Remote Sensing for Water Quality Monitoring with Particular Reference to NOAA AVHRR Application in the Hawkesbury River Catchment Area.
by Li Li
A thesis submitted to the University of New South Wales in part fulfilment of the requirements for the Degree of Master of Surveying
The University of New South Wales, Australia February, 1994 UNIVERSITY OF N.S.W. I ' ’ 'Ml. 1395
1 LI B FI A R I E S STATEMENT
I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of the university or other institute of a higher learning, except where due acknowledgement is made in the text.
Li Li
February, 1994 ABSTRACT
Questions have been raised regarding the rapid deterioration of water quality in the Hawkesbury River. These problems are expected to escalate as the population in Sydney's West doubles to an expected 800,000 people residing in the catchment area by the year 2000. Regular monitoring of the Hawkesbury River tends to be limited, especially in the temporal and spatial domains, because of the costs associated with data collection and laboratory analyses. Satellite remote sensing provides an alternative means for obtaining simultaneous data over a large geographic area, and providing very valuable information at relatively low cost. However, little or no systematic study has been made to monitor the whole of the Hawkesbury River catchment area using such data.
For the first time this study uses NOAA AVHRR data to investigate the relationship between surface temperature change, land use change and water quality in the Hawkesbury River Catchment during the period of 1986 to 1991. The aim of this study, therefore, is to develop suitable techniques to relate remote observations from NOAA AVHRR satellite sensors to in situ measurements. However prior to the application of these data a comprehensive analysis of water quality and the application of satellite derived remotely sensed data to its measurement is undertaken.
The procedures for the application study involve four steps: 1) Determining the accurate location of the catchment area on the NOAA image using geometric correction and digitized methods; 2) Use of NDVI and Hybrid classification methods to analyse the land use change; 3) Application of a split-window technique to determine the surface temperature of the study area; 4) Comparison of the image processing results with the ground truth data (e.g. land use map, air temperature, water quality data).
The results confirm the possibility of using the NOAA AVHRR data for temperature and land use change detection in the catchment area and suggest that the methods obtained can be used for catchment planning and management, and further that NOAA AVHRR data is a useful and economic tool for investigating the catchment environment problem especially for long term monitoring.
Because of the limitation of the NOAA resolution it is difficult to quantitatively determine the water quality using NOAA data alone, and better results should be achieved using combined NOAA AVHRR data and high resolution data such as Landsat TM and MSS, or SPOT data. TABLE OF CONTENTS
Page ABSTRACT i TABLE OF CONTENTS jjj LIST OF FIGURES vii LIST OF TABLES viii ACKNOWLEDGEMENTS ix
CHAPTER 1 INTRODUCTION 1
1.1 Research Objectives 3 1.2 Study Area 4 1.2.1 Physical Characteristics 4 1.2.2 Climatic 8 1.2.3 Population and Land Use 10 1.2.4 Use of the River 12 1.2.5 Transport 13 1.3 The Structure of the Thesis 15
CHAPTER 2 WATER QUALITY 18
2.1 General Concepts of Water Pollution 18 2.2 The Necessity for Monitoring Water Quality 20 2.3 The Source of Water Pollution 21 2.4 Water Quality Parameters 24 2.4.1 Physical Properties 25 2.4.2 Chemical Properties 27 2.4.3 Biological Properties 28 2.5 Water Quality in the Study Area 29
iii CHAPTER 3 THE OPTICAL PROPERTIES OF WATER 32
3.1 Definition of Some Basic Radiometric Quantities 33 3.2 Inherent and Apparent Optical Properties 35 4.2.1 Inherent Optical Properties 36 4.2.2 Apparent Optical Properties 39 3.3 Scattering and Absorption in Pure Water 40 3.4 Scattering and Absorption in Natural Water 42 3.5 Application of Water Optics to Remote Sensing of Water Quality 46
CHAPTER 4 REMOTE SENSING IN WATER QUALITY MONITORING - AN OVERVIEW 51
4.1 Introduction 51 4.2 Historical Overview 52 4.3 General Approach 55 4.4 The Measurement of Water Quality Using Remote Sensing Data 57 4.4.1 The Measurement of Suspended Sediment 58 4.4.2 The Measurement of Chlorophyll 61 4.4.3 The Measurement of Dissolved Organic Matter 64 4.4.4 The Measurement of Colour 66 4.4.5 The Measurement of Temperature 68 4.5 The Impacts of Land Use on Water Quality 71 4.6 Problems and Limitations 73
CHAPTER 5 SOURCES OF INFORMATION 76
5.1 NOAA AVHRR Data 76 5.2 Water Quality Data 77 5.3 Air Temperature Data 79 5.4 Land Use Map 80
iv CHAPTER 6 IMAGE PREPROCESSING 82
6.1 Geometric Correction 83 6.1.1 Sources of Geometric Distortion 83 6.1.2 Methodology of Geometric Correction 84 6.1.2.1 Selecting GCPs 85 6.1.2.2 Calculation of the Transformation Matrix 87 6.1.2.1 Interpolation Using Coordinate Transformation 89 6.2 Image to Image Registration 90 6.3 Atmospheric Correction 96
CHAPTER 7 CHANGE DETECTION 100
7.1 The Techniques for Change Detection 100 7.1.1 Image Differencing 100 7.1.2 Image Regression 101 7.1.3 Image Ratioing 102 7.1.4 Normalized Vegetation Index Differencing 103 7.1.5 Principal Component Analysis 104 7.1.6 Post Classification Comparison 104 7.1.7 Direct Multidate Classification 105 7.1.8 Change Vector Analysis 106 7.1.9 Summary 107 7.2 Application of the NDVI Method 108 7.3 Application of Post Classification Comparison 116 7.4 Surface Temperature Retrievals 121 CHAPTER 8 RESULTS AND CONCLUSIONS 126
8.1 Land Use Change Discussion 126 8.1.1 Comparison of Classified Map 126 8.1.2 Land Use Change Discussion 127 8.1 Temperature Change Discussion 130 8.2.1 Temperature Change Analysis 130 8.2.2 Urban Heat Island Effect 137 8.2.3 The Relationship between Urbanization and Water Quality 138 8.1 Conclusion 139
REFERENCES 142
APPENDIX A 161 SATELLITES EMPLOYED FOR WATER POLLUTION MONITORING 161
A.1 NOAA VHRR/AVHRR (Tiros-N) 162 A.2 Landsat Series 164 A.3 Spot Series 168 A.4 The Coastal Zone Colour Scanner on Nimbus-7 170
vi LIST OF FIGURES Figure Page 1-1: Hawkesbury River Catchment 5 1-2: Hawkesbury River Catchment Area on the NOAA Image 6
3-1: Interaction of Light and Water 32 3-2: Absorption Coefficient a 36 3-3: Volume Scattering Function p(0,(p) 37 3-4: Volume Scattering Function for Isotopic Scattering, Rayleigh (Molecular) Scattering, and Mie (Particle Scattering) 38 3-5: Inherent Optical Properties a and b of Pure Water 42 3-6: Cumulative Particle Size Distributions 43
5- 1: Sampling Sites along Hawkesbury River 78
6- 1: Control Point Residual Plot 94 6- 2: Disk Pixel vs Disk Lines Plot 95
7- 1: Histograms of the NDVI's for the 1987 Image 110 7-2: Histograms of the NDVI's for the 1989 Image 111 7-3: Histograms of the NDVI's for the 1991 Image 112 7-4: NDVI Image of 24 September 1987 113 7-5: NDVI Image of 24 September 1989 114 7-6: NDVI Image of 30 September 1991 115 7-7: Classified Image of 24 September 1987 118 7-8: Classified Image of 24 September 1989 119 7-9: Classified Image of 30 September 1991 120 7-10: Surface Temperature of 24 September 1987 123 7-11: Surface Temperature of 24 September 1989 124 7- 12: Surface Temperature of 30 September 1991 125
8- 1: Land Use Map in the Hawkesbury River Catchment 128 8-2: Mean Daily Maximum Temperature at Sydney 132 8-3: Mean Daily Maximum Temperature at Richmond 132 8-4: Mean Daily Maximum Temperature at Camden 133 8-5: Mean Daily Maximum Temperature at Katoomba 133 8-6: Mean Daily Maximum Temperature at Goulburn 134 8-7: Some Meteorological Station on a NOAA AVHRR Image 135
vii LIST OF TABLES
Table Page 1-1: Rainfall Characteristics of 30 Years Record to 1979 9 1-2: Population Growth 11 1-3: Land-Use Patterns 12
3-1: Radiometric Quantities for Remote Sensing Use 34 3- 2: Specific Absorption Coefficient ac(X) of Phaeopigments and of Suspended and Dissolved Matter aM(A.) 45
4- 1: Some Applications in Remote Sensing of Water Quality 54 4- 2: List of Some Published Spilt Window Functions 70
5- 1: NOAA AVHRR Data Details 77 5-2: Mean Daily Minimum Temperature (°C) at Different Station 79 5- 3: Mean Daily Maximum Temperature (°C) at Different Station 80
6- 1: The Coordinates of the GCPs from the Map 86 6-2: Statistics for GCPs 93
8-1: Classified Difference of 1987, 1989 and 1991 127 8-2: Comparison of Land Use Patterns Changes 129 8-3: Comparison between Ground and Satellite Derived Temperature 136
A-1: Wavebands of Tiros-N/NOAA 163 A-2: Characteristics of the Evolving Family of Multichannel Radiometers on NOAA Satellites, 1972-2000 164 A-3: Wavebands and Applications of MSS on Landsat 1-3 165 A-4: Characteristics of the MSS and TM Carried on Landsat 4-7 166 A-5: Orbital and Sensor Characteristics of the SPOT-1 Satellite 169 A-6: Spectral Range and Applications of CZCS 170
viii ACKNOWLEDGEMENTS
It is very difficult to express in words my gratitude to my supervisor, Associate Professor Bruce Forster for the professional advice, and patient guidance, especially for the many critical and constructive comments during the period of my study. Bruce's extensive knowledge of remote sensing helped me in understanding, what were for me, new concepts and ideas. It was his warm and friendly nature that has made the greatest impact.
I would also like to thank with great appreciation all others, in the School of Surveying and Centre for Remote Sensing and GIS, who contributed to the completion of my research and shared a good and humorous atmosphere with me. In particular, thanks are due to Associate Professor Tony Milne, John Klingberg, Mark Hall and John Steer. I thank them all for their valuable suggestions and for taking an interest in my work. I am deeply grateful to Associate Professor Bill Kearsley for his very able and willing assistance, and for his time in reviewing drafts of my seminar.
There were many other organizations whose assistance in supplying such things as temperature data, water quality data, land use map and NOAA AVHRR data is gratefully acknowledged. They include: Water Board Bureau of Meteorology Division of Oceanography, CSIRO at Hobart Archives Authority of NSW
My study was made possible by an Australian Development Cooperation Scholarship Scheme. I am most grateful for this opportunity and the financial support.
My final appreciation is to my family for their love, never ending encouragement, support and understanding throughout my studies.
ix CHAPTER 1
INTRODUCTION
Questions have been raised regarding the rapid deterioration of water quality in the Hawkesbury River. The urban, agriculture, and industrial developments in the catchment area are a major contribution to the water pollution. These problems are expected to escalate as the population in Sydney's West doubles to an expected 800,000 people residing in the catchment area by the year 2000 (Dept, of Planning, 1989).
Regular monitoring of the Hawkesbury River has been undertaken using on-site measurements since the 1930's by the Water Board (Williams and Callaghan, 1991). Observations of chlorophyll, turbidity, suspended sediment concentration, and secchi disk depth can provide quantitative information concerning water quality. Traditional technology to measure these indicators of water quality involves in situ measurements or collection of water samples for subsequent laboratory measurements. However, in situ measurements of water quality characteristics tend to be limited, especially in the temporal and spatial domains, because of the costs associated with data collection and laboratory analyses. Although this technology gives accurate measurements for a point in time and space, it is expensive, time-consuming, and more importantly, does not give either the spatial or temporal view of water quality that is needed for accurate assessment and monitoring of surface water quality problems. INTRODUCTION Page 2
Therefore a measurement strategy is required to acquire information in a more efficient way. A technique is needed for monitoring changes in surface water quality parameters to provide a rapid assessment of both the spatial and temporal variability in surface water quality. Satellite remote sensing, with its wide area spatial coverage and synoptic view, provides an alternative means for obtaining relatively low-cost, simultaneous information on water quality within a large geographic area. And very valuable information can be acquired at very economical costs compared to field measurements.
However, to get meaningful information on water quality or the source of its change, remote sensing data needs to be processed and interpreted in the context of geographic and historical information. Interest in water quality have lead to efforts to relate remote observations from satellite sensors to in situ light and optical measurements. Therefore it is desirable that research work be conducted to develop suitable techniques and methods for its use in water pollution monitoring.
Since the late 1960s, many researchers have attempted to derive techniques for the estimation of water quality from remotely sensed data.
Many methods have been developed for the estimation of water quality at a number of temporal and spatial scales, and these have been applied in many different areas and situations with varying degrees of success.
A large number of scientific studies have been conducted over the
Hawkesbury River, and its catchment area, in a variety of disciplines including climatology, geology, and geomorphology. In relation to the present research, previous studies have provided a large amount of information on many aspects of water pollution, thus making it possible to establish a comprehensive remote sensing method for monitoring water pollution. INTRODUCTION Page 3
1.1 Research Objective
The aim of this study, therefore, is firstly to undertake a comprehensive analysis of water quality and the relationship between satellite observed remotely sensed data and water quality parameters and than to develop a suitable technique to relate remote observations from NOAA satellite sensors to in situ measurements. For the first time this study will use NOAA AVHRR data to investigate the relationship between surface temperature change, land use change and water quality in the Hawkesbury River Catchment area during the period 1986 to 1991.
In order to accomplish this investigation, the experimental procedures will involve the following steps: 1) Determine the accurate location of catchment area on the NOAA image using geometric correction and digitized method; 2) Use NDVI and Hybrid classification method analysis the land use change;
3) Apply split-window technique to determine the surface temperature of study area;
4) Compare the image processing result with the ground truth data
(e.g. land use map, air temperature, water quality data). INTRODUCTION Page 4
1.2 Study Area
By the most common usage the name "Nepean" is given to the main river above its junction with the Grose River and the name "Hawkesbury" is given to the river below this junction. Generally in this study the entire river, for convenience, will be named the Hawkesbury River.
The Hawkesbury River drains most of the developing areas to the west of
Sydney and its catchment will accommodate much of the City's future metropolitan growth. The river frequently experiences low flows because of the extended dry periods which are characteristic of New South Wales' weather. The river receives treated sewage and urban stormwater, and both cause a deterioration in water quality, and will become progressively worse as the city grows, unless corrective actions are taken.
1.2.1 Physical Characteristics
The catchment of the Hawkesbury River system encloses an area of almost 21,750 square kilometres (8,400 square miles) and is shown in Figure 1-1 (Minister for Environment Control, 1973). Figure 1-2 shows the catchment area on a NOAA AVHRR image. The area lies between latitude 33°15'S- 34°10'S, and longitude 150°35'E-151°15'E. The catchment extends from near Goulburn in the south to the boundaries of the Hunter Valley near Cessnock in the north. Westward it incorporates Lithgow and eastward, apart from the Georges and Parramatta River catchments, it continues to the Pacific Ocean. It constitutes the largest single catchment on the eastern coast of New South Wales. INTRODUCTION Page 5
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SYDNEY
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MOSS VAlt
Figure 1-1: Hawkesbury River Catchment INTRODUCTION Page 6
Figure 1-2: Hawkesbury River Catchment Area on the NOAA AVHRR Image of 24 September 1989 INTRODUCTION Page 7
Geographically, the catchment can be divided into seven discreet units (Conybeare, 1976). These are: • the Colo Highlands;
• the Blue Mountains Plateau; • the Southern Tablelands; • the Nepean Ramp; • the Emu Plains; • the Hornsby Plateau;
• the Macdonald River. Within these geographical areas there are certain consistencies regarding their geologic, topographic and physiographic form.
Topography in the catchment varies widely, from high plateau mountains and upland valleys in the west to the low plateau and shallow valleys of the east. The highest elevations are in the west, where Mount Bindo (1,362 meters) is the highest point. The lowest extensive land units, the Emu and Cumberland plains, are less than ten meters above sea level.
Most of the northern and northwestern areas are heavily timbered, rugged, mountainous terrain with land slopes greater than 15 degrees. Undulating hilly areas occur in the southwest near Goulburn and Moss Vale. The relatively gentle sloping or flat terrain borders the Hawkesbury River from the vicinity of Camden to Wallacia and from Penrith to a location about 8 miles downstream from Windsor. INTRODUCTION Page 8
1.2.2 Climate
The dominant factors controlling the climate of the Hawkesbury River catchment are its latitude and proximity to the Pacific ocean. The catchment enjoys a temperate climate with some variations in certain areas. A cool temperature climate prevails in the western and southern elevated parts of the catchment. The western section of the Cumberland Plain has a subhumid temperate climate and coastal areas have a humid climate.
A wide variation in temperature exists in the area. On the coastal fringe average maxima range from 28°C in summer to 18°C in the winter, while average minimum range from 18°C to 9°C. Further inland where the moderating effect of the ocean is reduced, the average maxima tend to be 3°C warmer in summer and 5°C cooler in winter. Extremes of temperature carry the range from 38°C to -3°C but these occur only on rare occasion (Rogers, 1982).
Rainfall distribution is largely dependent on topographic effects, which cause relatively high rainfall over elevated regions close to the coast and over the Blue Mountains. Annual rainfall in these areas range from 1000 to 16000 millimetres. There are rainshadows in the headwaters of the Wollondilly River, in the middle reaches of the Nepean River and in much of the Colo River catchment. Annual rainfall in these areas varies from 650 to 750 millimetres (Table 1-1). Large seasonal variations of rainfall can occur in the area and these have been adequately summarised in the report by the State Pollution Control Commission (1985). INTRODUCTION Page 9
Table 1-1: Rainfall Characteristics of 30 Years Record to 1979
Station 10 Percentile 50 Percentile Rainfall (mm/y) Rainfall(mm/y) Rainfall(mm/y)
Camden 468 779 Goulburn 462 678 Katoomba 900 1,501 Lithgow 611 893 Penrith 499 831 Windsor 484 731
Source: State Pollution Control Commission, 1983
On a monthly basis rainfall is generally heaviest near the coast in June but further inland the wettest month is February or March; September is generally the driest month. Summer is the wettest season over inland parts and Spring the driest over all of the region.
The long term average annual run-off for the Hawkesbury River catchment is 11 percent (State Pollution Control Commission, 1983). Direct storm run-off provides the supply for short period rises in streamflows, while the normal dry weather flows are derived mainly from percolating ground- water, discharges from sewage treatment works and occasional controlled releases from upstream dams. Since most of the major streams in the catchment continue to flow for a long periods after direct run-off ceases, ground-water provide significant contributions to dry weather streamflows.
However, low flows in tributaries such as South, Eastern and Cattai consist largely of treated sewage. INTRODUCTION Page 10
1.2.3 Population and Land Use
The Hawkesbury River catchment is conceived as a large scale green belt 'containing' Sydney's ever expanding population. Nevertheless, the catchment is destined to accommodate the greater part of metropolitan Sydney's growth in the foreseeable future. It is, therefore, an area that will be subject to dramatic change.
The future water quality of the Hawkesbury River and its tributaries will be governed by the catchment population, volumes and characteristics of treated sewage effluent and discharge location, volumes and characteristics of stormwater run-off and the extent to which it is controlled, and land use practices.
The total population living in the Hawkesbury River catchment is approaching 500,000 persons (State Pollution Control Commission of NSW, 1983). With more than half residing in urban areas at Blacktown, St Marys, Penrith, Liverpool, Campbelltown, Camden, Richmond, Windsor and the Baulkham Hills area. The population growth for different local government areas and the average annual rate of population increase between 1981 and 1991 are shown in Table 1-2. According to the Department of Planning (1989) the population will double to an expected 800,000 people by the year 2000. INTRODUCTION Page 11
Table 1-2: Population Growth
Local Government Estimated Population Rate of Change Area 1971(a) 1981(b) 1991(b) 1981-1991 (%/Year)
Camden 11,150 17,400 22,528 2.6 Penrith 60,300 110,900 149,789 3.1 Blue Mountains 36,750 57,500 69,380 1.9 Wollondilly 12,650 20,400 30,338 4.0 Hawkesbury (c) 28,150 37,550
(a) Minister for Environment Control (1973) (b) Australian Bureau of Statistics (1982, 1991) (c) Includes the former Windsor Municipal and Colo Shire local government areas.
The land use can been divided into six basic existing land patterns. They are shown in Table 1-3 (State Pollution Control Commission of NSW, 1983).
Table 1-3: Land-Use Patterns
Land use Approx, area (ha) Proportion of total (%)
Forest 1,365,000 62.8 Agricultural and grazing 633,000 29.1 Urban and industrial 100,000 4.6 Waterways and storage dams 50,000 2.3 Other-mining, extraction, waste disposal 26,000 1.2
About half of the total basin is forested. Much of the forested area is in rugged topography suitable mainly for national parks, water supply catchments and state forests. INTRODUCTION Page 12
In addition to the forested areas, the next most significant land use is agriculture and grazing, which occurs on the flatter undulating country and accounts for about 633,000 square kilometres of land. The rich, alluvial river flats provide land for fruit growing, market gardening, and dairy and beef production. The remaining uses are essentially urban in character and tend to be located along the major regional transportation corridors out of Sydney.
More than half population of the catchment area reside in urban areas. Other smaller population centres are located to the west, in the towns of the
Blue Mountains, and at Lithgow, and to the southwest, at Mittagong, Moss Vale, Bowral, Marulan and Goulburn. Because of natural restriction to the east, south and north of Sydney, the Hawkesbury River catchment is destined to accommodate the greater part of metropolitan Sydney's growth in the foreseeable future.
1.2.4 Use of the River
The major uses of the river include water supply for potable and non- potable domestic uses, water supply for landholders along the river for the irrigation of horticultural and grazing lands and for stock watering, recreation and aesthetic pursuits, water supply for the sand and gravel industry, habitats for aquatic flora and fauna and wastewater transport and assimilation.
While all these uses are important, the greatest consumptive use is the irrigation of pastures and cornland. This demand for irrigation is high during summer, and in drought periods there is often insufficient water in the river to meet requirements. Irrigation demands are generally greatest during periods when water quality is poor, particularly in the river's wastewater assimilation INTRODUCTION Page 13
zones. Regulated release from the water supply dams are not normally provided during low flow periods to meet this need. Further irrigation development in these areas is expected to be limited, in view of the continuing expansion of the urban areas.
Significant volumes of water are diverted from the Hawkesbury River under licence for the processing of sand and gravel. The greater proportion of this processing takes place in the areas of the proposed Penrith Lakes Scheme. Water extracted from the river for processing requirements totalled about 13,500 million litres / year (ML7Y). After processing, the wastewaters are settled and returned to the river or reused within sand and gravel plants, although a proportion of these waters would return to the river via underground routes.
The river is also an important recreational resource due to its proximity to Australia's largest city. The principal recreational activity on the river is water skiing, but other forms of boating as well as picnicking, swimming, fishing, and holidaying are also popular. With a wide variety of activities available along the river, it it not unusual to find several thousand people taking advantage of the waterway on a fine day.
1.2.5 Transport
Accessibility is a prime determinant of urban form and the real originator of market forces. Improvements in the inter-city links will increase accessibility and stimulate development within the Hawkesbury River catchment for both urban and recreational activities. INTRODUCTION Page 14
All roads serving the region are two lane rural roads. A distinction has been made between state highways, express ways, trunk and main roads (Minister for Environment Control, 1973). The major movement in the Hawkesbury River catchment is along the radial tendrils of major regional transportation corridors out of Sydney: the Great Western Highway, the Hume
Highway, and the Pacific Highway.
Development along these movement corridors has become increasingly intensified, opening up quite extensive sectors of new urban land. The construction of a series of radial freeways further accelerate and extend development. The western and Castlereagh express ways, together with the south western and northern expressways to Newcastle, place vast areas of the valley within easy reach of Sydney - jobs, shopping and educational activities.
Limited forms of public transport to some points along the river are available. Rail access is provided by motor rail from Riverstone to Windsor and Richmond. Riverstone may be reached by regular suburban train from Blacktown. INTRODUCTION Page 15
1.3 The Structure of the Thesis
Since the main body of this study deals with the problems and potentialities of monitoring the water quality using remote sensing data from the Hawkesbury River catchment area, it is important that the concepts and fundamental principles of the water quality, optical properties of the water, and remote sensing water quality monitoring techniques be understood. The early chapters therefore give an overview of the background information and latest developments in this field. The later chapters use this knowledge to investigate a few specific topics. The thesis is divided into eight chapters. A brief synopsis of each chapter is presented below:
Chapter 1 gives an introduction to the thesis. Firstly the research project and objective of the thesis are outlined. Secondly the study area is introduced. The physical and socio-economic environment of the Hawkesbury River catchment area are examined based on previous studies. The information considered essential for an understanding of the changes in water quality that occurred in the Hawkesbury River is summarised. Then the structure of the thesis is described.
