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Monitoring Area Variation and Sedimentation Patterns in Poyang Lake, China Using MODIS

Medium-Resolution Bands

Liu Qian March 2006

Monitoring Area Variation and Sedimentation Patterns in Poyang Lake, China Using MODIS Medium-Resolution

Bands

by

Liu Qian Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Nature Resources Management. Thesis Assessment Board Chairman External Examiner ITC Supervisor Dr. David Rossiter Wuhan Supervisor Prof. Zhang Wanshun

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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Abstract

Poyang Lake is the largest fresh water lake in China. The Ramsar Convention proclaimed Poyang Lake Basin as a Wetland of International Importance in 1992. Receiving water from five inland rivers and moderating floodwater from the Yangtze River lead to a complex hydrology condition in Poyang Lake. The large variation of water level between low-water season and high-water season makes a great difference of water area. The suspended sediment in Poyang Lake directly affects many water columns, benthic processes, and the water quality of the lower reaches of the Yangtze River. The area variation makes Poyang Lake wetlands changing between different ecological categories. This study aims to understand the dynamics distribution of suspended sediment concentration (SSC) and area variation in Poyang Lake. The Moderate-resolution Imaging Spectroradiometer (MODIS) of medium-resolution data were used in this research. During the pre-processing procedures, dark objected subtraction (DOS) method was used for atmospheric correction. The maximum likelihood method (MLM) of supervised classification was used to classify the remote sensing data into lake and non-lake. The water bodies were extracted and the area of water bodies was calculated from the number of pixels and their pixel size. The area of Poyang Lake is less than 1000 km2 in low-water season and more than 3000 km2 in the flood season. A strong linear relationship (R2 = 0.81; n = 31) was established between band 1 MODIS Terra 250 m reflectance and in situ measurements of SSC acquired from different environments in Poyang Lake Basin. The spectral characteristics of water surface with high SSC were determined by hyperspectral measurements during the field survey in seven sample points. The moderate resolution of MODIS 250 m data and the operating characteristics of the instrument provide data well suited for assessing temporal and spatial patterns of the lake area and SSC in Poyang Lake. The approach used in this study could be applied to other coastal or inland regions but the specific relationship between MODIS reflectance and SSC may vary as a consequence of optical characteristics of suspended sediment. The mixed pixels and the spatial aggregation effects limit the MLM of supervised classification to assess the lake area during low-water season. The presence of other constituents such as chlorophyll, Chromophoric Dissolved Organic Material (CDOM) can also be investigated in the further research and many other factors such as fluctuation of water surface, the bottom of the lake, proper sensor calibration, and accurate atmospheric correction are the main challenges in operational application of MODIS in water monitoring.

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Acknowledgements

I am extremely grateful to NRM department in ITC, the Netherlands, giving me an invaluable opportunity to study there. Thanks to all the NRM and other ITC staff, my classmates. The precious time together with you will have a special place in my heart! My deepest gratitude goes to Dr. David Rossiter, my ITC supervisor, for his timely supervision, present and long distance guidance, valuable comments and suggestions. His support and encouragement accompanied with me during the whole research. His scientific and critical attitude for the research gave me a deep impression that will guide me forever in my future research. Thanks to Prof. Zhang Wanshun, my Wuhan supervisor, for his valuable knowledge of hydrology, all his guidance in the fieldwork and comments of the draft. Special thanks go to Wu Guofeng, (PhD. candidate in NRM Department in ITC), for providing numerous useful data and material on Poyang Lake and his always “ready to help” spirit. Many thanks go to my colleagues, Fei Teng and Bian Meng, for helping me with the fieldwork; and to Zhou Yunlong in Poyang Lake National Nature Reserve Protection Station for his wonderful guidance in the field. I also thank Dai Jiangshan, and other staff in MODIS Receiving Centre in Wuhan University for providing a large quantity of MODIS data. Thanks to Mrs. Wu in Chemistry Research Laboratory in SRES Department in Wuhan University for her help and guidance of the lab work. Finally, my appreciation goes to my mother and father for their love, confidence, support and encouragement in me. They provide me a good environment to live and study. Thanks to them for giving me a wonderful life!

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Table of contents

Abstract ................................................................................................................................................ i Acknowledgements ............................................................................................................................. ii Table of contents ................................................................................................................................ iii List of figures .......................................................................................................................................v List of tables ...................................................................................................................................... vii List of frequently used abbreviations ............................................................................................... viii

1. Introduction................................................................................................... 1

1.1. Problem definition............................................................................................................1

1.1.1. Special sedimentation phenomenon in Poyang Lake.................................1

1.1.2. Special landscape of Poyang Lake ....................................................................2

1.1.3. Unknown and researchable...................................................................................3

1.1.4. Research problems involving remote sensing...............................................4

1.2. Study objectives ................................................................................................................4

1.3. Specific research questions...........................................................................................5

1.4. Outline of the thesis.........................................................................................................5

2. Description of the study area ....................................................................... 6

2.1. General description of Poyang Lake.........................................................................6

2.2. Hydrological characteristics in Poyang Lake........................................................7

2.3. Sedimentation characteristics in Poyang Lake ...................................................10

2.4. General description of Poyang Lake National Nature Reserve (PNR).....11

3. Literature Review........................................................................................ 13

3.1. Characteristics and application of MODIS ..........................................................13

3.2. Estimating SSC by remote sensing .........................................................................14

3.2.1. Empirical approach ................................................................................................14

3.2.2. Semi-empirical approach.....................................................................................15

3.2.3. Analytical approach...............................................................................................16

3.3. Estimating area variation by remote sensing.......................................................16

4. Methods........................................................................................................ 18

4.1. General set-up ..................................................................................................................18

4.2. Field data collection and analysis ............................................................................20

4.2.1. Field survey...............................................................................................................20

4.2.2. Laboratory analysis of water samples ............................................................22

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4.3. Remotely sensed data acquisition and pre-processing ....................................23

4.3.1. MODIS image acquisition and geometric correction ..............................23

4.3.2. Resizing the data .....................................................................................................24

4.3.3. Atmospheric correction........................................................................................24

4.4. Assessing areas variation with MODIS images .................................................28

4.5. Assessing SSC with hyperspectral data.................................................................31

4.6. Assessing SSC with MODIS images......................................................................32

5. Results and discussions............................................................................... 33

5.1. Area variation of Poyang Lake .................................................................................33

5.2. Statistical relation of SSC with hyperspectral data...........................................35

5.2.1. Spectral characteristics of water with high SSC ........................................35

5.2.2. Linear regression between reflectance and SSC........................................36

5.3. Statistical analysis of SSC with MODIS image.................................................37

5.4. Analysis and interpretation of SSC variation......................................................40

5.4.1. SSC variation in the main part of Poyang Lake .........................................43

5.4.2. SSC variation in Poyang Lake Nature Reserve..........................................44

5.5. Comparison of MODIS 250m and 500m data ....................................................46

5.6. Error analysis....................................................................................................................48

5.6.1. Error analysis of assessing area variation.....................................................48

5.6.2. Error analysis of assessing SSC........................................................................49

6. Conclusion and recommendation .............................................................. 50

6.1. Conclusions on monitoring area variation............................................................50

6.2. Recommendations and future research on monitoring area variation.......51

6.3. Conclusions on monitoring SSC ..............................................................................51

6.4. Recommendations and future research on monitoring SSC..........................53 References ..........................................................................................................................................55 Appendix A: Description of Hukou hydrological station ..................................................................58 Appendix B: Field survey ..................................................................................................................59 Appendix C: Geographical coordinates of sample points ..................................................................61 Appendix D: Laboratory analysis of water samples...........................................................................63 Appendix E: Specification of spectrometer........................................................................................65 Appendix F: Importing MODIS Level 1B data into IP software packages........................................66 Appendix G: Pixel information of area variation of Poyang Lake using MODIS 250m data............69 Appendix H: MODIS imagery used...................................................................................................70 Appendix I: List of Equipment requirement ......................................................................................72

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List of figures

Figure 1-1: Landsat images of Poyang Lake of different time ...........................................2 Figure 1-2: Landscape of Poyang Lake of different season................................................3 Figure 2-1: Study area: Poyang Lake..................................................................................6 Figure 2-2: Main rivers connected with Poyang Lake ........................................................7 Figure 2-3: Average monthly rainfall nearly 50 years at Hukou Hydrological station ......8 Figure 2-4: Average monthly water level nearly 50 years at Hukou hydrological station .9 Figure 2-5: Average discharge measurement of 2004 in Hukou hydrological station .......9 Figure 2-6: Transport rate (kg s-1) of sediment measurement in Hukou hydrological

station ........................................................................................................................11 Figure 4-1: Flowchart of Research Methodology for assessing SSC ...............................19 Figure 4-2: Flowchart for the historical analysis ..............................................................20 Figure 4-3: Sampling site for Poyang Lake ......................................................................22 Figure 4-4: MODIS Terra 250m data (full image) for October 18th 2005 (Left) and

resized image for October 18th 2005 (Right).............................................................24 Figure 4-5: Diagram of the solar radiation interactions in the atmosphere (Pietro, 2001)26 Figure 4-6: MODIS Terra Band 1 data for October 18th 2005 and atmospheric corrected

image using DOS atmospheric correction method....................................................27 Figure 4-7: Flowchart of Research Methodology for assessing area ................................28 Figure 4-8: Supervised classification Figure 4-9: Subsetting the classified images

...................................................................................................................................30 Figure 4-10: Extracting water bodies Figure 4-11: Giving the water bodies the

original values ...........................................................................................................30 Figure 4-12: Averaged reflectance of water surface obtained in NO 11 sample point.....31 Figure 4-13: Reflectance spectra of water surface with a hand-held hyperspectral

radiometer..................................................................................................................32 Figure 5-1: Area variation of Poyang Lake in 2003 and 2004..........................................34 Figure 5-2: Landscapes of Poyang Lake during different season in 2004 derived from

MODIS 250 m data ...................................................................................................35 Figure 5-3: The relationship between SSC and surface reflectance at 665 nm (Red band)

...................................................................................................................................37 Figure 5-4: Regression coefficients (R2) between SSC and reflectance (Sensor:

Spectrometer) ............................................................................................................37 Figure 5-5: The distribution of in-situ measurement of SSC............................................38 Figure 5-6: The relationship between MODIS band 1 and SSC.......................................39 Figure 5-7: Plot of residuals against predicted values ......................................................39 Figure 5-8: The relationship between MODIS band 1 and SDD ......................................40 Figure 5-9: SSC maps of Poyang Lake (Sources MODIS images)...................................43 Figure 5-10: SSC maps of the water coming from Yangtze in July 2004 ........................44

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Figure 5-11: SSC map of Poyang Area in September 30th 2004, with detail of Dahuchi Lake in Poyang Lake Nature Reserve .......................................................................45

Figure 5-12: SSC map of Poyang Area in November 28th 2004, with detail of Dahuchi Lake in Poyang Lake Nature Reserve .......................................................................45

Figure 5-13: Comparison of MODIS 250 m and 500 m data in the high-water season....46 Figure 5-14: Comparison of MODIS 250 m and 500 m data at low-water season...........47 Figure A-1: The location of Hukou hydrological station..................................................58

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List of tables Table 3-1: Basic characteristics for MODIS medium-resolution bands ...........................14 Table 4-1: Solar irradiance for the sensor of Terra ...........................................................27 Table 5-1: Summary of the regression analysis between band or band ratios and SSC. In

all cases n = 31 ..........................................................................................................39 Table 5-2: Comparison of MODIS 250 m and 500 m data in lake area estimation………….48

Table B-1: Field survey for the samples taken in Poyang Lake........................................59 Table C-1: Geographical coordinates of each sample points............................................61 Table H-1: Pixel information of water body of Poyang Lake in 2004..............................69 Table H-2: Pixel information of water body of Poyang Lake in 2003..............................69 Table G-1: 2005 MODIS imagery used ............................................................................70 Table G-2: 2004 MODIS imagery used ............................................................................70 Table G-3: 2003 MODID imagery used ...........................................................................71

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List of frequently used abbreviations

SSC Suspended Sediment Concentration MODIS Moderate-resolution Imaging Spectroradiometer WWF World Wildlife Fund TM Thematic Mapper ETM+ Enhanced Thematic Mapper AVHRR Advanced Very High Resolution Radiometer SeaWIFS Sea-viewing Wide Field Sensor MERIS Medium Resolution Imaging Spectrometer PNR Poyang Lake National Nature Reserve IOP Inherent Optical Properties LAC Local Area Coverage GAC Global Area Coverage TD Transformed Divergence JM Jeffrey-Matushita Distance WTD Weighted Transform Divergence WJM Weighted Jeffrey-Matushita Distance MLM Maximum Likelihood Method CZCS Coastal Zone Color Scanner DOS Dark Object Subtraction DTs Dark Targets SDD Secchi Disk Depth R2 Determination Factor Near-IR Near Infra Red RGB Red-Green-Blue AOI Area Of Interest CDOM Chromophoric Dissolved Organic Material WDEM Water Digital Elevation Model NASA National Aeronautics and Space Administration RSIE School of Remote Sensing Information Engineering SRES School of Resources and Environmental Science

MONITORING AREA VARIATION AND SEDIMENTATION PATTERNS IN POYANG LAKE, CHINA USING MODIS MEDIUM-RESOLUTION BANDS

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1. Introduction

Located in the middle and lower reaches of the Yangtze River, China’ s longest river, Poyang Lake wetland is one of the world’s six top wetlands designated for the List of Wetlands of International Importance1. Poyang Lake wetland provides countless benefits; it is the source of food, protection and other vital habitat factors for a variety of fish and wildlife species, in many cases including endangered and threatened species. However, the wetlands face several environmental pressures, including suspended sediment in lake waters and a wide seasonal fluctuation in lake level and area. Many environmental experts and local decision makers have recommended the conservation and wise use of Poyang Lake wetland. Scientific research has an important role to play in achieving sustainable development of this area. Earth observation from satellites has proven to be a key technology in environmental assessment and monitoring of large areas such as Poyang Lake; it is especially well suited for assessing lake area and has shown promise for monitoring suspended sediment.

