Impact of Flash Flood on Boro Rice Production in Taherpur Upazila

IMPACT OF FLASH FLOOD ON BORO RICE PRODUCTION IN TAHERPUR UPAZILA

By Md. Abdullah All Sourav Master of Science in Water Resources Development

Institute of Water and Flood Management BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY June 2017

BANGLADESH UNIVERSITY OF ENGINEERING AND TECHNOLOGY Institute of Water and Flood Management

The thesis titled “Impact of Flash Flood on Boro Rice Production in Taherpur Upazila” submitted by Md. Abdullah All Sourav, Roll No. 1014282018 F, Session: October/2014 has been accepted as satisfactory in partial fulfillment of the requirement for the degree of Master of Science in Water Resources Development on June 3, 2017.

BOARD OF EXAMINERS

1. ______Dr. G. M. Tarekul Islam Chairman Professor IWFM, BUET, Dhaka (Supervisor)

2. ______Dr. Mashfiqus Salehin Member Professor and Director (Ex-Officio) IWFM, BUET, Dhaka

3. ______Dr. A.K.M. Saiful Islam Member Professor IWFM, BUET, Dhaka

4. ______Dr. Sujit Kumar Bala Member Professor IWFM, BUET, Dhaka

5. ______Dr. Muhammod Nazrul Islam Member Professor (External) Department of Geography and Environment Jahangirnagar University, Savar, Dhaka

CANDIDATE’S DECLARATION It is hereby declared that this thesis or any part of it has not been submitted elsewhere for the award of any degree or diploma.

Signature of the Candidate ______Md. Abdullah All Sourav

To my mother, Sultana Begum, my father, Hachhen Ali, my brothers, Shaikat and Shihab, and the most influential person for last five years of my life, my wife, Rumpa.

TABLE OF CONTENTS TABLE OF CONTENTS ...... i

LIST OF FIGURES ...... vii

LIST OF TABLES ...... x

LIST OF ABBREVIATIONS ...... xi

ACKNOWLEDGEMENT ...... xii

ABSTRACT ...... xiii

Chapter One Introduction ...... 1

1.1 Background ...... 1

1.2 Objectives ...... 3

1.3 Possible Outcome ...... 3

1.4 Limitation of the Study ...... 3

Chapter Two Review of Literature ...... 4

2.1 Introduction ...... 4

2.2 Flood in Bangladesh ...... 4

2.3 Haor in North-East Region of Bangladesh ...... 7

2.4 Flash Flood in North-East Region of Bangladesh ...... 9

2.5 Agriculture in Haor Region and Flash Flood ...... 11

2.6 Application of Remote Sensing in Flood Mapping ...... 13

2.7 MODIS data in Flood Mapping ...... 16

2.9 Density Slicing in Flood Mapping ...... 20

Chapter Three Study Area ...... 25

3.1 Introduction ...... 25

3.2 Location of the Study Area ...... 25

3.3 Haors and Beels of Taherpur ...... 25

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3.3.1 Tanguar Haor ...... 27

3.3.2 Shonir Haor ...... 28

3.3.3 Beels ...... 28

3.4 Land Use Pattern ...... 28

3.5 Climate Condition ...... 29

3.5.1 Rainfall ...... 29

3.5.2 Temperature ...... 30

3.5.3 Humidity ...... 32

3.6 Soil Condition ...... 33

3.7 Agricultural Production ...... 34

3.8 Natural Resources ...... 34

3.9 River Systems and Drainage Networks ...... 35

3.9.1 Jadukata – Rokti River ...... 36

3.9.2 Baulai ...... 37

3.9.3 Patnay River ...... 37

Chapter Four Data and Data Collection ...... 38

4.1 Introduction ...... 38

4.2 Category of Data ...... 38

4.3 Remote Sensing Data ...... 38

4.3.1 MODIS TERRA and MODIS AQUA Data ...... 39

4.3.2 Shuttle Radar Topography Mission (SRTM) 30m Global DEM ...... 40

4.4 Field Data ...... 43

4.5 Boro Production and Miscellaneous Data ...... 43

4.6 Jadukata River Water Level Data...... 44

Chapter Five Methodology ...... 45

5.1 Introduction ...... 45

5.2 Flash Flood Date Identification ...... 45

5.3 MODIS Data Download ...... 47

5.4 Cloud Free Data Identification ...... 48

5.5 Projection of Raw Data and Conversion to “tif” Format through MODIS Re- projection Tools ...... 49

5.6 Masking out the Study Area Using QGIS ...... 50

5.7 Threshold Value of Digital Number of Water Pixel in Near-infrared Band and NDVI Data ...... 50

5.8 Inundation Mapping from Near-infrared Band Data ...... 51

5.9 NDVI Extraction and Inundation Mapping ...... 52

5.10 Software Used ...... 53

Chapter Six Impact of Flash Flood on Boro Rice Production ...... 54

6.1 Introduction ...... 54

6.2 Identification of Threshold Values of Water Pixel DN in Near-infrared Band and NDVI Image ...... 54

6.3 Inundation Mapping for Pre-monsoon Season from 2007 to 2014 ...... 54

6.4 Inundation Map of 2007 to 2014 ...... 56

6.4.1 Inundation Maps of 2007 ...... 56

6.4.2 Inundation Map of 2008 ...... 58

6.4.3 Inundation Map of 2009 ...... 60

6.4.4 Inundation Map of 2010 ...... 62

6.4.5 Inundation Map of 2011 ...... 64

6.4.6 Inundation Map of 2012 ...... 66

6.4.7 Inundation Map of 2013 ...... 68

6.4.8 Inundation Map of 2014 ...... 70

6.5: Summary of Inundation Map and their Comparative Analysis ...... 72

6.6 Accuracy Assessment ...... 75

6.6.1 Accuracy of Inundation Map Derived from Near-infrared Band Data ...... 76

6.6.2 Accuracy of Inundation Map Derived from NDVI Image ...... 77

6.7 Impact of Flash Flood on Boro Production ...... 79

6.7.1 Effect of Timing of Flash Flood on Upshi Boro Rice Production ...... 79

6.7.2 Effect of Timing of Flash Flood on Upshi Boro Rice Production ...... 81

6.7.3 Effect of Timing of Flash Flood on Local Rice Production ...... 82

6.7.4 Effect of Timing of Flash Flood on Hybrid Boro Rice Production ...... 83

6.7.2 Effect of Flash Flood Inundation on Boro Rice Production ...... 83

6.9 Summarization of Semi-structured Interview of Farmers ...... 84

Chapter Seven Conclusions and Recommendations ...... 86

7.1 Conclusions ...... 86

7.2 Recommendation ...... 87

References ...... 88

Appendix A : Graph of Historical Water Level Data Set for Jadukata River (Provided by BWDB) ...... xiv

Appendix B : Inundation Mapping with Density Slicing Technique ...... xix

Appendix C : Semi-structured Interview Question for Assessing the Impact of Flash Flood on Boro Rice Production ...... xxii

Appendix D : Some of the Photos of Study Area in Monsoon and Spring ...... xxiii

LIST OF FIGURES

Figure 2.1 The GBM basins’ image 5

Figure 2.2 Haor of north east region of Bangladesh 7

Figure 2.3 North-east region of Bangladesh and its associated watersheds 9

Figure 2.4 Flood prone areas in Bangladesh 10

Figure 2.5 Impacts of flood on crop production in haor areas of two 12 upazilas in Kishoregonj Figure 2.6 Data collection by remote sensing 13

Figure 2.7 Active and passive remote sensing 14

Figure 2.8 Inundation map of Bangladesh using active sensor on August 03, 15 2007 (RADARSAT Satellite) Figure 2.9: Inundation map using passive sensor on July 29, 2007 (MODIS) 16

Figure 2.10 Inundation map based on combination of Terra and Aqua 17 MODIS images Figure 2.11 Extraction of different land cover (water bodies, vegetation, and 20 mixed land cover) from NDVI original image Figure 2.12 The flood extent in Pitt County 22

Figure 3.1 Map of Bangladesh showing Sunamganj district 26

Figure 3.2 Location of the study area 26

Figure 3.3 Mean monthly rainfall in Sylhet and Bangladesh 30

Figure 3.4 Mean minimum monthly temperature distributions for 1960, 31 1980, 2000 and 2013 Figure 3.5 Mean maximum monthly temperature distributions for 1960, 31 1980, 2000 and 2013 Figure 3.6 Mean monthly long-term humidity 32

Figure 3.7 Mean monthly humidity distributions for 1980, 2000, and 2013 32

Figure 3.8 Agro Ecological Zone of Taherpur 33

Figure 3.9 River network of Taherpur Upazila 35

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Figure 3.10 Jadukata River at India-Bangladesh boarder (spring) 36

Figure 3.11 Rokti River near Taherpur upazila 36

Figure 3.12 Baulai River at Taherpur (spring) 37

Figure 4.1 Collection of coordinate of different points with mobile GPS 42

Figure 4.2 Location of the ground truthing points and their inundation status 42

Figure 4.3 Interview of Upazila Agricultural Extension Officer, Taherpur 43

Figure 4.4 Semi-structured interview of farmers 44

Figure 4.5 Water level measuring station at Laurergarh Saktiarkhola 44

Figure 5.1 Outline of methodology 46

Figure 5.2 Input for density slicing of near-infrared band 51

Figure 5.3 NDVI image viewed in ILWIS Outline of methodology 52

Figure 6.1 Inundated area and non-inundated area in the map (May 20, 55 2014) Figure 6.2 Density sliced inundation map generated from near-infrared 56 band for pre-monsoon season of 2007 Figure 6.3 Inundation map generated from NDVI image for pre-monsoon 57 season of 2007 Figure 6.4 Density sliced inundation map generated from near-infrared 58 band for pre-monsoon season of 2008 Figure 6.5 Inundation map generated from NDVI image for pre-monsoon 59 season of 2008 Figure 6.6 Density sliced inundation map generated from near-infrared 60 band for pre-monsoon season of 2009 Figure 6.7 Inundation map generated from NDVI image for pre-monsoon 61 season of 2009 Figure 6.8 Density sliced inundation map generated from near-infrared 62 band for pre-monsoon season of 2010 Figure 6.9 Inundation map generated from NDVI image for pre-monsoon 63 season of 2010

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Figure 6.10 Density sliced inundation map generated from near-infrared 64 band for pre-monsoon season of 2011 Figure 6.11 Inundation map generated from NDVI image for pre-monsoon 65 season of 2011 Figure 6.12 Density sliced inundation map generated from near-infrared 66 band for pre-monsoon season of 2012 Figure 6.13 Inundation map generated from NDVI image for pre-monsoon 67 season of 2012 Figure 6.14 Density sliced inundation map generated from near-infrared 68 band for pre-monsoon season of 2013 Figure 6.15 Inundation map generated from NDVI image for pre-monsoon 69 season of 2013 Figure 6.16 Density sliced inundation map generated from near-infrared 70 band for pre-monsoon season of 2014 Figure 6.17 Inundation map generated from NDVI image for pre-monsoon 71 season of 2014 Figure 6.18 Comparison between inundation percentage derived from NIR 74 band and NDVI data Figure 6.19 Map showing spatial distribution of ground truthing points 75

Figure 6.20 Cross performance of sample set and inundation map derived 76 from near-infrared band data Figure 6.21 Cross performance of sample set and inundation map derived 78 from NDVI image Figure 6.22 Effect of flash flood timing on total boro rice production 80

Figure 6.23 Effect of flash flood timing on Upshi rice production 81

Figure 6.24 Effect of flash flood timing on local rice production 81

Figure 6.25 Effect of flash flood timing on local rice production 82

Figure 6.26 Effect of flash flood inundation on boro rice production 83

LIST OF TABLES

Table 2.1 The GBM basins 5

Table 2.2 Evaluation of supervised classifications relative to CIR aerial 24 photography for the density slicing method applied to SWIR band 5 and the classification tree and feature extraction methods applied to four band combinations. Table 4.1 SRTM product specifications 41

Table 5.1 MODIS Data used in flood mapping 49

Table 5.2 Input for density slicing of near-infrared band 51

Table 5.3 Input for density slicing of NDVI data 53

Table 6.1 Summary of inundation of Taherpur upazila due to flash flood, 72 derived using near-infrared band with density slicing technique Table 6.2 Summary of inundation of Taherpur upazila due to flash flood, 73 derived from NDVI image Table 6.3 Percentage of inundated area for 2007 to 2014 74

Table 6.4 Confusion matrix of inundation map derived from near-infrared 77 band data Table 6.5 Confusion matrix of inundation map derived from NDVI image 78

Table 6.6 Flash flood timing and boro rice production 79

LIST OF ABBREVIATIONS

AVHRR Advanced Very High Resolution Radiometer

BCM Billion Cubic Meters

BMD Bangladesh Meteorological Department

BUET Bangladesh University of Engineering and Technology

BWDB Bangladesh Water Development Board

DEM DEM Digital Elevation Model

DN Digital Number

ETM+ Enhanced Thematic Mapper Plus

GBM Ganges Brahmaputra Meghna

GIS Geographic Information System

GPS Global Positioning System

ILWIS Integrated Land and Water Information System

IWFM Institute of Water and Flood Management

MODIS Moderate Resolution Imaging Spectroradiometer

MRT MODIS Reprojection Tool

MSS Multispectral Scanner System

NASA National Aeronautics and Space Administration

QGIS Quantum Geographic Information System

SRTM Shuttle Radar Topography Mission

SWIR Short Wave Infrared

TIF Tagged Image File

TM Thematic Mapping

USGS United States Geological Survey

WL Water Level

ACKNOWLEDGEMENT

All praises are due to the Almighty Allah who has provided me with the opportunity to complete this research.

The author acknowledges his heartfelt gratitude to Dr. G. M. Tarekul Islam, Professor, Institute of Water and Flood Management, BUET for his invaluable inputs, guidance, cooperation and constant encouragement during the time of research and course work. I truly appreciate his esteemed guidance and encouragement from beginning to the end of the thesis, his knowledge and company at the time of crisis would be remembered lifelong.

The author is also grateful to Dr. Sujit Kumar Bala, Professor, Institute of Water and Flood Management, BUET, Dr. A.K.M. Saiful Islam, Professor, Institute of Water and Flood Management, BUET, who helped by providing guidance during the course of the research. Their valuable comments on this study are duly acknowledged.

The author would like to thank his parents and Tanzila Tahmin for their encouragement, inspiration and blessings. Without their help and support it would not be possible to finish M.Sc. The author is grateful for their limitless sacrifice and love.

The author would like to express his sincere gratitude and appreciation to his all friends for their help, support and inspiration.