Chapter 2 focuses on the concepts and theoretical basis of water pollution from the point of view of civil engineering. The necessity for water pollution monitoring, the definition of water quality, the source of pollution, and the principal parameters of water quality are discussed. An examination of the water quality in the study area is also presented. INTRODUCTION Page 16
Chapter 3 addresses the basic theory of the interaction between electromagnetic radiation and water by answering two questions, "How does downwelling light from sun and sky interact with natural water bodies?" and "How can the upwelling light be used for obtaining detailed information about the quality and quantity of organic and inorganic matter, suspended in the water?".
Chapter 4 reviews some of work to date on water quality assessment using remote sensing data with reference to a large number of research publications. It examines former studies which include:
• the historical overview of some applications in remote sensing of water quality;
• the general approach to monitoring water quality using remote sensing data; • the major parameters to be monitored using remote sensing methods; and the problems and limitations of the methods involved, are discussed in depth.
Chapter 5 introduces the sources of information for an operational application of satellite remotely sensed data. This study has been carried out using NOAA AVHRR data over the same area and at the same time of the year, corresponding to a 7 year change period. The NOAA data were carefully selected in order to get reasonable haze-free images. Relevant meteorological data, water quality data and land use information were collected from all available sources to provide the basis for assessment of existing conditions. INTRODUCTION Page 17
Chapter 6 deals with some items which play an important role regarding the quantitative application of remote sensing data over land and water bodies. These items involved geometric distortion, impact of atmospheric anomalies and accurate spatial registration of multiple data sets from different dates of acquisition.
In chapter 7 various techniques for change detection are described. These techniques vary in complexity of computation, data requirements and accuracy of results. In order to detect changes NDVI and hybrid classification methods were applied. The temperature were derived using the split-window method, which method reduced the atmospheric affect on the image.
The results are compared with those obtained from in-situ measurement, in Chapter 8. Chapter 8 is also the concluding chapter, which summaries the research work and provides conclusions. The methodology is assessed in terms of its strengths and weakness and suggestions for further research are also presented.
The appendix describes the satellites employed for water pollution monitoring, which have not been elaborated upon in the main text. The characteristic of scanner, spectral resolution and spectral bands are also briefly reviewed in the appendix. CHAPTER 2
WATER QUALITY
2.1 General Concepts of Water Pollution
Water quality is the general term that describes whether or not water is usable or whether or not the surrounding environment may be endangered by pollutants in the water. The sources of pollution are varied and unpredictable both in time and amount. The scientific community has had great difficulties in addressing the water quality problems due to their inherently multidisciplinary character. In many countries water quality has even been seen as a speciality of its own, developing its own concepts basically from a chemical/biological aspect.
From the point of view of civil engineering, water pollution has been defined in the literature as follows: • "A condition or state of the water environment under which the usefulness of the water is impaired or eliminated for domestic, industrial, and recreational purposes; aquatic biota suffers or is
destroyed; and offensive and unnatural sights, smells and taste are
present" (Grava, 1969). • "A change in the properties of water in a water resource in physical
chemical, organoleptic, biological, radioactive or other respect, or a
change as a result of which water is dangerous to public health or WATER QUALITY Page 19
likely to harm animal or plant life or less suitable for the purpose for which it is used or intended to be used" (Arlosoroff, 1974). • "To 'pollute', according to the Concise Oxford Dictionary, is 'to destroy the purity or sanctity of; 'to make foul or filthy'. Water
pollution, within the meaning of these definitions, may therefore be considered as the alteration of the quality of a receiving body, for
example by wastewater discharge, in such a manner as to make it
unfit for some intended use. It may also refer more generally to any change in the inherent physical, chemical or biological
characteristics of the receiving stream caused by material entering the water" (Barnes et a!., 1983). • "Pollution of the aquatic environment means the introduction by
man, directly or indirectly, of substances or energy which result in such deleterious effects as (i) harm to living resources, (ii) hazards to human health, (iii) hindrance to aquatic activities including fishing, (iv) impairment of water quality with respect to its use in agricultural, industrial and often economic activities, and (v) reduction of amenities" (Chapman, 1992).
The definition of water pollution is vague and uncertain. This is due to several factors. To begin with, there is no agreement among the experts as to what specific standards should be maintained or even what degree of pollution under any given circumstances is harmful to human beings, fish-life, and aquatic plants. Furthermore, there is a continuing upgrading of expectations and requirements of cleanliness. So that even if a level of tolerance in pollution could be defined for today, it is bound to change tomorrow. WATER QUALITY Page 20
2.2 The Necessity for Monitoring Water Quality
Water is the most important natural resource in the world since without it life cannot exist and most industries could not operate. Although human life can exist for many days without food, the absence of water for only a few days has fatal consequences. The presence of a safe and reliable source of water is thus an essential prerequisite for the establishment of a stable community. It is for these reasons that water has become one of our most precious natural resources and hydrological and water management problems now represent key areas of applied science in the current world-wide economic and ecological situation.
The reasons for monitoring of water quality may be summarized into seven main categories (Starosolszky, 1987): 1. To classify water resources according to water quality criteria; 2. To collect data for detection of long-term changes of water quality,
which may influence water uses or capacity of a water body to accept effluents; 3. For continuous monitoring of water quality in relation to its ongoing
or expected uses; 4. For determining the efficiency of water pollution control and water suitability for certain uses ( e.g., recreation); 5. For investigation of the cause of pollution;
6. For forecasting water quality on a short-range, middle-range and
long-range horizon; 7. To supply data for water quality models and computations. WATER QUALITY Page 21
However, the water pollution problem is far more complex than air pollution. The quality of water and its influence on ecosystems is difficult to define or measure. While safe levels of air pollutants have been defined the pollutants in water act more subtly. The effects on food chains and species distribution are often slow and long term, though important.
2.3 The Source of Water Pollution
Water pollution results from a great variety of causes, includes complex changes in receiving waters, and affects subsequent water uses in numerous ways. A number of major categories of pollution sources or water quality affecting human activities are commonly recognized, such as: 1. Urban and domestic; 2. Industrial activities; 3. Agriculture activities; 4. Recreation and navigation.
Pollution may result from point sources or non-point sources. There is no clear-cut distinction between the two, because a non-point source on a regional or even local scale may result from a large number of individual point sources. An important difference between a point and a non-point source is that a point source may be collected, treated or controlled (non-point sources consisting of many point sources may also be controlled if all point sources can be identified).
A point source is a pollution input that can be related to a single outlet. The major point sources of pollution to freshwater originate from the collection and discharge of domestic wastewaters, industrial wastes or certain WATER QUALITY Page 22
agricultural activities, such as husbandry (most other agricultural activities, such as pesticide spraying or fertilizer application, are considered as non point sources). Untreated, or inadequately treated, sewage disposal is probably the major point source of pollution to the world's waters. Other important point sources include mines and industrial effluents.
Non-point sources cannot be ascribed to a single point or a single human activity. They may be due to many individual point sources to a water body over a large area. Typical examples are: • Agricultural run-off, including soil erosion from surface and sub-soil drainage. These processes transfer organic and inorganic soil
particles, nutrients, pesticides and herbicides to adjacent water
bodies. • Urban run-off from city streets and surrounding areas (which is not channelled into a main drain or sewer). Urban development results in a number of changes in water characteristics. Run-off is more
rapid due to the increased areas, such as house roofs and roads, which are impervious to water penetration. This results in increased sediment loads and siltation in adjacent streams. Likely contaminates include derivatives of fossil fuel combustion, bacterial, metals (particularly lead) and industrial organic pollutants.
Fertilizers, pesticides and herbicides may also be derived from urban gardening, landscaping, horticulture and their regular use on
railways, airfields and roadsides. In the worst circumstances
pollutants from a variety of non-point sources may be diverted into combined storm/sewer systems during storm-induced, high
drainage flow conditions, where they then contribute to major point
sources. WATER QUALITY Page 23
• Waste disposal sites which include municipal and industrial solid waste disposal facilities; liquid waste disposal (particularly if
groundwater is impacted); dredged sediment disposal sites (both
confined and open lake). Depending on the relative sizes of the
disposal sites and receiving water bodies, these sources of pollution can be considered as either non-point or point sources. • Other diffuse sources including waste from navigation, harbour and
marina sediment pollution, and pollution from open lake resource exploitation.
Until recently, pollution control in most countries was directed mainly at the point source of pollution. There are two important reason for this. Firstly, the large concentrations of pollution from domestic and industrial wastewaters and the potential health, aesthetic and economic effect of uncontrolled discharges demand that effective disposal systems be provided. Secondly, the fact that these sources of pollution are so concentrated, mainly within urban areas, simplifies the problem of providing sewerage systems for collecting and conveying wastewaters to a small number of plants so that they can be treated to the required standard prior to discharge. This results in economies of scale in plant construction and operation.
Since the early 1970s, increasing attention has been paid to non-point sources of pollution such as stormwater run-off in both urban and rural areas, and other forms of rural pollution. Although their diffuse nature tends to make them relatively insignificant for much of the time, these sources can give rise to particularly heavy pollution during storms, when the storm intensity is sufficiently high to produce surface run-off. The effects of pollutants from these sources can be especially important where streams discharge into either large WATER QUALITY Page 24
inland water bodies, such as natural lakes and man-made impoundments, or to poorly-flushed estuaries.
The time variability of pollutant release into the aquatic environment falls into four main categories. Sources can be considered as permanent or continuous (e.g., domestic wastes from a major city and many industrial wastes), periodic (e.g., seasonal variation associated with the influx of tourist populations, or food processing wastes), occasional (e.g., certain industrial waste releases), or accidental (e.g., tank failure, truck or train accidents, fires etc.). The effects of these various types of pollutants on receiving water bodies are rather different. The continuous discharge of municipal sewage, for example, may be quite acceptable to a river during high discharge periods when dilution is high and biodegradation is sufficient to cope with the pollution load. During low discharges, however, pollution levels and effects may exceed acceptable levels in downstream river stretches.
2.4 Water Quality Parameters
Based on the environmental target most affected, the pollutants may be divided into those impacting or interacting, namely
• physical properties: temperature, suspended sediment, chlorophyll- a, colour, and electric conductivity;
• chemical properties: pH value, ammonia, nitrogen, phosphates;
• biological properties: dissolved oxygen and biochemical oxygen demand. WATER QUALITY Page 25
2.4.1 Physical Properties
• Water Temperature Water temperatures are strongly influenced by natural factors, especially the seasonal variation of air temperature. The change of
temperature in natural waters from human activities is primarily due to the discharge of wastes from industries and sewage treatment plants, the return of irrigation water, the release of cold water from
dams and cooling water from thermal power plants. Temperature influences aquatic life and suitability of water for drinking purposes.
• Suspended Sediment Suspended sediment refers to solid matter suspended in water. It may affect water or effluent quality adversely in a number of ways. Water with high sediment are generally of inferior palatability and may induce an unfavorable physiological reaction in the transient consumer. Highly mineralized waters also are unsuitable for many industrial applications. Sediment sources may be classified as: • sheet and rill erosion;
• erosion from gullies, roads, construction sites, etc.; • channel bed and bank erosion.
The natural sequence of the entire sedimentation process is soil erosion, then transportation, and finally deposition.
• Chlorophyll-a
Chlorophyll-a is a major photosynthetic pigment of plants, and has been widely used as a convenient measure for estimating
phytoplankton biomass and productivity. Important ecological factors that influence the growth and distribution of phytoplankton WATER QUALITY Page 26
include sunlight, temperature and nutrient levels. If one or more of these factors is in short supply it is said to be the limiting factor controlling population size and composition. Chlorophyll-a concentrations therefore provide one indication of the biological
response to water quality in an aquatic system. Eutrophic (bloom)
conditions exist in water if chlorophyll-a concentrations exceed 20 pg/L (Williams & Callaghan, 1991).
• Colour Colour in water may result from the presence of natural iron, humus and peat materials, plankton, weeds, and industrial wastes. The term "colour" is used here to mean true colour, that is, the colour of water from which turbidity has been removed. The term "apparent colour" includes not only colour due to substances in solution, but also that due to suspended matter. Apparent colour is determined on the original sample without filtration or centrifugation. In some highly coloured industrial wastewaters colour is contributed principally by colloidal or suspended material. In such cases both true colour and apparent colour should be determined.
• Electric Conductivity Electric conductivity is an expression of the ability of water to
conduct an electric current, and is directly related to the concentration of dissolved salts. The salinity level can affect
physiological behaviour of aquatic organisms and can lead to
mortality of some species. Furthermore, salinity levels are
particularly relevant to the extraction of water for irrigation.
Conductivity provides a rapid estimation of dissolved solids. Since WATER QUALITY Page 27
temperature influences conductivity, measurements are normalized to 20 °C.
2.4.2 Chemical Properties
• pH pH is a measurement of the acidity or alkalinity of water. It usually lies within the range 6-9 units. Very high or low pH levels can occur
due to industrial discharges, but may also occur naturally. Sharp changes in the pH of water can have adverse effects on aquatic life, the effect can be both direct or indirect. Direct effects include
mortalities, reduced growth rates and reproduction of aquatic organisms. Indirect effects include a modification in toxicity of certain chemical agents by affecting their solubilities. For example, the toxicity of ammonia is known to increase with increase in pH (Williams & Callaghan, 1991).
• Ammonia Ammonia enters the surface waters from decomposition of organic
matter (e.g. sewage, agricultural run-off and decay of biological population in the stream) and industrial discharges. In pure water
bodies with high dissolved oxygen concentration, the ammonia
concentration is low. High ammonia concentrations are also an indicator of recent pollution.
• Nitrogen
Nitrogen is an important micronutrient for the growth of plants. Since nitrogen is the product of biochemical oxidation of ammonia, increased concentrations may indicate faecal pollution. The self- WATER QUALITY Page 28
purifying capacity of water can be characterized by nitrogen concentration changes.
• Phosphates
Phosphorus is a nutrient that simulates plant growth, leading to an increase in plant biomass and restriction of water usage. Phosphorus typically has a long residence time in a waterway, and is therefore available for recycling by physical, chemical and biological processes. Phosphorus exists in a range of forms and can be rapidly changed from one to another.
2.4.3 Biological Properties
• Dissolved Oxygen The dissolved oxygen is vital for the aquatic life. It depends on the water temperature, salinity and the partial pressure of the oxygen in the atmosphere. The concentration may be reduced by respiration of plants, animals and bacterial, and increased by photosynthetic production of oxygen by plants and also by turbulent mixing of the water column.
• Biochemical Oxygen Demand
Biochemical Oxygen Demand (BOD) is defined as the total amount
of oxygen required by microorganisms to oxidize decomposable organic material. BOD is used mainly for estimating the relative
oxygen demand in waste and polluted surface waters. WATER QUALITY Page 29
2.5 Water Quality in the Study Area
The Hawkesbury River is the most important waterway in Sydney's west. Studies by the State Pollution Control Commission (SPCC) (1983, 1985) and the Water Board have shown that water quality in the Hawkesbury River is often unsatisfactory for water supply, recreation, ecology and agriculture.
Human activity in the Hawkesbury River catchment has resulted in a rapid deterioration of water quality since settlement. During the late 1940's and 1950's the influence of treated sewage on Hawkesbury River water quality was minimal. These discharges and associated water quality deterioration have increased with increasing catchment population. During the 1970's and the early 1980's there have been increasing public complaints concerning excessive aquatic plant growth including algae blooms interfering with and degrading the suitability of the river for public uses. Complaints have arisen from members of the public using the river for recreation and aesthetic purposes and from landholders adjacent to the river who use water for both irrigation and non-potable domestic uses. Amateur fishing clubs and local councils have also been active and vocal in their complaints about water quality of the waters in the river. Complaints about water quality of the Hawkesbury River have concerned taste and odour problems, aesthetic deterioration in quality and loss of amenity for swimming and water sports.
The river is impacted upon by pollutants from a wide range of sources, which can be divided into two groups: a) non-point sources such as urban and agricultural run-off;
b) point discharges such as effluent from Water Board and Council
operated Sewage Treatment Plants (STP's). Other point sources include the various licensed discharges from light industry. WATER QUALITY Page 30
The addition of pollutants from these sources has resulted in an increased incidence of algae blooms in the river and concerns for public health due to pathogenic bacteria and viruses.
Eutrophication is the major water quality problem in the river. It is caused by the process of gradual nutrient enrichment of the water system. Human influence, by altering land-use practices, accelerates the rate of eutrophication causing excessive plant growth and oxygen depletion. More than half the length of the river between Camden and Broken Bay could suffer excessive plant growth. This would diminish aesthetic appeal and create problems with irrigation and drinking water. Deoxygenation of the water and ammonia toxicity will kill many animals and some areas will be unsafe for swimming.
As a river flowing through both agricultural and urban areas, the inflows of sewage, animal excreta and fertilizers are potentially serious problems. Among the problems arising from such inflows are health hazards to people indulging in water sports, and excessive algae growth. Excessive algae growth (or algae blooms) is not usually a serious problem in fast flowing rivers or in estuaries with strong tidal flushing. However, if water movement is slight, high nutrient levels and adequate sunlight penetration into the water can greatly accelerate the natural growth of aquatic weeds. The problem is most apparent during dry periods, when the flow of water in the river is low and there is excessive growth of aquatic plants and algae. During low-flow periods which occur for varying periods in most years, effluents from sewage treatment works dominated river-water quality.
Ironically, another undesirable feature of the river induced by man, that of added turbidity due to sand extraction and increased erosion within the catchment, reduces the risk of algae blooms. This occurs because the turbidity WATER QUALITY Page 31
reduces the penetration depth of sunlight and limits the photosynthesising processes required for plant growth. The retarding effect of increased turbidity should not, however, be used to justify present extraction and land use practices; it merely illustrates the complex interactions between pollutants and that the elimination of one form of pollution, namely excessive turbidity, may not necessarily lead to an improvement in water quality. Clearly, a comprehensive approach is required for the establishment and maintenance of an acceptable water quality in the Hawkesbury River and its tributaries.
Future water quality of the Hawkesbury River and its tributaries will be governed by catchment population, volumes and characteristics of treated sewage effluent and discharge locations, volumes and characteristics of stormwater run-off and the extent to which it is controlled, and land use practices. Water quality problems are expected to escalate as the population in Sydney's West doubles to an expected 800,000 people residing in the catchment by the year 2000 (Dept, of Planning, 1989). CHAPTER 3
THE OPTICAL PROPERTIES OF WATER
Direct sunlight and diffuse skylight penetrate the water surface and are absorbed and scattered by water molecules, suspended and/or dissolved matter. Since absorption and scattering processes depend on wavelength, the spectral composition of the light emerging from the water depends on the relative amounts of those scattering and absorption materials. By measuring the spectral composition, the nature and concentrations of the substances present in the water can be determined quantitatively.
If we do not consider the effect of the atmosphere, that is: neither the alteration of spectral composition and intensity of the upwelling light during its path to the sensor, nor the
light scattered into the sensor by
dust particles and air molecules, and
that the electromagnetic radiation
that reaches the water from the sun comprises direct solar irradiance
(Esun) and diffuse irradiance (ESky), Figure 3-1: Interaction of Light and then the sun, water, sensor Water (after Sturm, 1980) relationship will be shown in Fig.3-1. THE OPTICAL PROPERTIES OF WATER Page 33
Direct sun irradiance (Esun) falls with a zenith angle 0o onto the water surface (s) where it is specularly reflected and transmitted into the water. The specularly reflected flux (about 2-3% for 0O<45°) is reflected without being detected by a sensor looking vertically down (Sturm, 1980). The part which is transmitted into the water is either absorbed or scattered by suspended matter or water molecules. The light scattered in the direction of the sensor can again be either absorbed or scattered, or diffusely leaves the water surface being detected by the sensor.
The diffuse irradiance falling on the water surface is ESky; a part of it is again reflected diffusely (6.6%) and the part transmitted into the water can be absorbed or scattered, some radiation coming finally back to the sensor.
3.1 Definition of Some Basic Radiometric Quantities
The main radiometric quantities relevant in remote sensing are radiance
L and irradiance E which are derived from radiant energy Q and radiant flux F which are experimentally observable quantities; the definitions and the units of measurements are listed in Table 3-1. Since all these quantities are generally wavelength dependent it is useful to define spectral quantities by introducing their derivatives with respect to wavelength, Qx=dQ/d>. [W s nnrr1],
F^=dF/d>. [W nrrr1], U=dL7d>. [Wsr1 nrrr1] etc. So for example E^ [W cm'2 nrrr1] is the radiant flux per unit area of the receiving surface per wavelength interval of 1nm. THE OPTICAL PROPERTIES OF WATER Page 34
Table 3-1: Radiometric Quantities for Remote Sensing Use
Symbol Equation Unit Definition
Q W sec,J Radiant energy, total energy in form of radiation emitted by a radiating body or contained in a volume of space.
Q F W,J sec"1 Radiant flux, the energy per unit f=t time flowing across a given surface.
I Wsr1 Radiant intensity, the radiant flux dec per unit solid angle dco leaving the source in a given direction.
L L = W nrr2 sr"1 Radiance, radiant flux per unit solid d2F angle leaving an extended source dAcosBdco per unit projected area of a surface.
P dFin W m-2 Irradiance, the total radiant flux E b" dA falling on an infinitesimal element of surface area from all directions.
., dFout Wrrr2 Emittance, the total radiant flux M M~ dA emitted by an infinitesimal element of surface area into a hemisphere. THE OPTICAL PROPERTIES OF WATER Page 35
3.2 Inherent and Apparent Optical Properties
According to Preisendorfer (1976) the optical properties of water can be broadly grouped into two classes, a) inherent optical properties; b) apparent optical properties.
An optical property is inherent if its operational value at a given point in a medium is invariant with changes of the radiance distribution at that point. Inherent optical properties directly specify the true scattering and absorbing characteristics of the medium and are dependent upon the dissolved and suspended material in the water and the electromagnetic properties of the medium. These properties are of particular practical importance when considering high-resolution image transmittance through waters.
An optical property is apparent if its operational value at a given point in a medium is dependent upon the radiance distribution at that point. Apparent optical properties can be related to inherent optical properties by means of radiative transfer theory and, like the inherent properties, are dependent on the dissolved and suspended material in the water in addition to the geometry of the lighting distribution. Apparent optical properties are of particular importance when considering the penetration of radiant energy to depths in water. THE OPTICAL PROPERTIES OF WATER Page 36
3.2.1 Inherent Optical Properties
The fundamental inherent optical properties of water are: • absorption coefficient a, absorptance A; • volume scattering function (3(0);
• scattering coefficient b, scatterance B; • attenuation coefficient c, attenuance C.
The absorption coefficient, a, is defined as the relative amount of radiant flux removed from the incident flux per unit pathlength in a homogeneous medium by absorption (see Figure 3-2), dFa a = FTd7 (3.1) its unit is inverse length [rrr1]. I/ r scattscattering
absorption F-dFa-dFb
<- dr >
Figure 3-2: Absorption Coefficient a (after Sturm, 1990)
The volume scattering function (3(0,(p) is defined as the relative amount of radiant flux removed by scattering into an infinitesimal spherical angle dco per unit pathlength (see Figure 3-3), its unit is per meter per steradian [rrr1 sr1]. In the most general case (3(0,cp) is a function of scattering direction 0, THE OPTICAL PROPERTIES OF WATER Page 37
P(e,cp) = (3.2) F*dr»dco with dco being the infinitesimal spherical angle dco = sin6d0dcp.