1.1. Problem definition Poyang Lake is the largest freshwater lake in China. Although the water quality is still good, erosion, degradation, negative influences of toxic pollution from heavy metals and human activities (exploiting sands, fisheries, transport and industry) all threaten its water quality. More awareness should be put on this great potential of environmental damage. Using a scientific approach to monitor, protect and manage water resources in Poyang area can help relieve this threat; in particular, satellite remote sensing for assessment and monitoring of lake conditions can supply timely and reliable information to decision-maker.

1.1.1. Special sedimentation phenomenon in Poyang Lake The lake is connected to the Yangtze River through a 1 km long channel and serves as a natural overflow reservoir (Wang and Dai, 2005). Poyang Lake is slightly higher than the Yangtze River and the water normally flows into the Yangtze. While during the high-water season, usually July to September, the water level in the Yangtze River increases and the

1 Source: http://www.chinaview.cn/


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water flows back to Poyang Lake. This phenomenon brings much Yangtze sediment into Poyang Lake. The remotely sensed images below (Figure 1-1) show the Poyang Lake of different times. The left Landsat data for 22 June 2004 shows the ordinary statue of Poyang Lake; the right one for 24 July 2004 shows the statue in water coming back from the Yangtze River. The penetration of sediment from the Yangtze to Poyang Lake can be observed obviously from the remotely sensed image.

Figure 1-1: Landsat images of Poyang Lake of different time

Landsat data for 22 June 2004 (Left) and for 24 July 2004 (Right) Suspended sediment directly affects many water column and benthic processes such as phytoplankton productivity (Cole and Cloern, 1987), productivity of submerged aquatic dynamics (Dennison et al., 1993), nutrient dynamics (Mayer et al., 1998) and transport of pollutants (Martin and Wisdom, 1991) and other materials (Miller and McKee, 2004). The animals within the lake or living along the bank may change their habitats because of the changing environment. The increasing sediment in Poyang Lake changes the storage capacity of the lake and decreases the water depths. If the sedimentation phenomenon is serious, the capability of Poyang Lake to mediate the flooding of the Yangtze River will decrease.

1.1.2. Special landscape of Poyang Lake All wetlands in the world are in dynamics during different season, but the extent of changes in Poyang Lake wetland is rarely seen. According to the hydrological archives from the office of state flood control and drought relief headquarters in China, the mean water level of Poyang Lake averaged over fifty years is 12.86 m; the highest was 22.59 m on Jul.31st, 1998, and the lowest 5.9 m on Feb.6th, 1963 (at Hukou Hydrological Station, Wusong Base Level) (Appendix A). The amplitude of variation of water level is from 9.79 m to 15.36 m in a year. The absolute fluctuation of water level is up to 16.69 m. With variation of water amount, the fluctuating range of water level is relatively large. It can naturally store floodwater. The area of the lake greatly varies with the fluctuation of its water level, which rises at high-water


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season so that the water surface suddenly expands. And it drops at low-water season and bottomland emerges, and only several wandering watercourses remain. The landscape looks like a line at low water level and an ocean at flood. The remotely sensed images (Figure 1-2) show Poyang Lake at different season. The left MODIS Terra image (30th June 2004) shows the landscape of Poyang Lake during high-water season; the right MODIS Terra image (28th January 2003) shows the landscape during low-water season. Almost all parts of Poyang Lake join together as a water body when water level is high. While more marshland appears when water level is low and the lake is consisted with hundreds of small water bodies. The big difference of the area of Poyang Lake can be observed obviously from remotely sensed images.

Figure 1-2: Landscape of Poyang Lake of different season

MODIS Terra data for 30th June 2004 (Left) and 28th January 2003 (Right) The big changes of water level and water area make Poyang Lake wetlands change between different ecological categories. When the water level is high, water body is the main part of Poyang Lake wetlands; when the water level is low, marshland is dominant in Poyang Lake wetlands. The whole wetlands system changes regularly over the year, although there are large differences in the magnitude and timing of the annual cycle among different years.

1.1.3. Unknown and researchable The following are not known, and can be answered by appropriate research:

The pattern of the Yangtze sedimentation flow How far the Yangtze sedimentation will go into Poyang Lake The variation of suspended sediment concentration (SSC) in different season The spatial-temporal pattern of sedimentation among years The causes of SSC variation The pattern of area variation of Poyang Lake within one year


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The area variation of Poyang Lake between years, both total area and pattern The area of water body lasting all of the year and the area of water body just lasting

several months a year Causes of the area variation and its spatial pattern

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1.1.4. Research problems involving remote sensing Methods currently used to measure suspended sediment in lakes and reservoirs are based on in situ measurements and subsequent laboratory analyses. These approaches, while accurate, are time-consuming and do not easily lend themselves to understanding the spatial dimension of suspended sediment within the lake which contribute toward more fully understanding the limnology processes (Nellis et al., 1998). Satellite systems for remote sensing provide an ability to measure the optical characteristics of suspended sediment in water. Satellite-based estimates of suspended sediment concentration have been derived from statistical relationships between spectral reflectance by the suspended sediment near the surface of the water and corresponding ground truth data (water samples) (Nellis et al., 1998). The purpose of this study was to evaluate the potential for using multi-temporal digital data from the Moderate-resolution Imaging Spectroradiometer (MODIS) to estimate the magnitude and spatial variability in Poyang Lake. The following are not known, and can be answered by appropriate research:

Which bands or bands combination of MODIS medium-resolution can account for spatial distribution of SSC best?

What the relation between SSC and reflectance is in Poyang Lake. The most accurate method of calculating the lake area with MODIS. The relation between pixel resolution and area estimation.

1.2. Study objectives The main objective is to develop a method based on MODIS medium-resolution bands to monitor the size of the lake, distribution and flux of sediment in Poyang Lake over a broad spectrum of time and space scales. Specific objective of this study are: (1) To determine the relation between SSC and reflectance value of MODIS image (2) To map the SSC in Poyang Lake during flooding periods among years (3) To explain the causes of SSC changes in Poyang Lake in many aspects (4) To extract water body from MODIS image during different season (5) To determine the area variation of Poyang Lake during different season


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1.3. Specific research questions Answers for the following questions were sought in this research: (1) Which band or bands combination of MODIS are most correlated with SSC? (2) What is the relation between MODIS reflectance in the selected band or combination and

SSC in Poyang Lake? (3) How much of the variation does this explain? (4) What is the temporal variability of SSC in Poyang Lake during the flooding season in this

year? (5) What is the temporal variability of SSC in Poyang Lake among years? (6) What are the causes of SSC variation? (7) What is the area of Poyang Lake during different season? (8) What is the pattern of the changes in lake area? (9) Which area of Poyang Lake last all of the year, and which area last just several months? (10) What are the causes of area variation? (11) What is the relation between pixel resolution and area estimation?

1.4. Outline of the thesis

Chapter 1: General introduction of the area, interesting phenomena, problem statement, general and specific objectives, research questions are described.

Chapter 2: A description of the study area that focused on physical characteristics, geology, nature resources, biological resources, hydrological and sedimentation characteristics.

Chapter 3: Gives a literature review, related to the use of MODIS image and the methods for assessment of SSC and area. The review is focused on methods part of this thesis.

Chapter 4: Explains the research methods in detail. Chapter 5: The results obtained from this research, analysis and interpretation on the

results, error analyses are described. Some graphs and figures have been used to present the results.

Chapter 6: Presents the conclusions found in this study, shortcomings in this research and corresponding recommendations as well as future research possibilities for the area.


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2. Description of the study area

2.1. General description of Poyang Lake

Figure 2-1: Study area: Poyang Lake

Poyang Lake is located at latitude 28°22′- 29°45′ north, longitude 115°47′- 116°45′ east. It lies in the northern part of Jiangxi Province, at the southern bank of the middle and lower reaches of the Yangtze River (Figure 2-1). It varies from 12-18 m above sea level. It is divided into two parts by Songmenshan Mountain. The northern part is the water channel joining the Yangtze River, with the length of 40 km and the width of 3-5 km (the most narrow point is 2.8 km or so). The southern part is the main lake, with the length of 133 km and the furthest width of 74 km. Poyang Lake is 173 km long from north to south. The furthest width is 74 km, mean width 16.9 m from west to east. The lakeshore is 1200 km long, and the area of the water body 3,283 km² (when the water level at Hukou is 21.71 m), mean depth 8.4 m, and the inmost depth about 25.1 m. Its volume is 27.6 billion m³. It is the largest freshwater lake in China. Poyang Lake Basin is an important international wetland, and an important storing lake of the main stream of the Yangtze River. It possesses very important ecological functions, e.g. floodwater storage and biological diversity protection, in the watershed of the Yangtze River. It is one of the 10 ecological conservation areas in China, and also one of the global important ecological areas regulated by World Wildlife Fund (WWF). It plays very important roles in maintaining the ecological safety of the region and the nation.


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Since it possesses humid monsoon climate, Poyang Lake becomes accordingly “the country of rivers and green grass, and blooming plums in the misty rain”, and “a land flowing with milk and honey”. Its environment and climatic conditions are suitable for migratory birds to live through the winter. At late autumn and early winter in every year, thousands and thousands of birds migrate over here from Siberia of Russia, Mongolia, Japan, Korea, and northeastern and northwestern China. At spring (April) of next year, they gradually migrate away. So far, there are more than 300 kinds and a million plumes of birds, among which 50 kinds are rare birds, in the conservation area. It becomes one of the biggest bird conservation areas in the world. Especially, it is here where the biggest group of white cranes was found in the world. The total number of wintering populations was above 4000 in 2002, accounting for more than 95% of total of white crane in the world. This is why Poyang Lake comes to be known as “the world of white cranes” and “the kingdom of rare birds”2.

2.2. Hydrological characteristics in Poyang Lake

Figure 2-2: Main rivers connected with Poyang Lake

Figure 2-2 is an overview of the Poyang Lake area. The hydrological condition is very complicated in Poyang Lake. It is a seasonal lake with the feature of taking in and sending out waters. It receives water from the five rivers -- Gan River, Fu River, Xin River, Rao River and Xiu River, and empties into the Yangtze River, at Hukou. A large amount of the water received is stored before discharge although some flows straight through the lake.

2 Source: http://www.poyanglake.net/


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According to the record from Jiujiang Hydrological Bureau, the total precipitation in the Poyang area is ranged from1341.4 to1934 mm per year. As an example of the rainfall oscillation, Figure 2-3 shows the monthly average rainfall measured from 1956 to 2005 in Hukou hydrological station (Appendix A).

Figure 2-3: Average monthly rainfall nearly 50 years at Hukou Hydrological station

Its annual afflux of water to the Yangtze River exceeds the total water amount of the three rivers -- Yellow River, Huaihe River and Haihe River. The amount of water from Poyang Lake into the Yangtze River is 1470×108 m³per year. The sediment from Poyang Lake directly affects the water quality of lower reaches of the Yangtze River. The difference between low water levels in winter and high water levels at the height of the summer flood is a staggering eleven metres. Figure 2-4 shows the average monthly water level from 1956 to 2005 measured in the same station as for rainfall. It clearly illustrates that from June to September the water level is high (above 16.0 m). Notice the water level decreases from November to the following February and the water level increases from February to July steadily.


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Figure 2-4: Average monthly water level nearly 50 years at Hukou hydrological station

Water discharge (m3 s-1) was measured by Hukou hydrological station. The Figure 2-5 shows the average water discharge in the mouth of Poyang Lake during different months of 2004. Most of the time the water goes from Poyang Lake into theYangtze River, and during the flood period the water goes back from the Yangtze River into Poyang Lake, such as July 2004 and September 2004. Extremely low values happen in July and September because the values are average values of a month. When the water goes back from the Yangtze River the discharge values became negative, so the average values are low in these two month. It is shown by Figure 2-5 that in the low-water season the water discharge is near zero, whereas in the high-water season the water discharge becomes high.

