_5 0.984 0.982 0.131 814.181 8.33E-14
Turbidity -32.166+599.642*reflec_8 0.889 0.881 8.448 112.602 4.46E-08
6th TSS 4.014-407.597*reflec_1+328.357*reflec_5 0.948 0.931 2.205 54.995 0.000138
95
The overall conclusion from the previous analysis is that for each mission
one can note that there are common bands suitable for predicting both
optical and non-optical water quality parameters for each mission. For
example, the retrieval models for both turbidity and transparency for
second mission contain reflec_2, reflec_5 and reflec_6, where models of
non-optical parameters have best correlation with reflec_5 and reflec_6.
The same can be found in other mission like fifth mission.
5.3.3. Validation of retrieval model
Water quality in Lake Nasser varied along the year due to the effect of
flood, especially in the southern part which influenced by suspended
sediment. Variation in water quality parameters resulted in different
regression models for each water quality parameter depending on the
season of measurement, as discussed in the previous sections.
Regression models validation will be based on selecting a model for each
water quality parameter; one per each season. Missions will be
categorized into groups depending on season. Each model will be
validated using all collected data for the same group; statistical
performance measures, such as MNB, NMSE, FB, MG, VG, Fac2 will be
used to select the suitable regression model for each season.
Field missions were conducted in three seasons at the start of rising
period, at end of flood season, and during the falling period. Figure ( 3-8)
illustrated the timing of each mission with respect to the change in water
level upstream AHD. There are four missions were conducted just after
the end of flood season, first three missions and sixth mission. So that,
the shared measured parameters will be validated using the proposed
method. For other two seasons, it is found that only one mission was
conducted per season, hence, it is not possible to validate its regression
models. So that, retrieval models for these seasons need to be validated in
order to have a graph for time series change for each mission.
Tables ( 5-9) and ( 5-10) show validation results for each retrieval model,
different statistical performance measures are calculated for each season.
96
The selection of the suitable retrieval model will be based on the criteria
used before in validating Case 2 water quality processors. Validation
process here will account for R-Squared value as additional criteria in
selecting the suitable retrieval model. Bold and underlined values means
that it comply with criteria of good model.
TSS were measured during three aforementioned seasons, during rising
and falling seasons it was measured only once while during end of flood
period it was measured three times. The statistical performance measures
indicate that retrieval model for the third mission, conducted during
November 2007, gives good comply with the criteria of good model for
both NMSE and FB. Also, it has the greatest value of R-Squared.
Chl-a was measured during two missions, in November 2007 and May
2009, as indicated before, but only one mission, during 2007, gives a
correlation with reflec_8, and the R-squared value for this model is
0.431.
Turbidity was measured twice during end of flood season, validation
results indicate that regression model for November 2003 is suitable to
get turbidity during this season where all statistical performance
measures have good comply with the criteria of good model except for
VG and has a greater R-squared.
Validation of retrieval models indicate that model for November 2006 is
the suitable model to get transparency during end of flood, all statistical
performance measures give values within limits of good model, in
addition, its R-squared value is the greater between other missions.
Total phosphorus (TP) was validated during end of flood season, the
results show that retrieval model of 2006 mission is suitable for
estimating TP as all statistical performance measures comply with criteria
of good model. Also, Silica (SiO2) validation results show that model for
2007 mission is suitable to estimate silica during the end of flood.
97
All statistical performance measures calculated for TDS comply with
limits for good model, although model of 2007 mission has the highest
R-squared value, but it will be excluded as the other models show a
relation with reflec_6 as a common band. So that, the suitable retrieval
model for estimating TDS is 2003 model.
98
Table (5-9) Retrieval Models Validation for different seasons (TSS, Chl-a, Turbidity and transperancy)
Parameter Mission Retrieval Model R-Sq MNB NMSE FB MG VG Fac2
TSS 2003 -3.954+270.496*reflec_5 0.913 80.908 0.633 -0.139 0.729 1.944 0.649
2007 1.914+258.003*reflec_6 0.969 111.571 0.463 -0.235 0.596 1.958 0.702
2011 4.014-407.597*reflec_1+328.357*reflec_5 0.948 -8.031 1.383 0.325 1.494 2.809 0.632
2009 0.954+120.622*reflec_5 0.578 - - - - - -
2010 -33.274+632.144*reflec_8 0.881 - - - - - -
Chl-a 2007 9.81-76.269*reflec_8 0.431 - - - - - -
Turbidity 2003 -11.412+316.079*reflec_5 0.917 46.585 0.242 -0.174 0.938 1.933 0.818
2006 8.189-365.413*reflec_2+334.423*reflec_6 0.913 65.850 0.240 0.031 0.795 1.789 0.591
2010 -32.166+599.642*reflec_8 0.889 - - - - - -
Transparency 2003 0.207+436.153*reflect_4-346.301*reflec_5 0.889 -238.301 5.915 1.129 - - 0.500
2006 2.45+50.634*reflec_2-38.832*reflec_5 0.983 9.430 0.099 0.061 0.978 1.123 0.938
2007 3.297+99.144*reflec_2-71.577*reflec_5 0.978 35.766 0.201 -0.279 0.845 1.352 0.844
2009 2.336-35.723*reflec_3+19.756*reflec_10 0.893 - - - - - -
2010 5.409-39.844*reflec_5 0.984 - - - - - -
99
Table (5-10) Retrieval Models Validation for different seasons (TP, SiO2, and TDS)
Parameter Mission Retrieval Model R-Sq MNB NMSE FB MG VG Fac2
TP 2003 -0.056+10.066*reflec_4-9.331*reflec_9 0.823 -15.677 0.558 0.276 1.685 9.320 0.848
2006 -0.0124+3.275*reflec_5 0.878 -0.509 0.063 0.054 1.072 1.166 0.909
2007 -0.019+8.246*reflec_2 0.986 107.866 0.451 -0.482 0.564 1.901 0.576
2009 0.0321+3.121*reflec_4-1.144*reflec_8 0.94 - - - - - -
2010 0.052+0.437*reflec_8 0.55 - - - - - -
SiO2 2003 2.082+225.371*reflec_8 0.47 -10.776 0.264 0.136 1.288 1.474 0.758
2006 6.542+233.258*reflec_5-155.372*reflec_6 0.796 10.719 0.038 -0.072 0.925 1.054 1.000
2007 11.769+102.322*reflec_4-85.277*reflec_9 0.847 17.603 0.049 -0.096 0.879 1.081 0.970
2009 9.295-180.949*reflec_1 0.45 - - - - - -
TDS 2003 164.488-395.906*reflec_6 0.808 -2.867 0.008 0.031 1.034 1.009 1.000
2006 164.505-150.108*reflec_6 0.446 5.687 0.006 -0.052 0.948 1.007 1.000
2007 154.985+2038.49*reflec_2-1701.5*reflec_4 0.923 4.901 0.013 -0.048 0.958 1.013 1.000
2010 134.257+176.93*reflec_6 0.814 - - - - - -
100
5.4. Time series analysis
The selected retrieval models will be used in mapping of the spatial
distribution for each water quality parameter for period of about ten years
form December, 2002 until March, 2012. An atmospheric correction will
be performed to MERIS image in order to get surface reflectance of each
band. MPT will be used as illustrated before to automate all process.
5.4.1. Image selection criteria
Total number of images used in time series analysis is 913 images. To
use image for time series analysis for water quality parameters, we
should assure that the cloud coverage over Lake Nasser is not so large,
also to assure that there is no sun glint found over the water surface. Sun
glint occurs in remote sensing image when sun light is reflected by water
surface towards the sensor. The sun glint can contribute in increasing the
water-leaving radiance from sub-surface features (Kay et al., 2009).
5.4.1.1. Cloud coverage
Clouds over Lake Nasser was calculated using Cloud Probability
Processor, it uses a clear sky conservative cloud detection algorithm
which is based on artificial neural nets developed by Rene Preusker. The
cloud probability algorithm has been developed and implemented by Free
University Berlin and Brockmann Consult. It is found as a plugin in
BEAM. The output product contains one raster indicating the cloud
probability for each pixel. The three flags indicate pixels which are
cloudy (probability > 80%), cloud free (probability < 20%) or where it is
uncertain (20% < probability < 80%).
Cloud probability processor is applied to the total number of 913 MERIS
images used in time series analysis using MPT. The output of cloud
probability is reprojected and converted to GEOTIFF file, and then water
pixels are extracted in separate product, in order to calculate the
percentage of each category of cloud probability over the surface of lake
in different seasons.
101
Figure ( 5-11) provides a summary of cloud analysis results; it indicates
the percentage of cloud free, cloud uncertain and cloudy pixels against
the date of each image. Criteria for excluding image from time series
analysis will be based on cloud free percentage, if the value of cloud free
percentage is less than 95%, the image will be excluded. So that, the total
number of excluded MERIS image is 185 images.
5.4.1.2. Sun glint
On the other hand, the excluded images due to sun glint will be
performed manually by extracting a true image and visually excluding
images that affected by sun glint. Total number of images excluded due
to sun glint is 284 images.
The total number of excluded images due to both clouds and sun glint is
469 images out of 913 images, distributed along the total period used for
time series analysis. A further analysis of the excluded images can be
done in order to find the best times for future water quality sampling to
be coupled with remote sensing images. Figure ( 5-12) is a bar chart
shows the percentage of excluded images due to clouds and sun glint
categorized by month. The minimum percentage of excluded images due
to cloud coverage is found during period from September to November,
the percentage of ranges from zero in October while during September
and November are 2.67% and 8.86% respectively. Also the period from
February to April also has relatively low percentage of excluded images
as the percentages are 14.86%, 10.96% and 24.62% respectively. The
maximum percentage of excluded images due to cloud coverage is
observed during May, August, and January, the percentages are 43.06%,
39.76 and 35.62% during the three periods respectively.
The excluded images percentage due to sun glint ranges from 18.46% to
14.29%, the percentage of exclusion is uniform during months, as
indicated in Figure ( 5-12).
102
a- Cloud free %
b- Cloud Uncertain%
c- Cloudy%
Figure (5-11) Cloud probability summary surface of Lake Nasser
0
20
40
60
80
100
120
1-Nov-02 9-Jan-05 20-Mar-07 28-May-09 6-Aug-11
Clo
ud
Fre
e (%
)
Date
95% Cloud Free %
-10
0
10
20
30
40
50
1-Nov-02 9-Jan-05 20-Mar-07 28-May-09 6-Aug-11
Clo
ud
Un
certa
in (
%)
Date
Cloud Uncertain % 5%
-20
0
20
40
60
80
100
1-Nov-02 9-Jan-05 20-Mar-07 28-May-09 6-Aug-11
Clo
ud
y (
%)
Date
Cloudy % 5%
103
Figure (5-12) Percentage of excluded images due to clouds and sun
glint
Hence, it can be concluded previous analysis that in May the cloud
probability is high, and then it decreases during June, while it starts to
increase during the next two month until August, followed by period that
has minimum cloud probability from September to November while it
increased again until it reaches to January, during February and March
there are another low cloud probability period.
5.4.2. Flood seasons
In order to create a time series for the different water quality parameters,
the dates of each season flood will be identified using US AHD
hydrograph. Figure ( 5-13) illustrates how to define start and end of
different seasons. The rising season begins with the start of flood and the
water levels starts to significantly increase until it reaches the maximum
water level, at this stage the end of flood season is started where it
characterized by a slowly decrease in water level, in this period the water
level may increase for period of two or three days. While the falling
period starts as illustrated in the figure where characterized by a
significant decrease in water level until the next flood. Based on this
definitions start and end of each flood season are identified for the period
from 2002 to 2012 as shown in Table ( 5-11).
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
% E
xcl
ued
im
ages
Month
Total
Clouds
Sunglint
104
MERIS images are seasonally categorized; different regression models
are applied to MERIS images using MPT in order to get the
concentrations of each water quality parameter.
Figure (5-13) Percentage of excluded images due to clouds and sun
glint
Table (5-11) Start/End of Flood Seasons
Rising End of Flood Falling
Start End Start End Start End
2-Nov-2002 14-Jan-2003 16-Jan-2003 25-Jul-2003
26-Jul-2003 31-Oct-2003 1-Nov-2003 15-Jan-2004 16-Jan-2004 24-Jul-2004
25-Jul-2004 8-Nov-2004 9-Nov-2004 9-Feb-2005 10-Feb-2005 25-Jul-2005
26-Jul-2005 6-Nov-2005 7-Nov-2005 2-Feb-2006 3-Feb-2006 24-Jul-2006
25-Jul-2006 3-Nov-2006 4-Nov-2006 3-Feb-2007 4-Feb-2007 8-Jul-2007
9-Jul-2007 19-Oct-2007 20-Oct-2007 18-Feb-2008 19-Feb-2008 27-Jul-2008
28-Jul-2008 8-Oct-2008 9-Oct-2008 10-Feb-2009 11-Feb-2009 4-Aug-2009
5-Aug-2009 28-Sep-2009 29-Sep-2009 30-Jan-2010 31-Jan-2010 1-Aug-2010
2-Aug-2010 10-Oct-2010 11-Oct-2010 13-Nov-2010 14-Nov-2010 3-Aug-2011
4-Aug-2011 16-Oct-2011 17-Oct-2011 16-Jan-2012 17-Jan-2012 5-Aug-2012
105
5.4.3. Time series of Water quality parameters
The time series for different water quality parameters will be used the
average monthly values for both curves and spatial distributions maps. So
that, after calculating the concentrations, images will be grouped by
months and average values were calculated for each group, a python code
are written to automate the calculation of average.
