ASSESSMENT OF SPATIO - TEMPORAL CHANGES OF IN DISTRICT BY USING LANDSAT IMAGES AND HYDROLOGICAL DATA

FARIDA YASMIN

Roll No: 0413162020 P

DEPARTMENT OF WATER RESOURCE ENGINEERING UNIVERSITY OF ENGINEERING AND TECHNOLOGY DHAKA, BANGLADESH

March 2018 ASSESSMENT OF SPATIO - TEMPORAL CHANGES OF HAORS IN BY USING LANDSAT IMAGES AND HYDROLOGICAL DATA

by

FARIDA YASMIN

Roll No: 0413162020 P

In partial fulfillment of the requirement for the degree of MASTER OF ENGINEERING IN WATER RESOURCES ENGINEERING

Department Of Water Resource Engineering BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY Dhaka, Bangladesh

March 2018

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TABLE OF CONTENTS

Page no.

LIST OF FIGURES viii

LIST OF TABLES xiii

ABBREVIATIONS xiv

ACKNOWLEDGEMEN xv

ABSTRACT xvi

CHAPTER 1 INTRODUCTION

1.1 Background of the Study 1

1.2 Scope of the Study 3

1.3 Objectives of the Study 4

1.4 Organization of Thesis Work 5

CHAPTER 2 LITERATURE REVIEW

2.1 General 6

2.2 Definition of 6

2.3 Importance of Wetland 8

2.4 around the World 9

2.5 Wetlands of Bangladesh 10

2.6 Previous Studies on Wetland in Bangladesh 16

2.7 Summary 19

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CHAPTER 3 THEORY AND METHODOLOGY Page no.

3.1 General 20

3.2 Remote Sensing (RS) and Global Information System (GIS) 20

3.2.1 Principles of Remote Sensing Systems 21 3.2.2 Primary Components of Remote Sensing 21 3.2.3 Types of Remote Sensing 22

3.3 Application of GIS 23

3.4 Components of GIS 24

3.5 Working Principle of GIS 25

3.6 Methodology of the Study 26

3.6.1 Study Area 28 3.6.2 Data Collection and Pre-processing 28 3.6.3 Band Reflectance Calculation 29 3.6.4 Calculation of Normalized Difference Water Index (NDWI) 30 3.6.5 Image Analysis 31

3.7 Summary 32

CHAPTER 4 RESULTS AND DISCUSSIONS

4.1 General 33

4.2 Assessment of Sunamganj Area by GIS 33

4.3 Evaluation of Water Area for Different Year 33

4.4 Changes in Water Area within a Year 47

4.5 Spatial Changes of Water Area in Sunamganj district 63

4.6 Hydrological Analysis 66 4.6.1 Changes of inundated water areas 66

4.7 Correlation between the results of Image analysis and Hydrological trend analysis for the year 1994, 2006,2011 and 2016 76

4.8 Frequency Analysis 78

4.9 Summary 79 vi

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS Page no.

5.1 General 80

5.2 Conclusions of the Study 80

5.3 Recommendations 81

References 82

Appendix 1 88

Appendix 2 94

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LIST OF FIGURES

Page no.

Figure 1.1: Location Map of Sunamganj 1

Figure 1.2: Haors in Sunamganj District 2

Figure 2.1: Location Map of Tanguar Haor 11

Figure 2.2: Location Map of Hakaluki Haor 12

Figure 2.3: Picture of Marjat Baor 12

Figure 2.4: Location Map of Baikka 13

Figure 2.5: Location Map of Arial Beel 14

Figure 2.6: Location Map of Chalan Beel 14

Figure 2.7: Picture of Ramsagar Dighi 15

Figure 2.8: Location map of Kaptai Lake 16

Figure 3.1: The satellite remote sensing process 22

Figure 3.2: Passive and Active Sensor System 22

Figure 3.3: GIS World Model 24

Figure 3.4: Components of GIS 25

Figure 3.5: GIS working Process 26

Figure 3.6: Steps that has been followed in this study 27

Figure 3.7: Sunamganj District Map 28

Figure 4.1: Sunamganj Water surface area detection map for the year 1973 34

Figure 4.2: Sunamganj Water surface area detection map for the year 1977 35

Figure 4.3: Sunamganj Water surface area detection map for the year 1994 36

Figure 4.4: Sunamganj Water surface area detection map for the year 1999 38

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Figure 4.5: Sunamganj Water surface area detection map for the year 2003 39

Figure 4.6: Sunamganj Water surface area detection map for the year 2007 40

Figure 4.7: Sunamganj Water surface area detection map for the year 2009 41

Figure 4.8: Sunamganj Water surface area detection map for the year 2011 43

Figure 4.9: Sunamganj Water surface area detection map for the year 2013 44

Figure 4.10: Sunamganj Water surface area detection map for the year 2015 45

Figure 4.11: Sunamganj Water surface area detection map for the year 2017 46

Figure 4.12: Sunamganj Water surface area detection map for the year 1994 (a) January. (b) February. (c) March. 48

Figure 4.13: Sunamganj Water surface area detection map for the year 1994 (a) April, (b) June. 49

Figure 4.14: Sunamganj Water surface area detection map for the year 1994 (a) July, (b) August. 50

Figure 4.15: Sunamganj Water surface area detection map for the year 1994 (a) October, (b) November, (c) December. 51

Figure 4.16: Sunamganj Water surface area detection map for the year 2006 (a) February, (b) March, (c) April. 52

Figure 4.17: Sunamganj Water surface area detection map for the year 2006 (a) July, (b) August, (c) September. 53

Figure 4.18: Sunamganj Water surface area detection map for the year 2006 (a) October, (b) November, (c) December. 54

Figure 4.19: Sunamganj Water surface area detection map for the year 2011 (a) January, (b) February, (c) March. 55

Figure 4.20: Sunamganj Water surface area detection map for the year 2011 (a) April, (b) May, (c) June. 56

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Figure 4.21: Sunamganj Water surface area detection map for the year 2011 (a) July, (b) August, (c) September. 57

Figure 4.22: Sunamganj Water surface area detection map for the year 2011 (a) October, (b) November, (c) December. 58

Figure 4.23: Sunamganj Water surface area detection map for the year 2016 (a) January, (b) February, (c) March. 59

Figure 4.24: Sunamganj Water surface area detection map for the year 2016 (a) April, (b) May, (c) July. 60

Figure 4.25: Sunamganj Water surface area detection map for the year 2016 (a) August, (b) September, (c) October. 61

Figure 4.26: Sunamganj Water surface area detection map for the year 2016 (a) November, (b) December. 62

Figure 4.27: Spatial distribution of NDWI for the Sunamganj Haor area during pre-monsoon season from the year 1977 to 2007. 64

Figure 4.28: Spatial distribution of NDWI for the Haor area during monsoon season from the year 1973 to 2007. 65

Figure 4.29: Water area changes of pre-monsoon period for different years. 66

Figure 4.30: Water area changes of monsoon period for different years. 67

Figure 4.31: Water area changes of post-monsoon period for different years. 67

Figure 4.32: Relationship between Sunamganj district water area and observed discharge at Sunamganj discharge station for the year 1994. 68

Figure 4.33: Relationship between Sunamganj district water areas and observed Discharge data at Sunamganj discharge station for the year 2006. 68

Figure 4.34: Relationship between Sunamganj district water areas and observed discharge data at Sunamganj discharge station for the year 2011. 69

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Page no. Figure 4.35: Relationship between Sunamganj district water areas and observed Discharge data at Sunamganj discharge station for the year 2016. 69

Figure 4.36: Relationship between Sunamganj district water areas and observed Rainfall at Sylhet station for the year 1994. 70

Figure 4.37: Relationship between Sunamganj district water areas and observed Rainfall data at Sylhet station for the year 2006. 70

Figure 4.38: Relationship between Sunamganj district water areas and observed Rainfall data at Sylhet station for the year 2011. 71

Figure 4.39: Relationship between Sunamganj district water areas and observed Rainfall data at Sylhet station for the year 2016. 71

Figure 4.40: Relationship between Sunamganj district water area and observed water level data at Markuli station for the year 1994. 72

Figure 4.41: Relationship between Sunamganj district water areas and observed water level data at Markuli station for the year 2006. 72

Figure 4.42: Relationship between Sunamganj district water areas and observed water level data at Markuli station for the year 2011. 73

Figure 4.43: Relationship between Sunamganj district water areas and observed water level data at Markuli station for the year 2016. 73

Figure 4.44: Water area for different years for the month January to April. 75

Figure 4.45: Water area for different years for the month May to August 75

Figure 4.46: Water area for different years for the month September to December. 76

Figure 4.47: Relationship between Sunamganj district water area and observed 76 monthly discharge for the year 1994, 2006, 2011 and 2016.

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Page no. Figure 4.48: Relationship between Sunamganj district water area and observed 77 monthly maximum water level for the year 1994, 2006, 2011 and 2016.

Figure 4.49: Relationship between Sunamganj district water area and observed 77 monthly maximum rainfall for the year 1994, 2006, 2011 and 2016.

Figure 4.50: Probability curve for maximum discharge value. 78

Figure 4.51: Probability curve for maximum Water Level value. 79

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LIST OF TABLES

Page no.

Table 1.1: List of Sunamganj Haors 3

Table 1.2: Topography of Haor area 4

Table 2.1: World’s top 10 largest lakes 10

Table 3.1: Different sensors for different Landsat Satellites 29

Table 3.2: Values of Esun 30

Table 3.3: The band designations for the Landsat satellite 31

Table 4.1: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 7 and 37

Landsat 8 images for the year 1973 to 1994.

Table 4.2: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 7 and 42 Landsat 8 images for the year 2003 to 2009.

Table 4.3: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 7 and 47 Landsat 8 images for the year 2011 to 2017.

Table 4.4: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 8 63 images for the year 1994, 2006, 2011 and 2016.

Table 4.5: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 8 64 images for the year 1994, 2006, 2011 and 2016.

Table 4.6: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 8 65 images for the year 1973 to 2007 in monsoon season.

Table 4.7: Water levels for the station Markuli, Maximum Discharge for the 74 station Sunamganj, Maximum Rainfall for the station Sylhet and Water Area of Sunamganj District for the year 1994, 2006, 2011 and 2016.

Table 4.8: For certain return period inundated area of Sunamganj district. 79

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ABBREVIATIONS

EROS Earth Resources Observation System

ETM+ Enhanced Thematic Mapper Plus

GIS Geographic Information System

NDWI Normalized Difference Water Index

NIR Near-Infrared

RADARSAT Radio Detection and Ranging Satellite

RS Remote Sensing

SLC Scan Line Correction

TM Thematic Mapper

USGS United States Geological Survey

WGS 84 World Geodetic System 1984

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ACKNOWLEDGEMENT

Firstly, I would like to praise Almighty Allah for giving me the strength, knowledge, and patience to successfully complete this Study.

I wish to express my profound gratitude and sincere appreciation to my supervisor Dr. Umme Kulsum Navera, Professor, Department of Water Resources Engineering (WRE), Bangladesh University of Engineering and Technology (BUET) for her continuous co-operation, relentless support and intellectual guidance to inspire me to carry out the Study systematically and smoothly. Her extended support and guidance in all aspects have definitely enriched my knowledge to accomplish my thesis work smoothly.

I would also like to thank my committee members, Dr. Md. Mostafa Ali, Professor and Head, Department of Water Resources Engineering, Bangladesh University of Engineering and Technology and Dr. K. M Ahtesham Hossain, Assis tant Professor, Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, for their valuable suggestions and comments.

My sincere thanks also go to all the teachers of my entire educational life, especially to the teachers of the Department of Water Resources Engineering, BUET. Without their help and support I could not have come this far in my life.

I would like to convey my deep appreciation and profound gratitude to all my friends of Department of Water Resources Engineering, BUET, for their continuous help support and encouragement throughout the Study.

Last but not the least, I would like to thank my family for supporting me spiritually throughout writing this thesis and my life in general.