Figure 2-5: Average discharge measurement of 2004 in Hukou hydrological station


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2.3. Sedimentation characteristics in Poyang Lake Among these several rivers connected with Poyang Lake, the Gan, Xiu and Yangtze Rivers contribute the majority of the sediment to Poyang Lake. According to the report from Jiujiang Hydrology Bureau, during 1976 to 1987, 112 Tg sediments came into Poyang Lake and 78 Tg sediments run out off from Poyang Lake into Yangtze River. The average rate of sedimentation is 7.09 Tg per year. During resent years, the amount of sediment coming from five rivers was 24 Tg per year and 12.09 Tg sediments left in Poyang Lake in one year. The sediment from five inland rivers goes into Poyang Lake all the year round while the sediment from the Yangtze River just during the flood period goes into Poyang Lake. The phenomenon of water from the Yangtze River flowing into Poyang Lake occurred twice in 2004: from 20th July to 27th July and 10th September to 15th September. Figure 2-6 shows the transport rate of sediment (kg s-1) for the months of July and September 2004 respectively. These data were measured in the mouth of Poyang Lake (the connected area between the Yangtze River and Poyang Lake) by Hukou hydrological station. In Figure 2-6 the positive values mean the sediment from Poyang Lake into the Yangtze River; the negative values mean the sediment coming from Yangtze into Poyang Lake. It is obviously shown that SSC in the Yangtze River is much higher than it from Poyang Lake.


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Figure 2-6: Transport rate (kg s-1) of sediment measurement in Hukou hydrological station

In order to get the information on recent sedimentation, four cores were taken from northwest corner of Poyang Lake by Institute of Geography and Limnology of Chinese Academy of Sciences in March 1997 (Xiang et al., 2002). The cores were divided into different segments depending on estimated sedimentation rates from field observations. The four cores indicate different linear sedimentation rates since 1963: 0.62 cm yr-1, 0.24 cm yr-1, 0.18cm yr-1, and 0.10 cm yr-1. The different rate demonstrates spatial variations, which are most likely due to variations in sediment inputs and in part post-depositional movement. The core with highest sedimentation rate was taken from the part of the lake controlled by Gan River from the southern Jiangxi province, which is well known for its severe soil erosion in the catchments.

2.4. General description of Poyang Lake National Nature Reserve (PNR)

Poyang Lake National Nature Reserve (PNR) is located in the northwest of Poyang Lake, at latitude 28°05′- 29°15′ north, longitude 115°55′- 116°03′ east (the intersection of Gan River and Xiu River). The nature reserve was established in 1983 and covers approximately 6% of the total area of the Poyang Lake Basin (ICF, 2004). The geographical and climatic conditions at PNR make it favourable place for migratory birds to escape the winter cold and most of the migrant birds in Poyang Lake Basin usually concentrate in PNR. It is known as the world’s largest nature reserve for birds. In November, many birds from Mongolia, Japan, North Korea, and Russia as well as the northeastern and northwestern parts of China fly to the nature reserve and spend the winter there with the egrets, wild ducks, and mandarin ducks till the following March.


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The average temperature of waters in PNR is 18.3 ºC. The lowest in the history is 0 ºC and highest is 37.8 ºC. The highest average monthly water temperature is reached in August of the year and the lowest is reached in January. The average of the water temperature variation in one year is about 24.3 ºC. The water coming from the western tributary of Gan River and Xiu River goes into into PNR, which goes through the northern part of Poyang Lake and flowes into the Yangtze River. The amount of water coming from Xiu River accounts for 9.20% of the total amount. In the low-water season, PNR is separated from Poyang Lake and the water in it becomes almost stable. Most of the small water bodies in PNR are gravity oscillation lakes at that time. In the high-water season, PNR and the grassland are submerged by flood. The PNR merges with the greater Poyang Lake. The water bodies of Poyang Lake appear to be contradicted oscillation lakes. The extent of sedimentation is relatively serious in PNR compared with other parts in Poyang Lake. The sedimentation rate in the western tributary riverbed of Gan River is 20 mm per year; the rate in the bank of water bodies in PNR is relative low, at 10-15 mm per year (Jiangxi, 2002).


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3. Literature Review

3.1. Characteristics and application of MODIS Many satellite sensors are potentially suitable for estimating the concentration of suspended sediment. The basis for sensor comparison and selection is the spectral, spatial and temporal resolution. The satellite images from Landsat series of instruments [Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+)] have been used to measure SSC in inland waters (Baban, 1993; Baban, 1995; Baruah et al., 2002; Dekker et al., 2001; Dekker et al., 2002; Giardino et al., 2001). But the orbit characteristics of the Landsat satellites yield a revisit time of about 16 days. Hence, Landsat sensors cannot capture the temporal dynamics of waters. Many researches demonstrate that the Advanced Very High Resolution Radiometer (AVHRR) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite images can be used in estuary waters and near-shore ocean waters successfully (Baban, 1995; Friodefond et al., 1999; Keiner and Yan, 1998; Ruhl et al., 2001; Stumpf and Goldschmidt, 1992). Although these data are adequate for studying mesoscale and larger-scale processes on the continental shelf, 1 -km spatial resolution data are often too large to examine horizontal gradients, particularly in small area water bodies (Nittrouer and Wright, 1994). The MODIS instrument was designed building on the AVHRR and TM experience, with its 2330 km viewing swath width flying onboard two satellites named Terra and Aqua, to provide almost complete global coverage in one day. It acquires data in 36 high spectral resolution bands between 0.415 and 14.235 µm with spatial resolutions of 250 m (2 bands), 500 m (5 bands), and 1000 m (29 bands) (Savtchenko et al., 2004). The medium-resolution bands (250 m and 500 m) were originally designed as “sharpening” bands for land studies and cloud detection, and therefore have lower sensitivities than the MODIS ocean bands for water application. However, through comparison with other sensors including Landsat-7 ETM+, CZCS (Coastal Zone Color Scanner), and SeaWiFS, these bands do provide sufficient sensitivity. The MODIS medium-resolution bands are 4-5 times more sensitive than Landsat-7/ETM+ bands, nearly twice as sensitive as the corresponding CZCS blue-green bands, and only 3-4 times (250 m, red and near-IR) and 1-2 times (500 m, blue and green) less sensitive than the corresponding SeaWiFS bands. Hence, from the perspective of radiometric sensitivity, the MODIS medium-resolution bands can be expected to be at least as useful for water application as CZCS (Hu et al., 2004).


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Primary Use Band Bandwidth1 (nm)

Spectral Radiance2 (W/m2 -µm-sr)

Required SNR3 (Signal-to-noise ratio)

1 Red 620 - 670 21.8 128 Land/Cloud/Aerosols Boundaries 2 Near-IR 841 - 876 24.7 201

3 Blue 459 - 479 35.3 243

4 Green 545 - 565 29.0 228

5 Near-IR 1230 - 1250 5.4 74

6 Middle-IR

1628 - 1652 7.3 275

Land/Cloud/Aerosols Properties

7 Middle-IR

2105 - 2155 1.0 110

Table 3-1: Basic characteristics for MODIS medium-resolution bands3

The characteristics of MODIS band 1 data (620-670 nm), such as its medium spatial resolution, red band reflectance, high sensitivity, and near daily coverage, suggest that these images may be well suited to examining the suspended particulates (Miller and McKee, 2004). Proper sensor calibration, accurate atmospheric correction, removing bottom interference are the three major challenges in moving toward operational application of MODIS in water monitoring (Hu et al., 2004).

3.2. Estimating SSC by remote sensing The concentrations of suspended sediments are highly variable in water and vary over a broad spectrum of time and space scales. Most traditional field sampling methods can not resolve sediment dynamics (Miller and McKee, 2004; Miller et al., 2003). Many studies have demonstrated that remotely sensed data could monitor the concentration of suspended sediments. The most common techniques for analyzing the remotely sensed data to determine water constituent concentration are based on the brightness of reflectance. Three different approaches can be used to determine the reflectance: empirical approach, semi-empirical approach and analytical approach (Gordon and Morel, 1983).

3.2.1. Empirical approach This is also called “statistical approach”, and is a simple and straightforward method. The main concept of this approach is retrieving suspended sediment concentration from statistical relationships between spectral reflectance by the suspended sediment near the surface of the water and corresponding ground truth data (water samples) (Nellis et al., 1998). 3 Source: http://modis.gsfc.nasa.gov/


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Linear and multiple regressions are proved useful for the study of the suspended sediment. These are the subjects of research done by Ritchie and Cooper (1998), Baban (1983). They yield sufficiently accurate concentration estimations. They gave better accuracy of the in-situ measurement is at the same time as the acquisition date of remotely sensed imagery. The results of empirical algorithms always need in-situ data because illumination, surface water, atmospheric conditions and subsequently underwater conditions may change between different remote sensing missions. A common method is to relate remotely sensed reflectance measured in the red portion (600-700 nm) of the visible spectrum to parameters of water column sediment or particulate matter concentration. This approach is reasonably robust in coastal and inland waters because scattering from suspended sediment frequently dominates the reflectance spectra when compared to pure water and phytoplankton absorption (Kirk, 1994).

3.2.2. Semi-empirical approach In this approach, the spectral characteristics of the water constituents are well known and this knowledge is used to improve the algorithms developed by statistical approach. Reasonable algorithms can be found by common sense and improved by experience. Quantitatively, the coefficients could be applied just to the data set at hand, so each application must be individually calibrated. Semi-empirical approach based on the volume reflectance R (0-) is significantly better than the empirical algorithms. This is because the only parameters that may change between different times are the relation between R (0-) and the inherent optical properties (IOP), and the relation between IOP and the optical water quality parameter (Dekker, 1993).This approach appears reasonable and constituents a simple method of estimating suspended sediment concentration but algorithms would be calibrated for each satellite image using simultaneous field measurements (Pulliainen et al., 2001). Pekka et al. (2001) tested semi-empirical algorithms with simulated satellite data against field observations using regression analysis. They concluded that the band combination to be included in Envisat4 and Medium Resolution Imaging Spectrometer (MERIS) instrument enables the interpretation of water quality using semi-empirical algorithms both for lakes and coastal waters. Spectral mixture analysis, as a data analysis tool, is done using a fixed reference (end-members). The end-members are represented by spectral data from either the purest pixel of a specific material on an image or the purest material in the laboratory (Kerner and Yan, 1998). They proved that spectral mixture analysis is a powerful tool for estimating SSC in the surface water using a neural network from three visible bands of Landsat TM as input. It was 4 Launched in 2002, Envisat is the largest Earth Observation spacecraft ever built.


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found that a neural network using three visible bands of Landsat TM as input has been successful in modelling the water quality parameters and was able to model the transfer function to a much higher accuracy than regression analysis.

3.2.3. Analytical approach The fully analytical approach is used optimally to retrieve the water constituents or parameter from the remotely sensed up welling radiance or radiance reflectance signal. Only initial measurements are needed to establish optical properties of the relevant waters in an area, requiring only a few in-situ measurements. It allows the analysis of error propagation, which enables us to predict errors in retrieved concentration. This approach is very complicated by its strict requirements for accurate knowledge of the inherent optical properties (IOP) to use in the modelling. The knowledge about the equipment and the availability of the equipment such as the spectrophotometer and equipment for the laboratory analysis to measure several optical water properties also are obstacles to implement this method (Ambarwulan, 2002). Several models for coastal and inland waters were investigate by Gordon and Morel (1983). They are similar to a solution of the radiative transfer equation: volume reflectance is expressed as a function of absorption and backscattering coefficients of the water constituents. The main differences in bio-optical models were in including the water constituents contributing to the subsurface reflectance. Advantage of using the analytical approach is that once the optical properties of studied water bodies are identified; the model could be applied to any remote sensing scenery irrespective to the time of its acquisition. In the research done by Dekker (2001), the algorithms based on analytical optical modelling using the in-situ inherent optical properties (IOP) were set up and he proved that satellite data could become an independent measurement tool for water management authorities. A disadvantage is the model uses various input parameter, which often are not available (Ulanbek, 2003).