The primary purpose when planning to create time series is to track the
change in water quality for surface layer of Lake Nasser all over the year
during the period from 2002 to 2012, Figure ( 5-14) shows the monthly
average discharge for TSS as calculated using seasonal regression model
found during this research, in addition to the measured TSS value. The
comparison between the average monthly calculated concentrations and
measured concentrations indicate that there is a good agreement between
them during different months except for months of rising period of the
flood as the measured concentration during August, 2010 at Arkeen cross
section is 84 mg/l while the calculated average value is about 35.7 mg/l,
this big error in TSS will be reflected on the estimation of other values.
So that, it is decided to only perform time series for the season which
have a validated regression models, i.e. during the end of flood season as
the invalidated model may give us unrealistic values. October, November
and December was selected to represent the end of flood season
Figure (5-14) Monthly average TSS at Arkeen Cross section as
calculated from MERIS Images
106
Figure ( 5-15) shows a sample of the spatial distribution maps of monthly
average concentration of TSS, Transparency, and TP as samples of
concentration maps. Time series curves for TSS, Transparency TP, SiO2,
and TDS are drawn for October, November and December at location of
monitoring sections as indicated in Figure ( 5-16) to Figure ( 5-20). The
measure values are identified in the figures to assure that the obtained
time series indicate values in the same range as the measured one.
It is noted that TSS values at the southern part of the lake increases until
it reaches its maximum values in 2005, after that TSS started to decrease
until 2009. This pattern of change is noted in sections from Arkeen to
Ebream, this part is influenced by the suspended sediment transported by
flood. Generally the pattern of change can be similar to a sin curve. Also
it is noted that there is no specific pattern of change for the northern part
of the lake, starting from Krosko to High dam, this may be due to the
small concentrations.
Transparency in the southern part of the lake is not showing a specific
pattern and the estimated values are greater than the measured values,
while in the northern part it indicates a change in range of 0.5 m but it
gives under estimation when compared with the measured transparency.
This can be due to that transparency measurements depend on the vision
of secchi disk which may differ from person to other.
Silica concertation for section found in the northern part from High Dam
until reaches Krosko cross section changes in range of 0.2 mg/l. it is
noted that for cross sections from Ebreem to Arkeen, the values of Silica
is started to decrease until 2005 and started to increase again until 2009.
The changing pattern of Total Phosphorus is the same as that of TSS,
while for Total dissolved Solids it is the same as that of Silica.
107
10/2005 11/2005 12/2005
a- TSS
10/2005 11/2005 12/2005
b- Transparency
10/2005 11/2005 12/2005
c- TP
Figure (5-15) Spatial distribution maps of monthly average
concentration of TSS, Transparency, and TP (mg/l)
Legend
0.95
- 5
5.01
- 10
10.0
1 - 1
5
15.0
1 - 2
0
20.0
1 - 2
5
25.0
1 - 3
0
30.0
1 - 3
5
35.0
1 - 4
0
40.0
1 - 4
5
Legend
0.95
- 1
1.01
- 1.
2
1.21
- 1.
4
1.41
- 1.
6
1.61
- 1.
8
1.81
- 2
2.01
- 2.
2
2.21
- 2.
4
2.41
- 2.
6
2.61
- 2.
8
2.81
- 3
3.01
- 40
.6
Legend
0 - 0
.1
0.11
- 0.
2
0.21
- 0.
3
0.31
- 0.
4
0.41
- 1
108
Figure (5-16) TSS during Period from October to December (mg/l)
109
Figure (5-17) Transparency during Period from October to
December (mg/l)
110
Figure (5-18) Silica during Period from October to December (mg/l)
111
Figure (5-19) Total Phosphorus during Period from October to
December (mg/l)
112
Figure (5-20) TDS during Period from October to December (mg/l)
113
CHAPTER (6)
CONCLUSION AND RECOMMENDATIONS
Lake Nasser is considered the strategic storage of water in Egypt, so that,
the preservation and continues monitoring of its water quality is
considered very important. Satellite-based remote sensing (RS) has
become a useful tool for coastal and inland waters
(management/preservation). This research introduces a detailed study on
estimating water quality parameters in Lake Nasser using remote sensing
techniques. Six field missions are used to fulfill the objectives of this
research, missions were conducted during period from 2003 to 2011, and
different water quality parameters were measured at a specific monitoring
location along the lake. MERIS images are used during this research
Match-up images are identified for each field mission. the research has
two main objectives, the first is to validate MERIS Case 2 water quality
processors, such as Case2 Regional (C2R), Eutrophic Lake and boreal
Lake to estimate Total suspended Solids (TSS) and Chlorophyll-a (Chl-a)
in Lake Nasser, some statistical performance measures are used in
validation for each individual mission, mission is grouped by season to
evaluate the processor for different seasons. The second objective is to
create a regression models for optical and non-optical water quality
parameters with different MERIS bands reflectance. Time series for
different water quality parameters are drawn especially for season that
has a validated regression models. In addition to the analysis of cloud
coverage and sun glint over Lake Nasser surface.
6.1. Conclusion
The conclusion of the current research can be summarized as follows:
1- Validation MERIS Case 2 water quality processors
The drawn conclusions resulted from validation of Case 2 water quality
processors, can be pointed out as follows:
114
A- Validation of Case 2 water quality processors to estimate TSS
- The statistical performance measure and overall ranking of TSS
estimation for first, third, and six missions which conducted
during end of flood season indicates that the most appropriate
processor that can be used in this period is eutrophic lake
processor without using ICOL.
- During falling period which represented by fourth mission it is
found that C2R and eutrophic lake processor without ICOL can
be used to estimate TSS values as the overall rank value equal to
one.
- The evaluation of water quality processor to estimate TSS during
rising period, fifth mission, did not indicate a specific processor to
estimate TSS as all processors failed to estimate TSS when its
value is greater than 40 mg/l.
B- Validation of Case 2 water quality processors to estimate Chl-a
The statistical performance measure and overall ranking of Chl-a third
and fourth missions indicates that eutrophic lake processor with using
ICOL process yields good results but it cannot be concluded that the
processor is valid to estimate Chl-a concentration for all year.
2- Regression analysis between MERIS reflectance and water quality
parameters
The Pearson correlation between measured parameters indicates a very
good correlation between optical water quality parameters and other non-
optical parameters which enables us to estimate different parameters
using remote sensing techniques.
The conclusion resulted from the regression between band reflectance
extracted from MERIS images and water quality parameters can be
summarized as follows:
A- Retrieval models for Total Suspended Solids (TSS)
- Different band reflectance can be used to estimate TSS in
different season. reflec_5 or reflec_6 can be used during the end
115
of flood and falling seasons either individually or with other band
such as reflec_1. While reflec_8 has good correlation with TSS
during rising season (flood season), but more repetitive
measurements is need during this period to get more reliable
regression model.
- Validation of regression models in the end of flood season
indicated that that retrieval model for the third mission, conducted
during November 2007, can be used to estimate TSS as it
indicated good comply with the criteria of good model.
B- Retrieval models for Chl-a
- reflec_8 can be used as a predictor of Chl-a during the third
mission, but with low R-squared value (0.431).
- Stepwise regression was failed to get an equation for fourth
mission.
C- Retrieval models for Turbidity indicate very good R-squared value,
but it did not show a correlation with a common band for all models
constructed to estimate turbidity. Validation results indicate that
regression model for first mission is suitable to get turbidity during
this season.
D- Retrieval models for Transparency
- Retrieval models for different missions indicate that reflec_5 can
be used as a predictor for transparency; it can be used alone or
with other band reflectances.
- Validation of retrieval models indicate that model for November
2006 is the suitable model to get transparency during the end of
flood.
E- Retrieval models for Total Phosphorus (TP)
- TP can be correlated to different band reflectance depending on
the season and mission.
- The validation indicates that regression model found for 2006
mission can be used for the end of flood season, and it is noted
that reflec_5 is suitable to predict TP.
116
F- Retrieval models for Silica (SiO2)
- Silica can be correlated to different band reflectance depending on
the season and mission.
- Validation results show that model for 2007 mission is suitable to
estimate silica at the end of flood.
G- Retrieval models for Total dissolved Solids (TDS)
- Results show that reflec_6 is commonly correlated to TDS.
- All regression models for the end of flood show good comply
with the criteria of good model. Model of 2006 mission was select
to estimate TDS.
The analysis and validation results shows that common bands (5 and 6)
are used to predict both optical especially TSS and non-optical water
quality parameters.
3- Time Series
It is concluded from time series curves of the three months (October,
November and December) which represent the end of flood season that
value of TSS concentration started to increase until it reaches its
maximum value in 2005 for the southern part of the lake. The change in
TP is the same as TSS. The change pattern is similar to sin curve. An
opposite behavior is found for both Silica and TDS as they decreased in
2005. Transparency in the southern part of the lake is not showing a
specific pattern and indicates values greater than the measured values.
4- Cloud and sun glint analysis
It concluded that period from September to November has minimum
cloud probability and it considered the best time for coupling between
field measurements and remote sensing. The second period that has low
cloud probability is during January and February.
117
6.2. Recommendations
The following represents the recommendation for the future researches
that can be done by the author or by others:
1- A complete field program, oriented only for water quality
monitoring, should be conducted in different seasons with repetitive
measurements for the same season with preforming some optical
measurements.
2- Creating a bio-optical model that can be used for the case of Lake
Nasser.
3- Creating a new processor that can be used for Lake Nasser.
4- Coupling between water quality modelling and remote sensing in
order to calibrate and predict different water quality parameters.
5- Maximize the benefits of all MERIS images used through this
research by using it in other applications such as evaporation or land
use/land cover maps.
6- Integration between MERIS processing Tool with other used tools to
create a new tool that can be easily used to have a near real time
status for water quality in the lake using remote sensing images.
119
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APPENDIX (A)
MERIS IMAGES CHARACTERISTICS AND
WATER QUALITY ANALYSIS
A.1. MERIS image characteristics
The Medium Resolution Imaging Spectrometer (MERIS) is one of the
main instruments on board Envisat platform lunched by the European
Space Agency (ESA)'s in March 2002. An array of nine instruments,
used in earth observation, was carried out by Envisat to collect
information about the Earth (land, water, ice, and atmosphere) using a
variety of measurement principles. A tenth instrument, DORIS, provided
guidance and control, see Figure ( A-1).
MESIS is a passive sensor that depends on sun solar radiation as its
source of energy. The observation of Earth using MERIS images is
performed simultaneously in 15 spectral bands (ESA, 2006).
Figure (A-1) Envisat Satellite Configuration and Payload
Instruments (ESA, 2006)
Name Description
ASAR Advanced Synthetic Aperture
Radar
MERIS Medium-Spectral Resolution
Imaging Spectrometer
AATSR Advanced Along Track Scanning
Radiometer
RA-2 Radar Altimeter
MWR Microwave Radiometer
GOMOS Global Ozone Monitoring by
Occultation of Stars
MIPAS Michelson Interferometer for
Passive Atmospheric Sounding
SCIAMACHY
Scanning Imaging Absorption
Spectrometer for Atmospheric
Chartography
DORIS Doppler Orbitography and Radio-
positioning Integrated by Satellite
LRR Laser Retro-Reflector
126
A.1.1. MERIS product processing levels
The Envisat products are delivered to researches in three processing
levels; Level 1B which contains data about Top Of Atmosphere (TOA)
radiance, Level 2 are produced by processing from Level 1B images to
get geophysical measurements, and Level 3 products which are synthesis
of more than one MERIS products (and possibly external data) to display
geophysical measurements for a time period.
The output or products of MERIS are image packages of several
parameters such as radiances at the top of the atmosphere (TOA) in
different bands. Different products are created according to the
parameters plotted and their resolution. In Table ( A-1), all the MERIS
products are listed with their respective ID’s and the working mode of the
instrument.
A.1.2. Resolutions
Spatial, Spectral and Temporal resolution of MERIS image are described
below.
- Spatial Resolution
MERIS was provided in three main spatial resolutions; Full-Resolution
(FR), Reduced Resolution (RR) and Low Resolution (LR). Pixel in an FR
image represents an area of 260 m × 290 m and in an RR image an area
of 1,040 m × 1,160 m, and in LR an area of 4,160 m × 4,640 m.
The instrument always records information with its full resolution. The
Reduced Resolution then can be generated using onboard averaging,
while LR images can be generated by averaging RR data in a ground
processor.
- Spectral Resolution
The MERIS sensor acquire an image contains fifteen (15) spectral bands
within the spectral range from 390 nm to 1040 nm, this range lies in the
visible and near infrared wavelengths of the electromagnetic spectrum.,
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Table ( A-2) illustrates the spectral characteristics of each band and the
application used for each band.
Table (A-1) MERIS products description (ESA, 2006).
Product ID Product Name Application
MER_RR__0P Reduced Resolution Level 0
Not generally available to
users
MER_FR__0P Full Resolution Level 0
MER_CA__0P Calibration Level 0
MER_RV__0P Reduced Field of View Level 0
MER_RR__1P Reduced Resolution Level 1 Serve as the basis for level
2 processing
MER_FR__1P Full Resolution Level 1
Application in atmospheric
modeling, land use
monitoring, ocean color
monitoring, vegetation
indices, and others
MER_RR__2P Reduced Resolution
Geophysical
Ocean, land or atmosphere
characterization at 1040 by
1160 m pixel spatial
resolution
MER_FR__2P Full Resolution Geophysical
Climatology, meteorology,
environmental monitoring,
etc.