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ABSTRACT

Haors are large bowl-shaped flood plain depressions located mostly in north-eastern part of Bangladesh covering a total area of over 19,998 square kilometers and very susceptible to flash flood. These haor basins are being used for fish production in monsoon and boro rice production in dry season. However, they often experience severe damage to boro rice in the pre-monsoon months of April and May due to flash flood’s sudden high discharges and velocities, and unexpected rise and recession. Therefore, an attempt was made for the year of 2007 to 2014 with a view to mapping flash flood inundation using remote sensing and Geographic Information System (GIS) data and assessig the impact of the flash flood on total boro rice production in Taherpur Upazila of northeastern Bangladesh. This study revealed the extent of the inundation occurred by flash flood in the Taherpur upazila from 2007 to 2014 and how they affected the boro rice production. The Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and AQUA satellite data were used for mapping flash flood inundation using two different methods viz. density slicing of near-infrared band and density slicing of Normalized Difference Vegetation Index (NDVI). Using the 30 ground truthing points, SRTM 30m DEM data, and field data, threshold values of the water pixel digital number of cloud free near-infrared band and NDVI image were identified to map flash flood. On the other hand, flash flood timing was found out by observing the water level of the Jadukata River. Satellite image processing, analysis and density slicing operation were carried out with MODIS Reprojection Tool, QGIS, and ILWIS. The accuracy of the inundation mapping was assessed with confusion matrix. The accuracy of inundation mapping using near-infrared band and NDVI image was 92.50% and 87.50%, respectively. Inundation mapping technique with higher accuracy was used for assessing the impact of flash flood on boro rice production. Flash flood timing was plotted against the boro rice production to find out the relationship between flash flood timing and boro rice production. Relationships between production of different types of boro rice and flash flood timing were also derived. It was found that the extent of inundation has weak correlation with the boro rice production. Studies using high temporal and spatial resolution satellite image may lead to better result. From semi-structured interview with farmers, it was observed that the impact of flash flood heavily relies on the duration of inundation, hailstorm, and some other factors. In addition, introduction of new rice varieties, cultivation of flood-tolerant rice varieties, availability of chemical fertilizer and local measures of rice harvesting were the reasons for rice production improvement.

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Chapter One Introduction

1.1 Background

Among all kinds of natural hazards of the world, flood is probably the most devastating, widespread and frequent natural calamity human beings have to face. In the humid tropics and subtropical climates, especially in the realms of monsoon, river flooding is a recurrent natural phenomenon (Sanyal and Lu, 2004). Like other South Asian countries, the flood is a very common phenomenon in Bangladesh due to its geographic location and immerse river network. The country is located at a floodplain delta of three major river basins: the Ganges, the Brahmaputra, and the Meghna. An extreme monsoon river flood occurs approximately once in every five to ten years in Bangladesh and has a significant negative impact on the country (Islam et al., 2008). The degree of these flood’s inundation sometimes become severe and causes damage to infrastructures, crops, communication system, and human being.

Haors with their unique hydro-ecological characteristics are large bowl shaped floodplain depressions located in the north-eastern region of Bangladesh in Sunamganj, Sylhet, Moulvibazar, Kishoreganj, Habiganj, Brahmanbaria, and Netrokona districts (CEGIS, 2012). The flooding in the haor area is associated with the rainfall in the upstream catchment in India (Meghalaya, Barak and Tripura basins in India) and often occurs in a short notice. These rapid flooding with a high discharge and velocity are called flash flood. Flash floods spill onto low-lying floodplain lands inundating crops, damaging infrastructure by erosion and often causing loss of lives and property. Its severity depends on duration, intensity, and total catchment area generated the run-off (Salauddin and Islam, 2010). Intense flow of rainfall fed water often washes out submersible embankment, bank of the rivers as well as canals and gets into the rice growing floodplain and inundate boro rice field.

Almost 80% of the haor area is covered by boro rice which is the main crop and is frequently affected by the flash floods in the pre-monsoon months of April and May (Khan et al., 2012). Recently, Climate change is also exacerbating the situation

(Bhattacharya and Suman, 2012). In haor region, the most vulnerable area to flash flood is Sunamganj district especially upazilas adjacent to the hilly area. Taherpur Upazila has a long history of boro production damage due to flash flood. However, boro rice damage assessment and prediction model are not even available for this region. A flash flood mapping model and relationship development of its impact on boro rice production will help planners to take necessary steps for food security.

With advancement in satellite technology, the remote sensing images provide access to spatial information at the global scale; of features and phenomena on earth on an almost real-time basis (Mohd et al., 1994). Application of remote sensing and GIS technology in flood inundation mapping was started in 1973 using Landsat MSS data alongside the geographic information system (GIS), it has become the key tool for flood monitoring in recent years. Landsat TM data with higher resolution became the prime source of monsoon flood inundation mapping data in South Asia and Africa (Sanyal and Lu, 2004). In Bangladesh, numerous researches have been conducted for mapping monsoon flood inundation, especially for the flood of 2004 and 2007. However, works on the real-time mapping of the flood inundation in Bangladesh has been limited compared to the wide range research that has been conducted in other countries (Islam et al., 2008).

For unique characteristics of a flash flood, its inundation mapping requires satellite images with higher temporal resolution. The MODIS satellite provides data in 36 different bands and some of them have been used for mapping flood inundation. Flood mapping systems utilizing MODIS data are now capable of generating near-real-time flood maps with a global coverage on a daily basis. An online flood mapping system has been developed by NASA to provide the observational information as well as real- time maps with a rapid mapping technique (NASA, 2007; 2016a).

In this study, an attempt has been made to map flash flood inundation and assess its impact on boro rice production specifically between 2007 and 2014 in the Taherpur Upazila using Geographic Information System (GIS) and Remote Sensing (RS) data and technologies. In this study, MODIS data with a spatial resolution of 250 meters and daily temporal resolution were used.

1.2 Objectives

The overall objectives of this study are as follows:

i) To assess the extent of inundation caused by flash flood using satellite remote sensing ii) To develop a relationship between area of flash flood inundation and boro rice production

The second objective will be accomplished by analyzing relations between flash flood timing and boro rice production, flash flood inundation and boro rice production, and if possible flash flood inundation duration and boro rice production.

1.3 Possible Outcome

The study will come up with an effective method of mapping flash flood and find its impact on boro rice production. The method of flood mapping and the relation between different aspects flash flood and boro production would provide very useful information for to be used in planning.

1.4 Limitation of the Study

The main limitations of this study are-

➢ The non-availability of the cloud free high-resolution images of the study area. The study results could be made more accurate if remote sensing data of higher resolution for the study area were used. ➢ Availability of cloud free data in pre-monsoon season is limited. In MODIS data, it is absent for a couple of weeks in a row. ➢ The study area is so remote that some of its parts could not be accessed and it was time-consuming to conduct field visit. ➢ Financial constraint inhibits buying high-resolution images.

Chapter Two Review of Literature

2.1 Introduction

The study focuses on flash flood inundation mapping with remotely sensed data and assessing its impact on boro rice production in the Taherpur Upazila that is covered by numerous haors and low laying land. The study was carried out between 2007 and 2014. A brief review of floods, flash floods, flash floods in haor region, and application of remote sensing and GIS techniques in flood mapping are presented in this chapter.

2.2 Flood in Bangladesh

Bangladesh is generally known as a small but fast developing country in South Asia. The country has low medium income with high population density (Ullah, 2015). It is the largest delta in the world that covers an area of 147,613 km2 and located in the confluence and delta area of three large rivers; the Ganges, the Brahmaputra, and the Meghna (Figure 2.1). Almost 60 percent of the country lies less than 6 meters above the sea level which makes it prone to flood and storm (USAID, 1988). With almost 700 rivers, it is also known as the land of rivers. The rivers are not, however, evenly distributed. Sothern part of the country has more rivers and water bodies than the northern part. The total length of all rivers, streams, creeks, and channels is about 24,140 km. The total discharge of this river system is almost 1,174 billion cubic meter per year (Banglapedia, 2014). However, a good picture of the water status in this country can only be understood by looking at the river system as well as international boundaries of GBM basins presented in figure 2.1. The details of GBM basins in terms of catchment area are also presented in Table 2.1.

Table 2.1: The GBM basins (Source: Joint River Commission, Bangladesh)

Catchment Areas of Major Rivers Rivers Total Catchment Area (Sq. Km.) Catchment India Nepal Bhutan China Bangladesh Area (Sq.Km.) Brahmaputra 552000 195000 - 47000 270900 39100 Ganges 1087300 860000 147480 - 33520 46300 Meghna 82000 47000 - - - 35000 Total 1721300 1102000 147480 47000 304420 120400 Catchment area of GBM basins Percentage 100 64.02 8.57 2.73 17.69 7

Figure 2.1: The GBM basins’ image (Source: Banglapedia)

Over 92 percent of the annual runoff generated in the GBM basins flow through Bangladesh. However, as shown in table 2.1, it only contributes 7 percent of the total GBM basins catchment area (Coleman, 1969). As most of the rivers of GBM basins fall into the Bay of Bengal after flowing through Bangladesh, the country has to drain water from an area 12 times its own size (Bingham, 1991; Miah, 1988). The amount

of water that annually reaches Bangladesh from the sources that are outside the country would form a lake of the size of the country and a depth of 10.3 meters (Ahmed, 1989).

Bangladesh as well as the Ganges–Brahmaputra–Meghna (GBM) basins are dominated by the Asian monsoon system. Most of the rainfall occurs in the GBM basins during the monsoon season- June to August. At this time, Bangladesh becomes a ‘‘water country’’, rivers, ponds, and depressions filled with water as well as the sea, which are all interlinked form large water bodies. In monsoon, heavy rainfall in the catchment area of this three major rivers creates overland flows and runoffs often causing floods.

Bangladesh has a long history of floods. Approximately, once in every five to ten years, it experiences an extreme monsoon river flood that leaves a negative impact on the economy and livelihood of many people (Islam et al., 2010). On the other hand, a regular monsoon flood causes an inundation to about one-fourth to one-third of the country (Islam et al., 2010; Kwak et al., 2015). After 1950, four extremely large devastating floods have so far been recorded: the 1988 floods affected approximately 63.1%, or about 89,970 km2, of the total area of the country, the 1998 floods affected 67.9%, or about 100,200 km2, while the 2004 floods affected approximately 37.2%, or about 55,000 km2. Also, the floods that hit the country in 2007 affected approximately 40%, or about 52,600 km2, of the total area of the country (Kwak et al., 2015). Among all sectors, agriculture was severely affected, where it shares 30% of the gross domestic product and 65% of the nation’s workforce (Kwak et al., 2015; BBS, 2012).

In contrast, small scale flooding comes with plenty of benefits. It replenishes the groundwater and helps to sustain the agricultural industry. The sediments carried out by flood water make the land more fertile. Flood water avails considerable amount of water required by crops. In the southern part of the country, salts deposited on fields are being washed and removed by flood water. Hence, prevent the agriculture lands from becoming infertile. In a nutshell, the benefits of flooding are immense, but at times, floods become destructive when they are on the extreme (Hasan, 2015).

2.3 Haor in North-East Region of Bangladesh

Haors are large bowl-shaped flood plain depressions located mostly in north-eastern part of Bangladesh covering about 859,000 hectares (19,998 km2) of area and accommodating about 19.37 million people (CEGIS, 2012). It is a mosaic of wetland habitats including rivers, streams, canals, large areas of seasonally flooded cultivated plains and beels.

Figure 2.2: Haor of north east region of Bangladesh (Source: Haor Masterplan, 2002)

There are 373 haors/wetlands located in the districts of Sunamganj, Sylhet, Habiganj, Moulvibazar, Netrakona, Kishoreganj and Brahmanbaria as shown in figure 2.2 (CEGIS, 2012). It is bounded by the Meghalaya state of India to the north, the Assam state of India to the East, the Old Brahmaputra to the west, the Nasir Nagar (to Madhabpur) and Meghna River to the south (NERP, 1995).

The physical setting and hydrology of the haor region of Bangladesh have created lots of opportunities as well as constraints for the people living in this region. It has distinctive hydrological characteristics. The amount of rainfall received annually ranges from 2200 mm along the western boundary to 5800 mm in its north east corner and is as high as 12000 mm in the headwaters of some catchments extending to India (CEGIS, 2012). The region receives water from the catchment slopes of the Shillong Plateau across the borders in India to the north and the Tripura Hills in India to the south-east (CEGIS, 2012). Flash flood is the main disaster here which not only engulfs the agriculture sector but also threatens the lives and livelihoods of the people. Excess rainfall in the upstream hilly areas of India and subsequent high runoff, sedimentation in the rivers, landslide, lack of proper drainage, deforestation and hill cuts, unplanned road and water management infrastructure and the effect of climate change can be viewed as the main reasons for the devastation caused by flash floods (CEGIS, 2012).

According to IWM, the north-east region of Bangladesh receives water from Meghalaya Basin, Barak Basin, and Tripura Basin of India as shown in figure 2.3. In terms of the catchment area, the Barak Basin contributes the most with an area of 26613 km2. There are 15 major cross border rivers that carry water from external catchment area of 44,500 km2 to the north-east haor region Bangladesh. They are Bhugai, Jadukata (Rakti), Jhalukhali(Chalti), Chela, Dhala Gang, Jafflong River, Sarigowain, Lubachara, Barak, Sonaibardal, Juri, Manu, Dhalai, and Khowai. Other major rivers are; Kangsha, Someswari, Dhanu, Baulai, Surma, Kushiyara, Kalni, Titas, Old Surma, Ghorautra, and Upper Meghna. The rainfall distribution is also uneven in the area; annual average rainfall ranges from 2000 mm in the southwest to 5900 mm in the northeast.

Figure 2.3: North-east region of Bangladesh and its associated watersheds

2.4 Flash Flood in North-East Region of Bangladesh

Flood implies abnormal submergence of land, which causes damage to crops and property as well as loss of lives (Brammer, 2000; Hasan, 2015). The term flood is generally used when the flows in the rivers and channels cannot be contained within natural or artificial river banks. By spilling over the river banks, when water inundates floodplains and adjoining high lands to some extent or when the water level in the river or channels exceeds certain stage, the situation can then be termed as a flood (Hasan, 2015; Rahman et al., 2007).

Bangladesh experiences four types of floods: flash flood, river flood, tidal flood, and storm surge (Islam, 2006). Figure 2.4 shows the types of flood experienced by different parts of the country. Majority of the areas are affected by at least one type of flood except for the hilly regions of Chittagong division, a large part of Rangpur and Rajshahi division, and a small part of the Dhaka and Sylhet division. The figure depicts that flash flood are common phenomena in northern and north-east hilly region. It is notorious for its sudden high discharges and velocities, and abrupt rise and recession.

Figure 2.4: Flood prone areas in Bangladesh (source: BARC, 2010)

The National Weather Service of USA defines flash flood as a rapid and extreme flow of high water into a normally dry area, or a rapid rise in a stream or creek above a predetermined flood level, beginning within six hours of the causative event (e.g.,

intense rainfall, dam failure). Often with high velocities of on-rush flood damages crops, properties and fish stocks of the wetland (Hassan, 2015).

In north-east Bangladesh, rivers are excessively flashy and provide a wide variety of flow as excessive rainfall in the Meghalaya state of India. Due to heavy rainfall in Meghalaya, water moves towards the haor area through several rivers causing flash floods (Islam et al., 2008). The flash floods not only carry the runoff but also huge sediments. Over time, these sediments are deposited on the river bank, river bed, canal bed and finally reduce the conveyance capacity of all hydrologic system. In recent time, flash flood water often exceeds the maximum conveyance capacity and can easily overtop to the adjacent floodplain of the river. Even submersible embankment cannot prevent the flash flood water from entering into the agricultural field. The water moves through agricultural field leaving a devastating effect on local agriculture and life (Salauddin and Islam, 2010).