Figure 3-3: Volume Scattering Function (3(0,cp) (after Sturm, 1990)
Integration of scattering over all directions, gives the scattering coefficient
(unit: [nr1]) which removes radiant power from the beam by scattering processes only. HFu 2rc n b = f VdF = J J P(0> b = 2tt j (3(0) sin0 d0 (3.4) o Polar views of three examples of the volume scattering function are shown in Figure 3-4. For isotropic scattering, (3*(3(0), i.e., p is a constant and not a function of 0 , Equation 4.4 can be rewritten b = 2tcP j sin0 d0 = 4rcp (3.5) THE OPTICAL PROPERTIES OF WATER Page 38 Isotropic scattering at visible wavelengths occurs in nature in thick clouds or in fog. Rayleigh or molecular scattering occurs where the wavelength of the radiation is much larger than the molecular diameters, such as daylight scattering in the atmosphere or in very pure water. ■"s------ISOTROPIC SCATTERING '■------RAYLEIGH SCATTERING ^-----MIE SCATTERING Figure 3-4: Volume Scattering Function for Isotropic Scattering, Rayleigh (Molecular) Scattering, and Mie (Particle Scattering) (after Maul, 1985) Mie scattering, on the other hand, occurs when the wavelength is of the same order of magnitude as the particle diameter. Most of the scattered radiation in Mie scattering is forward scattering which can be represented by a delta function. The phase-function is a normalised version of the volume scattering function defined as: 2ji t P(e. In nature there is both scattering and absorption. The sum of the scattering and absorption coefficients is called the total attenuation coefficient which is defined as c=a+b. The unit is inverse length [rrr1]. 3.2.2 Apparent Optical Properties The fundamental apparent optical properties of water are: • radiance attenuation coefficient, k; • irradiance attenuation coefficient, K. Both are determined directly from the up- and downwelling irradiance distribution Eu(z) and Ed(z), defined as energy densities falling on a unit horizontal surface at depth z in the water from below and above respectively. The reflectance R(z) is the ratio R(z)=|§ (3.7) and the irradiance attenuation coefficient K(z) is the relative decrease of irradiance per unit length: k lz) _ _dEx(z)_ (3.8) Kx(z) ~ Ex(z)dz where x=u, d for up- and downwelling respectively. Two other important apparent properties are average cosines of up- and downwelling radiance distribution |liu and |id or their inverses, the up- and down-welling radiance distribution functions Du and Dd: Du (z) = 1/pu and Dd= 1/pd (3.9) From balanced relations for up- and down-welling irradiances (see Figure. 3- 4) a system of differential equations for Eu and Ed is obtained from its solution, R and K: b1 2Dd R (3.10) a+b' * 4DuDd (1 +2b'a) Du+Dd+{1 +■ }1/2 (Du-Dd)2 • (a+b')2 THE OPTICAL PROPERTIES OF WATER Page 40 and (3.11) For the special case of a homogeneous infinitely deep water the distribution functions can be approximated by two constants Du=8/3 and Dd=4/3 (Preisendorfer, 1976), and one obtains very simple relations between inherent and apparent properties. For b'/a ,«1 which applies for most practical cases (Morel and Prieur, 1977): K1 R ~ ^ and k * 4/3 (a+b') (3.12) Solving these equations for b' and a, they can be expressed in terms of R and k which can easily be obtained from simple light distribution measurements. In this way inherent optical properties can be obtained from apparent properties. 3.3 Scattering and Absorption in Pure Water As mentioned above, the scattering of visible light by pure water is often considered as a problem of molecular scattering. It can qualitatively be explained by the Rayleigh theory (Rayleigh, 1871). If we express the volume function scattering, p(6), of Rayleigh scattering by P(90), we can write: P(0) = P(90) (1+cos20) (3.13) and by integrating over 4tc, we have K = 4^- k (3(90) = g- it P(0) (3.14) THE OPTICAL PROPERTIES OF WATER Page 41 The dependence on wavelength and scattering angle of the scattering coefficient b, and also the polarisation characteristics resulting from this theory, are found to be in good agreement with experiment. However, the scattering intensity is strongly overestimated because the theory does not take into account the interaction of the molecules in a dense liquid medium. Smoluchowski (1908) and Einstein (1910) developed a theory in which the scattering centres are not the molecules themselves but random fluctuations of the dielectric constant. The results of this theory have been reviewed by Morel (1974). P(A.,e,cp) = 1.21 • 10'4 • [A/550]'43 • (1+cos20) (3.15) Integration over 0 form 0 to n and q> from 0 to 2n gives then b(A) = 2.027 • 10'4 • [A/550]'43 (3.16) and due to the symmetry of p the backscattering ratio of pure water is r = 0.5. The attenuation of pure water has a minimum at X = 470nm; at this wavelength the contribution of scattering to the extinction is approximately 20% (b/c = 0.2). For increasing wavelength the scattering becomes less important and for A>600 nm more than 99% of the extinction is due to absorption. The absorption in the infrared is very high. The absorption coefficient of water, a, is obtained from measured values of total attenuation c(A) = a {X) + b (A) and the subtraction of b(X). The presence of the minimum makes the measurements difficult because of the great path length that must be used (Morel, 1974). The values of a(A) and b(A) are given in Figure. 3-5. THE OPTICAL PROPERTIES OF WATER Page 42 ■ I I.. - ; ; : '• V"' -.:J\ j ; : yS _ ,‘i . j. = . • "j: . -4- 1.000E-03 1.000E-04 200 250 300 350 400 450 500 550 610 660 710 760 Wavelength (nm) absorption ...... scattering Figure. 3-5: Inherent Optical Properties, a and b, of Pure Water (after Sturm, 1990) 3.4 Scattering and Absorption in Natural Water Attenuation of light by natural water is strongly dependent from the content of suspended and dissolved matter. Whereas the scattering is mainly influenced by suspended organic and inorganic particles, the absorption is mainly influenced by dissolved matter. The most important parameter for the scattering characteristics of natural water is the particle size distribution. The scattering by particles suspended in the water can be described by Mie theory. Microscopic examination, photographic techniques and Coulter counting techniques are used for the determination of size distributions. A hyperbolic distribution N(x) ~ x-y (3.17) THE OPT1CAI PROPERTIES OF WATER Page 43 is now assumed to best represent the particle size distributions measured by the various techniques. N is the number/cm3 of particles with diameter greater than x, y is an exponent ranging from 0.7 to 6. Figure 3-6 shows some typical examples of cumulative size distribution. N(cm ) 1- 1 Theoretical 1.9 10000 2- 2 Microscopic (Swedish Fjord) 1.1 3- -3 Microscopic (Mediterranean) 0.7 4- 4 Microscopic (North Atlantic) 100 1.3 5- -5 Coulter Counter (West North Atlantic) 2.0 6- 45 Coulter Counter (Mediterranean) 2.4 7- 7 Coulter Counter (West Pacific) x(|iim) 3.1 < y< 6 Figure 3-6: Cumulative Particle Size Distributions (after Cracknell, 1981) According to Morel and Prieur (1977) a good approximation would be a Junge distribution with y+1=4 which gives a wavelength dependence of the scattering coefficient of -1. We can therefore write the particle scattering in the form bPM = bp(A<,) (Xq/X)^ - bp(Xo) (W1 (3.18) THE OPTICAL PROPERTIES OF WATER Page 44 where bp(^0) is the scattering coefficient at a give wavelength (for example Xq = 0.55fim). bp(Xo) is related to the total number of particles per volume i.e. to the concentration of suspended sediment which is measured in mg/I. Experimental data on particle scattering in water can be obtained from in situ and in vitro measurements of the volume scattering function P(0). In situ measurements are generally more reliable because they are not hampered by the risk of contamination and changes in particle characteristics during the time lapse between samples and measurements. The following features appear as characteristic of the particle scattering volume function of natural water: a very strong forward scattering compared to pure water Rayleigh type scattering, and a pronounced difference in scattering in forward and backward directions, the slope of the forward scattering being greater for turbid waters. The wavelength dependence of scattering by natural waters is determined essentially by three effects: • selectivity of scattering by pure water; • selectivity of scattering due to the presence of small particles; • absorption by scattering particles. m=n-ix, x*0, x(X). The first effect is especially important for the back-scattering from clear waters, whereas the forward scattering for practically all natural waters depends only slightly on wavelength. Selectivity due to scattering by small particles is considered a minor contribution because of the small total area. The third effect can give rise to a certain inconsistency of experimental data. Whereas in almost all measurements the scattering in the blue predominates over scattering in the green and in the red, there also exist results (Jerlov, 1976) showing higher scattering coefficients in the red than in the blue. THE OPTICAL PROPERTIES OF WATER Page 45 The suspended matter which is the origin of scattering is also absorbing. From a great amount of experimental data Morel and Prieur (1976) have deduced specific absorption coefficients (rrrVconcentration) for phaeopigments (chlorophyll-a + phaeophytin), suspended matter and dissolved matter (other than phaeopigments). The first coefficient ac(A.) is given in rrr1 per mg/m3 of measured chlorophyll-a + phaeopigment, the second aM(A,) is associated to the scattering coefficient bp(X0) with k0 = 0.55 pm and is given in rrr1 per bp(0.55) = 1m-1. If we call C (mg/m3) the concentration of phaeopigments, then the absorption coefficient of suspended and dissolved matter becomes a(X) = ac(X) C + aM(X) bp(0.55) (3.19) The values of ac(A,) and aM(X) are tabulated in Table 3-2. Table 3-2: Specific Absorption Coefficient ac(X) of Phaeopigments and of Suspended and Dissolved Matter aM(A,) (after Cracknell,1981) Jt ac{X) aMW X ac{X) 3mM X acM aMW (nm) (m-1/m-3) (rTf1/m-1) (nm) (m'1/m-3) (nrr1/rTr1) (nm) (rTf1/nrr3) (m’1/m-1) 380 0.0467 0.1397 490 0.0442 0.0442 600 0.0067 0.0345 390 0.0483 0.1391 500 0.0370 0.0417 610 0.0064 0.0358 400 0.0502 0.1343 510 0.0280 0.0401 620 0.0063 0.0375 410 0.0530 0.1266 520 0.0205 0.0373 630 0.0060 0.0401 420 0.0560 0.1153 530 0.0155 0.0349 640 0.0066 0.0419 430 0.0577 0.1005 540 0.0124 0.0339 650 0.0089 0.0443 440 0.0570 0.0838 550 0.0100 0.0333 660 0.0105 0.0473 450 0.0531 0.0704 560 0.0087 0.0322 670 0.0171 0.0514 460 0.0506 0.0602 570 0.0079 0.0322 680 0.0189 0.0541 470 0.0486 0.0530 580 0.0077 0.0333 690 0.0039 0.0584 480 0.0466 0.0481 590 0.0073 0.0334 700 0.0020 0.0600 THE OPTICAL PROPERTIES OF WATER Page 46 Combining all the coefficients for the various components we can now write for the scattering coefficient of natural water b{X) = bwM + bp(X) (3.20) and for the absorption coefficient a(A,) = awM + ac(>-)C + aM(A.) bp(0.55) (3.21) With these inherent properties of the water radiance and irradiance distribution can now be evaluated. The simplest way to do this is to integrate the scattered radiation from direct sun radiation downwelling on the water surface (Jerlov, 1976). We assume that direct sunlight is falling on the water surface and the transmitted part of it creates an irradiance E just below the surface. On performing the integration we have at the surface z=0. 1/2bw(>0 + b'p(X) R(X,0)=0.307 ------^------(3.22) aw(X) + ac{X)C + a^ (X,)bp(0.55) + bw(A.) + bp(A.) where R(^,z) is the ratio Eu(X,z)/Ed(>.,z), the ratio of the upwelling and downwelling radiance for wavelength I, and „ E(M>.,e-j) + Pp(^.Q-j)) j (cosj-cose) u ’ ’ a„(?i) + ac(X)C + aM(X.)bp(0.55) + bw(X) + bp(X) ' f (k/2 These equations provide the possibility for evaluation of the concentration of phaeopigments, C, and the backscattering coefficient of particulate matter bp(X) from spectral measurements of R(^,0) or of L{X,0,Q). 3.5 Applications of Water Optics to Remote Sensing of Water Quality The application of remotely sensed data for determining the distribution of water optical properties is more strongly associated with radiation sensed by the satellite sensors than bio-chemical parameters such as chlorophyll or suspended sediments. The scattering and absorption of light within the water THE OPTICAL PROPERTIES OF WATER Page 47 column gives rise to the water colour or the upwelled radiation. Optical properties are direct functions of scattering and absorption and more closely associated with upwelling radiation than the bio-chemical parameters. Optical properties of waters are affected by different concentrations of suspended sediment, chlorophyll, and dissolved organic material and by the size and shape of the suspended particles. Any two bodies of water that differ in quality have a unique distribution of reflected, emitted and absorbed radiation. These characteristics can be used to distinguish the water. Certain parameters have a dominating influence on optical properties for different water types. As concentrations increase for each of the above water masses, the other parameters have less influence on the optical properties. Different parameters have different response in multichannel remotely sensed data. Remote sensing can only measure reflected or emitted energy from the surface or near surface of water. Remote sensing applications are limited to those characteristics that can be observed, such as turbidity, suspended sediment, chlorophyll, eutrophication and temperature. However, these characteristics of water quality can be used as indicators of more specific pollution problems. The main water quality parameters monitored by remote sensing methods can be grouped into two: the dissolved constituents and the suspended particles. THE OPTICAL PROPERTIES OF WATER Page 48 Dissolved constituents that do not add colour to water have no effect on the absorption and scattering of light, as measured by a multispectral scanner. Thus, clear sea water has the same spectral signature as distilled water. Pollutants generally must affect the colour, turbidity or temperature of water to be detectable by remote sensing. Dissolved coloured materials increase light absorption in water but do not affect light scatter. Nevertheless, the spectral distribution of backscattered energy is affected by additional absorption. A brown water, for example, backscatters much less blue light and slightly less red light than a clear water. The average path length for light in a coloured water is the same as in clear water. There has been little use of remote sensing to measure water colour, because inland waters are generally turbid, and it is difficult to separate the effects of colour and turbidity in remote sensing signatures. Differences in water colour have been used to detect discharge points of pollutants on aerial photographs and images. Suspended particles increase total scatter, increase backscatter, change the spectral distribution of light, and reduce average path length. The most important results of these effects are: 1. a turbid water is more reflective than clear water at all visible and near infrared wavelengths (a light tone on aerial photographs and satellite images); 2. the remote signal from a turbid water represents only near-surface conditions; THE OPTICAL PROPERTIES OF WATER Page 49 3. the measured signal at any wavelength interval is dependent on particle size and may be dependent on the absorption (reflection) and refraction characteristics of the suspended material. Suspended particles may occur in sizes that range from colloids to sand. Colloids do not occur in concentrations that are significant for sediment transport studies, so only their effects on the light flux need to be considered. Colloids produce Mie scattering because particle size is about equal to the wavelength of light. Colloids increase backscatter but do not produce a distinctive spectral signature, except possibly at high concentrations. The reason is that low to medium concentrations of any material (and any grain size) result in a long average path length for light; the shape of the spectral reflectance curve is determined mainly by the absorptance characteristics of water. For most purposes, the colloidal content of terrestrial waters can be ignored, because colloids may pass through the filter pads used to separate the larger particles. Particles more than 10 times the size of a wavelength of light scatter all wavelengths equally. This is the atmospheric process that produces white clouds. Low to medium concentrations of these particles increase backscatter but do not produce distinctive spectral signatures. High concentrations of large suspended particles produce a spectral signature that is characteristic of the particles. In this case, the average path length of light is short, and the light flux is strongly affected by the absorption and reflection characteristics of the particles. THE OPTICAL PROPERTIES OF WATER Page 50 Because the intensity and colour of light is modified by the volume of water and its contaminates, an empirical relationship can be established between the reflectance measurement and water quality parameter to infer the water quality status of a water body. The degree to which light is attenuated by water varies with the wavelength of the light and with the nature, concentration and colour of the water quality contaminate. The empirical nature of this approach limits its usefulness because a unique relationship must be developed for each environmental situation. Even after an empirical relationship has been established, it is likely to change because the type of constituent in the water may not remain constant. Also, the angular relationship between the sun and a satellite sensor will change with the time of year, and the composition of the atmosphere will not remain constant. The latter two effects can be compensated for, but there is no way to measure the temporal changes in the water quality constituents directly with a high degree of confidence. Periodic field sampling programmes must therefore be carried out to verify the empirical relationships, perhaps every year or several years. However, because the properties result from this interaction and this results in the upwelled radiation field, the properties should be closely related to the remotely sensed radiation. CHAPTER 4 REMOTE SENSING IN WATER QUALITY MONITORING - AN OVERVIEW 4.1 Introduction Water quality is one of the most important parameters of the environment, but also one of the most difficult to monitor. The estimation of water quality over large areas of water using in situ sampling is lengthy, expensive and often inaccurate. Remote sensing can provide an alternative, synoptic, speedy and economic method for assessing the water quality. Since the late 1960s, many researchers have attempted to derive techniques for the estimation of water quality from remotely sensed data. Many methods have been developed for the estimation of water quality at a number of temporal and spatial scales, and these have been applied in many different areas and situations with varying degrees of success. This chapter reviews some of the work to date on water quality assessment using remote sensing data. The general background of water quality parameters measurement using remote sensing data is discussed and the methodology used to exploit the relationship between the water quality and remote sensing data at different geographical locations is examined. LITERATURE REVIEW Page 52 4.2 Historical Overview One of the first attempts to evaluate the effect of water volume components on water reflectance was performed by Scherz et al. (1969). Despite the inadequate experimental design, their results showed a clear linear relationship between water spectral reflectance and concentration of volume components. An improved design was later used to measure the reflectance of four types of suspended material (Whitlock, 1977). The results showed that, at high concentrations, different types of sediments produced distinctive reflectance spectra. Various papers and articles followed which attempted to develop or explore the applications and methods in depth. These studies have used multispectral remote sensing data for monitoring vast areas of the landscape, effectively, efficiently and repeatedly (Tucker et al., 1985). Techniques have been used to estimate water quality in lakes and rivers (Lathrop & Lillesand, 1986; Lindell et al., 1986; Ritchie et al., 1990; Dekker et al., 1991), reservoirs (Ritchie et al., 1976; Verdin, 1985), coastal waters (Khorram, 1981; Lindell et al., 1985; Curran & Williamson, 1985; Hinton, 1991) and estuarine environments (Catts et al., 1985). Researchers in those studies related digital spectral data to various water characteristics in an attempt to identify spectral indices best suited to estimating certain water quality characteristics. Many water quality parameters have been studied and satellite-based water quality estimation techniques have been successfully used in many applications. Their results have LITERATURE REVIEW Page 53 demonstrated the value of satellite data (i.e., NOAA, MSS, TM and SPOT) in water quality studies. Among the remote sensors now operating in the visible domain, the most suitable for the quantitative determination of both chlorophyll and sediment concentration in water appears to be the Thematic Mapper (TM) onboard Landsat (Engel, 1983). Marine applications of the TM have been investigated by Lathrop and Lillesand (1986), Rimmer et al. (1987), Tassan (1987), Tassan & D'alcala, (1993). SPOT appears to be an effective system for monitoring small-scale and transient hydrodynamic phenomena such as river plumes. Initial studies of SPOT data have shown the utility of SPOT imagery for delineating estuarine water masses and analysing coastal sediment plumes. Lathrop & Lillesand (1989) used SPOT-1 multispectral digital data to assess water quality conditions in Green Bay, an embayment of lake Michigan. The results of the spectral analysis of satellite data have proved promising and some applications in remote sensing of water quality are summarized in Table 4-1. LITERATURE REVIEW______Page 54 Table 4-1: Some Applications in Remote Sensing of Water Quality Investigator & Year Geographical area Parameters Sensor Amos & Toplis (85) Bay of Fundy SS CZCS Bowker & Witte. (77) Chesapeake Bay Chlorophyll Landsat Carpenter & Carpenter (83) Inland water Water quality Landsat Catts et al.(85) Neuse river estuary Chlorophyll AMS Curran et al. (87) Nearshore SS ATM Dekker et al. (91) Lake Water quality MSS Froidefond et al.(91) Gironde estuary (France) Turbidity SPOT Froidefond et al.(93) Gironde estuary (France) SS AVHRR Harding et al.(92) Chesapeake bay Chlorophyll Aircraft Harrington et al.(92) Lake Chicot, Arkansas SS, Turbidity, SD MSS Hinton (91) Coastal SS, chlorophyll, & salinity ATM Huang & Lulla (86) Lake Michigan Water quality TM Khorram (81) San Francisco Bay-Delta Water Quality MSS Lathrop & Lillesand (88) Green Bay & central Water quality SPOT Lake Michigan Lathrop & Lillesand (91) Green Bay & central Water quality TM Lake Michigan Lindell et al.(86) Green Bay & Lake Michigan Turbidity TM Lodwick & Harrington (85) Lake Athabaska SS Landsat Lyon et a I. (88) Lake Erie SS Landsat, AVHRR Rimmer et al. (87) Coastal Water quality TM Ritchie et al.(76) Reservoirs SS MSS Ritchie et al.(90) Moor lake, Mississippi SS,chlorophyll,temperature MSS,TM Schiebe et al.(85) Lake Chicot, Arkansas SS TM Swift (79) Chesapeake Bay Temperature & salinity Radiometer Tassan & Sturm (86) Coastal SS CZCS Verdin (85) Reservoir Water quality MSS Week & Simpson (91) Shelf seas, UK SS CZCS Witte et al.,(82) DOM SS-Suspended Sediments SD-Secchi Depth AMS-Airborne Multispectral Scanner LITERATURE REVIEW Page 55 4.3 General Approach The general approach to monitoring water quality using remote sensing methods can be summarised in the following five stages (Curran et al., 1987, Tassan, 1987): 1. simultaneous measurement of water quality parameters and remotely sensed spectral radiance (L*,); 2. correct, as far as possible, for environmental influences on (1); 3. derive an empirical relationship between corrected L^and water quality parameters and on a training set of data; 4. use corrected La. and the relationship in (3) to estimate water quality; 5. determine the accuracy of water quality estimation using a testing set of corrected water quality data. It appears, however, that there is no general relationship, which is applicable to all water bodies. The degree to which light is attenuated by water varies with the wavelength of the light and with the nature, concentration and colour of the water quality contaminate. The presence of sediment in water changes backscattering characteristics of the water dramatically so there is a unique relationship for each environmental situation. Even after an empirical relationship has been established, it is likely to change because the type of constituent in the water may not remain constant. In addition, the angular relationship between the sun and the sensor will change with the time of year and the composition of atmosphere will not remain constant. The latter two effects can be compensated for, but there is no way to measure the temporal changes in the water quality constituents directly with a high degree of confidence. Periodic field sampling programmes must therefore be carried out to verify the empirical relationships, perhaps every year or every several years. LITERATURE REVIEW Page 56 The region of the electromagnetic spectrum that includes visible and infrared light is useful for detecting indicators of water quality. Measurements in this part of the spectrum utilize reflected electromagnetic energy. Thermal infrared is also used for measuring water quality but it uses a direct measure of the emitted energy. The microwave region is not particularly useful for determining indicators of water quality because there is little if any penetration into the water. The approach differs depending on whether the reflected or the emitted energy is being measured (Engman & Gurney, 1991). Applications of thermal infrared remote sensing for water quality generally takes advantage of the 8-14 pm waveband. Not only is this region of the spectrum an efficient atmospheric window, but it also contains the region of the maximum radiant emittance for most earth features. Water behaves much like a black body with its peak emission occurring between 9 and 10 pm and with an emissivity close to unity. In general, most water quality monitoring can assume constant emissivities in the 8-14 pm range, but it is important for quantitative work to know the variability that occurs over time and with wavelength. Water quality monitoring with thermal infrared is based on spatially measuring temperature differences. The interpretation of thermal data generally relies on some ancillary information, such as knowledge of the location of a discharge pipe, to infer a water quality impact. However, thermal pollution at the water surface can be measured directly with thermal scanners that can be used to derive a very detailed map of actual temperature. As can be seen from Table 4-1, water quality monitoring studies may be reviewed in various ways. According to the water parameters determined, the review may be classified by suspended sediment, turbidity, chlorophyll, colour LITERATURE REVIEW Page 57 and temperature. According to the the geographic location, the study can be grouped into inland water and coastal water and can also be catalogued by the different sensor employed. In this thesis, classification by water parameters is preferred. These parameters and some of the approaches used to evaluate them are discussed as follows. 