MER_LRC_2P
Extracted Cloud Thickness and
Water Vapour for
Meteorological Users
Intended only for
meteorological
applications
MER_RRC_2P Extracted Cloud Thickness and
Water Vapour
Intended for
meteorological
applications
MER_RRV_2P Extracted Vegetation Indices Intended for near real time
land monitoring
MER_RR__BP Browse Product
Support queries to a
MERIS archive for land,
sea, ice or cloud features,
to be viewed from a
remote user terminal
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Table (A-2) MERIS spectral bands and applications (ESA, 2006).
No
.
Band center
(nm)
Band width
(nm) Applications
1 412.5 10 Yellow substance and detrital pigments
2 442.5 10 Chlorophyll absorption maximum
3 490 10 Chlorophyll and other pigments
4 510 10 Suspended sediment, red tides
5 560 10 Chlorophyll absorption minimum
6 620 10 Suspended sediment
7 665 10 Chlorophyll absorption & fluorescence
reference
8 681.25 7.5 Chlorophyll fluorescence peak
9 708.75 10 Fluorescence reference, atmosphere
corrections
10 753.75 7.5 Vegetation, cloud, O2 absoption band
reference
11 760.625 3.75 O2 R- branch absorption band
12 778.75 15 Atmosphere corrections
13 865 20 Atmosphere corrections
14 885 10 Vegetation, water vapour reference
15 900 10 Water vapour
- Temporal Resolution
MERIS field-of-view angle is 68.5° and its swath width is about 1150
km, which allows covering the Earth every three days, this can be useful
to track changes in oceanographic, land, and in atmospheric
investigations. Figure ( A-2) shows the global coverage of MERIS in both
winter and summer.
129
Figure (A-2) MERIS Global coverage (ESA, 2006)
A.1.3. MERIS Case 2 water quality processors
Estimating water quality parameters, as discussed before, is one of the
main objectives of MERIS mission. So that, many water processors were
developed for that reason. All of these processors were developed as a
plugins for BEAM software.
Algorithms developed to process MERIS images and are reliable far from
land and on open oceans (These type of water are called Case 1 Waters).
However, in coastal and lake waters (Case 2 Waters) where high
concentrations of reflective particles can be found in the water surface,
traditional algorithms fail. Alternative and more sophisticated algorithms
have been developed to deal with these complications; three of these
processors will be described, analyzed and compared in a Case 2 Waters
scenario:
- Coastal Case 2 Regional Water Processor (C2R).
- Boreal Lakes Water Processor.
- Eutrophic Lakes Water Processor.
The three have been developed by the Gesellschaft fur
Kernenergieverwertung in Schiffbau und Schiffahrt mbH (GKSS). All
processors use neural networks and are based on the architecture of the
C2R processor developed by (Doerffer R., 2008)
130
The algorithm of these processors can be conceptually divided in two
main parts: the atmospheric correction algorithm and the water algorithm.
The first part, determines the surface reflectance spectrum RLw() using
the top of atmosphere radiance spectrum RLtoa() acquired by MERIS;
the second part, uses the water leaving radiances as input and computes
different products that provide information about the quality of the water.
A.2. Water Quality Analysis
A.2.1. Water sampling
At any site water samples were taken from five depths (50) cm from
surface, 25%, 50%, 65% and 80% from total depth) using water sampler
of different depths. Field measurements were determined in all water
samples for all sites.
A.2.2. Preservation
Sample bottles should be cleaned before use by soaking it in detergent,
followed by rinsing with tap water several times until free of detergent,
rinsed with 5% nitric acid and then thoroughly with distilled-deionized
water. Sample bottles were cleaned and sterilised according to the
standard procedures for microbiological analysis.
The collected water samples were preserved in glass and plastic clean
bottles for the parameter needed to be measured and preserved in icebox
to retard any chemical and biological changes.
A.2.3. Types of analysis
Field measurements (on site analysis)
Certain parameters can vary while transporting samples to the laboratory
and it is always required to determine the following parameters in the
field (pH, temperature, dissolved oxygen, turbidity and conductivity) by
using portable field equipment moreover water depths, Transparency,
flow velocity and discharges have been also measured.
131
A.2.4. Laboratory measurements
Sensitive parameters such as, nutrients (orthophosphate, total
phosphorous, nitrate, nitrite and ammonia), fecal coliform (FC), Silicate,
Chlorophyll a, alkalinity, sulphate, major ions, surfactants, total
suspended solids (TSS), total dissolved solids (TDS) and total hardness
were analyzed within 24 hours of sampling. The other parameters
including sodium, potassium and heavy metals were measured within one
month. Physicochemical analyses were performed according to the
standard methods for examination of water and wastewater suggested by
American Public Health Association (APHA, 2012).
A.2.5. Reagents
The standards, reagents, solvents, indicators and buffer solutions were
prepared following recommended procedures (APHA, 2012). All the
chemicals used in the analysis were of analytical grade.
A.2.6. Equipment
Field equipment
- Different depths water sampler.
- Secchi Disk.
- Portable conductivity meter, used for measurement of
conductivity, salinity and temperature (WTW, LF 197).
- Portable pH meter with electrode for measurement of pH and
temperature (WTW, pH 197).
- Portable dissolved oxygen meter with oxygen probe used for
measurement of dissolved oxygen (WTW, Oxi 197).
- Portable spectrophotometer (DR 2400 HACH) used for field
measurements of phosphorous, COD, nitrite, detergents.
- YSI 6-Series Multi-parameter Water Quality Monitors 650 MDS
used for measuring pH, DO, Conductivity, Temperature and
Depth.
132
A.2.7. Laboratory equipment
- Orion research ionanalyzer EA 940 for measuring cations, anions
and gases by using specific ion selective electrode.
- In-Spectra Analyzer used for measuring TOC, Nitrate, TSS and
Surfactant.
A.2.8. General equipment
- Incubator for Fecal test.
- Automatic autoclave for sterilization and digestion.
- Hot plates with magnetic stirrer.
- Electronic analytical balance.
- Oven.
133
APPENDIX (B)
MERIS PROCESSING TOOL (MPT) AND
EXCEL CUSTOM FUNCTIONS
B.1. Introduction
This appendix contains the programming details and visual basic code of
MERIS Processing tool, which create in order to automate the MERIS
images processing to save time need for manual processing. Also, it
contains visual basic code for excel custom function created to calculate
statistical performance measures used to evaluate water quality
processors and regression models.
B.2. MERIS PROCESSING TOOL (MPT)
This tool was developed in Excel using Visual Basic for Application
(VBA) to automate processing of MERIS images, to help in processing
of large number of MERIS images, to save time needed for processing
and to reduce the mistakes which may found in the manual process. The
advantage of using VBA in excel is that Excel is found in all computers,
so there is no need to have additional programs; also it is easy to convert
it to a stand-alone program if needed.
B.2.1. Main Idea of MPT
The main idea of MPT is based on using the BEAM Graph Processing
Framework (GPF), a processor is as a software module which uses one or
more input product to create an output product using a set of processing
parameters. The GPF allows constructing directed acyclic graphs (DAG)
of processing nodes. A node in the graph refers to a GPF operator, which
makes a certain algorithm to be executed. The main role of the node is to
configure the operation by identifying the operator’s source nodes and
providing values for different processing parameters. Figure ( B-1) shows
a sample of a processing graph comprising five nodes.
134
GPF operators can be executed in batch mode using Graph processing
Tool (GPT), the operators can be used stand-alone or combined as a
directed acyclic graph (DAG). The concept of using GPT is to use the
BEAM from command line; Processing graphs are represented using
XML. GPT command can be used as a single command or using a set of
commands using batch file to handle more than one processing operation
as well as handling more than one file. The role of MPT is to write and
runs the final batch and XML files in easy way.
Figure (B-1) Sample of A processing graph
B.2.2. MPT description
The flow chart of MPT programming is shown in Figure ( B-2), a detailed
programming code for the tool was represented below. MPT was found
in MS Excel workbook contains the VBA Code for MPT, Once you open
this workbook was opened, the shown window in Figure ( B-3a) will
appears, it is a simple window and easy to use. The first step in using
MPT is to select the directory in which MERIS image files were found,
the default output directory is a directory in the same location of the MPT
workbook. Also it is important to select the type of MERIS image format
even it in Envisat MERIS (*.N1) format or BEAM-DIMAP product files
135
(*.dim) and to select the format of final output image a description of the
processing operations for MERIS images are:
Figure (B-2) Flow chart of MPT programming
a. Subsetting of the MERIS products in order to avoid processing
area larger than required to decrease time needed for processing,
the boundaries of the region of interest (ROI) were defined in
MPT by two ways, by entering the coordinates of lower left
corner and upper right corner coordinates of ROI bounding box or
by loading a Google Earth (*.KML) file contains a rectangular
polygon that defines the ROI. If the image is already subsetted to
the ROI, this step can be skipped. MERIS image can be corrected
against adjacency effect using ICOL process at this step, as there
are two option, to preform subset with ICOL processor or to
preform ICOL processor only as show in Figure ( B-3b)
b. MPT have an option to reproject the final BEAM-DIMAP product
files, to geo-locate the final product in its original coordinates.
136
c. After subsetting and/or ICOL processor operation were selected,
there are two processes to preform; water processor operation or
to band math operation.
d. Validating the use of a three water processors (Case 2 Regional,
Boreal Lakes, Eutrophic Lakes) for use in Lake Nasser is one of
the main objectives this research. So that, MPT give the ability to
select one or all of water processors to be applied to MERIS
image in the same batch file. A parameter XML file, which stores
the settings, is need for each processor, and it can easily obtained
directly from BEAM, MPT take this parameter file and then
generates another output XML graph file that used in processor
operation.
e. The other process can be performed in MPT is band math, in
order to perform a mathematical operation of the level 1B MERIS
image, a simplified method for atmospheric correction (SMAC),
should be performed to the image to calculate the water leaving
reflectance for 15 bands. A request XML file contains the
parameters used in SMAC process is required. SMAC processor
can be skipped in case the images are already atmospherically
corrected. Applying the band math operations can be done by
check “Apply Band Math” box, a window will popups to enter the
required information, Figure ( B-3c) for each mathematical
expression, an XML graph file containing the all added
expression will be written to be used later by MPT.
f. Cloud probability processor can be applied using MPT. This
allows us to classify each pixel as Cloudy, Cloud uncertain or
Cloud free.
137
a.
b.
c.
Figure (B-3) MPT interface
138
B.2.3. MPT Code
This section illustrates the programming code for MTP, the code will be
shown for every object found in the tool and categorized as follows:
- Code for Main MPT window.
- Code for Subset region definition and ICOL processor window.
- Code for Band Math window.
The shown code is for buttons, check boxes and option buttons.