2.5 Agriculture in Haor Region and Flash Flood

North-east region of Bangladesh is widely known for its fisheries and boro cultivation activities. Almost 80% of haor area is cultivated by boro rice, while only about 10% is cultivated by T. Aman production (Huda, 2004). Wheat, potato, vegetable, and mustard are grown in negligible quantities. Thus, a flash flood is an acute threat to agriculture in pre-monsoon season, especially for boro rice (Upazila Disaster Management Committee, 2014).

A study conducted by Khan et al. (2012) in two upazilas in Kishoregonj districts which are mainly haor region showed that the river flood does minimum damage where the flash flood is the main reason for crop damage, especially boro rice production. As shown the result of that study in figure 2.5, almost 97% of the damage is done to boro rice production as flash flood water enters to the haors in the harvesting periods (Khan et al., 2012).

Category of Flood Which Damage the Crop

11%

19%

70%

Flash Flood Rainfed Flood River Flood

Types of crop damage by flood 96.64% responded

Percentange Percentange of the 0% 0% 3.36%

Boro Aush Aman Other Robi Crop Type of Crop

Figure 2.5: Impacts of flood on crop production in haor areas of two upazilas in Kishoregonj (Source: Khan et al., 2012)

In the north-eastern region of Bangladesh, flash floods of different intensities occur every year. The intensity differs from one basin to another. The most vulnerable area to flash flood is Sunamganj district, especially upazilas those are adjacent to the hilly area. Its degree of damage depends on the duration of the flash flood, intensity, and total catchment area of runoff generation (Salauddin and Islam, 2010). As per Upazila Level Disaster Management Plan, in 2010, flash flood water was flowing over 35 cm of the danger level and kept increasing for 2-3 days. It damaged almost 42% of the crops, which were 10,337 hectares of rice and vegetable fields. In addition, 2,600 homes (10.62%) were damaged along with 20 km of road. Due to its adverse effect on boro production, a flash flood is ranked as the number one hazard of Taherpur region (Upazila Disaster Management Committee, 2014).

2.6 Application of Remote Sensing in Flood Mapping

Remote sensing is the science (and to some extent, art) of acquiring information about the earth's surface without actually being in contact with it as shown in figure 2.6. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information. In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest. Remote sensing may also involve the sensing of an emitted energy and the use of non-imaging sensors. It is, therefore, an attempt to measure something at a distance, rather than in situ. Since we are not in direct contact with the object of interest, we must rely on propagated signals of some sort, for example optical, acoustical, or microwave (Hasan, 2015).

Figure 2.6: Data collection by remote sensing

In remote sensing, the energy is measured with two types of instruments: active sensor and passive sensor, as illustrated in figure 2.7. Passive remote sensing techniques employ natural sources of energy, such as the Sun or artificial light. In this type of remote sensing, sunlight is the most common source of radiation measured by passive sensors (Janssen and Huurneman, 2004). Active sensors provide their own source of

energy to illuminate the objects they observe. It emits and controls a beam of energy to the surface and measures the amount of energy reflected back to the sensor. The main advantage of active sensor systems is that they can be operated day and night (Janssen and Huurneman, 2004).

Figure 2.7: Active and passive remote sensing

Active remote sensing system as Synthetic Aperture Radar (SAR) system with its cloud-penetrating capability has been used with considerable success for mapping the extent of flooding in some of the major floodplains and river basins of the world (Wilson and Rashid, 2005). Imhoff et al. (1987) showed that SAR imagery is more effective than Landsat MSS image for monsoon flood mapping in Bangladesh. In recent time, Bhatt et al. (2010; 2013; 2016) conducted several studies to map the flood and inundated area in GBM basins with active remote sensing data.

In Bangladesh, SAR imagery proved to be useful for mapping river water flooding in an urban landscape (Dewan et al., 2006), accuracy assessment of other flood maps (Islam et al., 2008), analyze and compare flooding extant of Upper Meghna River (Hasan, 2015), and frequency analysis of flood in the north-eastern region (Hoque et al., 2011). Figure 2.8 shows a flood map of 2007 in entire Bangladesh. The flood map was derived from RADARSAT data and identified the river flood and the lower inundated area in the hoar region.

Figure 2.8: Inundation map of Bangladesh using active sensor on August 03, 2007 (RADARSAT Satellite) (Islam et al., 2008) Passive remote sensing data have been widely used for flood mapping as well as environmental change assessment. It is also used in land cover change detection, planning, yield prediction, and a lot more. The main reason for its wide use is some satellite’s images are freely available to download and they also provide moderate to better temporal and spatial resolution – e.g. MODIS and LANDSAT imagery. Numerous studies had been reported to use passive remote sensing data in flood mapping (Byun et al., 2015; De Groeve, 2010; Jain et al., 2005; Wang et al., 2002). In Bangladesh, Kwak et al., (2015) used MODIS data to map flood damage in the rice field, Islam and Sado (2000; 2002) used NOAA AVHRR data for mapping the extent of flood and food hazard. Islam et al. (2008) used MODIS image for mapping 2007’s flood in Bangladesh as illustrated in figure 2.9. By using different water indices, flood water, long term flood water, a mixture of water and vegetation were mapped in his study.

Figure 2.9: Inundation map using passive sensor on July 29, 2007 (MODIS) (Islam et al., 2008)

2.7 MODIS data in Flood Mapping

After launching MODIS satellite in December 1999, it has now become one of the main contributors of real time land cover mapping (Seo et al., 2016; Almeida et al., 2016; Gessner et al., 2015). Another important application of MODIS satellite data is mapping agricultural field as well as predict crop yield (Bala and Islam, 2009; Son et al., 2014; Mkhabela et al., 2011). However, the benefit of using MODIS instrument is that it freely broadcasts raw data throughout the world. In addition to this, it provides raw data with higher temporal resolution (Daily, 8 days, and 16 days), swath width range (approximately 2330 km) and moderate spatial resolution (250m and 500m and 1000m) (Pinheiro et al., 2007). Flood mapping systems utilizing MODIS data are now capable of generating near real-time flood maps with a global coverage on a daily basis. An online flood mapping system has been developed by NASA to provide

fundamental observational information as well as produce maps with a rapid mapping technique (NASA, 2007; 2016a). These online downloadable data are being used to monitor nation-wide flood all over the world with high temporal and spatial resolution. Using real time flood map and other related information, disaster managers, and other end users can monitor floods and evaluate larger-scale flood risk (Kwak et al., 2015). Flood map developed by Kwak and Iwami (2014) is a good example of the application of MODIS data in flood mapping (Figure 2.10). In that study, flood occurred in 2007 in Bangladesh was analyzed with a 10-year return period over the whole country from July to August 2007.

Figure 2.10: Inundation map based on combination of Terra and Aqua MODIS images (Kwak and Iwami, 2014)

Figure 2.10 illustrates the inundation map resulting from the combination of Terra and Aqua MODIS images. The combination of Terra and Aqua MODIS showed that the 42% of the country was inundated during the 2007’s flood. The inundation map in was produced by Modified Land Surface Water Index (MLSWI) which is given by the following equation:

1−NIR−SWIR MLSWI = 1−NIR+SWIR …………………… (2.1)

Where NIR= near-infrared and SWIR = short waved infrared

In the figure 2.10, the red color represents the flood waters during the period August 5 to12, 2007 and the blue color represents the permanent water bodies captured before flooding between March 22 and 29, 2007. Although flood areas smaller than 500m were not detected accurately because of low spatial resolution (Kwak and Iwami, 2014).

Using MODIS data, there are several water detection algorithms that have been developed and which rely on combinations of surface reflectance at two or more wavelengths, such as the Normalized Difference Water Index (NDWI) and the Land Surface Water Index (LSWI). For detecting water, the three most widely used water sensitive bands are –band 2 (841–876 nm), band 6 (1628–1652 nm), and band 7 (2105– 2155 nm) (Kwak et al., 2015). Near-infrared band (band 2) has solely been used to detect water-bodies with the simple process of density slicing. However, for better detection and higher accuracy, several indices are further used to derive new algorithms; for example, the Modified Land Surface Water Index (MLSWI), specifically LSWI and NDWI were used to develop this and provides better results (Kwak et al., 2015). For real time flood mapping, Dartmouth Flood Observatory uses a water detection algorithm based on a reflectance ratio of MODIS bands 1 and 2, and a threshold on band 7 to provisionally identify pixels as water (Kwak et al., 2015).

2.8 NDVI in Flood Mapping

Tucker (1979) first suggested Normalized Difference Vegetation Index (NDVI) as an index of vegetation health and density. According to Tucker, NDVI is defined as:

NIR−RED NDVI = NIR+RED ………………..(2.2)

NDVI = Normalized Difference Vegetation Index NIR = Near-infrared band of electromagnetic spectrum RED = Red band of electromagnetic spectrum

It is the most common vegetative index and its value ranges from -1 to +1. The lower value means no vegetation and the higher means highest possible density of green biomass (Salauddin and Islam, 2010). However, it has also been used for mapping inundation. NDVI uses the near-infrared band’s capability of detecting water effectively. It is well known that water has a unique spectral signature in the near- infrared band which is very different from other surface features. Therefore, if a surface feature is inundated its NDVI value changes considerably from the normal situation (Sanyal and Lu, 2004).

Wang et al. (2002) observed that in the lower reaches of the Yangtze River, the NDVI value for inundated surface features remains negative while the value for the non- inundated surface is commonly greater than 0. But the choice of this threshold is critical because different natural condition offers different threshold values. In some studies, NDVI values of flood water were found to be significantly positive where it supposed to be negative (Barton and Bathols, 1989). Thus, a straight forward approach of using simple NDVI values might not be universally effective in the delineation of inundated area. However, considering geographical location, the location of satellite, season, sedimentation concentration, the threshold NDVI value for water pixel for specific study area can be determined (Sanyal and Lu, 2004). Kwak and Iwami (2014) used MYD09A1 and MOD09A1 data for mapping flood for entire Bangladesh. They assumed threshold value of inundated area’s NDVI as -0.2 but the map underestimated flooded land and flood waters over mixed-water zones due to overflow from the Brahmaputra River. Salauddin and Islam (2010) used the same dataset with a different threshold value to map flood in haor region of Bangladesh. As shown in Figure 2.11, in that study, waterbody had NDVI value of -1 to 0.1, vegetation .4 to1.0 and mixed areas 0.1 to 0.4.

Figure 2.11: Extraction of different land cover (water bodies, vegetation, and mixed land cover) from NDVI original image

2.9 Density Slicing in Flood Mapping

Density slicing is a digital data interpretation method used in the analysis of remotely sensed imagery to enhance the information gathered from an individual brightness band. Density slicing is done by dividing the range of brightness in a single band into intervals, then assigning each interval to a color (Campbell, 2002). This method is considered popular for delineating water bodies because it is easy to use and less time- consuming than alternatives approaches (Ryu et al., 2002). In many studies, Landsat (TM and MSS), SPOT or IRS data have been used to identify water bodies using simple classification procedures, density slicing, usually with an infrared band. Water bodies have a unique spectral response in this range of electromagnetic radiation when

compared to the surrounding landscape or vegetation (Frazier and Page, 2000; Wang et al., 2002). Since Landsat data has become available at 1972, several early studies were conducted using Landsat MSS data especially with band 7 for distinguishing water bodies from adjacent soil and vegetation. Some of those studies were cited by Smith (1997). Bennett (1987) used density slicing of Landsat MSS band 7 to map water bodies to the west of Griffith, New South Wales, Australia (NSW).

A simple and efficient method for mapping food extent in a coastal floodplain was used by Wang et al. (2002). The method was based on a comparison of the reflectance feature of the water versus non-water targets on a pair of TM images (one acquired before and the other during the flood event), as well as by incorporating DEM data into the analysis. The objective of incorporating the DEM data into the analysis was to overcome the limitation of the TM data in distinguishing between flooded areas and forest canopies. Landsat 4 data were used for mapping flood. Once the representation of the reflectance values for water and non-water features was understood, a cut-off value was determined to separate the two categories. For the July TM image, the cut- off value was 141. If a pixel’s digital number value was less than 141, that pixel was assigned as a water category, otherwise, it would assign a non-water category. For the September TM image, the cut-off value was 109, thus, a pixel having digital number value less than 109 was classified as water, and otherwise, it was classified as non- water. Later, considering inundation status in July and September, flooded area, river or permanent water bodies, and non-flooded area were classified (Figure 2.12).

According to Johnson and Barson (1993), Landsat TM imagery was found to be useful for mapping riverine wetland extent in Central Victoria and small wetlands extent in Western Victoria. The map was compared to manually mapped ground truth data. In this study, density slicing of the TM5 (mid-infrared) was capable of detecting small wetlands with an accuracy of more than 95 percent. However, riverine was not mapped properly and the result was poor. Poor timing of data selection and narrow width of the oxbow lake were listed as causes of failure of the technique. Between 1993 and 2000, many other studies used density slicing of Landsat TM or MSS data to successfully identify water bodies; however, quantitative accuracy assessments were not conducted.

Figure 2.12: The flood extent in Pitt County, North Carolina on 30 September 1999, derived by density slicing from a pair of Landsat TM images of 28 July and 30 September 1999. The gray rectangle indicates the Greenville study area. (Wang, 2002)

Landsat TM band 4 was used to map the extent of the Bhadra Reservoir, India (Manavalan et. al., 1993). Overton (1997) used density slicing of Landsat TM band 5 along with a high flood spatial mask and successfully mapped water bodies on the Murray River, South Australia. Brady et al. (1999) solely used Landsat MSS band 7 to determine wetland inundation for the Cumbung Swamp, NSW, Australia. Baumann (1999) used band 4 of Landsat TM for flood mapping on the Mississippi River but certain urban features with quite similar spectral reflectance were the bottleneck toward getting an accurate result. Same issues were faced by Jain et al. (2005) for

Landsat TM band 4 data, but the result showed that it was capable of mapping major water bodies.

In recent time, Roach et al. (2012), used 3 different remote sensing image analysis techniques to estimate lake number and area; density slicing, classification trees, and feature extraction. In his study, he applied that technique and compared the result with CIR aerial photography. As shown in table 2.3, density slicing of Landsat infrared band 5 gave a better result than two other techniques. It was the best of the three at classifying small lakes as evidenced by its lower optimal minimum lake size criterion of 5850 m2 compared with the other methods (8550 m2). Roach et al. (2012) found that use of additional spectral bands and a more sophisticated method not only required additional processing effort but also had associated cost in terms of the accuracy and consistency of lake classifications.