4.4 The Measurement of Water Quality Using Remote Sensing Data Remote sensing can only measure reflected or emitted energy from the surface or near surface of water. Remote sensing applications are limited to those characteristics that can be observed. Of the ten water quality criteria listed by Oswald (1967), (epidemiological, dissolved oxygen, pH, biochemical oxygen demand, algae growth potential, floating solids, non-biological turbidity, colour, temperature, toxic materials) four can be determined directly by one or more remote sensing techniques. These four water quality parameters are: 1. floating solids or particulate matter which can be characterized by two measurable quantities: a) The suspended sediment; b) Chlorophyll; 2. non-biological turbidity (or dissolved organic matter); 3. colour; 4. temperature. It is also conceivable that dissolved oxygen, pH and algal growth potential could be determined by secondary effects that are associated with each of the above parameters. LITERATURE REVIEW Page 58 4.4.1 The Measurement of Suspended Sediment Suspended sediments are the inorganic particles kept temporarily in suspension within water. Traditionally, suspended sediments have been measured either directly or indirectly. In the direct method a container is lowered through a fixed vertical distance of water, the collected samples are filtered and the remaining sediment is expressed as a proportion of the original sample in parts per million (ppm) or more usually milligrammes per litre (mg/I) (Gray, 1973; Rodda et al., 1976). This method allow the accurate measurement of suspended sediments concentration (SSC) for single points in space and time. Indirect observations use a turbidity meter to measure the backscattering of a light beam within the water. The resultant value can be converted via a calibration equation to SSC. These techniques have been developed for the accurate measurement of SSC at a few points (Geraci et al., 1981; Alfoldi, 1982). However, suspended sediments have great spatial variability and points often quite close to each other can have very different values of SSC. This spatial variability has severely limited the extrapolation of such point samples over large areas (Ritchie et al., 1976; Curran & Wilkinson, 1985). Fortunately , remotely sensed data can put such point measurements into their spatial context (Robinson, 1985) and perhaps more importantly can provide a means of estimating SSC that is at least as accurate as the conventional methods discussed above (Catts et al., 1985). The determination of SSC using remote sensing method from water reflectance is based on the relationship between the absorption and scattering properties of water and its constitutents (Maul, 1985). Thus, the absorption and scattering properties of sediment affect the overall water reflectance. LITERATURE REVIEW Page 59 The spectral behaviour of sediments, however, is dependent on particle size distribution and mineral composition (Maul, 1985; Querry et al., 1977; Gordon, 1974). The reflectance of dry sediment increases with a decrease in particle size (Myers & Allen, 1968) and the same trend has been reported for sediment suspended in water (Moore 1977, Holyer, 1978). For any given concentration, fine-grained material contains more particles and thus scatters more than would an equal weight of coarse-grained material. The effect of sediment spectra on water spectra is affected by both particle size and concentration, with large suspended particles at high concentrations producing a marked increase in water reflectance at visible wavelengths (Moore, 1977). Some experimental results (Whitlock et al., 1978; Witte et al., 1982; McKim et al., 1987) suggest that distinctive water reflectance spectra are produced for high concentration of different minerals as a result of sediment colour. Consequently, sediment colour may be expected to affect the relationship between SSC and reflectance. On the basis of this theoretical background it was hypothesized that the strength of the correlation between SSC and reflectance is dependent upon sediment type. Several attempts have been made to estimate SSC from the remotely sensed reflectance of water (Ritchie et al., 1976; Holyer 1978; Munday & Alfoldi, 1979; Khorram, 1981; Whitlock et al., 1982; Catts et al., 1985; Curran et al., 1987, Rimmer et al., 1987; Tassan, 1987; Ritchie et al.,1990; Week & Simpson, 1991; Froidefond et al., 1993). A number of researcher have reported a strong positive correlation between SSC and spectral radiance (U,) (Ritchie et al , 1987; 1990; Novo et al., 1989; Bhargava & Mariam, 1990; 1991; Hinton, 1991). This relationship has been used in conjunction with remotely sensed LUo estimate SSC over large areas of oceans, reservoirs, lakes, rivers, and coastal waters as an aid to the management of water quality, the monitoring of pollution, and the modeling of sediment budgets (Curran & LITERATURE REVIEW Page 60 Novo, 1988; Ritchie & Cooper, 1988; Weeks & Simpson, 1991). However, relatively few studies have been produced that use to estimate SSC (Whitlock et al., 1982; Curran & Novo, 1988). There are many reasons for this reluctance. One is the large spread of points around any regression relationship between SSC and Lx. Other factors including changes in water parameters during the collection of SSC data; changes in atmospheric transmission and acquisition geometry during the collection of Lx. data; inadequate geometric correction of Lx. and a small sample size in relation to the known area; vertical and temporal variability in SSC (Curran & Novo, 1988). In addition, little agreement has emerged on the strength of, or even the optimum wavelengths to be used in the relationship between SSC and remotely sensed reflectance (reviewed in Curran & Novo, 1988). It seems likely that this lack of agreement is the result of variation in the following factors: 1) the range of SSC (Rouse & Coleman, 1976; Moore, 1977, Sydor, 1980); 2) the particle size distribution (Sturm, 1980, Whitlock et al., 1982); 3) the particle shape (Bukata et al., 1981); 4) the particle mineralogy (Witte et al., 1982); 5) the presence of covarying water substances such as chlorophyll and organic acids (Tassan & Sturm, 1986); 6) the geometry of measurement (Whitlock et al., 1981); 7) the area, vertical and temporal variability of SSC (Curran et al., 1987). LITERATURE REVIEW Page 61 It is clear that isolation and quantification of some or all of the above factors would enhance greatly the utility of remote sensing for water quality monitoring. 4.4.2 The Measurement of Chlorophyll In the past decade, there are a number of papers describing techniques to detect chlorophyll concentrations from remote sensing data (Gower & Boarstad, 1981; Amann & Doerffer, 1983; Gordon et al., 1983; Fischer & Kronfeld, 1990). Several remote sensors have been used to measure chlorophyll concentrations (Hoge & Swift, 1981; Campbell & Esaias, 1983, 85; Hoge et al., 1986, 1987). These studies have been conducted in a variety of water types including both inland and ocean waters, and using both active and passive systems. Active systems measure laser-induced fluorescence (LIF) of chlorophyll or phycobilins, whereas passive systems measure upwelled radiance in selected wavebands. Chlorophyll-a distributions have been studied in the English Channel (LeFever et al., 1983) and an airborne experiment to map chlorophyll-a was carried out in the James River, Virginia (Johnson, 1978). Gondon (1978) and Gondon et al. (1983) demonstrated a successful approach to estimate chlorophyll-a concentration in open oceans, which however, met with little success when applied to turbid and shallow coastal waters (Gower & Borstad, 1990). Landsat Multispectral Scanner (MSS) data have been widely used to measure chlorophyll concentrations in lakes and open ocean waters (Ritchie & Schiebe, 1984; Brown et al., 1985). With an atmospheric correction algorithm LITERATURE REVIEW Page 62 devised by Gordon and Clark (1980), and prelaunch calibration factors, data from the Coastal Zone Colour Scanner CZCS on the Nimbus 7 have been processed for chlorophyll concentrations in Florida waters. The CZCS on the Nimbus-7 satellite has also been used extensively to study chlorophyll-a distributions in the surface water of the English Channel (Holligan et al., 1983) and Mediterranean Sea (Caraux & Austin, 1983). There exist two kinds of approach to the measurement of chlorophyll-a concentration using remote sensing data. The first uses a two-channel ratio technique and the second a multiple regression technique. Duntley (1971) indicated that spectral responses at different wave lengths could be related to changes in chlorophyll-a concentrations. Taking advantage of the reflectance decrease in the blue wavelength and an increase in the red wavelength, the chlorophyll were determined by the ratio of the two different chlorophyll concentrations reflectance curve. Szekielda et al. (1975) reported satellite measurement of chlorophyll concentrations in the upwelling regions off the northwest coast of Africa. Photographic and scanner data from aircraft and satellite (Skylab and Landsat) were used to locate ocean colour- variations which correlated well with chlorophyll and temperature measurements, although a two-channel ratio approach was ineffective in determining surface chlorophyll concentrations. A number of investigators (Clark et al., 1980; Gordon 1980; Gordon et al., 1983) have used ratio techniques with remotely sensed data to detect and map chlorophyll distributions. But this simple techniques can successfully be applied for open ocean waters only (Fischer & Kronfeld, 1990). LITERATURE REVIEW Page 63 Other investigators have used multiple regression techniques for the quantitative mapping of chlorophyll-a concentration. By applying the appropriate algorithms, a group of the detailed images or maps can be prepared in ocean and even in coastal waters (Johnson, 1978; Gordon et al., 1980). These algorithms are derived from in situ measurements and mainly differ in the coefficients used. Johnson & Bahn (1977) applied a continuous function analysis technique, stepwise regression, to multispectral scanner digital data collected over the James River, Virginia. This approach identifies spectral bands for quantifying individual water quality parameters in a data set. The results indicated that changes in chlorophyll-a concentrations give unique spectral response compared to other water quality parameters. Remote sensing of chlorophyll-a concentrations using satellite and aircraft sensors have evolved greatly during the last decade, but this technology has been primarily applied to clear open ocean (Clark et al., 1980; Gordon et al., 1980; 1983), where the main contributors to light attenuation are plant pigments and water molecules (Morel & Prieur, 1977). Less work has been done in the estuarine and coastal regions with higher concentrations of dissolved and particulate substances. Bowker and Witte (1977) found that the correlation between suspended solids and chlorophyll concentration in the lower Chesapeake Bay interfered with Landsat measurement of chlorophyll alone. Chlorophyll-a could not be detected in concentrations below 5 to 10 pg/l due to the dominant influence of suspended sediment on reflectance. This is the general problem encountered in many other studies (Johnson, 1978; Kim et al., 1979; Tassan, 1979) and is severe in marine waters with large concentrations of inorganic particulates, such as in the confluence of strong currents, or in shallows along shorelines experiencing current-dependent resuspension of bottom sediments. Where LITERATURE REVIEW Page 64 inorganic particulates from such sources are lacking, the correlation between particulates and chlorophyll may be used to estimate chlorophyll (Clark et al., 1980). Even if chlorophyll concentrations are very high, as in phytoplankton blooms with values up to 1000 pg/l, simultaneous variations in chlorophyll and inorganic particulates will still impede accurate chlorophyll measurements (Munday & Zubkoff, 1981). In their study, Munday and Zubkoff (1981) concluded that while remote measurement of phytoplankton in turbid water is possible, the result from one particular study area cannot be generalized. Khorram et al. (1986) also concluded that universal estuarine models of remotely-sensed chlorophyll-a may not be possible to establish because of turbidity interference. 4.4.3 The Measurement of Dissolved Organic Matter The dissolved organic matter (DOM) is a final decomposition product of living matter. It is formed in shallow waters with high detritus content in the bottom sediment. Its absorption coefficient decreases exponentially with wavelength therefore its presence results in a reduction of the short-wave portion of the upwelling light, resulting in a yellowish colour in the water so it is commonly known as yellow substance (YS). It is associated with land run-off in near coastal environments. Concentration of dissolved organic matter is measured in terms of its absorption properties (i.e. the absorption coefficient in units nrr1 of filtered water, 0.45 pm pore-size) at a given wavelength (between 380 and 450 nm). At the concentration levels at which dissolved organic matter is found in water its contribution to scattering is negligible. LITERATURE REVIEW Page 65 There is an important distinction between DOM and suspended sediment. Suspended solids are concentrations of matter suspended in water and can be measured physically while DOM on the other hand can only be determined by its optical properties. Sometimes there is a strong positive correlation between them. Theoretically they are not identical. Like chlorophyll it affects the SSC/L*. relationship but without at least a few in situ measurements a correction for its influence has not been possible. The dissolved organic compounds is an important parameter for studying the environment, especially in reference to the estuarine areas (Ferrari, 1991). River organic matter, mixing of bottom releases of organic matter, and the organic decay products of plankton decomposition are some of many possible sources of YS in the water. YS is characterized by radiation absorption with exponential dependence on wavelength (Jerlov, 1976): Ay(k) = Ay(X0)e'k^0) (4.1) where Ay(k) is the absorption coefficient at a fixed wavelength X. YS interferes with the absorption of chlorophyll and suspended matter in the visible domain (400-700nm). The relatively low spectral resolution of the present satellite remote sensors prevents discrimination between chlorophyll, suspended matter, and yellow substance contents. Current algorithms for chlorophyll and suspended matter retrieval from remote measurements of sea colour (Bricaud & Morel, 1987) operate on the assumption that the optical signature of YS is either negligible, constant, or well correlated with chlorophyll or total suspended sediment (Ferrari, 1991). LITERATURE REVIEW Page 66 Several studies point to the significant influence of varying concentrations of dissolved organic matter (Witte et al., 1982; Bukata et al., 1988; Kirk, 1989; Vertucci & Likens, 1989) and relative proportions of humic and fulvic acids (Carder et al., 1989) on light absorption in water and , therefore, remote sensing reflectance. However YS must be carefully assessed as it can become an important source of error in remote sensing of water quality. This is especially so when attempting to determine parameters such as chlorophyll-like pigment and total suspended matter. Therefore, in situ measurements of YS are an essential support to biooptical assessments, especially in estuarine/deltaic waters where dissolved organic matter can be present as a variable independent from other parameters. 4.4.4 The Measurement of Colour Colour describes qualitative information about the biological productivity and the general chemical make-up of water bodies. The water colour is influenced by the presence of organic matter, with clean water being light blue, and organic matter concentrations modifying it to greenish-blue, green, greenish-yellow, yellow or brown. True colour is determined by substances in colloidal suspension. Apparent colour, on the other hand, is usually what is observed and is caused by light reflecting from suspended materials, the bottom, or reflected sky. To determine true colour, in situ sampling must be performed. LITERATURE REVIEW Page 67 Observations of colour are often made to evaluate the amounts of living and non-living substances in the water. True colour is due to dissolved matter; apparent colour is due to suspended matter. Dissolved chemicals can impart a true color to a water solution, but frequently these changes are too subtle or are obliterated by other sources of colour in natural water and thus are generally not detectable by common remote sensing techniques. Inexact corections for atmospheric absorption of reflected light also inhibit the measurement of true colour by remote sensing techniques (Engman & Gurney, 1991). Existing satellites can provide colour data useful for determining water quality indicators. The Nimbus-7 Coastal Zone Color Scanner, Landsat and SPOT series can provide valuable spatial and temporal maps of true colours that can be related to specific water quality indicators. Feldman et al. (1984) used Nimbus-7 data to track changes in colour that are associated with dissolved matter concentrations. These data documented a major redistribution of dissolved matter around the Galapogos Islands. Viollier et al. (1978) performed a series of ocean colour measurements with a special airborne radiometer to estimate the chlorophyll content over the Gulf of Guinea. Fourteen low-level flights provided data which, when mapped, showed the detailed structure of boundaries due to upwelling. Chlorophyll content was found to affect the albedos at the two wavelength bands of 466 and 525 pm, whereas the albedos of longer wavelengths were found to be affected primarily by light-scattering particles. LITERATURE REVIEW Page 68 4.4.5 The Measurement of Temperature Temperature and salinity have formed the basis of identification of coastal and open ocean waters for many decades. Water temperatures are required in the determination of evaporation, energy budget, stability of water stratification, concentration of chemical substances and thermal pollution due particularly to cooling water of thermal power plants. Temperature also influences aquatic life and suitability of water. There have been many studies about global scale sea surface temperature (SST) estimation by using remote sensing data, but there are only a few reports about local scale water surface temperature estimation in coastal zones or inland water where the spatial and temporal temperature changes rather quickly (Pearce et al., 1989). The SST imagery remotely sensed by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellite series have been conveniently used in various fields, for example, oceanography, meteorology, fishery, etc. As the thermal radiation from the sea surface is exposed to atmospheric effects, many studies have been undertaken to investigate its transmittance mechanism. There are two kinds of techniques that have been used to estimate SST. One is a multiple window technique (MWT) which was developed as an error correction algorithm (Anding & Kauth, 1970; Maul & Sidran, 1973; Prabhakara et al., 1974; McMillin, 1975; Deschamps & Phulpin, 1980). This method is based upon three spectral bands of channel 3 (3.5-3.9 pm), channel 4 (10.5- 11.5 pm) and channel 5 (11.5-12.5 pm) for which the difference between atmospheric absorption properties is more pronounced. Because of reflected LITERATURE REVIEW Page 69 solar radiation, only night measurements can be used at around 3.7 pm. The multichannel approach allows a satisfactory correction of atmospheric effects but is justified only for low instrumental noise levels because of the amplification of this noise by the retrieval algorithm. The other method which is more commonly used is called the split- window technique (SWT), which uses only the channel 4 and channel 5 brightness temperatures of AVHRR. This technique is based on the assumption that the average atmospheric brightness temperatures in the two channels are equal and the surface emissivity is unity. The principal advantage of this method is that the infrared window can be used both night and day and this technique is most commonly used nowadays. A general structure of the SWT via channel 4 and channel 5 brightness temperature (T4 and T5, respectively) can be described as Ts = ocT4 + P(T4 - T5) + y (4.2) where Ts is the estimated SST. The coefficients of a, p and y depend upon various factors, for instance, atmospheric effects, air-sea interacting effects, improper sensor calibration, contamination in the optical system, etc. The SST estimation function by the split window algorithm is characterized by the value of a. It is theoretically induced to be one from the transmission model of channel 4 and channel 5 radiations (McMillin & Crosby, 1984). The values can be modified around one by minor adjustments. Table 4-2 shows a list of some published split window functions. There have been three kinds of approach to derive SST using SWT. The first is a theoretical one, where the coefficients are calculated from a transmission model of the thermal radiation by using atmospheric profiles, which can be observed by radiosondes or specified as standard profiles LITERATURE REVIEW Page 70 (Deschamps & Phulpin, 1980; Barton, 1985; Maul, 1983). The error correction in this approach, however, is directed only to atmospheric effects. The second is an experimental approach such that the coefficients are calculated by the regression analysis to a match-up data set (McMillin & Crosby, 1984). By this, it is expected to compensate the various errors on the whole. The third approach is the combination of these two methods. That is, an SWT is originally calculated by the first method, then it can be adjusted by the second method (McClain et al., 1985). Table 4-2: List of Some Published Split Window Functions No. Algorithm Source 1 Ts = 1.000T4 + 2.098(T4-T5) - 1.280 Deschamps & Philpin (1980) 2 Ts = 1.035T4+ 3.046(T4-T5) - 1.209 McClain (1981) (for daytime data, NOAA-7) 3 Ts = 1 .OOOT4 + 2.830(T4-T5) - 0.070 Barton (1983) 4 Ts = 1 .OOOT4 + 3.350(T4-T5) - 0.320 Maul (1983) 5 Ts = I.OOOT4 + 2.702(T4-T5) - 0.582 McMillin & Crosby (1984) 6 Ts = 1,056T4 + 2.852(T4-T5) - 2.049 Llewellyn-Jones et al. (1984) 7 Ts = 1,000T4 + 2.760(T4-T5) - 0.420 Barton (1985) 8 Ts = 1.035T4 + 2.579(T4-T5) - 0.459 McClain et al. (1985) 9 Ts = 0.986T4 + 2.671 (T4-T5) - 0.525 Yokoyama & Tanba (1991) (for daytime data, NOAA-9) 10 Ts = 0.986T4 + 2.668(T4-T5) - 0.789 Yokoyama & Tanba (1991) (for nightime data, NOAA-9) LITERATURE REVIEW Page 71 4.5 The Impacts of Land Use on Water Quality Water quality in the catchment area is determined by activities within the catchment. The main factors affecting water quality are the surrounding land use. Different land uses will result in different rates of export of pollutants (Water Board, 1990). Pollutants mostly derive from industrial or agricultural activity, and the need to dispose of human waste. They enter river water by a number of routes: from direct run off from adjacent land, outfall from pipes, atmospheric fall out, deliberate dumping from ships. Population increases result in an increase in sewage effluent volumes. Although all treatment plants currently provide secondary treatment, which produces a clear, colourless effluent, the concentrations of dissolved plant nutrients, such as phosphorus and nitrogen compounds, can be very high. These nutrients may cause explosive growth of duckweed, algae and other plants whose respiration and decomposition cause deoxygenation of the water. This process of over-enrichment is called eutrophication. The water quality problems it causes can be serious. As a river flowing through both agricultural and urban areas, the inflows of sewage, animal excreta and fertilizers are potentially serious problems. Among the problems arising from such inflows are health hazards to people indulging in water sports, and excessive algae growth. The problem is most apparent during dry periods, when the flow of water in the river is low and there is excessive growth of aquatic plants and algae. During low-flow periods which occur for varying periods in most years, effluents from sewage treatment works dominated river-water quality. LITERATURE REVIEW Page 72 In many cases remote sensing is ideally suited to the task of assessing changes in land use. Remote sensing has many applications in helping in our understanding of the environment, particularly in determining change, and may thus help in decision of what are acceptable levels of disturbance by human influence. Different authors have shown that satellite multi-spectral data can provide information on rural to urban land conversion more frequently, at lower cost and comparable accuracy than ground surveys and analysis of aerial photographs (Forster, 1983; 1991; Gomarasca et al., 1993). For broad scale, regional analysis of land use change, low resolution satellite systems such as NOAA AVHRR may be appropriate. Increased urbanization and industrialization in a city increases the intensity and extent of the positive thermal anomalies that commonly are termed urban heat islands. The large heat capacity and high heat conductivity of urban building materials prevent rapid cooling of urban areas after sunset, contrasting with the situation in the rural environment. A variety of other factors, often equally important, enter into heat island formation. Rapid runoff of precipitation in urban areas, plus the waste heat from residential and other buildings are two such consideration. The increased 'roughness' of cities, with a resultant reduction of about 25% in wind speeds, is also significant factor (Henry et al., 1989). The higher temperature of runoff from urban areas in catchment area will increase the temperature of the river water ultimately causing changes in the ecological balance. Spatial and temporal variations in catchment area temperature can be determined using thermal infrared sensing systems such as NOAA AVHRR, on a daily basis. LITERATURE REVIEW Page 73 4.6 Problems and Limitations As can been seen from previous sections, considerable efforts have been made in the measurement of water quality using remote sensing methods. Each of these are unique in that they are site specific. Field data are necessary to develop quantitative relationships that are purely empirical. However, they do provide a large-scale view of the water body and its surrounding catchment area that in many cases cannot be obtained by any other manner. Also, the temporal nature of satellite data from differing dates allows the scientist to infer changes in the water body and catchment land use, and monitor programmes. A number of factors limit the current methods being successfully used to monitor water quality. These limitations are due to a number of interrelated causes: 1) Although measurements by satellites give an overview of the global distribution of oceanic constituents, most studies have been limited to areas where water constituents have been at low concentration. Less work has been done in the estuarine, coastal regions and inland water with higher concentrations of dissolved and particulate substances due to river discharge and bottom resuspension. These substances cannot be separated using remotely sensed data as most satellite instruments have too few and too broad spectral channels. LITERATURE REVIEW Page 74 2) There is a problem of difference in time between the remote sensing data acquisition and sampling by ship. Duggin and Robinove (1990) stated in this aspect: 'Failure to ensure this synchronization in image and in ground-data acquisition might result in the comparison of two unrelated datasets'. 3) The incomplete mathematical formulation of the relationship between volume reflectance and the inherent optical properties of a water body. The relationship given by Jerome et al. (1988) suggested that a further term involving the backscattering coefficient is required in the relationship defining volume reflectance. 