3.3. Estimating area variation by remote sensing

Multi-temporal remotely sensed data can be used to calculate the area variation of the surface types. Remote sensing provides a map-like representation of the Earth’s surface that is spatially continuous and internally consistent across the image, as well as available at a range of spatial and temporal scales. Differencing surface types is typically based on an image classification. This may be achieved by either visual or computer-aided analysis. The classification may be one that seeks to group together cases by their relative spectral similarity (unsupervised) or that aims to allocate cases on the basis of their similarity to set of


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predefined classes that have been characterized spectrally (supervised) (Jesus and Gregory, 1997). The maximum likelihood classifier (MLC) has generally been proven to be the one that obtains best results for classification of remotely sensed natural resource data (Bolstad and Lillesand, 1991; Mather, 1987; Shettigara, 1991). Although some author states that the minimum distance classifier, and nonparametrc classifiers are more robust, results from many research projects show that the classification accuracy obtained with an MLC is superior to that obtained with other classifiers (Gallup, 1991). In addition, four methods used to determine separability between classes, transformed divergence (TD), Jeffrey-Matushita distance (JM), weighted transform divergence (WTD) weighted Jeffrey-Matushita distance (WJM), are specifically constrained to classification with an MLC because of the spectral distances between classes they compute. Fine spatial resolution data, such as those from the Landsat and SPOT satellites, are useful for monitoring small, local areas, but the low temporal resolution of these data limit the availability of imagery, particularly in the regions with extensive cloud cover (Wagner et al., 1993). The need to maintain manageably sized data sets, combined with the advantages of multi-temporal data, points to increasing reliance on coarse spatial resolution data (Mary and Curtis, 1997). Nelson and Holben (1986) compared the feasibility of detecting deforested areas in Rondonia, Brazil with AVHRR local area coverage (LAC) and global area coverage (GAC) data to that of airborne Multi-Spectral Scanner (MSS) data. They found that while the 1.1 km LAC data was sufficient to differentiate forested from non-forested areas, linear features of deforestation could not be reliably identified with the 4 km GAC data. That is, not only is the size of the feature relevant to the ability to detect it with coarse resolution data, but also the spatial pattern of the feature is also important. Similarly, Moody and Woodcock (1996) aggregated a map of a forested landscape developed from 30 m TM imagery to various resolutions ranging from 90 m to 6000 m. They reported that estimation of class proportions varied as a function of the spatial resolution. The magnitude of error was hypothesized to be a function of the relationship between the spatial resolutions, the original size of the land cover classes, and the spatial patterns of the classes.


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4. Methods

4.1. General set-up

Preparation stage This stage composed of activities such as literature review, proposal finalization, collection of secondary data, and conduction of discussions. Literature review This activity was done transversally throughout the entire research process. The literatures on study area, wetlands management, analysis of SSC, statistics, characteristics of MODIS data and other topics was surveyed. Literature review was carried out in order to develop the researcher’s knowledge on scientific and technical aspects. Methodology development was the main subject of this stage. Collection of secondary data At the beginning of the research, the available data was collected. The data include the general information of study area, the hydrological data and research reports from some institutions or Internet. Remotely sensed data such as TM images of last year were also collected. Discussion Visiting, discussion and electronic mail communication with experts in hydrology and remote sensing were done in this stage. Valuable, scientific suggestions and comments about this research were obtained and taken into account.

Data acquisition and processing stage This stage includes the steps of in-situ data measurement, analysis in the laboratory, remotely sensed data acquisition and processing. Setting up the calibrated algorithm for SSC is the most important part in this stage. The detailed description of these steps is narrated in the following sections of Chapter 4. The main steps are shown in Figure 4-1.


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Fieldwork(Water samples)

Satellite images acquisition (MODIS)

Laboratory analysis

Pre-processingGeometric correction

Atmospheric correction

Land and cloud masking

In-situ SSC Corrected satellite imagery

Regression analysis between reflectance and in-situ SSC

Apply the algorithms into entire image

Map classification

SSC maps

Figure 4-1: Flowchart of Research Methodology for assessing SSC

Mapping and analysis stage This stage including mapping, making some tables and graphs from the processed data, result analysis. The causes of SSC variation and area variation of Poyang Lake were investigated in this stage. Figure 4-2 shows the flowchart for historical analysis of SSC.


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Historical satellite images acquisition (MODIS)

Pre-processing Geometric correction

Atmospheric correction

Apply the calibrated algorithms

Map classification

Analysis and interpretations of the resulting time-series maps

Historical corrected satellite images

SSC maps

Land and cloud making

Figure 4-2: Flowchart for the historical analysis

Evaluation and reporting stage

This stage includes the evaluation of the methods applied in this research, writing this present report and modifying it. Readers can understand what has been done in this research when a thesis is well organised and clearly presented.

4.2. Field data collection and analysis

4.2.1. Field survey In order to get samples ranging from high SSC to low SSC, transect sampling strategy was used in this research. A sampling strategy was developed after visual interpretation of acquired satellite images of the same season of previous years. The SSC variation in different


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part of the lake can be observed obviously from MODIS true colour composite combined by band 1 and band 2. The brightest part was shown in the northern part of the lake (close to the Yangtze River). The southeast part of the lake was very dark in the RGB image. The brightness decreased gradually from north to south, west to east. Field surveys were conducted on 14th to 17th October 2005 using a motorboat. Thirty-one (31) locations of water samples are shown in Figure 4-3 with red and dark blue dots (the satellite image will be discussed in Chapter 4-3). They were distributed in different environments in Poyang Area. Most of them were from the main part of Poyang Lake. Several samples were collected in Poyang National Nature Reserve, Chang Huchi Lake, Zhu Shihu Lake, Gan River and Xiu River (all belong to Poyang Lake Basin). At each sampling point, surface water was pumped from approximately 0.2 m below water surface into 300 mL plastic bottle and the bottle was then capped, labelled, and placed in a box to be shipped for laboratory analysis. The following parameters were measured at each location: longitude and latitude by portable GPS device (pinpoint locations within 15 metres), water temperature at 0.2 m below water surface and Secchi disk depth (SDD) (transparency). The equipments required in the field survey are listed in Appendix I. Water-leaving radiance spectra measurements were taken from the boat from a height of 1 m above the water surface with a hand-held hyperspectral radiometer in seven different locations, which were shown with dark blue dots in Figure 4-3. All the measurements were performed on a flat-water surface and in sunny condition. The upwelling radiance spectra of water surface were measured in triplicate with the sensor (Appendix E) viewing the water surface vertically and 30 cm above the water surface.


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Figure 4-3: Sampling site for Poyang Lake

4.2.2. Laboratory analysis of water samples Water samples were taken into the chemistry laboratory in School of Resources and Environmental Science (SRES) in Wuhan University two days after collection. The author spent three days to analyse the water samples and recorded all the parameters (Appendix D). The methods are based on ISO (International Standardization Organization). All the equipments required in this laboratory analysis of SSC are listed in Appendix I. SSC (mg L-1) was determined gravimetrically following the standard procedures outlined by Stickland and Parsions (1972). (1) One sample was shaken to make sure the sediment was evenly distributed in the water

before analysing. A 250 mL-graduated cylinder was used to extract 250 mL volume of each sample;

(2) A set of 0.2-μm Nuclepore membrane filters was labelled using a permanent maker on the outer edge of each filter. Then the filters were dried and weighed using a precision balance. Sufficient time was allowed to warm up the balance to operating temperature and was zeroed. The weight (g) of the dried filter was recorded on the Sampling Log Sheet before filtering.

(3) The filter was conditioned using a 350 mL vacuum filtration apparatus. 350 mL organic-free water was pass through each filter and the filters were placed onto the filtration


23

apparatus with the labelled side facing up. 250 mL lake water was poured onto the glass filtration funnel. The cylinder was rinsed using 100 mL non-organic water and pour the water onto the filtration.

(4) Using stainless steel forceps, the filter was removed from the filtration apparatus and placed into a large Petri dish in the oven at 105 ºC for two hours. Then an exsiccator (for storage at dry place) was used to make it cool down for five minutes. After that, the filter was weighed on a high precision balance. In order to decrease the influence of water vapor in the air on the weight of filter to a minimum, the exposure time of the dried filter exposing to air was as short as possible.

Using the recorded parameters in the laboratory analysis, the following formula was used to calculate the in-situ SSC of each water sample:

SSC = (Weight of dried filter after filtering － Weight of dried filter before filtering)

Sample volumn

4.3. Remotely sensed data acquisition and pre-processing

4.3.1. MODIS image acquisition and geometric correction An X-band antenna at School of Remote Sensing Information Engineering (RSIE) in Wuhan University captured MODIS direct-broadcast data. The MODIS data receiving and processing system, from Seaspace Company, USA was used to process the primitive, direct-broadcast data from MODIS sensors of Terra and Aqua satellite. This system automatically produced level 1B data set and all the MODIS images were calibrated and geo-located. The geo-positional accuracy of each file was assessed by overlaying a high-resolution vector file data onto each image. The file map coordinates were adjusted manually. The errors for the geographic locations of each image were within one or two pixels. According to the web site of RSIE5, the data that is received by this system cover the area of which the radius is 5000 km and the centre is at Wuhan. The scope is south to South China Sea, north to Baikal of Russia, east to Pacific Ocean to the east of Japan and west to Tibet and Xinjiang. The MODIS data has been received since February 2002 in RSIE. All the images can be previewed in the web site of RISE and a cloud-free coverage over study area can be selected. Specific data was ordered for time period using a web-based query and ordering system. All the images (Appendix H) obtained from RSIE were converted into IMAGIN img format by MODIS data receiving and processing system from Seaspace Company. Actually the primitive MODIS data obtained from most receiving centres are not calibrated and geo-located, but some software such as ERDAS 8.6 and ENVI 3.4 can also achieve this transform using the corresponding metadata file of each MODIS data (Appendix F). At the mean while,


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the corresponding angle files, such as solar zenith angle, sensor viewing zenith angle, were also obtained.

4.3.2. Resizing the data MODIS data is quite large; an image can cover half of China and takes a lot of disk space. Therefore it is useful to resize the scenes of the area of interest of the raw image data, before applying other calculations. The area chosen extends from 115°to 117°E and 28°to 30°N. Figure 4-4 shows the example of resizing the data of October 18th 2005 using ERDAS 8.6.

Figure 4-4: MODIS Terra 250m data (full image) for October 18th 2005 (Left) and resized image

for October 18th 2005 (Right)

4.3.3. Atmospheric correction Because of the atmospheric path existing between the space platform and the water surface, MODIS images must be adequately corrected before being properly analysed, especially if

5 http://rsgis.wtusm.edu.cn/


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several images will be compared, since there were acquired at different times with different atmospheric conditions. Scattering due to molecules and aerosols in the atmosphere and absorption due to the gaseous constituents of the atmosphere, all affect the illumination of the surface, as well as the surface reflectance information propagated through the atmosphere to the satellite sensor. Atmospheric effects are critical factors, particularly because the water-leaving radiance detected by the remote sensor is very low with respect to the contribution of the atmospheric path radiance (Pietro et al., 2001). Compared with other natural objects, such as soils and vegetation, surface reflectance from lake water is always quite small. Correction was accomplished with the spatial Modeler in ERDAS 8.6; the required model was built up as now explained. Dark object subtraction (DOS) atmospheric correction method (see, e.g., (Gordon and Morel, 1983) was used to get the water-leaving reflectance without in situ field measurements of the atmosphere optical properties. This method is based on the image itself and has been widely used when atmospheric properties are not measured during image acquisition. This approach assumes that the aerosol type and size distribution does not change over the distance from which the dark pixel is selected. This assumption has been demonstrated to be applicable for various studies (D'Sa et al., 2002; Hu et al., 2000). The specific steps are explained as follow: (1) Clear water was assumed to be dark targets (DTs) within the images. The DTs were

detected as the lower bound of the histogram for the uncorrected reflectance of MODIS band1. According to Kaufman (1989), the DT surface reflectance can be assumed as ρ(DT) = 0.045 in the red band.

(2) According to Moran at al. (1992), the general model expression is given by

Pg(λ) = π[L0(λ)-Ld￪(λ)]

τv↑(λ)[E0(λ)cosθzτz↓(λ)+Ed↓(λ)] (a)


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Figure 4-5: Diagram of the solar radiation interactions in the atmosphere (Pietro, 2001)

Where Pg(λ)is the surface reflectance values at the ground;

E0(λ) is the solar spectral irradiance in Wm⁻²μm⁻¹ on a surface perpendicular to the Sun’ rays at the top of the Earth’s atmosphere. E0(λ) includes the term d² which accounts for the effect of the sun-earth distance d at the time of scene acquisition;

L0(λ) is at-satellite radiance measured by the sensor in Wm⁻²μm⁻¹; θz is referred to as the solar zenith angle; Ld￪(λ) is the upwelling atmospheric spectral radiance scattered in the direction of and

at the sensor entrance pupil and within the sensor field of view in Wm⁻²μm⁻¹, also called atmospheric path radiance;

Ed↓(λ) is the downwelling spectral irradiance at the surface due to the scattered solar flux in the atmosphere in Wm⁻²μm⁻¹;

τv↑(λ) is the atmospheric transmittance along the path from the ground surface to the sensor;

τz↓(λ) is the atmospheric transmittance along the path from the Sun to the ground surface.

The values of L0(λ) has been calculated from L0(λ) = E(λ)cosθz Pag(λ)

π

Where Pag(λ) is the apparent surface reflectance from the original images without atmospheric correction. (3) Values of solar irradiance E0(λ) depend on the response function of the sensor filter. Values of E0(λ) for the different sensor were obtained from the website of National Aeronautics and Space Administration (NASA) (Table 4-1). There are two kinds of solar


27

irradiance for the sensor Terra. The value E0(λ) for Terra MODIS band 1 at wavelength of 667 was assumed to be 151.605 Wm⁻² μm⁻¹ in this research.

Wavelength (nm)

412 443 488 531 551 667 678 748 869

E0 172.205 187.509 195.237 185.608 186.532 151.605 147.500 127.936 95.840*E0 170.368 186.503 191.820 185.567 187.159 154.151 128.075 97.297

Table 4-1: Solar irradiance for the sensor of Terra6 (E means Thuilier solar spectrum; *E means Neckel and Labs solar spectrum)

Image-based approach has been proposed by Chavez (1996) for the solution ofτv↑(λ) andτz

↓(λ) representing the atmosphere transmittance for scattering and weak absorption conditions. The cosine technique was used in this research. The cosine of solar zenith angle (θz) was used for theτz↓(λ) and cosine of the sensor viewing angle (θv) was used for theτv↑(λ).