Table 2.2: Evaluation of supervised classifications relative to CIR aerial photography for the density slicing method applied to SWIR band 5 and the classification tree and feature extraction methods applied to four band combinations. (Source: Roach et al., 2012)

Method and No. of No. of No. of Optimal Mean band(s) omissionsx commissionsY mutually minimum lake absolute identified size threshold percent lakesz (m2)M errorN Density slicing 5 51.8 (5.68) 39.8 (6.29) 131.5 (4.26) 5850 27.3 (1.32) Classification tree 5 84.0b(11.48) 25.0 (5.08) 109.4c(8.29) 8550 34.9 (2.95) 4, 5 86.6 (11.06) 25.8 (5.68) 107.7 (8.25) 8550 36.1 (2.67) 5, 7 82.7 (11.75) 28.4 (6.06) 110.4 (8.56) 8550 34.6 (2.99) 4, 5, 7 85.0 (11.47) 30.0 (6.76) 109.3 (8.79) 8550 35.7 (2.76) Feature extraction 5 46.8a,b (6.34) 179.5 (74.91) 149.8c (5.45) 8550 27.2 (1.47) 4, 5 39.8a (6.94) 336.7 (163.99) 150.2 (6.20) 27450 27.4 (2.97) 5, 7 56.4 (10.96) 229.3 (118.39) 141.9 (8.65) 13050 31.4 (2.40) 4, 5, 7 41.0 (7.92) 399 (158.59) 152.9 (5.14) 50850 31.9 (1.68)

X Lakes present in CIR aerial photography and not classified from Landsat image. Y Lakes not present in CIR aerial photography and falsely classified from Landsat image. Z Lakes present in CIR aerial photography and classified from Landsat image. M Minimum lake size threshold to avoid omission and commission errors. N Mean absolute percent error of classified lake area relative to lake area delineated from CIR aerial photography.

Chapter Three Study Area

3.1 Introduction

The study was conducted in Taherpur Upazila of Sunamganj district. Taherpur Upazila is occupied by several haors which are large saucer shaped floodplain depressions located mostly in north-eastern part of Bangladesh covering about 25% of the entire region. The study area’s climatic condition, land use pattern, soil condition, agriculture, and river systems are discussed in this chapter.

3.2 Location of the Study Area

Taherpur Upazila is situated in Sunamganj district under Sylhet division (Figure 3.1). The upazila was named after Tahir Ali, it is also known as the Ancient Kalidaha Sea. The location of upazila is between 91°01´ E to 91°18´ E and 25°01´ N to 25°13´ N (Figure 3.2). Indian Meghalaya state is to the north, Maddanagar union to the northwest, Jamalganj Upazila to the south, Bishawmvarpur to the east and Dharmapasha Upazila to the west. The southern and western parts are taken up by the haor basin which comprises of the floodplains of the Meghna tributaries. There are 5 rivers, 14 canals, 129 km embankment and 300.04 km roads in this upazila (Upazila Disaster Management Committee, 2014). The total area of the upazila is 336.70 km2. According to Population and Housing census 2011 (BBS, 2012), 37931 households are living in the upazila, which has the total population of about 2,15,200.

3.3 Haors and Beels of Taherpur

According to Department of Agricultural Extension, there are 6 major haors in the Taherpur Upazila. They are Tanguar haor, Shonir haor, Matian haor, Mahalia haor, Halir haor and Gurmar haor. Shonir haor is one of the largest haors in the country with a total boundary of 8,237 hectares. The area of Matian haor, Mahalia haor, Halir haor, and Gurmar haor are 2,900 hectares, 425 hectares, 2,865 hectares and 550 hectares respectively.

Figure 3.1: Map of Bangladesh showing Sunamganj district

Figure 3.2: Location of the study area (Source: DAE, Taherpur)

All of them are well known for boro rice cultivation in boro season. Besides, they also play an important role in communication during the rainy season.

The term beel is also used for oxbow lakes and other permanent water bodies in abandoned river channels; these are found along the lower courses of the Baulai River. As the monsoon flood waters recede during the dry season, rich alluvial soils are exposed around the margins of the beels, which are extensively cultivated for rice.

3.3.1 Tanguar Haor

Tanguar haor is one of the most important wetlands not only in Bangladesh but also in the entire South Asia (BirdLife International, 2012). It is located in the north-east part of Bangladesh (25°09'-25°12'N 91°04'-91°07'E) in Taherpur and Dharmapasha upazilas of Sunamganj district. The haor covers roughly 10,000 hectares of land and has about 60,000 inhabitants and 82 villages (Alam et al., 2012).

This haor forms the core of the northern haor system, which includes several other haors (such as Gurmar haor, Kanamaiya haor and Matian haor). The Tangua haor site itself consists of a group of large beels to the west of the Patnai Gang, close to the Indian border; its principal beels are Pana, Rauar, Tangua, Ainna, Arabiakona, and Samsar (NERP, 1995). During the monsoon, almost the whole Tanguar haor gets inundated under water that flows through the Baulai-Surma river system. In the dry season, only 25-30% land area remains under water after most of the water recedes from the land (Alam et al., 2012).

At least 135 fish and 208 bird species, including 92 waterbird and 98 migratory bird species, including 10 "IUCN Red Book listed" species and 22 "CITES listed" species are being found in the Tanguar haor. Every winter, about 30-40,000 migratory waterfowl converge on the area. More than 140 species of freshwater fishes are found in this area also represent the rich biodiversity of Tanguar haor (Alam et al., 2012).

In the past few decades, the total environmental settings of the Tanguar haor have degraded a lot. It was declared as Bangladesh's second RAMSAR site on July 10, 2000, as it is particularly threatened by overexploitation of fishery stocks, deforestation and large scale waterfowl harvesting (Kjetil Bevanger, 2001). The government has also decided not to allow any developmental or commercial activity within the haor area,

which is harmful to haor environment. Besides these, the government has banned the digging of wells for gas or oil within the 10 km area of the haor (Banglapedia, 2007).

3.3.2 Shonir Haor

Shonir haor is located in the southern part of Taherpur Upazila (25°02'-25°06'N 91°08'-91°06'E). In 2016, it had a total area of 8,237 hectares while boro cultivable land was almost 6,300 hectares. The area is prone to flash flood and usually inundated by entering the Baulai River’s water in pre-monsoon season. It is known to be very fertile land and leads in the production of boro rice in this region.

3.3.3 Beels

According to Upazila Fishery Office, there are 81 beels in Taherpur Upazila. Among them, 36 beels are above 20 acres and 34 beels are under 20 acres while 11 of them are open beels. Beels are the main source of fish for being the deepest depressions in haors. Fishermen are heavily dependent on the fish resources as they lead their life by catching and selling fishes and beels are the breading ground and last place to shelter for the fishes in winter. Besides, the water of the beels is used for irrigation activities and also save the bio-diversity by inhabiting different birds. Some of the notable beels of Taherpur Upazila are Kataura Beel, Kazir Doba, Bonuagroup, Patabuka Dighor, Shingrar Daeer, Sonar Beel, Kopaura Mohishmara, and Kazir Doba.

3.4 Land Use Pattern

Fishing is the main commercial activity that takes place around the haors and beels. Water bodies are mainly used for fishing management and transportation of extractable wetland resources includes thatching materials, animal fodders, wild plants, fruits, foods and fuel wood supplements. During the winter season, when the water level is lower, marginal lands of the haors and relatively higher regions are cultivated with paddy. Apart from boro rice cultivation, some lands are used for vegetable cultivation. According to Department of Agricultural Extension, Taherpur Upazila has a total of 25,246 hectares land. Amongst them, the amounts of arable and uncultivated lands are 24,595 hectares and 650 hectares respectively. Among the arable land, single crop land is 13,455 hectares, two crop land is 8,986 hectares while three crop land is about 2,154 hectares. Almost 6,114 hectares of the total land is being used for settlement.

3.5 Climate Condition

The climatic condition of the region is sub-tropical in nature but has three prominent seasons. Winter runs from November to March, pre-monsoon runs from March to May, Southwest monsoon from June to September and northeast monsoon from October to November.

3.5.1 Rainfall

The north-eastern part of the country is characterized by high rainfall and relatively low temperature compared to the annual average received by the country. According to BMD, the mean annual rainfall in this area is 4,195 mm while the entire country receives an average of 2,427 mm. The study area receives a lot of rainwater from the Meghalaya hills found in the north. The rain normally begins in the second week of April and lasts until September (south-west monsoon) and it does not allow most of the cultivable land to grow anything. However, the month of June and July receive most of the rain. Winter starts in November and lasts until March (locally known as Rabi season). This is the only time when the lands are not inundated and paddy along with some other vegetables are grown. However, very little rainfall is expected during this period. Figure 3.3 shows the mean monthly rainfall distribution of Sylhet, which is near to the study area. The data are also compared to the mean annual rainfall distribution of the country.

900.0

800.0

700.0

600.0

500.0

400.0

Rainfall (mm) Rainfall 300.0

200.0

100.0

0.0

Month Rainfall in Sylhet (mm) Rainfall in Country (mm)

Figure 3.3: Mean monthly rainfall in Sylhet and Bangladesh (Source: BMD)

3.5.2 Temperature

Summers are hot but after the rain begins in the late May, there is a constant favorable nature shown by the temperature. Lowest temperatures are usually recorded in January. In March and October, on an individual day temperature may go up to more than 30°C. However, mean monthly maximum temperature varies between 23.3°C and 34.6°C and mean monthly minimum temperature varies between 10.6°C and 26.3°C. Figure 3.4 and 3.5 show the mean maximum monthly and mean minimum monthly temperature distributions for 1960, 1980, 2000 and 2013 in Sylhet respectively, which is very close to the study area. From the chart, it is clearly visible that, in recent years, the temperature has increased slightly.

28 26

) 24 C ° 22 20 18 16

Temperature ( Temperature 14 12 10

Month 1960 1980 2000 2013

Figure 3.4: Mean minimum monthly temperature distributions for 1960, 1980, 2000 and 2013 (Source: BMD)

34 C)

° 32

28 Temperature ( Temperature 26

Month 1960 1980 2000 2013

Figure 3.5: Mean maximum monthly temperature distributions for 1960, 1980, 2000 and 2013 (Source: BMD)

3.5.3 Humidity

The study area experiences a high degree of humidity throughout the year. The minimum humidity is felt during February and March, while the maximum is from June to August. Figure 3.6 and 3.7 show the mean monthly long-term humidity and mean monthly humidity distributions for 1980, 2000, and 2013 in Sylhet, which is very close to the study area.

Percentange of Humidity Humidity of Percentange 60

Month

Figure 3.6: Mean monthly long-term humidity (Source: BMD)

Percentange of Humidity Percentange Humidity of 65

Month

1980 2000 2013

Figure 3.7: Mean monthly humidity distributions for 1980, 2000, and 2013 (Source: BMD)

3.6 Soil Condition

Taherpur Upazila is under two Agro Ecological Zones with plenty of permanent deep water bodies. The Northern part of the upazila is under Eastern and Northern Piedmont Plan which is a part of discontinuous region occurring as a narrow strip of land at the foot of the northern and eastern hills. The soils found in this area range from loam to clay soil. Its texture ranges from slightly acidic to strongly acidic. General fertility level is low to medium. The Southern side of the upazila is on the Sylhet Basin, which is the region that occupies the lower western side of Surma-Kusiyara Floodplain. The area is mainly a smooth broad basin with narrow ridges of higher land along the rivers. The soils found in this area are gray clays in the wet basins and silt clay loams and clay loam in the higher parts which dry out seasonally. The soils have moderate content of organic matter while the soil reaction is mainly acidic. Fertility level is medium to high.

Figure 3.8: Agro Ecological Zone of Taherpur (Source: BARC)

According to Upazila level Disaster Management Plan Development, Taherpur Upazila, the nature of Taherpur Upazila’s soil is loamy, sandy loamy and bisuues where most of the homesteads are bisuues. The soil of the land used for agriculture is fertile as well as loamy and sandy loamy. Irrigation canal and pond shore are sandy

and loamy soils and road’s soils are bisuues and loamy. Mineral resources include sand and stones.

3.7 Agricultural Production

The main crops of Taherpur Upazila are rice (Boro, Aus and Aman), potato, vegetables, wheat, pepper, mustard, peanuts, etc. Boro production mostly contributes to rice production and Aus production is negligible. According to Department of Agricultural Extension, between 2007 and 2016, every year 13,000 to 18,000 hectors of land was put under the cultivation of Boro rice. Some vegetables are grown in the relatively upper lands of the region. In 2014, 350 hectares, 220 hectares, 100 hectares and 990 hectares of land were put under Mustard, Potato, Pepper, and vegetable cultivation subsequently (Upazila Disaster Management Committee, 2014).

3.8 Natural Resources

The haors are considered as the most productive wetland resources of Bangladesh and Taherpur Upazila is covered with several of them. The haor basin supports a large variety of wetland biodiversity and works as a natural reservoir as it plays a key role in basin water resources management and development by regulating water flows of the Meghna river system. Also, Tanguar haor is noted sanctuary of both permanent and migratory birds. With the recession of floodwater, a large variety of small fishes, oysters, water snails and bivalves, and pasture spread over the surface attracting a large number of migratory birds. These birds use this area as temporary resting and roosting ground before moving elsewhere. The Swamp forests, which were once dominant with the flood tolerant tree species like hijal (Barringtonia acutangula) and Koroch (Pongamia pinnata), have been reduced to a few small patches. In the past century or so, when the population pressure was less, most of the rimlands of the haors remained as cultivable wetland and were used for extensive grazing during the dry season. As the population increased, boro cultivation expanded onto these marginal lands leading to a large area being drained. Thus, the very existences of these wetlands are now threatened. The area is enriched with various aquatic biodiversity along with 140 species of fish. About 8,000 migratory wild birds visit the area annually (Salauddin and Islam, 2010).

3.9 River Systems and Drainage Networks

According to Bangladesh Water Development Board, Taherpur is endowed with an intricate network of river system formed by 5 rivers named the Jadukata, the Boulay, the Abuya, the Patnay and the Baglichara. They are highly infamous for their unpredictable nature and cause flash floods in pre-monsoon as well as bank erosion during the rainy season. The majority of the rivers in this region are originated from the Meghalaya plateau and enter Bangladesh from a north to north-west direction and flows south to south-east direction. The rivers play a vital role in the development of the area. Due to plenty of water transportation systems, sand, stones, and the fish can be transported from one place to another in a short time at a reduced cost. Besides, a significant amount of surface water is used for irrigation. However, some of the rivers are losing their navigability because of siltation.

Figure 3.9: River network of Taherpur Upazila

3.9.1 Jadukata – Rokti River

The Jadukata River is one of the most important rivers in north-east Bangladesh. In its source in the Meghalaya State of India.

Figure 3.10: Jadukata River at India-Bangladesh border (spring)

At the border with Bangladesh, it becomes Jadukata River and enters through Uttar Badaghat Union of Taherpur Upazila.

Figure 3.11: Rokti River near Taherpur Upazila (spring)

It covers a total distance of 37 km through Jamalganj and Taherpur Upazila and pours its content into the Surma River. Patnay, Boulay, Abuya Rivers are some of its notable

distributaries. In recent time, it has become wider and water discharge rate is increasing day by day. On March 4, 2011, the recorded discharge was 17.1 cubic metres per second (BWDB, 2011).