4) Many studies have reported on the high correlations between water parameters and L^. However, relatively few studies have proceeded to use to estimate water quality. There are considerable problems associated with matching remotely sensed Lx. and water parameters since the wavelength-dependent absorption and scattering cross sections for the component water quality parameters are not constant in space and time. 5) It is difficult to quantify water quality because of the many environmental factors that disturb the relationship between water quality and L^. These factors include the atmosphere, sensing geometry, water/atmosphere boundary, water components and concentration of water components. Because of many variables, it may not be practical to determine an atmospheric correction directly from one time to another. Variations in environmental factors preclude the result for the original field site being used at other locations. LITERATURE REVIEW Page 75 6) The sensors limitation. The poor temporal sampling frequency, small swath and insufficient spatial resolution to observe water pollution is a serious disadvantage for three reason. First, the probability of imagery co-inciding with the events developing within short times is slight. Second, more than one overpass may be required to monitor change. Third, in many parts of the world there will be a strong likelihood of cloud cover. Solution to these problems include the acquisition of more appropriate imagery and a more detailed investigation of the spectral signatures of pixels containing surface covers in the monitoring area. In addition, a more detailed analysis of the environmental effects including change in catchment land use is also significant in quantitatively estimating water quality using remote sensing data. Research is needed to (Gallie & Murtha, 1992): 1) find simpler and quicker methods for measuring the specific absorption and backscattering properties of individual variables; 2) determine the constancy of the specific properties; 3) determine in practical terms how the properties relate to other parameters such as particle size, chemical composition, or species composition; 4) determine the relationship between catchment land use change and water quality. CHAPTER 5 SOURCES OF INFORMATION In order to compare the changes in the Hawkesbury River catchment, relevant information was collected from all available sources. This information included: • NOAA AVHRR data; • Water quality data; • Temperature data; • Land use map. 5.1 NOAA AVHRR Data This study has been carried out using NOAA AVHRR data corresponding to a seven year period. The Advance Very High Resolution Radiometer (AVHRR), flown on the NOAA satellite has operated since 1972. The data was obtained from the Division of Oceanography, CSIRO at Hobart, where archiving has been undertaken since 1986. All data were carefully selected to cover the same area and approximately the same point in time, but in different years. Problems were encountered with obtaining the data at exactly the same day of different years due to cloud free requirements. Sky conditions at the time of image acquisition were reasonably haze-free. The data are listed in Table 5-1. SOURCE OF INFORMATION Page 77 Table 5-1: NOAA AVHRR Data Details No. Station Time Date Satellite Orbit No. 1 05:52 26 Sep 86 NOAA-9 9208 2 06:07 24 Sep 87 NOAA-9 14239 3 05:39 22 Sep 88 NOAA-9 14464 4 04:11 24 Sep 89 NOAA-11 5143 5 03:53 25 Sep 90 NOAA-11 10360 6 04:36 30 Sep 91 NOAA-11 15528 7 05:44 01 Oct 92 NOAA-11 20710 5.2 Water Quality Data The Water Board of NSW has been monitoring water quality in the Hawkesbury River since the 1930's. Water samples have been collected from approximately eighty stations on the Hawkesbury River and its tributaries. The choice of sampling points and frequency of sampling reflect the operational needs of the time. In this study, 20 sites were selected to indicate the general water quality of the Hawkesbury River. These sampling sites are shown in Figure 5-1 (Williams and Callaghan, 1991). Water quality parameters were chosen to reflect potential sources of effluent discharging into the river. The parameters consisted of surface (to a depth of 0.3m) measurements of total suspended solids (rngh1), secchi disk depth (m), chlorophyll-a (pg-1), turbidity (NTU), absorbance (rrr1, at 0.375 pm) and temperature (°C). Water quality data were extracted from charts published by the Water Board in 1990. SOURCE OF INFORMATION Page 78 Figure 5-1: Sampling Sites along Hawkesbury River N04 = Mooney-Mooney Br. NBII = Berowra Gc. N06 : Marlowes Q. N07 : Mangrove Ck. Nil : Gunderman NI4 Wisemans Ferry NISI MacDonald River NI6 Carinva NI8 Leers Vale N2I Lower Portland FerTy N220I = Colo River N26 = Sackville Ferry NCI = Cartai Ck. N35 = Wilberforce N504 = South Ck. N38 = Windsor Br. NR04 = Rickaby’s Ck. N4|| = Red batik Ck. N42 = North Richmond N430I = Grose River SYDNIIY SCALK (J l() km SOURCE OF INFORMATION Page 79 5.3 Air Temperature Data In the Hawkesbury River catchment area there is a good network of climatological stations, most of which have comparatively long records. Some of the stations have been in operation for more than 100 years. All temperature are measured in a Standard Stevenson Screen technique. Temperature records of varying periods are available from 9 stations. The discussion of temperature change is therefore quite comparative. In this study all temperature data were obtained from the 9am Weather Bulletin of the Meteorology Bureau of NSW. The mean daily minimum and maximum temperature are summarised in Table 5-2 and Table 5-3. Table 5-2: Mean Daily Minimum Temperature (°C) at Different Stations Year 86 87 88 89 90 91 92 Date 26/Sep 24/Sep 22/Sep 24/Sep 25/Sep 30/Sep 01/Oct Bankstown 11 10 08 06 10 10 06 Camden 17 07 02 17 14 04 Katoomba 10 04 02 09 07 03 Lithgow 06 05 02 -01 04 07 03 Liverpool 10 09 07 05 09 12 07 Nowra 13 11 08 07 12 15 07 Parramatta 11 07 12 10 09 06 Richmond 08 08 06 03 09 08 05 Sydney 14 13 11 10 13 15 10 Wollongong 17 11 15 08 SOURCE OF INFORMATION Page 80 Table 5-3: Mean Daily Maximum Temperature (°C) at Different Stations Year 86 87 88 89 90 91 92 Date 26/Sep 24/Sep 22/Sep 24/Sep 25/Sep 30/Sep 01/Oct Bankstown 30 23 21 21 24 27 21 Camden 30 23 19 25 25 20 Katoomba 20 16 18 20 17 12 Lithgow 23 22 16 20 21 17 11 Liverpool 31 25 22 22 24 27 21 Nowra 27 22 19 20 22 25 19 Parramatta 29 21 21 29 27 21 Richmond 31 24 22 23 24 28 21 Sydney 27 20 21 20 22 27 20 Wollongong 28 23 25 21 5.4 Land Use Map To study the land cover change in the Hawkesbury River catchment area, historical land use maps and the land use map of 1986 were compared. Historical land use maps can be obtained from Government Records Repository (GRR) of the Archives Authority of New South Wales. The earliest available land use map in the Hawkesbury River catchment area is the map named 'land use in Sydney and county of Cumberland' at 1:360,000 scale. This map was prepared by the Department of Main Roads of New South Wales in 1945. The land use map of 1986 at a scale of 1:5,000,000 was published by the Division of National Mapping of Canberra. Five different land use patterns were chosen and analysed: SOURCE OF INFORMATION Page 81 • Forest; • Agricultural and grazing; • Urban and industrial; • Waterways and dams; • Other-recreation; • Mining, extraction and waste disposal. CHAPTER 6 IMAGE PREPROCESSING The purpose of image preprocessing is to correct errors which stem from the image acquisition process and which can degrade the quality of the remote sensor data collected. These errors may have an impact on the accuracy of subsequent image analysis. Therefore, it is usually necessary to preprocess the remotely sensed data prior to analyse to remove some of these errors. To correct the image data, internal and external errors must be determined. Internal errors are created by the sensor itself. They are generally systematic (predictable) and stationary (constant), and may be determined from prelaunch or in-flight calibration measurements. External errors are due to platform perturbations and the modulation of scene characteristics, which are variable in nature. Such unsystematic errors may be determined by relating points on the ground to sensor system measurements. Geometric and atmospheric errors are the most common types of error encountered in remotely sensed imagery so the most important preprocessing operations are the correction of these. IMAGE PREPROCESSING Page 83 6.1 Geometric Correction Satellite images are subjected to different deformations due to the earth, the satellite, the orbit, and the image projection. The contribution of the earth comes from its rotation and its effect on the speed, attitude, and altitude of the satellite. The scan skew and the projection of a spherical surface on a flat image also give rise to geometric errors. These deformations, if not properly accounted for, will prevent meaningful comparison among images acquired at different times, by different sources, and with different geometries. In applications which involve change detection or image enhancement, the images must be geometrically registered to one another. 6.1.1 Sources of Geometric Distortion Remotely sensed data normally has both inherent systematic and non- systematic errors. The errors can arise in many ways. The following are some of the more important sources of geometric distortions: 1) Systematic distortions: • Earth rotation; • Scan skew; • Panoramic distortion; • Platform velocity; • Earth curvature; • Sensor scan nonlinearities. 2) Nonsystematic distortions: • Variations in platform altitude; • Variations in platform attitude. IMAGE PREPROCESSING Page 84 The geometric correction process is normally implemented as a two-step procedure. First, those distortions that are systematic, or predictable, are considered. Second, those distortions that are essentially random, or unpredictable, are considered. The systematic distortions can be corrected using data from platform ephemeris and knowledge of internal sensor. Non-systematic distortions which are caused by platform altitude and attitude (roll, pitch and yaw) cannot be corrected with acceptable accuracy without a sufficient number of ground control points (GCPs). Most digital remotely sensed data acquired have already had systematic errors removed by their master processors. Unless specified however, the non-systematic errors remain in the image, making it nonplanimetric. 6.1.2 Methodology of Geometric Correction Geometric correction is the process by which the geometry of the image is made planimetric. Two basic operations must be performed in order to geometrically rectify a remotely sensed image to a map coordinate system. 1) Spatial interpolation: The geometric relationship between the input pixel location (row and column) and the associated map coordinate of this same point (x, y) must be identified. The process always involves relating GCPs pixel image coordinates in rows and columns with their equivalent map coordinates. The GCPs are then taken and tested for the root mean square (RMS) error until all points are acceptable or until a RMS tolerance limit is met. IMAGE PREPROCESSING Page 85 2) Intensity interpolation: Pixel brightness values must be determined to fill the output matrix from the original image matrix. Unfortunately, there is not a direct one-to-one relationship between the position of input pixel values to output locations. It is more often the case that a pixel in the rectified output image requires a value from the input pixel grid that does not fall exactly on a row-and column coordinate. When this occurs a decision has to be made for determining the brightness value to be assigned to the new rectified pixel. The system used in this project was the MERIDIAN image processing system. The MERIDIAN image processing package implemented in the Image Analysis Laboratory of the Centre for Remote Sensing and GIS allows a wide range of display and analysis tasks to be performed. Utilities are available to convert AVHRR raw data from CCT to 512*512 8 bit image planes. A summary of the steps in the geometric correction process are as follows: 6.1.2.1 Selecting GCPs Selecting GCPs is the most crucial and most tedious part of the process, because the results of the processing greatly depends on the accuracy of the points being taken as GCPs. GCPs are points clearly distinguishable in both the image and the map being used. A point in the image is identified in row and column coordinates. This same point is also identified in the reference map in map coordinates. A general rule in the selection of GCPs is that there should be a distribution of control points around the edges remove, of the image to be corrected, with a scattering of points over the body of the image. This is IMAGE PREPROCESSING Page 86 necessary to ensure that the mapping polynomials are well-behaved over the image (Richards, 1986). Identified points may be road intersections, river curves, airport runway intersections and other topographic feature which are identifiable and that show clearly on both the image and the map. It is also advisable to use, if available, comparatively up-to-date reference maps with respect to the acquired image. However, this rule cannot be well implemented in this project for a number of reasons. The NOAA AVHRR image has 1.1 km resolution and was acquired many years after the map was produced. The map was produced in 1982 and the images obtained on the 26th September 1986, 24th September 1987, 22nd September 1988, 24th September 1989, 25th September 1990, 30th September 1991 and 1st October 1992 respectively. Because of the low resolution of the image, most of the distinguishable features on the map are hardly recognizable, particularly the roads and mountain peaks. The best possible part of the image for GCPs selection is prominent coastline features, lakes, reservoirs and dams because of their relatively unchanged position and distinguishing features. The coordinates of the GCPs taken from the map are shown in Table 6-1. Table 6-1: The Coordinates of the GCPs from the Map GCPs Location Latitude (S,C°) Longitude (E,C°) 1 Storage Reservoir 32.733 151.800 2 Norah Head 33.283 151.583 3 Botany Bay 34.000 151.250 4 Bass Point 34.594 150.902 5 Jervis Bay 35.117 150.767 6 Lake George 35.200 149.417 7 Lake Wyangala 34.000 148.967 8 Lake Burragorang 33.958 150.467 9 Lake Burrendong 32.639 149.111 IMAGE PREPROCESSING Page 87 In MERIDIAN, the commands CPDEF were used to locate ground control points on the image and to create a file containing GCPs. At the GCPs prompt, file coordinates and map coordinates were manually entered via the keyboard. 6.1.2.2 Calculation of the Transformation Matrix The selection of the polynomial to be used is an important point to be considered. Higher order polynomials can be used to map one image to the map, however, whilst the polynomials will be accurate in the area of the GCPs, considerable 'stretching' of the image will occur outside the areas of the GCPs which will result in image distortion. This situation is worsened if the GCPs are of low accuracy. In this study, the control points coordinates were defined from the 1:250,000 maps published in 1984. Because of the small sub-scene of the study area and the relatively low accuracy of GCPs, the second order polynomial was considered most suitable. The polynomials are shown below: x = a-i + a2U + a3V + a4U2 + asv2 + a6uv (6.1) y = bi + b2U + b3V + b4U2 + bsv2 + b6uv (6.2) where u, v = the map coordinates, x, y = the equivalent image pixel position, and a, b = the coefficients defining the transformation, which can be determined using an affine transformation with a minimum of three points for first order and 6 points for second order (Richards, 1986). Mathematically, this relationship can be represented as: N = (t + 1) (t + 2) / 2 (6.3) Where N = No. of GCPs. T = Degree of order of polynomial. In practice however, significantly more than these are chosen to allow for a least squares solution. Nine GCPs were selected for this project since a second order IMAGE PREPROCESSING Page 88 transformation was to be used. The next step was then to derive the coefficients for a and b of the polynomials for the second order equations. These coefficients were to be computed using the least-squares multiple regression analysis of the image and map coordinates. To determine how well these coefficients account for the geometric distortion in the input image involves the computation and analysis of the RMS error for each GCP which should be less than the specified threshold value. Any GCPs that has a RMS error of more than the threshold value could be deleted or edited until the total RMS error threshold value is met. The RMS for each control point can be calculated using the equation: RMS = (x'-xorig)2 + (y'-yorig)2 (6.4) where xorig and yorig are the original row and column coordinates of the GCP in the image, and x' and y' are the computed or estimated coordinates in the original image. The square root of the squared deviations represents a measure of the accuracy of this GCP in the image. In MERIDIAN, the command WRPDEF was used to automatically perform and derive the various coefficients and thereby obtain the transformation matrix (by calculating the inverse matrix of the coefficients) which was used to transform the image, and the corresponding RMS error. IMAGE PREPROCESSING Page 89 6.1.2.3 Interpolation Using Coordinate Transformation Having determined the mapping polynomials explicitly by use of ground control points, the image can be transformed to the new coordinate system. Every pixel in the original input image is rectified to the output image with the use of the transformation matrix. There are three techniques for geometric transformation which are commonly used, namely: 1) Nearest Neighbor: where the brightness value of the pixel closest to the map coordinate specified is assigned to the output image coordinate. 2) Bilinear Interpolation: which assigns output pixel values by interpolating brightness values in two orthogonal directions in the input image. 3) Cubic Convolution: which assigns values to output pixels in a similar manner as bilinear interpolation accept that the weighted values of input pixels surrounding the location of the desired image pixel are used to determine the value of the output image pixel. Each of these methods can be used for each of the orders of matrix transformation. Richards (1986) stated that the nearest neighbor resampling 'is the preferred technique if the new image is to be classified since it then consists of the original pixel brightness, simply rearranged in position to give a correct image geometry'. It is evident that for each order, nearest neighbor is the fastest to process. It is fast because it simply chooses the actual pixel that has its centre nearest the point located in the image. The bilinear interpolation method takes longer because it involves interpolation over the four pixels that IMAGE PREPROCESSING Page 90 surround the point found in the image corresponding to a given display grid position. The longest to process is cubic convolution because it uses the surrounding sixteen pixels. 6.2 Image to Image Registration Many applications of remote sensing image data require two or more scenes of the same geographical region, acquired at different dates, to be processed together. Such a situation arises, for example, when changes are of interest, in which case registered images allow a pixel by pixel comparison to be made (Richards, 1986). Image registration is the translation and rotation process by which two or more images of like geometries and the same set of objects are positioned coincident with respect to each other, so that corresponding elements of the same ground area appear in the same place on the registered images. The same general image processing principles are used in both image rectification and image registration. The difference is that in image rectification the reference is a map in a standard map projection, whereas in image registration the reference is another image. It should be obvious that if an image is used as the reference base (rather than a map), any other image registered to it will inherit the geometric errors existing in the reference image. It is recommended that image-to-image registration take place after image-to- map rectification, because it reduces the risk of having more error due to multiple misregistration (Jensen, 1986). IMAGE PREPROCESSING Page 91 The image to image registration procedure usually consists of three steps: 1) establishing a set of control points: to identify the image coordinates on the reference image corresponding to a set of well distributed image coordinates on the input image; 2) calculating the deformation model; 3) resampling: to use a resampling routine on a grid generated by the polynomial to remap the input image to its reference image. For the resampling, several methods can be applied. For example, the nearest neighbor selects the intensity of the closest input pixel and assigns that value to the output pixel. The bilinear interpolation uses four neighbouring input values to compute the output intensity by two-dimensional interpolation. The cubic convolution uses 16 neighbouring values to compute the output intensity (Bernstein, 1983). It was found that the different orders of transformation produced relatively different results and the higher the order of transformation the longer the processing time as would be expected. In this study, the nearest neighbor resampling method was used. Nearest neighbor resampling simply chooses the actual pixel that has its centre nearest the point located in the image. The pixel is then transferred to the corresponding display grid location. This is the preferred technique if the new image is to be classified since it then consist of the original pixel brightness, simply rearranged in position to give a correct image geometry (Richards, 1986). It is appropriate for this study, because it dose not alter the pixel brightness value during resampling. Jensen (1986) asserts that it is often very subtle changes in brightness values that greatly improve interpretation IMAGE PREPROCESSING Page 92 when discriminating between one type of vegetation and another. The actual image to image registration on the Meridian image processing system are as follows. The job control point definition (CPDFF) is invoked. This allows the user to define a suitable control point in one band in the AVHRR image. A suitable control point has the following attributes, well defined on the image and the topographic map, symmetrical and spectrally homogeneous. The statistic provided by the Meridian system enable examination of residuals present at each control point (i.e. the difference between the true value and the transformed value). The control points are used in the job WARP on the Meridian image processing system to calculate the transformation parameters. The actual hands on process of CPDEF and establishing the transformation parameters was iterative. As statistics are supplied by the WARP job on the quality of the GCPs the control points are defined and then tested and if necessary refined. When applying the GCPs to a particular band, it is important to note that if the individual standard deviations are near or below 0.5 pixel, then the model should be accepted. A combined standard deviation of less than 0.7 is another way of checking that model is sufficiently accurate. In this study, the pixel standard deviation was determined as 0.248, line standard deviation as 0.273 and combined standard deviation as 0.369 (see Table 6-2). The geometric correction result are plotted as Figure 6-1 and Figure 6-2, which show the spatial distribution of the residual errors, and the amount of image warping, respectively. IMAGE PREPROCESSING Page 93 A. This warping specification has been defined using 9 control points. Error in current model Mean Standard Deviation Pixel (x) 0.000 0.248 Line (y) 0.000 0.273 Combined 0.000 0.369 B. The order of the warping transformation equation is: 2 x space coefficients F(x,y) = a1 + a2x + a3y + a4x2 + a5y2 + a6xy a1 = -56.083 a2 = 1.262 a3 = -0.065 a4 = 0.000 a5 = 0.000 a6 = 0.000 y space coefficients G(x,y) = b1 + b2x + b3y + b4x2 + b5y2 + b6xy b1 = -4.013 b2 = -0.005 b3 = 1.014 b4 = 0.000 b5 = 0.000 b6 = 0.000 C. Control point locations and residuals No. Name Output Space Residual Line(y) Pixel(x) Line(y) Pixel(x) A0 3-Botany Bay 162 277 0.238 -0.400 A1 6-Lake George 280 127 -0.111 -0.180 A2 9-Lake Burrendong 29 101 -0.051 -0.070 A3 8-Lake Burragorang 158 213 0.035 0.064 A4 7-Lake Wyangala 162 90 0.103 0.160 A5 4-Bass Point 221 248 0.048 0.326 A6 2-Norah Head 92 305 -0.610 -0.120 A7 5-Jervis Bay 272 237 0.019 0.059 A8 1-Storage Res. 39 324 0.329 0.180 Table 6-2: Statistics for GCPs IMAGE PREPROCESSING Page 94 WARP SPECIFICATION Control Point Residual Plot DISK PIXELS V 100 150 200 250 300 344 2: o SCALE FACTOR: 47.855 1.245 PIXELS COMMENT: 89-9-24-B5 WARP TYPE: Control Point Database CREATED: 19-DEC-93 23:28 SOURCE IMAGE SET: LI-COM-ROT3 SOURCE FEATURE: 89-B5 DISK LINE/PIXEL CORNERS UPPER LEFT LINE: 1 PIXEL: 1 LOWER RIGHT LINE: 301 PIXEL: 351 9 point have been marked to derive the current model Figure 6-1: Control Point Residual Plot 95 Page URC 351 /\ 300 ULC Plot 1 351 + model 250 ir Lines LINES ----- ' Disk r MODEL current Database DISK 200 PIXEL: PIXEL: vs - 23:28 the VS t PIXELS Point Pixel -r SPECIFICATION 150 derive 1- DISK Disk PIXELS to 19-DEC-93 LI-COM-ROT3 Control 89-9-24-B5 89-B5 __SOURCE 1 WARP 6-2: 100 DISK marked CORNERS SET: Figure LINE: LINE: 301 LINE: LINE: 50 been IMAGE FEATURE: RIGHT LEFT have TYPE: 1 LINE/PIXEL PREPROCESSING point UPPER LOWER 9 DISK SOURCE LEGEND SOURCE CREATED: WARP DISK LIh COMMENT: IMAGE IMAGE PREPROCESSING Page 96 6.3 Atmospheric Correction Remote sensing images suffer from atmospheric effects which alter the direct relation between remotely observed spectral radiance and object spectral reflectance. The molecules and aerosol particles in the atmosphere cause selective extinction of light, i.e., scattering and absorption depending on the wavelength. In order to retrieve accurate values of land surface temperatures or sea surface temperatures from satellite thermal infrared data, it is necessary to perform atmospheric corrections. The atmosphere affects the upwelling radiance by changing the amount and quality of energy available at the earth surface and sensor (Egan and Fischbein,1975). The major factors can been divided into meteorological parameters and optical parameters (Bowker et al., 1985). The meteorological parameters include relative humidity, cloud cover and surface pressure. Relative humidity determines the strength of the water absorption bands within the electromagnetic spectrum and the type and amount of aerosol; unresolved cloud alters upwelling radiance independently of conditions at the water surface (Duggin, 1985) and surface pressure can affect the amount of molecular scattering. The most important optical parameters are aerosol content and skylight (Bowker et al., 1985). The amount of aerosol in the atmosphere can be expressed by the aerosol optical thickness which measures the aerosol transmissivity in a vertical path. However, a rough estimation of aerosol amount can be obtained by measuring the visual range in the horizontal direction (Bowker et al., 1985). An algorithm for suppressing the effects of aerosol content on the radiance of dark water surfaces, especially in IMAGE PREPROCESSING Page 97 blue/green wavelengths, has been developed by Bowker et al. (1985). This algorithm is based on radiative transfer equations and enables the calculation of multiple scattering and absorption by the atmosphere. However, like other similar algorithms it does not account for aerosol colour which can be an important consideration, especially for dark water surfaces. Skylight, or the reflectance of radiation by the atmosphere, is the largest single component of remotely sensed spectral radiance above water surfaces. For example, it has been reported that on a clear day the radiance received by the sensor could be around four to five times higher than the radiance from the volume of water (in California coastal waters, Austin, 1974). This effect being particularly severe at shorter wavelengths. However, Milne & Hall (1992) concluded that after 20 years there are still no easy methods of atmospheric correction that will work over a wide range of conditions or that are relatively easy to implement. The method of McClain et al. (1985) however, seems to be one of the more robust techniques available for performing split window atmospheric corrections to AVHRR data and has been verified for use over land by Cooper and Asrar (1989). According to McClain et al. (1985) the atmospheric absorption in three infrared window channels centred at 3.7 pm, 11 pm, and 12 pm occurs primarily at very low levels in the atmosphere. As a result one may assume that the mean atmospheric temperature is the same in these channels. Another result is that the mean atmospheric temperature is nearly equal to the sea surface temperature. IMAGE PREPROCESSING Page 98 Thus, when two channels, or more, corresponding to different atmospheric transmissions, are available, it is possible to use the differential absorption to estimate the atmospheric contribution to the signal. This method is called the split window technique (SWT). The algorithm consists simply of a linear combination of the thermal channels which gives a surface temperature corrected for atmospheric contribution. The equation is given by TBB = T4 + A(T4 - T5)+ B (6.5) where TBB is the satellite temperature corrected for the atmospheric effects but not for the emissivity effects, T4 and T5 are the brightness temperatures measured in the NOAA band4 (10.3 - 11.3 pm) and bands (11.5 - 12.5 pm), A=k4/(k5-k4), k4 and k5 being the absorption coefficients of water vapour in the NOAA band4 and band5, and B is the constant that takes into account the influence of surface reflection and CO2 emission (Caselles & Sobrino, 1989). The SWT has been developed for sea surfaces and has proved to be efficient and accurate in most cases. It is now used operationally. It is a simplified way to take into account atmospheric effects and thus relies on a number of assumptions such as: 1) the surface is lambertian; 2) the surface temperature is close to the temperature in the lower layers of the atmosphere, the latter varying slowly (Plank's law linearization); 3) the surface temperature does not exceed 305 °K; 4) absorption in the atmosphere is small and occurs essentially in the lower layers; 5) the surface emissivity is very stable spatially, and close to unity; 6) the emissivities of channel 4 and 5 are almost identical, and e4>e5. IMAGE PREPROCESSING Page 99 In a study of the distribution of minimum temperature (McClatchey et al., 1987) it was found that such climatological values could be usefully used as ground truth for AVHRR derived temperatures. For cloud free locations the 10-12 pm spectral interval is relatively transparent to radiation, except for the effects of atmospheric water vapour. The SWT algorithm used in this study for atmospheric correction was adopted from Caselles and Sobrino (1989) and can be written as TBB = T4 + 2.0 (T4 - T5)+ 0.5 (6.6) where the temperatures are given in °C. This result was used to derive the surface temperature of the Hawkesbury River catchment area with a detailed analysis being given in the next chapter. CHAPTER 7 CHANGE DETECTION Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times. Essentially, it involves the ability to quantify temporal effects using multi-temporal data sets (Singh, 1989). 