1- Code for Main MPT window
Dim CHL_CPfile
Dim TSM_CPfile
Dim c2r_xml
Dim eut_xml
Dim bor_xml
Dim FUBMath_xml
Dim SmacRfile
Private Sub bmath_Change()
If bmath.Value = -1 Then
fndloc.Form_bm.Enabled = True
fndloc.smac.Enabled = True
ElseIf bmath.Value = 0 Then
fndloc.Form_bm.Enabled = False
fndloc.Form_bm.Value = 0
fndloc.smac.Enabled = False
fndloc.smac.Value = 0
End If
End Sub
Private Sub c2r_Change()
Dim P_file_cont As String * 6000
quot = """"
Dim PF_Typ As String * 12
If c2r.Value = -1 Then
5:
fndloc.c2rer = ""
P_file = Application.GetOpenFilename("Case 2
Regional Parameters (*.xml),*.xml")
Open P_file For Binary As #2
Get #2, 1, P_file_cont
Get #2, 1, PF_Typ
Close #2
If P_file = flase Then
c2r.Value = 0
GoTo 20
End If
139
If PF_Typ <> "<parameters>" Then
MsgBox("Please Select Proper Parameter file")
fndloc.c2rer = ""
GoTo 5
End If
c2r_xml = Application.ActiveWorkbook.Path &
"\Processing Parameters\" & "Case2R.xml"
Open c2r_xml For Output As #1
Print #1, "<graph id="; quot; "Case2RGraph"; quot; ">"
Print #1, " <version>1.0</version>"
Print #1, " <node id="; quot; "case2r"; quot; ">"
Print #1, "
<operator>Meris.Case2Regional</operator>"
Print #1, " <sources>"
Print #1, " <source>${source}</source>"
Print #1, " </sources>"
Print #1, Left(P_file_cont, FileLen(P_file))
Print #1, " </node>"
If fndloc.reproj.Value = -1 Then
Print #1, " <node id="; """reprojNode"""; ">"
Print #1, " <operator>Reproject</operator>"
Print #1, " <sources>"
Print #1, " <source>case2r</source>"
Print #1, " </sources>"
Print #1, " <parameters>"
Print #1, " <crs>EPSG:63266405</crs>"
Print #1, "
<resampling>Nearest</resampling>"
Print #1, "
<includeTiePointGrids>false</includeTiePointGrids>"
Print #1, " </parameters>"
Print #1, " </node>"
End If
Print #1, "</graph>"
Close #1
ElseIf c2r.Value = 0 Then
End If
20:
End Sub
Private Sub Clouds_Click()
Dim objFSO, Infolder, fold, f1, fc
quot = """"
Dim SMAC_F_cont As String * 6000
If Clouds.Value = -1 Then
CloudsRfile = Application.GetOpenFilename("Clouds
Request File (*.xml),*.xml")
Const ForReading = 1
Const ForWriting = 2
objFSO = CreateObject("Scripting.FileSystemObject")
140
Infolder = Left(fndloc.SDname, Len(fndloc.SDname) -
1)
fold = objFSO.GetFolder(Infolder)
fc = fold.Files
For Each f1 In fc
If f1.Type = "BEAM-DIMAP file, BEAM's standard
EO data format" Or f1.Type = "Envisat data product in N1
format" Then
If f1.Type = "BEAM-DIMAP file, BEAM's
standard EO data format" Then
exlen = 4
extName = ".dim"
ElseIf f1.Type = "Envisat data product in
N1 format" Then
exlen = 3
extName = ".N1"
End If
In_Clouds_File =
objFSO.OpenTextFile(CloudsRfile, ForReading, True)
Out_Clouds_File =
objFSO.OpenTextFile(Application.ActiveWorkbook.Path &
"\Processing Parameters\Clouds\" & Left(f1.Name,
Len(f1.Name) - exlen) & ".xml", ForWriting, True)
Do While Not In_Clouds_File.AtEndofStream
myLine = In_Clouds_File.ReadLine
If Subset.Value = -1 Then
InValue = " <InputProduct
file=" & quot & Left(fndloc.TDname, Len(fndloc.TDname) - 1)
& "\Subset\" & Left(f1.Name, Len(f1.Name) - exlen) &
"_Subset.dim" & quot & " />"
ElseIf Subset.Value = 0 Then
InValue = " <InputProduct
file=" & quot & Left(f1.Path, Len(f1.Path) - exlen) &
extName & quot & " />"
End If
OutValue = " <OutputProduct
file=" & quot & Left(fndloc.TDname, Len(fndloc.TDname) - 1)
& "\Clouds\" & Left(f1.Name, Len(f1.Name) - exlen) &
"_Clouds.dim" & quot & " format=" & quot & "BEAM-DIMAP" &
quot & " />"
If InStr(myLine, "InputProduct") Then
myLine = InValue & whatever
ElseIf InStr(myLine, "OutputProduct")
Then
myLine = OutValue & whatever
End If
Out_Clouds_File.WriteLine(myLine)
Loop
End If
141
Next
End If
End Sub
Private Sub eut_l_Change()
Dim P_file_cont As String * 6000
quot = """"
Dim PF_Typ As String * 12
If eut_l.Value = -1 Then
6:
fndloc.c2rer = ""
P_file = Application.GetOpenFilename("Eutrophic
lakes Parameters (*.xml),*.xml")
Open P_file For Binary As #2
Get #2, 1, P_file_cont
Get #2, 1, PF_Typ
Close #2
If P_file = flase Then
eut_l.Value = 0
GoTo 21
End If
If PF_Typ <> "<parameters>" Then
MsgBox("Please Select Proper Parameter file")
fndloc.c2rer = ""
GoTo 6
End If
eut_xml = Application.ActiveWorkbook.Path &
"\Processing Parameters\" & "Eut_Lakes.xml"
Open eut_xml For Output As #1
Print #1, "<graph id="; quot; "Eut_Lakes"; quot; ">"
Print #1, " <version>1.0</version>"
Print #1, " <node id="; quot; "Eutrophic"; quot; ">"
Print #1, " <operator>Meris.Lakes</operator>"
Print #1, " <sources>"
Print #1, " <source>${source}</source>"
Print #1, " </sources>"
Print #1, Left(P_file_cont, FileLen(P_file))
Print #1, " </node>"
If fndloc.reproj.Value = -1 Then
Print #1, " <node id="; """reprojNode"""; ">"
Print #1, " <operator>Reproject</operator>"
Print #1, " <sources>"
Print #1, " <source>Eutrophic</source>"
Print #1, " </sources>"
Print #1, " <parameters>"
Print #1, " <crs>EPSG:63266405</crs>"
Print #1, "
<resampling>Nearest</resampling>"
Print #1, "
<includeTiePointGrids>false</includeTiePointGrids>"
142
Print #1, " </parameters>"
Print #1, " </node>"
End If
Print #1, "</graph>"
Close #1
ElseIf c2r.Value = 0 Then
End If
21:
End Sub
Private Sub bor_l_Change()
Dim P_file_cont As String * 6000
quot = """"
Dim PF_Typ As String * 12
If bor_l.Value = -1 Then
7:
fndloc.c2rer = ""
P_file = Application.GetOpenFilename("Boreal lakes
Parameters (*.xml),*.xml")
Open P_file For Binary As #2
Get #2, 1, P_file_cont
Get #2, 1, PF_Typ
Close #2
If P_file = flase Then
bor_l.Value = 0
GoTo 21
End If
If PF_Typ <> "<parameters>" Then
MsgBox("Please Select Proper Parameter file")
fndloc.c2rer = ""
GoTo 7
End If
bor_xml = Application.ActiveWorkbook.Path &
"\Processing Parameters\" & "Bor_Lakes.xml"
Open bor_xml For Output As #1
Print #1, "<graph id="; quot; "Eut_Lakes"; quot; ">"
Print #1, " <version>1.0</version>"
Print #1, " <node id="; quot; "Eutrophic"; quot; ">"
Print #1, " <operator>Meris.Lakes</operator>"
Print #1, " <sources>"
Print #1, " <source>${source}</source>"
Print #1, " </sources>"
Print #1, Left(P_file_cont, FileLen(P_file))
Print #1, " </node>"
If fndloc.reproj.Value = -1 Then
Print #1, " <node id="; """reprojNode"""; ">"
Print #1, " <operator>Reproject</operator>"
Print #1, " <sources>"
Print #1, " <source>Eutrophic</source>"
Print #1, " </sources>"
143
Print #1, " <parameters>"
Print #1, " <crs>EPSG:63266405</crs>"
Print #1, "
<resampling>Nearest</resampling>"
Print #1, "
<includeTiePointGrids>false</includeTiePointGrids>"
Print #1, " </parameters>"
Print #1, " </node>"
End If
Print #1, "</graph>"
Close #1
ElseIf bor_l.Value = 0 Then
End If
21:
End Sub
Private Sub exchl_Change()
If exchl.Value = -1 And out_typ.Value <> "GeoTIFF
product format (*.tif)" Then
CHL_CPfile = Application.GetOpenFilename("CHL Color
Palette (*.cpd),*.cpd")
End If
End Sub
Private Sub extsm_Click()
If extsm.Value = -1 And out_typ.Value <> "GeoTIFF
product format (*.tif)" Then
TSM_CPfile = Application.GetOpenFilename("TSM Color
Palette (*.cpd),*.cpd")
End If
End Sub
Private Sub fndlocext_Click()
fndloc.Hide()
End Sub
Private Sub Form_bm_Click()
If Form_bm.Value = -1 Then
FBmath.Show()
ElseIf Form_bm.Value = 0 Then
End If
End Sub
Private Sub fub_Change()
Dim objFSO, Infolder, fold, f1, fc
quot = """"
Dim FUB_F_cont As String * 6000
If fub.Value = -1 Then
8:
fubRfile = Application.GetOpenFilename("FUB\WEW
Request File (*.xml),*.xml")
If fubRfile = flase Then
fub.Value = 0
GoTo 22
144
End If
Const ForReading = 1
Const ForWriting = 2
objFSO = CreateObject("Scripting.FileSystemObject")
Infolder = Left(fndloc.SDname, Len(fndloc.SDname) -
1)
fold = objFSO.GetFolder(Infolder)
fc = fold.Files
For Each f1 In fc
If f1.Type = "BEAM-DIMAP file, BEAM's standard
EO data format" Or f1.Type = "Envisat data product in N1
format" Then
If f1.Type = "BEAM-DIMAP file, BEAM's
standard EO data format" Then
exlen = 4
extName = ".dim"
ElseIf f1.Type = "Envisat data product in
N1 format" Then
exlen = 3
extName = ".N1"
End If
In_FUB_File = objFSO.OpenTextFile(fubRfile,
ForReading, True)
Out_FUB_File =
objFSO.OpenTextFile(Application.ActiveWorkbook.Path &
"\Processing Parameters\FUB\" & Left(f1.Name, Len(f1.Name)
- exlen) & ".xml", ForWriting, True)
Do While Not In_FUB_File.AtEndofStream
myLine = In_FUB_File.ReadLine
If Subset.Value = -1 Then
InValue = " <InputProduct
file=" & quot & Left(fndloc.TDname, Len(fndloc.TDname) - 1)
& "\Subset\" & Left(f1.Name, Len(f1.Name) - exlen) &
"_Subset.dim" & quot & " />"
ElseIf Subset.Value = 0 Then
InValue = " <InputProduct
file=" & quot & Left(f1.Path, Len(f1.Path) - exlen) &
extName & quot & " />"
End If
OutValue = " <OutputProduct
file=" & quot & Left(fndloc.TDname, Len(fndloc.TDname) - 1)
& "\FUB\001 Main\" & Left(f1.Name, Len(f1.Name) - exlen) &
"_FUB.dim" & quot & " format=" & quot & "BEAM-DIMAP" & quot
& " />"
If InStr(myLine, "InputProduct") Then
myLine = InValue & whatever
ElseIf InStr(myLine, "OutputProduct")
Then
myLine = OutValue & whatever
145
End If
Out_FUB_File.WriteLine(myLine)
Loop
End If
Next
FUBMath_xml = Application.ActiveWorkbook.Path &
"\Processing Parameters\FUB\FUBExpBands.xml"
V_Express = "(reflec_2 > 0) and not
(l1_flags.INVALID or l1_flags.BRIGHT or bright)"
If Regdef.icol.Value = -1 Then
V_Express = "(reflec_2 > 0) and not
(l1_flags.INVALID or l1_flags.BRIGHT)"
End If
Open FUBMath_xml For Output As #1
Print #1, "<graph id="; """BandmathId"""; ">"
Print #1, " <version>1.0</version>"
Print #1, " <node id="; """BandmathNode"""; ">"
Print #1, " <operator>BandMaths</operator>"
Print #1, " <sources>"
Print #1, " <source>${source}</source>"
Print #1, " </sources>"
Print #1, " <parameters>"
Print #1, " <targetBands>"
Print #1, " <targetBand>"
Print #1, " <name>"; "algal_2";
"</name>"
Print #1, " <expression>";
V_Express; " ? (algal_2) : NaN</expression>"
Print #1, " <description>";
"Chlorophyll 2 content"; "</description>"
Print #1, " <type>float32</type>"
Print #1, " <unit>";
"log10(g/m^3)"; "</unit>"
Print #1, "
<noDataValue>0</noDataValue>"
Print #1, " </targetBand>"
Print #1, " <targetBand>"
Print #1, " <name>"; "yellow_subs";
"</name>"
Print #1, " <expression>";
V_Express; " ? (yellow_subs) : NaN</expression>"
Print #1, " <description>"; "Yellow
substance"; "</description>"
Print #1, " <type>float32</type>"
Print #1, " <unit>";
"log10(g/m^3)"; "</unit>"
Print #1, "
<noDataValue>0</noDataValue>"
Print #1, " </targetBand>"
146
Print #1, " <targetBand>"
Print #1, " <name>"; "total_susp";
"</name>"
Print #1, " <expression>";
V_Express; " ? (total_susp) : NaN</expression>"
Print #1, " <description>"; "Total
suspended matter"; "</description>"
Print #1, " <type>float32</type>"
Print #1, " <unit>";
"log10(g/m^3)"; "</unit>"
Print #1, "
<noDataValue>0</noDataValue>"
Print #1, " </targetBand>"
Print #1, " </targetBands>"
Print #1, " </parameters>"
Print #1, " </node>"
If fndloc.reproj.Value = -1 Then
Print #1, " <node id="; """reprojNode"""; ">"
Print #1, " <operator>Reproject</operator>"