τv↑(λ) = cosθv and τz↓(λ) = cosθz (4) The parameters of Ld￪(λ) and Ed↓(λ) were retrieved from the DTs pixels using all the equations. Finally, the spatial Modeler in ERDAS 8.6 was used to set up a model and all the parameters that have already been obtained were used to get the surface reflectance by the equation (a).

Figure 4-6: MODIS Terra Band 1 data for October 18th 2005 and atmospheric corrected image

using DOS atmospheric correction method Figure 4-6 shows an example of original MODIS Terra band 1 data on October 18th 2005 and the image after atmospheric corrected. All MODIS images used in this research were

6 Source: http://oceancolor.gsfc.nasa.gov/DOCS/RSR_tables.html


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corrected using this atmospheric correction model. Only the input data and output data needed to be changed every time.

4.4. Assessing areas variation with MODIS images Areas of Poyang Lake vary during different season in one year. In this research, time series images were used, so extracting the water bodies of Poyang Lake from other ground objects was very important. Figure 4-7 shows the flowchart of assessing area variation with MODIS images.

Supervised classification(Maximum Likelyhood)

Training pixels of different surface types

Extracting Poyang Area (Eliminating other water bodies not belong to Poyang Lake)

Classified images (water, grassland, bare soil and cloud)

Masking grassland, bare soil and cloud

Corrected MODIS images

Area of Poyang Lake

Pixel number of water bodies in Poyang Lake

Spatial resolution of MODIS images

Figure 4-7: Flowchart of Research Methodology for assessing area

Figure 4-8, Figure 4-9, Figure 4-10 and Figure 4-11 show the example of the following steps. ERDAS 8.6 and ILWIS 3.3 software were used for all the processes. (1) The local map of Poyang Area was obtained as a reference map and the MODIS RGB

map combined by band 1 and band 2 has been presented. Using the local map and


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experience, the pixels representing different surface types can be differentiated and these kinds of pixels can be trained using AOI (Area Of Interest) tool in ERDAS 8.6. Several patterns were identified and the pixels with similar characteristics of different regions were trained. The patterns can be combined into a new pattern to represent the similar pixels.

(2) Supervised classification using ERDAS 8.6. Using Maximum likelihood method to classify the image into three classes: water, grassland, bare soil (including clouds). By setting priorities to each class, each resulting class corresponding a pattern that has been identified can be assigned to a class value. (Figure 4-8)

(3) Subset the image using AOI (Area Of Interest) tool to select the whole Poyang Lake. The region of interest should be not too large or too small. Poyang Lake has been selected and the part far from it has been masked. So the water outside the Poyang Lake (the Yangzte River, Xiu River, Gan River and small water bodies) can also be masked. (Figure4-9)

(4) Only the water bodies need to be considered, and other surface types were masked. The processed image was imported into ILWIS 3.3 and given the water class value of 1 and other classes value of 0. The mask image had all land pixels set as 0 and the water pixel had values of 1. The example of formula is given as follows:

ter20040810_w = if (ter20040810_aoi = 1,1,0) (Figure 4-10) Where, ter20040810_aoi is a raster map that has been classified and the part of Poyang

Lake has been selected. (5) Original pixel values of water pixels were extracted by multiplying the original image of

MODIS interested band with the image obtained from above step to get a new image. This image only had the original values of water part in Poyang Lake. This kind of formula was used in ILWIS 3.3 such as:

ter20040810_water = ter20040810_band_1* ter20040810_w (Figure 4-11) Where, ter20040810_w is a raster map, where land has the value of 0 and water has the

value 1. ter20040810_band_1 is a raster map of MODIS band 1 that was imported from

ERDAS 8.6. For this step the original image of MODIS band 1 and the processed image obtained from step (4) must have the same coordinate system.


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Figure 4-8: Supervised classification Figure 4-9: Subsetting the classified images

Figure 4-10: Extracting water bodies Figure 4-11: Giving the water bodies the

original values When the water body of the lake was extracted from the other surface objects, the area of the water body can be calculated. MODIS image has three spatial resolutions depending on the band: 250 m, 500 m and 1000 m. The pixel information of the processed image can be obtained in the corresponding statistic histogram in ILWIS 3.3. Pixel number of water body and the spatial resolutions of the image can be used to calculate the area of water body. The formula is given as follow:


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the area of water body = pixel number of water body × spatial resolution²*10⁻⁶ (km²)

4.5. Assessing SSC with hyperspectral data The water-leaving reflectance was measured at seven different sample points (Appendix C). Figure 4-13 shows the spectral curves for all seven points; the legend shows the point number in the field survey. In order to make the result more reliable, each measurement was replicated three times. Some inconsistencies and noise were found. The results for each point were averaged to minimize this noise. Figure 4-12 shows the three measurements and the averaged result in NO. 11 sample point.

Figure 4-12: Averaged reflectance of water surface obtained in NO 11 sample point


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Figure 4-13: Reflectance spectra of water surface with a hand-held hyperspectral radiometer

4.6. Assessing SSC with MODIS images Because the image on 17th October 2005 is very cloudy, the image of the next day (10:02 GMT, 18th October 2005, satellite Terra) was substituted in this research. The longitude and latitude of each sampling point was converted into metric coordinates by Lambert Conformal Conic projection (Appendix C). Exploratory linear regression analyses between reflectance of different bands or band ratios (after correction) and SSC obtained from the field were tested. In ILWIS 3.3, a new table of geo-location for the sample points was set up and a point map of sample points was obtained by applying the function “table to point map”. Then the layer of sample point’s map was added into the layer of MODIS image that has been processed. A prerequisite for this step is that the two maps have the same coordinate system. All the sample points were in the corresponding pixels in the image. During the field survey, the rough locations of sample points as reported by the local tour guider were recorded (Appendix B). From the local map of Poyang Area, the sample points were in the right place according to the field survey notes. Some sample points were distributed near the bank of river or small hills in Poyang Lake. Thus the water surface reflectance from the corresponding pixels may be contaminated with other objects. The SSC doesn’t change sharply and it is almost homogeneous for large area. So the reflectance of close pixels can be used in the regression analysis. In ILWIS 3.3, using “pixel information” operation all the surface reflectance of sample points can be obtained. Due to the limited number of water samples, data match-ups were made by manually selecting an appropriate new pixel and recording its MODIS reflectance values. The in-situ SSC and MODIS reflectance values were imported into SYSTAT 11 statistical analysis software7. The in-situ SSC and MODIS reflectance values were corresponded with each other by their serial numbers. The reflectance values from medium-resolution bands and band combinations were tested using linear regression analysis and logarithmic regression analysis. Linear regression was proved to be useful for the study of SSC. A statistical model was set up between in situ SSC and MODIS reflectance values. The SSC maps were produced using this statistical model. The calibrated algorithm was applied for retrieving SSC and spatial distribution of the SSC in Poyang Lake. Finally, in order to make all the SSC maps present in a uniform style and can be compared among them easily; the operation of “slicing” was carried out to classify the image.


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5. Results and discussions

5.1. Area variation of Poyang Lake The MODIS 250 m data of different month from January 2003 to December 2004 were analysed and the area of Poyang Lake during different month were obtained. The pixel information of water body during different month is shown in Appendix G. Figure 5-1 shows the area variation of Poyang Lake of different month in 2003 and 2004. Missing data happen in May 2003 and May 2004. Because May is the raining season in Poyang Area, it is hard to find images without cloud and the quality of most images are bad at that time.

7 SYSTAT is a product from SYSTAT Software Inc. (SSI). It is a statistics and graphics package for technical professionals in the areas of Data Analysis and Modelling.


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Figure 5-1: Area variation of Poyang Lake in 2003 and 2004

From the Figure 5-1 and Figure 5-2 (landscape of Poyang Lake during different season derived from MODIS 250 m data), some characteristics of area variation can be drawn: (1) During 2003 and 2004, the largest area was achieved in June 2003 and reached more than

3300 km². The smallest area was achieved in February 2004 when the area decreased to about 990 km². This is a very large difference, which clearly will have major ecological consequences.

(2) In 2004 the areas of each month were consistently larger than in the corresponding month of 2003 by 5% - 40% approximately.

(3) Area variation in different years has almost the same pattern. During the high-water season (June to August), the area of Poyang Lake can approach 3000 km². During the low-water season (November to the following February), the area of Poyang Lake decreases to around 1000 km². Thus the area of low-water season may change to be less than one third of the area in the high-water season.

(4) Area variation has the similar pattern with the water level variation (Figure 2-4). Both the area and water level reach minimum in February. The relatively higher values are reached from June to September every year. From October to the following February, the water level decreases steadily and a similar pattern is also seen in area variation.

Spring (March 4th 2004) Summer (August 10th 2004)


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Autumn (October 20th 2004) Winter (December 7th 2004)

Figure 5-2: Landscapes of Poyang Lake during different season in 2004 derived from MODIS

250 m data

5.2. Statistical relation of SSC with hyperspectral data

5.2.1. Spectral characteristics of water with high SSC


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The components of suspended sediment are various in different regions. The size and shape of the sediment, colour and component of the mineral all affect the reflectance spectrum. The spectra features associated with SSC in Poyang Lake were concluded as follow (Figure 4-13): (1) There is a fixed relationship between SSC and reflectance from 480- to 720- nm.

Prominent reflectance maximum is at between 560- and 610- nm due to minimum in absorption and mainly scattering. It is near the yellow band.

(2) The reflectance of water surface increases when the SSC goes up. The reflectance gets close to a maximum when the SSC increases. The extents of increasing vary at different wavelength. Low extent happens between 400- and 500- nm. Sharp increase happens between 580- and 710- nm. The wavelength where the extent of increasing is highest coincides with the wavelength having the highest reflectance.

5.2.2. Linear regression between reflectance and SSC Linear regression analyses between the measured SSC and spectra at different wavelength were carried out. These should be used with caution, as only seven points were available to calibrate the relation. Figure 5-3 is an example of this analysis; it shows the relationship between SSC and surface reflectance at 665 nm (red band). The relation clearly is positive but there is an anomaly near 0.18 surface reflectance that is difficult to explain. With the small number of calibration point it’s not possible to investigate possible non-linear relations or conduct a proper regression diagnostic, so this linear relation seems satisfactory under the circumstances. The Figure 5-4 shows the regression coefficients of determination (multiple R²) at different wavelengths. From 675- to 710- nm, there are very good linear relationships (R²above 0.8) between SSC and reflectance. The regression coefficients (R²) at red region approximately range from 0.78 to 0.82.


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Figure 5-3: The relationship between SSC and surface reflectance at 665 nm (Red band)

Figure 5-4: Regression coefficients (R2) between SSC and reflectance (Sensor: Spectrometer)

These coefficients are affected by the number of measurements and by the origin of in-situ SSC. The constituents (chlorophyll, yellow substance) in other study area may also lead to different results. In this research, just seven points have been measured. Most of the sample points have high SSC (300-520 mg L-1), and the result of coefficients has regional limitation.

5.3. Statistical analysis of SSC with MODIS image

The in-situ SSC ranges from 15.6- to 518.8- mg L-1. Figure 5-5 is a histogram of the density of in situ SSC variable. Since the points were not taken at random, this is not intended as a frequency distribution of the SSC population over the whole lake. However, this data can be


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used to setting up a linear statistical model since there is a good spread of data values and they were taken independently.

Figure 5-5: The distribution of in-situ measurement of SSC In order to identify the most suitable medium-resolution band or band ratios for our purpose, their correlation for SSC was tested. Considering the characteristics of each medium-spatial resolution band, band 1, band 3, band 4 or their ratios were tested and linear regression analyses were used. Table 5-1 shows the results of the analysis. The regression analysis indicates the similar result with the analysis using hyperspectral data. The highest correlation (R2 = 0.81) occurs in the regression analysis between band 1 and SSC (Figure 5-6). It appears that the relationship between SSC and atmospherically corrected MODIS band1 data is significant (F (1,29) = 127; p<0.001) in a statistical sense; this calibrated relation was used for all further analyses. The MODIS band 1 data is at the wavelength of 620-670 nm (red band) and the correlation for in-situ SSC is 0.81. The correlation between the reflectance (at the wavelength of 620-670 nm) measured by the spectrometer and in-situ SSC ranges from 0.75 to 0.81. The reflectance from MODIS band 1 data and spectrometer measurements in the field had the same result that the relationship between the reflectance from the red band and SSC is significant. At the mean while, the MODIS band 3 (blue band), band 4 (green band) and spectrometer measurements indicated that the blue band and green band had lower correlation with SSC compared with red band.