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ABSTRACT

The wetlands of Bangladesh are precious gifts of nature. Wetland includes haors, baors, bills, jeels, lakes, depressed land etc. Among them haor is the most prominent and resourceful wetland which occupies North-Eastern part of Bangladesh known as Sylhet basin. There are a total of 373 no’s of haor in the Sylhet basin which covers almost 7 districts of Bangladesh. Sunamganj district consists of 95 haors and most of them are deeply flooded in the monsoon period in every year. According to Sunamganj district digital elevation model, the elevations of haors bathymetry are lower than the normal plain lands. Maximum people living in these haor areas are directly or indirectly beneficiary from these wetlands.

The inundation of wetland (haor) of Sunamganj district depends on the rainfall in the upstream catchment which lies within and outside of the country. Also the river discharge and water level within the district plays an important role in the inundation pattern. It has been found that the overall pattern of inundation in different times of the year and also in different years has not been documented in earlier studies.

Therefore, a study has been conducted to assess the spatio-temporal changes and water feature extraction of Sunamganj district for the period from 1973 to 2017 by using the multi-temporal Landsat 5-TM, 7-ETM+, 8-OLI/TIRS images. Hydrological analysis has also been carried out from the year 1983 to 2016 which has indicated the relationship of the inundated area with the parameters like water level, discharge and rainfall. There are different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), and Automated Water Extraction Index (AWEI) for the extraction of surface water from Landsat data. Based on previous studies, the NDWI has been found to be superior than other indexes and therefore it was used to analyze the spatio-temporal changes of Sunamganj district’s wetland area.

From the study it is seen that because of low land, the left side of Sunamganj district has been flooded earlier than the right side. During the monsoon time around 62.54- 77.42% of the district’s area become as haor area. Also in the dry season 1.2-5% area is under water. So there is a huge change in water area each year which effects the hydrological, morphological and anthropological conditions. The impact of hydrological changes based on the statistical significances has been provided an understanding about the inundation pattern of the study area.

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Chapter 1

Introduction

1.1 Background of the Study

Bangladesh possesses enormous area of wetlands including rivers, streams, haors, baors, , jheels, Lake etc. The north-eastern part of Bangladesh is known as Haor region. It spread over seven districts Sylhet, Sunamganj, Habiganj, Maulovi Bazar, Kishoreganj, Bhramanbaria and Netrokona. The Haor area altogether covers 1.99 million ha which is around 13.5% of the country’s total surface area (Khan, 2010).

In greater Sylhet the most prominent haors are Saneer haor, Hail haor, Hakaluki haor, Dekar haor, Maker haor, Chayer haor, Tanguar haor and Kawadighi haor (Shopan et. al., 2013). The haor basin is a low lying bowl-shaped basin covering about 6,000 km2 in , mostly in Sunamganj district in Bangladesh.

According to Haor master plan volume-2 (2012), Sunamganj total area is 3670 km2, number of haors are 95 and the haor area is 2685.31 km2. The major rivers are Surma and Kushiyara. Their tributaries are Manu, Khowai, Jadukhata, Piyain, Mogra, Mahadao and Kangsha. (Uddin et. al., 2013)

Figure 1.1: Location Map of Sunamganj (Source: BHWDB, 2012)

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Figure 1.2: Haors in Sunamganj District

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1.2 Scope of the Study

The topography of haor regions is uneven. According to digital elevation model haor region are lower than the normal plain lands. All of this land is below 8 meters and is flooded for 7-8 months to depths of 5 meters or more during the monsoon. Saucer shaped, seasonally flooded, inter fluvial areas called hoar characterize this unit (NERP, 1995).

Table 1.2: Topography of Haor area

Elevation (m) Area (km2) Elevation (m) Area (km2) <= 1 4 8 - 10 2,673 1 – 2 160 10 – 12 1,345 2 – 3 1,225 12 – 15 1,007 3 – 4 2,418 15 – 20 634 4 – 5 2,905 20 - 50 773 5 – 6 2,339 50 - 100 223 6 – 7 2,099 100 - 310 19 7 – 8 1,837 (Source: BHWDB, 2012)

These wetlands area very important habitats for the unique and dynamic ecosystems, which have immense productive and ecological value e.g., storage of rainfall-runoff, groundwater recharge, providing habitats for fish, wildlife, aquatic plants and animals, resort to migratory birds, support biodiversity, Haor area plant based socioeconomic activities, fishing and recreation. According to Meyer (1995), every parcel of land on the Earth’s surface is unique in the cover it possesses. Haor area can be altered by forces other than anthropogenic. Natural events such as weather, flooding, climate fluctuations, and ecosystem dynamics may also initiate modifications upon land cover of Haor area.

Land cover and human/natural modifications have largely resulted in deforestation, biodiversity loss, global warming and increase of natural disaster-flooding (Mas et al., 2004; Zhao et al., 2004). Both human-induced and natural land cover changes can influence the global change because of its interaction with terrestrial ecosystem (Houghton, 1994), biodiversity and landscape ecology (Reid and Landsberg, 2000). Therefore, accurate and up-to-date land cover information is essential for environmental planning, to understand the impact on terrestrial ecosystem (Muttitanon and Tripathi, 2005) and to achieve sustainable development (Alphan, 2003).

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Therefore, available data on land cover changes can provide critical input to decision- making of environmental management and planning the future (Fan, F. et al., 2007). Land cover changes modify the reflectance of the land surface, determining the fraction of the Sun’s energy absorbed by the surface and thus affecting heat and moisture fluxes. These processes also alter vegetation transpiration and surface hydrology and determine the partitioning of surface heat into latent and sensible heat fluxes. Land cover changes are expected to increase the risk of flash floods in the haor region. Thirty years ago, flash flood used to hit border area of Sunamganj and took 15 days to reach the hoar of Jamalganj Upazilla. The early flash floods of April 2004 in the northeast have been the worst of its kind in the history of floods in the country. The agriculture sector of Bangladesh is expected to be heavily impacted by land cover changes (Salaudin and Islam, 2011). This constrains agriculture activities. Only one crop is grown annually in the haor area, i.e. the Boro season rice (local and HYV variety of rice) is cultivated only during winter. Land-less and marginal farmers in particular are affected.

Remote Sensing (RS) and Geographic Information System (GIS) are now providing new tools for advanced ecosystem management. Land cover studies using remote sensing data have been received immense attention worldwide due to their importance in global change analysis (Cihlar, 2000). In this study, change detection comparison (pixel by pixel) technique was applied to the Land cover maps derived from satellite imagery. The aim of the study is to analyze land cover changes using satellite imagery and GIS techniques in Haor area of Bangladesh.

The above said study area is the wetland of Sunamganj district, lies on the north eastern region of Bangladesh. Which has been changing on temporal basis. About 2013738 people are beneficiary directly or indirectly of this wetland. Along with lot of threat still there is very little concern noticed about this sensitive issue. Therefore a study to assess the spatio-temporal changes of Sunamganj Haor area has been conducted.

1.3 Objectives of the Study

Sunamganj district is the main wetland of Sylhet basin which stands North-Eastern region of Bangladesh. It is going to lose its tradition and pride of her resources. Therefore, a study has been conducted to assess the spatio-temporal changes and water

4 feature extraction of Sunamganj in the period 1973-2017 using the multi-temporal Landsat 5-TM, 7-ETM+, 8-OLI/TIRS images and Hydrological analysis for the year 1983 to 2016 which will indicate whether the behavior of haor is changing.

The main objectives of this study are:

1. To assess the spatial and temporal changes of the haor area in Sunamganj district for the years 1973-2017 by using Landsat 5, Landsat 7 and Landsat 8 images. 2. To analyze selected hydrological parameters of Sunamganj district and to find a relationship between hydrological parameters and inundated area.

1.4 Organization of Thesis Work

This thesis comprises of five chapters illustrating the necessary steps taken to achieve the above mentioned objectives. The thesis is organized as follows:

Chapter 1 gives the context of the Study at a glance.

Chapter 2 describes the definition of wetland. It contains importance of wetland, wetland of Bangladesh and wetland around the world. It also contains previous works on Bangladesh.

Chapter 3 describes the theory, methodology, data collection and analysis followed for thesis.

Chapter 4 presents the results and discussions.

Chapter 5 provides conclusions and recommendations.

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Chapter 2

Literature Review

2.1 General

Bangladesh possesses enormous area of wetlands including rivers, streams, haors, baors, beels, jheels, Lake etc (Hossain, et. al., 2013). The haors, baors, beels and jheels are of fluvial origin and are commonly identified as freshwater wetlands. The greater part of northeast region is taken up by the wetland basin (Uddin, et. al., 2013). Sunamganj district covers a large number of haors and wetlands. Among those Hakaluki haor, Tanguar haor, Hail haor etc cover an extensive area (WRI, 1990). According to Haor Master Plan Volume-1 (2012), Sunamganj district total area is 3670 km2 and number of haors are 95.

2.2 Definition of Wetland

According to BHWDB (2012), wetland is an area of variable size filled with water, localized in a basin that is surrounded by land, apart from any river. Wetlands can be contrasted with rivers or streams, which are usually flowing. Most wetlands are fed and drained by rivers and streams.

The Ramsar Convention has defined wetlands as, Areas of marsh, fen, peat land, or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters. Thus the term wetland group together a wide range of inland, coastal and marine habitats which share a number of common features (Dugan, 1990).

According to Ralph (1994), Wetlands are those areas that are inundated or saturated by surface or groundwater at a frequency and duration sufficient to support, and that under normal circumstances do support, a prevalence of vegetation typically adapted for life in saturated soil conditions. Wetlands generally include swamps, marshes, bogs, and similar areas.

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Haors, which are bowl-shaped depressions between the natural levees of a river subject to monsoon flooding every year, are mostly found in the eastern region of the country, known collectively as Haor basin covering an area of approximately 24,500 km2. There are altogether 411 haors (Chakraborty, et. al., 2005).

Baors are formed by dead arms of rivers, are situated in the moribund delta of the in western part of the country. Locally, the feature is also known as beel and jheel. Usually, baors are deeply flooded during the monsoon, either through local rainfall and runoff water or by river flood. During the monsoon season it act as local water reservoirs, and help to control the local flood level. In some areas, these serve as valuable sources of irrigation during the dry season (Nishat, 1993).

Beels are large surface water bodies that accumulate surface runoff water through internal drainage channels; these depressions are mostly topographic lows produced by erosions. Many of the beels dry up in the winter but during the rains expand into broad and shallow sheets of water. In the active floodplains of the Surma-Meghna, the Brahmaputra-Jamuna, and the Ganges- systems, there are several large and small beels. In Bangladesh, there are thousands of beels of different sizes. Some of the most common names are Chalan beel, Chand Beel and Arial beel. Normally, beels remain deeply flooded for most of the wet season (IUCN, 2004).

Jheel a local term representing a reach of an old river channel bed. Usually it appears as an oxbow lake. It may originate in two ways: (i) when a river changes its course, the old course remains abandoned and in course of time its mouth get totally clogged with silt. The channel becomes a receiving basin of local surface runoff water; (ii) in an extremely curved channel meander the river straightens its course through plugging the narrow neck between adjacent reaches. The meander loop thus gets separated and both ends are rapidly filled by bed material washed in by eddies. An oxbow lake is then created. Jheels are commonly seen in the southwestern Ganges deltaic parts of the country.

A lake is an area of variable size filled with water, localized in a basin that is surrounded by land, apart from any river or other outlet that serves to feed or drain the lake. (Lake- Wikipedia) Lakes lie on land and are a not part of the ocean, and therefore are distinct from lagoons and are also larger and deeper than ponds, though there are no official or

7 scientific definitions. Lakes can be contrasted with rivers or streams, which are usually flowing. Most lakes are fed and drained by rivers and streams. (Hakanson, 2012)

2.3 Importance of Wetland

The life and livelihood on Bangladesh is dependent on the wetlands. The wetlands are the source of fisheries, aquatic vegetation’s and other biodiversity, irrigation, navigation and flood control etc. The Haor, Baors, Beels and Lakes play an important role in the ecology, economy and livelihood of the country.