Print #1, " <sources>"
Print #1, "
<source>BandmathNode</source>"
Print #1, " </sources>"
Print #1, " <parameters>"
Print #1, " <crs>EPSG:63266405</crs>"
Print #1, "
<resampling>Nearest</resampling>"
Print #1, "
<includeTiePointGrids>false</includeTiePointGrids>"
Print #1, " </parameters>"
Print #1, " </node>"
End If
Print #1, "</graph>"
Close #1
End If
22:
End Sub
Private Sub lakes_Change()
If lakes.Value = -1 Then
fndloc.c2r.Enabled = True
fndloc.eut_l.Enabled = True
fndloc.bor_l.Enabled = True
fndloc.fub.Enabled = True
fndloc.extsm.Enabled = True
fndloc.exchl.Enabled = True
ElseIf lakes.Value = 0 Then
fndloc.c2r.Enabled = False
fndloc.c2r.Value = 0
fndloc.eut_l.Enabled = False
147
fndloc.eut_l.Value = 0
fndloc.bor_l.Enabled = False
fndloc.bor_l.Value = 0
fndloc.fub.Enabled = False
fndloc.fub.Value = 0
fndloc.extsm.Enabled = False
fndloc.extsm.Value = 0
fndloc.exchl.Enabled = False
fndloc.exchl.Value = 0
End If
End Sub
Private Sub Run_Click()
Dim Ex_num As Integer
Mbat = Application.ActiveWorkbook.Path & "\Processing
Parameters\" & "Main Batch.bat"
quot = """"
reg_xml = Application.ActiveWorkbook.Path &
"\Processing Parameters\" & Regdef.XML_fn & ".xml"
bmath_xml = Application.ActiveWorkbook.Path &
"\Processing Parameters\Band Math.xml"
Open Mbat For Output As #1
Print #1, "@echo off"
Print #1, "setlocal ENABLEDELAYEDEXPANSION"
Print #1,
"::::::::::::::::::::::::::::::::::::::::::::"
Print #1, ":: User Configuration"
Print #1, "set gptPath=" & """C:\Program Files\beam-
4.10.3\bin\gpt.bat"""
If extsm.Value = -1 Or exchl.Value = -1 Or
Form_bm.Value = -1 Then
Print #1, "set PconvPath=" & """C:\Program
Files\beam-4.10.3\bin\pconvert.bat"""
If extsm.Value = -1 Then
Print #1, "set TSM_CP="; TSM_CPfile
'D:\Phd\GPT\MERIS Processing Tool\Input
Parameters\TSM_colours.cpd"
End If
If exchl.Value = -1 Then
Print #1, "set CHL_CP="; CHL_CPfile
'D:\Phd\GPT\MERIS Processing Tool\Input
Parameters\TSM_colours.cpd"
End If
End If
Print #1, "set sourceDirectory=" & quot &
Left(fndloc.SDname, Len(fndloc.SDname) - 1) & quot
If Subset.Value = -1 Then
Print #1,
"::::::::::::::::::::::::::::::::::::::::::::"
Print #1, ":: Subset commandline handling"
148
Print #1, "rem first parameter is a path to the
subset graph xml"
Print #1, "set subset_graphXml=" & quot & reg_xml &
quot
Print #1, "set sourceDirectory=" & quot &
Left(fndloc.SDname, Len(fndloc.SDname) - 1) & quot
Print #1, "set subset_TDirectory=" &
Left(fndloc.TDname, Len(fndloc.TDname) - 1) & "\Subset"
Print #1, "set subset_Suffix=Subset"
End If
If lakes.Value = -1 Then
Print #1,
"::::::::::::::::::::::::::::::::::::::::::::"
Print #1, ":: Water Processors commandline
handling"
If c2r.Value = -1 Then
Print #1, "set C2R_graphXml=" & quot & c2r_xml
& quot
Print #1, "set C2R_TDirectory=" &
Left(fndloc.TDname, Len(fndloc.TDname) - 1) & "\Case 2
Regional"
If extsm.Value = -1 Then
'Print #1, "md "; quot; "!TSM_TDirectory!";
quot
ElseIf exchl.Value = 0 Then
'Print #1, "md "; quot; "!CHL_TDirectory!";
quot
End If
Print #1, "set C2R_Suffix=C2R"
End If
If eut_l.Value = -1 Then
Print #1, "set EUT_graphXml=" & quot & eut_xml
& quot
Print #1, "set EUT_TDirectory=" &
Left(fndloc.TDname, Len(fndloc.TDname) - 1) & "\Eutrophic
lakes"
Print #1, "set EUT_Suffix=EUT"
End If
If bor_l.Value = -1 Then
Print #1, "set BOR_graphXml=" & quot & bor_xml
& quot
Print #1, "set BOR_TDirectory=" &
Left(fndloc.TDname, Len(fndloc.TDname) - 1) & "\Boreal
lakes"
Print #1, "set BOR_Suffix=BOR"
End If
If fub.Value = -1 Then
Print #1, "set FUB_Path=" & """C:\Program
Files\beam-4.10.3\bin\fub.bat"""
149
Print #1, "set FUB_XMLDirectory=";
Application.ActiveWorkbook.Path; "\Processing
Parameters\FUB"
Print #1, "set FUB_TDirectory=";
Left(fndloc.TDname, Len(fndloc.TDname) - 1) & "\FUB"
Print #1, "set FUB_BM_XML=" & quot &
FUBMath_xml & quot
End If
End If
If bmath.Value = -1 Then
Print #1,
"::::::::::::::::::::::::::::::::::::::::::::"
Print #1, ":: Band Math and SMAC commandline
handling"
If smac.Value = -1 Then
Print #1, "set SMAC_Path=" & """C:\Program
Files\beam-4.10.3\bin\meris-smac.bat"""
Print #1, "set SMAC_XMLDirectory=";
Application.ActiveWorkbook.Path; "\Processing
Parameters\SMAC"
Print #1, "set SMAC_TDirectory=";
Left(fndloc.TDname, Len(fndloc.TDname) - 1) & "\SMAC"
End If
If Form_bm.Value = -1 Then
Print #1, "set Bmath_Xml=" & quot & bmath_xml &
quot
Print #1, "set Bmath_TDirectory=" &
Left(fndloc.TDname, Len(fndloc.TDname) - 1) & "\Band Math"
Print #1, "set Bmath_Suffix=Bmath"
End If
End If
If Clouds.Value = -1 Then
Print #1,
"::::::::::::::::::::::::::::::::::::::::::::"
Print #1, ":: Clouds Propability commandline
handling"
Print #1, "set Clouds_Path=" & """C:\Program
Files\beam-4.10.3\bin\meris-cloud.bat"""
Print #1, "set Clouds_XMLDirectory=";
Application.ActiveWorkbook.Path; "\Processing
Parameters\Clouds"
Print #1, "set Clouds_TDirectory=";
Left(fndloc.TDname, Len(fndloc.TDname) - 1) & "\Clouds"
End If
Print #1,
"::::::::::::::::::::::::::::::::::::::::::::"
Print #1, ":: Main processing"
Img_Intyp = typ.Value
If typ.Value = "ENVISAT MERIS (*.N1)" Then
150
Img_Intyp = "N1"
ElseIf typ.Value = "BEAM-DIMAP product files (*.dim)"
Then
Img_Intyp = "dim"
Else
If MsgBox("MERIS Input file type not selected,
Select (*.N1) as a default value", vbOKCancel) = vbOK Then
Img_Intyp = "N1"
End If
End If
If out_typ.Value = "GeoTIFF product format (*.tif)"
Then
out_format = "tifp"
TSM_Color_p = ""
CHL_Color_p = ""
ElseIf out_typ.Value = "JPEG image format (*.jpg)" Then
out_format = "jpg"
TSM_Color_p = " -c " & """!TSM_CP!"""
CHL_Color_p = " -c " & """!CHL_CP!"""
ElseIf out_typ.Value = "Portal Network Graphics
(*.png)" Then
out_format = "png"
TSM_Color_p = " -c " & """!TSM_CP!"""
CHL_Color_p = " -c " & """!CHL_CP!"""
ElseIf out_typ.Value = "Geotif Image Format (*.tif)"
Then
out_format = "tif"
TSM_Color_p = " -c " & """!TSM_CP!"""
CHL_Color_p = " -c " & """!CHL_CP!"""
ElseIf out_typ.Value = "Microsoft Bitmap image (*.bmp)"
Then
out_format = "bmp"
TSM_Color_p = " -c " & """!TSM_CP!"""
CHL_Color_p = " -c " & """!CHL_CP!"""
End If
Print #1, "for %%F in (%sourceDirectory%\*." &
Img_Intyp & ") do ("
If Subset.Value = -1 Then
Print #1, "set subset_SFile=%%~fF"
Print #1, "set
subset_TFile=%subset_TDirectory%\%%~nF_%subset_Suffix%.dim"
Print #1, "set subset_procCmd=%gptPath%
%subset_graphXml% -Ssource=" & """!subset_SFile!""" & " -t
" & """!subset_TFile!"""
Print #1, "echo !subset_procCmd!"
Print #1, "call !subset_procCmd!"
End If
If c2r.Value = -1 Then
If Subset.Value = -1 Then
151
Print #1, "set C2R_SFile=!subset_TFile!"
ElseIf Subset.Value = 0 Then
Print #1, "set C2R_SFile=%%~fF"
End If
Print #1, "set C2R_TFile=%C2R_TDirectory%\001
Main\%%~nF_%C2R_Suffix%.dim"
Print #1, "set C2R_procCmd=%gptPath% %C2R_graphXml%
-Ssource=" & """!C2R_SFile!""" & " -t " & """!C2R_TFile!"""
Print #1, "echo !C2R_procCmd!"
Print #1, "call !C2R_procCmd!"
If extsm.Value = -1 Then
Print #1, "set
TSM_TDirectory=!C2R_TDirectory!\TSM"
Print #1, "md "; quot; "!TSM_TDirectory!"; quot
Print #1, "set TSMPconv_Cmd=%pconvPath% -f ";
out_format; " -b 35 " & """!C2R_TFile!""" & " -o " &
"""!TSM_TDirectory!""" & TSM_Color_p
Print #1, "echo !TSMPconv_Cmd!"
Print #1, "call !TSMPconv_Cmd!"
End If
If exchl.Value = -1 Then
Print #1, "set
CHL_TDirectory=!C2R_TDirectory!\CHL"
Print #1, "md "; quot; "!CHL_TDirectory!"; quot
Print #1, "set CHLPconv_Cmd=%pconvPath% -f ";
out_format; " -b 36 " & """!C2R_TFile!""" & " -o " &
"""!CHL_TDirectory!""" & CHL_Color_p
Print #1, "echo !CHLPconv_Cmd!"
Print #1, "call !CHLPconv_Cmd!"
End If
End If
If eut_l.Value = -1 Then
If Subset.Value = -1 Then
Print #1, "set EUT_SFile=!subset_TFile!"
ElseIf Subset.Value = 0 Then
Print #1, "set EUT_SFile=%%~fF"
End If
Print #1, "set EUT_TFile=%EUT_TDirectory%\001
Main\%%~nF_%EUT_Suffix%.dim"
Print #1, "set EUT_procCmd=%gptPath% %EUT_graphXml%
-Ssource=" & """!EUT_SFile!""" & " -t " & """!EUT_TFile!"""
Print #1, "echo !EUT_procCmd!"
Print #1, "call !EUT_procCmd!"
If extsm.Value = -1 Then
Print #1, "set
TSM_TDirectory=!EUT_TDirectory!\TSM"
Print #1, "md "; quot; "!TSM_TDirectory!"; quot
152
Print #1, "set TSMPconv_Cmd=%pconvPath% -f ";
out_format; " -b 35 " & """!EUT_TFile!""" & " -o " &
"""!TSM_TDirectory!""" & TSM_Color_p
Print #1, "echo !TSMPconv_Cmd!"
Print #1, "call !TSMPconv_Cmd!"
End If
If exchl.Value = -1 Then
Print #1, "set
CHL_TDirectory=!EUT_TDirectory!\CHL"
Print #1, "md "; quot; "!CHL_TDirectory!"; quot
Print #1, "set CHLPconv_Cmd=%pconvPath% -f ";
out_format; " -b 36 " & """!EUT_TFile!""" & " -o " &
"""!CHL_TDirectory!""" & CHL_Color_p
Print #1, "echo !CHLPconv_Cmd!"
Print #1, "call !CHLPconv_Cmd!"
End If
End If
If bor_l.Value = -1 Then
If Subset.Value = -1 Then
Print #1, "set BOR_SFile=!subset_TFile!"
ElseIf Subset.Value = 0 Then
Print #1, "set BOR_SFile=%%~fF"
End If
Print #1, "set BOR_TFile=%BOR_TDirectory%\001
Main\%%~nF_%BOR_Suffix%.dim"
Print #1, "set BOR_procCmd=%gptPath% %BOR_graphXml%
-Ssource=" & """!BOR_SFile!""" & " -t " & """!BOR_TFile!"""
Print #1, "echo !BOR_procCmd!"
Print #1, "call !BOR_procCmd!"
If extsm.Value = -1 Then
Print #1, "set
TSM_TDirectory=!BOR_TDirectory!\TSM"
Print #1, "md "; quot; "!TSM_TDirectory!"; quot
Print #1, "set TSMPconv_Cmd=%pconvPath% -f ";
out_format; " -b 35 " & """!BOR_TFile!""" & " -o " &
"""!TSM_TDirectory!""" & TSM_Color_p
Print #1, "echo !TSMPconv_Cmd!"
Print #1, "call !TSMPconv_Cmd!"
End If
If exchl.Value = -1 Then
Print #1, "set
CHL_TDirectory=!BOR_TDirectory!\CHL"
Print #1, "md "; quot; "!CHL_TDirectory!"; quot
Print #1, "Set CHLPconv_Cmd=%pconvPath% -f ";
out_format; " -b 36 " & """!BOR_TFile!""" & " -o " &
"""!CHL_TDirectory!""" & CHL_Color_p
Print #1, "echo !CHLPconv_Cmd!"
Print #1, "call !CHLPconv_Cmd!"
End If
153
End If
If fub.Value = -1 Then
Print #1, "Set
FUB_XML=%FUB_XMLDirectory%\%%~nF.xml"
Print #1, "Set FUB_procCmd=%FUB_Path% "; quot;
"!FUB_XML!"; quot
Print #1, "echo !FUB_procCmd!"
Print #1, "call !FUB_procCmd!"
Print #1, "set FUB_BM_SFile=%FUB_TDirectory%\001
Main\%%~nF_FUB.dim"
Print #1, "set FUB_BM_TFile=%FUB_TDirectory%\001
Main\%%~nF_FUB_BM.dim"
Print #1, "set FUB_BM_procCmd=%gptPath%
%FUB_BM_XML% -Ssource=" & """!FUB_BM_SFile!""" & " -t " &
"""!FUB_BM_TFile!"""
Print #1, "echo !FUB_BM_procCmd!"
Print #1, "call !FUB_BM_procCmd!"
If extsm.Value = -1 Then
Print #1, "set
TSM_TDirectory=!FUB_TDirectory!\TSM"
Print #1, "md "; quot; "!TSM_TDirectory!"; quot
Print #1, "set TSMPconv_Cmd=%pconvPath% -f ";
out_format; " -b 3 " & """!FUB_BM_TFile!""" & " -o " &
"""!TSM_TDirectory!""" & TSM_Color_p
Print #1, "echo !TSMPconv_Cmd!"
Print #1, "call !TSMPconv_Cmd!"
End If
If exchl.Value = -1 Then
Print #1, "set
CHL_TDirectory=!FUB_TDirectory!\CHL"
Print #1, "md "; quot; "!CHL_TDirectory!"; quot
Print #1, "set CHLPconv_Cmd=%pconvPath% -f ";
out_format; " -b 1 " & """!FUB_BM_TFile!""" & " -o " &
"""!CHL_TDirectory!""" & CHL_Color_p
Print #1, "echo !CHLPconv_Cmd!"