3.9.2 Baulai

Baulai River is a distributary of Jadukata-Rokti River and rises in Balijuri union of Taherpur Upazila. After flowing approximately 72 km through Taherpur and Dhamapasha it falls into Dhanu River at Jamalganj upazila. It is also known as Balua to local people. Due to severe river erosion, some parts of the river are filled with land and its width has also decreased. It is mainly responsible for the recurrent flash flood in Shonir haor. Every year, the local people build dams to protect boro rice from pre- monsoon flood. There has been no significant change in river discharge in last couples of years (BWDB, 2011).

Figure 3.12: Baulai River in Taherpur (spring)

3.9.3 Patnay River

Patnay River is a distributary of Jadukata-Rakti River. It rises in the Uttor Badaghat Union and flows for 40 km through Taherpur and Dharmapasha before pouring its content into Baulai River at Jamalganj upazila. A negligible amount of river erosion has been observed in Patnai River. On March 4, 2011, the recorded discharge was 0.8 cubic meter per second. However, from May to December, Patna River plays an important role in local transportation (BWDB, 2011).

Chapter Four Data and Data Collection

4.1 Introduction

This chapter gives a brief overview of data, data type, the source of collected data and finally data collection process. This study has been carried out based on secondary data as well as three field visits.

4.2 Category of Data

Data from different sources were used in this study. Three types of remote sensing data played the main role here. Following data were was used in this study:

• Satellite data i.e. Moderate Resolution Imaging Spectroradiometer (MODIS) data like- Surface Reflectance Daily L2G Global 250m (TERRA and AQUA) which is available in USGS website. MODIS satellite data from 2007 to 2014 were used to derive inundation map for the flood mapping and estimating the impact of the flash flood on boro production. Shuttle Radar Topography Mission (SRTM) 30m DEM data were also used to identify the depression and permanent water bodies. Coordinates of 30 points were taken from different locations of the upazila for ground truthing. • Field data i.e. flash flood timetable and their impact on local agriculture data were collected with a semi-structured interview. • Water level data of Jadukata River was purchased from Bangladesh water development board. The water measuring station is located at Laurergarh Saktiarkhola, near Bangladesh-India border. • Boro cultivated area and total production data were collected from Department of Agricultural Extension, Taherpur. • Meteorological data were collected from Bangladesh Meteorological Department (BMD). 4.3 Remote Sensing Data

MODIS surface reflectance product and SRTM 30m DEM data were used for accurate inundation mapping.

4.3.1 MODIS TERRA and MODIS AQUA Data

MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the TERRA (originally known as EOS AM-1) and AQUA (originally known as EOS PM-1) satellites as part of NASA's Earth Observing System (EOS). MODIS TERRA and AQUA instruments are viewing the entire Earth's surface every 1 to 2 days. TERRA's orbit around the Earth from north to south across the equator in the morning. On the other hand, AQUA passes from south to north over the equator in the afternoon (NASA, 2016). Both instruments are acquiring data in 36 spectral bands, or groups of wavelengths. Band 1 and band 2 has a spatial resolution of 250 meters, band 3 to band 7 has spatial resolution 500 meter, and spatial resolution of rest of the bands are 1000 meter. (NASA, 2016).

In this study, MODIS TERRA MOD09GQ data were used for 2007 to 2014 except 2013. In 2013, cloud free TERRA data were unavailable; hence MODIS AQUA MYD09GQ data were used. Both of them provide bands 1 and band 2 at a 250-meter spatial resolution in a daily gridded L2G product in the Sinusoidal projection. Science Data Sets provided for this product include reflectance for bands 1 and 2, a quality rating, observation coverage, and observation number. Band 1 and band 2 are known as the red and near-infrared band respectively. These products are meant to be used in conjunction with the MOD09GA and MYD09GA, where important quality and viewing geometry information are stored. Version-5 MODIS/TERRA and MODIS/AQUA Surface Reflectance products are Validated Stage 2, meaning that accuracy has been assessed over a widely distributed set of locations and time periods via several ground truthing and validation efforts.

Layers of Science Data Sets for MODIS TERRA Surface Reflectance Daily L2G Global 250m SIN Grid V005 (MOD09GQ) and Science Data Sets for MODIS AQUA Surface Reflectance Daily L2G Global 250m SIN Grid V005 (MYD09GQ) are the same.

MODIS data are freely available at http://earthexplorer.usgs.gov/ (Accessed on July, 2016) which is the website of United States Geological Survey. Login is required to download full data from this website. Satellite data for any specific area can be downloaded by providing the exact coordinate or path and row number of the satellite

for a specific location or shapefile of the area or making a search by writing the location name in the search box. In data set box, MOD09GQ or MYD09GQ needs to be selected to download the data, they are available under at NASA LPDAAC collection under MODIS Land Surface Reflectance.

MODIS data are available in Hierarchical Data Format (HDF) to download which is designed to store and organize large amounts of data. All MODIS data used are Surface Reflectance Daily L2G Global 250m (TERRA or AQUA) and the naming of the file has been done according to the Julian date calendar. For example, MOD09GQ.A2014129.h26v06.005.2014130132756.hdf is a Surface Reflectance Daily L2G Global 250m data. The first part of the name, MOD09GQ, MODIS product short name, A2014129 indicate the date of data acquisition, h26v06.005 is the version name and the last part contains production date. The format is MOD09GQ.AYYYYDDD.VVV.YYYYDDDDHHMMSS.HDF.

4.3.2 Shuttle Radar Topography Mission (SRTM) 30m Global DEM

The Shuttle Radar Topography Mission (SRTM) is the result of a collaborative effort by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA). The mission of the project was to acquire radar data which were used to create the first near-global set of land elevations. In SRTM mission, single-pass interferometry was used and it acquired two signals by using two different radar antennas. Digital surface elevation model was derived by calculating the difference between two signals (USGS, 2016). Initially, a 1-arc second data product (30m DEM) and 3-arc second data product (90m DEM) were produced. Only 3-arc second data product (90m DEM) were available for free to use. However, since January, 2015 NASA is providing the 1-arc second data freely for many countries including Bangladesh (Keeratikasikorn and Trisirisatayawong, 2008). SRTM product specifications are provided in Table 4.4.

Table 4.1: SRTM product specifications

Projection Geographic Horizontal Datum WGS84 Vertical Datum EGM96 (Earth Gravitational Model 1996) ellipsoid Vertical Units Meters Spatial Resolution 1 arc-second for global coverage (~30 meters) 3 arc-seconds for global coverage (~90 meters) 3 arc-seconds for global 1-degree tiles coverage (~90 meters) Raster Size 5.6 cm SRTM 1 Arc-Second Global elevation data are being distributed by NASA/USGS (finished product) contains ‘no-data’ termed as voids where water or heavy shadow prevented the quantification of elevation. SRTM data of GeoTIFF format was downloaded and used for this study. The format allows SRTM elevation data to be used by any Geographic Information System (GIS) tools or software.

The procedure of downloading SRTM data is quite similar to downloading MODIS data. They are also freely available at http://earthexplorer.usgs.gov/ (Accessed on 2016) which is the website of United States Geological Survey. Login is required to download full data from this website. SRTM elevation data for any specific area can be downloaded by providing the exact coordinate or path and row number of the satellite for a specific location or shapefile or making a search by writing the location name in the search box. In data set window, SRTM 1 Arc-Second Global are available at SRTM data option under Digital Elevation. SRTM 1 Arc-Second Global elevation data were available for Taherpur region.

4.3.3 Coordinate and Inundation Status of Different Points By visiting 30 different points of the upazila their inundation status along with coordinates were noted using free android application. Upon installing the application, it was run to get the exact location of the place where the phone was being operated as shown in figure 4.1. It automatically showed the latitudes and longitudes of the location. Spatial distribution of the points is shown in Figure 4.2.

Figure 4.1: Collection of coordinate of different locations with mobile GPS

Figure 4.2: Location of the ground truthing points and their inundation status

4.4 Field Data

Primary data on usual flash flood timing, reasons of a flash flood, reasons for crop production rise and some other information were collected from multiple field visits. Data were collected from several randomly selected areas using semi structured questionaries’. Data collection were conducted in different places among different level of farmers to get a better picture of the situation. To avoid biasness, sample group was selected from the central area of Taherpur Upazila as well as very remote places near Bangladesh-India border. Total 56 local farmers (Figure 4.4) and 2 Agriculture Extension Officer were interviewed.

4.5 Boro Production and Miscellaneous Data

Department of Agricultural Extension stores all data of crop production. Boro rice production data for 2007 to 2014 were collected from Department of Agricultural Extension, Taherpur as shown in figure 4.3. Along with the boro production data, natural resources of haor area, map of the upazila, and details of beels and haors data were also collected.

Figure 4.3: Interview of Upazila Agricultural Extension Officer, Taherpur

Figure 4.4: Semi-structured interview of farmers

4.6 Jadukata River Water Level Data

Jadukata River water level data were purchased from Bangladesh Water Development Board. A water level measuring station is located at Laurergarh Saktiarkhola to measure the water level of Jadukata River (figure 4.6).

Figure 4.5: Water level measuring station at Laurergarh Saktiarkhola

Chapter Five Methodology

5.1 Introduction This chapter provides a brief overview of the whole process of flash flood date identification, satellite data processing, and inundation map development with two different satellite image processing techniques. In the first part of this chapter, a flowchart of the whole process is presented followed by their description (Figure 5.1).

5.2 Flash Flood Date Identification

The study area, Taherpur Upazila, is located near to the Meghalaya state of India which is renowned for receiving a high amount of rainfall. Thus, the occurrence of flash flood in Taherpur Upazila depends on the amount of rainfall received by the Meghalaya State. Rain water from the huge basin of Meghalaya State comes through several rivers that flow downhill. One of those hilly rivers is Jadukata River that carries water from Meghalaya basin to the haor basin through the Badhaghat union. Baulai is one of its distributaries that directly supplies water into the Tanguar haor system and is mainly responsible for the flash flood at Shonir haor and Martian haor. Bangladesh Water Development Board has a water level measuring station for Jadukata River at Laurergarh Saktiarkhola (SW 131.5).

From knowledge based judgment, a threshold value of 5.35m of water level was considered for a possible flash flood event in the haor region. If the water level in Jadukata River was more than 5.35m then it was considered as a flash flood event. By observing the water level, flash flood timing was noted for analyzing the effect of different factors of a flash flood on boro rice production. Only the water level of the Jadukata River in April to mid-May was analyzed because flash flood on mid-April to mid-May (Boishakh month) poses threat to boro production as it is the harvesting period. Every year, several possible flash flood events and their dates were selected and noted down. Upon accessing the cloud-free MODIS data, a complete list of one flash flood date per year with their respected satellite image data collection date are shown in table 5.1.

Possible flash flood date SRTM and MODIS identification data acquisition

Clipping of NIR and red band Cloud free MODIS data with shapefile and NDVI image extraction and Reprojection creation

Threshold value of water pixel digital number of near- infrared band and NDVI image identification

Development of a Development of a Classification Scheme Classification Scheme Based Based on NIR Band on NDVI

Inundated Area Non-inundated Area Inundated Non- DN of NIR DN of NIR >1985 Area inundated <1985 NDVI <0.42 NDVI >0.42

Accuracy Assessment Accuracy Assessment

Inundation mapping method with higher accuracy identification

Effect of flash flood assessment on boro rice production with data derived from inundation mapping method with higher accuracy

Inundation Flash flood timing Flash flood duration Summarization of vs boro vs boro production vs boro production semi-structured production checklist

Figure 5.1: Outline of methodology

5.3 MODIS Data Download

MODIS data are available at USGS website (https://earthexplorer.usgs.gov/). Since 2008, the projects at U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center have been making the data available to the public at free of cost. Day by day, the data access system was improved for making the process more user-friendly. Previously, the user had to download one scene at a time where some user requires plenty of scenes to download from the portal which is time-consuming. To make the process easier, Bulk Download Application was introduced. This application allows the user to request bulk downloads to support their requirements for access to larger amounts of data. The Bulk Download Application (BDA) has been integrated with the EarthExplorer (EE) and Hazards Data Distribution Systems to allow this capability. The overall goal of the BDA is to streamline and enhance the capability of delivering bulk data to users while managing the resources that store these data. The EROS BDA service requires the user to use either the EE or HDDS as the front end systems to access to the query systems for the desired data. The BDA interfaces with the EROS Mass Storage System and other storage systems to facilitate download of data through a simple JAVA applet.

To download required data for this study, an account was created in USGS Earth Explorer website. After logged into Earth Explorer, MOD09GQ and MYD09GQ data set were searched for specific flash flood date for Taherpur Upazila. When search results appeared, under every image, “Add to Bulk Download” were checked. Once, every scene was selected, the order was submitted by proceeding to check out. An order number was provided by Earth Explorer when the checkout was completed.

Bulk Download Application requires Java platform to run. Before using Bulk Download Application, java platform was downloaded from Java official website and installed. After logging in to the Bulk Download Application, different product numbers based on orders appeared on the screen. Product number that was given after checkout was selected. To start download “Begin Download” button was clicked. BDA saved the satellite image to its own directory in a specific folder named after the order number.

5.4 Cloud Free Data Identification Taherpur is one of the very few upazilas of Bangladesh which receives the highest amount of rainfall. Like other north-eastern upazilas, in May and June, the upper atmosphere is covered with thin to high clouds in this area. Therefore, it is difficult to get cloud free data in any specified day. On the other hand, MODIS scene with cloud cover does not provide a real picture of the land cover, instead, it shows the relatively higher digital number in both near-infrared band and NDVI image data. Thus, cloud- free MODIS scenes are required to map flash flood inundation. To check whether MODIS scenes are cloud free, downloaded MODIS data were imported to GIS application in RGB layer made of band 1 and band 2 which are red band and near- infrared band respectively. The output offers an image where green represents vegetation, black represents water and white represents cloud (Figure 5.2).

Figure 5.2: MODIS Geo-TIF image geo-referenced to WGS – 84 Coordinate System (RGB layer of Band 1 and Band 2)

By activating administrative boundary area shapefile and zooming into the study area, it became easier to identify whether the collected satellite data was cloud free or not. If a cloud-free image was not available for a specific date then data from the next day’s satellite image was used for the analysis. If the water level of Jadukata River rose sharply such as on April 26, 2007, satellite image for April 27, 2007, was searched because river water takes several hours to reach haor or to inundate crop fields.

However, if cloud free data for April 27, 2007, was not available then satellite data for April 28, 2007, was used. If no cloud-free satellite data for next 4-5 days after the flash flood were available, then the next flash flood event was considered. By checking for several consecutive cloud-free satellite images for each flash flood event, one MODIS data was used for each flash flood. Their respective dates are provided in Table 5.1.