Flood water reservoir

A reservoir is a storage space for water. A reservoir usually means an enlarged natural or artificial lake, storage pond or impoundment created using a dam or lock to store water. Reservoirs can be created by controlling a stream that drains an existing body of water. They can also be constructed in river valleys using a dam. Alternately, a reservoir can be built by excavating flat ground and/or constructing retaining walls and levees. (Ahmed, et. al., 2004)

Irrigation

Irrigation needs fresh water which is being provided from surface fresh eater sources. More than half of the world’s wetland, which hold nearly 90 percent of all surface liquid fresh water, are facing massive ecological threats that are endangering the entire global environment; say a panel of experts for the 3rd World Water Forum. Some wetlands are dying fast (Reservoir, 2018). The amount of water that is currently stored in the world’s freshwater wetland is approximately 35 times that found in rivers and many of these lakes are important sources of water for human use. Wetlands in industrialized countries that are most endangered are shallow ones, especially those situated in areas of intensive agriculture, which sends tons of agricultural chemical run- off into the lakes, or which have been depleted for drinking water and industrial uses. (Ahmed, 2009)

Source of potable water

Wetland Water can be a great source of potable water. But untreated surface water in rivers, streams, lakes, and ponds is not safe to drink unless it is treated to remove

8 bacteria, viruses, and parasites. Chemical contaminants, like gasoline, oil, pesticides, and heavy metals, can come from discharge pipes, chemical storage areas, gasoline tanks, oil drums, or anywhere chemicals have been used close to open water (Galib, et. al., 2009).

Fisheries

About 260 species of freshwater fishes are found in the inland water bodies of Bangladesh. Inland fisheries alone cover an area of 4.3 million hectares of which 94% comprise openwater capture fisheries, and only 6% close water system (Chakraborty, et. al., 2005). The haors, beels and baors offer tremendous scope and potential to augment fish production by adoption of culture-based fishery enhancement technique. The haors, beels and baors are the main source and reserves of the brood stock of fish. Tanguar is one of the famous breeding ground for native crabs and flat fishes (Chital) of the country (Chakraborty, et. al., 2005).

Transport route

Wetlands are a great mean of communications. There are a number of wetlands which are being used as the mean of communications around the world (Alam and Chowdhury, 2003).

Recreation

Wetlands can be a great mean of recreation. There are a number of wetlands around the world which are being considered as the best recreational places. Some of the best recreational wetlands are: Kaptai Lake, Ramsagar Lake, Lake of the Ozarks, Big Bear Lake, West Okoboji Lake, Lake Cumberland, Lake Tahoe, Lake Havasu, Lake Michigan, Lake Coeur d'Alene, Flathead Lake, and Lake Powell (Haque, 2008).

2.4 Wetland around the World

There are large numbers of lakes around the world. The world's largest lakes are not only known for their enormous size, but are visited often because of their rich flora and fauna as well and the outdoor recreational activities and tours offered at many guarantee visitors to them very unique experiences indeed. While the passing seasons

9 and years frequently cause the surface areas of several of these lakes to vary considerably. (Ahmed, 2017)

Table 2.1: World’s top 10 largest lakes

Serial Name Of The Lake Location Area (km2) no

Kazakhstan, Russia, Azerbaijan, 1 Caspian Sea Iran, 436,000

2 Superior Canada, United States 82,100

3 Victoria Uganda, Kenya, Tanzania 68,870

4 Huron Canada, United States 59,600

5 Michigan United States 58,000

6 Tanganyika Burundi, Tanzania, Zambia, 32,600

7 Baikal Russia 31,500

8 Great Bear Lake Canada 31,000

9 Malawi Malawi, Mozambique, Tanzania 29,500

10 Great Slave Lake Canada 27,000

(Source: https://www.worldatlas.com/articles/10-largest-lakes-in-the-world.htm).

2.5 Wetlands of Bangladesh

Bangladesh is estimated to possess 70000 km2 to 80000 km2 of wetlands. Haor basin covering an area of approximately 24,500 km2. There are altogether 411 haors comprising an area of about 8000 km2 dispersed in the districts of Sunamgonj, Sylhet, Moulvibazar, Hobigonj, Netrokona & Kishoreganj (Nishat, 1993).

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1. Tanguar Haor

Tanguar Haor is one of the biggest wetland of Bangladesh. It's a beautiful natural sight of this country. Tanguar haor is lying in the Dharmapasha and Tahirpur of Sunamganj District in Bangladesh. Tanguar Haor is a unique wetland ecosystem of national importance and has come into international focus. The area of Tanguar haor is about 100 km2. Tanguar Haor is the source of livelihood for more than 40,000 people. In 1999 the Government of Bangladesh declared Tanguar haor as an Ecologically Critical Area. With this declaration, the Government takes many steps to protect the haor.

Figure 2.1: Location Map of Tanguar Haor, Bangladesh (Source: BHWDB, 2012)

Tanguar Haor is called "Mother Fishery" of Bangladesh. More than 140 species of fresh water fish in the haor. She plays an important role in fish productions. This hoar also provides home of many birds. Every winter the haor is home to about 200 types of migratory birds. It's a mind blowing place for take a tour. (Khan, et. al., 1994).

2. Hakaluki Haor

Hakaluki Haor is situated in greater Sylhet division. It is one of Bangladesh's biggest and one of Asia’s largest Haor. The haor is consisting with Kulaura, Juri and Baralekha Upazilla under Moulovibazar district and Fenchuganj and Gopalganj upazillas under

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Sylhet district. The total area of this haor is about 393.22 km2. But during the rainy season its total area increasing. (Hossain, 2009) This haor is a protected Ramsar site of international importance for the conservation and sustainable utilization of wetlands. The Hakaluke haor offers a very different type of ecosystem. According to IUCN report in 2008, the Hakaluki haor is home of many kind of fishes, birds and animal. There are 107 species of fishes available here but 75 species remain present day.

Figure 2.2: Location Map of Hakaluki Haor, Sylhet, Bangladesh. (Source: BHWDB, 2012). 3. Marjat Baor

The Marjat Baor is located at Chougacha, Jessore. The total surface are covered by the large Baor is 200 ha (2 km2).

Figure 2.3: Marjat Baor, Chougacha, Jessore (Source: Ahmed, 2017)

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4. Baikka Beel

Baikka Beel is a wetland in the eastern part of Hail Haor, 18 km from Sreemangal. Outstanding and Flagship Attractions are Migratory birds (seasonal) and resident birds Fish sanctuary, Swamp Plantation (Hijal, koroch).

Figure 2.4: Location Map of Baikka Beel, Sreemangal (Source: BHWDB, 2012)

5. Arial Beel

Arial Beel is a large water body of 136 km2, situated south of Dhaka in between Padma and . The Beel's features change from season to season. Specially, during the winter time, its diversity reaches a unique level. Arial Beel is a huge wetland. Tens of thousands of people live on this Beel's natural resources. After the monsoon is over, the region is filled with diverse fruits and crops. Famers don't use any fertilizer here because the soil is very fertile (Hossain, 2009).

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Figure 2.5: Location Map of Arial Beel, Munshiganj, Bangladesh. (Source: BHWDB, 2012)

6. Chalan Beel

Chalan Beel one of the largest inland depressions of marshy character and also one of the richest wetland areas of Bangladesh. It is the largest Beel of the country and comprises a series of depressions interconnected by various channels to form more or less one continuous sheet of water in the rainy season when it covers an area of about 368 km2.

Figure 2.6: Location Map of Chalan Beel, Natore, Bangladesh (Source: https://www.researchgate.net)

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7. Ram Sagar

Ramsagar is located in the village Tejpur in Dinajpur District, and is the largest man- made lake in Bangladesh. It is situated about 8 kilometers south of the Dinajpur town.

Figure 2.7: Ramsagar Dighi, Dinajpur, Bangladesh (Source: Ahmed, 2017) The lake is about 1,079 meters long from North to South, and 192.6 meters wide from East to West. It was created in the mid-1750s, funded by Raja Ram Nath, after whom the lake is named. The excavation cost 30,000 taka at that time, and about 1.5 million labourers took part in the project (Banglapedia, 2014).

8. Kaptai Lake

Kaptai Lake is the largest manmade lake in Bangladesh. It is located in the Kaptai under Rangamati District of Chittagong Division. The lake was created as a result of building the Kaptai Dam on the River, as part of the Karnaphuli Hydro-electric project. The Kaptai Lake's average depth is 100 feet (30 m) and maximum depth is 490 feet (150 m) (Banglapedia, 2014).

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Figure 2.8: Location map of Kaptai Lake, Rangamati, Bangladesh (Source: Banglapedia, 2014)

2.6 Previous Studies on Wetland in Bangladesh

In a study by Islam and Sado, 2000 flood hazard maps were developed using remote sensing (RS) data for the historical event of the 1988 flood with data of elevation height, and geological and physiographic divisions. Flood damage depends on the hydraulic factors which include characteristics of the flood such as the depth of flooding, rate of the rise in water level, propagation of a flood wave, duration and frequency of flooding, sediment load, and timing. In this study flood depth and "flood-affected frequency" within one flood event were considered for the evaluation of flood hazard assessment, where the depth and frequency of the flooding were assumed to be the major determinant in estimating the total damage function. Different combinations of thematic maps among physiography, geology, land cover and elevation were evaluated for flood hazard maps and a best combination for the event of the 1988 flood was proposed. Finally, the flood hazard map for Bangladesh and a flood risk map for the administrative districts of Bangladesh were proposed.

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The study of Uddin et al., 2013 Eco-environmental Changes of Wetland Resources of Hakaluki haor in Bangladesh Using GIS Technology. In this study, the time series data analysis by GIS technology, it is confirmed that wetland ecosystems of Hakaluki haor is decreasing in course of time due to sedimentation, irregular rainfall and other related anthropogenic causes. This decreasing trend is becoming serious due to the lack proper management planning. Henceforth, this wetland eco-system is degrading seriously which impacts on the bio-resources and biodiversity of the study areas. A sustainable technology should be adopted to prevent the degradation of hilly lands and conservation and caring of soil resources in the wetland basins.

The study of Khan, 2010 Wind Analysis for Wave Prediction in Haor Areas. An attempt has been undertaken in this research to obtained wind speed values for 25 years, 50 years and 100 years return period can be used for design of wave parameters in Haor areas of Bangladesh for more accurate results.

The study of Salaudin and Islam, 2011 Identification of land cover changes of the haor area of Bangladesh using MODIS images. This study was able to detect the land cover changes of the haor area using MODIS satellite data. It has found that water bodies of the haor area has been reduced around 6.88% whereas vegetation (agricultural) area has been increased 8.35% during the 9 years period from 2000 to 2008. The change pattern of mixed land cover is somewhat unchanged except 2001 and 2006. Water bodies has been reduced greatly and converted to other land cover areas.

The study of Uddin, et. al., 2013 Eco-Environmental Changes of Hail Haor Wetland Resources under Sylhet Basin of Bangladesh Due To Sedimentation: A GIS Approach. An attempt has been undertaken to the area covered by wetlands in the hail haor has been significantly reduced over the period from 1989 to 2003. According to GIS analysis using PAT (Polygon Attribute Tables) files, total hail haor area was about 10,000 ha in 1989. In 2003, the area of this water body was reduced and the area becomes 5,200 ha. In 2010, this water ecosystem also decreased significantly and the area of which becomes 2000 ha. The rate of reduction of the water body is alarming but in the dry season, the area of the Hail haor shrinks further which become 900 ha. The study also shows that considerable changes have occurred due to sedimentation and as a result depth and duration of inundation has changed. This change shows a

17 positive impact on agricultural aspects enhancing emergence of new soil boundaries and serious negative impact on eco-environmental aspects.

The study of Islam, 2014 undertakes a detailed assessment of flood risks of the Brahmaputra-Jamuna Floodplain. An attempt has been undertaken in this research to develop a GIS model. The underlying objective was to contribute to the flood management system in the country. The model involved the analysis of the hydrologic, topographic and the local resident’s coping capacity variables.