Print #1, "call !CHLPconv_Cmd!"
End If
End If
If bmath.Value = -1 Then
If smac.Value = -1 Then
Print #1, "set
SMAC_XML=%SMAC_XMLDirectory%\%%~nF.xml"
Print #1, "echo !SMAC_XML!"
Print #1, "Set SMAC_procCmd=%SMAC_Path% ";
quot; "!SMAC_XML!"; quot
Print #1, "echo !SMAC_procCmd!"
Print #1, "call !SMAC_procCmd!"
End If
If Form_bm.Value = -1 Then
154
If Subset.Value = -1 Then
If smac.Value = -1 Then
Print #1, "set
Bmath_SFile=%SMAC_TDirectory%\%%~nF_Smac.dim"
Print #1, "set
Bmath_TFile=%Bmath_TDirectory%\001
Main\%%~nF_%Bmath_Suffix%.dim"
Print #1, "set Bmath_procCmd=%gptPath%
%Bmath_Xml% -Ssource=" & """!Bmath_SFile!""" & " -t " &
"""!Bmath_TFile!"""
Print #1, "echo !Bmath_procCmd!"
Print #1, "call !Bmath_procCmd!"
ElseIf smac.Value = 0 Then
Print #1, "set
Bmath_SFile=!subset_TFile!"
Print #1, "set
Bmath_TFile=%Bmath_TDirectory%\001
Main\%%~nF_%Bmath_Suffix%.dim"
Print #1, "set Bmath_procCmd=%gptPath%
%Bmath_Xml% -Ssource=" & """!Bmath_SFile!""" & " -t " &
"""!Bmath_TFile!"""
Print #1, "echo !Bmath_procCmd!"
Print #1, "call !Bmath_procCmd!"
End If
ElseIf Subset.Value = 0 Then
If smac.Value = -1 Then
Print #1, "set
Bmath_SFile=%SMAC_TDirectory%\%%~nF_Smac.dim"
Print #1, "set
Bmath_TFile=%Bmath_TDirectory%\001
Main\%%~nF_%Bmath_Suffix%.dim"
Print #1, "set Bmath_procCmd=%gptPath%
%Bmath_Xml% -Ssource=" & """!Bmath_SFile!""" & " -t " &
"""!Bmath_TFile!"""
Print #1, "echo !Bmath_procCmd!"
Print #1, "call !Bmath_procCmd!"
ElseIf smac.Value = 0 Then
Print #1, "set Bmath_SFile=%%~fF"
Print #1, "set
Bmath_TFile=%Bmath_TDirectory%\001
Main\%%~nF%Bmath_Suffix%_.dim"
Print #1, "set Bmath_procCmd=%gptPath%
%Bmath_Xml% -Ssource=" & """!Bmath_SFile!""" & " -t " &
"""!Bmath_TFile!"""
Print #1, "echo !Bmath_procCmd!"
Print #1, "call !Bmath_procCmd!"
End If
End If
For Ex_loop = 1 To Application.Cells(100, 100)
155
Ex_Var = Application.Cells(100 + Ex_loop,
101) & "_TDirectory"
Print #1, "set "; Ex_Var;
"=%Bmath_TDirectory%\"; Application.Cells(100 + Ex_loop,
101)
Print #1, "md "; quot; "!"; Ex_Var; "!";
quot
Ex_Cmd = Application.Cells(100 + Ex_loop,
101) & "Pconv_Cmd"
Print #1, "set "; Ex_Cmd; "=%pconvPath% -f
"; out_format; " -b "; Application.Cells(100 + Ex_loop,
100); """!Bmath_TFile!""" & " -o " & """!"; Ex_Var; "!""" &
TSM_Color_p
Print #1, "echo !"; Ex_Cmd; "!"
Print #1, "call !"; Ex_Cmd; "!"
Next Ex_loop
End If
End If
If Clouds.Value = -1 Then
Print #1, "set
Clouds_XML=%Clouds_XMLDirectory%\%%~nF.xml"
Print #1, "echo !Clouds_XML!"
Print #1, "Set Clouds_procCmd=%Clouds_Path% ";
quot; "!Clouds_XML!"; quot
Print #1, "echo !Clouds_procCmd!"
Print #1, "call !Clouds_procCmd!"
End If
Print #1, ")"
Print #1, "echo msgbox "; quot; "Process finished!";
quot; ", vbInformation, "; quot; "MERIS Processing Tool";
quot; " > %tmp%\tmp.vbs"
Print #1, "cscript /nologo %tmp%\tmp.vbs"
Print #1, "del %tmp%\tmp.vbs"
Print #1, "pause"
Close #1
Shell(Mbat, vbMaximizedFocus)
fndloc.Hide()
End Sub
Private Sub S_Dir_Click()
in_file = Application.GetOpenFilename("All Supported
Products (*.dim; *.N1),*.dim;*.N1,BEAM-DIMAP product files
(*.dim), *.dim , ENVISAT MERIS (*.N1),*.N1")
If in_file = flase Then GoTo 10
fndloc.SDname = Left(in_file, Len(in_file) -
Len(Dir(in_file))) 'Left((in_file))
10:
End Sub
Private Sub smac_Change()
Dim objFSO, Infolder, fold, f1, fc
156
quot = """"
Dim SMAC_F_cont As String * 6000
If smac.Value = -1 Then
SmacRfile = Application.GetOpenFilename("SMAC
Request File (*.xml),*.xml")
Const ForReading = 1
Const ForWriting = 2
objFSO = CreateObject("Scripting.FileSystemObject")
Infolder = Left(fndloc.SDname, Len(fndloc.SDname) -
1)
fold = objFSO.GetFolder(Infolder)
fc = fold.Files
For Each f1 In fc
If f1.Type = "BEAM-DIMAP file, BEAM's standard
EO data format" Or f1.Type = "Envisat data product in N1
format" Then
If f1.Type = "BEAM-DIMAP file, BEAM's
standard EO data format" Then
exlen = 4
extName = ".dim"
ElseIf f1.Type = "Envisat data product in
N1 format" Then
exlen = 3
extName = ".N1"
End If
In_SMAC_File =
objFSO.OpenTextFile(SmacRfile, ForReading, True)
Out_SMAC_File =
objFSO.OpenTextFile(Application.ActiveWorkbook.Path &
"\Processing Parameters\SMAC\" & Left(f1.Name, Len(f1.Name)
- exlen) & ".xml", ForWriting, True)
Do While Not In_SMAC_File.AtEndofStream
myLine = In_SMAC_File.ReadLine
If Subset.Value = -1 Then
InValue = " <InputProduct
file=" & quot & Left(fndloc.TDname, Len(fndloc.TDname) - 1)
& "\Subset\" & Left(f1.Name, Len(f1.Name) - exlen) &
"_Subset.dim" & quot & " />"
ElseIf Subset.Value = 0 Then
InValue = " <InputProduct
file=" & quot & Left(f1.Path, Len(f1.Path) - exlen) &
extName & quot & " />"
End If
OutValue = " <OutputProduct
file=" & quot & Left(fndloc.TDname, Len(fndloc.TDname) - 1)
& "\SMAC\" & Left(f1.Name, Len(f1.Name) - exlen) &
"_Smac.dim" & quot & " format=" & quot & "BEAM-DIMAP" &
quot & " />"
If InStr(myLine, "InputProduct") Then
157
myLine = InValue & whatever
ElseIf InStr(myLine, "OutputProduct")
Then
myLine = OutValue & whatever
End If
Out_SMAC_File.WriteLine(myLine)
Loop
End If
Next
End If
End Sub
Private Sub Subset_Change()
If Subset.Value = -1 Then
Regdef.Show()
'fndloc.Reproj.Enabled = True
ElseIf Subset.Value = 0 Then
fndloc.RegCoord = ""
'fndloc.Reproj.Enabled = False
End If
End Sub
Private Sub T_Dir_Click()
out_file = Application.GetOpenFilename("All Files
(*.*),*.*")
If out_file = flase Then GoTo 20
fndloc.TDname = Left(out_file, Len(out_file) -
Len(Dir(out_file))) 'Left((out_file))
20:
End Sub
Private Sub UserForm_Initialize()
fndloc.TDname = Application.ActiveWorkbook.Path &
"\Output\"
typ.AddItem("ENVISAT MERIS (*.N1)")
typ.AddItem("BEAM-DIMAP product files (*.dim)")
out_typ.AddItem("GeoTIFF product format (*.tif)")
out_typ.AddItem("JPEG image format (*.jpg)")
out_typ.AddItem("Portal Network Graphics (*.png)")
out_typ.AddItem("Geotif Image Format (*.tif)")
out_typ.AddItem("Microsoft Bitmap image (*.bmp)")
fndloc.c2r.Enabled = False
fndloc.eut_l.Enabled = False
fndloc.bor_l.Enabled = False
fndloc.fub.Enabled = False
fndloc.extsm.Enabled = False
fndloc.exchl.Enabled = False
fndloc.Form_bm.Enabled = False
fndloc.smac.Enabled = False
bmath.Value = 0
c2r.Value = 0
End Sub
158
2- Code for Subset region defination and ICOL processor
window
Dim icol_P_file
Dim icolP_file_cont
Private Sub ClrFld2_Click()
Regdef.ll_lat = ""
Regdef.ll_long = ""
Regdef.ur_lat = ""
Regdef.ur_long = ""
End Sub
Private Sub icol_Change()
Dim icol_P_file_cont As String * 6000
quot = """"
Dim PF_Typ As String * 12
If icol.Value = -1 Then
5:
icol_P_file = Application.GetOpenFilename("ICOL
Parameters (*.xml),*.xml")
Open icol_P_file For Binary As #2
Get #2, 1, icol_P_file_cont
Get #2, 1, PF_Typ
Close #2
If icol_P_file = flase Then
icol.Value = 0
GoTo 20
End If
If PF_Typ <> "<parameters>" Then
MsgBox("Please Select Proper Parameter file")
GoTo 5
End If
Close #1
fndloc.bmath.Enabled = False
ElseIf icol.Value = 0 Then
'fndloc.extsm.Enabled = False
'fndloc.exchl.Enabled = False
fndloc.bmath.Enabled = True
End If
20:
icolP_file_cont = icol_P_file_cont
End Sub
Private Sub ReadKML_Click()
Dim xmlDom As MSXML2.DOMDocument
Dim xmlPlaceMark As MSXML2.IXMLDOMNode
Dim xmlPolygon As MSXML2.IXMLDOMNode
Dim xmlCoord As MSXML2.IXMLDOMNode
Dim sName As String
159
Dim vaSpace As Object, vaComma As Object
Dim i As Long, j As Long
xmlDom = New MSXML2.DOMDocument
Kmlfile = Application.GetOpenFilename("SMAC Request
File (*.kml),*.kml")
xmlDom.Load(Kmlfile)
For i = 0 To
xmlDom.ChildNodes(1).ChildNodes(0).ChildNodes.Length - 1
'Polygone
If
xmlDom.ChildNodes(1).ChildNodes(0).ChildNodes.Item(i).nodeN
ame = "Placemark" Then
xmlPlaceMark =
xmlDom.ChildNodes(1).ChildNodes(0).ChildNodes.Item(i)
xmlPolygon = xmlPlaceMark.ChildNodes(2)
If xmlPolygon.nodeName = "Polygon" Then
xmlCoord =
xmlPolygon.ChildNodes(1).ChildNodes(0).ChildNodes(0)
'MsgBox xmlCoord.nodeName
With Sheet1.Cells(Sheet1.Rows.Count,
1).End(xlUp).Offset(1, 0)
vaSpace =
Split(xmlCoord.ChildNodes(0).Text, " ")
For j = LBound(vaSpace) To
UBound(vaSpace)
vaComma = Split(vaSpace(j), ",")
.Offset(j + 1, 1).Value =
vaComma(0)
Regdef.ll_lat = vaComma(1)
.Offset(j + 1, 2).Value =
vaComma(1)
Regdef.ll_long = vaComma(0)
Next j
End With
End If
'Path
xmlLine = xmlPlaceMark.ChildNodes(2)
If xmlLine.nodeName = "LineString" Then
'MsgBox xmlLine.nodeName
xmlCoord = xmlLine.ChildNodes(1)
'MsgBox xmlCoord.nodeName
With Sheet1.Cells(Sheet1.Rows.Count,
1).End(xlUp).Offset(1, 0)
vaSpace =
Split(xmlCoord.ChildNodes(0).Text, " ")
For j = LBound(vaSpace) To
UBound(vaSpace)
If j = 0 Then
160
vaComma = Split(vaSpace(j),
",")
'.Offset(j + 1, 1).Value =
vaComma(1)
Regdef.ll_lat = vaComma(1)
'.Offset(j + 1, 2).Value =
vaComma(0)
Regdef.ll_long = vaComma(0)
End If
If j = 1 Then
vaComma = Split(vaSpace(j),
",")
'.Offset(j + 1, 1).Value =
vaComma(1)
Regdef.ur_lat = vaComma(1)
'.Offset(j + 1, 2).Value =
vaComma(0)
Regdef.ur_long = vaComma(0)
End If
Next j
End With
End If
End If
Next i
End Sub
Private Sub regset_Click()
quot = """"
reg_xml = Application.ActiveWorkbook.Path &
"\Processing Parameters\" & Regdef.XML_fn & ".xml"
'MsgBox Reg_xml
If Regdef.ll_lat = "" Or Regdef.ll_long = "" Or
Regdef.ur_lat = "" Or Regdef.ur_long = "" Then
MsgBox("Please Define Region coordinate")
Else
URlat = Regdef.ur_lat ': Application.Cells(11, 3) =
URlat
URlong = Regdef.ur_long ': Application.Cells(11, 2)
= URlong