Table 5.1 MODIS Data used in flood mapping

Year Flood date Flood water Data acquisition Optical Sensor level (meter) date 2007 26 April, 2007 5.77 29 April, 2007 TERRA 2008 18 April, 2008 5.35 22 April, 2008 TERRA 2009 2 May, 2009 6.37 5 May, 2009 TERRA 2010 26 April, 2010 6.13 3 May, 2010 TERRA 2011 2 May, 2011 5.65 7 May, 2011 TERRA 2012 2 May, 2012 5.39 8 May, 2012 TERRA 2013 12 May 2013 7.09 12 May 2013 AQUA 2014 13 May 2014 5.74 20 May -2014 TERRA

5.5 Projection of Raw Data and Conversion to “tif” Format through MODIS Re- projection Tools

MODIS data files come in HDF format and not well geo-referenced. USGS freely distributes MODIS Re-projection Tools to project and extract different layers of raw MODIS data. MODIS Re-projection Tools (MRT) enables users to read data files in HDF-EOS format (MODIS Level-2G, Level-3, and Level-4 land data products), specify a geographic subset or specific science data sets as input to process, perform geographic transformation to a different coordinate system/cartographic projection, and write the output to file formats other than HDF-EOS. Each MODIS HDF raw data contains 5 layers where second and third layers are red band and near-infrared band. MODIS Re-projection Tools (MRT) was used for extraction and conversion of TERRA and AQUA HDF image files into GEO-TIF format. In that process, the

resampling type was the nearest neighbor where WGS-84 coordinate system was selected for output files.

5.6 Masking out the Study Area Using QGIS

Geo-referenced images, afterward, were masked to extract the study area for carrying out further analysis. Administrative boundary shapefile was used for masking out. From the country’s shapefile only attributes of Taherpur Upazila were selected and a new shapefile was created. QGIS, a free GIS tool, was used to mask out the study area. Near-infrared band data, red band data, and shapefile of Taherpur Upazila were imported into QGIS. For clipping the near-infrared band data, Taherpur Upazila shapefile was selected as the Mask Layer and the resolution of the image was kept as the input file. The output raster file was in tif format and saved in the specific directory. Same procedures were followed for masking out SRTM 30m DEM data.

5.7 Threshold Value of Digital Number of Water Pixel in Near-infrared Band and

NDVI Data

In near-infrared band data, vegetation is represented by gray where black area represents water. In addition, water body has lower digital number compared to vegetation. On the other hand, NDVI value is lower for water and higher for vegetation (For NDVI image derivation, see 5.10).

Two types of data were used to identify threshold values of the water pixel digital number in the near-infrared band and NDVI image, they are 30 ground truthing points and SRTM 30m DEM data. The ground truthing points used were locations of inundated areas, locations of non-inundated areas, and locations of inundated areas after early April (before April the areas were non-inundated). The field visit was made on June 15, 2016, and it was found that the most of the agricultural lands were started to inundate from the first week of April. Thus, there was a considerable change in digital number value of the same pixel before and after early April in the near-infrared band and NDVI image, especially for the paddy field. On the other hand, the inundated area of Shonir haor and Martian haor clearly showed differences in digital number with north-eastern part of the upazila which is relatively higher land.

SRTM 30m DEM data helped to identify the permanent water bodies as they have relatively lower elevation compared any other land cover features. Permanent water bodies usually have the lowest digital number in both near-infrared band and NDVI image. A MODIS scene generated on June, 2016 was downloaded, processed and NDVI image was created. The near-infrared band and NDVI image were imported in QGIS along with the ground truthing points. Every ground truthing point’s digital number in the near-infrared band and NDVI image was noted. Another MODIS scene for early April was downloaded, processed, NDVI created from it and the near-infrared band, as well as NDVI image, was imported into the QGIS application. The digital number of the points which got inundated after early April was also noted. Last of all, importing and activating SRTM 30m DEM, the digital number of permanent water bodies were noted too. The results are mentioned in chapter six (6.2).

5.8 Inundation Mapping from Near-infrared Band Data

Density slicing is a digital data interpretation method used in the analysis of remotely sensed imagery to enhance the information gathered from an individual brightness band. It is a technique, whereby the digital numbers distributed along the horizontal axis of an image histogram, are divided into a series of user-specified intervals or slices. The number of slices and the boundaries between the slices depends on the different inundation status in the area. All the digital numbers falling within a given interval in the input image are then displayed using a single class name in the output map.

Using specific threshold value for each feature class the inundation was mapped using ILWIS which is a GIS and remote sensing application. The following feature classes were used for mapping and their respective colors, as well as code and threshold values, are shown in Table 5.2. However, for the details procedures see appendix B.

Table 5.2: Input for density slicing of near-infrared band

Name Code Upper Boundary Color Inundated Area I A 1985 Red Non-inundated Area N I A 10000 Green

5.9 NDVI Extraction and Inundation Mapping

Normalized Difference Vegetation Index is a measure of the amount and vigor of vegetation in the surface. The magnitude of the NDVI (Normalized Difference Vegetation Index) is related to the level of vegetation. In general, the higher NDVI indicates the greater vegetation and lower usually indicates water. For MODIS surface reflectance 250m resolution images, NDVI is universally defined as

푛푒푎푟−푖푛푓푟푎푟푒푑 − 푣푖푠푖푏푙푒 푟푒푑 NDVI = 푛푒푎푟−푖푛푓푟푎푟푒푑 + 푣푖푠푖푏푙푒 푟푒푑 …………….. (5.1)

A sample NDVI image derived using the following equation is shown in figure 5.3.

Figure 5.3: NDVI image viewed in ILWIS

Table 5.3: Input for density slicing of NDVI data

Name Code Upper Boundary Color Inundated Area I A 0.415 Red Non-inundated Area N I A 1 Green

5.10 Software Used

The following remote sensing and GIS application packages have been used to perform remote sensing data processing and analysis, i. e.,

• Bulk Download Application (version 1.3) to download multiple MODIS satellite data at a time. • MODIS Reprojection Tool to reproject and extract MODIS data. • QGIS (2.14.0 version) was used to mask out the study area from band 1 and band 2 MODIS data with administrative shapefile. By using QGIS, threshold DN value for water was identified with different location’s inundation status. NDVI images for each year were generated using QGIS raster map calculator. It is a cross-platform, free and open-source desktop geographic information system (GIS) application that provides data viewing, editing, and analysis. • ILWIS was used for Density Slicing as well as histogram analysis. Two versions of ILWIS were used in image analysis. ILWIS version 3.8 was used for Density Slicing and ILWIS version 3.4 was used to create map layout. Integrated Land and Water Information System (ILWIS) is a remote sensing and GIS software which integrates image, vector, and thematic data in one unique and powerful package on the desktop. As of July 1, 2007, ILWIS Open 3.X is available from 52°North for free. • Microsoft excel was used for the arrangement of data and producing different graphs.

Chapter Six Impact of Flash Flood on Boro Rice Production

6.1 Introduction

The objective of the study forms the basis of all analysis carried out in this chapter. The results are presented in the form of maps, charts, and statistical tables. They include inundation maps, accuracy assessment maps and tables, charts, and tables on flash flood impact on boro rice production.

6.2 Identification of Threshold Values of Water Pixel DN in Near-infrared Band and NDVI Image

By analyzing a digital number of different land cover features in the near-infrared band and NDVI image over the entire study area, threshold values of the water pixel digital number in the near-infrared band and NDVI image were found 1985 and 0.415 respectively. The threshold value of the water pixel digital number in the near-infrared band had to be multiplied by .0001 (USGS, 2017). Thus, the value became 0.1985, however, for density slicing, the upper boundary was entered as 1985 as all pixel values are multiplied with 10000 in the data. Later, the threshold values were used for mapping inundation. A similar approach had been followed by Salauddin (2008) for mapping vegetation and water with NDVI image.

6.3 Inundation Mapping for Pre-monsoon Season from 2007 to 2014

In both NDVI image and near-infrared band data, a digital number of a pixel is lower when it is full of water and higher when it has plenty of vegetation. A specific digital number can be used for classifying water and vegetation. The threshold value of the digital numbers of near-infrared band and NDVI image were found to be 0.1985 and 0.415, and used to map inundation. Inundation area was mapped using density slicing, an image processing technique that uses histogram analysis. Different colors were assigned for an inundated and non-inundated area- which are red and green respectively (Figure 6.1). Using defined domain classes and their maximum limits, each pixel was categorized either as inundated or non-inundated area. The inundated

area includes surface water on haor, ponds, and rivers. The south-west part of the upazila showed comparatively more inundated areas than the north-east part which is near to hilly region and has relatively higher grounds. The total inundated area was calculated by counting the number of pixels. In MOD09GQ and MYD09GQ data, each pixel represents an area of 250m x 250m. Based on the MODIS data and shapefile of the upazila, the total area was found to be 326.125 km2 which was used in this study. However, according to population census 2011, Taherpur has an area of 315.4 km2.

Inundated Area Non-inundated Area

Figure 6.1: Inundated area and non-inundated area in the map (May 20, 2014)

6.4 Inundation Map of 2007 to 2014

Flash flood inundation map for Taherpur Upazila, from 2007 to 2014 (Figures 6.2 - 6.17), and their respective discussions are given below.

6.4.1 Inundation Maps of 2007

The MODIS scene identification number used for generating this inundation map (Figure 6.2) is MOD09GQ.A2007119.h26v06.005 (Image courtesy of the U.S. Geological Survey). The scene was downloaded four days after the flash flood with the water level of 5.77 meters found on April 26. However, at the time of data acquisition, on April 29, the water level was 5.29 meter. The cloud free satellite scene was qualified for mapping inundation with density slicing techniques. Total inundated and non-inundated areas were 208.81 and 117.31 km2 respectively. The corresponding percentage of the inundated and non-inundated areas were 35.97% and 64.03% subsequently. The Jadukata River in the north-east region and relatively lower part of the south-west region of the Taherpur Upazila were identified as the inundated area.

Figure 6.2: Density sliced inundation map generated from near-infrared band for pre- monsoon season of 2007

The MODIS scene used for generating previous inundation map (Figure 6.2) was also used to create the inundation map shown in Figure 6.3. The inundation map generated from NDVI image showed comparatively less inundated area for the year of 2007. The percentages of inundated area and non-inundated areas were 18.2% and 81.8% respectively which are 59.4 km2 and 266.8 km2 subsequently. The inundation map successfully identified the Jadukata River in the north-east, but the river extend in the inundation map was far wider than the possible inundation.

Figure 6.3: Inundation map generated from NDVI image for pre-monsoon season of 2007

6.4.2 Inundation Map of 2008

The MODIS scene identification number used for generating the inundation map shown in figure 6.4 is MOD09GQ.A2008113.h26v06.005 (Image courtesy of the U.S. Geological Survey). The scene was generated on April 22, which was 4 days after flash flood occurred on April 18, 2008. The water level at the time of the flash flood occurrence and data acquisition were 5.35 meter and 5.17 meter respectively. The cloud free near-infrared band image was density sliced for deriving the inundation map (Figure 6.4). Total inundated and non-inundated areas were 248.75 km2 and 77.38 km2 respectively. The corresponding percentages of the non-inundated and inundated area were 76.27% and 23.73% subsequently. Compared to the inundation of 2007, the flash flood of 2008 caused less inundation. A small part of the South and most of the northwest region of the Taherpur Upazila were found to be inundated.

Figure 6.4: Density sliced inundation map generated from near-infrared band for pre- monsoon season of 2008

The MODIS scene used for generating previous inundation map (Figure 6.4) was also used for generating the inundation map shown in Figure 6.5. The inundated and non- inundated area of the Taherpur Upazila in 2008 were 41.12 km2 and 284.99 km2 respectively and their corresponding percentages were 12.61% and 87.39%. Similar to 2007, inundation map derived from NDVI image showed comparatively less inundated area than inundation map generated from the near-infrared band.

Figure 6.5: Inundation map generated from NDVI image for pre-monsoon season of 2008

6.4.3 Inundation Map of 2009

The MODIS scene identification number used for the inundation map shown figure 6.6 is MOD09GQ.A2009125.h26v06.005 (Image courtesy of the U.S. Geological Survey). The scene was generated on May 5, 2009 which was 3 days after the flash flood occurred on May 2, 2009. In the Jadukata River, the water level during the time of the flash flood occurrence and satellite data generation were 6.37 meter and 4.98 meter respectively. Total inundated and non-inundated area were 136.44 km2 and 189.69 km2 subsequently and their corresponding percentages were 41.84% and 58.16% respectively.

Figure 6.6: Density sliced inundation map generated from near-infrared band for pre- monsoon season of 2009

The MODIS scene used for generating inundation map (Figure 6.6) was also used for generating the inundation map shown in Figure 6.7. The inundation map generated from NDVI image showed comparatively less inundated area for the year of 2009 than the map generated from the near-infrared band. Only 20.99% of the study area was inundated compared to 79.1% of the non-inundated area. On the other hand, they are 68.44 km2 and 257.68 km2 respectively. The whole north-east area was shown as inundated when a significant amount of low land in the south-west region of the Taherpur Upazila was identified as non-inundated area.

Figure 6.7: Inundation map generated from NDVI image for pre-monsoon season of 2009

6.4.4 Inundation Map of 2010

The MODIS scene identification number used for inundation map shown in figure 6.8 is MOD09GQ.A2010123.h26v06.005 (Image courtesy of the U.S. Geological Survey). The scene was generated on May 3, 2010 which was 7 days after the flash flood occurred on April 26, 2010. The water level of the Jadukata River during satellite data generation and flash flood were 6.13 meter and 5.63 meter respectively. In 2010, Taherpur experienced a severe flash flood. The inundated area was more than any other previous years and which was 83.77% where the non-inundated area was only 16.23%. The corresponding area of inundated and non-inundated areas were 273.19 km2 and 52.94 km2. The whole study area was inundated except the northeastern higher ground.

Figure 6.8: Density sliced inundation map generated from near-infrared band for pre- monsoon season of 2010

The MODIS scene used for generating inundation map shown in figure 6.8 was also used for generating the inundation map presented in Figure 6.9. In 2010, inundation map derived from NDVI image and inundation map (Figure 6.9) generated from the near-infrared band had (Figure 6.8) lots of similarities. In both images, the Jadukata River in the north-east region was identified clearly. The inundation map generated from NDVI image has 72.12% inundated area and 27.88% non-inundated area whereas their corresponding areas were 235.18 km2and 90.94 km2.

Figure 6.9: Inundation map generated from NDVI image for pre-monsoon season of 2010

6.4.5 Inundation Map of 2011

The MODIS scene identification number used for generating inundation map shown in figure 6.10 is MOD09GQ.A2011127.h26v06.005 (Image courtesy of the U.S. Geological Survey). The scene was generated on May 7, 2011 which was 5 days after the flash flood occurred on May 2, 2011 because cloud free scene was not available immediately after the flash flood. In the Jadukata River, the water level during the time of the flash flood and satellite data generation were 5.65 meter and 4.67 meter respectively. The total inundated and non-inundated areas were 62.81 km2 and 263.31 km2 subsequently and their corresponding percentages were 19.26% and 80.74% consequently. In this inundation map, the river in north-east region was not even identified properly.

Figure 6.10: Density sliced inundation map generated from near-infrared band for pre-monsoon season of 2011

The MODIS scene used for generating inundation map shown in figure 6.10 was also used for generating the inundation map showed in Figure 6.11. Firstly, NDVI image was developed using near-infrared band and red band then inundation map was derived. Both image showed relatively less inundated area than any other previous years. The total inundated and non-inundated areas were 28.19 km2 and 297.93 km2 respectively where their corresponding percentages were 8.64% and 91.36%. A portion of southern part of the upazila and the river in the north were identified as an inundated region.