Band or band ratios R2 Equation8

8 Y means SSC; x means the reflectance of each band or band ratios.


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Band1 0.81 Y=7388x-46

Band3 0.64 Y=16708x-220

Band4 0.58 Y=9145x-19

Band1/Band3 0.73 Y=1227x-792

Band1/Band4 0.49 Y=1077x-721 Table 5-1: Summary of the regression analysis between band or band ratios and SSC. In all

cases n = 31

Figure 5-6: The relationship between MODIS band 1 and SSC

However, Figure 5-6 shows some difficulties with this analysis. First, there is a large spread of SSC at the same reflectance throughout the range. For example, at 0.06 reflectance SSC varied from 300 to 500, almost a twofold range. This does not invalidate the overall conclusion but does show that analysis of individual pixels is problematic.

Figure 5-7: Plot of residuals against predicted values Figure 5-7 shows the plots of residuals against predicted values in regression analysis between MODIS band 1 and SSC. The high scatter of values around the estimated values makes the derivation of a correction function from such a relationship unreliable. We can


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assume that the possibility of autocorrelation is low and the independence hypnosis is valid. The spread noted above is also seen in the residuals, as is to be expected.

Figure 5-8: The relationship between MODIS band 1 and SDD

Linear and logarithmic regression analyses were tested between MODIS Band 1 and SSD. Logarithmic regression analysis (Figure 5-8) was proved to have a better correlation (Linear regression analyses R2=0.31; logarithmic regression analyses R2=0.53). The relationship was significant but the regression coefficient of determination (R²=0.53) was not very high. SSD is a controversial parameter: on the one hand, it is easy to measure; on the other hand, turbidity is a rather loose term – it is a lumped consequence of the inorganic and organic materials presence in the water column (Ulanbek, 2003). So there was not much attention paid to this parameter.

5.4. Analysis and interpretation of SSC variation The historical images were analysed using the algorithm calibrated from the field data. The situation in Poyang Nature Reserve was different from the main part of Poyang Lake. So this sub-chapter was divided into two parts to explain them separately by some SSC maps. More SSC maps are presented in Figure 5-9.

February 6th 2003 (1) February 28th 2004 (2)


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April 16th 2003 (3) April 2nd 2004 (4)

June 30th 2003 (5) June 30th 2004 (6)


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September 4th 2003 (7) September 16th 2004 (8)

November 30th 2003 (9) November 28th 2004 (10)


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Figure 5-9: SSC maps of Poyang Lake (Sources MODIS images)

5.4.1. SSC variation in the main part of Poyang Lake From the SSC maps of different times (Figure 5-9), higher SSC appears near the mouths of inland rivers, the connected area between Poyang Lake and the Yangtze River. The distribution of suspended sediment agrees with the fact that inland rivers and Yangtze River bring much sediment into Poyang Lake. From the north to south, SSC decreases. It is clearly shown in the maps that the SSC is extremely high in the middle of Poyang Lake at the high-water season, which agrees with the fact that there are lots of sand hills and many ships go there to exploit sand at that time. Since at the high-water season, Poyang Lake appears to be as a whole and the ship can navigate conveniently. From local information it is known that, the water from Yangtze River flowed into Poyang Lake from July 20th to July 27th 2004. The MODIS images of that time were processed and the SSC was retrieved using the calibrated algorithm. The Figure 5-10 shows the pattern of suspended sediment coming from Yangtze.

July 21st 2004 July 25th 2004 July 27th 2004


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Figure 5-10: SSC maps of the water coming from Yangtze in July 2004

These maps show that when the water goes from Yangtze Rive into Poyang Lake, the SSC is extremely high and the suspended sediment backs up into the middle part of the Lake (See the middle map). Figure 5-10 illustrates the sedimentation pattern clearly. High spatial variability is observed within each image as well as significant differences in the horizontal distribution of suspended sediment between the several days. This result agrees with the fact that the SSC is very high in the Yangtze River and it brings much sediment into Poyang Lake during the flood.

5.4.2. SSC variation in Poyang Lake Nature Reserve Poyang Lake Nature Reserve is situated in the west of Poyang Area, shown by the red square frame in the figure below. Figure 5-11 and Figure 5-12 are the SSC maps of Poyang Area in 2004.


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Figure 5-11: SSC map of Poyang Area in September 30th 2004, with detail of Dahuchi Lake in

Poyang Lake Nature Reserve

Figure 5-12: SSC map of Poyang Area in November 28th 2004, with detail of Dahuchi Lake in

Poyang Lake Nature Reserve Every year in the winter, many swans, geese and ducks migrate to Poyang Nature Reserve for breeding. The grassland and the lake provide them abundant food. The migrants resuspend the sediment in the bottom of the lake. In Figure 5-11 (the SSC map of September 30th 2004), the SSC in Poyang Nature Reserve is below 100 mg L-1. The migrants always come to Poyang Nature Reserve at November every year. In September, the number of birds is very low. Figure 5-12 shows the SSC distribution in winter 2004. The SSC is very high (around 200- to 400- mg L-1) in Poyang Nature Reserve. This is in contrast the situation in most of Poyang Lake and the SSC is very low (below 100 mg L-1). The number of birds observed by the Poyang Nature Reserve station at that time reaches up to 15666. Food resources that are, in turn, affected by water quality and quantity in the water affect the population and distribution


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of water birds within PNR. The inference is that the high SSC found mostly in the Nature Reserve is caused by the bottom-feeding birds; this can be checked by future fieldwork. More SSC maps show the same result that from April to November, the SSC in Poyang Nature Reserve is very low (below 100 mg L-1); from November to next March, the SSC is very high. The SSC seems to be seriously affected by the swans in Poyang Nature Reserve. The swans eat aquatic vegetation, fish eggs, fish, small aquatic insects and sometimes clams in the lake. They suspend the sediment on the bottom of the lake and their long necks allow them to forage and pick food from shallow water. Whether there is a relationship between SSC and the number of swans can be proved by more field survey on swans. Whether the distribution of swans can be mapped through SSC using remote sensing can be investigated in the future research.

5.5. Comparison of MODIS 250m and 500m data A comparison of moderate spatial resolution MODIS 250- and 500- m is shown in Figure 5-13 and Figure 5-14. As expected, the 250 m data provide more detail in the water features of Poyang Lake and more detail in the distribution of suspended sediment; containing more pixels in small areas. The differences of water features between 250- and 500-m data are not very obvious during the high-water season (Figure 5-13), while sharp differences can be observed during the low-water season (Figure 5-14).

October 18th 2005 250 m (Area: 2175km2) October 18th 2005 500 m (Area: 2319 km2)

Figure 5-13: Comparison of MODIS 250 m and 500 m data in the high-water season


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December 7th 2004 250 m (Area: 1158 km2)

December 7th 2004 500 m (Area: 834 km2))

Figure 5-14: Comparison of MODIS 250 m and 500 m data at low-water season

A simple difference was carried out to compare 250 m and 500 m data in lake area estimation (Table 5-2). More details are presented by MODIS 250 m data, but were not discriminated by


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500 m data since they were too small for the spatial resolution. It illustrates that in the high-water season, the ratio of area (500 m) to area (250 m) (1.02; 1.07) is more than one and the lower spatial resolution data (500 m) tend to overestimate lake area when the water cover is relatively large; in the low-water season, the ratio is less than one (0.72) and the lower spatial resolution data (500 m) tend to underestimate lake area for sparsely water area. The relative difference between area (250 m) and area (500 m) estimation in the low-water season is larger (28%) than it in the high-water season and mean-water season (<10%). It is shown that the area estimation in the low-water season is more sensitive to the varying spatial resolution than in the other seasons. This study demonstrates that the lake area estimation in the low-water season is indeed influenced by the spatial resolution and landscape characteristics such as the small water size, discontinuous water bodies.

Date of MODIS image Area (250 m)9

(km2) Area (500 m)10

(km2) Ratio=Area (500 m)/

Area (250 m) Relative

difference2004-8-10

(High-water season) 2500 2558 1.02 2%

2005-10-8 (Mean-water season)

2175 2319 1.07 7%

2004-12-7 (Low-water season)

1158 834 0.72 28%

Table 5-2: Comparison of MODIS 250 m and 500 m data in lake area estimation

5.6. Error analysis Human and physical factors may cause some effects on the results of this research. Some effects are unavoidable, and some effects can be considered and relieved in the future research.

5.6.1. Error analysis of assessing area variation The errors of assessing area are mainly related to the accuracy of image classification. The errors of training pixels of different surface types may be made by human’s wrong recognition. But there are still several shortcomings need to be improved: (1) All the images contain a combination of pure and mixed pixels. Mixed pixels may be due

to the presence of sub-pixel objects, such as the bank of the rivers and the lake, some grasslands and small hills in the lake, small water bodies in the marshlands. The mixed

9 Area (250 m) means the lake area assessed by MODIS 250 m spatial resolution data. 10 Area (500 m) means the lake area assessed by MODIS 500 m spatial resolution data.


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pixels happen along the contacted area between two spectrally different land cover types (Cracknell, 1998; Fisher, 1997). The mixed pixel should be assigned into several land cover types in proportion of the specific classes.

(2) The estimation of land cover area from MODIS data is associated with a systematic bias due to spatial aggregation effects. The magnitude of this error depends on the spatial resolution, the initial proportions of the landscape in the different land covers, and the spatial arrangement of the land covers at the fine resolution (Turner et al., 1989).

(3) In this research, the 250 m spatial resolution MODIS images have been used. During the high-water season, Poyang Lake appears as one water body and almost all parts of the lake connect as a whole. It covers a very large area (about 3000 km²) and 250 m spatial resolution is quite suitable to assessing the area at the high-water season. But in the low-water season, Poyang Lake is consisted with hundreds of small water bodies. Some water bodies are so small that they cannot be classified as water and mixed pixels occur with relatively high probability. The area of the lake may be very sensitive to the spatial resolution of satellite images and the accuracy of assessing the area at the low-water season may be seriously affected by the spatial resolution of satellite images.

5.6.2. Error analysis of assessing SSC The errors of assessing SSC mainly originate from two sources: field survey and MODIS image processing. The specific errors are presented as follows: (1) The water in the lake is dynamic in time and space. The water is moving all the time and

the suspended sediments are not stable. It is difficult to correspond the in-situ measurement of SSC with satellite images acquisition so exactly, especially since in this study there was a one-day lag between field and image.

(2) During the field survey, Secchi disk depths (SDD) were measurement on a small motorboat. The boat attempted to stop at each sample point, but the water was always moving which made the boat vibrating. Therefore, the SDDs were not so accurate due to the disturbed water.

(3) In the laboratory analysis of water samples, there may have been some loss of sediment when the water was poured into different containers. And the effects originated from the vapor in the air cannot be negligible. The errors should be minimized.

(4) Although only the SSC has been investigated in this research, the presence of other constituents, such as chlorophyll and yellow substance, also affects the reflectance spectra.

(5) The images may be not have been correctly converted to absolute surface reflectance. The DOS atmospheric correction method has been applied in this research, but the atmospheric correction algorithm has been simplified. Many factors that have been neglected can affect the water surface reflectance, including: fluctuation of water surface, wind, waves, and the bottom of the lake.


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6. Conclusion and recommendation

The main objective of the study was monitoring the area variation and sedimentation patterns using optical remote sensing in Poyang Lake. Methods were developed and tested to quantify the area and SSC. Quantification of the area has been achieved by using an MLM classification method to extract water bodies; quantification of SSC has been achieved by using a systematic approach through the establishment of the empirical relationship between the reflectance of MODIS data and in-situ SSC. For the investigation of the possibilities of area and SSC monitoring using optical remote sensing, the characteristics of the MODIS Terra instrument provide data well suited for the study of dynamics suspended matter, and the MODIS data were found to be suitable to a limited extent for monitoring of area variation. Detailed conclusions and recommendations are given in the following sections.

6.1. Conclusions on monitoring area variation The landscapes of Poyang Lake during different seasons and the pattern of the changes in lake size have been obtained successfully by suitable classification method. The lake area variation during different seasons exhibited that a high degree of variability in water level was associated with a high level of lake size variation. A simple comparison of MODIS 250 m and 500 m data in lake area estimation was carried out. Compared with the lake area assessed by 250 m data, the lower spatial resolution data (500 m) tend to overestimate the lake area in high-water season and underestimate the lake area in low-water season. It is shown that the area estimation in the low-water season is more sensitive to the varying spatial resolution than in the other seasons. This analysis demonstrates that the lake area estimation in the low-water season is indeed influenced by the spatial resolution and landscape characteristics such as the small water size, discontinuous water bodies. This conclusion found in this research is similar to the ones found by the former researches. The current interest in regional scale phenomena requires data from sensors with coarse spatial resolution to meet both the demand for high temporal resolutions and the need for the demand for data sets of manageable size (Mary and Curtis, 1997). This study indicated that the MODIS medium spatial resolution data with high temporal frequency, coarse spatial


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resolution and free charge was potential for the study of area estimation in seasonal lakes. The MODIS data was useful for mapping the spatial patterns of the landscape of seasonal lakes. Using simple processing procedures and suitable land cover classification method, the water bodies can be extracted and the size of the water bodies can be calculated by the pixel information and spatial resolution. MODIS data can potentially be used in a variety of researches in land cover area estimate, which is especially useful for monitoring agricultural lands.