The study of Haque and Basak, 2017 Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on Tanguar Haor Sunamganj,

Bangladesh. . The overall analysis found the anthropogenic influence behind the change. The result shows that 71% extent of deep water body change its state mostly to shallow water. The NDWI analysis shows around 25.4583 square kilometer of deep water body has degraded within 1989–2010. According to the local peo-ple rice cultivation becoming more popular than fishing. Once high land vegetation or forested vegetation was the second dominating land cover feature of Tanguar Haor. But within 30 years from 1980 it is decreased more than 50%. The transition matrix shows that; the forested vegetation land was either trans-ferred to shallow water or settlement. The NDVI analysis evaluate a more precise result. Around 15.9967 square kilometer of forested vegetation land has degraded in 30 years from 1980. The accelera-tion of degradation was maximum within 1989–2001 (12.4902 sq. km) when most of the forested land was transferred to semi emer-gent crop land. Popularity of crop cultivation and hence emergence of settlement is the main culprit behind such clear cutting of emer-gent forested trees. The change statistics shows that the shallow water body increased by 33% in 30 years. As the shallow water is friendlier for agricultural activities, overabundance of shallow water encourage the local people to build semi- permanent or permanent settlement which resultant a serious damage to the haor ecosystem e.g. fish diversity. According to image difference statistics settlement feature inclined by 140% within the study period which imposes serious threat to other land features. Population pressure and insufficient land is the main cause of settlement expansion in the adjacent area of Haor Basin. The doubling rate of settlement is only 20–25 year.

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The study of Ahmed, 2017 Landsat images are downloaded from the Earth Explorer. The downloaded images are then processed using ArcGIS. The Processing starts with the atmospheric correction for the calculation of radiance and then the reflectance is calculated. Finally the Normalized Difference Water Index (NDWI) is calculated. The analysis shows that the Chalan Beel area has been significantly decreased and the Beel disappears within a short period of time. And the area is still decreasing day by day. This critical condition is due to both natural and human interventions. Chalan Beel almost disappears during dry season

2.7 Summary

In the north-eastern part of Bangladesh is known as Haor region. It is the only sources of occupation for thousands of people. It supplies fresh water as well as abundant of aquatic resources. This wetland is the large source of native fishes. It plays a vital role to keep the environment of the surrounding vast region balanced. But recently due to various natural and anthropogenic activities Haor is under threat. This Study will help to find out the temporal changes through the application of Remote Sensing and Global Information System.

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Chapter 3

Theory and Methodology

3.1 General

Geo-informatics is a science and technology of gathering, analyzing, interpreting, and disseminating geographical information. It encompasses a broad range of disciplines and technologies including landscape ecology, remote sensing (RS), spatial statistics, geographical information systems (GIS) and global positioning systems (GPS). (Haque and Basak, 2017). RS and GIS together has become very important in monitoring vegetation, water feature extraction, environmental preservation, detecting land cover and land uses and so on (Aggarwal, 2004). The collection of remotely sensed data facilitates the synoptic analyses of Earth - system function, patterning and change at local, regional and global scales over time; such data also provide an important link between intensive, localized ecological research and regional, national and international conservation and management of biological diversity (Wilkie and Finn, 1996)

3.2 Remote Sensing (RS) and Global Information System (GIS)

According to Colwell (1983), Remote Sensing includes all methods of obtaining pictures or other forms of electromagnetic records of Earth’s surface from a distance, and the treatment and processing of the picture data. Remote Sensing then in the widest sense is concerned with detecting and recording electromagnetic radiation from the target areas in the field of view of the sensor instrument. This radiation may have originated directly from separate components of the target area, it may be solar energy reflected from them; or it may be reflections of energy transmitted to the target area from the sensor itself (Patra, 2010).

According to Campell (1996), Remote Sensing is the practice of deriving information about the earth’s land and water surfaces using images acquired from an overhead perspective, using electromagnetic radiation in one or more regions of the electromagnetic spectrum, reflected or emitted from the earth’s surface.

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International Training Centre (ITC), Holland defined GIS as a computerised system that facilitates the phases of data entry, data analysis and data presentation especially in cases when we are dealing with geo referenced data (Patra, 2010).

According to Burrough (1998), GIS as a set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world for a particular set of purpose.

3.2.1 Principles of Remote Sensing Systems

The principles of remote sensing are based primarily on the properties of the electromagnetic spectrum and the geometry of airborne or satellite platforms relative to their targets. Remote sensors are the instruments which detect various objects on the earth’s surface by measuring electromagnetic energy reflected or emitted from them. The sensors are mounted on the platforms discussed above. Different sensors record different wavelengths bands of electromagnetic energy coming from the earth’s surface (Patra, 2010).

3.2.2 Primary Components of Remote Sensing

Primary components of remote sensing are as follows:

• Electromagnetic energy is emitted from a source.

• This energy interacts with particles in the atmosphere.

• Energy interacts with surface objects.

• Energy is detected and recorded by the sensor.

• Data are displayed digitally for visual and numerical interpretation on a computer.

(Aggarwal, 2004)

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Figure 3.1: The satellite remote sensing process (Source: Aggarwal, 2004).

3.2.3 Types of Remote Sensing

(i) Based on Source of Energy

Remote sensing can be either passive or active. Active systems have their own source of energy (such as RADAR) whereas the passive systems depend upon external source of illumination (such as SUN) or self-emission for remote sensing (Aggarwal, 2004). a) Passive Sensors: Remote sensing systems, which measure this naturally available energy, are called passive sensors. This can only take place when the sun is illuminating the earth. There is no reflected energy available from the sun at night. Solar energy and radiant heat are examples of passive energy sources. (Daptardar and Kesti, 2013).

Figure 3.2: Passive and Active Sensor System. (Source: https://en.wikipedia.org/wiki/Remote_sensing) b) Active Sensors: Remote sensing systems, which provide their own source of energy for illumination, are known as active sensors. These sensors have the advantage of obtaining data any time of day or season. RADAR is an example of active remote

22 sensing where the time delay between emission and return is measured, establishing the location, height, speeds and direction of an object (Kerle, 2004).

(ii) Based on Range of Electromagnetic Spectrum a) Optical Remote Sensing: The optical remote sensing devices operate in the visible, near infrared, middle infrared and short wave infrared portion of the electromagnetic spectrum. These devices are sensitive to the wavelengths ranging from 300 nm to 3000 nm. Most of the remote sensors record the EMR in this range, e.g., bands of IRS P6 LISS IV sensor are in optical range of EMR (Kerle, 2004). b) Thermal Remote Sensing: The sensors, which operate in thermal range of electromagnetic spectrum record, the energy emitted from the earth features in the wavelength range of 3000 nm to 5000 nm and 8000 nm to 14000 nm. The previous range is related to high temperature phenomenon like forest fire and later with the general earth features having lower temperatures. Hence thermal remote sensing is very useful for fire detection and thermal pollution. e.g., the last five bands of ASTER and band 6 of Landsat ETM+ operates in thermal range (Daptardar and Kesti, 2013). c) Microwave Remote Sensing: A microwave remote sensor records the backscattered microwaves in the wavelength range of 1 mm to 1 m of electromagnetic spectrum. Most of the microwave sensors are active sensors, having their own sources of energy, e.g., RADARSAT. These sensors have edge over other type of sensors, as these are independent of weather and solar radiations. (Patra, 2010).

3.3 Application of GIS

According to Centre for Spatial Database Management and Solutions (CSDMS), GIS is a computer based tool for mapping and analyzing things that exist and events that happen one earth. It is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data (Patra, 2010).

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Figure 3.3: GIS World Model (Source: http://grindgis.com/what-is-gis/what-is-gis-definition.)

GIS user expects support from the system to enter geo referenced data to analyze it in various ways and to produce output (maps and other) from the data. GIS draws on concepts and ideas from many different disciplines, such as cartography, cognitive science, computer science, engineering, environmental sciences, geodesy, landscape architecture, law, photogrammetry, public policy, remote sensing, statistics and surveying. So, it involves not only the study of the fundamental issues arising from the creation, handling, storage and use of geographic information, but it also examines the impacts of GIS on individuals and society and the influences of society on GIS. In this study Arc Map 10.2 has been used for mapping (Campbell, 1996).

3.4 Components of GIS

According to Tveito and Schoner, 2002 GIS is a technology system, not just one particular component. GIS is composed of: 1. Data: Both Spatial and Attribute Data are used. 2. Software: Data is entered into GIS Software which provides a user interface and data functionality. 3. GIS Professional: Software is accessed by GIS Professionals. 4. Hardware: Increasingly become more mobile, this is where the GIS Software resides. 5. Organization: Their need for data which drives the entire process. 6. Output: Analysis. Reports. Tracking. Decision Making. Planning and Work Flow Management.

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Figure 3.4: Components of GIS (Source: Tveito and Schoner, 2002)

3.5 Working Principle of GIS

According to Maroju 2007, five-step process applying GIS to any problem that requires a geographic decision.

1) Asking: What is the problem being try to solve or analyze and where is it located? Framing the question will help to decide what to analyze and how to present the results to the audience.

2) Acquiring: Next finding the data to complete the project. The type of data and the geographic scope of project helping direct for methods of collecting data and conducting the analysis.

3) Examining: Knowing for certain data is appropriate for the Study after thoroughly examining. This includes how the data is organized, how accurate it is and where the data came from.

4) Analyzing: Geographic analysis is the core strength of GIS. Depending on project, there are many different analysis methods to choose from GIS modeling tools make it relatively easy to make these changes and create new output.

5) Acting: The analysis results can be shared through reports, maps, tables and charts and delivered in printed format or digitally over a network or on the web.

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Figure 3.5: GIS working Process (Source: Aggarwal, 2004)

3.6 Methodology of the Study

The study has been carried out according to the steps shown in figure 3.6. The analysis has been started with the selection of study area and the necessary Landsat images have been downloaded from the Earth Explorer website. The downloaded images then processed using ArcGIS. The processing starts with the atmospheric correction for the calculation of radiance and then the reflectance has been calculated. Finally the Normalized Difference Water Index (NDWI) has been calculated. Then developed a relationship between Sunamganj water areas and hydrological parameters (discharge, water level and rainfall).

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Site Selection

Data Collection and Pre-Processing

Band Reflectance Calculation

NDWI Calculation

Reclassify and Extraction of Water Feature

Map Preparation

Sunamganj area Change Detection

(a)

Sunamganj Water Areas Hydrological Parameters Extracted from part (a) (Discharge, Water Level and Rainfall)

Correlation between Sunamganj Water Areas And Hydrological Parameters

(b)

Figure 3.6: Steps that has been followed in this study (a) NDWI calculation. (b) Relationship with hydrological parameters.

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3.6.1 Study Area

Sunamganj district area 3,669.58 km2, located in between 24°34' and 25°12' north latitudes and in between 90°56' and 91°49' east longitudes. It is bounded by state of India on the north, Habiganj and Kishoreganj districts on the south, Sylhet district on the east and Netrokona district on the west. Total population is 2013738. Main rivers are Surma, Kushiyara, Dhamalia, Jadukata (Hossain and Islam, 2017)

Figure 3.7: Sunamganj District Map. (Source: BHWDB, 2012)

3.6.2 Data Collection and Pre-processing

Landsat images have been downloaded from the Landsat Archive of USGS website where Landsat imagery is available from the archive, free for registered users. Downloaded imagery is composed of a .tiff file for each Landsat band and an MTL.txt file which contains metadata information. Images are already georeferenced in WGS 84 datum. (Yunus, 2005)

Different Landsat and their information about sensor and spatial resolutions are being provided in table 3.1.

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Table 3.1: Different sensors for different Landsat Satellites

Satellite Sensor Bandwidths Resolution RBV (1) 0.48 to 0.57 80 (2) 0.58 to 0.68 80 (3) 0.70 to 0.83 80 Landsat 1-2 MSS (4) 0.5 to 0.6 79 (5) 0.6 to 0.7 79 (6) 0.7 to 0.8 79 (7) 0.8 to 1.1 79 (1) 0.505 to 0.75 40 (4) 0.5 to 0.6 79 Landsat 3 RBV (5) 0.6 to 0.7 79 MSS (6) 0.7 to 0.8 79 (7) 0.8 to 1.1 79 (8) 10.4 to 12.6 240 (4) 0.5 to 0.6 82 MSS (5) 0.6 to 0.7 82 (6) 0.7 to 0.8 82 (7) 0.8 to 1.1 82 (1) 0.45 to 0.52 30 Landsat 4-5 (2) 0.52 to 0.6 30 TM (3) 0.63 to 0.69 30 (4) 0.76 to 0.90 30 (5) 1.55 to 1.75 30 (6) 10.4 to 12.5 120 (7) 2.08 to 2.35 30 (1) 0.45 to 0.52 30 (2) 0.52 to 0.60 30 (3) 0.63 to 0.69 30 Landsat 7 ETM+ (4) 0.76 to 0.90 30 (5) 1.55 to 1.75 30 (6) 10.4 10 12.5 60 (7) 2.08 to2.35 30 (8) PAN 0.5 to 0.9 15

(Source: Chander, et. al., 2009)

3.6.3 Band Reflectance Calculation

Radiance calculation: Radiance is calculated from the Landsat imagery using the following equation:

Lλ = (Gain) × (Digital Number) + (Bias)

Where, Gain and Bias values are obtained from the .MTL file provided with the data set.