7.1 The Techniques for Change Detection Many different procedures have been proposed and used to detect changes. The fundamental assumption is that changes in land cover will result in changes in reflectance values and other factors (Ingram et al. 1981). Accurate spatial registration of images is a basic requirement for most of the change detection techniques. There are many and varied approaches to change detection analysis, and these techniques can be broadly categorized as follows. 7.1.1 Image Differencing Image differencing involves subtracting the imagery of one date at time ti from that of another at time t2) pixel by pixel, to produce a further image which represent the change between the two dates. The operation is expressed CHANGE DETECTION Page 101 mathematically as, DXy = Xy (t2)- Xy(t,) + C (7.1) where Xy = pixel value for band k, i and j are line and pixel numbers in the image, ti = first date, t2 = second date and C = a constant to produce positive digital numbers. This procedure yields a differenced distribution for each band, where pixels of no radiance change are distributed around the mean while pixels of radiance change are found in the tail of the distribution. This technique has been used in a variety of geographic environments with varying degree of success (Ingram et al., 1981; Nelson, 1983; Weismiller et al., 1977). However, this method has its limitations. Simple image differencing failed to consider the start and end location of a pixel in the feature space (Singh, 1989). Weismiller et al. (1977) concluded that the method may be too simple to deal adequately with all the factors involved in detecting changes in a natural scene. 7.1.2 Image Regression In the regression method of change detection, pixels from time ti are assumed to be a linear function of the pixels at time t2- So one image can be regressed against the other using a least squares regression. Image differencing is obtained by subtracting the predicted value obtained from the regression line from the values in the other image, pixel by pixel (Ingram et al., 1981). CHANGE DETECTION Page 102 The regression technique accounts for differences in the mean and variance between pixel values for different dates so that adverse effects from differences in atmospheric conditions or sun angles are reduced (Jenson, 1986). Ingram et al. (1981) and Singh (1989) have reported that the regression procedure performed marginally better than the image differencing technique in detecting urban land cover changes and tropical forest cover changes, respectively. 7.1.3 Image Ratioing In ratioing two registered images from different dates with one or more bands in an image are ratioed, band by band. The data are compared on a pixel by pixel basis. The mathematical expression of the ratio function is: k xij(ti) (7.2) (t2) where Xy (t2) is the pixel value of band k for pixel x at row i and column j at time t2- Basically, a pixel that has not changed in land cover will have the same brightness value on both dates, yielding a ratio value of 1.0. Areas of change in the multiple date imagery will have values either higher or lower than 1.0. Thus, as with the image differencing method, a change histogram is produced with the tails of the distribution containing the change information. The selection of thresholds is again based on empirical judgment (Singh, 1989). CHANGE DETECTION Page 103 Changes in viewing conditions (such as shadows, seasonal reflectance differences due to sun angle, etc.) degrade the ability of a classifier to identify surface materials correctly. The ratio transformations can remain in-variant when changes result from such factors. These ratios are especially useful for change detection when several dates of imagery are used in an analysis, because they reduce the effect of environmental and system multiplicative factors that may be present. The better these factors are controlled, the higher the probability that accurate change detection can take place. 7.1.4 Normalized Vegetation Index Differencing The NDVI (Normalized Differencing Vegetation Index) method analysis spectral radiance values on a band by band basis or in combinations of two or more bands. The ratio images have two important properties. First, large differences in the intensities of the spectral response curves of different features may be emphasized in ratioed images (Singh, 1989). Secondly, ratios can suppress the topographic effects and normalized differences in irradiance when using multidate images. But the ratio technique may enhance random noise or coherent noise that is not correlated in different bands. In vegetation studies the ratios, commonly known as vegetation indices, have been developed for the enhancement of spectral differences on the basis of strong vegetation absorbtance in the red and strong reflectance in the near- infrared part of the spectrum. As far as change detection is concerned, the difference in vegetation indices should provide an avenue for deciding whether or not a vegetation canopy has been significantly altered (Nelson, 1983). CHANGE DETECTION Page 104 7.1.5 Principal Component Analysis Principal Component Analysis (PCA) is a data transformation technique commonly applied in multivariate statistical analysis. The aim of principal components analysis in remote sensing is to remove redundancy in the multi- spectral image by rotating the axes into principal components which maximize variance in the data. The first component will contain the largest possible amount of the total variance, the second component will contain the largest possible amount of the remaining variance, and so on. PCA is commonly applied technique for remote sensing image analysis. It has been used in remote sensing for image encoding and image data compression, for image enhancement (Richards, 1986), for digital change detection, and for examining the underlying multi-temporal dimensions of data sets (Singh, 1989). Byrne et al. (1980) concluded that principal component analysis provides an effective way of identifying areas in which change has occurred between two images on different dates. The effect of using of PCA in land cover change detection was examined by Fung and LeDrew (1987). They concluded that PCA is scene dependent and is not sensitive to the absolute value of the image densities. 7.1.6 Post Classification Comparison The most obvious method of change detection is comparative analysis of independently produced classified images. By properly coding the classification results from times ti and t2, the analyst can produce change maps which show a complete matrix of changes. In addition, selective grouping of classification results allows the analyst to observe any subset of CHANGE DETECTION Page 105 changes which may be of interest. Postclassification comparison holds promise because data from two dates are separately classified, thereby minimizing the problem of normalizing for atmospheric and sensor differences between two dates. The method also by-passes the problem of getting accurate registration of multidate images (Singh, 1989). It is likely that the change map product of two classifications is bound to exhibit accuracies similar to the product of multiplying the accuracies of each individual classification (Malila, 1980). Hence it can produce a large number of error change indications since an error on either date gives a false indication of change. For example, two images classified with 80 percent accuracy might have only a 0.80x0.80x100=64 percent correct joint classification rate. Computationally it is more demanding than other methods since it requires classification of the whole images twice (Robinson, 1979). Singh (1989) noted that the poor performance of this approach may, in part, be attributed to 'the difficulty of producing comparable classifications from one date to another'. 7.1.7 Direct Multidate Classification Such methods are based on a single analysis of a combined data set of two or more times to identify areas of change (Swain 1976). For example in a two date Landsat data set eight bands are analysed at one time in supervised or unsupervised modes of classification. The basic drawback of this method is coupling between the spectral and temporal changes. CHANGE DETECTION Page 106 While this method requires only a single classification it is very complex one, often requiring many classes and too many features, i.e. bands, some of which may be redundant in information content (Estes et al., 1982). The problem of redundancy can be overcome by using a principal component transformation on the original data set. The first few components, containing significant amounts of variance from the two dates, can be used in the classification analysis. Another problem is that the temporal and spectral features have equal status in the combined data set (Schowengerdt, 1983). Thus spectral changes within one multispectral image cannot be easily separated from temporal changes between images in the classification. Singh (1989) conclude that this method has not attracted further attention, apparently because of the complexity of the algorithms and computational requirements. 7.1.8 Change Vector Analysis If two spectral variables are measured for the area, both before and after change occurs, then the vector describing the magnitude of change from the first to the second date is a spectral change vector. Given multidate pairs of spectral measurements one computes spectral change vectors and compares their magnitudes to a specified threshold criterion. The decision that a change has occurred is made if a threshold is exceeded. The direction of the vector contains information about the type of change (Malila, 1980). CHANGE DETECTION Page 107 In this method, a multitemporal data set is transformed into greenness and brightness data sets. The transformated data set is clustered using a spectral/spatial clustering algorithm called BLOB (Singh, 1989). Each BLOB has four components, consisting of the means of the greenness and brightness values for the two dates. BLOBs formed over change areas should vary significantly in the transformed channel values. The method is computationally very demanding as the data have to be geometrically corrected and digitally merged, then transformation coefficients have to be developed and finally spectral/spatial clustering is done. Also, it has been reported (Malila, 1980) that the performance of the procedure is sensitive to its parameter setting. However, there are no guidelines for their selection. Singh (1989) concluded that the disadvantage of this method is 'unavailability of a reference data set makes any assessment of true performance somewhat uncertain'. 7.1.9 Summary Comparative evaluation of various methods are few. The type of change detection procedure implemented can significantly affect the estimate of change. Change detection is a difficult task to perform accurately. The selection of an appropriate change detection technique is based on an analysis of several important factors. First, the analyst must know the cultural and biophysical characteristics of the study area. Next, it is imperative to know the precision with which the multiple-date imagery is registered. Finally, one should be aware of change detection techniques alternatives, their degree of flexibility, and availability. CHANGE DETECTION Page 108 The objective of this study is to use the NOAA AVHRR data to investigate how the Hawkesbury River catchment area land use and temperature have changed over a 7 year period. According to the land use map published by the Division of National Mapping of Canberra in 1976 at a scale of 1:5,000,000, the land use in the catchment area can be divided into the following major categories: 1) Urban land (residential, industrial and commercial); 2) Agriculture and grazed land; 3) Land not commercially used; 4) Large water bodies; 4) Forest. In this study, NDVI, visual interpretation, and post classification comparison are used and are described below. 7.2 Application of the NDVI Method According to Forster (1991), changes in urban areas can be divided into two types: absolute change for example, where previously vegetated areas are cleared for housing development, and relative change which is a result of seasonal changes. As many land use changes involve changes in the type or amount of vegetation present, an NDVI approach should be suitable for determining these change areas (Forster, 1991). The mathematical formulation for NDVI is give as follows: ____ ^ . Band2-Band1 ^ NDVI = Gain Band2+Band1r.__ + Offset (7.3) NDVI can move theoretically from -1.0 to 1.0, with +1 representing very high vegetation cover and vigour while -1 represents non-vegetation areas, although gain and offset can be adjusted to give positive values. Thus the change can be represented by multitemporal data. The gain and offset factor CHANGE DETECTION Page 109 are also applied to avoid the result values falling outside the range of the 8 bit dynamic range of the original AVHRR images. In this study, the gain=200 and the offset=50, giving results in the theoretical range between -150 to 250. However, in practice the majority of values range between 70 and 200. The NDVI histograms for the 1987, 1989, 1991 are shown in Figures 7-1, 7-2, 7-3 respectively. Figures 7-4, 7-5, 7-6 show the NDVI images for the Hawkesbury River catchment area for the 24th September 1987, 24th September 1989, and 30th September 1991. While some differences in NDVI can be identified from the visual inspection of these images, post classification comparison can be used to quantitatively assess the extent of change occurring. 110 Page -5 Image 340 33.0 1 142.0 140 255 1987 HISTOGRAM the 200 22:40 for NDVI COUNT 150 NDVI NDVI-87 VS the 24/09/87 27-JAN-94 of 100 INTENSITY 50 HISTOGRAM INTENSITY DEVIATION Histogram 0 7-1: 0 8 00 2 400 GOO 800 GOO 400 l 1200 1 15I MODE MEDIAN MEAN STANDARD Figure CREATED: COMMENT: 1NH0 u DETECTION HANGE CHANGE DETECTION Page 111 HISTOGRAM NDVI-89 INTENSITY VS COUNT j 40 72 yH'; 4:! ' ,lms' 3Q0O Mltti h- z 2000 o u mmmm ,JJ| n i ft! -• i H!-~-i i. 1000 ,-|*yl!i!S?i?imfSH| : f rtiil 0 M . '■ ‘ l:\ . : o 50 100 150 200 255 INTENSITY COMMENT: 24/09/89 NDVI HISTOGRAM CREATED: 27-JAN-94 22:44 MEAN 147.0 STANDARD DEVIATION 33.0 MEDIAN 154.0 MODE 165.0 Figure 7-2: Histogram of the NDVI for the 1989 Image CHANGE DETECTION Figure CREATED: COMMENT: STANDARD MODE MEDIAN MEAN COUl 4187 3000 2000 1000 7-3: 0 -I - ’ - 1 Histogram 0 iji '■ ■ i • : ■r.:U ■t ! ! DEVIATION ?• 'r> * i 1 1 , u , ftii !?h sy i: ! • '- ; 1 ; INTENSITY HISTOGRAM . : I : ; ; ‘ • 50 i of INTENSITY 30/09/91 27-JAN-94 the 100 ,. VS NDVI NDVI-91 COUNT Y' jiiiipinnitjiiiu ISO fii tmjiiititttii! mAm NDVI iii mm for Mi 22:45 4 • [ “ * the 200 HISTOGRAM 1991 160.0 139.4 175.0 255 55.1 Image Page 112 IGURE7-4: NDVI IMAGE OF 24 SEPTEMBER 1987 1 i IGURE 7-5: NDVI IMAGE OF 24 SEPTEMBER 1989 IGURE7-6: NDVI IMAGE OF 30 SEPTEMBER 1991 CHANGE DETECTION Page 116 7.3 Application of Post Classification Comparison This approach involves the independent classification of data from each end of the time interval of interest by automatic pattern recognition techniques, followed by a pixel-to-pixel comparison to detect changes. The spectral signatures for each data set may be produced by using an unsupervised approach employing a clustering analysis algorithm. However, in order to obtain adequate signatures, a supervised approach is often applied. A maximum likelihood classifier may then be used to map the data into the statistically defined boundaries of the spectral signature. The use of supervised and unsupervised methods of classification represents two very different approaches towards the problem of automated classification of remotely sensed imagery. Initially supervised classification appears superior since the analyst controls the classes that are to be discriminated and it makes better use of ground data which is available. However, if little or no ground data is available, or if the analyst has no prior knowledge of the area, an unsupervised approach has several advantages. Unsupervised classification obviates the need to select training areas in order to provide unique spectral signatures which are fully representative of the terrain classes throughout the entire image. Selection of these representative areas can prove extremely difficult due to variations in slope and aspect as well as spectral differences in the cover types. CHANGE DETECTION Page 117 Because of the advantages and disadvantages of each approach, hybrid methods combining the two methods are being developed which are considered the most efficient method for classification. The steps, using the ERDAS program, involved in the classification are described below. • an initial unsupervised classification into 15 classes using the ERDAS program ISODATA; • using SIGMAN command, examination and manipulation of the class signature statistics produced by ISODATA, removing any unnecessary classes and merging any closely related classes; • using SEED command, definition of any additional classes required using supervised training methods; • maximum likelihood classification of the image into the final defined classes using MAXCLAS command. It accepts an input NOAA AVHRR image file and a class signature definition file obtained from the program SIGMAN. By optionally using an initial parallelepiped decision rule to determine the possible classes for each pixel, the number of calculations required to perform the classification may be considerably reduced. This dramatically speeds up the classification process without greatly affecting the results; • COLORMOD command is used to adjust the colour of the classification map. Classified images are shown in Figures 7-7, 7-8, 7-9. LEGEND: UNCLASSIFIED URBAN AGRICULTURE AND GRAZING LAND FOREST AREA LARGE WATER BODIES MASK Figure 7-7: Classified image of 24 September 1987 LEGEND: UNCLASSIFIED URBAN AGRICULTURE AND GRAZING LAND FOREST AREA LARGE WATER BODIES MASK Figure 7-8: Classified image of 24 September 1989 LEGEND: UNCLASSIFIED URBAN AGRICULTURE AND GRAZING LAND FOREST AREA LARGE WATER BODIES MASK Figure 7-9: Classified image of 30 September 1991 CHANGE DETECTION Page 121 7.4 Surface Temperature Retrievals Surface temperature can be derived from the radiance measured in thermal band4 and band5 of NOAA AVHRR. These bands are sensitive to a narrow range of electromagnetic radiation. A calibration formula is then used to convert the radiance observed by the sensor into temperature. Badenas and Caselles (1992) concluded that the radiometric temperature given by a radiance measurement is not the same as the kinetic temperature which might be given by a thermometer, because sensor calibration is based on the assumption that the object being observed is a blackbody, i.e., a surface with emissivity equal to unity. The temperature determination was based on the calibration coefficients provided for the satellite data and the split-window technique, using the both band4 and band5 radiance values for correction of atmospheric effects. The satellite-derived surface brightness temperature (Tbb) was then determined according to (Caselles and Sorbrino, 1989); from: Tbb = T4+2(T4-T5)+0.5 (7.3) . / Badenas and Caselles (1992) developed a simple technique for estimating surface temperature from surface brightness temperature. The technique is based on the relationship: b+1-e T = Tbb (-5—)-AT (7.4) where T is the surface temperature, Tbb is surface brightness temperature given by the atmospheric models, b is the contribution of ground temperature and depend on the ground emissivities, e is the average emissivity value for band4 and band5, which can easily obtained by using the box method (Caselles et al., 1988), and AT is a correcting term to reduce error. CHANGE DETECTION Page 122 For the 10.5-12.5pim window, b=4.774, £=0.945, AT=1.0, were selected according to the AT-e relationship curve determined by Badenas and Caselles (1992). Thus 4.774+1-0.945, T = TBB(---- 4^774-----■)-1.0= 1.0108 TBB-1-0 (7.5) Applying this equation to the NOAA AVHRR images, surface temperature images were obtained and are shown in Figures 7-10, 7-11, 7-12. LEGEND: ^ < 7.9 II 17.0 - 19.9 CD 8.0 - 10.9 20.0 ~ 22.9 □ 11.0 ~ 13.9 23.0 ~ 23.9 14.0 ~ 16.9 > 24.0 Figure 7-10: Surface Temperature of 24 September 1987 LEGEND: < 7.9 m 17.0 ~ 19.9 mi 8.0 ~ 10.9 20.0 ~ 22.9 gm 11.0 - 13.9 m 23.0 ~ 23.9 14.0 ~ 16.9 > 24.0 Figure 7-11: Surface Temperature of 24 September 1989 Figure 7-12: Surface Temperature of 30 September 1991 CHAPTER 8 RESULTS AND CONCLUSIONS 8.1 Land Use Change Discussion 8.1.1 Comparison of Classified Map The classified images (see Figures 7-7, 7-8, 7-9) give an indication of the change between the three data of the same area observed in later September of 1987, 1989 and 1991. The major cover types include urban, agriculture and grazing land, forest and water. These classes were determined by inspection of the images and comparison with a land use map in the Hawkesbury river catchment area of 1973. (It is noted that this map is out-of date, but non change areas were clearly observable and could be used in the classification process.) It can be seen from the final hybrid classification that the classification has been reasonably successful. Areas identified as urban, agriculture, forest and water have all been successfully classified into the correct classes. The difference between the three images can be distinguished from the number of pixels and percentage cover for each class as shown in Table 8-1. RESULTS AND CONCLUSIONS Page 127 Table 8-1: Classified Difference of 1987,1989 and 1991 Class 87 pixel 89 pixel 91 pixel Difference pixel (91-87) Urban 1351 1603 1906 545 Agriculture 12612 17816 19747 7135 Forest 37759 32363 29958 -7801 The result of the classification revealed the following points: 1. The more homogeneous and the more spectrally unique the classes are, the greater the classification accuracy is likely to be. 2. Certain misclassifications were also present the most obvious being the classes of agriculture and forest. 