6.2. Recommendations and future research on monitoring area variation

Some recommendations offered to the future research with this topic are the following: (1) For mixed pixels, high spatial resolution remote sensing data can assist and perform a

field survey on the land covers. The fraction values of the land covers can be assigned to sub-pixel mapping algorithm, based on the assumption of the spatial dependence and application of linear optimisation technique (Jan and Robert, 2002). This study can get more precise result of the area.

(2) Use some high spatial resolution images to assess the area during the low-water season. High-resolution data is recommended to help resolve potential uncertainties in coarser resolution data. The effects of spatial resolution on the area estimation during the low-water season need a further investigation in the future research.

(3) Get more data on the water levels and the corresponding water area. This study can make it clear whether there is a consistent relationship between water levels and the size of the lake and prove that optical multi-spectra sensors can be used to reach the water levels.

(4) Using the water level data and multi-temporal water patterns, water digital elevation model (WDEM) can be set up. This future study can help us to know the distribution of the flooding hazard through the water levels during the flooding season in Poyang Lake.

6.3. Conclusions on monitoring SSC The field survey was conducted in Poyang Lake Basin, from 14 to 17 October 2005, to measure the suspended sediment concentration as well as remote sensing reflectance. The aims were to study the characteristics of Poyang Lake, to help interpret MODIS data, and to evaluate if and how MODIS medium-resolution data can be used for water monitoring. The statistical approach based on the reflectance of MODIS band 1 for the estimation of SSC was found to be sufficiently accurate. Several bands or band ratios of MODIS medium resolution (250- and 500-m) data were tested to assess the SSC in Poyang Lake using a simple


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regression method, aided by limited, yet concurrent field data. The reflectance of MODIS band 1 (red band) showed the highest correlation with in-situ SSC and a strong linear relationship (R2=0.81; n=31) was established. The regression equation found in this research is similar to the ones found by other researches. The statistical analysis based on the field spectroscopy and instantaneous water sampling produced the similar results and produced more consistent regression equations than the statistical approach based on the image spectra. Because there was a time interval between water sampling and satellite overpass and precision of the atmospheric correction also affected the results. The statistical analyses based on the field spectroscopy and remote sensing data draw the same conclusion that the red band had sufficient sensitivity for suspended sediment. The atmospheric correction scheme also appears to be effective for the wide range of sky condition that occurred during the field survey. The historical images were analysed using the algorithm calibrated from the field data. The temporal variability of SSC in Poyang Lake can be shown by the time serial SSC maps obtained from MODIS data. It is shown that higher SSC appears near the mouths of inland rivers, some sand hills in Poyang Lake, the connected area between Poyang Lake and the Yangtze River. The sedimentation pattern from the Yangtze River into Poyang Lake in 2004 was clarified and high SSC from the Yangtze can be observed distinctly. The sediment from the Yangtze penetrated into the middle part of Poyang Lake. Through the historical analysis of SSC maps, the causes of SSC variation were tried to interpret. In the main part of Poyang Lake, the main reasons are the phenomenon that the sediment comes from the Yangtze River during the flood season, the human activity of exploiting sand. In Poyang Nature Reserve, the bottom-feeding swans cause high SSC in winter and this can be checked by future fieldwork. Mapping the suspended sediment distribution is critical to many scientific studies and the data are always required involving expensive and laborious field survey. Although previous studies have shown a direct relationship between SSC and remotely sensed data, the spatial resolution or frequency of remotely sensed data was inadequate to fully examine the dynamics of most inland or coastal water bodies. This study demonstrated that the MODIS medium-resolution bands have sufficient sensitivity for suspended sediment work in dynamic inland waters. The MODIS 250 m data was useful for mapping small-scale features of suspended sediment in different inland water. Using moderate processing procedures and readily available software, the rapid acquisition, processing, and analysis of MODIS data were possible. The near daily revisit period of the MODIS instrument enabled an analysis of short term, yet significant, changes in the horizontal distribution of the sediment of the Poyang Lake. This approach can be applied to other coastal or inland regions but the specific relationship between MODIS reflectance and


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SSC may vary because of the optical characteristic of suspended particulars such as sediment type, partial size and mineral types. This methodology used here may be applied to monitor other water quality parameters, such as chlorophyll concentration, CDOM coefficient, based on the regression between sensor-derived remote sensing reflectance and in-situ measured, water parameters. Due to the sensor characteristics and calibration, this method may be transferred to other sensors (e.g. SeaWiFS) due to the different study area and different research aims.

6.4. Recommendations and future research on monitoring SSC Improving the accuracy of SSC prediction is critical and some recommendations offered to the future research with this topic are the following: (1) Measure the transparency with a more accurate Secchi Disk Depth (SDD). These

measurements can confirm whether there is a relationship between transparency and reflectance from an optical multi-spectral sensor. It can prove whether SDD can be used in monitoring changes in this lake.

(2) Perform the measurements of chlorophyll and chromophoric dissolved organic material (CDOM), which are also visible parameters in the water samples. Future studies will prove if these parameters can be used in a monitoring plan.

(3) Repeat the measurements of SSC during the low-water season. This study can help us to understand the accuracy of this empirical approach during different seasons.

(4) Perform more measurements of SSC during the field survey. Some water samples can be collected near or in the Yangtze River with higher SSC and some water samples in the reservoirs with clear water can be collected. These can help us to understand the consistency of the relationship over a more wide range.

(5) Take it into account that different suspended sediment types will be contained in the water at various times of the year. These different types, grain sizes, shapes and compositions may have distinct spectral signatures, so as to change the transfer function to be modelled.

(6) Perform a field survey on the number of swans during the winter that can prove whether there is a relationship between SSC and swans numbers in PNR. This study will make it clear whether SSC can be used to monitor the number of swans through the optical multi-spectra sensor.

(7) Remove the factors of bottom reflectance, so as thin cloud detection and spectrum remove. The data of wind can be reached from the weather station and the water surface fluctuation can be considered. It can improve the accuracy of the SSC calculation.

(8) Deploy a few buoys strategically mounted with instruments to monitor SSC continuously at representative locations. These spatially sparse yet continuous data may be used to fine-tune the concurrent MODIS measurement effectively.


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(9) The effects of the Three Gorge Dam on the hydrology and ecology of the lake system are not clear at present, so a research and long-term monitoring can be carried out at Poyang Lake Basin to assess impacts.


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57

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58

Appendix A: Description of Hukou hydrological station

Hukou hydrological station has been set up since October 1956. It is located in Jiujiang City, Jiangxi Province, at the connected area between the Yangtze River and Poyang Lake, at longitude116º13′25′′ east, latitude 29º45′34′′ north. The measurements in this station are based on Wusong Base Level. The warning water level is 19.0m. Water level, water discharge, precipitation, suspended sediment concentration, and water temperature has been recorded in this station. The location of Hukou hydrological station is shown by the red triangle in Figure A-1.

Figure A-1: The location of Hukou hydrological station

MO

NIT

OR

ING

AR

EA

VA

RIA

TIO

N A

ND

SE

DIM

EN

TATI

ON

PA

TTE

RN

S IN

PO

YAN

G L

AK

E, C

HIN

A U

SIN

G M

OD

IS M

ED

IUM

-RE

SO

LUTI

ON

BA

ND

S

59

A

ppen

dix

B: F

ield

sur

vey

Tab

le B

-1: F

ield

surv

ey fo

r th

e sa

mpl

es ta

ken

in P

oyan

g L

ake

NO

Secc

hi D

isk

Dep

th (m

) Te

mpe

ratu

re

Dat

e Ti

me

Posi

tion

Not

e

1 0.

20

25°C

14-O

ct-0

5 11

:15

AM

Con

nect

ed a

rea

betw

een

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er a

nd X

iu R

iver

2 0.

40

25°C

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ct-0

5 3:

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M

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ng H

u C

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21°C

16-O

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M

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5 7:

43 A

M

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g La

ke

12

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56 A

M

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Tab

le B

-1 (C

ontin

ued)

MO

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OR

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AR

EA

VA

RIA

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N A

ND

SE

DIM

EN

TATI

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60

17

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5 10

:18

AM

The

cent

ral p

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f Poy

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18

0.10

21°C

17-O

ct-0

5 10

:41

AM

The

cent

ral p

art o

f Poy

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Lake

19

0.16

21°C

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5 10

:58

AM

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cent

ral p

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f Poy

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20

0.21

21°C

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ct-0

5 11

:27

AM

The

cent

ral p

art o

f Poy

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21

0.20

22°C

17-O

ct-0

5 11

:45

AM

The

cent

ral p

art o

f Poy

ang

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22

0.15

21°C

17-O

ct-0

5 12

:10

PMTh

e ce

ntra

l par

t of P

oyan

g La

ke

23

0.15

21°C

17-O

ct-0

5 12

:25

PMTh

e ce

ntra

l par

t of P

oyan

g La

ke

24

0.25

22°C

17-O

ct-0

5 1:

13 P

M

The

East

ern

part

of P

oyan

g La

ke

25

0.15

22°C

17-O

ct-0

5 1:

30 P

M

The

East

ern

part

of P

oyan

g La

ke

26

0.35

22°C

17-O

ct-0

5 1:

41 P

M

The

East

ern

part

of P

oyan

g La

ke

27

0.40

22°C

17-O

ct-0

5 2:

07 P

M

The

East

ern

part

of P

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g La

ke

28

0.30

22°C

17-O

ct-0

5 4:

25 P

M

The

cent

ral p

art o

f Poy

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29

0.22

22°C

17-O

ct-0

5 4:

48 P

M

The

cent

ral p

art o

f Poy

ang

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30

0.15

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17-O

ct-0

5 5:

15 P

M

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cent

ral p

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f Poy

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31

0.12

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17-O

ct-0

5 5:

25 P

M

The

cent

ral p

art o

f Poy

ang

Lake


61

Appendix C: Geographical coordinates of sample points

Project type: Lambert Conformal Conic Project Ellipsoid: Krassovsky False easting 5000000.00 False northing 5000000.00 Latitude of projection original: 36º00.00´ Longitude of projection original: 115º00.00´ Latitude of standard parallel Ⅰ: 25°00.00´ Latitude of standard parallel Ⅱ: 47°00.00´

Table C-1: Geographical coordinates of each sample points NO Latitude (N) Longitude (E) X Y 1 29º11.654´ 116º00.689´ 6056800.00 4317306.70 2 29º11.752´ 116º00.725´ 6053383.70 4315163.60 3 29º11.260´ 116º00.886´ 6057082.50 4315836.10 4 29º09.713´ 116º00.512´ 6056948.00 4313751.30 5 29º08.370´ 115º59.393´ 6055443.30 4311109.10 6 29º08.151´ 115º56.803´ 6051297.00 4310726.30 7 29º06.454´ 115º56.614´ 6051245.70 4308007.30 8 29º12.628´ 116º01.111´ 6057181.80 4319961.80 9 29º13.076´ 116º01.096´ 6055535.70 4322426.90

10 29º14.425´ 115º59.875´ 6053652.70 4325991.30 11 29º16.452´ 115º59.465´ 6058642.40 4330053.50 12 29º17.847´ 116º02.775´ 6058863.40 4328926.10 13 29º16.635´ 116º03.537´ 6060327.70 4326865.60 14 29º15.628´ 116º04.348´ 6061827.80 4325186.20 15 29º14.814´ 116º04.532´ 6062290.30 4323742.60 16 29º14.337´ 116º05.309´ 6063626.50 4323019.10 17 29º13.921´ 116º06.649´ 6065441.20 4324276.00 18 29º13.741´ 116º07.537´ 6065846.30 4322509.60 19 29º13.578´ 116º08.325´ 6068586.00 4322194.90 20 29º13.425´ 116º08.908´ 6069546.10 4322024.50 21 29º13.110´ 116º09.850´´ 6071111.90 4321626.30 22 29º13.779´ 116º10.523´ 6073236.80 4321276.80 23 29º12.786´ 116º11.142´ 6072042.50 4322964.50 24 29º12.506´ 116º14.066´ 6077951.10 4321309.10 25 29º11.749´ 116º16.033´ 6081243.30 4320301.30 26 29º11.428´ 116º18.438´ 6085140.10 4320166.90

Table C-1 (Continued)

27 29º11.548´ 116º20.860´ 6088969.80 4320838.80 28 29º13.779´ 116º10.523´ 6064951.10 4319333.20


62

29 29º14.814´ 116º04.524´ 6062059.20 4317651.90 30 29º12.305´ 116º05.614´ 6065790.10 4315073.20 31 29º11.992´ 116º04.926´ 6061924.70 4313280.50

MO

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63

App

endi

x D

: Lab

orat

ory

anal

ysis

of w

ater

sam

ples

T

able

D-1

: Lab

orat

ory

anal

ysis

res

ults

of w

ater

sam

ples

NO

W

eigh

t of f

ilter

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per b

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ied

(g)

Wei

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men

t (g)

Susp

ende

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Con

cent

ratio

n (m

g L-

1 )

1 1.

9925

1.

8634

1.

9000

0.