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Reflectance calculation: Once the radiation is calculated by the above equation then reflectance is calculated by the following equation:

2 π Lλ d ……….. (1) Reflectance = (Esun∗sinθE) Where, Esun = Mean exoatmospheric solar irradiance θE= Sun Elevation θ°= (θ* π)/180 rad d = Earth sun distance provided in the MTL file. (Chander et al., 2009).

Table 3.2: Values of Esun

Band Landsat 4' Landsat 5' Landsat 7'

1 1957 1983 1997 2 1825 1769 1812

3 1557 1536 1533 4 1033 1031 1039

5 214.9 220 230.8

7 80.72 83.44 84.90

(Source: Chander et al., 2009).

3.6.4 Calculation of Normalized Difference Water Index (NDWI)

Normalized Difference Water Index (NDWI) may refer to one of at least two remote sensing-derived indexes related to liquid water: One is used to monitor changes in water content of leaves, using Near-Infrared (NIR) and short-wave infrared (SWIR) wavelengths, proposed by Gao in 1996.

Another index is used to monitor changes related to water content in water bodies, using Green and Near-Infrared (NIR) wavelengths, defined by Mc Feeters in 1996.

Xgreen - Xnir NDWI = ………… (2) Xgreen + Xnir

Where the Water feature has positive value.

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3.6.5 Image Analysis

Normalized Difference Water Index (NDWI) is calculated using the Landsat satellites images. The images are consisting of different spectral bands with specific spatial resolution. The spectral band information for different Landsat satellites is given table 3.3. Table 3.3: The band designations for the Landsat satellite

(Source: https://landsat.usgs.gov)

The analysis of the Landsat images has been done according to the following steps: Band-2(Green) and Band-4(NIR) images are loaded in ArcGIS.

1. The images reclassified with unique values and the zero value is classified as No Data value.

2. The radiance was then calculated using Raster Calculator field.

3. Then the reflectance has been calculated in Raster Calculator field using equation (1).

4. The reflectance has been corrected for negative value.

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5. The Normalized Difference Water Index (NDWI) has been calculated then using equation (2). 6. Sunamganj Haor area was then clipped by Raster Clip. 7. Then raster file has been reclassified and the water feature has been presented by the positive value. 8. The area has been calculated by the classified pixel number multiplying by the spatial resolution.

3.7 Summary

The analysis had been started with the selection of study area and the necessary Landsat images are downloaded from the Earth Explorer official website of USGS. The downloaded images are then processed using ArcGIS. The processing starts with the atmospheric correction for the calculation of radiance and then the reflectance is calculated. Finally the Normalized Difference Water Index (NDWI) is calculated.

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Chapter 4

Results and Discussions

4.1 General

The assessment of spatial and temporal changes of Haor in Sunamganj district has been performed using the Landsat satellite images. Normalized Difference Water Index (NDWI), one of the satellite-derived indices, has been calculated for the detection of change of Sunamganj area. The study has been conducted according to the procedures explained in the theory and methodology section.

4.2 Assessment of Sunamganj Haor Area by GIS

From the study the Normalized Difference Water Index (NDWI) has been calculated then the spatial and temporal changes of Sunamganj Haor have been detected. Maps showing the Normalized Difference Water Index (NDWI) values have been prepared. The images were downloaded from different Landsat satellites and the results has been organized accordingly. The higher water area observed in the month of June to October in image which might be presence of surface water by rain before the satellite pass over, which increased the mean value of dry season. However, the situation is that very low amount of water was observed in the month of January and February in almost every year images, which is the real threat to the fish and aquatic diversity of the Haor.

4.3 Evaluation of Water Area for Different Year

After the calculation of NDWI a map has been prepared for every image of different years. From figure 4.1 to 4.11 presents Sunamganj wetland area derived from Landsat 4, Landsat 5, Landsat 7 and Landsat 8. For the analysis Green and NIR images has been used. Green color represents land area and blue color represents water surface. The images have a spatial resolution of 30 m. These images are not Scan Line Corrected (SLC) images. For this reason the missing values have been classified as No Data value and then the NDWI is calculated. From the analysis it can be observed that the Sunamganj area has been decreased and it almost disappears during the month of July to August. Sunamganj district has the largest land area during the months January to May and the least surface area has been observed during November to December. The

33 whole area becomes dry during January and February. The maps are presented on the following page.

Figure 4.1: Sunamganj Water surface area detection map for the year 1973 (a) September, (b) November.

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Figure 4.2: Sunamganj Water surface area detection map for the year 1977 (a) February, (b) August, (c) November.

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Figure 4.3: Sunamganj Water surface area detection map for the year 1994 (a) February, (b) July.

From the above Figure 4.1 to 4.3, it has been observed that the land area of Sunamganj from the year 1973 to 1994 has been inundated and maximum area went under water during the months June to October. For the year 1973, the water area is 1360.87 km2 in September and November the water area is 1366.91 km2. But for the year 1977 the water area is 975.95 km2 in February where as in November the water cover area is 2.5

36 times in February (2552.75 km2), in 1990 the water area is 137.57 km2 in February where as in August the water cover area is 16 times of February (2295.29 km2) and in February 1999, the water cover area is 17 times of February, 1990 (2334.27 km2) and in June the water area is 19 times. Results from the images of Landsat 4, Landsat 5 and Landsat 8 for the year 1973 to 1994 are presented in the table 4.1.

Table 4.1: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 7 and

Landsat 8 images for the year 1973 to 1994.

Area of % of Area of % of

Year Month water water land land Land : Water (km2) area (km2) area

September 1360.87 37.08 2311.14 1.70 62.97 1973 November 1366.91 37.25 2314.22 63.06 1.69 February 975.95 26.59 2705.17 2.77 73.71

1977 August 2452.72 66.83 1218.32 33.20 0.50 November 2552.75 69.56 1128.38 0.44 30.75

February 137.57 3.75 3511.57 95.68 25.53

1990 August 2295.29 62.54 1374.89 37.46 0.60

December 1309.03 35.67 2371.97 64.63 1.81

February 142.75 3.89 3510.23 95.65 24.59 1994 July 2659.52 72.47 1009.51 27.51 0.38

From figure 4.4 to 4.7, it has been observed that the Sunamganj land area from the year 1999 to 2009 has been decreased and it almost disappears during the months June to October. In the year 1999, the water area of February and June are nearly. The maps are following pages.

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Figure 4.4: Sunamganj Water surface area detection map for the year 1999 (a) February, (b) June, (c) November.

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Figure 4.5: Sunamganj Water surface area detection map for the year 2003 (a) February, (b) August, (c) November.

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Figure 4.6: Sunamganj Water surface area detection map for the year 2007 (a) February, (b) September, (c) November.

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Figure 4.7: Sunamganj Water surface area detection map for the year 2009 (a) August, (b) November.

From the above map, Sunamganj District has been observed the largest land area during the months January to May and the largest surface area during the months November to December. The whole area becomes dry during January and February. The area corresponding to the years 1999 to 2009 and months has been shown in the table 4.2.

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Table 4.2: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 7 and Landsat 8 images for the year 2003 to 2009.

Area of Area of Year Month water % of % of land land (km2) (km2) water area Land : Water area

February 2334.27 63.60 1286.33 35.05 0.55

1999 June 2610.03 71.12 993.02 27.06 0.38

November 1678.06 45.72 1990.93 54.25 1.19

February 4.30 95.08 22.13 157.68 3489.35

2003 August 2330.17 63.49 1330.42 36.25 0.57

November 2147.36 58.51 1482.47 40.39 0.69

February 141.29 3.85 3498.32 95.32 24.76

2007 September 2725.59 74.27 955.42 26.03 0.35

November 1589.30 43.31 2091.71 56.99 1.32

August 2047.80 55.80 1633.21 44.50 0.80 2009 November 1387.48 63.60 2293.52 35.05 1.65

From figure 4.8 to 4.11 it has been observed that the Sunamganj land area from the year 2011 to 2017 is increased in January to May and it almost disappears during the months June to October. The maps are presented on the following pages.

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Figure 4.8: Sunamganj Water surface area detection map for the year 2011 (a) June, (b) August.

From figure 4.8, we have been observed that water area are nearly same for the month June and August in 2011. But figure 4.9 shows that for the year 2013 the water area are different in the month February and August.

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Figure 4.9: Sunamganj Water surface area detection map for the year 2013 (a) February, (b) August.

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Figure 4.10: Sunamganj Water surface area detection map for the year 2015 (a) June, (b) November.

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Figure 4.11: Sunamganj Water surface area detection map for the year 2017 (a) February, (b) April.

From figure 4.10 to 4.11, we have been observed that the water areas for the month June and November in 2015 are increasing. But for the month February and April in 2017 it has been decreasing. The area corresponding to the years 2011 to 2017 and months has been shown in the table 4.3.

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Table 4.3: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 7 and Landsat 8 images for the year 2011 to 2017.

Area of % of Area of % of Year Month water water land land Land : Water (km2) area (km2) area

June 1805.45 49.19 1875.56 51.11 1.04 2011 August 2261.72 61.63 1419.29 38.67 0.63

2013 February 114.47 3.12 3566.54 97.18 31.15

August 1545.13 42.10 2135.87 58.20 1.38

June 2367.78 64.52 1313.22 35.78 0.55 2015 November 1499.86 40.87 2181.14 59.43 1.45

February 152.11 4.14 3528.89 96.16 23.19 2017 April 161.86 4.41 3519.14 95.89 21.74

4.4 Changes in Water Area within a Year

Water cover maps for the study area Sunamganj District were developed by using NDWI values during the pre-monsoon season (February to May) and monsoon season (June to October) from the year 1973 to 2017. Figure 4.12 to 4.15, Sunamganj water area has been observed for the year 1994 corresponding to the month. From figure 4.12 and figure 4.13, it has been observed that the Sunamganj land area for the year 1994 was increased in January to April. The water area in January to April varies 728.4258 km2 to 280.0548 km2. But From the figure 4.13, the water area has been increased in June (1244.63 km2). The maps are presented on the following pages.

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Figure 4.12: Sunamganj Water surface area detection map for the year 1994 (a) January, (b) February, (c) March.

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Figure 4.13: Sunamganj Water surface area detection map for the year 1994 (a) April, (b) June.

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From figure 4.14, It almost disappears during the month of July (2171.19 km2). But the water area has been decreased in August (909.06 km2). Again the water area has been increased in the month of October (1952.51 km2) shown in figure 4.15. It may occurs for the high rainfall, high water level or high discharge. Again in November and December water area has been decreasing.

Figure 4.14: Sunamganj Water surface area detection map for the year 1994 (a) July, (b) August.

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Figure 4.15: Sunamganj Water surface area detection map for the year 1994 (a) October, (b) November, (c) December.

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From figure 4.16 to 4.18, it has been observed that the Sunamganj water area for the year 2006 is decreased in February to April (198.92 km2 to 148.89 km2) and it almost disappears during the months July to October (2849.93 km2 to 1956.82 km2). It may occurs for the high rainfall, high water level or high discharge. In November and December the water area has been decreasing.

Figure 4.16: Sunamganj Water surface area detection map for the year 2006 (a) February, (b) March, (c) April.

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Figure 4.17: Sunamganj Water surface area detection map for the year 2006 (a) July, (b) August, (c) September.

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Figure 4.18: Sunamganj Water surface area detection map for the year 2006 (a) October, (b) November, (c) December.

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From Figure 4.19 to 4.22, it has been observed that Sunamganj Haor area for the year 2011. The maps are presented on the following pages.

Figure 4.19: Sunamganj Water surface area detection map for the year 2011 (a) January, (b) February, (c) March.

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Figure 4.20: Sunamganj Water surface area detection map for the year 2011 (a) April, (b) May, (c) June.

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Figure 4.21: Sunamganj Water surface area detection map for the year 2011 (a) July, (b) August, (c) September.