8.1.2 Land Use Change Discussion As indicated in the Hawkesbury River Valley Environmental Study Background Report (1973) (see Figure 8-1), about half of the total basin is Crown Land. Most of this is forested, comprising national parks and state forest, and lies in the more rugged and dissected parts of the basin. Nine national parks occupy about 26 percent of the catchment area. The largest are the Blue Mountains (215,890 ha), Kanangra-Boyd (68,103 ha), Wollemi (457,020 ha, 40% of which is in the Hawkesbury basin), Marramarra (11,500 ha) and Dharug (14.782 ha) National Parks (State Pollution Control Commission, 1983). RESULTS AND CONCLUSIONS Page 128 ?L'^uMnilanO 21 I * 3l /' MAWKE S5URY ' > ri > ^ y/ lYOHtY * |UTHGOWpC-r- :hmq'wd1 I^ewwi' IsVoweyI ICAMBENl [pic.ton! Ihittagong] |moss val' residential ]goul»u«n special USES AGRICULTURE AND GRAZING FOREST AREA SCALE AILOME Figure 8-1: Land Use Map in the Hawkesbury River Catchment (Source: Minister for Environment Control, 1973) RESULTS AND CONCLUSIONS Page 129 The land use patterns in the Hawkesbury River Catchment Area from available sources and the classified map are summarised in Table 8-2. Table 8-2: Comparison of Land Use Patterns Changes Land use Urban % Agriculture % Forest % Water % Other 1945 map 0.5 7.7 89.0 2.6 0.2 1973 map 4.6 29.1 62.8 2.3 1.2 1987 image 2.5 23.2 69.4 1.2 3.7 1989 image 2.9 32.8 59.5 1.1 3.7 1991 image 3.5 36.4 55.1 1.3 3.7 From the above table it can be seen that there has been a substantial increase in the urban and agriculture classes but a decrease in the forest class. One obvious change that has occurred during the 1987 to 1991 period in the Hawkesbury River catchment area, is that the urban area has increased in size from 2.5 percent in 1987 to 3.5 percent in 1991 (table 8-2). These increases in urban size, shown spatially in Figures 7-7, 7-8, and 7-9, respectively, are due to the rapid growth of Sydney's population. The change from forest to agriculture areas, in many cases, was the result of the expansion of the urban areas into agriculture, and the need for more agricultural production as the population of Sydney grew. RESULTS AND CONCLUSIONS Page 130 There are some statistical differences between the satellite and map data. The main reason for these are the different land data collections methods being used. Due to the 1.1 Km resolution of NOAA AVHRR data, some small areas can be misclassified, especially at the boundary of different classes. Despite this, the trend of changes are well indicated by the NOAA AVHRR data. 8.2 Temperature Change Discussion 8.2.1 Temperature Change Analysis The temperature maps were derived from NOAA AVHRR for the three dates, 24 September, 1987, 24 September, 1989, and the 30 September 1991, which were acquired over the catchment area at about 0500 local time. Visual assessment of the three temperature change maps revealed a distinct spatial pattern with respect to the changes in urban areas over the 5 year period. The 24 September 1987 map exhibited lower temperatures over the urban areas compared to the 24 September 1989 and 30 September 1991 temperature maps which exhibited an obvious increase in the urban areas and intensities. The infrared channels of the AVHRR derived land surface temperatures are prone to greater errors due to variations in emissivity and substantial changes in temperature over short distances. The spatial temperature variability has been reported as being less at night (Saunders & Kriebel, 1988) but the emissivity variations remain, although in some places they may be relatively small (Kalma et al., 1986). RESULTS AND CONCLUSIONS Page 131 The brightness temperatures observed by satellites are normally lower than surface temperatures principally due to atmospheric attenuation. However, maximum temperatures recorded at climatological stations may well be lower before or well after the time of satellite overpass. Therefore in situ maximum temperature does not necessarily occur at the same time as maximum brightness temperature observed by the satellite. Quantification of the effect of time of overpass is thus complicated and requires more information than is routinely observed, even at synoptic stations. Satellite derived temperatures are generally verified by ground truth. The in situ observation of mean daily maximum temperature used in this study were acquired by the climatological stations distributed in the catchment area. Unfortunately the distribution of climatological stations are less than ideal. Furthermore, climatological observations are mostly made only once a day. Figures 8-2, 8-3, 8-4, 8-5, and 8-6 give mean daily maximum temperature changes at some stations during the month of September from 1984 -1991. Figure 8-7 shows the locations of all these stations on a NOAA AVHRR image. The mean maximum temperature was calculated based on 9am observation, while the satellite passed at about 5am. Due to the time lag between the ground observation time and the satellite passes, and also the difference between a mean monthly value and a daily value, there should be some differences in the temperatures obtained. RESULTS 2 Temperature Temperal AND Figure CONCLUSIONS 8-3: 1984 1 9 8 4 1 9 8 5 1 9 8 6 1 9 8 7 1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 / / xxx XXX XXX 8-2: y / y y Mean 1985 Mean 1986 r f ^ Daily xxxxxx XXX y y / Daily y y / 1987 Maximum Maximum Year Year 1988 f f f f f f f f f f f * xxxxxxxxx xxx xxxxxxxxxxxx XXX XXX XXXXXX y y y y y y y y / s s s y y y y y y y y s s / y 1989 T e m p e r a t u r e T e m p e r a t u r e 1990 ' ' ' f ' * f xxx xxx XXX XXX XXX XXXxxx y y s s y y y y y / / y y / 1991 at at Richmond □ Sydney □ September September Page 132 RESULTS 3 Temperature Temperal 0 Figure Figure AND CONCLUSIONS 8-5: 1984 1984 8-4: Mean 1985 1985 Mean S. ^ \ ^ / / / / \ \ \ \ s / / / \ \ N s / / / / 1986 Daily 1986 ^ t f \ \ Daily / / y \ \ / / y \ V 1987 1987 Maximum Maximum Year Year 1988 1988 f s s v. / \ 1989 1989 '■ s Temperature / Temperature 'T'" / 1990 1990 f ' \ \ \ / X \ \ \ / / \ \ \ 1991 1991 at at Katoomba Camden □ □ September September Page 133 RESULTS 3 Q) * Figure Temperal 1989 AND 8-6: data CONCLUSIONS 1984 Mean are 1985 \ / \ / not \ / Daily 1986 f \ available y \ y v Maximum 1987 Year 1988 1989 Temperature 1990 ' ' ' \ \ \ \ y y y \ \ \ \ y y y \ \ \ \ 1991 at Goulburn □ September Page 134 Lithoaw RICHMOND J&A TODMBA CAMDEN, V □ L L □ N 0 □ N O SOULBURN igure 8-7: The Location of Some Meteorological Stations on a NOAA AVHRR Image RESULTS AND CONCLUSIONS Page 136 Table 8-3 shows the comparison of satellite derived surface temperature and ground mean daily maximum temperature. It can be seen that while the absolute values of the NOAA AVHRR derived data within the catchment area are of the order of 1.8 to 3.8 °C lower than the ground based temperature (averaged for the month), that the relative differences between the four sites are of the correct order. The Lithgow site was not included due to limited ground based temperatures data. However the satellite derived temperature give a comprehensive view of the spatial distribution of temperature in the catchment area, which is closely related to the land use patterns (see figure 7- 7, 7-8 and 7-9). In general, the surface temperature derived from satellite data are underestimated when compared to ground surface temperature. The under estimation of satellite derived surface temperature are mainly due to the presence of high atmospheric water vapour content during the early morning, and because of the four hours time difference between ground and satellite temperature observations. In general, the satellite derived surface temperatures are in good agreement with the ground truth data and the difference is less than 4 °C. Table 8-3: Comparison between Ground and Satellite Derived Temperature Mean Max. Daily T. (°C) Satellite Derived T. (°C) 87 89 91 87 89 91 Richmond 23.0 22.5 22.6 19.9 19.7 19.6 Camden 22.3 21.5 21.6 18.7 18.9 17.8 Katoomba 15.5 14.3 14.4 13.7 11.9 11.8 Goulburn 17.3 no data 15.3 13.9 13.5 12.4 RESULTS AND CONCLUSIONS Page 137 8.2.2 Urban Heat Island Effect Increased urbanization and industrialization in a city increases the intensity and extent of the positive thermal anomalies that commonly are termed urban heat islands. The large heat capacity and high heat conductivity of urban building materials prevent rapid cooling of urban areas after sunset, contrasting with the situation in the rural environment. A variety of other factors, often equally important, enter into heat island formation. Rapid runoff of precipitation in urban areas, plus the waste heat from residential and other buildings are two such consideration. The increased 'roughness' of cities, with a resultant reduction of about 25% in wind speeds, is also significant factor (Henry et al., 1989). The NOAA AVHRR thermal IR temperature measurements have shown a change in the intensity and shape of the urban area heat island over the 5 year period from 1987-1991. Results show that thermal infrared digital data can effectively present the spatial variation of the urban thermal patterns and the relationship between land use/land cover and satellite-derived temperatures. These temperature changes can be used for detecting and monitoring the extent, shape, and thermal structure of urban heat islands. These changes are also an indicator for the growth and development of urban and suburban areas and their impact on the environment. The warm regions denoted by the red colour in Figures 7-10, 7-11 and 7- 12 are attributable mostly to urban areas. The overall effect of urban development on the temperature distribution is clearly indicated. However, the larger heat island type effects observed in the satellite data obtained are not all attributable to urban development . Additionally, meteorological conditions (particularly wind speed) are a critical value for the elimination of the post- RESULTS AND CONCLUSIONS Page 138 sunset heat island over a city. Ultimately the temperature of the catchment area surfaces will determine the temperature of the water flowing in the Hawkesbury River. As the catchment area was effectively totalled forested prior to the coming of Europeans, the increase in urban and agricultural areas will have had a considerable effect on the temperature regime in the catchment area, and consequently a substantial impact on the river temperature. If the growth of urban land use continues as the trends show in table 8-2, then a further considerable impact can be predicted. 8.2.3 The Relationship between Urbanization and Water Quality Population increases result in an increase in sewage effluent volumes. Although all treatment plants currently provide secondary treatment, which produces a clear, colourless effluent, the concentrations of dissolved plant nutrients, such as phosphorus and nitrogen compounds, can be very high. These nutrients may cause explosive growth of duckweed, algae and other plants whose respiration and decomposition cause deoxygenation of the water. This process of over-enrichment is called eutrophication. The water quality problems it causes can be serious. More than half the length of the river between Camden and Broken Bay is expected to suffer excessive plant growth due to these effects (Minister for Environment Control, 1973). RESULTS AND CONCLUSIONS Page 139 8.3 Conclusions The major benefit of this study, was the determination of environmental change and thus water quality change over the Hawkesbury river catchment area using NOAA AVHRR data. This information was previously unavailable using traditional methods. This study has demonstrated the potential of remote sensing techniques for identifying change in the catchment area by comparing multi-temporal images. Furthermore the result of this work will serve as a basis for the development of a new methodology to monitor environmental conditions in the catchment area, bringing together a variety of data and some unique information for such an analysis. There is clearly a potential for further development of such techniques. A satellite image of an affected area makes it possible to undertake ground-level impact surveys more efficiently. The analysis of vulnerable areas seen in the images can help in planning land use, developments and environmental protection. The systematic collection and archiving of remotely sensed data, according to carefully selected criteria balancing the scientific potential with commercial considerations, is a pressing need. Obtaining reliable water quality data using NOAA AVHRR data is still very difficult. One important problem is the image resolution relative to the size of the river or water quality features. It is difficult to study a specific class in the catchment area with the 1.1 km spatial resolution of the AVHRR as this is much larger than the average field size. The observed temporal variation mapped at 1.1 km is a result of the mixed response of agriculture land, urban areas forest and water, and not one specific class, except over large homogeneous cover type areas. RESULTS AND CONCLUSIONS Page 140 Although the NOAA AVHRR data cannot provide an estimate of water quality which is fully satisfactory and directly obtainable, the physical environment which affects water quality such as temperature and land use change, can be detected. Of the environmental factors, temperature is the most useful and practical factor, which directly affects the water quality. Thus, NOAA AVHRR is considered to be a useful tool for environmental monitoring in the catchment area. NOAA AVHRR data can also provide a means of monitoring the growth and development of urban and suburban areas and their impact on the environment. However, satellite imagery data cannot replace in situ data completely because some ground reference data are essential to check the reliability of the satellite data or to calibrate data in terms of the variables being examined. The data analysis procedure was developed with the aim of relating the observed land use change and land surface temperature change to the water temperature change. There are three important steps designed to achieve the goal of this particular application. The first was to create an image showing the changes. This spatial analysis was performed using standard classification procedures. The second step was to compare existing land use maps to the image-based change map. The third step was to interpret the changes with the assistance of the maps and other ancillary data. Because of the age of the land use map, changes between the image and map were also used to determine whether any relationship existed between the change and the physical characteristics of the area. RESULTS AND CONCLUSIONS Page 141 Much work remains in the development of quantitative tools for estimating water quality using remote sensing method in the Hawkesbury River catchment area. This study is one small step toward that end. Further work will be based upon the simultaneous acquisition of satellite and airborne data with high resolution and field observed surface water measurements to provide a more reliable database. It is recommended that multiple observation from low resolution to high resolution take place in future research. Low resolution observation is necessary for routine monitoring and high resolution observation is required for precise monitoring in rapidly changing critical areas. The high resolution images such as those produced by SPOT, with 20m resolution, in multispectral mode, and 10m in panchromatic, should show many water quality features of interest to water quality catchment studies. In addition very high spatial and spectral resolution data acquired from airborne imaging spectrometers will become increasingly important. REFERENCES ALFOLDI, T.T., 1982. Remote sensing for water quality monitoring. In Remote Sensing for Resource Management, Johannsen, C.J. & Sanders, J.L., (Eds), Soil Conservation Society of America, p317-328. AMANN, V., & DOERFFER, R., 1983. Aerial survey of the temporal distribution of phytoplankton during FLEX'76. In North Sea Dynamics, Sundermann, J. & Lenz, W. (Eds), Springer-Verlag, Berlin, p517-529. AMOS, C.L., & TOPLIS, B.J., 1985. Discrimination of suspended particulate matter in the Bay of Fundy using the CZCS. Canadian J. Remote Sensing, 11, p85-92. ANDING, D., & KAUTH, D., 1970. Estimation sea surface temperature from space. Remote Sensing Environ., 1, p217-220. ARLOSOROFF, S., 1974. Water conception and policy in Israel. Proceedings of the Syposium of Water Pollution and the Environment, 20-21, May, 1974, University of NSW, Australia, p5-6. AUSTIN, R.W., 1974. The remote sensing of spectral radiance from below the ocean surface. In Optical Aspects of Oceanography, Jerlov, N.G., & Nielsen, E.S. (Eds), Academic Press, New York, p317-344. BADENAS.C., & CASELLES, V., 1992. A simple technique for estimating surface temperature by means of a thermal infrared radiometer. Int. J. Remote Sensing, 13, p2953-2956. BARNES, D., BLISS, P.J., GOULD, B.W., & VALLENTINE, H.R., 1983. Water and Wastewater Engineering Systems, Pitman, London, p131. BARRETT, E. C., & CURTIS, L.F.,1992. Introduction to Environmental Remote Sensing, Chapman & Flail, Singapore, p73-100. BARTON, I.J., 1983. Dual channel satellite measurements of sea surface temperature. Quarterly Journal of Royal Meteorological Society, 109, p365-378. REFERENCES Page 143 BARTON, I.J., 1985. Transmission model and ground-truth investigation of satellite-derived sea surface temperature. J. Climate and Applied meteorological, 24, p508-516. BERNSTEIN, R., 1983. Image geometry and rectification. In Manual of Remote Sensing, Colwell, R.N., (Ed.), p873-922. BHARGAVA, D.S., & MARIAM, D.W., 1990. Spectral reflectance relationships to turbidity generated by different clay materials. Photogramm. Eng. Remote Sens., 56, p225-229. BOWKER, D.E., & WITTE, W.G., 1977. The use of Landsat for monitoring water parameters in the coastal zone. Proc. AIAA Joint Conf. Satellite Applications to marine technology, p193-198. BOWKER, D.E., DAVIS, R.E., MYRICK, D.L., STACY, K. & JONES, W.T., 1985. Spectral Reflectances of Natural Targets for Use in Remote Sensing Studies, Hampton, Virginia, NASA Reference Publication No. 1139. BRICAUD, A., & MOREL, A., 1987. Atmospheric corrections and interpretation of marine radiances in CZCS imagery: use of a reflectance model. Oceanol. Acta, SP, p33-50. BROWN, O.B., EVANS, R.H., BROWN, J.W., GORDON, R.H., SMITH, R.C., & BAKER, K.S., 1985. Phytoplankton blooming off the US east coast: a satellite description. Science, 229 p163-167. BUKATA, R.P., JEROME, J.H., & BRUTON, J.E., 1988. Particulate concentrations in Lake St. Clair as recorded by a shipborne multispectral optical monitoring system. Remote Sensing Environ., 25, p201-229. BUKATA, R.P., JEROME, J.H., BRUTON, J.E., JAIN, S. C., & ZWICK, H.H., 1981. Optical water quality model of Lake Ontario, 2: Determination of chlorophyll and suspended mineral concentrations of natural waters from submersibles and low altitude optical sensors. Applied Optics, 20, p1704-1714. REFERENCES Page 144 BYRNE, G.F., CRAPPER, P.F., & MAYO, K.K., 1980. Monitoring land cover change by principal components analysis of multitemporal Landsat data. Remote Sensing Environ., 10, p175. CAMPBELL, J.W., & ESAIAS, W.E., 1983. Basis for spectral curvature algorithms in remote sensing of chlorophyll. Applied Optics, 22, p1084- 1093. CAMPBELL, J.W., & ESAIAS, W.E., 1985. Spatial patterns in temperature and chlorophyll on Nantucket Shoals from airborne remote sensing data. J. Mar. Res., 43, p139-161. CARAUX,D., & AUSTIN, R.W., 1983. Delineation of seasonal changes of chlorophyll frontal boundaries in Mediterranean coastal waters with Nimbus-7 Coastal Zone Color Scanner data. Remote Sensing Environ., 13, p239. CARDER, K.L., STEWARD, R.G., HARVEY, G.R., & ORTNER, P.B., 1989. Marine humic and fulvic acids: their effects on remote sensing of ocean chlorophyll, Limnol. Oceanogr.,34, p68-81. CARPENTER, D.S., & CARPENTER, S.M., 1983. Monitoring inland water quality using Landsat data. Remote Sensing Environ., 13, p345-352. CASELLES, V., & SOBRION, J.A., 1989. Determination of frosts in orange groves using NOAA-9 AVHRR data. Remote Sensing Environ., 29, p135-146. CASELLES, V., SOBRINO, J.A., & BECKER, F., 1988. Determination of the effective emissivity and temperature, int J. Remote Sensing, 9, p715- 727. CATTS, G.P., KHORRAM, S., CLOERN, J.E., KNIGHT, A.W., & DeGLORIA, S.D., 1985. Remote sensing of tidal chlorophyll-a variations in estuaries. Int. J. Remote Sensing, 6, p1685-1706. CHAPMAN, D., (Ed), 1992. Water Quality Assessments, Chapman & Hall, London, p5-6. REFERENCES Page 145 CLARK, D.K., BAKER, E.T., & STRONG, A.E., 1980. Upwelled spectral radiance distribution in relation to particulate matter in sea water. Boundary-Layer Meteorology, 18, p287-298. CONYBEARE, D., 1976. The extent of the Hawkesbury River valley and its resources. In: Hawkesbury River Symposium Proceedings, National Trust of Australia, NSW, p3-7. COOPER, D.I., & ASRAR, G., 1989. Evaluating atmospheric correction models for retrieving surface temperature from the AVHRR over a tallgrass prairie. Remote Sensing Environ., 27, p93-102. CRACKNEL.L, A.P., (Ed.) 1981. Remote Sensing in meteorology, Oceanography and Hydrology, Ellis Horwoos Limited, Chichester, England, p163-197. CURRAN, P.J., & NOVO, E.M.M., 1988. The relationship between suspended sediment concentration and remotely sensed spectral radiance: a review. J. Coastal Res., 4, p351-368. CURRAN, P.J., & WILKINSON, W.B., 1985. Mapping the concentration and dispersion of dye from a long sea outfall using digital aerial photography. Int. J. Remote Sensing, 6, p1735-1748. CURRAN, P.J., & WILLIAMSON, H.D., 1985. The accuracy of ground data used in remote sensing investigations. Int. J. Remote Sensing, 6, p17-31. CURRAN, P.J., HANAON, J.D., PLUMMER, S.E., & PEDLEY, M.I., 1987. Multispectral remotely sensing of near-shore suspended sediments: a pilot study. Int. J. Remote Sensing, 8, p103-122. DEKKER, A.G., MALTHUS, T.J., & SEYHAN, E., 1991. Quantitative modeling of inland water quality for high-resolution MSS systems. IEEE Transaction on Geoscience and Remote Sensing, 29, p89-95. DEPARTMENT OF PLANNING, 1989. Hawkesbury-Nepean River, Sydney Regional Environmental Plan No. 20, Department of Planning. REFERENCES Page 146 DESCHAMPS, P.Y., & PHILPIN, T., 1980. Atmospheric correction of infrared measurements of sea surface temperature using channels at 3.7, 11 and 12 pm. Boundary Layer Meteorology, 18, p131 -141. DUGGIN, M.J., 1985. Factors limiting the discrimination and quantification of terrestrial features using remotely sensed radiance. Int. J. Remote Sensing, 6, p3-27. DUNTLEY, S.Q., 1971. Optical methods of water pollution. Proceedings of the Environmental Quality Sensor Workshop at Western Environmental Research Laboratory, Environment Protection Agency, Washington, D.C., Nov. 30 - Dec. 2, 1971, pll-15 to II-27. EGAN, W.G., & FISCFIBEIN, W.L., 1975. Optical atmospheric scattering and absorption limitations on offset pointing from earth observatory satellite (EOS) sensors. Proceedings of the 10th International Symposium on Remote Sensing of Environment, Ann Arbor, University of Michigan, p783-792. EINSTEIN, A., 1910. Ann. Physik., 33, p1275. ENGEL, J.L., 1983. The Thematic Mapper-instrument overview and preliminary on-orbit results. Proc. of the Soc. of Photo-Optical Instrumentation Engineers, p242-235. ENGMAN, E.T., & GURNEY, R.J., 1991. Remote Sensing in Hydrology, Chapman and Hall, London, p174-192. ESTES, J.E., STOW, D., & JENSON, J.R., 1982. Monitoring land use and land cover changes. In: Remote Sensing for Resource Management, Johannsen, C.J., & Sanders, J.L., (Eds), Soil Conservation Society of America, p100-110. FELDMAN, G., CLARK, D., & HALPERN, D., 1984. Satellite colour observations of the phytoplankton distribution in the Eastern Equatorial Pacific during 1982-1983. Science, 226, p1069-1071. REFERENCES Page 147 FERRARI, G.M., 1991. Influence of pH and heavy metals in the determination of yellow substance in estuarine Areas. Remote Sensing Environ., 37, p89-100. FISCHER, J., & KRONFELD, U., 1990. Sun-stimulated chlorophyll fluorescence 1: influence of oceanic properties. Int. J. Remote Sensing, 11, p2125-2147. FORSTER, B., 1983. Some urban measurements from Landsat data. Photogramm. Eng. Remote Sensing, 49, p1693-1707. FORSTER, B., 1991. Application of Normalized Vegetation Index Differencing for urban change monitoring: urban development on the fringe of Sydney. In: Applications of Remote Sensing in Asia and Oceania, Murai, S., (Ed), Asian Association on Remote Sensing, p215-220. FROIDEFOND, J.M., CASTAING, P., JOUANNEAU, J.M., PRUD'HOMME, R.,1993. Method for the quantification of suspended sediments from AVHRR NOAA-11 satellite data. Int. J. Remote Sensing, 14, p885-894. FROIDEFOND, J.M., CASTAING, P., MIRMAND, M., & RUCH, P., 1991. Analysis of the turbid plume of the Gironde Estuary (Francy) based on SPOT radiometer data. Remote Sensing Environ., 36, p149-164. FUNG, T., & LeDREW, E., 1987. Application of principal components analysis to change detection. Photogramm. Eng. Remote Sensing, 53, p1649- 1658. GALLIE, E.A., & MURTHA, P.A., 1992. Specific absorption and backscattering spectra for suspended minerals and chlorophyll-a in Chilko Lake, British Columbia. Remote Sensing Environ., 39, p103-118. GERACI, A.L., COLWELL, R.N., & KHORRAM, S., 1981. Remote sensing of water quality for estuarine environments. Matching Remote Sensing Technologies and their Applications, Remote Sensing Society, p199-208. REFERENCES Page 148 GOMARASCA, M.A., BRIVIO, P.A., & PAGNONI, F., 1993. One century of land use changes in the metropolitan area of Milan, Int. J. Remote Sensing, 14, p211-223. GORDON, H. R., & MOREL, A., 1983. Remote assessment of ocean colour for interpretation of satellite visible imagery: a review. In Lecture Notes on Coastal and Estuarine Studies, Bowman, M., (Ed.), Springer-Verlag, Berlin, p1 -114. GORDON, H.R., & CLARK, D.K., 1980. Atmospheric effects in the remote sensing of phytoplankton pigments. Boundary-Layer Meteorology, 18, p299-313. GORDON, H.R., 1974. Mie theory of light scattering by ocean particulates. In Suspended Solids in Water, GIBBS R.J., (Ed), Plenum Press, New York, p73-86. GORDON, H.R., 1978. Remote sensing of optical properties in continuously stratified waters. Applied Optics, 17, p1893-1896. GORDON, H.R., 1980. Phytoplankton pigments from the Nimbus-7 Coastal Zone Color Scanner: comparisons with surface measurements. Science, 210, p63. GORDON, H.R., CLARK, D.K., BROWN, J.W., BROWN, O.B., EVANS, R.H., & BROENKOW, W.W., 1983. Phytoplankton pigment concentrations in the Middle Atlantic Bight: comparison of ship determinations and CZCS estimates. Applied Optics, 22, p20-36. GORDON, H.R., CLARK, D.K., MUELLERT, J.L., & HOVIS, W.A., 1980. Phytoplankton pigments from the Nimbus-7 Coastal Zone Color Scanner: comparisons with surface measurements. Science, 210, p63-66. GOWER, J.F.R., & BORSTAD, G.A., 1981. The information content of different optical spectral ranges for remote chlorophyll fluorescence measurements. In Oceanography from Space, Gower, J.F.R., (Ed), Plenum, New York, P329-338. REFERENCES Page 149 GOWER, J.F.R., & BORSTAD, G.A., 1990. Mapping of phytoplankton by solar- simulated fluorescence using an imaging spectrometer. Int. J. Remote Sensing, 11, p313-320. GRAVA, S., 1969. Urban planning aspects of water pollution control. Columbia University Press, p174-175. GRAY, D.H., 1973. Handbook on the Principles of Hydrology, Water Information Centre, New York. HARDING, L.W.Jr., ITSWEIRE, E.C., & ESAIAS, W.E., 1992. Determination of phytoplankton chlorophyll concentrations in the Chesapeake Bay with aircraft remote sensing. Remote Sensing Environ., 40, p79-100. HARRINGTON, J.A.Jr., SCHIEBE, F.R., & NIX, J.F., 1992. Remote sensing of Lake Chicot, Arkansas: monitoring suspended sediments, turbidity, and secchi depth with Landsat MSS data. Remote Sensing Environ., 39, p15-27. HARRIS, R.A., 1987. Satellite Remote Sensing: An Introduction, Routledge and Kegan Paul, London, p49-79. HENRY, J.A., DICKS, S.E., WETTERQUIST, O.F., & ROGUSKI, S.J., 1989. Comparison of satellite, ground-based, and modeling techniques for analyzing the urban heat island. Photogramm. Eng. Remote Sensing, 55, p69-76. HINTON, J.C., 1991. Application of eigenvector analysis to remote sensing of coastal water quality. Int. J. Remote Sensing, 12, p1441-1460. HOGE, F.E., & SWIFT, R.N., 1981. Airborne simultaneous spectroscopic detection of laser-induced Raman backscatter and fluorescence from chlorophyll and other naturally occurring pigments. Applied Optics, 20, p3197-3205. HOGE, F.E., SWIFT, R.N., & YUNGEL, J.K., 1986. Active-passive airborne ocean color measurement. Applied Optics, 25, p48-57. REFERENCES Page 150 HOGE, F.E., WRIGFIT, C.W., & SWIFT, R.N., 1987. Radiance-ratio algorithm wavelengths for remote oceanic chlorophyll determination. Applied Optics, 26, p2082-2094. HOLLIGAN, P.M., VIOLLIER, M., HARBOUR, D.S., CAMUS, P., & CHAMPAGNE-PHILIPPE, M., 1983. Satellite and ship studies of coccolithophore production along a continental shelf edge. Nature, 304, P339-342. HOLYER, R.J., 1978. Towards universal multispectral suspended sediment algorithms. Remote Sensing Environ., 7, p323-338. HUANG, K., & LULLA, K., 1986. Modeling water quality using thematic mapper data: Case of Lake Michigan. Geocartography International, 2, p3-16. INGRAM, K., KNAPP, E., & ROBINSON, J.W., 1981. Change Detection Technique Development for Improved Urbanized Area Delineation, CSC/TM-81/6078, Computer Sciences Corp., Silver Springs, Md. JENSON, J.R., 1986. Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice-Hall, New Jersey. JERLOV, N.G., 1976. Marine Optics, Elsevier, Amsterdam, p231. JEROME, J.H., BUKATA, R.P, & BRUTON, J.E., 1988. Utilizing the components of vector irradiance to estimate scalar irradiance in natural waters. Applied Optics, 27, p4012-4018. JERSEN, J.R., 1986. Introductory Digital Image processing: A Remote Sensing Perspective, Prentice-Hall, New Jersey. JOHNSON, R W., 1978. Mapping of chlorophyll-a distributions in coastal zones. Photogramm. Eng. Remote Sensing, 44, p617-624. REFERENCES Page 151 JOHNSON, R.W., & BAHN, G.S., 1977. Quantitative Analysis of Aircraft Data and Mapping of Water Quality in the James River in Virginia. NASA Technical Report 1021, Langley Research Centre, Hampton, Virginia, p 1 -13. KALMA, J.D., LAUGHLIN, G.P., GREEN, A.A., & O'BRIEN, M.T., 1986. Minimum temperature surveys based on near-surface temperature measurements and airborne thermal scanner data. J. of Climatology, 6, p413-430. KHORRAM, S., 1981, Water quality mapping from LANDSAT digital data. Int. J. Remote Sensing, 2, p145-154. KHORRAM, S., CATTS, G. P., CLOERN, J.E., & KNIGHT, A.W., 1986. Modeling of estuarine chlorophyll-a from an airborne scanner. Proc. of the IGARSS'86 Symposium, 8-11 Sept. 1986, European Space Agency, p1387-1397. KIM, H.H., McCLAIN, C.R., BLAINE, L.R., HART, W.D., ATKINSON, L.P., & YODES, J.A., 1979. Ocean color studies from a U-2 aircraft platform. NASA, TM80574, Washington D.C. KIRK, J.T.O., 1989. The upwelling light stream in natural waters. Limnol. Oceanogr.,34, p1410-1425. KRAMER, H. J., 1992. Earth Observation Remote Sensing: Survey of Missions and Sensors, Springer-Verlag, Berlin, p93. LATHROP, R.G., & LILLESAND, T.M., 1986. Use of Thematic Mapper data to assess water quality in Green Bay and Central Lake Michigan. Photogramm. Eng. Remote Sensing, 52, p671-680. LATHROP, R.G., & LILLESAND, T.M., 1988. Utility of SPOT-1 data to assess water quality in southern Green Bay Lake Michigan. Tech. Papers, 1988, ACSM-ASPRS, St. Louis, 4, p102-111. LATHROP, R.G., & LILLESAND, T.M., 1989. Monitoring water quality and river plume transport in Green Bay, Lake Michigan with SPOT-1 imagery. Photogramm. Eng. Remote Sensing, 55, p349-354. REFERENCES Page 152 LATHROP, R.G., & LILLESAND, T.M., 1991. Testing the Utility of Simple Multi- Date Thematic Mapper Calibration Algorithms for Monitoring Turbid Inland Waters. Int. J. Remote Sensing, 12, p2045-2063. LEFEVER, J., VIOLLIER, M., LE CORRE, P., DUPOUY, C., & GRALL, J., 1983. Remote sensing observations of biological material by LANDSAT along a tidal thermal front and their relevancy to the available field data. Estuar. Coastal-shelf Sci., 16, p37. LINDELL, L.T., STEINVALL, O., JONSSON, H., & CLAESSON, T.H., 1985, Mapping of coastal water turbidity using Landsat imagery. Int. J. Remote Sensing, 6, p629-642. LINDELL, T., KARLSSON, B., ROSENGREN, M., & ALFOLDI, T., 1986. Use of Thematic Mapper data to assess water quality in Green Bay and Central Lake Michigan. Photogramm. Eng. Remote Sensing, 52, p1521 - 1529. LLEWELLYN-JONES, D. T., MINNETT, P.J., SAUNDERS, R.W., & ZAVODY, A.M., 1984. Satellite multichannel infrared measurements of sea surface temperature of the N.E. Atlantic Ocean using AVHRR-2. Quarterly Journal of Royal Meteorological Society, 110, p613-631. LODWICK, G.D., & FIARRINGTON, R.D., 1985. Deriving sediment information for Lake Athabasca using principal components analysis of Landsat data. Canadian J. Remote Sensing, 11, p4-16. LYON, J.C., BEDFORD, K.W., YEN, J.C., LEE, D.H., & MARK, D.J., 1988. Determinations of suspended sediment from multiple day Landsat and AVHRR data, Remote Sensing Environ., 25, p107-115. MALILA, W.A., 1980. Change vector analysis: an approach for detecting changes with Landsat. Proceedings. Machine Processing of Remotely Sensed Data Symposium, Purdue University, Indiana, p326. REFERENCES Page 153 MASSOM, R., 1991. Satellite Remote Sensing of Polar Regions: Applications, Limitations and Data Availability, Belhaven Press, London, p262-263. MAUL, G.A., & SIDRAN, M., 1973. Atmospheric effects on ocean surface temperature sensing from NOAA satellite scanning radiometer. J. Geophysical Research, 78, p1909-1916. MAUL, G.A., 1983. Zenith angle effects in multichannel infrared sea surface remote sensing. Remote Sensing Environ., 13, p439-451. MAUL, G.A., 1985. Introduction to Satellite Oceanography, Martinus Nijhoff Publishers, Dordrecht, Netherlands, p152-162. McCLAIN, E.P., PICHEL, W.G., & WALTON, C.C.,1985. Comparative performance of AVHRR-based multichannel sea surface temperatures. J. Geophysical Research, 20, p11,587-11,601. McCLAIN,E.P., 1981. Multiple atmospheric-window techniques for satellite- derived sea surface temperature. Oceanography from Space, Gower, J.F.R., (Ed), Plenum Press, New York, p73-85. McCLAIN,E.P., PICHEL, W.G., & WALTON, C.C., 1985. Comparative performance of AVHRR-based multichannel sea surface temperatures. J. Geophysical Research, 90, p11587-11601. McKIM, H.L., MERRY, C.J., & LAYMAN, R.W., 1987. Water quality monitoring using an airborne spectroradiometer. Photogramm. Eng. Remote Sensing, 50, p353-360. McMILLIN, L.M., & CROSBY, D.S., 1984. Theory and validation of the multiple window sea surface temperature technique. J. Geophysical Research, 89, p3655-3661. McMILLIN, L.M., 1975. Estimation of sea surface temperatures from two infrared window measurements with different absorption. J. Geophysical Research, 80, p5113-5117. REFERENCES Page 154 MILNE, A.K., & HALL, A.M., 1992. Towards the operational detection and mapping of bushfires. Proceedings of the 6th Australasian Remote Sensing Conference, 2-6 Nov., 1992, Wellington, New Zealand, p2- 156 - 2-165. MINISTER FOR ENVIRONMENTAL CONTROL, NSW, 1973. The Hawkesbury River Valley Environmental Study, Sydney, pp311. MOORE, G.K., 1977. Satellite surveillance of physical water-quality characteristics. In Proceedings, 12th International Symposium on Remote Sensing of Environment, University of Michigan, Ann Arbor, Ml, p445-461. MOREL, A., 1974. Optical properties of pure water and pure sea water. In Optical Aspects of Oceanography, Jerlov, N.G., & Steemann N. E., (Eds), Academic press, London and New York. MOREL, A.Y., & PRIEUR, L., 1977. Analysis of variations in ocean colour. Limnol. Oceanogr., 22, p709-722. MUNDAY, J. C., & ALFOLDI, T.T., 1979. Landsat test to diffuse reflectance models of aquatic suspended solid measurement. Remote Sensing Environ., 8, p169-183. MUNDAY, J. C., & ZUBKOFF, P.L., 1981. Remote sensing of dinoflagellate blooms in a turbid estuary. Photogramm. Eng. Remote Sensing, 47, P523-532. MYERS, V.I., & ALLEN, W.A., 1968. Electrooptical remote sensing methods as nondestructive testing and measuring techniques in agriculture. Applied Optics, 7, p1819-1836. NELSON, R.F., 1983. Detecting forest canopy change due to insect activity using Landsat MSS. Photogramm. Eng. Remote Sensing, 49, p1303. REFERENCES Page 155 NOVO, E.M.M., HANSON, J.D., & CURRAN, P.J., 1989. The effect of viewing geometry and wavelength on the relationship between reflectance and suspended sediment concentration. Int. J. Remote Sensing, 10, P1357-1372. NRSC, 1987. UK National Remote Sensing Centre Data Users Guide, NRSC, Farnborough. OSWALD, W.J., 1967. Remote sensing data and evaluation of water quality. Proc. of 3rd Annual Conf.: Remote Sensing of Air and Water Pollution, Sacramento, Calif.. PEARCE, A.F., PRATA, A.J., & MANNING, C.R., 1989. Comparison of NOAA/AVNRR-2 sea surface temperatures with surface measurements in coastal waters. Int. J. Remote Sensing, 10, p37-52. PRABHAKARA, C., DALU, G., & KUNDE, V.G., 1974. Estimation of sea surface temperature from remote sensing in the 11- to 13-fim window region. J. Geophysical Research, 79, p5039-5044. PREISENDORFER, R.W., 1976. Hydrologic Optics, 5, Properties, NTIS PB-259 797/9ST, National Technical Information Services, Springfield. QUERRY, M.R., HOLLAND, W.E., WARING, R.C., EARLS, I.M., & QUERRY, H.D., 1977. Relative reflectance and complex refractive index in the infrared for a saline environment. J. Geophysical Research, 82, p1425- 1433. RAYLEIGH, L., & STRUTT, J.W., 1871. Phil. Mag., 41, p107, 274, 447. RICHARDS J.A., 1986. Reomte Sensing Digital Image Analysis: An Introduction, Springer-Verlag, Heidelberg, Berlin, Germany, p33-65. RIMMER, J.C., COLLINS, M.B., & PATTIARATCHI, C.B., 1987. Mapping of water quality in coastal waters using airborne TM data. Int. J. Remote Sensing, 8, p85-102. REFERENCES Page 156 RITCHIE, J.C., & COOPER, C.M., 1988. Comparison of measured suspended sediment concentrations with suspended sediment concentrations estimated from LANDSAT MSS data. Int. J. Remote Sensing, 9, p379- 387. RITCHIE, J.C., & SCHIEBE, F.R., 1984. A Comparison of Landsat 4 MSS and TM Data for Inland Lake. Agriculture Research service, Washington. RITCHIE, J.C., COOPER, C.M., & SCHIEBE, F.R., 1990. The relationship of MSS and TM digital data with suspended sediments, chlorophyll, and temperature in Moon Lake, Mississippi. Remote Sensing Environ., 33, p137-148. RITCHIE, J.C., COOPER, C.M., & YONGGING, J., 1987. Using LANDSAT multispectral scanner data to estimate suspended sediments in Moon Lake, Mississippi. Remote Sensing Environ., 23, p65-81. RITCHIE, J.C., SCHIEBE, F.R., & McHENRY, J.R., 1976. Remote sensing of suspended sediments in surface waters. Photogramm. Eng. Remote Sensing, 42, p1539-1554. ROBINSON, I S., 1985. Satellite Oceanography: An Introduction for Oceanographers and Remote Sensing Scientists, Ellis Horwood Ltd., Chichester. ROBINSON, J.W., 1979. A Critical Review of Change Detection and Urban classification Literature, CSC/TM-79/6235, Computer Sciences Corp.. RODDA, J.C., DOWNING, R.A., & LAW, F.M., 1976. Systematic Hydrology, Butterworth, London. ROGERS, A.J., 1982. Hydrogeochemistry of the Hawkesbury River Catchment, PhD. Thesis, University of NSW, Sydney, p4-8. REFERENCES Page 157 ROUSE, L.J., & COLEMAN, J.M., 1976. Circulation observations in the Louisiana Bight using Landsat imagery. Remote Sensing Environ., 5, P55-66. SAUNDERS, R.W., & KRIEBEL, K.L., 1988. An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sensing, 9, p 123-150. SCHERZ, J.P., GRAFF, D.R., & BOYLE, W.C., 1969. Phtographic characteristics of water pollution. Photogramm. Eng., 35, p38-43. SCHIEBE, F.R., RITCHIE, J.C., & BOATWRIGHT, G.O., 1985. A first evaluation of Landsat to monitor suspended sediment in lakes. In Landsat-4 Science Characterization Early Results, NASA Conference Publication 2355, 4, p337-347. SCHOWENGERDT, R.A., 1983. Techniques of Image Processing and Classification in Remote Sensing, Academic Press, New York. SINGH, A., 1989. Digital changes detection techniques using remotely sensed data. Int. J. Remote Sensing, 10, p989-1003. SMOLUCHOWSKI, M., 1908. Ann. Physik, 25, p205. STAROSOLSZKY, O., (Ed), 1987. Applied Surface Hydrology, Water Resource Publications, Colorado, USA, p290-297. STATE POLLUTION CONTROL COMMISSION, NSW, 1983. Water Quality in the Hawkesbury-Nepean River: A Study and Recommendations, Sydney: SPCC, p5-17. STATE POLLUTION CONTROL COMMISSION, NSW, 1985. A Strategy for the Management of the Water Quality of the Hawkesbury- Nepean River, Sydney: SPCC, p1-28. REFERENCES Page 158 STURM, B., 1980. Optical properties of water-applicatiions of remote sensing to water quality determination. In: Remote Sensing Application in Agriculture and Hydrology, Fraysse, G. (Ed.), Rotterdam, Balkema, p471 -495. STURM, B., 1981. The atmospheric correction of remotely sensed data and the quantitative determination of suspended matter in marine water surface layers. In: Remote sensing in Meteorology, Oceanography and Hydrology, Cracknell, A.P., & Phil, D., (Ed), Ellis Horwood, Chichester, UK, p163-197. STURM, B., 1990. Ocean colour remote sensing: A status report. In: Satellite Remote Sensing for Hydrology and Water Management, Barrett, E.C., & Power, C.H., (Eds), Billing & Sons Ltd., Worcester, UK, p243-277. SWAIN, P H., 1976. Land use classification and mapping by machine assisted analysis of Landsat MSS data, LARS information report 112776, Laboratory for application of remote sensing, Purdue University. SWIFT, C., 1979. Sea surface salinity and temperature by microwave techniques. In Remote Sensing in the Coastal and Marine Environment, Zaitzeff, J.B., Cornillon, P., & Aubrey, D.A., (Eds.), p163- 174. SYDOR, M., 1980. Remote sensing of particulate concentrations in water. Applied Optics, 19, p2797-2800. SZEKIELDA, K.D., SUSZKOWSKI, D.J., & TABOR, P.S., 1975. Skylab investigation of the upwelling off the north-west coast of Africa. Proc. Earth Res. Survey Symp., NASA, Johnson Space Centre, p2005- 2022. SZEKIELDA, K.H., 1988. Satellite Monitoring of the Earth, John Wiley & Sons, New York, pp326. TASSAN, S. & STURM, B., 1986. An algorithm for the retrieval of sediment content in turbid coastal waters from CZCS data. Int. J. Remote Sensing, 7, p643-655. REFERENCES Page 159 TASSAN, S., & D'ALCALA, M.R., 1993. Water quality monitoring by Thematic Mapper in coastal environments. Remote Sensing. Environ., 45, p177- 191. TASSAN, S., 1979. The usefulness of model sensitivity analysis in remote sensing of marine water quality. Proc. Workshop EURASEP Ocean Color Scanner Expts, Italy, p 155-170. TASSAN, S., 1987. Evaluation of the potential of the TM for marine application. Int. J. Remote Sensing, 8, p1455-1487. TUCKER, C.J., TOWNSHEND, J.R.G., & GROFF, T.E., 1985. African land-cover classification using satellite data. Science, 227, p369-375. VERDIN, J.P., 1985. Monitoring water quality conditions in a large western reservoir with Landsat imagery. Photogramm. Eng. Remote Sensing, 51, p343-353. VERTUCCI, F.A., & LIKENS, G.E., 1989. Spectral reflectance and water quality of Adirondark mountain region lakes. Limnol. Oceanogr.,34, p1656- 1672. VIOLLIER, M., DESCHAMPS, P.Y., & LECOMTE, P., 1978. Airborne remote sensing of chlorophyll content under cloudy sky as applied to the tropical waters in the Gulf of Guinea. Remote Sensing Environ., 7, p235-248. WATER BOARD, 1990. A'Becketts creek. Catchment Management Study, Vol. 2, p13. WEEKS, A., & SIMPSON, H., 1991. The measurement of suspended particulate concentrations from remotely sensed data. Int. J. Remote Sensing, 12, p725-737. WEISMILLER, R.A., KRISTOF, S.J., SCHOLZ, D.K., ANUTA, P.E., & MOMIN, S.A., 1977. Change in coastal zone environment. Photogramm. Eng. Remote Sensing, 43, p1533. REFERENCES Page 160 WHITLOCK, C.H., 1977. Fundamental analysis of the quantification of water quality parameters from remote sensing data. NASA TM X-74600. NASA Scientific and Technical Information Branch, Washington DC. WHITLOCK, C.H., KUO, C.Y., & LECROY, S.R., 1982. Criteria for the use of regression analysis for the remote sensing of sediment and pollutants. Remote Sensing Environ., 12, p1405-1410. WHITLOCK, C.H., POOLE, L.R., USRY, J.W., HOUGHTON, W.M., WITTE, W.G., MORRIS, W.D., & GURGANUS, E.A., 1981. Comparison of reflectance with backscatter and absorption parameters for turbid waters. Applied Optics, 20, p517-522. WHITLOCK, C.H., WITTE, W.G., USRY, J.W., & GURGANUS, E.A., 1978. Penetration depth at green wavelengths in turbid water. Photogramm. Eng. Remote Sensing, 44, p151 -168. WILLIAMS, S., & CALLAGHAN, R., 1991. Water Quality in the Hawkesbury River and its Tributaries; Grose River to Mooney- Mooney Bridge 1984-1989, Scientific Services Report No. 91/26, Water Board, pp73. WITTE, W.G., WHITLOCK, C.H., HARRISS, R.C., USRY, J.W., POOLE, L.R., HOUGHTON, W.M., MORRIS, W.D., & GURGANUS, E.A.,1982. Influence of dissolved organic materials on turbid water optical properties and remote sensing reflectance. J. Geophysical Research, 87, p443-446. YOKOYAMA, R. & TANBA, S., 1991. Estimation of Sea Surface via AVHRR-9 Comparison with Fixed Buoy Data. Int. J. Remote Sensing, 12, p2513- 2528. APPENDIX SATELLITES EMPLOYED FOR WATER POLLUTION MONITORING Satellite selection and sensor characteristics are closely related to the strategy required to achieve the desired results. Different types of satellites are required to achieve continuous monitoring, repetitive coverage of different periodicities, global mapping, or selective imaging. Water quality is able to be detected within the spectral and thermal wavelengths by a range of satellite sensors and the area and frequency is a function of both spatial and temporal resolution. A high temporal resolution is important, particularly for monitoring changes in water quality. The optimal spatial resolution depends upon the size of the area to be observed and the required detail in water quality parameters. The potential for water quality description is also dependent upon the wavelength regions employed, as selected use of spectral data may allow certain water conditions to be identified. The basic characteristics of satellites employed in water quality studies are described as follows. APPENDIX Page 162 A.1 NOAA VHRR/AVHRR (Tiros-N) Polar orbiting meteorological satellites were designed to operate in sun- synchronous polar-orbits at altitudes of about 850km. With each pass, data are collected along a swath about 2700km wide. The orbital period is about 102 minutes. Each 24 hours, global coverage consisting of 14.1 orbits is achieved. Satellites image every point on the Earth's surface twice daily, once at night and once during the day. Because the number of orbits per day is not an integer, the suborbital tracks do not repeat on a daily basis. For this reason, the orbital equator crossing occurred at varying longitudes. The major polar orbiting programmers have been TIROS (Television and Infrared Observation Satellite) (1960-1966); ITOS (Improved TIROS Observational satellite) (1970-1976): TIROS-N (1979-present) and NOAA satellites (two series) (1973-present). These have been, or are, operated by the US National Oceanic and Atmospheric Administration. The well equipped NOAA polar-orbiting satellites are of particular interest in environmental management on a global scale. The NOAA satellite initially incorporated a VHRR (Very High Resolution Radiometer) sensor which observed the Earth in the visible (0.6|jm - 0.7pm) and the thermal infrared (10.5 pm-12.5pm ). This was superseded by the AVHRR (Advanced Very High Resolution Radiometer ) sensor which observed the Earth initially in four channels, with a fifth added at a later date. The wavebands are shown in Table A-1. The current NOAA series satellites are named simply NOAA-9 through NOAA-12 in the order of launches. APPENDIX Page 163 Table A-1: Wavebands of Tiros-N/NOAA (Source: Harris, 1987) Band Four-band instrument (|im) Five-band instrument (4m) 1 0.58-0.68 0.58-0.68 2 0.725-1.10 0.725-1.10 3 3.55-3.93 3.55-3.93 4 10.5-11.5 10.3-11.3 5 11.5-12.5 The AVHRR provides high-quality digital measurements that have a basic spatial resolution of 1.1km at nadir. The current five channel AVHRR instrument will increase to six channels with the NOAA K, L, M, and N satellites launched from 1994. From 1997 to 2000 it is expected that a further development of the AVHRR will be flown, namely a seven-channel VIRSR (Visible and Infrared Scanning Radiometer) (Barrett & Curtis, 1992). Along with the general program evolution, there has also been an enormous sophistication in sensor technology, hence in observation capability. Table A- 2 shows the characteristics and evolution of AVHRR instruments. Of the existing satellite-borne earth observation instruments the AVHRR on the NOAA/TIROS-N platforms is arguably the best for environmental monitoring at regional and global scales. Although the spatial resolution is lower than the high spatial resolution systems, such as Landsat, NOAA has an advantage in frequency of observation. The AVHRR sensors aboard the two satellites are capable of observing the same area twice a day and provide a better opportunity for obtaining cloud-free data than could be expected from satellites having longer return intervals. The daily global coverage largely overcomes problems of restricted cloud free image availability, and lower data APPENDIX Page 164 volumes, which make the analysis of long time series, over large geographical areas, possible. Table A-2: Characteristics of the Evolving Family of Multichannel Radiometers on NOAA Satellites, 1972-2000 (Source: Barrett &Curtis, 1992) VHRR AVHRR/1 AVHRR/2 AVHRR/3 VIRSR Date first flown 1972 1979 1981 1994 97-2000 Spatial resolution (m) 900 1100 1100 1100 1100 Thermal resolution (°C) 0.5 0.12 0.12 0.12 0.10 Radiometric resolution (bits) 900 1100 1100 1100 1100 Number of channels 2 4 5 6 7 IR calibration Yes Yes Yes Yes Yes Visible calibration No No No No Yes A.2 Landsat Series The US launched the first satellite designed specifically to collect data from the Earth's surface, the Earth Resources Technology Satellite-1 (ERTS-1, later renamed Landsat 1), on 23 July 1972. The Landsat system was initially designed to make automatic observations using a payload consisting of a return beam vidicon (RBV) camera system and multispectral scanner (MSS). The RBV system on Landsat-1 and Landsat-2 (launched on 22 January 1975) operated by shuttering three independent cameras simultaneously, each sensing a different spectral band in the range 0.48-0.83 pm. On Landsat-3 (launched on 5 March 1978) the RBV system was changed to two RBV panchromatic cameras operating in the range 0.51-0.75 pm. These cameras produced two side-by-side images, each covering a ground scene of APPENDIX Page 165 approximately 98x98 km. The geometric resolution obtained was about 40m (Kramer, 1992). The MSS system is a line scanning device using an oscillating mirror to scan at right angles to the space craft flight direction. Optical energy is sensed simultaneously by an array of detectors in four spectral bands from 0.5 to 1.1 pm (see Table A-3). The area of coverage was similar to that of the two RBV images. Table A-3: Wavebands and Applications of MSS on Landsat 1-3 (Source: Harris, 1987) Band Wavelength range (pm) Applications 4 0.5-0.6 Sediment loads; shallow water 5 0.6-0.7 Vegetation; cultural features 6 0.7-0.8 Land/water separation 7 0.8-1.1 Vegetation and geological studies The early Landsat satellite platforms operated in near circular, sun- synchronous, near-polar orbits at an altitude of 915 km, circling the Earth every 103 minutes, completing 14 orbits per day and viewing the entire Earth every 18 days. The orbit was selected so that the satellite ground trace repeated its Earth coverage at the same local time every 18-day period to within 37 km of its first orbit. Each day the paths shifted 160 km westward. The amount of overlap between successive passes varied from 14% sidelap at the equator to 70% at polar latitudes. A major improvement in the Landsat system was introduced on 16 July, 1982 with the launch of Landsat-4 and continued with subsequent satellites of APPENDIX Page 166 this series. Whilst an MSS of the same design as on L.andsat -1, 2 and 3 is carried, the RBV has been replaced by a new instrument! termed the Thematic Mapper (TM), so called because of its intended use for the mapping of different surface types, categories or 'themes'. The different bands investigated by the Landsats are shown in Table A-4. Table A-4: Characteristics of the MSS and TM carried on Landsat 4-7 (Source: Barrett & Curtis, 1992) Band MSS Wavelength MSS Wavelength Applications for TM (nm) (urn) 1 0.5-0.6 0.45-0.52 Co>astal water mapping, Soil differentiation & Vegetation differentiatio 2 0.6-0.7 0.52-0.6 Gneen reflectance by healthy Vegetation 3 0.7-0.8 0.63-0.69 Chlorophyll absorption for plant species 4 0.8-1.1 0.76-0.9 Biomass surveys 5 1.55-1.75 Ve*getation moisture, Snow discrimination & Cloud discrimination 6 10.4-11.7 Thermal mapping including plant stress 7 2.08-2.35 Vegetation moisture & geological studies Pixel size 82m 30m (Band 1-5, 7) 120m (Band 6) Quantization levels 64 256 Data rate 15Mbps 85Mbps APPENDIX Page 167 The improvements achieved by the TM included three additional bands, yielding greater detail at an improved spatial resolution (30m for VIS and IR, 120m for TIR), night time viewing, and offering better calibration and more separation between spectral bands. Alongside changes in instrumentation, the orbital characteristics of Landsat-4, 5 and 6 were modified in relation to Landsat 1-3. The orbit was reduced to 705 km altitude (as opposed to 915 km for the first three Landsats) with an inclination of 98.2° (as opposed to 99.2°), there by providing coverage slightly further forward (82.5°) and changing the repeat cycle from 18 to 16 days (Kramer, 1992). This lower orbit resulted in less overlap between images obtained on adjacent orbit paths than occurred in the case of Landsat 1-3: Landsat-4, 5 and 6 have areas near the sub-satellite point where only single images are obtained every 16 days. The pattern of overpass is now such that adjacent swaths are imaged at 8 days intervals instead of on successive days as in Landsat-1 to Landsat-3. The spatial resolution of the TM as measured by the Instantaneous Field of View (IFOV) is 30m compared with a value of 79m for the MSS sensor of the earlier Landsat missions. This spatial improvement provides for greatly improved visual analysis of TM images. The geometric characteristics of the TM data are superior to those of the earlier MSS systems owing to improved sensor pointing and finer spatial resolution. As a result TM data requires less geometric correction in order to achieve mapping tasks and the data can be used for mapping at much finer scales than MSS data. APPENDIX Page 168 A.3 Spot Series The first French satellite 'System Probatoire de ('Observation de la Terre' (SPOT) was launched on 22 February 1986. It marked a departure from the conventional multispectral scanners of the Landsat series. Whereas Landsat used the mechanical systems of the scanning mirror, SPOT was designed to adopt a 'pushbroom' sensor. The pushbroom scanner (or 'multispectral solid state linear array') consists of a line of detectors on a fixed assembly. With the pushbroom device the scanning of an individual line of a scene is performed electronically by successively measuring the current generated by each detector within the linear array. Each spectral band uses four linear arrays with each array consisting of 1728 detectors. The characteristics of the SPOT-1 Satellite, including its High Resolution Visible (HRV) imaging instruments and orbital parameters are shown in Table A-5. It will be apparent that the ground resolution is higher than that obtained in Landsat TM data, particularly in the panchromatic (black and white) mode which provides 10m resolution (Barrett & Curtis, 1992). One significant difference between SPOT and Landsat is that SPOT is able to view to the side of its orbital path. This off-nadir viewing, at up to 27° from the vertical, has two important implications. Firstly, stereoscopic coverage can be obtained by imaging the same area on different orbits at different viewing angles. Secondly, although SPOT will pass over the same point on the surface only every twenty-six days, the off-nadir viewing means that imagery can be acquired of the same area on successive days and that the average imaging interval can be as little as 2.5 days for priority areas (Harris, 1987). APPENDIX Page 169 Table A-5: Orbital and Sensor Characteristics of the SPOT-1 Satellite (Source: NRSC, 1987) Orbital Parameters: Orbit: near polar, sun-synchronous Altitude: 823 km Inclination: 98.70 Repeat cycle: 26 days Sensors: High Resolution Visible (HRV) Wavelength Resolution (nm) (m) Multi-spectral mode: Band 1 0.50-0.59 20 Band 2 0.61-0.68 20 Band 3 0.79-0.89 20 Panchromatic mode: 0.51-0.73 10 The main applications of stereoscopic imagery are as follows: • compilation of topographic maps with uniform contour intervals of 20-50m; • direct compilation of digital terrain models; • improved perception and interpretation of large scale vegetation and built features. SPOT-1 is the first operational satellite for Earth surface monitoring and it is operated by a commercial company, SPOT Image. SPOT-2 was launched on 22 January 1990 into an 850 km sun-synchronous orbit. Orbital parameters for SPOT-2 are identical to SPOT-1, except 180° apart. SPOT-3 was launched in 1992. SPOT-4 and SPOT-5 are scheduled for launch in 1995 and 1999 respectively (Massom,1991). APPENDIX Page 170 A.4 The Coastal Zone Colour Scanner on NIMBUS-7 The Nimbus programme began in 1964 and functioned for almost a quarter of a century. It has been principally been used to develop new sensors and remote-sensing techniques. Nimbus still merits special mention today because of the large literature of research results based on their data. Seven Nimbus spacecraft have been launched, each representing a significant advance in sophistication capability and performance over its predecessor. Nimbus 7 (launched on 24 October 1978) was used to carry the first visible/near-visible wavelength scanning radiometer. It was designed primarily to observe ocean colour, known as the Coastal Zone Colour Scanner (CZCS). The spectral bands and applications are presented in Table A-6. Table A-6: Spectral Range and Applications of CZCS (Source: Szekielda, 1988) Band Spectral Range (pm) Indicator 1 0.443 -0.453 Chlorophyll absorption 2 0.510 -0.530 Chlorophyll correlation 3 0.540 - 0.560 Yellow substance 4 0.660 - 0.680 Chlorophyll absorption 5 0.700 - 0.800 Surface vegetation 6 10.5 - 12.5 Surface temperatures