Figure 6.11: Inundation map generated from NDVI image for pre-monsoon season of 2011

6.4.6 Inundation Map of 2012

The MODIS scene identification number used for this inundation map (Figure 6.12) is MOD09GQ.A2012129.h26v06.005 (Image courtesy of the U.S. Geological Survey). The scene was generated on May 8, 2012 which was 6 days after the flash flood occurred on May 2, 2012 because cloud free scene was not available immediately after the flash flood. In the Jadukata River, the water level during the time of the flash flood and satellite data generation were 5.39 meter and 4.77 meter respectively. With comparatively lower water level in the river, the inundated area was 72.35% compared to 27.65% of the non-inundated area.

Figure 6.12: Density sliced inundation map generated from near-infrared band for pre-monsoon season of 2012

The MODIS scene used for generating inundation map shown in figure 6.12 was also used for generating the inundation map shown in Figure 6.13. In this map, total inundated area is less than the inundation observed in the map (Figure 6.12) generated using only near-infrared band. Both maps have identified the Jadukata River in the north-east area, however, previous inundation map, showed on Figure 6.12, gives greater details and illustrated longer river course. Total inundated and non-inundated area in this map were 114.25 km2 and 211.87 km2 respectively and their corresponding percentages were 35.03% and 64.96% sequentially.

Figure 6.13: Inundation map generated from NDVI image for pre-monsoon season of 2012

6.4.7 Inundation Map of 2013

The MODIS scene identification number used for inundation map shown in figure 6.14 is MYD09GQ.A2013132.h26v06.005 (Image courtesy of the U.S. Geological Survey). The satellite data generation and the flash flood occurrence date was the same, which was May 12. The water level of the Jadukata River at the time of data collection and the flash flood occurrence was 7.09 meter. The inundated and non- inundated areas were 222.50 km2 and 103.63 km2 and they were 68.23% and 31.77% respectively.

Figure 6.14: Density sliced inundation map generated from near-infrared band for pre-monsoon season of 2013

The MODIS scene used for generating previous inundation map (Figure 6.14) was also used for generating the inundation map shown in Figure 6.15. Both maps provided almost the same pattern of inundation, however, inundation map generated from near- infrared band provides more inundated area. Total inundated and non-inundated area in the inundation map generated from NDVI image were 159.7 km2 and 166.4 km2 where they comprise of 48.98% and 51.02% of the total area (Figure 6.15).

Figure 6.15: Inundation map generated from NDVI image for pre-monsoon season of 2013

6.4.8 Inundation Map of 2014

The MODIS scene identification number used for inundation map shown in figure 6.16 is MOD09GQ.A2014140.h26v06.005 (Image courtesy of the U.S. Geological Survey). The scene was generated on May 20, 2014 which was 7 days after the flash flood occurred on May 13, 2014 because cloud free scene was not available immediately after the flash flood. In the Jadukata River, the water level during the time of the flash flood and satellite data generation were 5.74 meter and 4.47 meter respectively. Total inundated and non-inundated area were 154.63 km2 and 171.50 km2 subsequently and their corresponding percentages were 47.41% and 52.59% sequentially.

Figure 6.16: Density sliced inundation map generated from near-infrared band for pre-monsoon season of 2014

The MODIS scene used for generating inundation map shown in figure 6.16 was also used for generating the inundation map shown below (Figure 6.17). The inundation map generated from NDVI image showed comparatively less inundated areas for the year of 2014 than the map generated from the near-infrared band. Only 40.84% of the study area was inundated compared to 59.2% of the non-inundated area. On the other hand, they were 133.2 km2 and 192.9 km2 respectively.

Figure 6.17: Inundation map generated from NDVI image for pre-monsoon season of 2014

6.5: Summary of Inundation Map and their Comparative Analysis

Table 6.1: Summary of inundation of Taherpur Upazila due to flash flood, derived using near-infrared band data with density slicing technique

Year Water Flash Data Inundated Non- Percentage Percentag Level Flood acquisition area in inundated of e of non- meter date date km2 area in km2 inundation inundatio n 2007 5.77 April April 29, 117.31 208.81 35.97 64.03 26, 2007 2007 2008 5.35 18 April 22, 77.38 248.75 23.73 76.27 April, 2008 2008 2009 6.37 May 2, May 5, 136.44 189.69 41.84 58.16 2009 2009 2010 6.13 April May 3, 273.19 52.94 83.77 16.23 26, 2010 2010 2011 5.65 May 2, May 7, 62.81 263.31 19.26 80.74 2011 2011 2012 5.39 May 2, May 8, 235.94 90.19 72.35 27.65 2012 2012 2013 7.09 May 12, May 12, 222.50 103.63 68.23 31.77 2013 2013 2014 5.74 May 13, May 20, 154.63 171.50 47.41 52.59 2014 2014

According to the table, the flash flood inundation was maximum in 2010 followed by the inundation of 2012 when their corresponding percentages were 83.77% and 72.35%. On the other hand, only 19.26% area was inundated in 2011, which is the lowest between 2007 and 2014.

Table 6.2: Summary of inundation of Taherpur Upazila due to flash flood, derived from NDVI image

Year Water Flash Data Inundated Non- Percentage Percentage Level Flood acquisition area in inundated of of non- meter date date km2 area in inundation inundation km2 2007 5.77 April April 29, 59.4 266.8 18.2 81.8 26, 2007 2007 2008 5.35 18 April 22, 41.12 284.99 12.61 87.39 April, 2008 2008 2009 6.37 May 2, May 5, 68.44 257.68 20.99 79.01 2009 2009 2010 6.13 April May 3, 235.18 90.94 72.12 27.88 26, 2010 2010 2011 5.65 May 2, May 7, 28.19 297.93 8.64 91.36 2011 2011 2012 5.39 May 2, May 8, 114.25 211.87 35.03 64.96 2012 2012 2013 7.09 May May 12, 159.75 166.37 48.98 51.02 12, 2013 2013 2014 5.74 May May 20, 133.19 192.93 40.84 59.16 13, 2014 2014

The table 6.2 depicts that the inundation due to the flash flood was maximum in 2010 followed by inundation of 2013 when their corresponding percentages of inundation were 72.12% and 48.98% respectively. On the other hand, only 6.64% area was inundated in 2011, which is the lowest.

By analyzing inundation map from 2007 to 2014, it can be said that north-east part of the study area usually stays non-inundated due to its high elevation. In contrast, south, west, and south-west regions are susceptible to flash flood inundation as most of the permanent depressions are located in those areas.

Table 6.3: Percentage of inundated area for 2007 to 2014

Year Percentage of inundation by year Derived from near-infrared Derived from NDVI image data band image 2007 35.97 18.2 2008 23.73 12.61 2009 41.84 20.99 2010 83.77 72.12 2011 19.26 8.64 2012 72.35 35.03 2013 68.23 48.98 2014 47.41 40.84

It is seen that for every year, inundation map derived using near-infrared band data give a higher percentage of the inundated area than inundation map derived from NDVI image.

0 2007 2008 2009 2010 2011 2012 2013 2014

Inundation Map Derived From NIR Band Inundation Map Derived From NDVI

Figure 6.18: Comparison between inundation percentages derived from near-infrared band and NDVI data

6.6 Accuracy Assessment

A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. According to the thumb rule of confusion matrix, the number of samples for each class should be ten times the number of classes. Hence, to use confusion matrix for accuracy assessment of our inundation map, 20 inundated and 20 non-inundated points were used. The satellite image used for confusion matrix analysis was generated on May 9, 2016 by MODIS TERRA satellite. The spatial distribution of the points is shown in Figure. 6.19, while the accuracy assessment maps are shown in Figure 6.20 and Figure 6.21. The overall accuracy of the classification is expressed as a percentage of classified inundated and non-inundated status against actual status.

Figure 6.19: Map showing spatial distribution of ground truthing points

6.6.1 Accuracy of Inundation Map Derived from Near-infrared Band Data

Inundation map generated using near-infrared band has a higher percentage of accuracy. The overall accuracy of the classification as found from confusion matrix was 92.50% (shown in Figure 6.20 and Table 6.4). It was also observed that the confusion matrix identified one more non-inundated area correctly (19 out of 20) than the inundated area (18 out of 20).

Figure 6.20: Cross performance of sample set and inundation map derived from near- infrared band data

Table 6.4: Confusion matrix of inundation map derived from near-infrared band data

Classification

Class Inundated Non-inundated Unclassifi Tota Producer’ Area Area ed l s Accuracy

% Inundated Area 18 2 0 20 90%

Non- inundated Area 1 19 0 20 95% Reference Total 19 21 0 40

User’s Accuracy % 94.7% 90.5%

6.6.2 Accuracy of Inundation Map Derived from NDVI Image

Inundation maps derived from NDVI image has relatively lower accuracy than inundation map generated from near-infrared band data. The average accuracy of the classification as found from confusion matrix was 87.5% (shown in Figure 6.21 and Table 6.5). It was also observed that the confusion matrix identified one more the inundation map generation method works better for identifying non-inundated area correctly (18 out of 20) identification than the inundated area (17 out of 20).

Figure 6.21: Cross performance of sample set and inundation map derived from NDVI image

Table 6.5: Confusion matrix of inundation map derived from NDVI image

Classification

Class Inundated Non-inundated Unclassifi Tota Producer’ Area Area ed l s Accuracy

% Inundated Area 17 3 0 20 85%

Non- inundated Area 2 18 0 20 90% Reference Total 19 21 0 40

User’s Accuracy % 89.4% 85.6%

6.7 Impact of Flash Flood on Boro Production

Taherpur Upazila is known for its fisheries and agriculture, especially boro production. Harvesting time of the boro rice is from mid-April to mid-May. At that time, water from Meghalaya basin is carried out by the Jadukata and Baulai Rivers that creates a flash flood in different parts of Sunamganj district, especially in the Taherpur Upazila.

The impact of the flash flood on boro rice was analyzed with 3 different factors. Flash flood timing is known as a crucial factor in boro rice damage. Thus, an approach was made to develop a relationship between flash flood timing and total boro rice production followed by another relationship between the extent of flash flood inundation and total boro rice production. At last, a summarization of the findings of a semi-structured interview. The boro rice production data and the percentage of flash flood inundation data are shown in table 6.6.

6.7.1 Effect of Timing of Flash Flood on Upshi Boro Rice Production

An upward trend was observed when the total boro rice production data were plotted against the flash flood timing and the value of regression coefficient was 0.53 (Figure 6.22). As shown in figure 6.22, a flash flood in mid-April to early May significantly reduced the boro rice production. On the other hand, the occurrence of flash flood after the first week of May did not affect the total boro rice production at all as farmers could harvest the paddy on time. In 2013 and 2014, when Taherpur Upazila experienced flash floods in mid-May, it made almost no impact on the production and in those years, the total production was considerably higher than any other year from 2007 to 2014.

Table 6.6: Flash flood timing and boro rice production

Year Date of Flash Hybrid Upshi Local Total Inundation Flood % Roman Julian Area Production Area Production Area Production Area Production 2007 April 26 116 13650 34850 35.97 2008 April 18 108 2000 9400 6000 22200 5100 8976 13100 40576 23.7 2009 May 2 122 600 2490 7900 27650 5500 8250 14000 38390 41.8 2010 April 26 116 1050 4462 7200 27360 5000 9500 13250 41322 83.7 2011 May 2 122 1520 6688 9400 32900 3600 7200 14520 46788 19.3 2012 May 2 123 1100 4730 9500 29450 3800 7182 14400 41362 72.35 2013 May 12 132 800 3840 9700 38315 3600 7560 14100 49715 68.2 2014 May 13 133 800 3440 13000 57200 3600 8424 17400 69064 47.4

80000

70000 y = 930.64x - 67815 R² = 0.5303 60000

50000

40000

30000

20000 Total production in metric in metric production ton Total 10000

0 105 110 115 120 125 130 135 140 Julian Date

Figure 6.22: Effect of flash flood timing on total boro rice production

6.7.2 Effect of Timing of Flash Flood on Upshi Boro Rice Production

Upshi boro rice was the main contributor of boro rice production in Taherpur Upazila with a contribution of 55% to 82% between 2007 and 2014. Similar to total boro rice production, it also showed an upward trend when plotted against flash flood timing, the regression coefficient value was 0.69 (Figure 6.23). The occurrence of the flash flood from May 1 to May 3 usually caused Upshi boro rice production damage where flash floods in mid-May had no impact at all. Production data from 2008 to 2014 were plotted as for 2007, only total boro production data was provided by DAE, Taherpur Upazila. Thus, analysis of different types of boro rice production for 2007 was not possible.

60000

55000 y = 1102x - 101176 50000 R² = 0.6873

45000

40000

35000

30000

25000

Total production in metric in metric production ton Total 20000

15000 100 105 110 115 120 125 130 135 Julian Date

Figure 6.23: Effect of flash flood timing on Upshi rice production

6.7.3 Effect of Timing of Flash Flood on Local Rice Production

Production of local boro rice varieties showed a negative correlation with flash flood timing. Thus, flash floods in early to mid-May caused damage to boro rice production of local rice varieties. However, the correlation was relatively weak and regression coefficient was just 0.26 as shown in figure 6.24.

10000 9500 9000 8500 8000 7500 y = -51.894x + 14502 R² = 0.2556 7000 6500 Total production in metric in metric production ton Total 6000 100 105 110 115 120 125 130 135 Julian Date

Figure 6.24: Effect of flash flood timing on local rice production

6.7.4 Effect of Timing of Flash Flood on Hybrid Boro Rice Production

Hybrid boro rice varieties are the varieties that have a longer growing period. Thus, flash floods in mid-May caused significant hybrid boro rice production damage. A downward trend was observed with a regression coefficient of 0.52 when boro production was plotted against Julian dates as shown in figure 6.25. However, there was a decreasing trend in the cultivated area of hybrid rice too.

10000 9000 8000 7000 6000 5000 4000 3000 y = -192.79x + 28583 2000

Total production in metric in metric production ton Total R² = 0.5162 1000 0 100 105 110 115 120 125 130 135 Julian date

Figure 6.25: Effect of flash flood timing on hybrid boro rice production

6.7.2 Effect of Flash Flood Inundation on Boro Rice Production

Total boro rice production does not show correlation with the percentage of the inundated area as shown in figure 6.26 It was observed that even with the inundation of 83.7 percent in 2010, the production was higher than 2007 where the inundation percentage was just 35.97. More studies with high-resolution remote sensing data on a longer period of time could reveal the relation between flash flood inundation and boro rice production. Classification of land cover in more classes could play a pivotal role in future research.