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Figure 4.22: Sunamganj Water surface area detection map for the year 2011 (a) October, (b) November, (c) December.

From the above study we has been observed that left part of Sunamganj district are always inundated every year. From the DEM value of Sunamganj district, we observed the right part of DEM value is higher than the left part of Sunamganj district. The higher water area observed in the month of June to October in image which might be presence of surface water by rain before the satellite pass over. However, the actual situation is that very low amount of water was observed in the month of January to May in almost every year images, which is the real threat to the fish and aquatic diversity of the Haor. The situation was deteriorated further through the over use of surface and underground water.

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From Figure 4.23 to 4.25, it has been observed that Sunamganj Haor area for the year 2016 are dry during January to April (505.15 km2 to 19.14 km2). The water area has been increasing in the month May which is 737.76 km2. It almost disappeared during the month July to November. The maps are presented on the following pages.

Figure 4.23: Sunamganj Water surface area detection map for the year 2016 (a) January, (b) February, (c) March.

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Figure 4.24: Sunamganj Water surface area detection map for the year 2016 (a) April, (b) May, (c) July.

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Figure 4.25: Sunamganj Water surface area detection map for the year 2016 (a) August, (b) September, (c) October.

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Figure 4.26 shows that the whole Sunamganj Haor area becomes the land area during the month December for the year 2016.

Figure 4.26: Sunamganj Water surface area detection map for the year 2016 (a) November, (b) December.

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The above images area corresponding to the years (1994, 2006, 2011 and 2016) and months has been shown in the table 4.4.

Table 4.4: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 8 images for the year 1994, 2006, 2011 and 2016.

Area Of Water (km2) Area Of Land (km2) Month 1994 2006 2011 2016 1994 2006 2011 2016 1792.1 January 728.43 960.09 9 505.15 2952.58 2719.91 1888.82 3175.86

February 142.75 198.91 193.07 71.09 3538.25 3482.09 3487.93 3609.92

March 280.06 136.78 96.77 47.13 3400.64 3604.22 3584.23 3663.87

April 380.06 148.89 105.91 37.13 3400.65 3532.11 3575.09 3643.76

May 762.31 551.7 541.07 1237.76 2917.69 3128.3 3339.94 2943.24 1244.6 1581.2 1917.9 June 3 9 5 1295.52 2404.72 2098.71 1763.05 2384.48 2171.1 2849.9 2587.0 July 9 3 1 1453.29 1497.53 831.07 1093.99 2227.71 1993.0 2293.8 August 909.06 9 5 933.61 2771.95 1687.91 1387.15 3147.39 Septemb 1430.7 2087.0 1969.1 er 9 6 3 1735.53 2250.21 1593.94 1711.87 1845.47 1952.5 1956.8 2303.3 October 2 3 4 1742.58 1728.49 1724.18 1378.64 2038.42 Novemb 1380.7 1316.2 1242.9 er 1 7 3 1849.82 2300.29 2363.24 2438.92 2031.18 Decembe 1259.0 1026.3 r 912.85 9 8 1159.09 2597.90 2758.65 2655.21 2421.91

4.5 Spatial Changes of Water Area in Sunamganj District

Water cover maps for the study area Sunamganj district were developed by using NDWI values during the pre-monsoon season and monsoon season from the year 1973 to 2017. Figure 4.23 and figure 4.24 shows spatial distribution of water areas for the Sunamganj Haor areas during the pre-monsoon season (February to May) and monsoon season (June to October). Water area is shown in blue and land area is shown in green.

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Figure 4.27: Spatial distribution of NDWI for the Sunamganj Haor area during pre- monsoon season from the year 1977 to 2007.

From figure 4.23, we have been observed that Sunamganj Haor areas are small in Pre- monsoon season except in the year 1977 and 1999. Sunamganj Haor areas are increased in monsoon season. According to Spatial distribution of NDWI Sunamganj Haor areas during pre-monsoon season from the year 1977 to 2007 are presented in the table 4.5.

Table 4.5: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 8 images for the year 1994, 2006, 2011 and 2016.

Year Area Of Water (km2) Area Of Land % Of Water Area (km2)

1977 975.95 2705.17 26.59 1990 137.57 3511.57 3.748 1994 142.75 3510.23 3.89 1999 2334.27 1286.33 63.60 2003 157.68 3489.35 4.30 2007 141.29 3498.32 3.85

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Figure 4.28: Spatial distribution of NDWI for the Haor area during monsoon season from the year 1973 to 2007.

From figure 4.24, we have been observed that Sunamganj Haor areas are increased in monsoon season. The water areas varies in year to year. According to Spatial distribution of NDWI Sunamganj Haor areas during monsoon season from the year 1973 to 2007 are presented in the table 4.6.

Table 4.6: Sunamganj Haor area derived from Landsat 4, Landsat 5, Landsat 8 images for the year 1973 to 2007 in monsoon season. Year Month Area Of Water Area Of Land % Of Water (km2) (km2) Area

1973 September 1360.87 2311.14 37.08 1990 August 2295.29 1374.89 62.54 1994 July 2659.52 1009.51 72.47 1999 June 2610.03 993.02 71.12 2003 August 2330.17 1330.42 63.49 2007 September 2725.59 955.42 74.27

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4.6 Hydrological Analysis

Trend analysis by using hydrological parameters (Extreme value distribution and Frequency analysis using frequency factors) of water level, discharge and rainfall data for different station of Sunamganj district. From this, we observe the present situation in Sunamganj district for pre-monsoon season, monsoon season and post-monsoon season. Pre-monsoon season consists of February to May, Monsoon season consists of June to October and post-monsoon season consists of November to January.

4.6.1 Changes of Inundated water areas

Figure 4.25 to 4.27 shows water area change patterns from the year 1977to 2016 for pre-monsoon season, monsoon season and post-monsoon season. It has been found that when the vegetation cover increases, the water body decreases. For example, the water bodies rises from 142.75 km2 to 2334.2706 km2 in 1999, the vegetation cover decrease from 3510.23 km2 to 1286.33 km2.For the year 2007, in pre-monsoon season the land cover is 3498.318 Km2 and water area is 141.29 km2, in monsoon season the water body is 2725.59 km2 and the land cover is 955.4157 km2. The change in water cover does not very much from year to year.

Water Area Changes for pre-monsoon season

4000

3000 ) 2

2000 Area, (km Area, 1000

0 1977Feb, 1990 Feb, 1994 Feb, 1999 Feb, 2003 Feb, 2007 Feb, 2013 Feb, 2016 Feb Year Area Of Land (Km2) Area Of Water (Km2)

Figure 4.29: Water area changes of pre-monsoon period for different years.

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Water Area Changes for monsoon season

4000 3500 ) 2 3000 2500 2000 1500

Area, (km Area, 1000 500 0 1994, 1999, 2003, 2006, 2007, 2009, 2011, 2013, 2015, 2016, Jul Jun Aug Jul Sep Aug Aug Aug Jun Jul Year

Area Of Land (Km2) Area Of Water (Km2)

Figure 4.30: Water area changes of monsoon period for different years.

Water Area Changes for post-monsoon season 4000

3000 ) 2

2000

Area, (km Area, 1000

0 1977, 1990, 1999, 2006, 2008, 2009, 2015, 2016, Nov Dec Nov Nov Nov Nov Nov Nov Year Area Of Land (Km2) Area Of Water (Km2)

Figure 4.31: Water area changes of post-monsoon period for different years.

From Figure 4.32 to 4.34, relationship has been developed between Sunamganj water areas and different hydrological parameters like discharge, rainfall and water level for different month in the year 1994, 2006, 2011 and 2016.

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Relationship between Water Area and Discharge for the year 1994 4000 3000 2500 3000 2 2000 /s 3 Km 2000 1500 m 1000 1000 500 0 0 Discharge, Discharge, Water Area, Area, Water Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

Water area, km2 Discharge,m^3/s

Figure 4.32: Relationship between Sunamganj district water area and observed discharge at Sunamganj discharge station for the year 1994.

From the above figure 4.32 we have been observed that for the year 1994, when discharge is low water area is small and increasing discharge value water areas increasing.

Relationship between Water Area and Discharge for the year 2006 4000 3500 2 3000 /s 3

Km 3000 2500 m 2000 2000 1500 1000 1000 Discharge, Discharge, Water Area, Area, Water 500 0 0

Water Area(km2) Discharge(m^3/s)

Figure 4.33: Relationship between Sunamganj district water areas and observed Discharge data at Sunamganj discharge station for the year 2006.

From the above figure 4.33, we have been observed that when discharge is low water area is small and increasing discharge value water areas increasing. But for the month November and December discharge value is low also the water area is large it may occurs for increasing rainfall and water level. 68

Relationship between Water Area and Discharge for the year 2011

3000 3000 2 /s 2500 2500 3 2000 2000 1500 1500 1000 1000 Discharge, m Discharge,

Water Area, km Area, Water 500 500 0 0

Water Area, km2 Discharge (m3/s, BWDB)

Figure 4.34: Relationship between Sunamganj district water areas and observed discharge data at Sunamganj discharge station for the year 2011.

From the above figure 4.34, we have been observed that increasing discharge water area of sunamganj district are increasing in 2011 for the month June to September. But for the month January discharge is small but inundated area is large except for the month March.

Relationship between Water Area and Discharge for the year 2016

3000 3000 2

2500 2500 /s 3 2000 2000 1500 1500 1000 1000

Water Area, km Area, Water 500 500 Discharge, m Discharge, 0 0

Water Area, km2 Discharge (m3/s, BWDB)

Figure 4.35: Relationship between Sunamganj district water areas and observed Discharge data at Sunamganj discharge station for the year 2016.

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From the above figure 4.35, we have been observed that increasing discharge water area of sunamganj district are increasing in 2016. But for the month November and December discharge varies in 120 m3/s to 350 m3/s.

Relationship between Water Area and Rainfall for the year 1994 4000 160

2 140 3000 120 Km 100 2000 80 60 1000 40 Rainfall, mm Rainfall,

Water Area, Area, Water 20 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Water area, km2 rainfall, mm

Figure 4.36: Relationship between Sunamganj district water areas and observed Rainfall at Sylhet station for the year 1994.

From the above figure 4.36, we have been observed that for the year 1994 rainfall undulation could not affect the water areas of Sunamganj. When rainfall is high the small quantity of land are watered in the month March to May. Again rainfall shows low but the water areas are large. It means water areas are less affected by rainfall.

Relationship between Water Area and Rainfall for the year 2006 3000 200 2 2500 150 2000 1500 100 1000 50 500 Rainfall, mm Rainfall,

Water Area, Km Area, Water 0 0

Water Area(km2) Rainfall (mm)

Figure 4.37: Relationship between Sunamganj district water areas and observed Rainfall data at Sylhet station for the year 2006. From the figure 4.37, we have been observed that for the year 2006 rainfall variation affected the water areas of Sunamganj. When rainfall is high the large part of 70

Sunamganj are watered in the month April to July. But for the month of October to December it shows the different results. For small rainfall large area of Sunamganj are watered. It may occurs for the high water level.

Relationship between Water Area and Rainfall for the year 2011 3000 150 2 2500 2000 100 1500 1000 50 500 Rainfall, mm Rainfall,

Water Area, km Area, Water 0 0

Water Area, km2 Rainfall (mm)

Figure 4.38: Relationship between Sunamganj district water areas and observed Rainfall data at Sylhet station for the year 2011.

From the figure 4.38, we have been observed that for the year 2011 rainfall variation affected the water areas of Sunamganj. For high rainfall in June to September the large part of Sunamganj are watered. For the month October to December rainfall has been decreasing but water areas are increasing.

Relationship between Water Area and Rainfall for the year 2016 2000 250 2

1500 200 150 1000 100 500 50 Rainfall, mmRainfall,

Water Area, km Area, Water 0 0

Water Area, km2 Rainfall (mm)

Figure 4.39: Relationship between Sunamganj district water areas and observed Rainfall data at Sylhet station for the year 2016. From the above figure 4.36 to 4.39, we have been observed that rainfall variation affected the water areas of Sunamganj. When rainfall is high the large part of

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Sunamganj are watered in the month April to July. But for the month of October to December it shows the different results. For small rainfall large area of Sunamganj are watered. It may occurs for the high water level.