ULlat = Regdef.ur_lat ': Application.Cells(12, 3) =
ULlat
ULlong = Regdef.ll_long ': Application.Cells(12, 2)
= ULlong
LLlat = Regdef.ll_lat ': Application.Cells(13, 3) =
LLlat
LLlong = Regdef.ll_long ': Application.Cells(13, 2)
= LLlong
LRlat = Regdef.ll_lat ': Application.Cells(14, 3) =
LRlat: Application.Cells(15, 3) = URlat
161
LRlong = Regdef.ur_long ': Application.Cells(14, 2)
= LRlong: Application.Cells(15, 2) = URlong
If Regdef.SubIcol.Value = -1 Then
Open reg_xml For Output As #1
Print #1, "<graph id=" & """SubSetId""" & ">"
Print #1, " <version>1.0</version>"
Print #1, " <node id=" & """SubSetNode""" & ">"
Print #1, " <operator>Subset</operator>"
Print #1, " <sources>"
Print #1, " <source>${source}</source>"
Print #1, " </sources>"
Print #1, " <parameters>"
Print #1, " <geoRegion>POLYGON((" & URlong
& " " & URlat & "," & ULlong & " " & ULlat & "," & LLlong &
" " & LLlat & "," & LRlong & " " & LRlat & "," & URlong & "
" & URlat & "))</geoRegion>" 'Coordinates
Print #1, "
<copyMetadata>True</copyMetadata>"
Print #1, " </parameters>"
Print #1, "</node>"
fndloc.RegCoord = "Only Subset was select"
If icol.Value = -1 Then
Print #1, " <node id="; quot; "ICOL"; quot;
">"
Print #1, " <operator>icol.Meris</operator>"
Print #1, " <sources>"
Print #1, "
<sourceProduct>SubSetNode</sourceProduct>"
Print #1, " </sources>"
Print #1, Left(icolP_file_cont,
FileLen(icol_P_file))
Print #1, " </node>"
fndloc.RegCoord = "Subset And ICOL
Processor were selected"
End If
Print #1, "</graph>"
Close #1
End If
If Regdef.ICOLonly.Value = -1 Then
Open reg_xml For Output As #1
Print #1, "<graph id=" & """SubSetId""" & ">"
Print #1, " <version>1.0</version>"
Print #1, " <node id="; quot; "ICOL"; quot; ">"
Print #1, " <operator>icol.Meris</operator>"
Print #1, " <sources>"
Print #1, "
<sourceProduct>${source}</sourceProduct>"
Print #1, " </sources>"
162
Print #1, Left(icolP_file_cont,
FileLen(icol_P_file))
Print #1, " </node>"
Print #1, "</graph>"
Close #1
fndloc.RegCoord = "Only ICOL Processor was
selected"
End If
End If
Regdef.Hide()
End Sub
Private Sub SubIcol_Change()
If Regdef.SubIcol.Value = -1 Then
Regdef.Frame1.Enabled = True
Regdef.Frame2.Enabled = True
Regdef.ur_lat.Enabled = True
Regdef.ur_long.Enabled = True
Regdef.ll_lat.Enabled = True
Regdef.ll_long.Enabled = True
Regdef.ReadKML.Enabled = True
Regdef.Label1.Enabled = True
Regdef.Label2.Enabled = True
Regdef.Label3.Enabled = True
Regdef.Label4.Enabled = True
End If
End Sub
Private Sub ICOLonly_Change()
If Regdef.ICOLonly.Value = -1 Then
Regdef.Frame1.Enabled = False
Regdef.Frame2.Enabled = False
Regdef.ur_lat.Enabled = False
Regdef.ur_long.Enabled = False
Regdef.ll_lat.Enabled = False
Regdef.ll_long.Enabled = False
Regdef.ReadKML.Enabled = False
Regdef.Label1.Enabled = False
Regdef.Label2.Enabled = False
Regdef.Label3.Enabled = False
Regdef.Label4.Enabled = False
End If
End Sub
Private Sub UserForm_Initialize()
Regdef.ll_lat = 21.483242
Regdef.ll_long = 30.366176
Regdef.ur_lat = 23.965172
Regdef.ur_long = 33.48221
Regdef.XML_fn = "Subset Region"
End Sub
163
3- Code for Band Math window
Dim Ex_num As Integer
Public Sub Bm_add_Click()
bmath_xml = Application.ActiveWorkbook.Path & "\Processing
Parameters\Band Math.xml"
Open bmath_xml For Append As #1
Print #1, " <targetBand>"
Print #1, " <name>"; FBmath.Ex_Name;
"</name>"
Print #1, " <expression>";
FBmath.Bm_Ex; "</expression>"
Print #1, " <description>";
FBmath.Ex_Des; "</description>"
Print #1, " <type>float32</type>"
Print #1, " <unit>"; FBmath.Ex_Un;
"</unit>"
Print #1, "
<noDataValue>0</noDataValue>"
Print #1, " </targetBand>"
add_Ex = MsgBox("Add Another Expression", vbYesNo)
Ex_num = Ex_num + 1
Application.Cells(100 + Ex_num, 100) = Ex_num
Application.Cells(100 + Ex_num, 101) =
FBmath.Ex_Name.Value
If add_Ex = 6 Then
FBmath.Ex_Name.Value = ""
FBmath.Bm_Ex.Value = ""
FBmath.Ex_Des.Value = ""
FBmath.Ex_Un.Value = ""
End If
Close #1
Application.Cells(100, 100) = Ex_num
End Sub
Private Sub Bm_OK_Click()
bmath_xml = Application.ActiveWorkbook.Path & "\Processing
Parameters\Band Math.xml"
If Ex_num > 0 Then
Open bmath_xml For Append As #1
Print #1, " </targetBands>"
Print #1, " </parameters>"
Print #1, " </node>"
If fndloc.reproj.Value = -1 Then
Print #1, " <node id="; """reprojNode"""; ">"
Print #1, " <operator>Reproject</operator>"
Print #1, " <sources>"
Print #1, "
<source>BandmathNode</source>"
Print #1, " </sources>"
164
Print #1, " <parameters>"
Print #1, " <crs>AUTO:42001</crs>"
Print #1, "
<resampling>Nearest</resampling>"
Print #1, "
<includeTiePointGrids>false</includeTiePointGri
ds>"
Print #1, " </parameters>"
Print #1, " </node>"
End If
Print #1, "</graph>"
Close #1
Else
Open bmath_xml For Output As #1
Close #1
End If
FBmath.Hide()
End Sub
Private Sub UserForm_Activate()
Ex_num = 0
bmath_xml = Application.ActiveWorkbook.Path & "\Processing
Parameters\Band Math.xml"
Open bmath_xml For Output As #1
Print #1, "<graph id="; """BandmathId"""; ">"
Print #1, " <version>1.0</version>"
Print #1, " <node id="; """BandmathNode"""; ">"
Print #1, " <operator>BandMaths</operator>"
Print #1, " <sources>"
Print #1, " <source>${source}</source>"
Print #1, " </sources>"
Print #1, " <parameters>"
Print #1, " <targetBands>"
Close #1
End Sub
165
B. 1. Excel Custom functions
Function MNB(ByVal obs As Range, ByVal comp As Range)
MNB = 0
nRows = obs.Rows.Count
For i = 1 To nRows
If obs(i, 1) = 0 Or comp(i, 1) = 0 Then GoTo 10
MNB = MNB + 100 * (comp(i, 1) - obs(i, 1)) / obs(i,
1)
n = n + 1
10:
Next i
Result:
MNB = MNB / n
End Function
Function ModBias(ByVal obs As Range, ByVal comp As Range)
ModBias = 0
n = 0
nRows = obs.Rows.Count
For i = 1 To nRows
If obs(i, 1) = 0 Or comp(i, 1) = 0 Then GoTo 10
ModBias = ModBias + (comp(i, 1) - obs(i, 1))
10:
Next i
Result:
ModBias = ModBias
End Function
Function FracBias(ByVal obs As Range, ByVal comp As Range)
obs_Sum = 0
comp_Sum = 0
n = 0
nRows = obs.Rows.Count
For i = 1 To nRows
If obs(i, 1) = 0 Or comp(i, 1) = 0 Then GoTo 10
obs_Sum = obs_Sum + obs(i, 1)
comp_Sum = comp_Sum + comp(i, 1)
n = n + 1
10:
Next i
obs_avg = obs_Sum / n
comp_avg = comp_Sum / n
Result:
FracBias = 2 * ((obs_avg - comp_avg) / (obs_avg +
comp_avg))
End Function
Function NMSE(ByVal obs As Range, ByVal comp As Range)
DifSqr = 0
obs_avg = 0
comp_avg = 0
166
n = 0
nRows = obs.Rows.Count
For i = 1 To nRows
If obs(i, 1) = 0 Or comp(i, 1) = 0 Then GoTo 10
DifSqr_Sum = DifSqr_Sum + ((comp(i, 1) - obs(i, 1))
^ 2)
obs_Sum = obs_Sum + obs(i, 1)
comp_Sum = comp_Sum + comp(i, 1)
n = n + 1
10:
Next i
DifSqr_avg = DifSqr_Sum / n
obs_avg = obs_Sum / n
comp_avg = comp_Sum / n
Result:
NMSE = DifSqr_avg / (obs_avg * comp_avg)
End Function
Function GMBias(ByVal obs As Range, ByVal comp As Range)
lnobs_Sum = 0
lncomp_Sum = 0
nRows = obs.Rows.Count
For i = 1 To nRows
If obs(i, 1) = 0 Or comp(i, 1) = 0 Then GoTo 10
lnobs_Sum = lnobs_Sum +
Application.WorksheetFunction.Ln(obs(i, 1))
lncomp_Sum = lncomp_Sum +
Application.WorksheetFunction.Ln(comp(i, 1))
n = n + 1
10:
Next i
Result:
lnobs_avg = lnobs_Sum / n
lncomp_avg = lncomp_Sum / n
GMBias = Exp(lnobs_avg - lncomp_avg)
End Function
Function GMVariance(ByVal obs As Range, ByVal comp As
Range)
GMVar1 = 0
nRows = obs.Rows.Count
For i = 1 To nRows
If obs(i, 1) = 0 Or comp(i, 1) = 0 Then GoTo 10
lnobs = Application.WorksheetFunction.Ln(obs(i, 1))
lncomp = Application.WorksheetFunction.Ln(comp(i,
1))
GMVar1 = GMVar1 + ((lnobs - lncomp) ^ 2)
n = n + 1
10:
Next i
Result:
167
GMVariance = Exp(GMVar1 / n)
End Function
Function fac_2(ByVal obs As Range, ByVal comp As Range)
nRows = obs.Rows.Count
For i = 1 To nRows
If obs(i, 1) = 0 Or comp(i, 1) = 0 Then GoTo 10
comp_obs_R = comp(i, 1) / obs(i, 1)
If comp_obs_R >= 0.5 And comp_obs_R <= 2 Then
m = m + 1
End If
n = n + 1
10:
Next i
Result:
fac_2 = m / n
End Function
168
Figure (B-4) ArcMap Model Builder used to cloud coverage percentage
169
Figure (B-5) ArcMap Model Builder used to Extract Lake Nasser Surface
171
APPENDIX (C)
Stepwise Regression charts
This appendix contains Matlab output of stepwise regression, the output
was represent as a graph for each parameter showing coefficient value for
each independent variable in means of error bar and t-stat and p-value.
The intercept of the regression model and R-squared value and RMSE are
presented as well. The variables with blue color are that included in the
selected regression model.
172
TDS
Transparency
SiO2
TP Turbidity
TSS
Figure (C-1) Stepwise Regression Analysis Results For 2003
173
TDS
TP
SiO2
Transparency
Turbidity
Figure (C-2) Stepwise Regression Analysis Results For 2006
174
Chl-a
TSS
TP
Transparency
SiO2
TDS
Figure (C-3) Stepwise Regression Analysis Results For 2007
175
TSS
SiO2
TP
Transparency
Figure (C-4) Stepwise Regression Analysis Results For 2009
176
TSS
TDS TP
Transparency
Turbidity
Figure (C-5) Stepwise Regression Analysis Results For 2010
177
TSS
Figure (C-6) Stepwise Regression Analysis Results For 2011
جامعة عين شمس
كلية الهندسة
قسم الري والهيدروليكا
بحيرة ناصر باستخدام تقنية التوزيع المكاني لمعامالت نوعية المياه في
االستشعار من بعد
مهندس
محمد علي حامد غريب (2003) جامعة بنها – كلية الهندسة بشيرا -مدنيةالهندسة البكالوريوس
(2010) جامعة عين شمس -كلية الهندسة - الهندسة المدنية في ماجستير
رسالة مقدمة كجزء من متطلبات الحصول على
ري وهيدروليكا –في الهندسة المدنية الفلسفة دكتوراهدرجة
شراف البحراوي نبيه علي .د أ.