80000 70000 60000

50000 y = 20.188x + 44268 R² = 0.002 40000 30000 Total Production Total 20000 10000 0 0 20 40 60 80 100 Percentage of Inundated Area

Figure 6.26: Effect of flash flood inundation on boro rice production

6.9 Summarization of Semi-structured Interview of Farmers

From the semi-structured interview of farmers, several reasons were identified behind increasing production in recent years. The reasons include: • The new rice varieties played a crucial role in increasing the production and reducing flash flood damage to the crop. Some well-known local varieties like Gochi, Bakkaiya, Aji and Raittar Ail have been replaced by BRRI dhan28 and BRRI dhan29. Also, the Department of Agricultural Extension (DAE), Taherpur - promoted flood-tolerant rice varieties like BINA dhan11, BINA dhan12, BRRI dhan51, and BRRI dhan52. Shawra dhan has become the most popular flood-tolerant rice verities in this area that provides better production than any local rice varieties. On the other hand, short duration rice varieties are being distributed by DAE to farmers. • Chemical fertilizer enhanced the production rate. Availability of adequate chemical fertilizer along with better rice varieties reduced the impact of the flash flood. • The duration of inundation was a crucial factor. If paddy submerged under water for 3-7 days, the flash flood effect is minimum. In some cases, three days

submergence kills pests and enhance the crop production. However, if flash flood water does not drain quickly, it significantly damages the productivity. • Hailstorm causes severe damage and reduces productivity. • There are several measures taken by local people to compensate the amount damaged by the flash flood. Paddy stem and branch takes nearly two weeks to rot when fully inundated. On the other hand, the paddy grain remains harvestable even after fully submerged for 15-20 days. Local people use a fork- like instrument to harvest it. The quality of the rice is not good, and it produces a bad smell with a blackish look, the poor and landless farmers still harvest and consume the rice as no one holds property right to submerged crops.

Chapter Seven Conclusions and Recommendations

7.1 Conclusions

Near-infrared band (841–876 nm) and NDVI image of MODIS satellite were used to map flash flood inundation at Taherpur upazila. Location of 30 ground truthing points and their inundation status noted. SRTM 30m Digital Elevation Data used for identification of permanent waterbodies whereas ground truthing points inundation status showed a difference in digital numbers of inundated and non-inundated areas. By taking this into consideration a threshold values of the water pixel digital number in the near-infrared band and NDVI image were determined for inundation mapped using these threshold values. The accuracies of the inundation mapping methods’ were assessed with confusion matrix. Inundation map derived from the near-infrared band (841–876 nm) was much more reliable and accurate than inundation maps derived from NDVI data. Their overall accuracy of inundation mapping using NIR band was 92.50% compared to 87.5% accuracy of inundation mapping using NDVI image.

The study examined the relationship between flash flood timing and boro rice production. Total boro rice production and Upshi boro rice production showed upward trends by generating positive regression coefficients of 0.53 and 0.68 sequentially when plotted against the timing of the flash flood (Julian date). On the contrary, boro rice production of local rice varieties and high yield verities showed downward trends with regression coefficients of 0.26 and 0.51 respectively.

Later, assessment of the impact of extent of flash flood inundation on boro rice was carried out by plotting the total boro rice production against the percentage of inundation occurred between 2007 and 2014. A strong relation was absent, thus, rigorous analysis and land cover mapping with more classes are suggested for further studies to understand the in-depth relation between boro rice production and percentage of inundation.

Summarization of semi structured interview also revealed some measures that have been minimizing flash flood impact on boro rice production. Harvesting water submerged paddy, replacement of local rice varieties with high yield varieties, the

introduction of short duration and water tolerate rice varieties, and advancement of technologies are some of the notable observations made from the semi-structured interview. Hailstorm and long inundation duration play crucial roles in damaging the total rice production whereas submergence of paddy for 3-7 days lead to the least damage.

Generating accurate and reliable inundation map on a small scale, especially for the small rivers and non-inundated areas surrounded by water, were limited when satellite image with 250m spatial resolution is in use. Hence, studies using high spatial and temporal resolution data and different water detection techniques should be followed up by a more accurate and detailed investigation into this field.

7.2 Recommendation

Based on the experience gained during this study, the following recommendations are made:

• The research result could be more accurate if satellite data with higher spatial and temporal resolution were used. Therefore, further study using high- resolution image is recommended. • Similar studies with longer study period could lead to a more accurate result. • Land cover mapping (including inundated area) with more land cover classes can offer better result on flash flood inundation percentage. • Flash flood inundation duration is a crucial factor for assessing flash flood impact. The factor was absent in this study as cloud free data was unavailable. If possible using SAR data, flash flood duration should incorporate into similar studies.

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Appendix A Graph of Historical Water Level Data Set for Jadukata River (Provided by BWDB)

Station Name: Laurergarh Saktiarkhola Station ID: SW131.5 Red dot represents flash flood date Orange dot represents data acquisition date

a) Water level change of Jadukata River during April to May in 2007

6.00

5.80

5.60

5.40

Axis Axis Title 5.20

5.00

4.80

4.60 4/1/2007 4/3/2007 4/5/2007 4/7/2007 4/9/2007 5/1/2007 5/3/2007 5/5/2007 5/7/2007 5/9/2007 4/11/2007 4/13/2007 4/15/2007 4/17/2007 4/19/2007 4/21/2007 4/23/2007 4/25/2007 4/27/2007 4/29/2007 5/11/2007 5/13/2007 5/15/2007 5/17/2007 5/19/2007 5/21/2007 5/23/2007 5/25/2007 5/27/2007 5/29/2007 5/31/2007 Axis Title b) Water level change of Jadukata River during April to May in 2008

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6.00

5.90

5.80

5.70

5.60

5.50

Water Water Level 5.40

5.30

5.20

5.10

5.00 4/1/2008 4/3/2008 4/5/2008 4/7/2008 4/9/2008 5/1/2008 5/3/2008 5/5/2008 5/7/2008 5/9/2008 4/11/2008 4/13/2008 4/15/2008 4/17/2008 4/19/2008 4/21/2008 4/23/2008 4/25/2008 4/27/2008 4/29/2008 5/11/2008 5/13/2008 5/15/2008 5/17/2008 5/19/2008 5/21/2008 5/23/2008 5/25/2008 5/27/2008 5/29/2008 5/31/2008 Date c) Water level change of Jadukata River during April to May in 2009

7.60

7.10

6.60

6.10 Water Water Level

5.60

5.10

4.60 4/1/2009 4/3/2009 4/5/2009 4/7/2009 4/9/2009 5/1/2009 5/3/2009 5/5/2009 5/7/2009 5/9/2009 4/11/2009 4/13/2009 4/15/2009 4/17/2009 4/19/2009 4/21/2009 4/23/2009 4/25/2009 4/27/2009 4/29/2009 5/11/2009 5/13/2009 5/15/2009 5/17/2009 5/19/2009 5/21/2009 5/23/2009 5/25/2009 5/27/2009 5/29/2009 5/31/2009 Date

d) Water level change of Jadukata River during April to May in 2010

7.20

6.70

6.20 Water Water Level 5.70

5.20

4.70 4/1/2010 4/3/2010 4/5/2010 4/7/2010 4/9/2010 5/1/2010 5/3/2010 5/5/2010 5/7/2010 5/9/2010 4/11/2010 4/13/2010 4/15/2010 4/17/2010 4/19/2010 4/21/2010 4/23/2010 4/25/2010 4/27/2010 4/29/2010 5/11/2010 5/13/2010 5/15/2010 5/17/2010 5/19/2010 5/21/2010 5/23/2010 5/25/2010 5/27/2010 5/29/2010 5/31/2010 Date

e) Water level change of Jadukata River during April to May in 2011

5.90

5.70

5.50

5.30

5.10

4.90 Water Water Level 4.70

4.50

4.30

4.10

3.90 4/1/2011 4/3/2011 4/5/2011 4/7/2011 4/9/2011 5/1/2011 5/3/2011 5/5/2011 5/7/2011 5/9/2011 4/11/2011 4/13/2011 4/15/2011 4/17/2011 4/19/2011 4/21/2011 4/23/2011 4/25/2011 4/27/2011 Date4/29/2011 5/11/2011 5/13/2011 5/15/2011 5/17/2011 5/19/2011 5/21/2011 5/23/2011 5/25/2011 5/27/2011 5/29/2011 5/31/2011

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f) Water level change of Jadukata River during April to May in 2012

5.60

5.10

4.60 Water Water Level 4.10

3.60

3.10 4/1/2012 4/3/2012 4/5/2012 4/7/2012 4/9/2012 5/1/2012 5/3/2012 5/5/2012 5/7/2012 5/9/2012 4/11/2012 4/13/2012 4/15/2012 4/17/2012 4/19/2012 4/21/2012 4/23/2012 4/25/2012 4/27/2012 4/29/2012 5/11/2012 5/13/2012 5/15/2012 5/17/2012 5/19/2012 5/21/2012 5/23/2012 5/25/2012 5/27/2012 5/29/2012 5/31/2012 Date

g) Water level change of Jadukata River during April to May in 2013

7.50

7.00

6.50

6.00

5.50

5.00 Water Water Level

4.50

4.00

3.50

3.00 4/1/2013 4/3/2013 4/5/2013 4/7/2013 4/9/2013 5/1/2013 5/3/2013 5/5/2013 5/7/2013 5/9/2013 4/11/2013 4/13/2013 4/15/2013 4/17/2013 4/19/2013 4/21/2013 4/23/2013 4/25/2013 4/27/2013 4/29/2013 5/11/2013 5/13/2013 5/15/2013 5/17/2013 5/19/2013 5/21/2013 5/23/2013 5/25/2013 5/27/2013 5/29/2013 5/31/2013 Date

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h) Water level change of Jadukata River during April to May in 2014

6.50

6.00

5.50

5.00

Water Water Level 4.50

4.00

3.50

3.00 4/1/2014 4/3/2014 4/5/2014 4/7/2014 4/9/2014 5/1/2014 5/3/2014 5/5/2014 5/7/2014 5/9/2014 4/11/2014 4/13/2014 4/15/2014 4/17/2014 4/19/2014 4/21/2014 4/23/2014 4/25/2014 4/27/2014 4/29/2014 5/11/2014 5/13/2014 5/15/2014 5/17/2014 5/19/2014 5/21/2014 5/23/2014 5/25/2014 5/27/2014 5/29/2014 5/31/2014 Date

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Appendix B Inundation Mapping with Density Slicing Technique

Inundation Mapping from Near-infrared Band Data with Density Slicing Technique:

Firstly, the ranges of the digital number values representing inundated and non- inundated area need to be determined. Secondly, a domain group with the slice boundaries, names, and codes has to be created. The procedure is described for a masked near-infrared band data of 2014 below. The file was saved as 140.tif.

• The masked near-infrared image was imported in ILWIS using GDAL. Inside a GDAL imported file, there is 3 different files – one coordinate system file, one georeferenceed file and one raster file. Density slicing was done for the raster file which was named as “140_1”. • Slicing operation was started by double clicking the Slicing operation in the Operation-list. “140_1” raster file was selected as the input raster map and output raster map was named as “140slicing”. • “Create Domain” button was clicked. The Create Domain dialog box was opened. To give a domain name, “140slicing” was typed in the Domain Name text box. • The “Group” check box was selected. New domain was created with a width of 5. All other default settings were accepted by clicking the OK button. The Domain Group editor was opened.

• To add new domain item was clicked. Upper boundary and Name are mandatory information to be provided. The use of a Code and a Description is optional.

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Name Code Upper Boundary Color Inundated Area I A 1985 Red Non-inundated Area N I A 10000 Green • Representation editor and Domain group editor were closed and the map was shown. • Coordinate, legend and north arrow were added as required.

Inundation Mapping from IDVI Data with Density Slicing Technique:

Following operations were conducted to map inundation with NDVI for the year of 2014.

• Both near-infrared and red band were imported in QGIS. WGS-1964 geo- referencing systems were used in the both images, named; 1.tif and 2.tif. • Upon starting Raster Calculator from Raster option, both band 1 and band 2 images name was shown in the Raster Calculator window. • The equation of the NDVI calculation is 푛푒푎푟−푖푛푓푟푎푟푒푑 − 푣푖푠푖푏푙푒 푟푒푑 NDVI = 푛푒푎푟−푖푛푓푟푎푟푒푑 + 푣푖푠푖푏푙푒 푟푒푑 푏푎푛푑 2− 푏푎푛푑 1 Hence the equation will be, NDVI = 푏푎푛푑 2+푏푎푛푑 1 • The band 1 and band 2 images were shown in the raster calculator window as 1@1 and 2@1 respectively. In Raster calculation expression box in the Raster Calculator,

( "2@1" - "1@1" ) / ( "2@1" + "1@1" )

was written and output layer name was provided. A sample NDVI image is shown in Figure 5.3. • The output file was saved in geotif format and named NDVI2014.tif. It was imported in ILWIS using GDAL. In GDAL imported file, there are 3 different files – one coordinate system file, one georeferenced file, and one raster file. Density slicing was done for the raster file which was named after NDVI2014.tif image.

• Slicing operation was started by double clicking the Slicing operation in the Operation-list. “NDVI2014.tif” raster file was selected as the input raster map and output raster map was named after “NDVI2014slicing”. • “Create Domain” button was clicked. The Create Domain dialog box was opened. To give a domain name “NDVI2014slicing” was typed in the Domain Name text box. • The “Group” check box was selected. New domain was created with a width of 1. All other default settings were accepted by clicking the OK button. The Domain Group editor was opened.

• To add new domain item was clicked. Upper boundary and Name are mandatory information to be provided. The use of a Code and a Description is optional.

• To open Representation button in the toolbar was clicked. The Representation Class editor was appeared, showing the groups/classes that are present in the domain with different colors. By double clicking a color box in the editor, different colors were assigned for different classes. • Three domain item was added, namely, Inundated Area and Non-inundated Area. Specific colors were also defined from the representation editor window as follows; Name Code Upper Boundary Color Inundated Area I A 0.415 Red Non-inundated Area N I A 1 Green • Representation editor and Domain group editor were closed and the map was shown. • Coordinate, legend and north arrow were added as required.

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Appendix C Semi-structured Interview Question for Assessing the Impact of Flash Flood on Boro Rice Production

Name: Age:

1. What are the boro rice transplanting and harvesting time?

2. When flash flood usual occurs in this area?

3. How many days before the rice harvesting the occurrence flash flood cause the most damage?

4. How many day’s inundations requires to fully deteriorate the grain?

5. What steps you have taken so far to protect the rice from flash flood damage?

6. What are the most common cultivated boro rice varieties in this area?

7. What are the flash flood damage porne verities?

8. Do you use any flood tolerate rice variety?

9. Does submersible embankment play a role in protecting the crop from the flash flood?

10. What are the reasons for improvement of rice production improvement in recent years?

11. In recent time, the flash flood did more or less damage than it did before?

12. Does river siltation happen in this area? Does it play any roles in a flash flood?

13. Provide your own opinion on flash flood

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Appendix D Some of the photos of study area in monsoon and spring

Photo E1: Taherpur Upazila in moonson season

Photo E2: Taherpur Upazila in moonson season

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Photo E3: Taherpur Upazila in moonson season

Photo E4: Paddy harvested after being inundated for several weeks

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Photo E5: Boro rice field at Badaghat union

Photo E6: Boro rice field near Taherpur bazar

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Photo E7: Maize field at Badaghat union

Photo E8: Fellow land of Taherpur uapzila

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Photo E9: Shonir haor in April, 2017

Photo E10: Shonir Haor in April, 2017

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Photo E11: Earth dam construction by local people at Taherpur Upazila

Photo E12: Transportation of early harvested rice using Baulai River

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