Relationship between Water Area and Water Level for the year 1994

4000 10 2 3000 8 Km 6 2000 4 1000 2 Water Level, Level, m Water 0 0 Water Area, Area, Water Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month Water area, km2 Water level, m

Figure 4.40: Relationship between Sunamganj district water area and observed water level data at Markuli station for the year 1994.

Relationship between Water Area and Water Level for the year 2006

4000 10 2 3000 8 Km 6 2000 4 1000 2

0 0 Level, m Water Water Area, Area, Water

Water Area(km2) Water Level (m)

Figure 4.41: Relationship between Sunamganj district water areas and observed water level data at Markuli station for the year 2006.

From the above figure 4.40 and figure 4.41, for the pre-monsoon season water level data has been large but the water areas are small. In monsoon season water areas are increasing with the water level. Although in the year 1994, July to September water level data has been increased but the water areas are being decreased. But for the year 2006 up to the month December water level has been increased but the water areas are decreasing. It means this water did not causes flood. Whether in the year 1994 water 72 areas are being fluctuated. From figure 4.42 and 4.43, we have been observed the same thing. Observed water level, discharge and rainfall data are presented in the table 4.7. Which was presented on the next page.

Relationship between Water Area and Water Level for the year 2011

3000 10 2 2500 8 2000 6 1500 4 1000 500 2 Water Level, m Level, Water Water Area, km Area, Water 0 0

Water Area, km2 Water Level (m, BWDB)

Figure 4.42: Relationship between Sunamganj district water areas and observed water level data at Markuli station for the year 2011.

Relationship between Water Area and Rainfall for the year 2016 2000 20 2 1500 15

1000 10

500 5 Water Level, Level, m Water Water Area, km Area, Water 0 0

Water Area, km2 Water Level (m, BWDB)

Figure 4.43: Relationship between Sunamganj district water areas and observed water level data at Markuli station for the year 2016.

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Table 4.7: Water levels for the station Markuli, Maximum Discharge for the station Sunamganj, Maximum Rainfall for the station Sylhet and Water Area of Sunamganj District for the year 1994, 2006, 2011 and 2016.

Discharge (m3/s, Water Level (m, BWDB) Rainfall (mm) BWDB) Water Area, km2

Month 1994 2006 2011 2016 1994 2006 2011 2016 1994 2006 2011 2016 1994 2006 2011 2016

January 178 152 126.01 1457 16 8 0 16 5.66 5.14 4.62 2.44 728.43 960.09 1792.19 505.15

February 57 188.7 107.48 138 16 24 3 18 3.46 4.07 4.25 2.09 142.75 198.91 193.07 71.09

March 420 60.62 1461.7 37.2 130 3 32 26 6.87 3.85 3.63 2.74 280.06 136.78 96.77 47.13

April 1050 125.3 401.53 1880 60 16 20 82 7.22 5.66 4.56 7.56 380.06 148.89 105.91 37.13

May 1605 1212 817.83 2240 102 82 63 151 7.15 7.5 7.5 7.85 762.31 551.7 541.07 1237.76

June 2160 1760 1570.2 2591 143 125 131 122 7.09 7.8 7.8 8.47 1244.63 1581.29 1917.95 1295.52

July 2730 2308 2528.6 2941 150 168 141 93 7.28 8.79 7.8 9.08 2171.19 2849.93 2587.01 1453.29

August 1240 1729 2428.9 1562 121 113 121 144 7.32 7.79 7.84 7.86 909.06 1993.09 2293.85 933.61

September 1815 1884 1800.6 2942 80 121 72 204 6.94 8.02 7.77 7.57 1430.79 2087.06 1969.13 1735.53

October 2390 1690 1310.3 2564 39 118 24 25 6.56 6.85 7.4 6.58 1952.52 1956.83 2303.34 1742.58

November 1980 1304 358.63 449 3 75 0 4 5.17 5.6 6.2 5.35 1380.71 1316.27 1242.93 1849.82

December 1570 169.8 124.87 115 2 67 0 6 7.82 4.65 4.8 7.6 912.85 1259.09 1026.38 1159.09

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Figure 4.44 to 4.46, shows the water area for different year’s corresponding to the month. Observed water level, discharge and rainfall data are presented in the table 4.7.

Water Area for Different Years 2000

2 1500

1000

500

0 January February March April Water Area , km Area Water Water Area, km2 1994 Water Area, km2 2006 Water Area, km2 2011 Water Area, km2 2016

Figure 4.44: Water area for different years for the month January to April.

From the above figure 4.44, we have been observed that the water area are significantly increasing in January but in 2011 its swiftly increasing. Again in 2016 it’s falling. In February it’s significantly increasing different in 2016. For the month March and April the water area has been decreasing.

Water Area for Different Years 3000

2 2500 2000 1500 1000 500

Water Area , km Area Water 0 May June July August

Water Area, km2 1994 Water Area, km2 2006 Water Area, km2 2011 Water Area, km2 2016

Figure 4.45: Water area for different years for the month May to August.

Figure 4.45 shows that for the monsoon season the water area varies from 500 to 3000 km2. There is huge change in inundated area between April and May (from 50% to 97% increase in water area). This is the critical time for haor as the croplands turned into the area for aquaculture. So the calculated inundated area from NDWI reflects the main characteristics of the haors in Sunamganj.

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Water Area for Different Years 2500 2 2000 1500 1000 500 0 September October November December Water Area , km Area Water Water Area, km2 1994 Water Area, km2 2006 Water Area, km2 2011 Water Area, km2 2016

Figure 4.46: Water area for different years for the month September to December.

From the above figure 4.46, we have been observed that for the post- monsoon season the water area varies from 900 km2 to 2300 km2. The inundated area simultaneously change its pattern. From the above figure, we have been observed that in the past flash flood does not occur in May and November but in present Sunamganj district has been affected by flash flood in those month.

4.7 Correlation between the results of Image analysis and Hydrological trend analysis for the year 1994, 2006, 2011 and 2016

From figure 4.47 to 4.49, we shows the relationship between the water areas and observed monthly discharge, rainfall and water level data for the year 1994, 2006, 2011 and 2016. From figure 4.47, we have been plotted water areas against discharge data and we got a relationship between inundated areas and discharge.

Relationship between Water Area and Discharge for the year 1994, 2006,2011 and 2016

4000 ) 2 y = 0.6844x + 111.63 3000 R² = 0.7644

2000

1000 Water Area (km Area Water 0 0 500 1000 1500 2000 2500 3000 Discharge (m3/s, BWDB)

Figure 4.47: Relationship between Sunamganj district water area and observed monthly discharge for the year 1994, 2006, 2011 and 2016.

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The correlation co-efficient (R2) value for the year (1994, 2006, 2011 and 2016) is

0.7644 (figure 4.47), between GIS simulated water areas and observed discharge. From visual observation, it is indicated that the trend and shape of the simulated is matched with observed stage hydrograph.

Relationship between Water Area and Water Level for the year 1994, 2006, 2011 and 2016 ) 2 3000 y = 149.68x + 32.747 2500 R² = 0.6237 2000 1500 1000 500 Water Area (km Area Water 0 0 2 4 6 8 10 12 14 Water Level (m, BWDB)

Figure 4.48: Relationship between Sunamganj district water area and observed monthly maximum water level for the year 1994, 2006, 2011 and 2016.

The correlation co-efficient (R2) value for the year (1994, 2006, 2011 and 2016) is

0.6237 (Figure 4.48) between GIS simulated water areas and observed monthly water level. From visual observation, it is indicated that the trend and shape of the simulated is matched with observed stage hydrograph. From figure 4.49 we also get a relationship.

Relationship between Water Area and Rainfall for the year 1994, 2006, 2011 and 2016

3000 y = 0.5218x + 984.56 ) 2500 R² = 0.1766 2000 1500 1000 500 Water Area (km2 Area Water 0 0 50 100 150 200 Rainfall (mm, BWDB)

Figure 4.49: Relationship between Sunamganj district water area and observed monthly maximum rainfall for the year 1994, 2006, 2011 and 2016.

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The correlation co-efficient (R2) value for the year 1994, 2006, 2011 and 2016 has been found as 0.1766 (Figure 4.49) between GIS simulated water areas and observed monthly rainfall data. It can be seen that the correlation value is far lower than unity. So the relationship exhibits a weak correlation. Therefore it is not suggested to follow equation 4.3 to obtain the inundated area from rainfall data. From the above analysis, it has been found that,

(i) Relationship between Inundated water area (km2) and discharge (m3/s)

Inundated Water Area = 0.6844*Discharge + 111.63 (4.1)

(ii) Relationship between Inundated water area (km2) and water level (m)

Inundated Water Area = 149.68*Water level + 32.75 (4.2)

(iii) Relationship between Inundated water area (km2) and rainfall (mm)

Inundated Water Area = 0.5218*Rainfall + 984.56 (4.3)

4.8 Frequency Analysis

By using Gumble’s Distribution water level and discharge values for 2.5, 5.0, 7.5 and 10.0 years return period has been estimated. After the analysis of discharge data and water level data for the year 1983 to 2016 probability curve has been plotted, which are shown in figure 4.50 and 4.51.

Probability curve for discharge 5000

/s /s 3825.318621 3 4000 3462.027721 3046.838121 3000 2216.458921 2000 y = 209.67x + 1827.2 R² = 0.9574 1000

0

Max. Discharge, m Discharge, Max. 0 2.5 5 7.5 10 Return period, T(Year)

Figure 4.50: Probability curve for maximum discharge value.

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Probability curve for water level 14.00 11.28 11.82 12.00 10.49 9.03 10.00 8.00 6.00 y = 0.3657x + 8.3703 4.00 R² = 0.9496 2.00 - 0 2.5 5 7.5 10 12.5

Max. Water Level, m Level, Water Max. Return period, T(Year)

Figure 4.51: Probability curve for maximum Water Level value.

From the frequency analysis it has been observed that for the selected return period the values of discharge and water level are increasing. For selected return period, discharge and water level data has been plotted by using equation 4.1 and 4.2. Based on those values, the inundated area of Sunamganj district has been calculated and the results are presented in table 4.8.

Table 4.8: For certain return period inundated area of Sunamganj district. Return Discharge, m3/s Water Level, m Inundated % of Inundated period Area, km2 Area 2.5 2216.46 9.03 1628.57 44.24% 5 3046.84 10.49 2196.88 59.68% 7.5 3462.03 11.28 2481.04 67.4% 10 3825.32 11.82 2729.68 74.15%

4.9 Summary

From the observation of satellite images and the above tables it is evident that discharge and water level are the major causes increasing water areas of Sunamganj district. From the NDWI maps it is also observed that the area which watered most in the last 44 years from 1973 to 2017. The above analysis shows that the Sunamganj district land area has been significantly decreased in monsoon and post-monsoon season. When discharge and water level are increasing Sunamganj district go through under water which is very harmful for our environmental and geological system. This critical condition is due to both natural and human interventions.

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Chapter 5

Conclusions and Recommendations

5.1 General

Sunamganj district is one of the main areas where the wetland (haor) occupies the maximum area during the monsoon period. The rainfall and the river network in and surrounding the district has a great influence on this vast water body. The spatio- temporal changes in Sunamganj haors has been assessed in this study for the study period 1973-2017. The hydrological parameters like rainfall, water level and discharge in the study area has been evaluated and a relationship of the hydrological parameters has been established with the inundated area.

5.2 Conclusions of the Study

From the overall study, some specific conclusions can be drawn which are listed below:

1. Based on the hydrological analysis and the NDWI extraction for water area, the following relationship has been found for Sunamganj district.

Relationship between Inundated water area (km2) and discharge (m3/s)

Inundated Water Area = 0.6844*Discharge + 111.63

Relationship between Inundated water area (km2) and water level (m)

Inundated Water Area = 149.68*Water level + 32.75

2. It has been found from the study that during the monsoon time around 62.54% to 77.42% of the area can be considered as haor area. Also in the dry season 1.2% to 5.0% area is under water. So there is a huge change in water area each year which effects the hydrological, morphological and anthropological conditions.

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3. From the study, it has been found that, there is an enormous change in inundated area between the month April and May. The percent of area increase varies from 50% to 97%. This is the critical period for the haors as the croplands turned into area of aquaculture.