الهيدروليكاأستاذ
كليــــــــــة الهندســـــــــة - والهيــــدروليكا قسم الري
جامعــــــة عيـــن شمــــس
د. محسن محمود يسري معهد بحوث النيل - أمين عام
المركز القومي لبحوث المياه
والريوزارة الموارد المائية
عطية محمود كريمة .د أ. بحوث الموارد المائية دمدير معه
المركز القومي لبحوث المياه
والريوزارة الموارد المائية
جمهورية مصر العربية – القاهرة
2016
كليــة الهندســة
قسم الري والهيدروليكا
نوعية المياه في بحيرة ناصر باستخدام تقنية التوزيع المكاني لمعامالت
االستشعار من بعد
إعداد
محمد علي حامد غريبالمهندس / (2003بكالوريوس الهندسة المدنية )
(2010ري وهيدروليكا ) –ماجستير في الهندسة المدنية
رسالة مقدمة كجزء من المتطلبات للحصول علي درجة الدكتوراه
في الهندسة المدنية )ري وهيدروليكا(
لجنة الحكم
التوقيع
……………… سعد عزيز مدحت .د أ. معهد بحوث النيل مدير
المركز القومي لبحوث المياه
والريوزارة الموارد المائية
……………… إيمان العزيزي .د أ. الهيـــدروليكاأستاذ
ةالهندســكليــة -الري والهيـــدروليكا قسم
جامعـــــة عيـــن شمــــس
……………… علي نبيه البحراوي .د أ. أستاذ الهيـــدروليكا
كليــة الهندســة -قسم الري والهيـــدروليكا
جامعـــــة عيـــن شمــــس
……………… كريمة محمود عطية .د أ. بحوث الموارد المائية معهد مدير
المركز القومي لبحوث المياه
والريوزارة الموارد المائية
2016/----/----التاريخ
كلية الهندسة
قسم الري والهيدروليكا
: محمد علي حامد غريب الطــــالب اسم
التوزيع المكاني لمعامالت نوعية المياه في بحيرة ناصر : عنوان الرسالة
باستخدام تقنية االستشعار من بعد
دكتوراه الفلسفة : الدرجـــة
شرافلجنة اإل
قسم الري والهيــــدروليكا دروليكاـــالهيأستاذ البحراوي نبيه علي .د أ.
جامعــــــة عيـــن شمــــس -ـة الهندســـــــــةـكليــــ
معهد بحوث الموارد المائية مدير عطية محمودد. كريمة أ.
والريوزارة الموارد المائية - المركز القومي لبحوث المياه
معهد بحوث النيل -أمين عام د. محسن محمود يسري
والريوزارة الموارد المائية - المركز القومي لبحوث المياه
2016تاريخ البحث : / /
2016أجيزت الرسالة بتاريخ / / :الدراسات العليا
:ختم اإلجازة
موافقة مجلس الجامعة الكليةموافقة مجلس
/ /2016 / /2016
هداءإ
إلى زوجتي العزيزة إلى ابنائي علي واحمد
إلى والدي العزيز إلى والدتي العزيزة
إلى أخي أحمد وأسرته
الرسالة بمقدم التعريف
محمد علي حامد غريب: االســــــــــم
6/12/1980: تاريــــخ الميــالد
القليوبية -شبرا الخيمة ثان -بهتيم : محــــل الميــالد
: بكالوريوس الهندسة المدنية الدرجة الجامعية االولـي
مدنية: هندسة التــخـــصـــص
بنهاجامعة –كلية الهندسة بشبرا : االوليالجهة المانحة الدرجة الجامعية
2003: تاريخ المنــــــح
لمدنية: ماجستير الهندسة ا الدرجة الجامعية الثانية
ري وهيدروليكا: التــخـــصـــص
جامعة عين شمس -كلية الهندسة : الجهة المانحة الدرجة الجامعية الثانية
2010 ليويو: تاريخ المنــــــح
.المركز القومي لبحوث المياه –معهد بحوث النيل – باحث مساعد: الوظيفــــــــة
2016التاريخ .../...../
جـــامعــة عيـــن شمس
كليـــــــة الهنـــــدســـــة
قسم الري والهيدروليكا
محمد علي حامد غريب اسم مقدم الرسالة:
المكاني لمعامالت نوعية المياه في بحيرة ناصر باستخدام تقنية التوزيع عنوان الرسالة:
االستشعار من بعد
مستلخص الرسالةيهدف هذا البحث الى استخدام االستشعار من بعد في تقدير معامالت نوعية المياه ببحيرة ناصر،
رحالت تم استخدام نتائج ستة حيث تعتبر بحيرة ناصر المخزون االستراتيجي للمياه بمصر.
للتحقق من (Envisat/MERIS)تم قياسها في فترات مختلفة، مع استخدام صور ،حقلية
Case 2 Regional (C2R), Eutrophic lake, and Boreal)استخدام بعض االدوات مثل
Lake)باستخدام ين الناتج عن االنعكاسات االرضيةاب، سواء بعد تصحيح الت(Improved
Contrast between ocean and Land – ICOL) ،لحساب المعامالت وذلك او بدونه
الضوئية )المواد العالقة والكلوروفيل(. تم التحقق من هذه االدوات باستخدام بعض المقاييس
اوضحت نتائج البحث انه بالنسبة للمواد العالقة يمكن استخدام االحصائية. بالنسبة للمواد العالقة،
كما يمكن استخدام في فترة نهاية الفيضان، (ICOL)بدون استخدام (Eutrophic Lake)اداة
لتقدير المواد العالقة في فترة (ICOL)بدون استخدام (Eutrophic Lake)و (C2R)كال من
عدم مالئمة كل االدوات في تقدير تركيز المواد العالقةالتصرفات المنخفضة، كما اظهر البحث
مع استخدام (Eutrophic Lake) لكلوروفيل، يمكن استخدامفي فترة بداية الفيضان .اما ا
(ICOL) تم .فترة نهاية الفيضانو التصرفات المنخفضةفي تقدير تركيز الكلوروفيل في فترة
والغير ضوئية في فترات الفيضان المختلفة ةاستنتاج معادالت خاصة لتقدير المعامالت الضوئي
يمكن 6و 5الغالف الجوي ووجد ان البندين باستخدام الصور بعد تصحيحها من تأثير
استخدامها في تقدير المعامالت المختلفة. تم التحقق من المعادالت في فترة انتهاء الفيضان
صورة لعمل سالسل زمنية )منحنيات وخرائط( بعد استبعاد الصور المتأثرة 913وتطبيقها على
وبعدها 2005ة وإجمالي الفسفور حتي عام بالسحب. حيث تم مالحظة ازدياد تركيز المواد العالق
بدأ التركيز في االنخفاض، وبالنسبة للسليكا والمواد الصلبة الذائبة فوجد انهما يتغيران بشكل
في التغير شكل معين. وقد وجد ان الفترة من عمعاكس. وبالنسبة لشفافية المياه فوجد انها ال تتب
لية وجود سحب وبالتالي تعتبر مناسبة إلجراء سبتمبر الى نوفمبر هي االقل من حيث احتما
قياسات ضوئية بالبحيرة والتي تفيد في عمل نموذج ضوئي خاص بالبحيرة.
البحراوي نبيه علي .د أ. المشرفون:
ا.د. كريمة محمود عطية
د. محسن محمود يسري
باللغة العربية ملخص الرسالة
عنوان الرسالة
المكاني لمعامالت نوعية المياه في بحيرة ناصر باستخدام تقنية االستشعار من التوزيع
مقدمة من المهندس/ محمد علي حامد غريب بعد
استخدام الطرق التقليدية في قياس نوعية المياه تعتمد على اخذ عينات عند نقاط محددة وبذلك
حالة يكون من الصعب معرفة فهي تعبر فقط عن التغييرات عند نفس نقط القياس، وفي هذه ال
هذا باالضافة الى ارتفاع تكاليف المأموريات الحقلية مما التغيرات في باقي المسطح المائي،
االستشعار تقنية لذا فإن استخدام يصعب مع تكرار هذه المأموريات في اوقات مختلفة من العام.
في حالة البحيرات ذات المساحات معرفة المعامالت المختلفة لنوعية المياه يكون مفيدا لمن بعد
السطحية الكبيرة مثل بحيرة ناصر التي تعتبر من اكبر البحيرات الصناعية في العالم، كما تعتبر
.المخزن الرئيسي للمياه العذبة بمصر
تنحصر مشاكل دراسة نوعية المياه في ارتفاع تكاليف القياسات الحقلية المتكررة باإلضافة الى
بقا عن عدم امكانية دراسة التغير في المسطح المائي بالكامل، ما تم ذكرة سا
تي:وتتلخص اهداف البحث في اآل
( في Case 2 water quality processorsالتحقق من استخدام بعض االدوات ) .1
Case 2تقدير المواد العالقة والكلوروفيل ببحيرة ناصر. ومن هذه االدوات )
Regional (C2R), Eutrophic lake, and Boreal Lake وهي عبارة عن )
ادوات تم تجربتها في مناطق مختلفة من العالم.
نوعية المياه واالنعكاسات السطحية المختلفة لمعامالت الن ايجاد معادالت تربط بي .2
( بعد تصحيحها من اثر الغالف الجوي.MERISالمستنتجة من صور )
فة لنوعية المياه.عمل سالسل زمنية للتغير في المعامالت المختل .3
يمكن حساب نسبة السحاب واالنعكاس الشمسي فوق البحيرة في االوقات المختلفة حيث .4
إلجراء القياسات الحقلية المستقبلية لربطها ةالمناسباالستفادة منها في تحديد االشهر
ببيانات االقمار الصناعية
وقد إشتملت الدراسة على األبواب األتية:
المقدمة الباب االول:
بالتغير في تركيز المواد العالقةبمشكلة البحث وعالقة التعريف يشمل على تقديم يهدف الى
محتويات كل باب. واستعراضكما يحتوي أيضا على أهداف الرسالة معامالت نوعية المياه.
الدراسات السابقة الباب الثاني:
التعريف باالستشعار من بعد وتاريخه يحتوي هذا الباب على الدراسات السابقة في مجال البحث و
في مجال نوعية المياه على مستوى العالم، كما يتضمن على ملخص االبحاث وعن استخدامه
التي أُستخدم فيها االستشعار من بعد في بحيرة ناصر في مجاالت عدة مثل البخر. كما يعطي
ايضا اسباب اختيار نقطة البحث.
والبيانات المستخدمة اسةخصائص منطقة الدر الباب الثالث:
يحتوي هذا الباب على التعريف ببحيرة ناصر من حيث موقعها الجغرافي واهميتها، كما يتضمن
اقبة البحيرة المستخدم حاليا. باإلضافة الى الخصائص الفزيائية توصيف لبرنامج مر
المتاحة للدراسة توصيف البيانات . كما يحتوي على والهيدرولوجية وخصائص الترسيب للبحيرة
.سواء كانت بيانات حقلية او بيانات اقمار صناعية
منهجية البحث الباب الرابع:
Case)البحث سواء المستخدمة في التحقق من استخدامالمتبعة بمنهجية العلى هذا البابيحتوي
2 water quality processors) بكل معامل من ةاو المستخدمة في ايجاد معادالت خاص
. كما يحتوي ايضا ، باإلضافة الى توصيف االدوات المستخدمة بالبحثت نوعية المياهمعامال
على توصيف للمقاييس االحصائية المستخدمة في تقييم النتائج.
تحليل النتائج الباب الخامس:
ناتجة عن التحقق من استخدام ادوات سواء البحث نتائج ليحتوي هذا الباب عل تحليل تفصيلي
(Case 2 Water quality processor) لتقدير كال من المواد العالقة والكلورفيل مع ايضاح
كما يحتوي على المعادالت التي تم .االدوات المالئمة لالستخدام في الفترات المختلفة في البحيرة
بين معامالت نوعية (Stepwise regression)استنتاجها باستخدام طريقة االنحدار المتدرج
مع تحديد معادلة واحدة لكل معامل لكل فترة من (MERIS)النعكاسات السطحية لصورالمياه وا
على تحليل مفصل عن نسبة السحاب فترات الفيضان باستخدام المقاييس االحصائية. كما يحتوي
االنعكاس الشمسي على سطح البحيرة حيث تفيد النسبة في استبعاد بعض الصور المستخدمة في و
ل الزمني للمعامالت المختلفة ويمكن االستفادة منها ايضا في تحديد افضل عمل منحني التسلس
يحتوي على االشهر إلجراء القياسات الحقلية المستقبلية لربطها ببيانات االقمار الصناعية. و
التي توضح تغير المعامالت المختلفة. منحنيات التسلسل الزمني
التوصياتالخالصة و الباب السادس:
من نتائج باالضافة الى التوصيات التي يجب ان البحث أهم ما توصل إليه ىلباب عليحتوي هذا ا
تؤخذ في االعتبار في االبحاث المستقبلية.
: كما يلي كما تحتوى الرسالة على ثالثة مالحق
من حيث دقة الصورة (MERIS)خصائص الصور المستخدمة يحتوي الملحق على ملحق )أ(:
ومستويات المعالجة الخاصة بها واالستخدامات المختلفة لها. كما يحتوي على شرح مفصل
خالل البحث. باالضافة الى توضيح طريقة (Case 2 water quality)لالدوات المستخدمة
.(APHA, 2012)أخذ وتحليل العينات من الطبيعة طبقا للمواصفات العالمية
الذي تم برمجته (MERIS Processing Tool)علي عرض لبرنامج يحتوي ملحق )ب(:
لتسهيل عملية معالجة الصور (Visual Basic) خالل هذا البحث باستخدام لغة الفيجوال بيسك
(MERIS) التحقيق واستنباط المعادالت او في مرحلة عمل التسلسل الزمني مرحلة سواء في
توي لب معالجة عدد كبير من صور االقمار الصناعية. كما يحللمعامالت نوعية المياه حيث تتط
الى بعض االدوات التي باإلضافةعلى الكود المستخدم في حساب المقاييس االحصائية المختلفة.
وذلك ايضا لتسهيل عملية معالجة بيانات صور (ArcMap)تم تطويرها باستخدام برنامج
االقمار الصناعية
بواسطة برنامج المتدرج اشكال توضح نتائج استخدام طريقة االنحدار يحتوي على ملحق )جـ(:
.(Matlab) مات الب