0366

14

6.4

2 2.

0326

1.

9135

1.

9177

0.

0042

16

.8

3 2.

0477

1.

9186

1.

9567

0.

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15

2.4

4 2.

0031

1.

8740

1.

9196

0.

0456

18

2.4

5 2.

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1.

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.8

6 2.

0478

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9187

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50

6.6

7 2.

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2.

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8.9

8 2.

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9174

1.

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0.

0565

22

6.0

9 2.

0491

1.

9200

1.

9489

0.

0289

11

5.6

10

2.03

91

1.92

25

1.96

83

0.04

58

183.

2 11

2.

0527

1.

9236

2.

0047

0.

0811

32

4.4

12

2.04

89

1.91

98

2.03

19

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21

448.

4 13

2.

0443

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9152

2.

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36

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45

2.01

97

0.09

52

380.

8 15

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12

2.03

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0.11

31

452.

4

Ta

ble

D-1

(Con

tinue

d)

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17

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1.91

67

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76

70.4

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1.

9319

2.

0474

0.

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46

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19

2.05

73

1.92

82

2.03

13

0.10

31

412.

4 20

2.

0586

1.

9295

2.

0097

0.

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0.8

21

2.04

89

1.91

98

2.06

01

0.14

03

561.

2 22

2.

0517

1.

9226

2.

0334

0.

1108

44

3.2

23

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47

1.92

56

1.98

64

0.06

08

243.

2 24

2.

0479

1.

9188

1.

9579

0.

0391

15

6.4

25

2.03

86

1.91

95

1.93

31

0.01

36

54.4

26

2.

0485

1.

9194

1.

9295

0.

0101

40

.4

27

2.04

69

1.91

78

1.92

19

0.00

41

16.4

28

2.

0504

1.

9213

1.

9756

0.

0543

21

7.2

29

2.04

84

1.91

93

1.96

41

0.04

48

179.

2 30

2.

0512

1.

9221

2.

0453

0.

1232

49

2.8

31

2.04

03

1.91

12

2.04

03

0.12

91

516.

4 A

vera

ge

2.04

56

1.91

76

1.98

64

0.06

90

276.

0 M

ax

2.06

10

1.93

19

2.10

02

0.14

03

561.

2 M

in

1.99

25

1.86

34

1.90

00

0.00

41

16.4


65

Appendix E: Specification of spectrometer

Spectrometer GER 150011

Table E-1: Specification of spectrometer

Spectral Range 350 nm to 1050 nm

Internal Memory 500 scans

Channels 512

Linear Array (1) 512 Si

Bandwidth (nominal) 1.5 nm

Scan Time 5 ms and up (selectable)

FOV 4° to 23° options with fiber optic

Head Size 8.3 cm x 15.2 cm x 19.7 cm 3.25" x 6" x 7.75"

Weight 2 kg, 4.5 lbs.

Battery Type 6 Volt NiMH

Battery Life 4 hours

Digitization 16 bit

Wavelength Repeatability +0.1 nm

Noise Equivalent Radiance

0.5 s integration time 400 nm: 6.0 x 10-10W · cm-2 ·nm-1· sr-1 700 nm: 5.7 x 10-10W · cm-2 ·nm-1· sr-1 900 nm: 1.7 x 10-9 W · cm-2 ·nm-1 · sr-

1

Maximum Radiance Levels12 ms integration time 700 nm: 1.5 x 10-4W · cm-2 ·nm-1· sr-1

Radiometric Calibration (Traceable to NIST)

400nm : ± 5% 700nm : ± 4% 1000nm : ± 5%

Dark Current Correction automatic

Spectrum Averaging selectable

Humidity to 90% RH (non-condensing)

Temperature -10° to 50°C

Sighting Laser

11 Source: http://www.ger.com/1500.html


66

Appendix F: Importing MODIS Level 1B data into IP software packages12

The MODIS Level 1B map product now includes ER Mapper (.ers) and ENVI (.hdr) headers with each band. ERDAS Imagine version 8.6 and above now have the capacity to directly read MODIS data by selecting ER Mapper (*.ers) from the Files of type drop down menu on the Select Layer To Add dialog window. Please note that the direct read method only partially geo-codes the image. In order to correctly geo-code the image please follow the procedure outlined below. Note: Registration co-ordinates in metadata refer to the top left corner of the pixel. I. ENVI Step 1. Create a new projection On ENVI main menu, click on Map and open Customise Map Projection window. Create a new projection called China-Lambert Conformal Conic (LCC) projection with the following parameters.

Latitude of projection origin = 36 N Longitude of Central meridian = 115 E Latitude of Standard Parallel 1 = 25 N Latitude of Standard Parallel 2 = 47 N Datum = WGS84

Select File/Save Projections… and save this projection information to Map_Proj.txt. Note: the projection parameters are different according to different images. These parameters can be obtained from the metadata file with each band. Step 2: Import MODIS data into ENVI Click on File from main menu, open image file and choose input file, enter number of samples (columns) and lines (rows) - from metadata file, bands (always 1), pick data type as integer, set byte order to Network IEEE and press OK. Step 3. Georeference MODIS image

12 Source: http://www.ga.gov.au/acres/prod_ser/importmodis.jsp The author calibrated some projection parameters, which were used in this research.


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Click on File from main menu and open the Edit ENVI header window, select input file and press OK. On the next screen, click on Edit Attribute button and choose Map Info option from pull down menu and enter image coordinate, pixel size. Change projection to LCC, enter latitude and longitude for the given image coordinate and press OK. Georeferencing of the MODIS image is now complete. II. ERDAS - Imagine Step 1. Create a new projection

From Main Menu, select Tools/Coordinate Calculator to convert Latitude and Longitude values for upper left corner in metadata file to Lambert Conformal Conic Projection (metres). In the Coordinate Calculator window, select Projection/Set Input Projection and Units. From the Input Projection and Units setup window, click Set Input Projection. In the Projection Chooser window, select Custom page and Geographic (Lat/Lon) from the Projection Type drop down list and WGS84 from the Spheroid Name drop down list. Then click OK, OK.

Select Projection/Set Output Projection and Units. From the Output Projection and Units setup window, click Set Output Projection. In the Projection Chooser window, select Custom page and Lamberts Conformal Conic from the Projection Type drop down list and WGS84 from the Spheroid Name drop down list. Enter the Latitude of the first standard parallel as 25 N, the Latitude of the second standard parallel as 47 N, the Longitude of the central meridian as 115 E and the Latitude of origin of projection as 36 N. Leave the False easting at central meridian and False northing at origin at 0.0m. To save projection information select Save, enter new projection name and click OK. Press OK in the Output Projection and units Setup dialog box.

Input the Latitude and Longitude of the top left corner of the image, given in the metadata file, to the Input Latitude and Input Longitude columns of the Coordinate Calculator window. The coordinate calculator will automatically convert the geographic coordinates to Lambert Conformal Conic projection in metres. Leave the Coordinate Calculator window open to enable entry of top left coordinates to Layer info in following steps. Step 2: Import MODIS data into Imagine Click on Import from main menu. In the Import/Export window, select generic binary from the type drop down menu and select appropriate media. Select one image file .img from Input file drop down list. Select an appropriate output from the Output file drop down menu. From the Import Generic Binary Window, select BSQ from the Data Format drop down menu and Signed 16 bit from the Data Type drop down menu. Tick the Swap bytes option, and fill the rows and columns fields from metadata file. If importing several channels, import options


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parameters may be saved by clicking Save options. Leave all other options as defaults. Press Preview to generate preview of resultant image. Then OK to commence import.

Step 3. Georeference MODIS image

Display the image in the Viewer window. From the Viewer window, select Utility/Layer Information. From the Image Info window, select Edit/Change Map Model. Enter image corner values generated above (Coordinate Calculator) to Upper left X and Upper Left Y. In Pixel size X and Pixel size Y enter pixel size in metres. From the Projection drop down list, select Lambert Conformal Conic and then OK. In the Attention message box, click Yes to Change map model in this layer? In the ImageInfo Window, select Edit/Add/Change Projection. From the Standard page, select the projection information created in Coordinate Calculator and OK. In the Attention message box, click Yes to Add projection information to this layer.

Reopen image to display changes by clicking File/Open/Raster Layer in the current viewer and selecting the image to be displayed.

Download Importing and georeferencing MODIS data into image processing systems.


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Appendix G: Pixel information of area variation of Poyang Lake using MODIS 250m data Table H-1: Pixel information of water body of Poyang Lake in 2004

Month Number of pixels Area (m2) Date of MODIS image 1 18148 1134240000 2004-1-21 2 14554 909625000 2004-2-13 3 32570 2035625000 2004-3-4 4 18532 1158250000 2004-4-20 6 45801 2862562500 2004-6-30 7 45987 2874187500 2004-7-23 8 39992 2499500000 2004-8-10 9 45709 2856812500 2004-9-16

10 28712 1794500000 2004-10-20 11 24559 1534940000 2004-11-28 12 18532 1158250000 2004-12-7

Table H-2: Pixel information of water body of Poyang Lake in 2003

Month Number of pixels Area (m2) Date of MODIS image 1 29197 1824812500 2003-1-28 2 19446 1215380000 2003-2-6 3 26116 1632290000 2003-3-10 4 30654 1915880000 2003-4-16 6 53572 3348250000 2003-6-30 7 49182 3037880000 2003-7-3 8 47509 2969310000 2003-8-24 9 43368 2710500000 2003-9-4

10 39952 2497000000 2003-10-9 11 35811 2238190000 2003-11-30 12 29156 1800230000 2003-12-8


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Appendix H: MODIS imagery used

Table G-1: 2005 MODIS imagery used

Index Recording data

Recording time (GMT) Platform

Morning /Afternoon Spatial Resolution

Figure 4-3 Figure 4-4 Figure 4-6 Figure 5-13 (left) Table 5-2

2005-10-18 10:02 Terra M 250 m

Figure 5-13 (right)Table 5-2 2005-10-18 10:02 Terra M 500 m

Table G-2: 2004 MODIS imagery used

Index Recording dataRecording time

(GMT) PlatformMorning

/Afternoon Spatial Resolution

Table H-1 2004-1-21 13:05 Aqua A 250 m Table H-1 2004-2-13 13:05 Aqua A 250 m Figure 5-9 (2) 2004-2-28 11:03 Terra M 250 m Figure 5-2 (1) Table H-1 2004-3-4 11:03 Terra M 250 m

Figure 5-9 (4) 2004-4-2 11:03 Terra M 250 m Table H-1 2004-4-20 10:02 Terra M 250 m Figure 1-2 (left) Figure 4-8 Figure 4-9 Figure 4-10 Figure 4-11 Figure 5-9 (6) Figure A-1 Table H-1

2004-6-30 10:02 Terra M 250 m

Figure 5-10 (1) 2004-7-21 10:02 Terra M 250 m Figure 5-10 (2) Table H-1 2004-7-25 10:02 Terra M 250 m

Figure 5-10 (3) 2004-7-27 10:02 Terra M 250 m Figure 2-2 Figure 5-2 (2) Table H-1 Table 5-2

2004-8-10 10:02 Terra M 250 m

Table G-2 (Continued)


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Table 5-2 2004-8-10 10:02 Terra M 500 m Figure 5-9 (8) Table H-1 2004-9-16 11:03 Terra M 250 m

Figure 5-11 2004-9-30 10:02 Terra M 250 m Table H-1 2004-10-20 10:02 Terra M 250 m Figure 5-9 (10) Figure 5-12 Table H-1

2004-11-28 10:02 Terra M 250 m

Figure 5-2 (4) Figure 5-14 (left) Table H-1 Table 5-2

2004-12-7 10:02 Terra M 250 m

Figure 5-14 (right) Table 5-2

2004-12-7 10:02 Terra M 500m

Table G-3: 2003 MODID imagery used

Index Recording data

Recording time (GMT) Platform

Morning /Afternoon Spatial Resolution

Table H-2 2003-1-28 10:02 Terra M 250 m Figure 5-9 (1) Table H-2 2003-2-6 10:02 Terra M 250 m


Figure 5-9 (5) Table H-2 2003-6-30 10:02 Terra M 250 m

Table H-2 2003-7-3 11:03 Terra M 250 m Table H-2 2003-8-24 10:02 Terra M 250 m Figure 5-9 (7) Table H-2 2003-9-4 10:02 Terra M 250 m


Table H-2 2003-12-13 13:05 Aqua A 250 m


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Appendix I: List of Equipment requirement

Equipments used in field survey Quantity Motorboat 1 Several plastic bottles (depend on the number of samples) GPS device and GPS receiver 1 Secchi disk 1 Water sampler 1 Spectrometer 1 Field survey log sheet 1 Equipments used in laboratory analysis (some quantities depend on number of samples) 350 mL all-glass filtration apparatus 0.2-μm Nuclepore membrane filters Stainless steel forceps Support stand for filtration apparatus Dessicator Drying oven Precision balance (200 mg capacity and 0.1 mg resolution) Permanent marker Petri dishes Laboratory analysis log sheet

monitoring area variation and sedimentation patterns in ... · monitoring area variation and...

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