4. The monsoon season in Bangladesh usually ends in the month October. In haor area, it has been observed that the inundated water area decreases in slow rate from October to January. The maximum inundated area reduces in January-February.

5. Because of low land elevation, the left side of Sunamganj district is always flooded before the right part.

5.3 Recommendations for Further Study

Sunamganj Haor is playing a vital role to keep the balance of the surrounding vast region. From the above study some recommendations can be summarized for future study:

1. The changes that occur in the haor area each year, need to be monitored and the inundation depth and duration data at different location of haor area is necessary for future research.

2. River network in the Sunamganj district need to be revived as all of them facilitates themselves as the main connectiors within the haors.

3. The detail analysis of satellite images (Landsat 8) can provide improved data for future mathematical modeling which will enable us for better understanding of the haor behavior.

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Appendix 1

Landsat Satellite Specification

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Landsat Satellite

1. Landsat-4 and Landsat-5

The Landsat Thematic Mapper (TM) sensor was carried onboard Landsats 4 and 5 from July 1982 to May 2012 with a 16-day repeat cycle, referenced to the Worldwide Reference System-2. Very few images were acquired from November 2011 to May 2012. The satellite began decommissioning activities in January 2013.

Landsat 4-5 TM image data files consist of seven spectral bands. The resolution is 30 meters for bands 1 to 7. (Thermal infrared band 6 was collected at 120 meters, but was resampled to 30 meters. The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).

Figure A 1: Illustration of the Landsat-4 and 5 spacecraft (Source: https://directory.eoportal.org)

Sensors

Multispectral Scanner (MSS)

Acquisitions of Landsat 5 MSS data over the United States ceased in 1992; global acquisitions ended in 1999. Limited acquisitions were made from June 2012 through January 2013, after the loss of the TM sensor on the satellite.

Four spectral bands (identical to Landsat 1 and 2):

Band 4 Visible green (0.5 to 0.6 µm) Band 5 Visible red (0.6 to 0.7 µm)

Band 6 Near-Infrared (0.7 to 0.8 µm) Band 7 Near-Infrared (0.8 to 1.1 µm) 89

Six detectors for each spectral band provided six scan lines on each active scan Ground Sampling Interval (pixel size): 57 x 79 m

Thematic Mapper (TM)

Added the mid-range infrared to the data

Seven spectral bands, including a thermal band:

Band 1 Visible (0.45 - 0.52 µm) 30 m

Band 2 Visible (0.52 - 0.60 µm) 30 m

Band 3 Visible (0.63 - 0.69 µm) 30 m

Band 4 Near-Infrared (0.76 - 0.90 µm) 30 m

Band 5 Near-Infrared (1.55 - 1.75 µm) 30 m

Band 6 Thermal (10.40 - 12.50 µm) 120 m

Band 7 Mid-Infrared (2.08 - 2.35 µm) 30 m

Ground Sampling Interval (pixel size): 30 m reflective, 120 m thermal

Product Processing Parameters

Most Landsat 4-5 TM scenes are processed through the Level 1 Product Generation System (LPGS). Some TM scenes are processed through the National Land Archive Production System (NLAPS). All Landsat 4 TM scenes were processed through NLAPS prior to April 15, 2009. Product Type L1T Terrain Corrected*

Pixel Size 30-meters (prior to February 25, 2010: thermal

Meters)

Output Format GeoTIFF

Resampling method cubic convolution (CC)

Map Projection UTM–WGS 84 Polar Stereographic for the

Antarctica.

Band 6=60

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While most Landsat scenes are processed with the Standard Terrain Correction (Level 1T), some scenes do not have the ground-control or elevation data necessary to perform these corrections. In these cases, the best level of correction is applied. (See Landsat Processing Details for details on correction levels.)

Specific Level 1T scenes are available for most of the globe under the Global Land Surveys (GLS) collections of 1975, 1990, 2000, 2005, and 2010. These datasets can be found on Earth Explorer or the USGS Global Visualization Viewer (GloVis).

Landsat 4-5 TM Collection 1

Landsat Tiers are the inventory structure for Landsat Collection 1 Level-1 data products and are based on data quality and level of processing. All scenes in the Landsat archive are assigned to a Collection category. The purpose of Collection categories is to support rapid and easy identification of suitable scenes for time-series pixel level analysis. During Collection 1 reprocessing, all Landsat 4-5 TM scenes in the USGS archive are assigned to a specific “Tier”. These data have well-characterized radiometric quality and are cross-calibrated among the different Landsat sensors.

Collection 1 Categories

Tier 1 (T1) – Contains the highest quality Level-1 Precision Terrain (L1TP) data considered suitable for time-series analysis. The georegistration is consistent and within prescribed tolerances [<12m root mean square error (RMSE)].

Tier 2 (T2) – Contains L1TP scenes not meeting Tier 1 criteria and all Systematic Terrain (L1GT) and Systematic (L1GS) scenes. Users interested in Tier 2 scenes can evaluate the L1TP RMSE and other properties to determine suitability for use in their applications and studies.

The Landsat Collections web page contains additional information about changes applied to Landsat Level-1 data products for Collection 1.

2. Landsat 7

Landsat 7 is the seventh satellite of the Landsat program. Launched on April 15, 1999, Landsat 7's primary goal is to refresh the global archive of satellite photos, providing

91 up-to-date and cloud-free images. The Landsat Program is managed and operated by the USGS, and data from Landsat 7 is collected and distributed by the USGS. The NASA World Wind project allows 3D images from Landsat 7 and other sources to be freely navigated and viewed from any angle. The satellite's companion, Earth Observing-1, trails by one minute and follows the same orbital characteristics. Landsat 7 was built by Lockheed Martin Space Systems Company.

Figure A 2: Illustration of Landsat-7 Spacecraft (Source: http://science.nasa.gov/missions/landsat-7/)

Main Features

1. A panchromatic band with "15 m (49 ft.)" spatial resolution (band 8)

2. Visible (reflected light) bands in the spectrum of blue, green, red, near-infrared (NIR), and mid-infrared (MIR) with 30 m (98 ft.) spatial resolution (bands 1-5, 7)

3. A thermal infrared channel with 60 m spatial resolution (band 6)

4. Full aperture, 5% absolute radiometric calibration

Scan Line Corrector failure

On May 31, 2003 the Scan Line Corrector (SLC) in the ETM+ instrument failed. The SLC consists of a pair of small mirrors that rotate about an axis in tandem with the motion of the main ETM+ scan mirror. The purpose of the SLC is to compensate for

92 the forward motion (along-track) of the spacecraft so that the resulting scans are aligned parallel to each other. Without the effects of the SLC, the instrument images the Earth in a "zig-zag" fashion, resulting in some areas that are imaged twice and others that are not imaged at all. The net effect is that approximately 22% of the data in a Landsat 7 scene is missing when acquired without a functional SLC.

Following the SLC failure, an Anomaly Response Team (ART) was assembled, consisting of representatives from the USGS, NASA, and Hughes Santa Barbara Remote Sensing (the manufacturer of the ETM+ instrument). The team assembled a list of possible failure scenarios, most of which pointed at a mechanical problem with the SLC itself. Since there is no backup SLC, a mechanical failure would indicate that the problem was permanent. However, the team was unable to rule out the possibility of an electrical failure, though such a possibility was deemed remote. Nevertheless, on September 3, 2003, USGS director Charles G. Groat authorized the Landsat project to reconfigure the ETM+ instrument and various other subsystems on board Landsat 7 to use the spacecraft's redundant ("Side-B") electrical harness.

With this authorization, the USGS flight operations team at the NASA Goddard Space Flight Center uploaded a series of commands to the spacecraft, instructing it to operate using the redundant electrical harness. This operation was successful, and on September 5, 2003, the ETM+ instrument was turned on and acquired data that was sent to the Landsat ground system at EROS outside Sioux Falls, South Dakota. It was immediately apparent that the migration to the Side-B electrical harness had not fixed the problem with the SLC. Following this, the instrument was reconfigured again to use its primary electrical harness. The subsequent conclusion of the ART was that the SLC problem was mechanical and permanent in nature.

Landsat 7 continues to acquire data in this mode. Data products are available with the missing data optionally filled in using other Landsat 7 data selected by the user. In 2013, Landsat 7 was joined by Landsat 8.

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Appendix 2

List of Sunamganj Haors

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Table 2.1: List of Sunamganj Haors (Source: BHWDB, 2012) Haor Area in Haor Name of Haor Area in Haor Name of Haor Area in Haor Name of Haor Area in Haor Name of Haor Area in Id Name of Haor km2 Id km2 Id km2 Id km2 Id km2

0 Sangshar Beel Haor 9.25 20 Togar Haor 43.58 40 Halir Haor 78.89 60 Khai Haor 53.87 80 Meda Beel Haor 1.86 1 Lubar Haor 16.36 21 Kalnikuri Beel 1.52 41 Angurali Haor 25.48 61 Chawlar Haor 37.81 81 Sullal Haor 8.8 2 Harinagar Haor 18.13 22 Chichrar Haor 14.6 42 Dekar Haor 251.3 62 Kalikota Haor 178.6 82 Haor 4.28 Kahilani-Sreekuli 3 Gomrar Haor 14.22 23 Haor 16.09 43 Jaliar Haor 24.66 63 Udgal Beel Haor 45.69 83 Sakitpur Haor 4.9 4 Sonadubi Haor 26.24 24 Shaldighar Haor 4.2 44 Kachibanga Haor 30.71 64 Chayer Haor 93.11 84 Jamaikata Haor 14.14 5 Dharam Haor 26.28 25 Atla Beel Haor 2.48 45 Pagnar Haor 173.82 65 Choukhali Haor 19.6 85 Chilaura Haor 7.43 6 Naya Beel Haor 2.66 26 Holdir Haor 2.23 46 Tanguar Haor 116.5 66 Bedar Dohar Haor 21.33 86 Parua Haor 5.98 7 Kalianibeel Haor 3.73 27 Kumuria Beel Haor 3.54 47 Ruiyer Beel Haor 2.3 67 Bhanda Haor 42.07 87 Rowail Haor 1.03 Suraya Bibiyana 8 Kainjar Haor 7.71 28 Morichapuri Haor 0.92 48 Bahara Haor 6.22 68 Haor 43.02 88 Pingla Haor 15.36 Maddhanagar 9 Sonamorol Haor 23.02 29 Boalar Haor 18.65 49 Katare Beel Haor 7.11 69 Tangua Haor 46.74 89 Saratir Haor 18.36 Bhera Mohana 10 Maheshpur Haor 6.46 30 Hasharani Beel Haor 2.74 50 Haor 7.03 70 Baram Haor 46.41 90 Chaliyar Haor 11.49 Balda Gulaghat 11 Kuri Haor 6.55 31 Bainchapra Haor 16.21 51 Joal Bhanga Haor 45.29 71 Naluar Haor 99.57 91 Haor 8.82 12 Jaydhuna Haor 4.2 32 Arong Beel Haor 6.76 52 Karchar Haor 64.11 72 Chaptir Haor 45.74 92 Kuti Beel Haor 4.07 13 Sashkar Haor 9.89 33 Gurmar Haor 12.19 53 Kalnar Haor 84.91 73 Doyalong Haor 14.66 93 Dhakua Haor 44.52

14 Dhankuniar Haor 17.6 34 Kanir Haor 4.84 54 Naidar Haor 68.29 74 Suktiar Haor 18.98 94 Dhamai Haor 17.43 15 Saytankhali Beel 3.67 35 Lusni Beel Haor1 17.08 55 Naingoan Haor 25.06 75 Lepa Haor 6.53 16 Dubail Haor 22.65 36 Medir Beel-1 Haor 9.14 56 Jamkhola Haor 21.04 76 Jahidpur Haor 4.69 Choto Hijla- Baro 17 Hijlar Beel Haor 5.76 37 Shanir Haor 78.71 57 Chaol Haor 14.74 77 Kurshi Chak Haor 13.8 Huramondira 18 Jaldhara/ Keuti Haor 7.05 38 Lusni Beel Haor2 3.59 58 Haor 17.7 78 Saidabad Haor 11.11 Shimultala -Jalla 19 Soilchapra Haor 18.75 39 Matian Haor 53.36 59 Shanghair Haor 37.6 79 Haor 18.68

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